From 4061d4d62fe3ef9b5e9137557b19730ca66a63ad Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Fri, 6 Jan 2023 13:24:17 -0500 Subject: [PATCH] update tfjs --- CHANGELOG.md | 7 +- dist/face-api.esm-nobundle.js | 2 +- dist/face-api.esm.js | 464 ++++++++--------- dist/face-api.esm.js.map | 6 +- dist/face-api.js | 488 +++++++++--------- dist/face-api.node-gpu.js | 2 +- dist/face-api.node-wasm.js | 2 +- dist/face-api.node.js | 2 +- dist/tfjs.esm.js | 460 ++++++++--------- dist/tfjs.version.js | 2 +- typedoc/classes/AgeGenderNet.html | 8 +- typedoc/classes/BoundingBox.html | 8 +- typedoc/classes/Box.html | 8 +- typedoc/classes/ComposableTask.html | 8 +- .../ComputeAllFaceDescriptorsTask.html | 8 +- .../ComputeFaceDescriptorsTaskBase.html | 8 +- .../ComputeSingleFaceDescriptorTask.html | 8 +- .../classes/DetectAllFaceLandmarksTask.html | 8 +- typedoc/classes/DetectAllFacesTask.html | 8 +- .../classes/DetectFaceLandmarksTaskBase.html | 8 +- typedoc/classes/DetectFacesTaskBase.html | 8 +- .../DetectSingleFaceLandmarksTask.html | 8 +- typedoc/classes/DetectSingleFaceTask.html | 8 +- typedoc/classes/Dimensions.html | 8 +- typedoc/classes/FaceDetection.html | 8 +- typedoc/classes/FaceDetectionNet.html | 8 +- typedoc/classes/FaceExpressionNet.html | 8 +- typedoc/classes/FaceExpressions.html | 8 +- typedoc/classes/FaceLandmark68Net.html | 8 +- typedoc/classes/FaceLandmark68TinyNet.html | 8 +- typedoc/classes/FaceLandmarkNet.html | 8 +- typedoc/classes/FaceLandmarks.html | 8 +- typedoc/classes/FaceLandmarks5.html | 8 +- typedoc/classes/FaceLandmarks68.html | 8 +- typedoc/classes/FaceMatch.html | 8 +- typedoc/classes/FaceMatcher.html | 8 +- typedoc/classes/FaceRecognitionNet.html | 8 +- typedoc/classes/LabeledBox.html | 8 +- typedoc/classes/LabeledFaceDescriptors.html | 8 +- typedoc/classes/NetInput.html | 8 +- typedoc/classes/NeuralNetwork.html | 8 +- typedoc/classes/ObjectDetection.html | 8 +- typedoc/classes/Point.html | 8 +- typedoc/classes/PredictedBox.html | 8 +- typedoc/classes/Rect.html | 8 +- typedoc/classes/SsdMobilenetv1.html | 8 +- typedoc/classes/SsdMobilenetv1Options.html | 8 +- typedoc/classes/TinyFaceDetector.html | 8 +- typedoc/classes/TinyFaceDetectorOptions.html | 8 +- typedoc/classes/TinyYolov2.html | 8 +- typedoc/classes/TinyYolov2Options.html | 8 +- typedoc/classes/draw.DrawBox.html | 8 +- typedoc/classes/draw.DrawBoxOptions.html | 8 +- typedoc/classes/draw.DrawFaceLandmarks.html | 8 +- .../draw.DrawFaceLandmarksOptions.html | 8 +- typedoc/classes/draw.DrawTextField.html | 8 +- .../classes/draw.DrawTextFieldOptions.html | 8 +- typedoc/enums/Gender.html | 8 +- typedoc/enums/draw.AnchorPosition.html | 8 +- typedoc/functions/allFaces.html | 8 +- typedoc/functions/allFacesSsdMobilenetv1.html | 8 +- typedoc/functions/allFacesTinyYolov2.html | 8 +- typedoc/functions/awaitMediaLoaded.html | 8 +- typedoc/functions/bufferToImage.html | 8 +- typedoc/functions/computeFaceDescriptor.html | 8 +- typedoc/functions/createCanvas.html | 8 +- typedoc/functions/createCanvasFromMedia.html | 8 +- typedoc/functions/createFaceDetectionNet.html | 8 +- .../functions/createFaceRecognitionNet.html | 8 +- typedoc/functions/createSsdMobilenetv1.html | 8 +- typedoc/functions/createTinyFaceDetector.html | 8 +- typedoc/functions/createTinyYolov2.html | 8 +- typedoc/functions/detectAllFaces.html | 8 +- typedoc/functions/detectFaceLandmarks.html | 8 +- .../functions/detectFaceLandmarksTiny.html | 8 +- typedoc/functions/detectLandmarks.html | 8 +- typedoc/functions/detectSingleFace.html | 8 +- typedoc/functions/draw.drawContour.html | 8 +- typedoc/functions/draw.drawDetections.html | 8 +- .../functions/draw.drawFaceExpressions.html | 8 +- .../functions/draw.drawFaceLandmarks-1.html | 8 +- typedoc/functions/euclideanDistance.html | 8 +- typedoc/functions/extendWithAge.html | 8 +- .../functions/extendWithFaceDescriptor.html | 8 +- .../functions/extendWithFaceDetection.html | 8 +- .../functions/extendWithFaceExpressions.html | 8 +- .../functions/extendWithFaceLandmarks.html | 8 +- typedoc/functions/extendWithGender.html | 8 +- typedoc/functions/extractFaceTensors.html | 8 +- typedoc/functions/extractFaces.html | 8 +- typedoc/functions/fetchImage.html | 8 +- typedoc/functions/fetchJson.html | 8 +- typedoc/functions/fetchNetWeights.html | 8 +- typedoc/functions/fetchOrThrow.html | 8 +- typedoc/functions/fetchVideo.html | 8 +- typedoc/functions/getContext2dOrThrow.html | 8 +- typedoc/functions/getMediaDimensions.html | 8 +- typedoc/functions/imageTensorToCanvas.html | 8 +- typedoc/functions/imageToSquare.html | 8 +- typedoc/functions/inverseSigmoid.html | 8 +- typedoc/functions/iou.html | 8 +- typedoc/functions/isMediaElement.html | 8 +- typedoc/functions/isMediaLoaded.html | 8 +- typedoc/functions/isWithAge.html | 8 +- typedoc/functions/isWithFaceDetection.html | 8 +- typedoc/functions/isWithFaceExpressions.html | 8 +- typedoc/functions/isWithFaceLandmarks.html | 8 +- typedoc/functions/isWithGender.html | 8 +- typedoc/functions/loadAgeGenderModel.html | 8 +- typedoc/functions/loadFaceDetectionModel.html | 8 +- .../functions/loadFaceExpressionModel.html | 8 +- typedoc/functions/loadFaceLandmarkModel.html | 8 +- .../functions/loadFaceLandmarkTinyModel.html | 8 +- .../functions/loadFaceRecognitionModel.html | 8 +- .../functions/loadSsdMobilenetv1Model.html | 8 +- .../functions/loadTinyFaceDetectorModel.html | 8 +- typedoc/functions/loadTinyYolov2Model.html | 8 +- typedoc/functions/loadWeightMap.html | 8 +- typedoc/functions/locateFaces.html | 8 +- typedoc/functions/matchDimensions.html | 8 +- typedoc/functions/minBbox.html | 8 +- typedoc/functions/nonMaxSuppression.html | 8 +- typedoc/functions/normalize.html | 8 +- typedoc/functions/padToSquare.html | 8 +- typedoc/functions/predictAgeAndGender.html | 8 +- .../functions/recognizeFaceExpressions.html | 8 +- typedoc/functions/resizeResults.html | 8 +- typedoc/functions/resolveInput.html | 8 +- typedoc/functions/shuffleArray.html | 8 +- typedoc/functions/sigmoid.html | 8 +- typedoc/functions/ssdMobilenetv1-1.html | 8 +- typedoc/functions/tinyFaceDetector-1.html | 8 +- typedoc/functions/tinyYolov2-1.html | 8 +- typedoc/functions/toNetInput.html | 8 +- .../utils.computeReshapedDimensions.html | 8 +- typedoc/functions/utils.getCenterPoint.html | 8 +- typedoc/functions/utils.isDimensions.html | 8 +- typedoc/functions/utils.isEven.html | 8 +- typedoc/functions/utils.isFloat.html | 8 +- typedoc/functions/utils.isTensor.html | 8 +- typedoc/functions/utils.isTensor1D.html | 8 +- typedoc/functions/utils.isTensor2D.html | 8 +- typedoc/functions/utils.isTensor3D.html | 8 +- typedoc/functions/utils.isTensor4D.html | 8 +- typedoc/functions/utils.isValidNumber.html | 8 +- .../functions/utils.isValidProbablitiy.html | 8 +- typedoc/functions/utils.range.html | 8 +- typedoc/functions/utils.round.html | 8 +- typedoc/functions/validateConfig.html | 8 +- typedoc/index.html | 8 +- typedoc/interfaces/IBoundingBox.html | 8 +- typedoc/interfaces/IDimensions.html | 8 +- typedoc/interfaces/IFaceDetecion.html | 8 +- typedoc/interfaces/IFaceLandmarks.html | 8 +- typedoc/interfaces/IFaceMatch.html | 8 +- typedoc/interfaces/IPoint.html | 8 +- typedoc/interfaces/IRect.html | 8 +- .../interfaces/ISsdMobilenetv1Options.html | 8 +- typedoc/interfaces/ITinyYolov2Options.html | 8 +- typedoc/interfaces/draw.IDrawBoxOptions.html | 8 +- .../draw.IDrawFaceLandmarksOptions.html | 8 +- .../draw.IDrawTextFieldOptions.html | 8 +- typedoc/modules/draw.html | 8 +- typedoc/modules/utils.html | 8 +- typedoc/types/AgeAndGenderPrediction.html | 8 +- typedoc/types/BatchNorm.html | 8 +- typedoc/types/ConvWithBatchNorm.html | 8 +- typedoc/types/DefaultTinyYolov2NetParams.html | 8 +- typedoc/types/Environment.html | 8 +- typedoc/types/FaceDetectionFunction.html | 8 +- typedoc/types/FaceDetectionOptions.html | 8 +- typedoc/types/FileSystem.html | 8 +- typedoc/types/ITinyFaceDetectorOptions.html | 8 +- typedoc/types/MobilenetParams.html | 8 +- typedoc/types/NetOutput.html | 8 +- typedoc/types/NetParams.html | 8 +- typedoc/types/TMediaElement.html | 8 +- typedoc/types/TNetInput.html | 8 +- typedoc/types/TNetInputArg.html | 8 +- typedoc/types/TResolvedNetInput.html | 8 +- typedoc/types/TinyYolov2Config.html | 8 +- typedoc/types/TinyYolov2NetParams.html | 8 +- typedoc/types/WithAge.html | 8 +- typedoc/types/WithFaceDescriptor.html | 8 +- typedoc/types/WithFaceDetection.html | 8 +- typedoc/types/WithFaceExpressions.html | 8 +- typedoc/types/WithFaceLandmarks.html | 8 +- typedoc/types/WithGender.html | 8 +- .../types/draw.DrawFaceExpressionsInput.html | 8 +- .../types/draw.DrawFaceLandmarksInput.html | 8 +- typedoc/types/draw.TDrawDetectionsInput.html | 8 +- typedoc/variables/FACE_EXPRESSION_LABELS.html | 8 +- typedoc/variables/env.html | 8 +- typedoc/variables/nets.html | 8 +- typedoc/variables/version.html | 8 +- types/face-api.d.ts | 124 ++++- 196 files changed, 1569 insertions(+), 1470 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 56731bef..914fe51b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # @vladmandic/face-api - Version: **1.7.7** + Version: **1.7.8** Description: **FaceAPI: AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using TensorFlow/JS** Author: **Vladimir Mandic ** @@ -9,7 +9,10 @@ ## Changelog -### **HEAD -> master** 2022/12/04 mandic00@live.com +### **1.7.8** 2023/01/06 mandic00@live.com + + +### **origin/master** 2022/12/21 mandic00@live.com ### **1.7.7** 2022/12/01 mandic00@live.com diff --git a/dist/face-api.esm-nobundle.js b/dist/face-api.esm-nobundle.js index 3a6cdcb9..02a75874 100644 --- a/dist/face-api.esm-nobundle.js +++ b/dist/face-api.esm-nobundle.js @@ -4,4 +4,4 @@ author: ' */ -var lr=Object.defineProperty;var bo=Object.getOwnPropertyDescriptor;var go=Object.getOwnPropertyNames;var xo=Object.prototype.hasOwnProperty;var vo=(o=>typeof 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Me=class extends I{constructor(e=new Ee(2)){super("AgeGenderNet");this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){let{params:r}=this;if(!r)throw new Error(`${this._name} - load model before inference`);return n.tidy(()=>{let a=e instanceof rt?this.faceFeatureExtractor.forwardInput(e):e,s=n.avgPool(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),i=se(s,r.fc.age).as1D(),c=se(s,r.fc.gender);return{age:i,gender:c}})}forwardInput(e){return n.tidy(()=>{let{age:r,gender:a}=this.runNet(e);return{age:r,gender:n.softmax(a)}})}async forward(e){return this.forwardInput(await M(e))}async predictAgeAndGender(e){let r=await M(e),a=await this.forwardInput(r),s=n.unstack(a.age),i=n.unstack(a.gender),c=s.map((p,u)=>({ageTensor:p,genderTensor:i[u]})),m=await Promise.all(c.map(async({ageTensor:p,genderTensor:u})=>{let f=p.dataSync()[0],l=u.dataSync()[0],d=l>.5,b=d?"male":"female",x=d?l:1-l;return 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locateFaces(t,e={}){let{maxResults:r,minConfidence:a}=new z(e),s=await M(t),{boxes:i,scores:c}=this.forwardInput(s),m=i[0],p=c[0];for(let v=1;v{let[D,N]=[Math.max(0,h[v][0]),Math.min(1,h[v][2])].map(O=>O*w),[Y,q]=[Math.max(0,h[v][1]),Math.min(1,h[v][3])].map(O=>O*x);return new E(u[v],new St(Y,D,q-Y,N-D),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return m.dispose(),p.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return qr(t)}extractParams(t){return Jr(t)}};function qo(o){let t=new wt;return t.extractWeights(o),t}function bl(o){return qo(o)}var eo=class extends wt{};var ro=.4,oo=[new g(.738768,.874946),new g(2.42204,2.65704),new g(4.30971,7.04493),new g(10.246,4.59428),new g(12.6868,11.8741)],no=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],ao=[117.001,114.697,97.404],so="tiny_yolov2_model",io="tiny_yolov2_separable_conv_model";var Se=o=>typeof 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${JSON.stringify(o.meanRgb)}`)}function Xt(o){return n.tidy(()=>{let t=n.mul(o,n.scalar(.10000000149011612));return n.add(n.relu(n.sub(o,t)),t)})}function ot(o,t){return n.tidy(()=>{let e=n.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=n.conv2d(e,t.conv.filters,[1,1],"valid"),e=n.sub(e,t.bn.sub),e=n.mul(e,t.bn.truediv),e=n.add(e,t.conv.bias),Xt(e)})}function nt(o,t){return n.tidy(()=>{let e=n.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return e=n.separableConv2d(e,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),e=n.add(e,t.bias),Xt(e)})}function Zo(o,t){let e=$t(o,t);function r(i,c){let m=n.tensor1d(o(i)),p=n.tensor1d(o(i));return t.push({paramPath:`${c}/sub`},{paramPath:`${c}/truediv`}),{sub:m,truediv:p}}function a(i,c,m){let p=e(i,c,3,`${m}/conv`),u=r(c,`${m}/bn`);return{conv:p,bn:u}}let s=Ot(o,t);return{extractConvParams:e,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function mo(o,t,e,r){let{extractWeights:a,getRemainingWeights:s}=A(o),i=[],{extractConvParams:c,extractConvWithBatchNormParams:m,extractSeparableConvParams:p}=Zo(a,i),u;if(t.withSeparableConvs){let[f,l,d,b,x,w,h,y,v]=r,D=t.isFirstLayerConv2d?c(f,l,3,"conv0"):p(f,l,"conv0"),N=p(l,d,"conv1"),Y=p(d,b,"conv2"),q=p(b,x,"conv3"),O=p(x,w,"conv4"),at=p(w,h,"conv5"),st=y?p(h,y,"conv6"):void 0,it=v?p(y,v,"conv7"):void 0,gt=c(v||y||h,5*e,1,"conv8");u={conv0:D,conv1:N,conv2:Y,conv3:q,conv4:O,conv5:at,conv6:st,conv7:it,conv8:gt}}else{let[f,l,d,b,x,w,h,y,v]=r,D=m(f,l,"conv0"),N=m(l,d,"conv1"),Y=m(d,b,"conv2"),q=m(b,x,"conv3"),O=m(x,w,"conv4"),at=m(w,h,"conv5"),st=m(h,y,"conv6"),it=m(y,v,"conv7"),gt=c(v,5*e,1,"conv8");u={conv0:D,conv1:N,conv2:Y,conv3:q,conv4:O,conv5:at,conv6:st,conv7:it,conv8:gt}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function Ko(o,t){let e=B(o,t);function r(c){let m=e(`${c}/sub`,1),p=e(`${c}/truediv`,1);return{sub:m,truediv:p}}function a(c){let m=e(`${c}/filters`,4),p=e(`${c}/bias`,1);return{filters:m,bias:p}}function s(c){let m=a(`${c}/conv`),p=r(`${c}/bn`);return{conv:m,bn:p}}let i=Ht(e);return{extractConvParams:a,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function po(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}=Ko(o,e),i;if(t.withSeparableConvs){let c=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?r("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:c>7?s("conv6"):void 0,conv7:c>8?s("conv7"):void 0,conv8:r("conv8")}}else i={conv0:a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:a("conv6"),conv7:a("conv7"),conv8:r("conv8")};return L(o,e),{params:i,paramMappings:e}}var J=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var cr=class extends I{constructor(e){super("TinyYolov2");co(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,r){let a=ot(e,r.conv0);return a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv1),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv2),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv3),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv4),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv5),a=n.maxPool(a,[2,2],[1,1],"same"),a=ot(a,r.conv6),a=ot(a,r.conv7),_t(a,r.conv8,"valid",!1)}runMobilenet(e,r){let a=this.config.isFirstLayerConv2d?Xt(_t(e,r.conv0,"valid",!1)):nt(e,r.conv0);return a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv1),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv2),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv3),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv4),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv5),a=n.maxPool(a,[2,2],[1,1],"same"),a=r.conv6?nt(a,r.conv6):a,a=r.conv7?nt(a,r.conv7):a,_t(a,r.conv8,"valid",!1)}forwardInput(e,r){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return n.tidy(()=>{let s=n.cast(e.toBatchTensor(r,!1),"float32");return s=this.config.meanRgb?X(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,a):this.runTinyYolov2(s,a)})}async forward(e,r){return this.forwardInput(await M(e),r)}async detect(e,r={}){let{inputSize:a,scoreThreshold:s}=new J(r),i=await M(e),c=await this.forwardInput(i,a),m=n.tidy(()=>n.unstack(c)[0].expandDims()),p={width:i.getInputWidth(0),height:i.getInputHeight(0)},u=await this.extractBoxes(m,i.getReshapedInputDimensions(0),s);c.dispose(),m.dispose();let f=u.map(h=>h.box),l=u.map(h=>h.score),d=u.map(h=>h.classScore),b=u.map(h=>this.config.classes[h.label]);return xr(f.map(h=>h.rescale(a)),l,this.config.iouThreshold,!0).map(h=>new ct(l[h],d[h],b[h],f[h],p))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return po(e,this.config)}extractParams(e){let r=this.config.filterSizes||cr.DEFAULT_FILTER_SIZES,a=r?r.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return mo(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,a){let{width:s,height:i}=r,c=Math.max(s,i),m=c/s,p=c/i,u=e.shape[1],f=this.config.anchors.length,[l,d,b]=n.tidy(()=>{let y=e.reshape([u,u,f,this.boxEncodingSize]),v=y.slice([0,0,0,0],[u,u,f,4]),D=y.slice([0,0,0,4],[u,u,f,1]),N=this.withClassScores?n.softmax(y.slice([0,0,0,5],[u,u,f,this.config.classes.length]),3):n.scalar(0);return[v,D,N]}),x=[],w=await d.array(),h=await l.array();for(let y=0;ya){let Y=(v+fe(h[y][v][D][0]))/u*m,q=(y+fe(h[y][v][D][1]))/u*p,O=Math.exp(h[y][v][D][2])*this.config.anchors[D].x/u*m,at=Math.exp(h[y][v][D][3])*this.config.anchors[D].y/u*p,st=Y-O/2,it=q-at/2,gt={row:y,col:v,anchor:D},{classScore:pr,label:ur}=this.withClassScores?await this.extractPredictedClass(b,gt):{classScore:1,label:0};x.push({box:new Nt(st,it,st+O,it+at),score:N,classScore:N*pr,label:ur,...gt})}}return l.dispose(),d.dispose(),b.dispose(),x}async extractPredictedClass(e,r){let{row:a,col:s,anchor:i}=r,c=await e.array();return Array(this.config.classes.length).fill(0).map((m,p)=>c[a][s][i][p]).map((m,p)=>({classScore:m,label:p})).reduce((m,p)=>m.classScore>p.classScore?m:p)}},Pt=cr;Pt.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Jt=class extends Pt{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:ro,classes:["face"],...t?{anchors:no,meanRgb:ao}:{anchors:oo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(a=>new E(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?io:so}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function id(o,t=!0){let e=new Jt(t);return e.extractWeights(o),e}var Le=class extends J{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var V=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function Ft(o,t,e,r,a=({alignedRect:s})=>s){let s=o.map(m=>Yt(m)?a(m):m.detection),i=r||(t instanceof n.Tensor?await oe(t,s):await re(t,s)),c=await e(i);return i.forEach(m=>m instanceof n.Tensor&&m.dispose()),c}async function qt(o,t,e,r,a){return Ft([o],t,async s=>e(s[0]),r,a)}var uo=.4,fo=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],lo=[117.001,114.697,97.404];var Zt=class extends Pt{constructor(){let t={withSeparableConvs:!0,iouThreshold:uo,classes:["face"],anchors:fo,meanRgb:lo,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(a=>new E(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var T={ssdMobilenetv1:new wt,tinyFaceDetector:new Zt,tinyYolov2:new Jt,faceLandmark68Net:new jt,faceLandmark68TinyNet:new Ie,faceRecognitionNet:new Ut,faceExpressionNet:new Pe,ageGenderNet:new Me},Qo=(o,t)=>T.ssdMobilenetv1.locateFaces(o,t),Bd=(o,t)=>T.tinyFaceDetector.locateFaces(o,t),Rd=(o,t)=>T.tinyYolov2.locateFaces(o,t),tn=o=>T.faceLandmark68Net.detectLandmarks(o),$d=o=>T.faceLandmark68TinyNet.detectLandmarks(o),Od=o=>T.faceRecognitionNet.computeFaceDescriptor(o),Hd=o=>T.faceExpressionNet.predictExpressions(o),zd=o=>T.ageGenderNet.predictAgeAndGender(o),en=o=>T.ssdMobilenetv1.load(o),Vd=o=>T.tinyFaceDetector.load(o),Yd=o=>T.tinyYolov2.load(o),Gd=o=>T.faceLandmark68Net.load(o),jd=o=>T.faceLandmark68TinyNet.load(o),Ud=o=>T.faceRecognitionNet.load(o),Xd=o=>T.faceExpressionNet.load(o),Jd=o=>T.ageGenderNet.load(o),qd=en,Zd=Qo,Kd=tn;var Ae=class extends V{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Dt=class extends Ae{async run(){let t=await this.parentTask,e=await Ft(t,this.input,async r=>Promise.all(r.map(a=>T.faceExpressionNet.predictExpressions(a))),this.extractedFaces);return t.map((r,a)=>tr(r,e[a]))}withAgeAndGender(){return new Mt(this,this.input)}},Et=class extends Ae{async run(){let t=await this.parentTask;if(!t)return;let e=await qt(t,this.input,r=>T.faceExpressionNet.predictExpressions(r),this.extractedFaces);return tr(t,e)}withAgeAndGender(){return new Ct(this,this.input)}},ut=class extends Dt{withAgeAndGender(){return new lt(this,this.input)}withFaceDescriptors(){return new ht(this,this.input)}},ft=class extends Et{withAgeAndGender(){return new dt(this,this.input)}withFaceDescriptor(){return new bt(this,this.input)}};var We=class extends V{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Mt=class extends We{async run(){let t=await this.parentTask,e=await Ft(t,this.input,async r=>Promise.all(r.map(a=>T.ageGenderNet.predictAgeAndGender(a))),this.extractedFaces);return t.map((r,a)=>{let{age:s,gender:i,genderProbability:c}=e[a];return sr(ir(r,i,c),s)})}withFaceExpressions(){return new Dt(this,this.input)}},Ct=class extends We{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:a}=await qt(t,this.input,s=>T.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return sr(ir(t,r,a),e)}withFaceExpressions(){return new Et(this,this.input)}},lt=class extends Mt{withFaceExpressions(){return new ut(this,this.input)}withFaceDescriptors(){return new ht(this,this.input)}},dt=class extends Ct{withFaceExpressions(){return new ft(this,this.input)}withFaceDescriptor(){return new bt(this,this.input)}};var ke=class extends V{constructor(e,r){super();this.parentTask=e;this.input=r}},ht=class extends ke{async run(){let t=await this.parentTask;return(await Ft(t,this.input,r=>Promise.all(r.map(a=>T.faceRecognitionNet.computeFaceDescriptor(a))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,a)=>ar(t[a],r))}withFaceExpressions(){return new ut(this,this.input)}withAgeAndGender(){return new lt(this,this.input)}},bt=class extends ke{async run(){let t=await this.parentTask;if(!t)return;let e=await qt(t,this.input,r=>T.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return ar(t,e)}withFaceExpressions(){return new ft(this,this.input)}withAgeAndGender(){return new dt(this,this.input)}};var Be=class extends V{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?T.faceLandmark68TinyNet:T.faceLandmark68Net}},Re=class extends Be{async run(){let t=await this.parentTask,e=t.map(i=>i.detection),r=this.input instanceof n.Tensor?await oe(this.input,e):await re(this.input,e),a=await Promise.all(r.map(i=>this.landmarkNet.detectLandmarks(i)));return r.forEach(i=>i instanceof n.Tensor&&i.dispose()),t.filter((i,c)=>a[c]).map((i,c)=>ie(i,a[c]))}withFaceExpressions(){return new ut(this,this.input)}withAgeAndGender(){return new lt(this,this.input)}withFaceDescriptors(){return new ht(this,this.input)}},$e=class extends Be{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof n.Tensor?await oe(this.input,[e]):await re(this.input,[e]),a=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof n.Tensor&&s.dispose()),ie(t,a)}withFaceExpressions(){return new ft(this,this.input)}withAgeAndGender(){return new dt(this,this.input)}withFaceDescriptor(){return new bt(this,this.input)}};var Oe=class extends V{constructor(e,r=new z){super();this.input=e;this.options=r}},me=class extends Oe{async run(){let{input:t,options:e}=this,r;if(e instanceof Le)r=T.tinyFaceDetector.locateFaces(t,e);else if(e instanceof z)r=T.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof J)r=T.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(a=>At({},a)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new Re(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Dt(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Mt(this.runAndExtendWithFaceDetections(),this.input)}},He=class extends Oe{async run(){let t=await new me(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?At({},e):void 0)})}withFaceLandmarks(t=!1){return new $e(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Et(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Ct(this.runAndExtendWithFaceDetection(),this.input)}};function Jh(o,t=new z){return new He(o,t)}function mr(o,t=new z){return new me(o,t)}async function rn(o,t){return mr(o,new z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function eb(o,t={}){return mr(o,new J(t)).withFaceLandmarks().withFaceDescriptors()}var rb=rn;function ho(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((a,s)=>a-r[s]).reduce((a,s)=>a+s*s,0))}var ze=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let a=1,s=()=>`person ${a++}`;this._labeledDescriptors=r.map(i=>{if(i instanceof Q)return i;if(i instanceof Float32Array)return new Q(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Q(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>ho(r,t)).reduce((r,a)=>r+a,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Kt(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>Q.fromJSON(r));return new ze(e,t.distanceThreshold)}};function yb(o){let t=new Zt;return t.extractWeights(o),t}function on(o,t){let{width:e,height:r}=new S(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(a=>on(a,{width:e,height:r}));if(Yt(o)){let a=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(a.box.width,a.box.height);return ie(At(o,a),s)}return tt(o)?At(o,o.detection.forSize(e,r)):o instanceof $||o instanceof E?o.forSize(e,r):o}var Nb=kr;export{Me as AgeGenderNet,Nt as BoundingBox,F as Box,V as ComposableTask,ht as ComputeAllFaceDescriptorsTask,ke as ComputeFaceDescriptorsTaskBase,bt as ComputeSingleFaceDescriptorTask,Re as DetectAllFaceLandmarksTask,me as DetectAllFacesTask,Be as DetectFaceLandmarksTaskBase,Oe as DetectFacesTaskBase,$e as DetectSingleFaceLandmarksTask,He as DetectSingleFaceTask,S as Dimensions,Lr as FACE_EXPRESSION_LABELS,E as FaceDetection,eo as FaceDetectionNet,Pe as FaceExpressionNet,pt as FaceExpressions,jt as FaceLandmark68Net,Ie as FaceLandmark68TinyNet,Yr as FaceLandmarkNet,$ as FaceLandmarks,yr as FaceLandmarks5,Lt as FaceLandmarks68,Kt as FaceMatch,ze as FaceMatcher,Ut as FaceRecognitionNet,rr as Gender,Qt as LabeledBox,Q as LabeledFaceDescriptors,rt as NetInput,I as NeuralNetwork,ct as ObjectDetection,g as Point,_r as PredictedBox,St as Rect,wt as SsdMobilenetv1,z as SsdMobilenetv1Options,Zt as TinyFaceDetector,Le as TinyFaceDetectorOptions,Jt as TinyYolov2,J as TinyYolov2Options,rb as allFaces,rn as allFacesSsdMobilenetv1,eb as 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m=e(`${c}/sub`,1),p=e(`${c}/truediv`,1);return{sub:m,truediv:p}}function a(c){let m=e(`${c}/filters`,4),p=e(`${c}/bias`,1);return{filters:m,bias:p}}function s(c){let m=a(`${c}/conv`),p=r(`${c}/bn`);return{conv:m,bn:p}}let i=Ht(e);return{extractConvParams:a,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function po(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}=Ko(o,e),i;if(t.withSeparableConvs){let c=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?r("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:c>7?s("conv6"):void 0,conv7:c>8?s("conv7"):void 0,conv8:r("conv8")}}else i={conv0:a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:a("conv6"),conv7:a("conv7"),conv8:r("conv8")};return L(o,e),{params:i,paramMappings:e}}var J=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var cr=class extends I{constructor(e){super("TinyYolov2");co(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,r){let a=ot(e,r.conv0);return a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv1),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv2),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv3),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv4),a=n.maxPool(a,[2,2],[2,2],"same"),a=ot(a,r.conv5),a=n.maxPool(a,[2,2],[1,1],"same"),a=ot(a,r.conv6),a=ot(a,r.conv7),_t(a,r.conv8,"valid",!1)}runMobilenet(e,r){let a=this.config.isFirstLayerConv2d?Xt(_t(e,r.conv0,"valid",!1)):nt(e,r.conv0);return a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv1),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv2),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv3),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv4),a=n.maxPool(a,[2,2],[2,2],"same"),a=nt(a,r.conv5),a=n.maxPool(a,[2,2],[1,1],"same"),a=r.conv6?nt(a,r.conv6):a,a=r.conv7?nt(a,r.conv7):a,_t(a,r.conv8,"valid",!1)}forwardInput(e,r){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return n.tidy(()=>{let s=n.cast(e.toBatchTensor(r,!1),"float32");return s=this.config.meanRgb?X(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,a):this.runTinyYolov2(s,a)})}async forward(e,r){return this.forwardInput(await M(e),r)}async detect(e,r={}){let{inputSize:a,scoreThreshold:s}=new J(r),i=await M(e),c=await this.forwardInput(i,a),m=n.tidy(()=>n.unstack(c)[0].expandDims()),p={width:i.getInputWidth(0),height:i.getInputHeight(0)},u=await this.extractBoxes(m,i.getReshapedInputDimensions(0),s);c.dispose(),m.dispose();let f=u.map(h=>h.box),l=u.map(h=>h.score),d=u.map(h=>h.classScore),b=u.map(h=>this.config.classes[h.label]);return xr(f.map(h=>h.rescale(a)),l,this.config.iouThreshold,!0).map(h=>new ct(l[h],d[h],b[h],f[h],p))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return po(e,this.config)}extractParams(e){let r=this.config.filterSizes||cr.DEFAULT_FILTER_SIZES,a=r?r.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return mo(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,a){let{width:s,height:i}=r,c=Math.max(s,i),m=c/s,p=c/i,u=e.shape[1],f=this.config.anchors.length,[l,d,b]=n.tidy(()=>{let y=e.reshape([u,u,f,this.boxEncodingSize]),v=y.slice([0,0,0,0],[u,u,f,4]),D=y.slice([0,0,0,4],[u,u,f,1]),N=this.withClassScores?n.softmax(y.slice([0,0,0,5],[u,u,f,this.config.classes.length]),3):n.scalar(0);return[v,D,N]}),x=[],w=await d.array(),h=await l.array();for(let y=0;ya){let Y=(v+fe(h[y][v][D][0]))/u*m,q=(y+fe(h[y][v][D][1]))/u*p,O=Math.exp(h[y][v][D][2])*this.config.anchors[D].x/u*m,at=Math.exp(h[y][v][D][3])*this.config.anchors[D].y/u*p,st=Y-O/2,it=q-at/2,gt={row:y,col:v,anchor:D},{classScore:pr,label:ur}=this.withClassScores?await this.extractPredictedClass(b,gt):{classScore:1,label:0};x.push({box:new Nt(st,it,st+O,it+at),score:N,classScore:N*pr,label:ur,...gt})}}return l.dispose(),d.dispose(),b.dispose(),x}async extractPredictedClass(e,r){let{row:a,col:s,anchor:i}=r,c=await e.array();return Array(this.config.classes.length).fill(0).map((m,p)=>c[a][s][i][p]).map((m,p)=>({classScore:m,label:p})).reduce((m,p)=>m.classScore>p.classScore?m:p)}},Pt=cr;Pt.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Jt=class extends Pt{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:ro,classes:["face"],...t?{anchors:no,meanRgb:ao}:{anchors:oo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(a=>new E(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?io:so}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function id(o,t=!0){let e=new Jt(t);return e.extractWeights(o),e}var Le=class extends J{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var V=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function Ft(o,t,e,r,a=({alignedRect:s})=>s){let s=o.map(m=>Yt(m)?a(m):m.detection),i=r||(t instanceof n.Tensor?await oe(t,s):await re(t,s)),c=await e(i);return i.forEach(m=>m instanceof n.Tensor&&m.dispose()),c}async function qt(o,t,e,r,a){return Ft([o],t,async s=>e(s[0]),r,a)}var uo=.4,fo=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],lo=[117.001,114.697,97.404];var Zt=class extends Pt{constructor(){let t={withSeparableConvs:!0,iouThreshold:uo,classes:["face"],anchors:fo,meanRgb:lo,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(a=>new E(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var T={ssdMobilenetv1:new wt,tinyFaceDetector:new Zt,tinyYolov2:new Jt,faceLandmark68Net:new jt,faceLandmark68TinyNet:new Ie,faceRecognitionNet:new Ut,faceExpressionNet:new Pe,ageGenderNet:new Me},Qo=(o,t)=>T.ssdMobilenetv1.locateFaces(o,t),Bd=(o,t)=>T.tinyFaceDetector.locateFaces(o,t),Rd=(o,t)=>T.tinyYolov2.locateFaces(o,t),tn=o=>T.faceLandmark68Net.detectLandmarks(o),$d=o=>T.faceLandmark68TinyNet.detectLandmarks(o),Od=o=>T.faceRecognitionNet.computeFaceDescriptor(o),Hd=o=>T.faceExpressionNet.predictExpressions(o),zd=o=>T.ageGenderNet.predictAgeAndGender(o),en=o=>T.ssdMobilenetv1.load(o),Vd=o=>T.tinyFaceDetector.load(o),Yd=o=>T.tinyYolov2.load(o),Gd=o=>T.faceLandmark68Net.load(o),jd=o=>T.faceLandmark68TinyNet.load(o),Ud=o=>T.faceRecognitionNet.load(o),Xd=o=>T.faceExpressionNet.load(o),Jd=o=>T.ageGenderNet.load(o),qd=en,Zd=Qo,Kd=tn;var Ae=class extends V{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Dt=class extends Ae{async run(){let t=await this.parentTask,e=await Ft(t,this.input,async r=>Promise.all(r.map(a=>T.faceExpressionNet.predictExpressions(a))),this.extractedFaces);return t.map((r,a)=>tr(r,e[a]))}withAgeAndGender(){return new Mt(this,this.input)}},Et=class extends Ae{async run(){let t=await this.parentTask;if(!t)return;let e=await qt(t,this.input,r=>T.faceExpressionNet.predictExpressions(r),this.extractedFaces);return tr(t,e)}withAgeAndGender(){return new Ct(this,this.input)}},ut=class extends Dt{withAgeAndGender(){return new lt(this,this.input)}withFaceDescriptors(){return new ht(this,this.input)}},ft=class extends Et{withAgeAndGender(){return new dt(this,this.input)}withFaceDescriptor(){return new bt(this,this.input)}};var We=class extends V{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Mt=class extends We{async run(){let t=await this.parentTask,e=await Ft(t,this.input,async r=>Promise.all(r.map(a=>T.ageGenderNet.predictAgeAndGender(a))),this.extractedFaces);return t.map((r,a)=>{let{age:s,gender:i,genderProbability:c}=e[a];return sr(ir(r,i,c),s)})}withFaceExpressions(){return new Dt(this,this.input)}},Ct=class extends We{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:a}=await qt(t,this.input,s=>T.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return sr(ir(t,r,a),e)}withFaceExpressions(){return new Et(this,this.input)}},lt=class extends Mt{withFaceExpressions(){return new ut(this,this.input)}withFaceDescriptors(){return new ht(this,this.input)}},dt=class extends Ct{withFaceExpressions(){return new ft(this,this.input)}withFaceDescriptor(){return new bt(this,this.input)}};var ke=class extends V{constructor(e,r){super();this.parentTask=e;this.input=r}},ht=class extends ke{async run(){let t=await this.parentTask;return(await Ft(t,this.input,r=>Promise.all(r.map(a=>T.faceRecognitionNet.computeFaceDescriptor(a))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,a)=>ar(t[a],r))}withFaceExpressions(){return new ut(this,this.input)}withAgeAndGender(){return new lt(this,this.input)}},bt=class extends ke{async run(){let t=await this.parentTask;if(!t)return;let e=await qt(t,this.input,r=>T.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return ar(t,e)}withFaceExpressions(){return new ft(this,this.input)}withAgeAndGender(){return new dt(this,this.input)}};var Be=class extends V{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?T.faceLandmark68TinyNet:T.faceLandmark68Net}},Re=class extends Be{async run(){let t=await this.parentTask,e=t.map(i=>i.detection),r=this.input instanceof n.Tensor?await oe(this.input,e):await re(this.input,e),a=await Promise.all(r.map(i=>this.landmarkNet.detectLandmarks(i)));return r.forEach(i=>i instanceof n.Tensor&&i.dispose()),t.filter((i,c)=>a[c]).map((i,c)=>ie(i,a[c]))}withFaceExpressions(){return new ut(this,this.input)}withAgeAndGender(){return new lt(this,this.input)}withFaceDescriptors(){return new ht(this,this.input)}},$e=class extends Be{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof n.Tensor?await oe(this.input,[e]):await re(this.input,[e]),a=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof n.Tensor&&s.dispose()),ie(t,a)}withFaceExpressions(){return new ft(this,this.input)}withAgeAndGender(){return new dt(this,this.input)}withFaceDescriptor(){return new bt(this,this.input)}};var Oe=class extends V{constructor(e,r=new z){super();this.input=e;this.options=r}},me=class extends Oe{async run(){let{input:t,options:e}=this,r;if(e instanceof Le)r=T.tinyFaceDetector.locateFaces(t,e);else if(e instanceof z)r=T.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof J)r=T.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(a=>At({},a)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new Re(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Dt(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Mt(this.runAndExtendWithFaceDetections(),this.input)}},He=class extends Oe{async run(){let t=await new me(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?At({},e):void 0)})}withFaceLandmarks(t=!1){return new $e(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Et(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Ct(this.runAndExtendWithFaceDetection(),this.input)}};function Jh(o,t=new z){return new He(o,t)}function mr(o,t=new z){return new me(o,t)}async function rn(o,t){return mr(o,new z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function eb(o,t={}){return mr(o,new J(t)).withFaceLandmarks().withFaceDescriptors()}var rb=rn;function ho(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((a,s)=>a-r[s]).reduce((a,s)=>a+s*s,0))}var ze=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let a=1,s=()=>`person ${a++}`;this._labeledDescriptors=r.map(i=>{if(i instanceof Q)return i;if(i instanceof Float32Array)return new Q(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Q(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>ho(r,t)).reduce((r,a)=>r+a,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Kt(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>Q.fromJSON(r));return new ze(e,t.distanceThreshold)}};function yb(o){let t=new Zt;return t.extractWeights(o),t}function on(o,t){let{width:e,height:r}=new S(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(a=>on(a,{width:e,height:r}));if(Yt(o)){let a=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(a.box.width,a.box.height);return ie(At(o,a),s)}return tt(o)?At(o,o.detection.forSize(e,r)):o instanceof $||o instanceof E?o.forSize(e,r):o}var Nb=kr;export{Me as AgeGenderNet,Nt as BoundingBox,F as Box,V as ComposableTask,ht as ComputeAllFaceDescriptorsTask,ke as ComputeFaceDescriptorsTaskBase,bt as ComputeSingleFaceDescriptorTask,Re as DetectAllFaceLandmarksTask,me as DetectAllFacesTask,Be as DetectFaceLandmarksTaskBase,Oe as DetectFacesTaskBase,$e as DetectSingleFaceLandmarksTask,He as DetectSingleFaceTask,S as Dimensions,Lr as FACE_EXPRESSION_LABELS,E as FaceDetection,eo as FaceDetectionNet,Pe as FaceExpressionNet,pt as FaceExpressions,jt as FaceLandmark68Net,Ie as FaceLandmark68TinyNet,Yr as FaceLandmarkNet,$ as FaceLandmarks,yr as FaceLandmarks5,Lt as FaceLandmarks68,Kt as FaceMatch,ze as FaceMatcher,Ut as FaceRecognitionNet,rr as Gender,Qt as LabeledBox,Q as LabeledFaceDescriptors,rt as NetInput,I as NeuralNetwork,ct as ObjectDetection,g as Point,_r as PredictedBox,St as Rect,wt as SsdMobilenetv1,z as SsdMobilenetv1Options,Zt as TinyFaceDetector,Le as TinyFaceDetectorOptions,Jt as TinyYolov2,J as TinyYolov2Options,rb as allFaces,rn as allFacesSsdMobilenetv1,eb as allFacesTinyYolov2,Tr as awaitMediaLoaded,wr as bufferToImage,Od as computeFaceDescriptor,Rt as createCanvas,be as createCanvasFromMedia,bl as createFaceDetectionNet,bf as createFaceRecognitionNet,qo as createSsdMobilenetv1,yb as createTinyFaceDetector,id as createTinyYolov2,mr as detectAllFaces,tn as detectFaceLandmarks,$d as detectFaceLandmarksTiny,Kd as detectLandmarks,Jh as detectSingleFace,Wr as draw,_ as env,ho as euclideanDistance,sr as extendWithAge,ar as extendWithFaceDescriptor,At as extendWithFaceDetection,tr as extendWithFaceExpressions,ie as extendWithFaceLandmarks,ir as extendWithGender,oe as extractFaceTensors,re as extractFaces,Fi as fetchImage,Dr as fetchJson,Ii as fetchNetWeights,mt as fetchOrThrow,ki as fetchVideo,k as getContext2dOrThrow,Bt as getMediaDimensions,Pr as imageTensorToCanvas,Fr as imageToSquare,On as inverseSigmoid,br as iou,Qe as isMediaElement,he as isMediaLoaded,yf as isWithAge,tt as isWithFaceDetection,Ar as isWithFaceExpressions,Yt as isWithFaceLandmarks,Pf as isWithGender,Jd as loadAgeGenderModel,qd as loadFaceDetectionModel,Xd as loadFaceExpressionModel,Gd as loadFaceLandmarkModel,jd as loadFaceLandmarkTinyModel,Ud as loadFaceRecognitionModel,en as loadSsdMobilenetv1Model,Vd as loadTinyFaceDetectorModel,Yd as loadTinyYolov2Model,Mr as loadWeightMap,Zd as locateFaces,Vi as matchDimensions,gr as minBbox,T as nets,xr as nonMaxSuppression,X as normalize,vr as padToSquare,zd as predictAgeAndGender,Hd as recognizeFaceExpressions,on as resizeResults,Wt as resolveInput,Rn as shuffleArray,fe as sigmoid,Qo as ssdMobilenetv1,n as tf,Bd as tinyFaceDetector,Rd as tinyYolov2,M as toNetInput,hr as utils,co as validateConfig,Nb as version}; diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js index 43e2e3d0..513df14a 100644 --- a/dist/face-api.esm.js +++ b/dist/face-api.esm.js @@ -4,65 +4,65 @@ author: ' */ -var iF=Object.defineProperty;var jr=(e=>typeof require!="undefined"?require:typeof Proxy!="undefined"?new 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this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*Ib(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof ss||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let 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this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*hh(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof ss||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let 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t={x:_(e,"x","tanh","float32")};return O.runKernel(go,t)}var ii=L({tanh_:ZD});function JD(e,t,n,a,r,s){let i=_(e,"forgetBias","basicLSTMCell"),o=_(t,"lstmKernel","basicLSTMCell"),l=_(n,"lstmBias","basicLSTMCell"),u=_(a,"data","basicLSTMCell"),p=_(r,"c","basicLSTMCell"),d=_(s,"h","basicLSTMCell"),c=Ze([u,d],1),h=Re(c,o),m=X(h,l),f=m.shape[0],g=m.shape[1]/4,y=[f,g],b=Ve(m,[0,0],y),x=Ve(m,[0,g],y),w=Ve(m,[0,g*2],y),I=Ve(m,[0,g*3],y),T=X(z(da(b),ii(x)),z(p,da(X(i,w)))),C=z(ii(T),da(I));return[T,C]}var sS=L({basicLSTMCell_:JD});function QD(e,t,n){let a=_(e,"x","batchToSpaceND"),r=t.reduce((o,l)=>o*l);A(a.rank>=1+t.length,()=>`input rank is ${a.rank} but should be > than blockShape.length ${t.length}`),A(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),A(a.shape[0]%r===0,()=>`input tensor batch is ${a.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let s={x:a},i={blockShape:t,crops:n};return O.runKernel($l,s,i)}var Cc=L({batchToSpaceND_:QD});function eM(e){let t;return e.rank===0||e.rank===1?t=W(e,[1,1,1,e.size]):e.rank===2?t=W(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=W(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function tM(e,t,n,a,r,s){s==null&&(s=.001);let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),u;r!=null&&(u=_(r,"scale","batchNorm"));let p;a!=null&&(p=_(a,"offset","batchNorm")),A(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),A(p==null||o.rank===p.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),A(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let d={x:eM(i),scale:u,offset:p,mean:o,variance:l},c={varianceEpsilon:s},h=O.runKernel(Di,d,c);return W(h,i.shape)}var vs=L({batchNorm_:tM});function nM(e,t,n,a,r,s){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),u;r!=null&&(u=_(r,"scale","batchNorm"));let p;return a!=null&&(p=_(a,"offset","batchNorm")),A(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),A(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),A(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),p!=null&&A(p.rank===2||p.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${p.rank}.`),vs(i,o,l,p,u,s)}var zx=L({batchNorm2d_:nM});function aM(e,t,n,a,r,s){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),u;r!=null&&(u=_(r,"scale","batchNorm"));let p;return a!=null&&(p=_(a,"offset","batchNorm")),A(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),A(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),A(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),p!=null&&A(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${p.rank}.`),vs(i,o,l,p,u,s)}var Wx=L({batchNorm3d_:aM});function rM(e,t,n,a,r,s){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),u;r!=null&&(u=_(r,"scale","batchNorm"));let p;return a!=null&&(p=_(a,"offset","batchNorm")),A(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),A(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),A(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),p!=null&&A(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${p.rank}.`),vs(i,o,l,p,u,s)}var Bx=L({batchNorm4d_:rM});function sM(e,t,n){let a=_(e,"x","bincount"),r=_(t,"weights","bincount");A(a.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${a.dtype}`),A(n>=0,()=>`size must be non-negative, but got ${n}.`),A(r.size===a.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${a.shape}, weights shape: ${r.shape}.`);let s={x:a,weights:r},i={size:n};return O.runKernel(Zh,s,i)}var Vx=L({bincount_:sM});function iM(e,t){let n=_(e,"s0","broadcastArgs","int32"),a=_(t,"s1","broadcastArgs","int32");if(n.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${n.rank}`);if(a.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${a.rank}`);let r={s0:n,s1:a};return O.runKernel(Jh,r)}var iS=L({broadcastArgs_:iM});function oM(e,t){let n=_(e,"broadcastTo","x"),a=n.shape;if(na(t),t.lengthn.rank){let l=n.shape.slice();for(;l.length=0;l--)if(r[l]===t[l])s[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${a}] cannot be broadcast to [${t}].`);if(s.map((l,u)=>l>1?u:-1).filter(l=>l>=0).length===0)return sr(n);let i={x:n},o={reps:s};return O.runKernel(ys,i,o)}var Ys=L({broadcastTo_:oM});function lM(e){let t={x:_(e,"x","ceil","float32")};return O.runKernel(ki,t)}var Ux=L({ceil_:lM});function bn(e,t,n){na(e);let a={shape:e,value:t,dtype:n};return O.runKernel(uc,{},a)}function uM(e,t,n){let a=_(e,"x","clipByValue");if(A(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`),t===n)return bn(a.shape,t,a.dtype);let r={x:a},s={clipValueMin:t,clipValueMax:n};return O.runKernel(gs,r,s)}var tn=L({clipByValue_:uM});function pM(e){return Ze(e,0)}var Gx=L({concat1d_:pM});function cM(e,t){return Ze(e,t)}var Hx=L({concat2d_:cM});function dM(e,t){return Ze(e,t)}var qx=L({concat3d_:dM});function hM(e,t){return Ze(e,t)}var jx=L({concat4d_:hM});function mM(e,t,n,a,r="NHWC",s=[1,1],i){let o=_(e,"x","conv2d","float32"),l=_(t,"filter","conv2d","float32"),u=o,p=!1;o.rank===3&&(p=!0,u=W(o,[1,o.shape[0],o.shape[1],o.shape[2]])),A(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),A(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),Tn("conv2d",a,i);let d=r==="NHWC"?u.shape[3]:u.shape[1];A(d===l.shape[2],()=>`Error in conv2d: depth of input (${d}) must match input depth for filter ${l.shape[2]}.`),A(cr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),A(si(s),()=>"Error in conv2D: Dilated rates should be larger than 0."),A(si(n),()=>"Error in conv2D: Strides should be larger than 0.");let c={x:u,filter:l},h={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i},m=O.runKernel(Ii,c,h);return p?W(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Rt=L({conv2d_:mM});function fM(e,t,n,a,r="NWC",s=1,i){let o=_(e,"x","conv1d"),l=_(t,"filter","conv1d"),u=o,p=!1;o.rank===2&&(p=!0,u=W(o,[1,o.shape[0],o.shape[1]])),A(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),A(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),Tn("conv1d",a,i),A(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),A(cr(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),A(si(s),()=>"Error in conv1D: Dilated rates should be larger than 0."),A(si(n),()=>"Error in conv1D: Stride should be larger than 0."),A(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let d=W(l,[1,l.shape[0],l.shape[1],l.shape[2]]),c=W(u,[u.shape[0],1,u.shape[1],u.shape[2]]),h=Rt(c,d,[1,n],a,"NHWC",[1,s],i);return p?W(h,[h.shape[2],h.shape[3]]):W(h,[h.shape[0],h.shape[2],h.shape[3]])}var Fm=L({conv1d_:fM});function gM(e,t,n,a,r,s="NHWC",i){A(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,u=!1;t.rank===3&&(u=!0,l=W(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),A(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),A(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),A(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let p=s==="NHWC"?o[3]:o[1],d=s==="NHWC"?l.shape[3]:l.shape[1];A(p===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${p}) must match input depth for filter ${n.shape[2]}.`),A(d===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${d}) must match output depth for filter ${n.shape[3]}.`),Tn("conv2dDerInput",r,i);let c={dy:l,filter:n},h={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,inputShape:o},m=O.runKernel(Si,c,h);return u?W(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Kx=L({conv2DBackpropInput_:gM});function yM(e,t,n,a,r,s){let i=_(e,"x","conv2dTranspose"),o=_(t,"filter","conv2dTranspose");return Kx(n,i,o,a,r,"NHWC",s)}var Rm=L({conv2dTranspose_:yM});function bM(e,t,n,a,r="NDHWC",s=[1,1,1]){let i=_(e,"x","conv3d"),o=_(t,"filter","conv3d"),l=i,u=!1;i.rank===4&&(u=!0,l=W(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),A(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),A(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),A(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),A(cr(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. 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Got strides ${n} and dilations '${s}'`);let g=Nc(h.shape,c.shape,n,s,a,i),y;o!=null&&(y=_(o,"bias","fused conv2d"),[y]=Et(y,d),r==="NHWC"?ot(g.outShape,y.shape):(A(y.shape.length<=1,()=>`Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${y.shape.length}.`),A(y.shape.length===0||y.shape[0]===g.outChannels||y.shape[0]===1,()=>`Error in fused conv2d: bias shape (${y.shape}) is not compatible with the number of output channels (${g.outChannels})`)));let b;if(u!=null){let T=u.shape;if(A(T.length<=1||T.length===3,()=>`Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${T.length}.`),T.length===1)A(T[0]===1||T[0]===g.outChannels,()=>`Error in fused conv2d: PReLU activation weights (${T}) is not compatible with the number of output channels (${g.outChannels}).`);else if(T.length===3)try{ot(T,g.outShape)}catch(C){let E=`Error in fused conv2d: PReLU activation weights (${T}) is not compatible with the output shape of the conv2d (${g.outShape}).`;throw Error(E)}b=_(u,"prelu weights","fused conv2d")}let x=(T,C)=>{A(r==="NHWC",()=>`Error in gradient of fused conv2D: got dataFormat of ${r} but only NHWC is currently supported.`);let[E,$,R,F]=C,S=nf(T,R,l);A(is(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let M=Kx($.shape,S,E,n,a),B=Dv($,S,E.shape,n,a),U=[M,B];if(F!=null){let G=af(F,S);U.push(G)}return U},w={x:h,filter:c,bias:y,preluActivationWeights:b},I={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:p};return o==null?ur((T,C,E)=>{let $=O.runKernel(ei,w,I);return E([C,T,$]),m&&($=W($,[$.shape[1],$.shape[2],$.shape[3]])),{value:$,gradFunc:x}})(h,c):ur((T,C,E,$)=>{let R=O.runKernel(ei,w,I);return $([C,T,R,E]),m&&(R=W(R,[R.shape[1],R.shape[2],R.shape[3]])),{value:R,gradFunc:x}})(h,c,y)}var g3=L({fusedConv2d_:f3});function y3(e,t,n,a,r,s=[1,1],i){let o=e;e.rank===3&&(o=W(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=W(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={x:o,dy:l},p={strides:a,pad:r,dimRoundingMode:i,dilations:s,filterShape:n};return O.runKernel(rm,u,p)}var JS=L({depthwiseConv2dNativeBackpropFilter_:y3});function b3(e,t,n,a,r,s=[1,1],i){let o=t,l=!1;t.rank===3&&(l=!0,o=W(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={dy:o,filter:n},p={strides:a,pad:r,dimRoundingMode:i,dilations:s,inputShape:e},d=O.runKernel(sm,u,p);return l?W(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var QS=L({depthwiseConv2dNativeBackpropInput_:b3});function x3({x:e,filter:t,strides:n,pad:a,dataFormat:r="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:p}){if(sf(O.state.gradientDepth,l)===!1){let I=ws(e,t,n,a,r,s,i);return o!=null&&(I=X(I,o)),rf(I,l,u,p)}let d=_(e,"x","depthwiseConv2d","float32"),c=_(t,"filter","depthwiseConv2d","float32"),h=d,m=!1;d.rank===3&&(m=!0,h=W(d,[1,d.shape[0],d.shape[1],d.shape[2]])),A(h.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${h.rank}.`),A(c.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${c.rank}.`),A(h.shape[3]===c.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${h.shape[3]}) must match the inChannels dimension in filter ${c.shape[2]}.`),s==null&&(s=[1,1]),A(cr(n,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. 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Got dilations '${s}'`);let[C,E,$,R]=T,F=nf(I,$,l),S=QS(E.shape,F,C,n,a,s,i),M=JS(E,F,C.shape,n,a,s,i);if(R!=null){let B=af(g,F);return[S,M,B]}return[S,M]},x={x:h,filter:c,bias:g,preluActivationWeights:y},w={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:p};return o==null?ur((I,T,C)=>{let E=O.runKernel(ti,x,w);return C([T,I,E]),m&&(E=W(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:b}})(h,c):ur((I,T,C,E)=>{let $=O.runKernel(ti,x,w);return E([T,I,$,C]),m&&($=W($,[$.shape[1],$.shape[2],$.shape[3]])),{value:$,gradFunc:b}})(h,c,g)}var v3=L({fusedDepthwiseConv2d_:x3});function w3({a:e,b:t,transposeA:n=!1,transposeB:a=!1,bias:r,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:o=.2}){if(sf(O.state.gradientDepth,s)===!1){let R=Re(e,t,n,a);return r!=null&&(R=X(R,r)),rf(R,s,i,o)}let l=_(e,"a","fused matMul"),u=_(t,"b","fused matMul");[l,u]=Et(l,u);let p=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=a?u.shape[u.rank-1]:u.shape[u.rank-2],c=n?l.shape[l.rank-1]:l.shape[l.rank-2],h=a?u.shape[u.rank-2]:u.shape[u.rank-1],m=l.shape.slice(0,-2),f=u.shape.slice(0,-2),g=mt(m),y=mt(f);A(p===d,()=>`Error in fused matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${l.shape} and ${u.shape} and transposeA=${n} and transposeB=${a} must match.`);let b=ot(l.shape.slice(0,-2),u.shape.slice(0,-2)).concat([c,h]),x=n?W(l,[g,p,c]):W(l,[g,c,p]),w=a?W(u,[y,h,d]):W(u,[y,d,h]),I;r!=null&&(I=_(r,"bias","fused matMul"),[I]=Et(I,l),ot(b,I.shape));let T;i!=null&&(T=_(i,"prelu weights","fused matMul"));let C=(R,F)=>{let[S,M,B,U]=F,G=nf(W(R,B.shape),B,s),j,K;if(!n&&!a?(j=Re(G,M,!1,!0),K=Re(S,G,!0,!1)):!n&&a?(j=Re(G,M,!1,!1),K=Re(G,S,!0,!1)):n&&!a?(j=Re(M,G,!1,!0),K=Re(S,G,!1,!1)):(j=Re(M,G,!0,!0),K=Re(G,S,!0,!0)),r!=null){let Z=af(U,G);return[j,K,Z]}else return[j,K]},E={a:x,b:w,bias:I,preluActivationWeights:T},$={transposeA:n,transposeB:a,activation:s,leakyreluAlpha:o};return r==null?ur((R,F,S)=>{let M=O.runKernel(Qs,E,$);return S([R,F,M]),{value:W(M,b),gradFunc:C}})(x,w):ur((R,F,S,M)=>{let B=O.runKernel(Qs,E,$);return M([R,F,B,S]),{value:W(B,b),gradFunc:C}})(x,w,I)}var k3=L({fusedMatMul_:w3});function I3(e){return tf(e,.54,.46)}var S3=L({hammingWindow_:I3});function T3(e){return tf(e,.5,.5)}var eT=L({hannWindow_:T3});function N3(e,t,n,a=!1,r=0){let s=0,i=[];for(;s+t<=e.size;)i.push(Ve(e,s,t)),s+=n;if(a)for(;s`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),A(o.rank===2&&o.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${u},4] but had shape ${o.shape}.`),A(l.rank===1&&l.shape[0]===u,()=>`Error in cropAndResize: boxInd must be have size [${u}] but had shape ${o.shape}.`),A(a.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${a.length}.`),A(a[0]>=1&&a[1]>=1,()=>`cropSize must be atleast [1,1], but was ${a}`),A(r==="bilinear"||r==="nearest",()=>`method must be bilinear or nearest, but was ${r}`);let p={image:i,boxes:o,boxInd:l},d={method:r,extrapolationValue:s,cropSize:a};return O.runKernel(Dl,p,d)}var A3=L({cropAndResize_:E3});function $3(e){let t=_(e,"image","flipLeftRight","float32");A(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let n={image:t};return O.runKernel(Wl,n,{})}var F3=L({flipLeftRight_:$3});function R3(e){let t=_(e,"image","grayscaleToRGB"),n=t.rank-1,a=t.shape[n];A(t.rank>=2,()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`),A(a===1,()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${a}.`);let r=new Array(t.rank);return r.fill(1,0,n),r[n]=3,Ln(t,r)}var D3=L({grayscaleToRGB_:R3});function M3(e,t,n=0,a=.5){let r=_(e,"image","rotateWithOffset","float32");A(r.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);let s={image:r},i={radians:t,fillValue:n,center:a};return O.runKernel(ku,s,i)}var P3=L({rotateWithOffset_:M3});function Au(e,t,n,a,r,s){a==null&&(a=.5),r==null&&(r=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),A(0<=a&&a<=1,()=>`iouThreshold must be in [0, 1], but was '${a}'`),A(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),A(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),A(t.rank===1,()=>"scores must be a 1D tensor"),A(t.shape[0]===i,()=>`scores has incompatible shape with boxes. 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a=Math.exp(t*n*n);return n<=e?a:0}function H1(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function H3(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY){let s=_(e,"boxes","nonMaxSuppressionAsync"),i=_(t,"scores","nonMaxSuppressionAsync"),o=Au(s,i,n,a,r);n=o.maxOutputSize,a=o.iouThreshold,r=o.scoreThreshold;let l=await Promise.all([s.data(),i.data()]),u=l[0],p=l[1],{selectedIndices:d}=nT(u,p,n,a,r);return s!==e&&s.dispose(),i!==t&&i.dispose(),je(d,"int32")}var q3=H3;function j3(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let i=_(e,"boxes","nonMaxSuppression"),o=_(t,"scores","nonMaxSuppression"),l=Au(i,o,n,a,r,s);n=l.maxOutputSize,a=l.iouThreshold,r=l.scoreThreshold,s=l.softNmsSigma;let u={boxes:i,scores:o},p={maxOutputSize:n,iouThreshold:a,scoreThreshold:r,softNmsSigma:s},d=O.runKernel(au,u,p);return{selectedIndices:d[0],selectedScores:d[1]}}var K3=L({nonMaxSuppressionWithScore_:j3});async function X3(e,t,n,a=.5,r=Number.NEGATIVE_INFINITY,s=0){let 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i=_(e,"boxes","nonMaxSuppressionAsync"),o=_(t,"scores","nonMaxSuppressionAsync"),l=Au(i,o,n,a,r,null),u=l.maxOutputSize,p=l.iouThreshold,d=l.scoreThreshold,[c,h]=await Promise.all([i.data(),o.data()]),{selectedIndices:m,validOutputs:f}=aT(c,h,u,p,d,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:je(m,"int32"),validOutputs:xe(f,"int32")}}var eL=Q3;function tL(e,t,n=!1,a=!1){let r=_(e,"images","resizeBilinear");A(r.rank===3||r.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${r.rank}.`),A(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),A(a===!1||n===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=r,i=!1;r.rank===3&&(i=!0,s=W(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:a,size:t},u=O.runKernel(no,o,l);return i?W(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var sT=L({resizeBilinear_:tL});function nL(e,t,n=!1,a=!1){let r=_(e,"images","resizeNearestNeighbor");A(r.rank===3||r.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`),A(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),A(r.dtype==="float32"||r.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),A(a===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=r,i=!1;r.rank===3&&(i=!0,s=W(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:a,size:t},u=O.runKernel(to,o,l);return i?W(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var iT=L({resizeNearestNeighbor_:nL});function aL(e,t="binary",n=!1,a=.5){let r=_(e,"image","threshold"),s=.2989,i=.587,o=.114,l=r.shape[0]*r.shape[1],u=z(je([a]),255),p,d,c,h;if(A(r.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${r.rank}.`),A(r.shape[2]===3||r.shape[2]===1,()=>`Error 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i=O.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:qe(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:qe(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedWeightedInfNorm[s].variable,d=Y(z(u,this.beta1),z(l,1-this.beta1)),c=z(p,this.beta2),h=Lt(l),m=dr(c,h);u.assign(d),p.assign(m);let f=Y(z(me(a,n),me(d,Y(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(Y(this.iteration,1)),this.accBeta1.assign(z(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ee(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ee(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};cf.className="Adamax";vs(cf);var Vc=class extends Dr{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=O.registeredVariables[t];P(()=>{let s=Y(z(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(xe(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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Pz(e,t){if(e==null||t==null)return;let n=e.length,a=t.length;if(n>=a)throw new Error(`defaultValue.shape=${e} and ragged tensor flatValues.shape=${t}, are incompatible: defaultValue.rank = ${n} must be less than ragged tensor input flatValues.rank = ${a})`);for(let r=0;r=0&&i>=0&&s!==1&&s!==i)throw new Error(`defaultValue.shape=${e}, and ragged tensor input flatValues.shape=${t} are incompatible: defaultValue.shape[${r-e.length}] = ${s} but ragged tensor input.flatValues.shape[${r-e.length}] = ${i}`)}}var Kv=30;function Oz(e){return e<=Kv?e:fh(e,Math.floor(Math.sqrt(e)))}function Lz(e,t,n){let a=n*(typeof e=="number"?e:e[0]),r=t*(typeof e=="number"?e:e[1]);return[a,r]}function zz(e,t,n,a=!0){let r=[];if(a)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let s=t.length;for(let i=0;i=t*2+1||i%2===1?s.push(i):r.push(i);a.push(...r),a.push(0),a.push(...s)}return a}function Bz(e,t,n,a=!0){let r=[];a?r.push(e[0]/n):r.push(e[0]*n);for(let 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e(t)}},Hs=class{constructor(){this.classNameMap={}}static getMap(){return Hs.instance==null&&(Hs.instance=new Hs),Hs.instance}static register(e){Hs.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function hT(e){A(e.className!=null,()=>"Class being registered does not have the static className property defined."),A(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),A(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),Hs.register(e)}var Rr=class extends dT{minimize(e,t=!1,n){let{value:a,grads:r}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(s)}else this.applyGradients(r);return _e(r),t?a:(a.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return 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a=O.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:P(()=>qe(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:P(()=>qe(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;P(()=>{let l=X(z(i,this.rho),z(it(s),1-this.rho)),u=z(he(pn(X(o,this.epsilon)),pn(X(i,this.epsilon))),s),p=X(z(o,this.rho),z(it(u),1-this.rho));i.assign(l),o.assign(p);let d=X(z(u,-this.learningRate),a);a.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(_e(this.accumulatedGrads.map(e=>e.variable)),_e(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}},Lv=class extends Rr{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}static get className(){return"Adagrad"}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=O.registeredVariables[t];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:P(()=>bn(a.shape,this.initialAccumulatorValue).variable(!1))});let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;P(()=>{let i=X(s,it(r));s.assign(i);let o=X(z(he(r,pn(X(i,O.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&_e(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}},zv=class extends Rr{constructor(e,t,n,a=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],P(()=>{this.accBeta1=xe(t).variable(),this.accBeta2=xe(n).variable()}),a==null&&(this.epsilon=O.backend.epsilon())}static get className(){return"Adam"}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);P(()=>{let n=pe(1,this.accBeta1),a=pe(1,this.accBeta2);t.forEach((r,s)=>{let i=O.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:P(()=>qe(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:P(()=>qe(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedSecondMoment[s].variable,d=X(z(u,this.beta1),z(l,1-this.beta1)),c=X(z(p,this.beta2),z(it(l),1-this.beta2)),h=he(d,n),m=he(c,a);u.assign(d),p.assign(c);let f=X(z(he(h,X(pn(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(z(this.accBeta1,this.beta1)),this.accBeta2.assign(z(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&_e(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&_e(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),P(()=>{this.accBeta1.assign(Ar(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ar(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}},Wv=class extends Rr{constructor(e,t,n,a=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],P(()=>{this.iteration=xe(0).variable(),this.accBeta1=xe(t).variable()}),a==null&&(this.epsilon=O.backend.epsilon())}static get className(){return"Adamax"}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);P(()=>{let n=pe(1,this.accBeta1),a=he(-this.learningRate,X(z(this.iteration,this.decay),1));t.forEach((r,s)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}},of=class extends Rr{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}static get className(){return"SGD"}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=O.registeredVariables[t];P(()=>{let s=X(z(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Gt(xe(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}},Vv=class extends Rr{constructor(e,t=.9,n=0,a=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,a==null&&(this.epsilon=O.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}static get className(){return"RMSProp"}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=O.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:P(()=>qe(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:P(()=>qe(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:P(()=>qe(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;P(()=>{let l=X(z(i,this.decay),z(it(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[n].variable,p=X(z(u,this.decay),z(s,1-this.decay)),d=he(z(s,this.learningRate),pn(pe(l,X(it(p),this.epsilon)))),c=X(z(o,this.momentum),d);i.assign(l),u.assign(p),o.assign(c);let h=pe(a,c);a.assign(h)}else{let u=X(z(i,this.decay),z(it(s),1-this.decay)),p=X(z(o,this.momentum),he(z(s,this.learningRate),pn(X(u,this.epsilon))));i.assign(u),o.assign(p);let d=pe(a,p);a.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&_e(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&_e(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&_e(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}},jL=[Ov,Lv,zv,Wv,Bv,Vv,of];function KL(){for(let e of jL)hT(e)}var 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this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},a=!1){let r,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");r=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof gl))throw new Re(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=Ga(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new V("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new V("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};gl.className="Sequential";ne.registerClass(gl);function IU(e){return new Er(e)}function SU(e){return new gl(e)}function wN(e){return eN(e)}function TU(e,t){Na.registerCallbackConstructor(e,t)}var Un=class extends ne.Serializable{getConfig(){return{}}},kN=class extends Un{apply(e,t=1){return X4(e,t)}};kN.className="elu";ne.registerClass(kN);var IN=class extends Un{apply(e){return Km(e)}};IN.className="selu";ne.registerClass(IN);var SN=class extends Un{apply(e){return Xe(e)}};SN.className="relu";ne.registerClass(SN);var TN=class extends Un{apply(e){return P(()=>$u(6,Xe(e)))}};TN.className="relu6";ne.registerClass(TN);var NN=class extends Un{apply(e){return e}};NN.className="linear";ne.registerClass(NN);var CN=class extends Un{apply(e){return da(e)}};CN.className="sigmoid";ne.registerClass(CN);var EN=class extends Un{apply(e){return Z4(e)}};EN.className="hardSigmoid";ne.registerClass(EN);var _N=class extends Un{apply(e){return xo(e)}};_N.className="softplus";ne.registerClass(_N);var AN=class extends Un{apply(e){return Y4(e)}};AN.className="softsign";ne.registerClass(AN);var $N=class extends Un{apply(e){return oi(e)}};$N.className="tanh";ne.registerClass($N);var gw=class extends Un{apply(e,t=-1){return Xa(e,t)}};gw.className="softmax";ne.registerClass(gw);var FN=class extends Un{apply(e,t=-1){return Wm(e,t)}};FN.className="logSoftmax";ne.registerClass(FN);var DN=class extends Un{apply(e,t=1){return P(()=>z(da(z(e,t)),e))}};DN.className="swish";ne.registerClass(DN);var RN=class extends Un{apply(e){return P(()=>z(e,oi(xo(e))))}};RN.className="mish";ne.registerClass(RN);function ls(e){return e.getClassName()}function bb(e,t={}){return Uc(e,ne.SerializationMap.getMap().classNameMap,t,"activation")}function us(e){if(e==null){let t={};return t.className="linear",t.config={},bb(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},bb(t)}else return e instanceof Un?e:bb(e)}function yw(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var MN=class extends ne.Serializable{},Kc=class extends MN{constructor(e){super(),yw(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return P(()=>{let t=It([1]);return this.hasL1&&(t=Y(t,fe(z(this.l1,Lt(e))))),this.hasL2&&(t=Y(t,fe(z(this.l2,Hc(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Kc.className="L1L2";ne.registerClass(Kc);function NU(e){return yw(e),new Kc({l1:e!=null?e.l1:null,l2:0})}function CU(e){return yw(e),new Kc({l2:e!=null?e.l2:null,l1:0})}var Nk={l1l2:"L1L2"};function ct(e){return Xv(e)}function Ck(e,t={}){return Uc(e,ne.SerializationMap.getMap().classNameMap,t,"regularizer")}function Tt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in Nk?Nk[e]:e,config:{}};return Ck(t)}else return e instanceof MN?e:Ck(e)}var bw=class extends Ve{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ne(e);let n=Xe(e);return this.maxValue!=null&&(n=tn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};bw.className="ReLU";ne.registerClass(bw);var xw=class extends Ve{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Ne(e);return Dc(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};xw.className="LeakyReLU";ne.registerClass(xw);var vw=class extends Ve{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=St(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Tt(e.alphaRegularizer),this.alphaConstraint=Yt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Je(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a(Rt(t),t==="channelsFirst"?_e(e,[0,2,3,1]):e))}function PN(e,t){return P(()=>(Rt(t),t==="channelsFirst"?_e(e,[0,2,3,4,1]):e))}function EU(e,t,n,a=1,r="valid",s,i=1){return P(()=>{if(s==null&&(s=qa()),Rt(s),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=_e(e,[0,2,1])),r==="causal")throw new Re("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Dm(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ya(o,n)),o})}function Ek(e,t,n,a=[1,1],r="valid",s,i,o=null){return P(()=>{if(s==null&&(s=qa()),Rt(s),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=Sw(e,s);if(r==="causal")throw new Re("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=hl.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=_e(l,[0,3,1,2])),l})}function _U(e,t,n,a=[1,1,1],r="valid",s,i){return P(()=>{if(s==null&&(s=qa()),Rt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=PN(e,s);if(r==="causal")throw new Re("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=pv(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ya(o,n)),s==="channelsFirst"&&(o=_e(o,[0,4,1,2,3])),o})}var Tw=class extends Ve{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Tw.verifyArgs(t),this.rank=e,en(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Re(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=rl(t.kernelSize,e,"kernelSize"),this.strides=rl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,xa(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Rt(this.dataFormat),this.activation=us(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=St(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(t.biasConstraint),this.biasRegularizer=Tt(t.biasRegularizer),this.activityRegularizer=Tt(t.activityRegularizer),this.dilationRate=rl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new V(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(nr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Yv(e.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:ls(this.activation),useBias:this.useBias,biasInitializer:Ct(this.biasInitializer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Xc=class extends Tw{constructor(e,t){super(e,t),this.kernel=null,Xc.verifyArgs(t),this.filters=t.filters,en(this.filters,"filters"),this.kernelInitializer=St(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(t.kernelConstraint),this.kernelRegularizer=Tt(t.kernelRegularizer)}build(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return P(()=>{e=Ne(e);let n,a=this.bias==null?null:this.bias.read(),r=GT(this.activation.getClassName());if(r!=null&&this.rank===2)n=Ek(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=EU(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Ek(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=_U(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Re("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=Je(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r 0 but got ${JSON.stringify(e.filters)}`)}},Yc=class extends Xc{constructor(e){super(2,e),Yc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Yv(e.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Yc.className="Conv2D";ne.registerClass(Yc);var Zc=class extends Xc{constructor(e){super(3,e),Zc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Zc.className="Conv3D";ne.registerClass(Zc);var Nw=class extends Yc{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Ne(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],u=this.kernelSize[0],p=this.kernelSize[1],d=this.strides[0],c=this.strides[1],h=ar(o,d,u,this.padding),m=ar(l,c,p,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=_e(n,[0,2,3,1]));let g=Rm(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=_e(g,[0,3,1,2])),this.bias!=null&&(g=Ya(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=Je(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=ar(t[a],o,s,this.padding),t[r]=ar(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Nw.className="Conv2DTranspose";ne.registerClass(Nw);var Cw=class extends Zc{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Ne(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],p=a[i],d=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],y=ar(l,m,d,this.padding),b=ar(u,f,c,this.padding),x=ar(p,g,h,this.padding),w=[r,y,b,x,this.filters];this.dataFormat!=="channelsLast"&&(n=_e(n,[0,2,3,4,1]));let I=cv(n,this.kernel.read(),w,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(I=_e(I,[0,4,1,2,3])),this.bias!==null&&(I=Ya(I,this.bias.read(),this.dataFormat)),this.activation!==null&&(I=this.activation.apply(I)),I})}computeOutputShape(e){e=Je(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],p=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[a]=ar(t[a],u,i,this.padding),t[r]=ar(t[r],p,o,this.padding),t[s]=ar(t[s],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Cw.className="Conv3DTranspose";ne.registerClass(Cw);var ON=class extends Xc{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Tt(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Tt(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Je(e),e.length{e=Ne(e);let n;if(this.rank===1)throw new Re("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=_e(e,[0,2,3,1])),n=Ss(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ya(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=_e(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Ct(this.depthwiseInitializer),e.pointwiseInitializer=Ct(this.pointwiseInitializer),e.depthwiseRegularizer=ct(this.depthwiseRegularizer),e.pointwiseRegularizer=ct(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};ON.className="SeparableConv";var Ew=class extends ON{constructor(e){super(2,e)}};Ew.className="SeparableConv2D";ne.registerClass(Ew);var Nf=class extends Xc{constructor(e){super(1,e),Nf.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Yv(e.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Nf.className="Conv1D";ne.registerClass(Nf);var _w=class extends Ve{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return P(()=>{if(e=Ne(e),this.dataFormat==="channelsLast"){let n=Xd(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Xd(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Xd(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Xd(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};_w.className="Cropping2D";ne.registerClass(_w);var Aw=class extends Ve{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,V4(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return P(()=>{let n=Ne(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=_e(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?_a.resizeNearestNeighbor(n,[r,s]):_a.resizeBilinear(n,[r,s]);return _e(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?_a.resizeNearestNeighbor(n,[r,s]):_a.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};Aw.className="UpSampling2D";ne.registerClass(Aw);function AU(e,t,n=[1,1],a="valid",r,s){return P(()=>{r==null&&(r=qa()),Rt(r);let i=Sw(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=ks(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=_e(i,[0,3,1,2])),i})}var $w=class extends Tw{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Tt(e.depthwiseRegularizer)}build(e){if(e=Je(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return P(()=>{e=Ne(e);let n=AU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ya(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Ha(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ha(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Ct(this.depthwiseInitializer),e.depthwiseRegularizer=ct(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};$w.className="DepthwiseConv2D";ne.registerClass($w);function LN(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function zN(e,t,n,a=!1,r,s,i=!1,o=!1){return P(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(ja(2,l));if(t=_e(t,u),s!=null)throw new Re("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=le(le(r,"bool"),"float32"),r.rank===l-1&&(r=Qt(r,-1)),r=_e(r,u)),a&&(t=ga(t,0),r!=null&&(r=ga(r,0)));let p=[],d,c=n,h=t.shape[0],m=ut(t),f;r!=null&&(f=ut(r));for(let y=0;ye(b,c));if(r==null)d=x[0],c=x[1];else{let w=P(()=>{let I=f[y],T=pe(ea(I),I),C=Y(z(x[0],I),z(c[0],T)),_=c.map(($,R)=>Y(z(x[1][R],I),z($,T)));return{output:C,newStates:_}});d=w.output,c=w.newStates}o&&p.push(d)}let g;return o&&(g=$t(p,1)),[d,g,c]})}var mr=class extends Ve{constructor(e){super(e);let t;if(e.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new _f({cells:e.cell}):t=e.cell,t.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ja(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Ub(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return P(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;ns.shape[s.shape.length-1]),r))throw new V(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=r.map(s=>new zt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){P(()=>{if(!this.stateful)throw new kr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>It([n,a])):this.states_=[It([n,this.cell.stateSize])];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>It([n,a])):this.states_[0]=It([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let a=0;aHt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=LN(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new zt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Va){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let p=super.apply(o,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return P(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Ne(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new V(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=zN((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],p=o[2];this.stateful&&this.resetStates(p,a);let d=this.returnSequences?u:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return P(()=>{let t=It(e.shape);return t=fe(t,[1,2]),t=Gc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Bb(t,[1,n]):t):this.cell.stateSize>1?[Bb(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===mr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),e),t)}static fromConfig(e,t,n={}){let a=t.cell,r=Ga(a,n);return new e(Object.assign(t,{cell:r}))}};mr.className="RNN";ne.registerClass(mr);var Jc=class extends Ve{},Cf=class extends Jc{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,en(this.units,"units"),this.activation=us(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=ml([1,os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ml([1,os([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return P(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0ea(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0ea(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=or(z(e,s),this.kernel.read()):r=or(e,this.kernel.read()),this.bias!=null&&(r=Ya(r,this.bias.read())),i!=null&&(n=z(n,i));let o=Y(r,or(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ls(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),recurrentRegularizer:ct(this.recurrentRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};Cf.className="SimpleRNNCell";ne.registerClass(Cf);var Fw=class extends mr{constructor(e){e.cell=new Cf(e),super(e)}call(e,t){return P(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};Fw.className="SimpleRNN";ne.registerClass(Fw);var Ef=class extends Jc{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,en(this.units,"units"),this.activation=us(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=us(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=ml([1,os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ml([1,os([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return P(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0ea(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0ea(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Dw.className="GRU";ne.registerClass(Dw);var Qc=class extends Jc{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,en(this.units,"units"),this.activation=us(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=us(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=ml([1,os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ml([1,os([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Je(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends Ra{apply(i,o){let l=r.apply([s]),u=new gf().apply([s]),p=r.apply([s*2]);return dk(dk(l,u),p)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return P(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0ea(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0ea(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Rw.className="LSTM";ne.registerClass(Rw);var _f=class extends Jc{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return P(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{Js(`RNNCell_${a}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign(Object.assign({},e),n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ga(r,n));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Gb(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;ss!=null?s(t(),n):ZT(t(),n),o=()=>jc(i,t,a);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var $U=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return P(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=It(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){P(()=>{if(!this.stateful)throw new kr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>It(r)):this.states_=[It(r)];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>It(r)):this.states_[0]=It(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let s=0;sHt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],p=Ha(l,a[0],r,s[0],i[0]),d=Ha(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,p,d]:[p,d,n]]}};WN.className="ConvRNN2D";var Af=class extends Qc{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign(Object.assign({},e),{units:t})),this.filters=t,en(this.filters,"filters"),this.kernelSize=rl(n,2,"kernelSize"),this.kernelSize.forEach(o=>en(o,"kernelSize")),this.strides=rl(a||1,2,"strides"),this.strides.forEach(o=>en(o,"strides")),this.padding=r||"valid",xa(this.padding),this.dataFormat=s||"channelsLast",Rt(this.dataFormat),this.dilationRate=rl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>en(o,"dilationRate"))}build(e){var t;e=Je(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Ra{apply(p,d){let c=l.apply([u]),h=Zn([u]),m=l.apply([u*2]);return Zv([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return P(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0ea(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0ea(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[x,w,I,T]=zn(this.kernel.read(),i,b),[C,_,$,R]=this.useBias?zn(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,w,_,this.padding),d=this.inputConv(d,I,$,this.padding),c=this.inputConv(c,T,R,this.padding);let[F,S,M,B]=zn(this.recurrentKernel.read(),i,b);m=this.recurrentConv(m,F),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),y=this.recurrentConv(y,B);let U=this.recurrentActivation.apply(Y(u,m)),G=this.recurrentActivation.apply(Y(p,f)),q=Y(z(G,s),z(U,this.activation.apply(Y(d,g)))),K=z(this.recurrentActivation.apply(Y(c,y)),this.activation.apply(q));return[K,K,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=$U(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=Ft(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ya(r,n,this.dataFormat):r}recurrentConv(e,t){return Ft(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Af.className="ConvLSTM2DCell";ne.registerClass(Af);var Mw=class extends WN{constructor(e){let t=new Af(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};Mw.className="ConvLSTM2D";ne.registerClass(Mw);var $f=class extends Ve{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a{this.invokeCallHook(e,t);let n=Ne(e);if(0ZT(n,this.rate,r,this.seed),()=>n,a)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};$f.className="Dropout";ne.registerClass($f);var Pw=class extends $f{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Pw.className="SpatialDropout1D";ne.registerClass(Pw);var Ow=class extends Ve{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,en(this.units,"units"),this.activation=us(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Tt(e.kernelRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.activityRegularizer=Tt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Je(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=Je(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ne(e),a=GT(this.activation.getClassName()),r;return a!=null?r=or(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=or(n,this.kernel.read()),this.bias!=null&&(r=Ya(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ls(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ow.className="Dense";ne.registerClass(Ow);var Lw=class extends Ve{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Je(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],as(e,1)]}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ne(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r{this.invokeCallHook(e,t);let n=Ne(e);return this.activation.apply(n)})}getConfig(){let e={activation:ls(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};zw.className="Activation";ne.registerClass(zw);var Ww=class extends Ve{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return P(()=>(e=Ne(e),j4(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Ww.className="RepeatVector";ne.registerClass(Ww);var Bw=class extends Ve{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t{this.invokeCallHook(e,t);let n=Ne(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return W(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Bw.className="Reshape";ne.registerClass(Bw);var Vw=class extends Ve{constructor(e){if(super(e),e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=ja(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new zt({ndim:this.dims.length+1})]}computeOutputShape(e){e=Je(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return _e(Ne(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Vw.className="Permute";ne.registerClass(Vw);var Uw=class extends Ve{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Ne(e),a=-1;return Kp(ui(n,this.maskValue),a)}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ne(e),a=-1,r=!0,s=Kp(ui(n,this.maskValue),a,r);return z(n,le(s,n.dtype))})}};Uw.className="Masking";ne.registerClass(Uw);var Gw=class extends Ve{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(yt(e.inputLength))}this.inputDim=e.inputDim,en(this.inputDim,"inputDim"),this.outputDim=e.outputDim,en(this.outputDim,"outputDim"),this.embeddingsInitializer=St(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Tt(e.embeddingsRegularizer),this.activityRegularizer=Tt(e.activityRegularizer),this.embeddingsConstraint=Yt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return P(()=>this.maskZero?(e=Ne(e),ui(e,qe(e))):null)}computeOutputShape(e){if(e=Je(e),this.inputLength==null)return[...e,this.outputDim];let t=yt(this.inputLength);if(t.length!==e.length-1)throw new V(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a{this.invokeCallHook(e,t);let n=Ne(e);n.dtype!=="int32"&&(n=ir(n,"int32"));let a=YT(this.embeddings.read(),W(n,[n.size]));return W(a,Je(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Ct(this.embeddingsInitializer),embeddingsRegularizer:ct(this.embeddingsRegularizer),activityRegularizer:ct(this.activityRegularizer),embeddingsConstraint:Xt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Gw.className="Embedding";ne.registerClass(Gw);var So=class extends Ve{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Re}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length1)throw new V(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;rr.length);e.indexOf(null)===-1&&ns(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return P(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=os(a);for(let s of e){let i=s.rank;for(let o=0;o1){let u=ja(1,l).concat([0]);n.push(_e(o,u)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,l=o.length,u=o[l-1],p=[u].concat(o.slice(0,o.length-1));s=W(_e(W(s,[-1,u]),[1,0]),p)}else if(i>1){let o=[i-1].concat(ja(0,i-1));s=_e(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a{if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an Array");if(!Array.isArray(e))throw new V("`inputs` should be an Array");if(t.length!==e.length)throw new V(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:Qt(a,0));let n=t[0];for(let a=1;a{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new V("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Re("batchDot is not implemented for complex64-type Tensors yet.");let a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return P(()=>{let i;if(a>r){i=a-r;let l=[];for(let u=0;ua){i=r-a;let l=[];for(let u=0;u0){let l;a>r?l=a+r-3:l=a-1;let u=[];for(let p=l;p"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Re("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new V(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new V(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],a;return Array.isArray(this.axes)?a=this.axes.map((r,s)=>Np(r,e[s].shape.length)):a=[Np(this.axes,t.shape.length),Np(this.axes,n.shape.length)],this.normalize&&(t=Ah(t,a[0]),n=Ah(n,a[1])),FU(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Np(this.axes,e.length),Np(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Re("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Zw.className="Dot";ne.registerClass(Zw);var Jw=class extends Ve{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ne(e);return jc(()=>Y(ff(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Jw.className="GaussianNoise";ne.registerClass(Jw);var Qw=class extends Ve{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ne(e);return this.rate>0&&this.rate<1?jc(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return z(n,ff(n.shape,1,a))},()=>n,t.training||!1):n})}};Qw.className="GaussianDropout";ne.registerClass(Qw);var e0=class extends Ve{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new V(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new zt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return P(()=>{let n=t.training==null?!1:t.training,a=Ne(e),r=a.shape,s=r.length,i=ja(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=ci(1,s);l[o]=r[o];let u=i.slice();u.sort();let p=!v.arraysEqual(u,ja(0,s).slice(0,s-1)),d=()=>{if(p){let g=W(this.movingMean.read(),l),y=W(this.movingVariance.read(),l),b=this.center?W(this.beta.read(),l):null,x=this.scale?W(this.gamma.read(),l):null;return Jp(a,g,y,b,x,this.epsilon)}else return Jp(a,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return d();let[c,h,m]=MU(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,b)=>{P(()=>{let x=1-b,w=g.read(),I=z(pe(w,y),x);g.write(pe(w,I))})};return f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum),c})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ct(this.betaInitializer),gammaInitializer:Ct(this.gammaInitializer),movingMeanInitializer:Ct(this.movingMeanInitializer),movingVarianceInitializer:Ct(this.movingVarianceInitializer),betaRegularizer:ct(this.betaRegularizer),gammaRegularizer:ct(this.gammaRegularizer),betaConstraint:Xt(this.betaConstraint),gammaConstraint:Xt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};t0.className="BatchNormalization";ne.registerClass(t0);var n0=class extends Ve{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=St(e.betaInitializer||"zeros"),this.gammaInitializer=St(e.gammaInitializer||"ones"),this.betaRegularizer=Tt(e.betaRegularizer),this.gammaRegularizer=Tt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Je(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==ns(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Ne(e),a=n.shape,r=a.length;return P(()=>{let{mean:s,variance:i}=Pc(n,this.axis,!0),o=ci(1,r);for(let h of this.axis)o[h]=a[h];let l=h=>h!=null&&h.shape.length!==r?W(h,o):h,u=this.scale?l(this.gamma.read()):null,p=this.center?l(this.beta.read()):null,d=[],c=[];for(let h=0;h{if(e.rank!==4)throw new V(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new V("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=qa()),n!=="channelsLast"&&n!=="channelsFirst")throw new V(`Unknown data format: ${n}. 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length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=Je(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return P(()=>PU(Ne(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};a0.className="ZeroPadding2D";ne.registerClass(a0);function Ff(e,t,n,a,r,s){return P(()=>{Rt(r),jT(s),xa(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=qa()),s==null&&(s="max"),e=Sw(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Dt(e,t,n,o):i=ya(e,t,n,o),r==="channelsFirst"&&(i=_e(i,[0,3,1,2])),i})}function BN(e,t,n,a,r,s){return P(()=>{Rt(r),jT(s),xa(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=qa()),s==null&&(s="max"),e=PN(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Cv(e,t,n,o):i=Qx(e,t,n,o),r==="channelsFirst"&&(i=_e(i,[0,4,1,2,3])),i})}var VN=class extends Ve{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(en(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new V(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,xa(this.padding),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){e=Je(e);let t=Ha(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return P(()=>{this.invokeCallHook(e,t),e=Gc(Ne(e),2);let n=this.poolingFunction(Ne(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Ts(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},r0=class extends VN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),xa(a),Ff(e,t,n,a,r,"max")}};r0.className="MaxPooling1D";ne.registerClass(r0);var s0=class extends VN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),xa(a),Ff(e,t,n,a,r,"avg")}};s0.className="AveragePooling1D";ne.registerClass(s0);var UN=class extends Ve{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new V(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];en(this.poolSize,"poolSize"),en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),xa(this.padding),this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ha(t,this.poolSize[0],this.padding,this.strides[0]),n=Ha(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return P(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ne(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},i0=class extends UN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),xa(a),Ff(e,t,n,a,r,"max")}};i0.className="MaxPooling2D";ne.registerClass(i0);var o0=class extends UN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),xa(a),Ff(e,t,n,a,r,"avg")}};o0.className="AveragePooling2D";ne.registerClass(o0);var GN=class extends Ve{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new V(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];en(this.poolSize,"poolSize"),en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),xa(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ha(t,this.poolSize[0],this.padding,this.strides[0]),n=Ha(n,this.poolSize[1],this.padding,this.strides[1]),a=Ha(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return P(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ne(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},l0=class extends GN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),xa(a),BN(e,t,n,a,r,"max")}};l0.className="MaxPooling3D";ne.registerClass(l0);var u0=class extends GN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),xa(a),BN(e,t,n,a,r,"avg")}};u0.className="AveragePooling3D";ne.registerClass(u0);var HN=class extends Ve{constructor(e){super(e),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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Ue{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Sw.verifyArgs(t),this.rank=e,en(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Pe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=tl(t.kernelSize,e,"kernelSize"),this.strides=tl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,xa(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Mt(this.dataFormat),this.activation=us(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=St(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(t.biasConstraint),this.biasRegularizer=Tt(t.biasRegularizer),this.activityRegularizer=Tt(t.activityRegularizer),this.dilationRate=tl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new V(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(nr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Xv(e.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:ls(this.activation),useBias:this.useBias,biasInitializer:_t(this.biasInitializer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},qc=class extends Sw{constructor(e,t){super(e,t),this.kernel=null,qc.verifyArgs(t),this.filters=t.filters,en(this.filters,"filters"),this.kernelInitializer=St(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(t.kernelConstraint),this.kernelRegularizer=Tt(t.kernelRegularizer)}build(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return P(()=>{e=Ce(e);let n,a=this.bias==null?null:this.bias.read(),r=WT(this.activation.getClassName());if(r!=null&&this.rank===2)n=kk(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=_U(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=kk(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=EU(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Pe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=Je(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r 0 but got ${JSON.stringify(e.filters)}`)}},jc=class extends qc{constructor(e){super(2,e),jc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Xv(e.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};jc.className="Conv2D";ne.registerClass(jc);var Kc=class extends qc{constructor(e){super(3,e),Kc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Kc.className="Conv3D";ne.registerClass(Kc);var Tw=class extends jc{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Ce(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],u=this.kernelSize[0],p=this.kernelSize[1],d=this.strides[0],c=this.strides[1],h=ar(o,d,u,this.padding),m=ar(l,c,p,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Ee(n,[0,2,3,1]));let g=Rm(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ee(g,[0,3,1,2])),this.bias!=null&&(g=Ya(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=Je(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=ar(t[a],o,s,this.padding),t[r]=ar(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Tw.className="Conv2DTranspose";ne.registerClass(Tw);var Nw=class extends Kc{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Ce(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],p=a[i],d=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],y=ar(l,m,d,this.padding),b=ar(u,f,c,this.padding),x=ar(p,g,h,this.padding),w=[r,y,b,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Ee(n,[0,2,3,4,1]));let I=Yx(n,this.kernel.read(),w,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(I=Ee(I,[0,4,1,2,3])),this.bias!==null&&(I=Ya(I,this.bias.read(),this.dataFormat)),this.activation!==null&&(I=this.activation.apply(I)),I})}computeOutputShape(e){e=Je(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],p=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[a]=ar(t[a],u,i,this.padding),t[r]=ar(t[r],p,o,this.padding),t[s]=ar(t[s],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Nw.className="Conv3DTranspose";ne.registerClass(Nw);var RN=class extends qc{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Tt(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Tt(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Je(e),e.length{e=Ce(e);let n;if(this.rank===1)throw new Pe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ee(e,[0,2,3,1])),n=Is(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ya(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ee(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.pointwiseInitializer=_t(this.pointwiseInitializer),e.depthwiseRegularizer=ct(this.depthwiseRegularizer),e.pointwiseRegularizer=ct(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};RN.className="SeparableConv";var Cw=class extends RN{constructor(e){super(2,e)}};Cw.className="SeparableConv2D";ne.registerClass(Cw);var vf=class extends qc{constructor(e){super(1,e),vf.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Xv(e.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};vf.className="Conv1D";ne.registerClass(vf);var _w=class extends Ue{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return P(()=>{if(e=Ce(e),this.dataFormat==="channelsLast"){let n=Kd(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Kd(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Kd(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Kd(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};_w.className="Cropping2D";ne.registerClass(_w);var Ew=class extends Ue{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,V4(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return P(()=>{let n=Ce(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Ee(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Ea.resizeNearestNeighbor(n,[r,s]):Ea.resizeBilinear(n,[r,s]);return Ee(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Ea.resizeNearestNeighbor(n,[r,s]):Ea.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};Ew.className="UpSampling2D";ne.registerClass(Ew);function AU(e,t,n=[1,1],a="valid",r,s){return P(()=>{r==null&&(r=ja()),Mt(r);let i=Iw(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=ws(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Ee(i,[0,3,1,2])),i})}var Aw=class extends Sw{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Tt(e.depthwiseRegularizer)}build(e){if(e=Je(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return P(()=>{e=Ce(e);let n=AU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ya(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Ha(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ha(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.depthwiseRegularizer=ct(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};Aw.className="DepthwiseConv2D";ne.registerClass(Aw);function DN(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function MN(e,t,n,a=!1,r,s,i=!1,o=!1){return P(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(qa(2,l));if(t=Ee(t,u),s!=null)throw new Pe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=ie(ie(r,"bool"),"float32"),r.rank===l-1&&(r=Qt(r,-1)),r=Ee(r,u)),a&&(t=ga(t,0),r!=null&&(r=ga(r,0)));let p=[],d,c=n,h=t.shape[0],m=lt(t),f;r!=null&&(f=lt(r));for(let y=0;ye(b,c));if(r==null)d=x[0],c=x[1];else{let w=P(()=>{let I=f[y],T=pe(ea(I),I),C=X(z(x[0],I),z(c[0],T)),E=c.map(($,R)=>X(z(x[1][R],I),z($,T)));return{output:C,newStates:E}});d=w.output,c=w.newStates}o&&p.push(d)}let g;return o&&(g=Ft(p,1)),[d,g,c]})}var mr=class extends Ue{constructor(e){super(e);let t;if(e.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new If({cells:e.cell}):t=e.cell,t.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return qa(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Ob(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return P(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;ns.shape[s.shape.length-1]),r))throw new V(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=r.map(s=>new zt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){P(()=>{if(!this.stateful)throw new kr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>It([n,a])):this.states_=[It([n,this.cell.stateSize])];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>It([n,a])):this.states_[0]=It([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let a=0;aGt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=DN(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new zt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Va){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let p=super.apply(o,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return P(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Ce(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new V(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=MN((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],p=o[2];this.stateful&&this.resetStates(p,a);let d=this.returnSequences?u:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return P(()=>{let t=It(e.shape);return t=fe(t,[1,2]),t=Bc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Mb(t,[1,n]):t):this.cell.stateSize>1?[Mb(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===mr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),e),t)}static fromConfig(e,t,n={}){let a=t.cell,r=Ga(a,n);return new e(Object.assign(t,{cell:r}))}};mr.className="RNN";ne.registerClass(mr);var Xc=class extends Ue{},wf=class extends Xc{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,en(this.units,"units"),this.activation=us(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=cl([1,os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=cl([1,os([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return P(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0ea(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0ea(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=or(z(e,s),this.kernel.read()):r=or(e,this.kernel.read()),this.bias!=null&&(r=Ya(r,this.bias.read())),i!=null&&(n=z(n,i));let o=X(r,or(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ls(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),recurrentRegularizer:ct(this.recurrentRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};wf.className="SimpleRNNCell";ne.registerClass(wf);var $w=class extends mr{constructor(e){e.cell=new wf(e),super(e)}call(e,t){return P(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};$w.className="SimpleRNN";ne.registerClass($w);var kf=class extends Xc{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,en(this.units,"units"),this.activation=us(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=us(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=cl([1,os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=cl([1,os([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return P(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0ea(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0ea(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Fw.className="GRU";ne.registerClass(Fw);var Yc=class extends Xc{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,en(this.units,"units"),this.activation=us(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=us(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=cl([1,os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=cl([1,os([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Je(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends Da{apply(i,o){let l=r.apply([s]),u=new pf().apply([s]),p=r.apply([s*2]);return ik(ik(l,u),p)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return P(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0ea(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0ea(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Rw.className="LSTM";ne.registerClass(Rw);var If=class extends Xc{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return P(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{Zs(`RNNCell_${a}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign(Object.assign({},e),n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ga(r,n));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Lb(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;ss!=null?s(t(),n):jT(t(),n),o=()=>Uc(i,t,a);return!r||r<=1?Gt(o().clone()):Array(r).fill(void 0).map(o).map(l=>Gt(l.clone()))}var $U=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r{if(this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return P(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=It(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){P(()=>{if(!this.stateful)throw new kr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>It(r)):this.states_=[It(r)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>It(r)):this.states_[0]=It(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let s=0;sGt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],p=Ha(l,a[0],r,s[0],i[0]),d=Ha(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,p,d]:[p,d,n]]}};PN.className="ConvRNN2D";var Sf=class extends Yc{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign(Object.assign({},e),{units:t})),this.filters=t,en(this.filters,"filters"),this.kernelSize=tl(n,2,"kernelSize"),this.kernelSize.forEach(o=>en(o,"kernelSize")),this.strides=tl(a||1,2,"strides"),this.strides.forEach(o=>en(o,"strides")),this.padding=r||"valid",xa(this.padding),this.dataFormat=s||"channelsLast",Mt(this.dataFormat),this.dilationRate=tl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>en(o,"dilationRate"))}build(e){var t;e=Je(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Da{apply(p,d){let c=l.apply([u]),h=Zn([u]),m=l.apply([u*2]);return Yv([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return P(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0ea(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0ea(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[x,w,I,T]=zn(this.kernel.read(),i,b),[C,E,$,R]=this.useBias?zn(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,w,E,this.padding),d=this.inputConv(d,I,$,this.padding),c=this.inputConv(c,T,R,this.padding);let[F,S,M,B]=zn(this.recurrentKernel.read(),i,b);m=this.recurrentConv(m,F),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),y=this.recurrentConv(y,B);let U=this.recurrentActivation.apply(X(u,m)),G=this.recurrentActivation.apply(X(p,f)),j=X(z(G,s),z(U,this.activation.apply(X(d,g)))),K=z(this.recurrentActivation.apply(X(c,y)),this.activation.apply(j));return[K,K,j]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=$U(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=Rt(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ya(r,n,this.dataFormat):r}recurrentConv(e,t){return Rt(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Sf.className="ConvLSTM2DCell";ne.registerClass(Sf);var Dw=class extends PN{constructor(e){let t=new Sf(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};Dw.className="ConvLSTM2D";ne.registerClass(Dw);var Tf=class extends Ue{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a{this.invokeCallHook(e,t);let n=Ce(e);if(0jT(n,this.rate,r,this.seed),()=>n,a)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Tf.className="Dropout";ne.registerClass(Tf);var Mw=class extends Tf{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Mw.className="SpatialDropout1D";ne.registerClass(Mw);var Pw=class extends Ue{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,en(this.units,"units"),this.activation=us(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Tt(e.kernelRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.activityRegularizer=Tt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Je(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=Je(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ce(e),a=WT(this.activation.getClassName()),r;return a!=null?r=or(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=or(n,this.kernel.read()),this.bias!=null&&(r=Ya(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ls(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Pw.className="Dense";ne.registerClass(Pw);var Ow=class extends Ue{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Je(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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Array.isArray(this.axes)?a=this.axes.map((r,s)=>kp(r,e[s].shape.length)):a=[kp(this.axes,t.shape.length),kp(this.axes,n.shape.length)],this.normalize&&(t=_h(t,a[0]),n=_h(n,a[1])),FU(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[kp(this.axes,e.length),kp(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Pe("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Yw.className="Dot";ne.registerClass(Yw);var Zw=class extends Ue{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ce(e);return Uc(()=>X(uf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Zw.className="GaussianNoise";ne.registerClass(Zw);var Jw=class extends Ue{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ce(e);return this.rate>0&&this.rate<1?Uc(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return z(n,uf(n.shape,1,a))},()=>n,t.training||!1):n})}};Jw.className="GaussianDropout";ne.registerClass(Jw);var Qw=class extends Ue{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ce(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return P(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Uc(()=>{let a=Ce(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=$r(Eu(n),this.rate);o=ir(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,p=X(z(a,o),z(X(o,-1),i));return X(z(p,l),u)},()=>Ce(e),t.training||!1)}return e})}};Qw.className="AlphaDropout";ne.registerClass(Qw);function Yp(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=zx(e,t,n,a,r,s);else if(e.rank===3)i=Wx(e,t,n,a,r,s);else if(e.rank===4)i=Bx(e,t,n,a,r,s);else throw new Pe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function RU(e,t,n,a,r=.001){return P(()=>{let s=Rc(e,a),i=s.mean,o=s.variance;return[Yp(e,i,o,n,t,r),i,o]})}function DU(e,t,n,a,r=.001){return P(()=>{let s=Rc(e,a),i=s.mean,o=s.variance,l=[];for(let h of 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t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new V(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new zt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return P(()=>{let n=t.training==null?!1:t.training,a=Ce(e),r=a.shape,s=r.length,i=qa(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=pi(1,s);l[o]=r[o];let u=i.slice();u.sort();let p=!v.arraysEqual(u,qa(0,s).slice(0,s-1)),d=()=>{if(p){let 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e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_t(this.betaInitializer),gammaInitializer:_t(this.gammaInitializer),movingMeanInitializer:_t(this.movingMeanInitializer),movingVarianceInitializer:_t(this.movingVarianceInitializer),betaRegularizer:ct(this.betaRegularizer),gammaRegularizer:ct(this.gammaRegularizer),betaConstraint:Xt(this.betaConstraint),gammaConstraint:Xt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};e0.className="BatchNormalization";ne.registerClass(e0);var t0=class extends Ue{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=St(e.betaInitializer||"zeros"),this.gammaInitializer=St(e.gammaInitializer||"ones"),this.betaRegularizer=Tt(e.betaRegularizer),this.gammaRegularizer=Tt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Je(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==ts(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Ce(e),a=n.shape,r=a.length;return P(()=>{let{mean:s,variance:i}=Rc(n,this.axis,!0),o=pi(1,r);for(let h of this.axis)o[h]=a[h];let l=h=>h!=null&&h.shape.length!==r?W(h,o):h,u=this.scale?l(this.gamma.read()):null,p=this.center?l(this.beta.read()):null,d=[],c=[];for(let h=0;h{if(e.rank!==4)throw new V(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new V("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ja()),n!=="channelsLast"&&n!=="channelsFirst")throw new V(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],ba(e,a)})}var n0=class extends Ue{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ja():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new V(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new V(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new V(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=Je(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return P(()=>PU(Ce(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};n0.className="ZeroPadding2D";ne.registerClass(n0);function Nf(e,t,n,a,r,s){return P(()=>{Mt(r),VT(s),xa(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=ja()),s==null&&(s="max"),e=Iw(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Dt(e,t,n,o):i=ya(e,t,n,o),r==="channelsFirst"&&(i=Ee(i,[0,3,1,2])),i})}function ON(e,t,n,a,r,s){return P(()=>{Mt(r),VT(s),xa(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=ja()),s==null&&(s="max"),e=FN(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=dv(e,t,n,o):i=Lx(e,t,n,o),r==="channelsFirst"&&(i=Ee(i,[0,4,1,2,3])),i})}var LN=class extends Ue{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(en(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new V(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,xa(this.padding),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){e=Je(e);let t=Ha(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return P(()=>{this.invokeCallHook(e,t),e=Bc(Ce(e),2);let n=this.poolingFunction(Ce(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Ss(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},a0=class extends LN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),xa(a),Nf(e,t,n,a,r,"max")}};a0.className="MaxPooling1D";ne.registerClass(a0);var r0=class extends LN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),xa(a),Nf(e,t,n,a,r,"avg")}};r0.className="AveragePooling1D";ne.registerClass(r0);var zN=class extends Ue{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new V(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];en(this.poolSize,"poolSize"),en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),xa(this.padding),this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ha(t,this.poolSize[0],this.padding,this.strides[0]),n=Ha(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return P(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ce(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},s0=class extends zN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),xa(a),Nf(e,t,n,a,r,"max")}};s0.className="MaxPooling2D";ne.registerClass(s0);var i0=class extends zN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),xa(a),Nf(e,t,n,a,r,"avg")}};i0.className="AveragePooling2D";ne.registerClass(i0);var WN=class extends Ue{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new V(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];en(this.poolSize,"poolSize"),en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),xa(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ha(t,this.poolSize[0],this.padding,this.strides[0]),n=Ha(n,this.poolSize[1],this.padding,this.strides[1]),a=Ha(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return P(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ce(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},o0=class extends WN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),xa(a),ON(e,t,n,a,r,"max")}};o0.className="MaxPooling3D";ne.registerClass(o0);var l0=class extends WN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),xa(a),ON(e,t,n,a,r,"avg")}};l0.className="AveragePooling3D";ne.registerClass(l0);var BN=class extends Ue{constructor(e){super(e),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Pe}},u0=class extends BN{constructor(e){super(e||{})}call(e,t){return P(()=>{let n=Ce(e);return Ct(n,1)})}};u0.className="GlobalAveragePooling1D";ne.registerClass(u0);var p0=class extends BN{constructor(e){super(e||{})}call(e,t){return P(()=>{let n=Ce(e);return ha(n,1)})}};p0.className="GlobalMaxPooling1D";ne.registerClass(p0);var VN=class extends Ue{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Pe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},c0=class extends VN{call(e,t){return P(()=>{let n=Ce(e);return this.dataFormat==="channelsLast"?Ct(n,[1,2]):Ct(n,[2,3])})}};c0.className="GlobalAveragePooling2D";ne.registerClass(c0);var d0=class extends VN{call(e,t){return P(()=>{let n=Ce(e);return this.dataFormat==="channelsLast"?ha(n,[1,2]):ha(n,[2,3])})}};d0.className="GlobalMaxPooling2D";ne.registerClass(d0);var UN=class extends Ue{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let a=t.layer,r=Ga(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},h0=class extends UN{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=Je(e),e.length<3)throw new V(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=Je(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return P(()=>(e=Ce(e),MN((n,a)=>[Ce(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};h0.className="TimeDistributed";ne.registerClass(h0);function OU(e){wo(B4,"BidirectionalMergeMode",e)}var LU="concat",m0=class extends UN{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Ga(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Ga(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?LU:e.mergeMode,OU(this.mergeMode),e.weights)throw new Pe("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,a,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,a=[n]):this.mergeMode==null?a=[n,n.slice()]:a=[n],this.returnState?this.mergeMode==null?a.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):On(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=DN(e,n,a,this.numConstants);if(e=r.inputs,n=r.initialState,a=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new V("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let u=n.map(p=>new zt({shape:p.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(a!=null)throw new Pe("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Va;for(let l of s)if(l instanceof Va!==o)throw new V("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),u=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=u;let d=super.apply(l,t);return this.inputSpec=p,d}else return super.apply(e,t)}call(e,t){return P(()=>{let n=t.initialState,a,r;if(n==null)a=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(a)&&(s=a.slice(1).concat(r.slice(1))),a=a[0],r=r[0]),this.returnSequences&&(r=ga(r,1));let i;return this.mergeMode==="concat"?i=Yv([a,r]):this.mergeMode==="sum"?i=X(a,r):this.mergeMode==="ave"?i=z(.5,X(a,r)):this.mergeMode==="mul"?i=z(a,r):this.mergeMode==null&&(i=[a,r]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Zs(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Zs(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let a=this.forwardLayer.states.map(r=>null);return Array.isArray(n)?n.concat(a).concat(a):[n].concat(a).concat(a)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=Ga(t.layer);if(delete t.layer,t.numConstants!=null)throw new Pe("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let a=t;return a.layer=n,new e(a)}};m0.className="Bidirectional";ne.registerClass(m0);var f0=class extends Ue{constructor(e){super(e),this.scale=e.scale,e.offset?this.offset=e.offset:this.offset=0}getConfig(){let e={scale:this.scale,offset:this.offset},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return P(()=>(e=Ce(e),e.dtype!=="float32"&&(e=ir(e,"float32")),X(z(e,this.scale),this.offset)))}};f0.className="Rescaling";ne.registerClass(f0);var{resizeBilinear:zU,cropAndResize:WU}=Ea,g0=class extends Ue{constructor(e){super(e),this.height=e.height,this.width=e.width}centerCrop(e,t,n,a,r,s,i,o){return P(()=>{let l,u=!1,p=t/s,d=n/i,c=(a+t)/s,h=(r+n)/i,m=[p,d,c,h],f=[];e.rank===3?(u=!0,l=Ft([e])):l=e;for(let x=0;x{let r=zU(e,[t,n]);return ir(r,a)})}call(e,t){return P(()=>{let n=Ce(e),a=n.dtype,r=n.shape,s=r[r.length-3],i=r[r.length-2],o=0;s!==this.height&&(o=Math.floor((s-this.height)/2));let l=0;return i!==this.width&&(l=Math.floor((i-this.width)/2),l===0&&(l=1)),o>=0&&l>=0?this.centerCrop(n,o,l,this.height,this.width,s,i,a):this.upsize(e,this.height,this.width,a)})}getConfig(){let e={height:this.height,width:this.width},t=super.getConfig();return Object.assign(e,t),e}computeOutputShape(e){e=Je(e);let t=e.length-3,n=e.length-2;return 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r=k("axis",e,t,n);return[a.squeeze(k("x",e,t,n),r)]}case"Reshape":return[a.reshape(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[a.mirrorPad(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[a.pad(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),s=k("paddings",e,t,n);return[a.spaceToBatchND(k("x",e,t,n),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),s=k("crops",e,t,n);return[a.batchToSpaceND(k("x",e,t,n),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),s=k("dataFormat",e,t,n).toUpperCase();return[a.depthToSpace(k("x",e,t,n),r,s)]}case"BroadcastTo":return[a.broadcastTo(k("x",e,t,n),k("shape",e,t,n))];case"BroadcastArgs":return[a.broadcastArgs(k("s0",e,t,n),k("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Pk(e,t,n,a,r=P){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>KH(i,o,l));case"basic_math":return r(()=>XH(i,o,l));case"control":return t6(i,o,l);case"convolution":return r(()=>n6(i,o,l));case"creation":return r(()=>a6(i,o,l));case"dynamic":return r6(i,o,l);case"evaluation":return r(()=>s6(i,o,l));case"image":return r(()=>u6(i,o,l));case"graph":return r(()=>i6(i,o,l));case"logical":return r(()=>p6(i,o,l));case"matrices":return r(()=>c6(i,o,l));case"normalization":return r(()=>d6(i,o,l));case"ragged":return r(()=>h6(i,o,l));case"reduction":return r(()=>m6(i,o,l));case"slice_join":return r(()=>f6(i,o,l));case"sparse":return r(()=>g6(i,o,l));case"spectral":return r(()=>y6(i,o,l));case"string":return r(()=>b6(i,o,l));case"transformation":return r(()=>x6(i,o,l));case"hash_table":return l6(i,o,l,a);case"custom":let u=a2(i.op);if(u&&u.customExecutor)return u.customExecutor(new qH(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return v.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Ok=class{constructor(e={},t={},n={},a={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Lk(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(c=>Xn(c)[0]),p=[];a!=null&&(p=a.map(c=>Xn(c.name)[0]));let d=[...t];for(;d.length>0;){let c=d.pop();if((T2(c)||S6(c)||T6(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&u.indexOf(c.name)===-1&&p.indexOf(c.name)===-1){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function v6(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(p=>Xn(p)[0]).map(p=>e.nodes[p]),o=e.initNodes;i.forEach(p=>{a.has(p.name)&&s.push(p)}),e.weights.forEach(p=>{a.has(p.name)&&s.push(p)}),o!=null&&o.forEach(p=>{a.has(p.name)&&s.push(p)});let l=new Set,u=[];for(;s.length>0;){let p=s.pop();l.add(p.name),t[p.name]||u.push(p),p.children.forEach(d=>{!l.has(d.name)&&a.has(d.name)&&d.inputs.every(c=>l.has(c.name))&&s.push(d)})}return u}var w6=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],k6=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],I6=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function T2(e){return w6.indexOf(e.op)>=0}function S6(e){return k6.indexOf(e.op)>=0}function T6(e){return I6.indexOf(e.op)>=0}var ix=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new ix(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=Lk(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(a.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return v6(this.graph,this.weightMap,n)}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return Ht(t),t}cloneTensorList(e){return e?e.map(t=>this.cloneAndKeepTensor(t)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,n])=>[t,this.cloneTensorList(n)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(p=>this.graph.nodes[Xn(p)[0]]),r=t.map(p=>Xn(p)[0]),s=r.map(p=>this.graph.nodes[p]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));try{this.keepIntermediateTensors=H().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(p){this.keepIntermediateTensors=!1,console.warn(p.message)}let l={},u={};return P(()=>{let p=new Ok(this.weightMap,l,u,this.functionExecutorMap),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(m=>{let[f,g]=Xn(m),y=[];y[g]=e[m],d[f]=y,this.keepIntermediateTensors&&(this.clonedTensorsMap[f]=this.cloneTensorList(y))});let c=this.getFrozenTensorIds(d),h={};for(let m=0;mwn(m,d,p))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(a=>a.id)));return new Set(t)}checkTensorForDisposal(e,t,n,a,r,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=NH(o.name,n,a);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let p=i[u.id];p===1?(u.dispose(),delete i[u.id]):p!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.clonedTensorsMap||(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,n=!1,a={},r={}){this.disposeIntermediateTensors(),n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=H().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let s=new Ok(this.weightMap,a,r,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,s,t,n),o=t.map(d=>wn(d,i,s)),l=o.map(d=>d.id),u=Object.keys(e).map(d=>e[d].id),p=new Set([...l,...u,...this.weightIds]);return Object.values(i).forEach(d=>{d.forEach(c=>{c&&!c.isDisposed&&!p.has(c.id)&&c.dispose()})}),this.parent==null&&s.dispose(p),o}async executeFunctionAsync(e,t,n){let a=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(a,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,a){let r=Object.keys(e),s=r.map(b=>this.graph.nodes[Xn(b)[0]]),i=n.map(b=>Xn(b)[0]),o=i.map(b=>this.graph.nodes[b]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:p,syncInputs:d}=Lk(e,o,this.weightMap,this._initNodes),c=[...s,...this.graph.weights,...this._initNodes||[]].map(b=>({node:b,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(b=>{let[x,w]=Xn(b),I=[];I[w]=e[b],h[x]=I});let m={},f=this.getFrozenTensorIds(h),g={};for(;c.length>0;){let b=this.processStack(s,c,t,h,g,f,i,m,l);await Promise.all(b)}p==null&&!a&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(b=>!T2(b)&&!wn(b.name,h,t)).map(b=>b.name);if(y.length>0){let b="";throw p!=null&&(b=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${b}`)}return h}processStack(e,t,n,a,r,s,i,o,l){let u=[];for(;t.length>0;){let p=t.pop();n.currentContext=p.contexts;let d="";if(p.node.op==="Enter"&&k("isConstant",p.node,a,n)&&([d]=Sr(p.node.name,n)),a[p.node.name]==null){let c=Pk(p.node,a,n,this._resourceManager);d||([d]=Sr(p.node.name,n));let h=n.currentContext;v.isPromise(c)?u.push(c.then(m=>(a[d]=m,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(m)),n.currentContext=h,this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l),m))):(a[d]=c,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(c)),this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l))}else this.processChildNodes(p.node,t,n,a,r,l)}return u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Sr(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!wn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!wn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Xn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){var t,n;let a={};for(let r in e){let s=(n=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||n===void 0?void 0:n[r];s!=null?a[s.name]=e[r]:a[r]=e[r]}return a}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Xn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var n,a;let r=(a=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||a===void 0?void 0:a[t];return r!=null?r.name:t},{})}checkOutputs(e){e.forEach(t=>{let[n]=Xn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},N6=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},C6="?tfjs-format=file",E6="model.json",T0=class{constructor(e,t={},n=Gt){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,t==null&&(this.loadOptions={}),this.resourceManager=new N6}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(n=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let a=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new ix(Fk.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Fk.Instance.transformGraph(e.modelInitializer);this.initializer=new ix(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof Te?[e]:e,n={};return t.forEach((a,r)=>n[this.structuredOutputKeys[r]]=a),n}return e}predict(e,t){let n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){let n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var t;if(!(e instanceof Te)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[a++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,n=Object.keys(t);for(let a=0;a1?n:n[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Ee(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function _6(e,t={},n=Gt){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof e=="string"&&(e=$6(e));let a=new T0(e,t,n);return await a.load(),a}function A6(e){if(e==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. 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a=e[0];if(n.has(a))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(bl(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(u=>u[i]),l=C2(o,t,n);s[i]=l}return n.delete(a),s}else throw new Error(`Can't recurse into non-iterable type: ${a}`);else return r.value}function E2(e){return e===null?null:bl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function _2(e,t){let n=new Map;Ph(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(v.isPromise(r)){let s=await r;n.set(a,s)}}return Ph(e,t,n)}function bl(e){let t=!1;if(H().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=SI();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Te)&&!(e instanceof Promise)&&!t)}function O6(e){return e==null||L6(e)||Array.isArray(e)||typeof 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A2{constructor(){super(N0.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let a=0;at===!0)}rowMajorBatch(e,t=!0){return new K6(this,e,t)}columnMajorBatch(e,t=!0,n=E2){return this.rowMajorBatch(e,t).map(a=>P6(a,n))}concatenate(e,t){return new F2($2([this,e]),t)}take(e){return e<0||e==null?this:new q6(this,e)}skip(e){return e<0||e==null?this:new j6(this,e)}prefetch(e){return new D2(this,e)}shuffle(e,t){return new ej(this,e,t)}serial(){return new H6(this)}},U6=class extends nn{constructor(e){super(),this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:z6(e),done:!1}}},G6=class extends nn{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return 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nn{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},X6=class extends nn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ee(e.value)}}},Y6=class extends nn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ua.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ua.getTensorsInContainer(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},Z6=class extends nn{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},zk=class extends nn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ua.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Ua.getTensorsInContainer(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},E0=class extends nn{constructor(){super(),this.outputQueue=new N0,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},J6=class extends E0{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Ua.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ua.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return!0}},F2=class extends nn{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries 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Mismatched at element ${this.count}.`);case Qr.SHORTEST:return{value:null,done:!0};case Qr.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},D2=class extends nn{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new A2(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},ej=class extends D2{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=R6.alea(n||v.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Pu=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is - ${e}`);let a;return this.size===1/0||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Kn(async()=>(await n.iterator()).columnMajorBatch(e,t,aj),a)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Kn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Kn(async()=>(await t.iterator()).filter(a=>P(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Kn(async()=>(await t.iterator()).map(n=>P(()=>e(n))),this.size)}mapAsync(e){let t=this;return Kn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Kn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Kn(async()=>{let a=C0(async()=>({value:await t.iterator(),done:!1}));return B6(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=D6.alea(t||v.now().toString());return Kn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Kn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Pu.MAX_BUFFER_SIZE=1e4;function Kn(e,t=null){return new class extends Pu{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function tj(e){return Kn(async()=>$2(e),e.length)}function nj(e){if(!bl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await _2(e,a=>{if(a instanceof Pu)return{value:a.iterator(),recurse:!1};if(bl(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return V6(n,Qr.SHORTEST)},t)}function aj(e){if(e===null)return null;let t=e[0];return O6(t)?{value:rj(e),recurse:!1}:{value:null,recurse:!0}}function rj(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Te?$t(e):mn(e)}var R2=class extends Pu{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` -`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Jd='"',Ep=Symbol("out"),Wk=Symbol("field"),Qd=Symbol("quote"),vb=Symbol("quoteafterquote"),Bk=Symbol("quoteinquote"),M2=class extends Pu{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new R2(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(v.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!H().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new P2(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&a({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),a({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),mn(n,t)}},O2=class extends nn{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ke([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=Aa([s,r,o,i],[1,4])}else this.cropBox=Aa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!H().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new O2(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=bo.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return P(()=>{let t=Qt(le(e,"float32"),0),n;n=_a.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return W(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},L2=class{},z2=class extends nn{split(e){return new sj(this,e)}},sj=class extends z2{constructor(e,t){super(),this.upstream=e,this.impl=new ij(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},ij=class extends E0{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},oj=class extends nn{decodeUTF8(){return new lj(this)}},lj=class extends z2{constructor(e){super(),this.upstream=e,this.impl=new uj(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},uj=class extends E0{constructor(e){if(super(),this.upstream=e,H().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=SI();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return H().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},W2=class extends oj{constructor(e,t={}){super(),this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(H().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let r=this.file.slice(this.offset,n);a.readAsArrayBuffer(r)}this.offset=n}),done:!1}}};async function pj(e,t={},n){let a,r;typeof e=="string"?a=e:(a=e.url,r=cj(e));let s=await(n||v.fetch)(a,r);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new W2(i,t)}else throw new Error(s.statusText)}var cj=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function B2(e){return typeof e=="string"&&e.slice(0,7)==="file://"}var V2=class extends L2{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(B2(this.input)&&H().get("IS_NODE")){let e=Ix();this.input=e.readFileSync(this.input.slice(7))}return new W2(this.input,this.options)}},U2=class extends L2{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return B2(this.url)?new V2(this.url,this.fileOptions).iterator():pj(this.url,this.fileOptions)}};function dj(e,t={}){return new M2(new U2(e),t)}function hj(e){let t=C0(e);return Kn(async()=>t)}function mj(e){return Kn(async()=>{let t=await e();return C0(()=>t.next())})}async function fj(e,t){return O2.create(e,t)}async function gj(e){return P2.create(e)}var yj="4.1.0";function ge(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var bj=hr.whereImpl,_0=class extends sc{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new qh(this,Ca())}nextDataId(){return _0.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,H().get("IS_NODE")&&N.warn(` + ${a}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=Qb(s,n),o=a===0?0:e.size/a,l=P(()=>{let p=[];e=W(e,[1,a,o]);for(let d=0;d{switch(e.op){case"If":case"StatelessIf":{let a=k("thenBranch",e,t,n),r=k("elseBranch",e,t,n),s=k("cond",e,t,n),i=k("args",e,t,n);return(await s.data())[0]?n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let a=k("body",e,t,n),r=k("cond",e,t,n),s=k("args",e,t,n),i=await n.functionMap[r].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(p=>p.id),l=await i[0].data();i.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&p.dispose()});let u=s;for(;l[0];){let p=u;u=await n.functionMap[a].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);let d=u.map(h=>h.id);p.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&d.indexOf(h.id)===-1&&h.dispose()});let c=await n.functionMap[r].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);l=await c[0].data(),c.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&d.indexOf(h.id)===-1&&h.dispose()})}return u}case"LoopCond":{let a=k("pred",e,t,n);return[Tr(a)]}case"Switch":{let a=k("pred",e,t,n),r=k("data",e,t,n);return r.kept||(r=Tr(r)),(await a.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let a=e.inputNames.find(r=>wn(r,t,n)!==void 0);if(a){let r=wn(a,t,n);return[Tr(r)]}return}case"Enter":{let a=k("frameName",e,t,n),r=k("tensor",e,t,n);return n.enterFrame(a),[Tr(r)]}case"Exit":{let a=k("tensor",e,t,n);return n.exitFrame(),[Tr(a)]}case"NextIteration":{let a=k("tensor",e,t,n);return n.nextIteration(),[Tr(a)]}case"TensorArrayV3":{let a=k("size",e,t,n),r=k("dtype",e,t,n),s=k("elementShape",e,t,n),i=k("dynamicSize",e,t,n),o=k("clearAfterRead",e,t,n),l=k("identicalElementShapes",e,t,n),u=k("name",e,t,n),p=new YH(u,r,a,s,l,i,o);return n.addTensorArray(p),[p.idTensor,xe(1)]}case"TensorArrayWriteV3":{let a=k("tensorArrayId",e,t,n),r=k("index",e,t,n),s=k("tensor",e,t,n),i=n.getTensorArray(a.id);return i.write(r,s),[i.idTensor]}case"TensorArrayReadV3":{let 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implemented`)}},p6=(e,t,n,a=an)=>{switch(e.op){case"Equal":return[a.equal(k("a",e,t,n),k("b",e,t,n))];case"NotEqual":return[a.notEqual(k("a",e,t,n),k("b",e,t,n))];case"Greater":return[a.greater(k("a",e,t,n),k("b",e,t,n))];case"GreaterEqual":return[a.greaterEqual(k("a",e,t,n),k("b",e,t,n))];case"Less":return[a.less(k("a",e,t,n),k("b",e,t,n))];case"LessEqual":return[a.lessEqual(k("a",e,t,n),k("b",e,t,n))];case"LogicalAnd":return[a.logicalAnd(k("a",e,t,n),k("b",e,t,n))];case"LogicalNot":return[a.logicalNot(k("a",e,t,n))];case"LogicalOr":return[a.logicalOr(k("a",e,t,n),k("b",e,t,n))];case"Select":case"SelectV2":return[a.where(k("condition",e,t,n),k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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implemented`)}},d6=(e,t,n,a=an)=>{switch(e.op){case"EuclideanNorm":return[a.euclideanNorm(k("x",e,t,n),k("axis",e,t,n),k("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"FusedBatchNormV3":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"LRN":return[a.localResponseNormalization(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[a.softmax(k("x",e,t,n))];case"LogSoftmax":return[a.logSoftmax(k("x",e,t,n))];case"SparseToDense":return[a.sparseToDense(k("sparseIndices",e,t,n),k("outputShape",e,t,n),k("sparseValues",e,t,n),k("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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r(()=>XH(i,o,l));case"control":return t6(i,o,l);case"convolution":return r(()=>n6(i,o,l));case"creation":return r(()=>a6(i,o,l));case"dynamic":return r6(i,o,l);case"evaluation":return r(()=>s6(i,o,l));case"image":return r(()=>u6(i,o,l));case"graph":return r(()=>i6(i,o,l));case"logical":return r(()=>p6(i,o,l));case"matrices":return r(()=>c6(i,o,l));case"normalization":return r(()=>d6(i,o,l));case"ragged":return r(()=>h6(i,o,l));case"reduction":return r(()=>m6(i,o,l));case"slice_join":return r(()=>f6(i,o,l));case"sparse":return r(()=>g6(i,o,l));case"spectral":return r(()=>y6(i,o,l));case"string":return r(()=>b6(i,o,l));case"transformation":return r(()=>x6(i,o,l));case"hash_table":return l6(i,o,l,a);case"custom":let u=QN(i.op);if(u&&u.customExecutor)return u.customExecutor(new jH(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. 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t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Fk(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(c=>Xn(c)[0]),p=[];a!=null&&(p=a.map(c=>Xn(c.name)[0]));let d=[...t];for(;d.length>0;){let c=d.pop();if((w2(c)||S6(c)||T6(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&u.indexOf(c.name)===-1&&p.indexOf(c.name)===-1){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function v6(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(p=>Xn(p)[0]).map(p=>e.nodes[p]),o=e.initNodes;i.forEach(p=>{a.has(p.name)&&s.push(p)}),e.weights.forEach(p=>{a.has(p.name)&&s.push(p)}),o!=null&&o.forEach(p=>{a.has(p.name)&&s.push(p)});let l=new Set,u=[];for(;s.length>0;){let p=s.pop();l.add(p.name),t[p.name]||u.push(p),p.children.forEach(d=>{!l.has(d.name)&&a.has(d.name)&&d.inputs.every(c=>l.has(c.name))&&s.push(d)})}return u}var w6=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],k6=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],I6=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function w2(e){return w6.indexOf(e.op)>=0}function S6(e){return k6.indexOf(e.op)>=0}function T6(e){return I6.indexOf(e.op)>=0}var ex=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new ex(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=Fk(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(a.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return v6(this.graph,this.weightMap,n)}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return Gt(t),t}cloneTensorList(e){return e?e.map(t=>this.cloneAndKeepTensor(t)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,n])=>[t,this.cloneTensorList(n)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(p=>this.graph.nodes[Xn(p)[0]]),r=t.map(p=>Xn(p)[0]),s=r.map(p=>this.graph.nodes[p]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));try{this.keepIntermediateTensors=H().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(p){this.keepIntermediateTensors=!1,console.warn(p.message)}let l={},u={};return P(()=>{let p=new $k(this.weightMap,l,u,this.functionExecutorMap),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(m=>{let[f,g]=Xn(m),y=[];y[g]=e[m],d[f]=y,this.keepIntermediateTensors&&(this.clonedTensorsMap[f]=this.cloneTensorList(y))});let c=this.getFrozenTensorIds(d),h={};for(let m=0;mwn(m,d,p))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(a=>a.id)));return new Set(t)}checkTensorForDisposal(e,t,n,a,r,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=NH(o.name,n,a);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let p=i[u.id];p===1?(u.dispose(),delete i[u.id]):p!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.clonedTensorsMap||(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,n=!1,a={},r={}){this.disposeIntermediateTensors(),n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=H().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let s=new $k(this.weightMap,a,r,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,s,t,n),o=t.map(d=>wn(d,i,s)),l=o.map(d=>d.id),u=Object.keys(e).map(d=>e[d].id),p=new Set([...l,...u,...this.weightIds]);return Object.values(i).forEach(d=>{d.forEach(c=>{c&&!c.isDisposed&&!p.has(c.id)&&c.dispose()})}),this.parent==null&&s.dispose(p),o}async executeFunctionAsync(e,t,n){let a=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(a,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,a){let r=Object.keys(e),s=r.map(b=>this.graph.nodes[Xn(b)[0]]),i=n.map(b=>Xn(b)[0]),o=i.map(b=>this.graph.nodes[b]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:p,syncInputs:d}=Fk(e,o,this.weightMap,this._initNodes),c=[...s,...this.graph.weights,...this._initNodes||[]].map(b=>({node:b,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(b=>{let[x,w]=Xn(b),I=[];I[w]=e[b],h[x]=I});let m={},f=this.getFrozenTensorIds(h),g={};for(;c.length>0;){let b=this.processStack(s,c,t,h,g,f,i,m,l);await Promise.all(b)}p==null&&!a&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(b=>!w2(b)&&!wn(b.name,h,t)).map(b=>b.name);if(y.length>0){let b="";throw p!=null&&(b=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${b}`)}return h}processStack(e,t,n,a,r,s,i,o,l){let u=[];for(;t.length>0;){let p=t.pop();n.currentContext=p.contexts;let d="";if(p.node.op==="Enter"&&k("isConstant",p.node,a,n)&&([d]=Sr(p.node.name,n)),a[p.node.name]==null){let c=Ak(p.node,a,n,this._resourceManager);d||([d]=Sr(p.node.name,n));let h=n.currentContext;v.isPromise(c)?u.push(c.then(m=>(a[d]=m,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(m)),n.currentContext=h,this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l),m))):(a[d]=c,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(c)),this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l))}else this.processChildNodes(p.node,t,n,a,r,l)}return u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Sr(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!wn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!wn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Xn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){var t,n;let a={};for(let r in e){let s=(n=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||n===void 0?void 0:n[r];s!=null?a[s.name]=e[r]:a[r]=e[r]}return a}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Xn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var n,a;let r=(a=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||a===void 0?void 0:a[t];return r!=null?r.name:t},{})}checkOutputs(e){e.forEach(t=>{let[n]=Xn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},N6=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},C6="?tfjs-format=file",_6="model.json",S0=class{constructor(e,t={},n=Ht){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,t==null&&(this.loadOptions={}),this.resourceManager=new N6}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(n=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let a=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new ex(Nk.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Nk.Instance.transformGraph(e.modelInitializer);this.initializer=new ex(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof Ne?[e]:e,n={};return t.forEach((a,r)=>n[this.structuredOutputKeys[r]]=a),n}return e}predict(e,t){let n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){let n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var t;if(!(e instanceof Ne)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[a++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,n=Object.keys(t);for(let a=0;a1?n:n[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&_e(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function E6(e,t={},n=Ht){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof e=="string"&&(e=$6(e));let a=new S0(e,t,n);return await a.load(),a}function A6(e){if(e==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let t;if(e instanceof Array){let[a,r]=e;if(!a)throw new Error("modelJSON must be the first element of the array");if(!r||!(r instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in a))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in a))throw new Error("Model JSON is missing 'weightsManifest'");let s=Ht.getWeightSpecs(a.weightsManifest),i=Ht.getModelArtifactsForJSONSync(a,s,r);t=Ht.fromMemorySync(i)}else if("load"in e)t=e;else if("modelTopology"in e&&"weightSpecs"in e&&"weightData"in e)t=Ht.fromMemorySync(e);else throw new Error("Unknown model format");let n=new S0(t);return n.load(),n}function $6(e){return e.endsWith("/")||(e=e+"/"),`${e}${_6}${C6}`}var F6="4.2.0",k2={};Ae(k2,{CSVDataset:()=>$2,Dataset:()=>Ru,FileDataSource:()=>L2,TextLineDataset:()=>A2,URLDataSource:()=>z2,array:()=>tq,csv:()=>dq,func:()=>hq,generator:()=>mq,microphone:()=>gq,version_data:()=>yq,webcam:()=>fq,zip:()=>nq});var R6=hs(Gh()),D6=hs(Gh());function M6(e,t){return Dh(e,t)}function Dh(e,t,n=new Map,a=new Set){if(e==null)return null;if(typeof Blob=="function"&&e instanceof Blob)return e.slice();if(a.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse)if(fl(e)){let s=Array.isArray(e)?[]:{};a.add(e);for(let i in e){let o=e[i],l=Dh(o,t,n,a);s[i]=l}return a.delete(e),e.__proto__&&(s.__proto__=e.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function P6(e,t=S2){return I2(e,t)}function I2(e,t,n=new Set){let a=e[0];if(n.has(a))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(fl(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(u=>u[i]),l=I2(o,t,n);s[i]=l}return n.delete(a),s}else throw new Error(`Can't recurse into non-iterable type: ${a}`);else return r.value}function S2(e){return e===null?null:fl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function T2(e,t){let n=new Map;Dh(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(v.isPromise(r)){let s=await r;n.set(a,s)}}return Dh(e,t,n)}function fl(e){let t=!1;if(H().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=yI();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ne)&&!(e instanceof Promise)&&!t)}function O6(e){return e==null||L6(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ne||v.isTypedArray(e)}function L6(e){return e===null||typeof e!="object"&&typeof e!="function"}function z6(e){return M6(e,W6)}function W6(e){return e instanceof Ne?{value:e.clone(),recurse:!1}:fl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var N2=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},T0=class extends N2{constructor(){super(T0.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let a=0;at===!0)}rowMajorBatch(e,t=!0){return new K6(this,e,t)}columnMajorBatch(e,t=!0,n=S2){return this.rowMajorBatch(e,t).map(a=>P6(a,n))}concatenate(e,t){return new _2(C2([this,e]),t)}take(e){return e<0||e==null?this:new j6(this,e)}skip(e){return e<0||e==null?this:new q6(this,e)}prefetch(e){return new E2(this,e)}shuffle(e,t){return new eq(this,e,t)}serial(){return new H6(this)}},U6=class extends nn{constructor(e){super(),this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:z6(e),done:!1}}},G6=class extends nn{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},H6=class extends nn{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},q6=class extends nn{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},K6=class extends nn{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},X6=class extends nn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;_e(e.value)}}},Y6=class extends nn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ua.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ua.getTensorsInContainer(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},Z6=class extends nn{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Rk=class extends nn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ua.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Ua.getTensorsInContainer(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},C0=class extends nn{constructor(){super(),this.outputQueue=new T0,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},J6=class extends C0{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Ua.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ua.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return!0}},_2=class extends nn{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},Jr;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Jr||(Jr={}));var Q6=class extends nn{constructor(e,t=Jr.FAIL){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function a(s){return s instanceof nn?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await T2(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Jr.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Jr.SHORTEST:return{value:null,done:!0};case Jr.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},E2=class extends nn{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new N2(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},eq=class extends E2{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=D6.alea(n||v.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Ru=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is + ${e}`);let a;return this.size===1/0||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Kn(async()=>(await n.iterator()).columnMajorBatch(e,t,aq),a)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Kn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Kn(async()=>(await t.iterator()).filter(a=>P(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Kn(async()=>(await t.iterator()).map(n=>P(()=>e(n))),this.size)}mapAsync(e){let t=this;return Kn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Kn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Kn(async()=>{let a=N0(async()=>({value:await t.iterator(),done:!1}));return B6(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=R6.alea(t||v.now().toString());return Kn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Kn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Ru.MAX_BUFFER_SIZE=1e4;function Kn(e,t=null){return new class extends Ru{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function tq(e){return Kn(async()=>C2(e),e.length)}function nq(e){if(!fl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await T2(e,a=>{if(a instanceof Ru)return{value:a.iterator(),recurse:!1};if(fl(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return V6(n,Jr.SHORTEST)},t)}function aq(e){if(e===null)return null;let t=e[0];return O6(t)?{value:rq(e),recurse:!1}:{value:null,recurse:!0}}function rq(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ne?Ft(e):fn(e)}var A2=class extends Ru{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` +`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Zd='"',Sp=Symbol("out"),Dk=Symbol("field"),Jd=Symbol("quote"),fb=Symbol("quoteafterquote"),Mk=Symbol("quoteinquote"),$2=class extends Ru{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new A2(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(v.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!H().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new F2(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&a({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),a({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),fn(n,t)}},R2=class extends nn{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=je([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=Aa([s,r,o,i],[1,4])}else this.cropBox=Aa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!H().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new R2(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=vo.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return P(()=>{let t=Qt(ie(e,"float32"),0),n;n=Ea.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return W(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},D2=class{},M2=class extends nn{split(e){return new sq(this,e)}},sq=class extends M2{constructor(e,t){super(),this.upstream=e,this.impl=new iq(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},iq=class extends C0{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},oq=class extends nn{decodeUTF8(){return new lq(this)}},lq=class extends M2{constructor(e){super(),this.upstream=e,this.impl=new uq(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},uq=class extends C0{constructor(e){if(super(),this.upstream=e,H().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=yI();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return H().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},P2=class extends oq{constructor(e,t={}){super(),this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(H().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let r=this.file.slice(this.offset,n);a.readAsArrayBuffer(r)}this.offset=n}),done:!1}}};async function pq(e,t={},n){let a,r;typeof e=="string"?a=e:(a=e.url,r=cq(e));let s=await(n||v.fetch)(a,r);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new P2(i,t)}else throw new Error(s.statusText)}var cq=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function O2(e){return typeof e=="string"&&e.slice(0,7)==="file://"}var L2=class extends D2{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(O2(this.input)&&H().get("IS_NODE")){let e=yx();this.input=e.readFileSync(this.input.slice(7))}return new P2(this.input,this.options)}},z2=class extends D2{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return O2(this.url)?new L2(this.url,this.fileOptions).iterator():pq(this.url,this.fileOptions)}};function dq(e,t={}){return new $2(new z2(e),t)}function hq(e){let t=N0(e);return Kn(async()=>t)}function mq(e){return Kn(async()=>{let t=await e();return N0(()=>t.next())})}async function fq(e,t){return R2.create(e,t)}async function gq(e){return F2.create(e)}var yq="4.2.0";function ge(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var bq=hr.whereImpl,_0=class extends ac{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Hh(this,Ca())}nextDataId(){return _0.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,H().get("IS_NODE")&&N.warn(` ============================ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. -============================`));let a={id:this.nextDataId()};return this.data.set(a,{values:e,dtype:n,refCount:1}),a}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(s=>v.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return{dataId:a,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,a,r){this.data.set(e,{values:t,dtype:a,refCount:r})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let a=this.readSync(n.real.dataId),r=this.readSync(n.imag.dataId);return N.mergeRealAndImagArrays(a,r)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(a=>v.decodeString(a));return Pe(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Pe(e.shape,e.dtype,t)}makeOutput(e,t,n){return Ca().makeTensorFromTensorInfo(this.makeTensorInfo(t,n,e),this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. 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G2={};Ae(G2,{addImpl:()=>q2,bincountImpl:()=>$0,bincountReduceImpl:()=>K2,castImpl:()=>j2,ceilImpl:()=>X2,concatImpl:()=>F0,equalImpl:()=>Y2,expImpl:()=>J2,expm1Impl:()=>eC,floorImpl:()=>tC,gatherNdImpl:()=>nC,gatherV2Impl:()=>aC,greaterEqualImpl:()=>sC,greaterImpl:()=>rC,lessEqualImpl:()=>oC,lessImpl:()=>iC,linSpaceImpl:()=>lC,logImpl:()=>uC,maxImpl:()=>pC,maximumImpl:()=>cC,minimumImpl:()=>dC,multiplyImpl:()=>D0,negImpl:()=>hC,notEqualImpl:()=>mC,prodImpl:()=>fC,raggedGatherImpl:()=>gC,raggedRangeImpl:()=>yC,raggedTensorToTensorImpl:()=>bC,rangeImpl:()=>M0,rsqrtImpl:()=>xC,scatterImpl:()=>el,sigmoidImpl:()=>dq,simpleAbsImpl:()=>H2,sliceImpl:()=>Lh,sparseFillEmptyRowsImpl:()=>wC,sparseReshapeImpl:()=>kC,sparseSegmentReductionImpl:()=>P0,sqrtImpl:()=>fq,squaredDifferenceImpl:()=>IC,stridedSliceImpl:()=>SC,stringNGramsImpl:()=>O0,stringSplitImpl:()=>L0,stringToHashBucketFastImpl:()=>z0,subImpl:()=>TC,tileImpl:()=>NC,topKImpl:()=>EC,transposeImpl:()=>R0,uniqueImpl:()=>_C});function 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jq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;ge(r,"argMin");let i=v.parseAxisParam(s,r.shape),o=N.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Vn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],N.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[p,d]=N.computeOutAndReduceShapes(l.shape,i),c=v.sizeFromShape(p),h=v.makeZerosTypedArray(c,"int32"),m=v.sizeFromShape(d),f=n.data.get(l.dataId).values;for(let g=0;gn.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(p,"int32",h)}var qq={kernelName:ic,backendName:"cpu",kernelFunc:jq},Kq=rt(_l,e=>Math.asin(e)),Xq={kernelName:_l,backendName:"cpu",kernelFunc:Kq},Yq=rt(Al,e=>Math.asinh(e)),Zq={kernelName:Al,backendName:"cpu",kernelFunc:Yq},Jq=rt($l,e=>Math.atan(e)),Qq={kernelName:$l,backendName:"cpu",kernelFunc:Jq},e5=Vt((e,t)=>Math.atan2(e,t)),t5=rn(Dl,e5),n5={kernelName:Dl,backendName:"cpu",kernelFunc:t5},a5=rt(Fl,e=>Math.atanh(e)),r5={kernelName:Fl,backendName:"cpu",kernelFunc:a5};function B0(e,t,n,a,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,p=r.effectiveFilterHeight,d=r.effectiveFilterWidth,c=r.padInfo.top,h=r.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=Pe(r.outShape,n),g=f.values,y=r.outShape[1]*r.outShape[2]*r.outShape[3],b=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let w=0;wq?q=oe:s==="avg"&&(K+=oe,Z++)}if(isNaN(q))break}let J=S+M*x+C;g[J]=s==="avg"?K/Z:q}}}return f}function PC(e,t,n,a,r=!1,s=!1){let i=Pe(a.outShape,"int32"),o=a.strideHeight,l=a.strideWidth,u=a.dilationHeight,p=a.dilationWidth,d=a.effectiveFilterHeight,c=a.effectiveFilterWidth,h=a.padInfo.top,m=a.padInfo.left,f=Pe(t,n,e);for(let g=0;gR&&(R=G,r?F=s?((g*a.inHeight+S)*a.inWidth+B)*a.inChannels+y:(S*a.inWidth+B)*a.inChannels+y:F=M*c+U)}}i.set(F,g,b,T,y)}}return i}function OC(e,t,n,a,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,p=r.dilationHeight,d=r.dilationWidth,c=r.effectiveFilterDepth,h=r.effectiveFilterHeight,m=r.effectiveFilterWidth,f=r.padInfo.front,g=r.padInfo.top,y=r.padInfo.left,b=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=Pe(r.outShape,n),w=x.values,I=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],T=r.outShape[2]*r.outShape[3]*r.outShape[4],C=r.outShape[3]*r.outShape[4],_=r.outShape[4];for(let $=0;$be?be=dt:s==="avg"&&(ke+=dt,Se++),isNaN(be))break}if(isNaN(be))break}if(isNaN(be))break}let Le=ue+S;w[Le]=s==="avg"?ke/Se:be}}}}return x}function s5(e,t){let n=Pe(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,p=t.effectiveFilterHeight,d=t.effectiveFilterWidth,c=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f=M&&(M=ee,B=G*p*d+K*p+J)}}}n.set(B,f,y,I,$,g)}}}return n}function i5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;ge(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;v.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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p=N.computePool3DInfo(s.shape,i,o,1,l,u),d=p.strideDepth,c=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,y=p.dilationDepth,b=p.dilationHeight,x=p.dilationWidth,w=p.effectiveFilterDepth,I=p.effectiveFilterHeight,T=p.effectiveFilterWidth,C=w-1-p.padInfo.front,_=T-1-p.padInfo.left,$=I-1-p.padInfo.top,R=Pe(s.shape,"float32"),F=1/(m*f*g),S=n.bufferSync(r);for(let M=0;M=p.outDepth||Math.floor(te)!==te))for(let ie=0;ie=p.outHeight||Math.floor(oe)!==oe))for(let ye=0;ye=p.outWidth||Math.floor(ue)!==ue)continue;let be=S.get(M,te,oe,ue,B);ee+=be}}}R.set(ee*F,M,U,G,q,B)}return n.makeTensorInfo(R.shape,R.dtype,R.values)}var c5={kernelName:Zh,backendName:"cpu",kernelFunc:p5};function d5(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ge([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=p.strideHeight,c=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,y=p.effectiveFilterHeight,b=p.effectiveFilterWidth,x=b-1-p.padInfo.left,w=y-1-p.padInfo.top,I=Pe(i.shape,"float32"),T=1/(h*m),C=n.data.get(r.dataId).values,_=Pe(r.shape,"float32",C);for(let $=0;$=p.outHeight||Math.floor(q)!==q))for(let K=0;K=p.outWidth||Math.floor(Z)!==Z)continue;let J=_.get($,q,Z,R);U+=J}}I.set(U*T,$,F,S,R)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var h5={kernelName:Yh,backendName:"cpu",kernelFunc:d5};function m5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient 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o=s.reduce((y,b)=>y*b),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=ft({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Vn({inputs:{x:h},backend:n,attrs:{perm:u}}),f=ft({inputs:{x:m},backend:n,attrs:{shape:p}}),g=hi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var y5={kernelName:Rl,backendName:"cpu",kernelFunc:g5};function b5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=$0(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var x5={kernelName:Jh,backendName:"cpu",kernelFunc:b5};function v5(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var w5={kernelName:Qh,backendName:"cpu",kernelFunc:v5},k5=rt(gs,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(v.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;uf.shape);N.assertParamsConsistent(i,s);let o=N.computeOutShape(t.map(f=>f.shape),s);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>v.sizeFromShape(f.shape)>0);if(l.length===1)return pr({inputs:{x:l[0]},backend:n});if(l[0].dtype==="complex64"){let f=l.map(w=>di({inputs:{input:w},backend:n})),g=l.map(w=>vl({inputs:{input:w},backend:n})),y=wl({inputs:f,backend:n,attrs:{axis:s}}),b=wl({inputs:g,backend:n,attrs:{axis:s}}),x=Yn({inputs:{real:y,imag:b},backend:n});return f.forEach(w=>n.disposeIntermediateTensorInfo(w)),g.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(b),x}let u=l.map(f=>{let g=[-1,v.sizeFromShape(f.shape.slice(s))];return ft({inputs:{x:f},backend:n,attrs:{shape:g}})}),p=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));o=N.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=F0(p,o,t[0].dtype,d),h=N.computeOutShape(l.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var C5={kernelName:Ml,backendName:"cpu",kernelFunc:wl};function LC(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a;ge([r,s],"conv2d");let d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,y=c.padInfo.left,b=c.padInfo.top,x=c.dataFormat==="channelsLast",w=new qt(c.outShape,r.dtype),I=v.computeStrides(r.shape),T=v.computeStrides(s.shape),C=I[0],_=x?I[1]:I[2],$=x?I[2]:1,R=x?1:I[1],F=w.strides[0],S=x?w.strides[1]:w.strides[2],M=x?w.strides[2]:1,B=x?1:w.strides[1],U=n.data.get(r.dataId).values,G=n.data.get(s.dataId).values,q=w.values;for(let K=0;K=c.inHeight)continue;let ye=ie*T[0],ue=Z+oe*_;for(let be=0;be=c.inWidth)continue;let nt=ye+Le*T[1],st=ue+Ue*$,Qe=nt;for(let at=0;at=u.inDepth)continue;let K=G*$[0],Z=F+q*_[1];for(let J=0;J=u.inHeight)continue;let oe=K+te*$[1],ye=Z+ie*_[2];for(let ue=0;ue=u.inWidth)continue;let Ue=oe+Se*$[2],nt=ye+Le*u.inChannels,st=Ue;for(let Qe=0;QeMath.cos(e)),W5={kernelName:Ni,backendName:"cpu",kernelFunc:z5},B5=rt(Ci,e=>Math.cosh(e)),V5={kernelName:Ci,backendName:"cpu",kernelFunc:B5};function U5(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,y=Pe([m,f,g,h],"float32"),b=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,w=n.data.get(r.dataId).values,I=v.computeStrides(r.shape),T=v.computeStrides(y.shape);for(let C=0;C=p)continue;let B=f>1?(F-$)*(d-1)/(f-1):0,U=g>1?(S-R)*(c-1)/(g-1):0;for(let G=0;G1?$*(d-1)+G*B:.5*($+F)*(d-1);if(q<0||q>d-1){for(let K=0;K1?R*(c-1)+ee*U:.5*(R+S)*(c-1);if(ae<0||ae>c-1){for(let ye=0;ye1?R*(c-1)+K*U:.5*(R+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;aey+m-b-1:(y,b)=>y+b;for(let y=0;yy+m-b-1:(y,b)=>y+b;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],d=l*s,c=u*s,h=p/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*d*c*h),g=0;for(let y=0;y`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=N.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:b}=h,x=b.left,w=b.top,I=h.outChannels/h.inChannels,T=new qt(h.outShape,r.dtype),C=n.data.get(r.dataId).values,_=n.data.get(s.dataId).values,$=T.values;for(let R=0;R=h.inHeight)continue;let K=G*d[0],Z=F+q*p[1];for(let J=0;J=h.inWidth)continue;let oe=K+te*d[1],ye=Z+ie*h.inChannels,ue=ee,be=oe;for(let ke=0;ke{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:b,padInfo:x,strideHeight:w,strideWidth:I,filterHeight:T,filterWidth:C,dilationHeight:_,dilationWidth:$,outShape:R}=N.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),F=v.sizeFromShape(R),S=R.length,M=v.getArrayFromDType(a.dtype,F);for(let B=0;B=0&&te=0&&oeJ&&(J=be)}}}let ee=v.locToIndex([B,U,q,Z],S,v.computeStrides(R));M[ee]=J}}}return{dataId:l.write(v.toTypedArray(M,a.dtype),R,a.dtype),shape:R,dtype:a.dtype}}},o8={kernelName:yh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=v.toNestedArray(a.shape,u.data.get(a.dataId).values),d=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:_,outShape:$}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===$.length,()=>`Error in ${yh}, dy must have the same rank as output ${$.length}, but got ${s.rank}`);let R=v.toNestedArray($,u.data.get(s.dataId).values),F=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&ae=0&&ieK&&(K=oe,Z=ee,J=te)}}}F[Z][J][q]+=R[S][M][U][q]}}}return{dataId:u.write(v.toTypedArray(F,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},l8={kernelName:gh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=v.toNestedArray(a.shape,u.data.get(a.dataId).values),d=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:_,outShape:$}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===$.length,()=>`Error in ${gh}, dy must have the same rank as output ${$.length}, but got ${s.rank}`);let R=v.toNestedArray($,u.data.get(s.dataId).values),F=v.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S=0&&ae=0&&ieK&&(K=oe,Z=ae,J=ie)}}}F[S][Z][J][q]+=R[S][M][U][q]}}}return{dataId:u.write(v.toTypedArray(F,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function ed(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ge(r,"sum");let o;r.dtype==="bool"?o=cs({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=pr({inputs:{x:r},backend:n});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),p=N.getAxesPermutation(u,l),d=u,c=o;p!=null&&(c=Vn({inputs:{x:o},backend:n,attrs:{perm:p}}),d=N.getInnerMostAxes(d.length,l)),N.assertAxesAreInnerMostDims("sum",d,c.shape.length);let[h,m]=N.computeOutAndReduceShapes(c.shape,d),f=N.upcastType(c.dtype,"int32"),g=Oh(n,h,f),y=v.sizeFromShape(m),b=n.data.get(g.dataId).values,x=n.data.get(c.dataId).values;for(let w=0;w=0&&(c=ed({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var c8={kernelName:lm,backendName:"cpu",kernelFunc:p8};function d8(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;ge([a,r],"eluGrad");let s=new Float32Array(v.sizeFromShape(r.shape)),i=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values;for(let l=0;l=1?s[l]=o[l]:s[l]=o[l]*(u+1)}return n.makeTensorInfo(r.shape,"float32",s)}var h8={kernelName:um,backendName:"cpu",kernelFunc:d8},m8=N.ERF_P,f8=N.ERF_A1,g8=N.ERF_A2,y8=N.ERF_A3,b8=N.ERF_A4,x8=N.ERF_A5,v8=rt(zl,e=>{let t=Math.sign(e),n=Math.abs(e),a=1/(1+m8*n);return t*(1-((((x8*a+b8)*a+y8)*a+g8)*a+f8)*a*Math.exp(-n*n))}),w8={kernelName:zl,backendName:"cpu",kernelFunc:v8};function Wh(e){let{inputs:t,backend:n,attrs:a}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),ft({inputs:{x:r},backend:n,attrs:{shape:o}})}var k8={kernelName:Bl,backendName:"cpu",kernelFunc:Wh},I8=Vt((e,t)=>e/t),V0=rn(Ai,I8),lx={kernelName:Ai,backendName:"cpu",kernelFunc:V0};function WC(e,t,n){let a=e.shape,r=a[0],s=a[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=v.sizeFromShape(u),d=v.getTypedArrayFromDType("float32",p),c=v.getTypedArrayFromDType("float32",p);for(let g=0;g{let{image:a}=e,r=n,s=v.getTypedArrayFromDType(a.dtype,v.sizeFromShape(a.shape)),[i,o,l,u]=a.shape,p=r.data.get(a.dataId).values;for(let d=0;d=0&&bMath.floor(e/t)),D8=rn(Ri,F8,null,"int32"),R8={kernelName:Ri,backendName:"cpu",kernelFunc:D8};function M8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=LC({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c}});if(i){let g=f;if(p==="NCHW"&&i.shape.length===1&&i.shape[0]!==1){let y=ft({inputs:{x:i},backend:n,attrs:{shape:[i.shape[0],1,1]}});f=xl({inputs:{a:f,b:y},backend:n}),n.disposeIntermediateTensorInfo(y)}else f=xl({inputs:{a:f,b:i},backend:n});n.disposeIntermediateTensorInfo(g)}if(h){let g=f;if(p==="NCHW"&&h==="prelu"&&o.shape.length===1&&o.shape[0]!==1){let y=ft({inputs:{x:o},backend:n,attrs:{shape:[o.shape[0],1,1]}});f=zh(n,f,h,y,m),n.disposeIntermediateTensorInfo(y)}else f=zh(n,f,h,o,m);n.disposeIntermediateTensorInfo(g)}return f}var P8={kernelName:ni,backendName:"cpu",kernelFunc:M8};function O8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=zC({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c}});if(i){let g=f;f=xl({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=zh(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var L8={kernelName:ai,backendName:"cpu",kernelFunc:O8};function z8(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=v.sizeFromShape(a.shape),i=r.shape,o=i[i.length-1],[l,u,p,d]=N.prepareAndValidate(a,r);if(u===0)return n.makeTensorInfo(l,a.dtype,[]);let c=n.data.get(r.dataId).values,h=n.bufferSync(a),m=nC(c,h,a.dtype,u,o,p,d,a.shape,s);return n.makeTensorInfo(l,a.dtype,m.values)}var W8={kernelName:Hl,backendName:"cpu",kernelFunc:z8};function B8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a;ge([r,s],"gatherV2");let l=v.parseAxisParam(i,r.shape)[0],u=n.data.get(s.dataId).values,p=r.shape[l];for(let w=0;w=0,()=>`GatherV2: the index value ${I} is not in [0, ${p-1}]`)}let d=o;o==null&&(d=0);let c=v.sizeFromShape(s.shape),h=N.segment_util.collectGatherOpShapeInfo(r,s,l,d),m=ft({inputs:{x:r},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),f=ft({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,c/h.batchSize]}}),g=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],y=n.bufferSync(f),b=n.bufferSync(m),x=aC(b,y,g);return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),n.makeTensorInfo(h.outputShape,x.dtype,x.values)}var V8={kernelName:Gl,backendName:"cpu",kernelFunc:B8};function U8(e){let{inputs:t,backend:n}=e,{input:a}=t,r=v.sizeFromShape(a.shape),s=a.shape[a.shape.length-1],i=r/s,o=ft({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),l=WC(o,!0,n),u=ft({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var G8={kernelName:cm,backendName:"cpu",kernelFunc:U8},H8=rt(ql,e=>Number.isFinite(e)?1:0,"bool"),j8={kernelName:ql,backendName:"cpu",kernelFunc:H8},q8=rt(Kl,e=>Math.abs(e)===1/0?1:0,"bool"),K8={kernelName:Kl,backendName:"cpu",kernelFunc:q8},X8=rt(Li,e=>Number.isNaN(e)?1:0,"bool"),Y8={kernelName:Li,backendName:"cpu",kernelFunc:X8};function Z8(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=lC(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var J8={kernelName:hm,backendName:"cpu",kernelFunc:Z8},Q8=rt(Zl,e=>Math.log1p(e)),eK={kernelName:Zl,backendName:"cpu",kernelFunc:Q8},tK=Vt((e,t)=>e&&t),nK=rn(Jl,tK,null,"bool"),aK={kernelName:Jl,backendName:"cpu",kernelFunc:nK},rK=rt(Ql,e=>e?0:1,"bool"),sK={kernelName:Ql,backendName:"cpu",kernelFunc:rK},iK=Vt((e,t)=>e||t),oK=rn(eu,iK,null,"bool"),lK={kernelName:eu,backendName:"cpu",kernelFunc:oK};function uK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;ge(r,"LRN");let 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qj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;ge(r,"argMin");let i=v.parseAxisParam(s,r.shape),o=N.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Vn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],N.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[p,d]=N.computeOutAndReduceShapes(l.shape,i),c=v.sizeFromShape(p),h=v.makeZerosTypedArray(c,"int32"),m=v.sizeFromShape(d),f=n.data.get(l.dataId).values;for(let g=0;gn.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(p,"int32",h)}var jj={kernelName:rc,backendName:"cpu",kernelFunc:qj},Kj=rt(Nl,e=>Math.asin(e)),Xj={kernelName:Nl,backendName:"cpu",kernelFunc:Kj},Yj=rt(Cl,e=>Math.asinh(e)),Zj={kernelName:Cl,backendName:"cpu",kernelFunc:Yj},Jj=rt(_l,e=>Math.atan(e)),Qj={kernelName:_l,backendName:"cpu",kernelFunc:Jj},e5=Vt((e,t)=>Math.atan2(e,t)),t5=rn(Al,e5),n5={kernelName:Al,backendName:"cpu",kernelFunc:t5},a5=rt(El,e=>Math.atanh(e)),r5={kernelName:El,backendName:"cpu",kernelFunc:a5};function W0(e,t,n,a,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,p=r.effectiveFilterHeight,d=r.effectiveFilterWidth,c=r.padInfo.top,h=r.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=Le(r.outShape,n),g=f.values,y=r.outShape[1]*r.outShape[2]*r.outShape[3],b=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let w=0;wj?j=se:s==="avg"&&(K+=se,Z++)}if(isNaN(j))break}let J=S+M*x+C;g[J]=s==="avg"?K/Z:j}}}return f}function FC(e,t,n,a,r=!1,s=!1){let i=Le(a.outShape,"int32"),o=a.strideHeight,l=a.strideWidth,u=a.dilationHeight,p=a.dilationWidth,d=a.effectiveFilterHeight,c=a.effectiveFilterWidth,h=a.padInfo.top,m=a.padInfo.left,f=Le(t,n,e);for(let g=0;gR&&(R=G,r?F=s?((g*a.inHeight+S)*a.inWidth+B)*a.inChannels+y:(S*a.inWidth+B)*a.inChannels+y:F=M*c+U)}}i.set(F,g,b,T,y)}}return i}function RC(e,t,n,a,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,p=r.dilationHeight,d=r.dilationWidth,c=r.effectiveFilterDepth,h=r.effectiveFilterHeight,m=r.effectiveFilterWidth,f=r.padInfo.front,g=r.padInfo.top,y=r.padInfo.left,b=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=Le(r.outShape,n),w=x.values,I=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],T=r.outShape[2]*r.outShape[3]*r.outShape[4],C=r.outShape[3]*r.outShape[4],E=r.outShape[4];for(let $=0;$ye?ye=dt:s==="avg"&&(ke+=dt,Se++),isNaN(ye))break}if(isNaN(ye))break}if(isNaN(ye))break}let We=ue+S;w[We]=s==="avg"?ke/Math.max(Se,1):ye}}}}return 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d5(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ge([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=p.strideHeight,c=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,y=p.effectiveFilterHeight,b=p.effectiveFilterWidth,x=b-1-p.padInfo.left,w=y-1-p.padInfo.top,I=Le(i.shape,"float32"),T=1/(h*m),C=n.data.get(r.dataId).values,E=Le(r.shape,"float32",C);for(let $=0;$=p.outHeight||Math.floor(j)!==j))for(let K=0;K=p.outWidth||Math.floor(Z)!==Z)continue;let J=E.get($,j,Z,R);U+=J}}I.set(U*T,$,F,S,R)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var h5={kernelName:Xh,backendName:"cpu",kernelFunc:d5};function m5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient 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o=s.reduce((y,b)=>y*b),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=ft({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Vn({inputs:{x:h},backend:n,attrs:{perm:u}}),f=ft({inputs:{x:m},backend:n,attrs:{shape:p}}),g=di({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var y5={kernelName:$l,backendName:"cpu",kernelFunc:g5};function b5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=A0(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var x5={kernelName:Zh,backendName:"cpu",kernelFunc:b5};function v5(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var w5={kernelName:Jh,backendName:"cpu",kernelFunc:v5},k5=rt(gs,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(v.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;uf.shape);N.assertParamsConsistent(i,s);let o=N.computeOutShape(t.map(f=>f.shape),s);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>v.sizeFromShape(f.shape)>0);if(l.length===1)return pr({inputs:{x:l[0]},backend:n});if(l[0].dtype==="complex64"){let f=l.map(w=>ci({inputs:{input:w},backend:n})),g=l.map(w=>yl({inputs:{input:w},backend:n})),y=bl({inputs:f,backend:n,attrs:{axis:s}}),b=bl({inputs:g,backend:n,attrs:{axis:s}}),x=Yn({inputs:{real:y,imag:b},backend:n});return f.forEach(w=>n.disposeIntermediateTensorInfo(w)),g.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(b),x}let u=l.map(f=>{let g=[-1,v.sizeFromShape(f.shape.slice(s))];return ft({inputs:{x:f},backend:n,attrs:{shape:g}})}),p=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));o=N.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=$0(p,o,t[0].dtype,d),h=N.computeOutShape(l.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var C5={kernelName:Fl,backendName:"cpu",kernelFunc:bl};function DC(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a;ge([r,s],"conv2d");let d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,y=c.padInfo.left,b=c.padInfo.top,x=c.dataFormat==="channelsLast",w=new jt(c.outShape,r.dtype),I=v.computeStrides(r.shape),T=v.computeStrides(s.shape),C=I[0],E=x?I[1]:I[2],$=x?I[2]:1,R=x?1:I[1],F=w.strides[0],S=x?w.strides[1]:w.strides[2],M=x?w.strides[2]:1,B=x?1:w.strides[1],U=n.data.get(r.dataId).values,G=n.data.get(s.dataId).values,j=w.values;for(let K=0;K=c.inHeight)continue;let be=re*T[0],ue=Z+se*E;for(let ye=0;ye=c.inWidth)continue;let pt=be+We*T[1],at=ue+Ge*$,Qe=pt;for(let et=0;et=u.inDepth)continue;let K=G*$[0],Z=F+j*E[1];for(let J=0;J=u.inHeight)continue;let se=K+te*$[1],be=Z+re*E[2];for(let ue=0;ue=u.inWidth)continue;let Ge=se+Se*$[2],pt=be+We*u.inChannels,at=Ge;for(let Qe=0;QeMath.cos(e)),W5={kernelName:Ti,backendName:"cpu",kernelFunc:z5},B5=rt(Ni,e=>Math.cosh(e)),V5={kernelName:Ni,backendName:"cpu",kernelFunc:B5};function U5(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,y=Le([m,f,g,h],"float32"),b=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,w=n.data.get(r.dataId).values,I=v.computeStrides(r.shape),T=v.computeStrides(y.shape);for(let C=0;C=p)continue;let B=f>1?(F-$)*(d-1)/(f-1):0,U=g>1?(S-R)*(c-1)/(g-1):0;for(let G=0;G1?$*(d-1)+G*B:.5*($+F)*(d-1);if(j<0||j>d-1){for(let K=0;K1?R*(c-1)+ee*U:.5*(R+S)*(c-1);if(ae<0||ae>c-1){for(let be=0;be1?R*(c-1)+K*U:.5*(R+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;aey+m-b-1:(y,b)=>y+b;for(let y=0;yy+m-b-1:(y,b)=>y+b;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],d=l*s,c=u*s,h=p/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*d*c*h),g=0;for(let y=0;y`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=N.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:b}=h,x=b.left,w=b.top,I=h.outChannels/h.inChannels,T=new jt(h.outShape,r.dtype),C=n.data.get(r.dataId).values,E=n.data.get(s.dataId).values,$=T.values;for(let R=0;R=h.inHeight)continue;let K=G*d[0],Z=F+j*p[1];for(let J=0;J=h.inWidth)continue;let se=K+te*d[1],be=Z+re*h.inChannels,ue=ee,ye=se;for(let ke=0;ke{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:b,padInfo:x,strideHeight:w,strideWidth:I,filterHeight:T,filterWidth:C,dilationHeight:E,dilationWidth:$,outShape:R}=N.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),F=v.sizeFromShape(R),S=R.length,M=v.getArrayFromDType(a.dtype,F);for(let B=0;B=0&&te=0&&seJ&&(J=ye)}}}let ee=v.locToIndex([B,U,j,Z],S,v.computeStrides(R));M[ee]=J}}}return{dataId:l.write(v.toTypedArray(M,a.dtype),R,a.dtype),shape:R,dtype:a.dtype}}},oK={kernelName:gh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=v.toNestedArray(a.shape,u.data.get(a.dataId).values),d=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:$}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===$.length,()=>`Error in ${gh}, dy must have the same rank as output ${$.length}, but got ${s.rank}`);let R=v.toNestedArray($,u.data.get(s.dataId).values),F=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&ae=0&&reK&&(K=se,Z=ee,J=te)}}}F[Z][J][j]+=R[S][M][U][j]}}}return{dataId:u.write(v.toTypedArray(F,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},lK={kernelName:fh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=v.toNestedArray(a.shape,u.data.get(a.dataId).values),d=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:$}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===$.length,()=>`Error in ${fh}, dy must have the same rank as output ${$.length}, but got ${s.rank}`);let R=v.toNestedArray($,u.data.get(s.dataId).values),F=v.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S=0&&ae=0&&reK&&(K=se,Z=ae,J=re)}}}F[S][Z][J][j]+=R[S][M][U][j]}}}return{dataId:u.write(v.toTypedArray(F,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function Zc(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ge(r,"sum");let o;r.dtype==="bool"?o=cs({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=pr({inputs:{x:r},backend:n});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),p=N.getAxesPermutation(u,l),d=u,c=o;p!=null&&(c=Vn({inputs:{x:o},backend:n,attrs:{perm:p}}),d=N.getInnerMostAxes(d.length,l)),N.assertAxesAreInnerMostDims("sum",d,c.shape.length);let[h,m]=N.computeOutAndReduceShapes(c.shape,d),f=N.upcastType(c.dtype,"int32"),g=Mh(n,h,f),y=v.sizeFromShape(m),b=n.data.get(g.dataId).values,x=n.data.get(c.dataId).values;for(let w=0;w=0&&(c=Zc({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var cK={kernelName:om,backendName:"cpu",kernelFunc:pK};function dK(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;ge([a,r],"eluGrad");let s=new Float32Array(v.sizeFromShape(r.shape)),i=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values;for(let l=0;l=1?s[l]=o[l]:s[l]=o[l]*(u+1)}return n.makeTensorInfo(r.shape,"float32",s)}var hK={kernelName:lm,backendName:"cpu",kernelFunc:dK},mK=N.ERF_P,fK=N.ERF_A1,gK=N.ERF_A2,yK=N.ERF_A3,bK=N.ERF_A4,xK=N.ERF_A5,vK=rt(Pl,e=>{let t=Math.sign(e),n=Math.abs(e),a=1/(1+mK*n);return t*(1-((((xK*a+bK)*a+yK)*a+gK)*a+fK)*a*Math.exp(-n*n))}),wK={kernelName:Pl,backendName:"cpu",kernelFunc:vK};function Lh(e){let{inputs:t,backend:n,attrs:a}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),ft({inputs:{x:r},backend:n,attrs:{shape:o}})}var kK={kernelName:Ll,backendName:"cpu",kernelFunc:Lh},IK=Vt((e,t)=>e/t),B0=rn(Ei,IK),nx={kernelName:Ei,backendName:"cpu",kernelFunc:B0};function PC(e,t,n){let a=e.shape,r=a[0],s=a[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=v.sizeFromShape(u),d=v.getTypedArrayFromDType("float32",p),c=v.getTypedArrayFromDType("float32",p);for(let g=0;g{let{image:a}=e,r=n,s=v.getTypedArrayFromDType(a.dtype,v.sizeFromShape(a.shape)),[i,o,l,u]=a.shape,p=r.data.get(a.dataId).values;for(let d=0;d=0&&bMath.floor(e/t)),RK=rn(Ri,FK,null,"int32"),DK={kernelName:Ri,backendName:"cpu",kernelFunc:RK};function MK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=DC({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c}});if(i){let g=f;if(p==="NCHW"&&i.shape.length===1&&i.shape[0]!==1){let y=ft({inputs:{x:i},backend:n,attrs:{shape:[i.shape[0],1,1]}});f=gl({inputs:{a:f,b:y},backend:n}),n.disposeIntermediateTensorInfo(y)}else f=gl({inputs:{a:f,b:i},backend:n});n.disposeIntermediateTensorInfo(g)}if(h){let g=f;if(p==="NCHW"&&h==="prelu"&&o.shape.length===1&&o.shape[0]!==1){let y=ft({inputs:{x:o},backend:n,attrs:{shape:[o.shape[0],1,1]}});f=Oh(n,f,h,y,m),n.disposeIntermediateTensorInfo(y)}else f=Oh(n,f,h,o,m);n.disposeIntermediateTensorInfo(g)}return f}var PK={kernelName:ei,backendName:"cpu",kernelFunc:MK};function OK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=MC({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c}});if(i){let g=f;f=gl({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=Oh(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var LK={kernelName:ti,backendName:"cpu",kernelFunc:OK};function zK(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=v.sizeFromShape(a.shape),i=r.shape,o=i[i.length-1],[l,u,p,d]=N.prepareAndValidate(a,r);if(u===0)return n.makeTensorInfo(l,a.dtype,[]);let c=n.data.get(r.dataId).values,h=n.bufferSync(a),m=J2(c,h,a.dtype,u,o,p,d,a.shape,s);return n.makeTensorInfo(l,a.dtype,m.values)}var WK={kernelName:Vl,backendName:"cpu",kernelFunc:zK};function BK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a;ge([r,s],"gatherV2");let l=v.parseAxisParam(i,r.shape)[0],u=n.data.get(s.dataId).values,p=r.shape[l];for(let w=0;w=0,()=>`GatherV2: the index value ${I} is not in [0, ${p-1}]`)}let d=o;o==null&&(d=0);let c=v.sizeFromShape(s.shape),h=N.segment_util.collectGatherOpShapeInfo(r,s,l,d),m=ft({inputs:{x:r},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),f=ft({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,c/h.batchSize]}}),g=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],y=n.bufferSync(f),b=n.bufferSync(m),x=Q2(b,y,g);return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),n.makeTensorInfo(h.outputShape,x.dtype,x.values)}var VK={kernelName:Bl,backendName:"cpu",kernelFunc:BK};function UK(e){let{inputs:t,backend:n}=e,{input:a}=t,r=v.sizeFromShape(a.shape),s=a.shape[a.shape.length-1],i=r/s,o=ft({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),l=PC(o,!0,n),u=ft({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var GK={kernelName:pm,backendName:"cpu",kernelFunc:UK},HK=rt(Gl,e=>Number.isFinite(e)?1:0,"bool"),qK={kernelName:Gl,backendName:"cpu",kernelFunc:HK},jK=rt(Hl,e=>Math.abs(e)===1/0?1:0,"bool"),KK={kernelName:Hl,backendName:"cpu",kernelFunc:jK},XK=rt(Oi,e=>Number.isNaN(e)?1:0,"bool"),YK={kernelName:Oi,backendName:"cpu",kernelFunc:XK};function ZK(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=rC(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var JK={kernelName:dm,backendName:"cpu",kernelFunc:ZK},QK=rt(Kl,e=>Math.log1p(e)),e8={kernelName:Kl,backendName:"cpu",kernelFunc:QK},t8=Vt((e,t)=>e&&t),n8=rn(Xl,t8,null,"bool"),a8={kernelName:Xl,backendName:"cpu",kernelFunc:n8},r8=rt(Yl,e=>e?0:1,"bool"),s8={kernelName:Yl,backendName:"cpu",kernelFunc:r8},i8=Vt((e,t)=>e||t),o8=rn(Zl,i8,null,"bool"),l8={kernelName:Zl,backendName:"cpu",kernelFunc:o8};function u8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;ge(r,"LRN");let u=r.shape[3],p=u-1,d=n.data.get(r.dataId).values,c=v.sizeFromShape(r.shape),h=new Float32Array(c);function m(f){let g=f%u,y=f-g+Math.max(0,g-s),b=f-g+Math.min(g+s,p),x=0;for(;y<=b;y++){let w=d[y];x+=w*w}return x}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. 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n=e.shapeInfo.logicalShape;switch(n.length){case 0:return TZ(e,t);case 1:return CZ(e,t);case 2:return EZ(e,t);case 3:return $Z(e,t);case 4:return RZ(e,t);case 5:return DZ(e);case 6:return MZ(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function g_(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return SZ(e);case 1:return NZ(e,t);case 2:return _Z(e,t);case 3:return AZ(e,t);default:return FZ(e,t)}}function aZ(e,t,n=!1,a){let r="";n?r+=g_(e,a):r+=Ou(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=PZ(e,t):r+=OZ(e,t)),r}function rZ(e,t,n){switch(e.length){case 0:return y_();case 1:return mZ(e,t,n);case 2:return kZ(e,t,n);case 3:return gZ(e,t,n);default:return bZ(e,t,n)}}function sZ(e,t,n){switch(e.length){case 0:return y_();case 1:return fZ(e,t,n);case 2:return IZ(e,t,n);case 3:return yZ(e,t,n);case 4:return xZ(e,t,n);case 5:return vZ(e,t);case 6:return wZ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function iZ(e){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${e.texture2D}(textureSampler, uv).r; } @@ -268,7 +268,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function wE(){return` +`;function y_(){return` int getOutputCoords() { return 0; } @@ -365,10 +365,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Rf(["r","c","d"],e)} + ${_f(["r","c","d"],e)} return ivec3(r, c, d); } -`;let a=To(["r","c","d"],e);return` +`;let a=So(["r","c","d"],e);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -422,10 +422,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Rf(["r","c","d","d2"],e)} + ${_f(["r","c","d","d2"],e)} return ivec4(r, c, d, d2); } - `;let a=To(["r","c","d","d2"],e);return` + `;let a=So(["r","c","d","d2"],e);return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -433,7 +433,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${a} return ivec4(r, c, d, d2); } - `}function vZ(e,t){let n=To(["r","c","d","d2","d3"],e);return` + `}function vZ(e,t){let n=So(["r","c","d","d2","d3"],e);return` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -445,7 +445,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec5 outShape = ivec5(r, c, d, d2, d3); return outShape; } - `}function wZ(e,t){let n=To(["r","c","d","d2","d3","d4"],e);return` + `}function wZ(e,t){let n=So(["r","c","d","d2","d3","d4"],e);return` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -543,7 +543,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int c = index - r * ${e[1]}; return ivec2(r, c); } - `}function No(e){return`offset${e}`}function SZ(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=Cn();return` + `}function To(e){return`offset${e}`}function SZ(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=Cn();return` vec4 ${n}() { return ${a.texture2D}(${t}, halfCR); } @@ -551,7 +551,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float ${a}() { return sampleTexture(${n}, halfCR); } - `;let i=No(n);if(t)return` + `;let i=To(n);if(t)return` float ${a}() { vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i}); return sampleTexture(${n}, uv); @@ -576,13 +576,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } `}function CZ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return` float ${a}(int index) { - ${Bu(e)} + ${Lu(e)} } `;let r=e.shapeInfo.texShape,s=r[0],i=r[1];if(i===1&&s===1)return` float ${a}(int index) { return sampleTexture(${n}, halfCR); } - `;let o=No(n);return i===1?t?` + `;let o=To(n);return i===1?t?` float ${a}(int index) { vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0])); return sampleTexture(${n}, uv); @@ -612,7 +612,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${s}, ${i}, index + ${o}); return sampleTexture(${n}, uv); } - `}function EZ(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=Cn();if(s!=null&&v.arraysEqual(n,s))return t?` + `}function _Z(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=Cn();if(s!=null&&v.arraysEqual(n,s))return t?` vec4 ${r}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]); @@ -636,7 +636,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = packedUVfrom2D(${p}, ${u[0]}, ${u[1]}, row, col); return ${l.texture2D}(${a}, uv); } - `}function _Z(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape;if(s!=null&&v.arraysEqual(n,s)){if(t)return` + `}function EZ(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape;if(s!=null&&v.arraysEqual(n,s)){if(t)return` float ${r}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]); return sampleTexture(${a}, uv); @@ -646,17 +646,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${c}.0); return sampleTexture(${a}, uv); } - `}let{newShape:i,keptDims:o}=v.squeezeShape(n),l=i;if(l.length=1?p="coords = 0;":p=o.map(g=>`coords.${d[g+u]} = 0;`).join(` + `}function PZ(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=f_(e.shapeInfo.logicalShape,t.logicalShape),l=gt(i),u=i-s,p,d=["x","y","z","w","u","v"];s===0?p="":i<2&&o.length>=1?p="coords = 0;":p=o.map(g=>`coords.${d[g+u]} = 0;`).join(` `);let c="";i<2&&s>0?c="coords":c=e.shapeInfo.logicalShape.map((g,y)=>`coords.${d[y+u]}`).join(", ");let h="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,f=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=` return vec4(outputValue.xy, outputValue.xy); `;else if(m&&!f)i===1?h=` @@ -977,16 +977,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float ${r}() { return sampleTexture(${n}, resultUV); } - `;let u=gt(l),p=xE(e.shapeInfo.logicalShape,t.logicalShape),d=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&p.length>=1?c="coords = 0;":c=p.map(f=>`coords.${h[f+d]} = 0;`).join(` + `;let u=gt(l),p=f_(e.shapeInfo.logicalShape,t.logicalShape),d=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&p.length>=1?c="coords = 0;":c=p.map(f=>`coords.${h[f+d]} = 0;`).join(` `);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+d]}`).join(", "),` float ${r}() { ${u} coords = getOutputCoords(); ${c} return get${a}(${m}); } - `}function gt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function K0(e,t,n){let{newShape:a,keptDims:r}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):a,l=!e&&s>1&&!v.arraysEqual(t,n)&&a.lengthe[n]).join(", ")}function LZ(e,t,n,a){let r=n.map((p,d)=>{let c={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(c.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[d],shapeInfo:c}}),s=r.map(p=>p.shapeInfo),i={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},o=nZ(r,i,t),l=ZC(e.gl,o),u=e.createProgram(l);return H().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},kE(e,t,u))}function kE(e,t,n){let a={},r={},s={},i=[],o,l,u,p=null,d=null;d=e.getUniformLocation(n,"NAN",!1),H().getNumber("WEBGL_VERSION")===1&&(p=e.getUniformLocation(n,"INFINITY",!1));let c=!1;for(let 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p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(c.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[d],shapeInfo:c}}),s=r.map(p=>p.shapeInfo),i={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},o=nZ(r,i,t),l=jC(e.gl,o),u=e.createProgram(l);return H().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},b_(e,t,u))}function b_(e,t,n){let a={},r={},s={},i=[],o,l,u,p=null,d=null;d=e.getUniformLocation(n,"NAN",!1),H().getNumber("WEBGL_VERSION")===1&&(p=e.getUniformLocation(n,"INFINITY",!1));let c=!1;for(let h=0;h{i[m]=e.getUniformLocation(n,h.name,c)}),{uniformLocations:a,customUniformLocations:i,infLoc:p,nanLoc:d,inShapesLocations:r,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function Vk(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,a)=>{let r=n.logicalShape,s=t[a],i=s.shape;if(!v.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function zZ(e,t,n,a,r){t.program.enableShapeUniforms||(Vk(t.inShapeInfos,n),Vk([t.outShapeInfo],[a]));let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),H().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,u)=>{let p=t.program.variableNames[u],d=t.uniformLocations[p],c=t.uniformLocations[`offset${p}`],h=t.inShapesLocations[`${p}Shape`],m=t.inTexShapesLocations[`${p}TexShape`];if(h){let{uniformShape:f}=j0(t.program.packedInputs,l.shape,l.texData.texShape);switch(f.length){case 1:e.gl.uniform1iv(h,new Int32Array(f));break;case 2:e.gl.uniform2iv(h,new Int32Array(f));break;case 3:e.gl.uniform3iv(h,new Int32Array(f));break;case 4:e.gl.uniform4iv(h,new Int32Array(f));break;default:break}}if(m&&e.gl.uniform2i(m,l.texData.texShape[0],l.texData.texShape[1]),d!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(d,l.uniformValues[0]);else{let f=l.uniformValues;f instanceof Float32Array||(f=new Float32Array(f)),e.gl.uniform1fv(d,f)}return}l.texData.slice!=null&&c!=null&&e.gl.uniform1i(c,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,d,u)}});let o=t.outShapeLocation;if(o)switch(a.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(a.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(a.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(a.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(a.shape));break;default:break}if(t.outShapeStridesLocation){let l=v.computeStrides(a.shape);switch(a.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,a.texData.texShape[0],a.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,u)=>{let p=t.customUniformLocations[u],d=r[u];if(l.type==="float")e.gl.uniform1fv(p,d);else if(l.type==="vec2")e.gl.uniform2fv(p,d);else if(l.type==="vec3")e.gl.uniform3fv(p,d);else if(l.type==="vec4")e.gl.uniform4fv(p,d);else if(l.type==="int")e.gl.uniform1iv(p,d);else if(l.type==="ivec2")e.gl.uniform2iv(p,d);else if(l.type==="ivec3")e.gl.uniform3iv(p,d);else if(l.type==="ivec4")e.gl.uniform4iv(p,d);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function WZ(e,t,n){let a="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:d}=j0(e.packedInputs,i.shape,l),c="",h="",m="";if(p.length===1&&e.packedInputs){let I=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];c=`${I[0]>1}_${I[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let I=v.computeStrides(p);m=`${I[0]===l[1]}_${I[I.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&v.arraysEqual(i.shape,l),y=v.sizeFromShape(i.shape)===1,b=N.getBroadcastDims(i.shape,n.shape),x=!e.packedInputs&&f===n.shape.length&&v.arraysEqual(l,n.texData.texShape),w=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;a+=`${f}_${x}_${u?d:""}_${p.length}_${y}_${b}_${g}_${c}_${h}_${m}_${w}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r+`${H().getNumber("WEBGL_VERSION")}`,s}function _n(e){return H().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var BZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Zp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Cn();this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Rf(["r","c","d"],e):To(["r","c","d"],e)} + ${this.enableShapeUniforms?_f(["r","c","d"],e):So(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1004,9 +1004,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},VZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Qp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Cn();this.outputShape=e,this.enableShapeUniforms=En(this.outputShape.length),this.userCode=` + `}},VZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Zp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Cn();this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Rf(["r","c","d"],e):To(["r","c","d"],e)} + ${this.enableShapeUniforms?_f(["r","c","d"],e):So(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1025,25 +1025,25 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } `}},UZ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=pa.DOWNLOAD;let t=Cn();this.outputShape=e,this.userCode=` - ${bE} + ${m_} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } `}},GZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=pa.DOWNLOAD;let t=Cn();this.outputShape=e,this.userCode=` - ${bE} + ${m_} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${t.output} = encode_float(x); } - `}},HZ={R:0,G:1,B:2,A:3},Xk=class{constructor(e,t=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let a=Cn();this.outputShape=e,this.enableShapeUniforms=En(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)");let s="";for(let i=0;iFE,createBufferFromOutputTexture:()=>ME,createFloat16MatrixTexture:()=>EE,createFloat16PackedMatrixTexture:()=>$E,createFloat32MatrixTexture:()=>CE,createIndexBuffer:()=>NE,createPackedMatrixTexture:()=>AE,createUnsignedBytesMatrixTexture:()=>_E,createVertexBuffer:()=>TE,createVertexShader:()=>SE,downloadByteEncodedFloatMatrixFromOutputTexture:()=>OE,downloadFloat32MatrixFromBuffer:()=>PE,downloadMatrixFromPackedOutputTexture:()=>zE,downloadPackedMatrixFromBuffer:()=>LE,getInternalFormatForFloat16MatrixTexture:()=>Y0,getInternalFormatForFloat16PackedMatrixTexture:()=>Q0,getInternalFormatForFloat32MatrixTexture:()=>X0,getInternalFormatForPackedMatrixTexture:()=>J0,getInternalFormatForUnsignedBytesMatrixTexture:()=>Z0,uploadDenseMatrixToTexture:()=>DE,uploadPixelDataToTexture:()=>RE});function SE(e){let t=Cn(),n=`${t.version} + 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t=H().getNumber("WEBGL_VERSION");if(e!=null?(this.gl=e,qC(t,e)):this.gl=Ka(t),e=this.gl,H().getNumber("WEBGL_VERSION")===2){let r=e;this.createVertexArray=()=>ce(r,()=>r.createVertexArray()),this.bindVertexArray=s=>ce(r,()=>r.bindVertexArray(s)),this.deleteVertexArray=s=>ce(r,()=>r.deleteVertexArray(s)),this.getVertexArray=()=>ce(r,()=>r.getParameter(r.VERTEX_ARRAY_BINDING))}else if(e!=null){let r=e.getExtension("OES_vertex_array_object");if(r==null)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>ce(e,()=>r.createVertexArrayOES()),this.bindVertexArray=s=>ce(e,()=>r.bindVertexArrayOES(s)),this.deleteVertexArray=s=>ce(e,()=>r.deleteVertexArrayOES(s)),this.getVertexArray=()=>ce(e,()=>e.getParameter(r.VERTEX_ARRAY_BINDING_OES))}let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),H().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Rp(this.gl,r),ca(this.gl,s))this.textureHalfFloatExtension=Rp(this.gl,s);else if(H().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),ca(this.gl,a))this.colorBufferHalfFloatExtension=Rp(this.gl,a);else if(H().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",ca(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(ca(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable 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this.throwIfDisposed(),$E(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),AE(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(cx(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>OE(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return LE(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return PE(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=ME(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),a}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(H().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let 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this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;lh(a,e,this.framebuffer),this.debug&&Mp(a),this.outputTexture=e,ce(a,()=>a.viewport(0,0,t,n)),ce(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,n,a))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function qZ(e){let t=0;for(;t`${e}.${n}`)}function kn(e,t){return t===1?[e]:UE(e,t)}function O7(e,t){if(e===1)return"rc";let n="";for(let a=0;ae.bindTexture(o,i)),ce(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ce(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ce(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),ce(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),H().getNumber("WEBGL_VERSION")===1?ce(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)):ce(e,()=>e.texStorage2D(o,1,a,t,n)),ce(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[n,t]}}function K0(e){return e.internalFormatFloat}function I_(e,t,n,a){let[r,s]=Jc(t,n);return Qc(e,r,s,K0(a),a.textureFormatFloat,e.FLOAT)}function X0(e){return e.internalFormatHalfFloat}function S_(e,t,n,a){let[r,s]=Jc(t,n);return Qc(e,r,s,X0(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function Y0(e){return e.downloadTextureFormat}function T_(e,t,n,a){let[r,s]=Jc(t,n);return Qc(e,r,s,Y0(a),e.RGBA,e.UNSIGNED_BYTE)}function Z0(e){return e.internalFormatPackedFloat}function N_(e,t,n,a){let[r,s]=Mu(t,n);return 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if(H().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",ca(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(ca(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=w_(this.gl),this.indexBuffer=k_(this.gl),this.framebuffer=e_(this.gl),this.textureConfig=U0(this.gl,this.textureHalfFloatExtension)}get debug(){return H().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),r_(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=Mu(t,n);this.setOutputMatrixTextureDriver(e,a,r)}setOutputMatrixWriteRegion(e,t,n,a){this.setOutputMatrixWriteRegionDriver(n,e,a,t)}setOutputPackedMatrixWriteRegion(e,t,n,a){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&ih(this.gl,this.program),$p(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and 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this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;oh(a,e,this.framebuffer),this.debug&&$p(a),this.outputTexture=e,ce(a,()=>a.viewport(0,0,t,n)),ce(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,n,a))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function jZ(e){let t=0;for(;t`${e}.${n}`)}function kn(e,t){return t===1?[e]:z_(e,t)}function O7(e,t){if(e===1)return"rc";let n="";for(let a=0;a= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}), cEdge ? 0. : getA(${t[1]}), rEdge ? 0. : getA(${t[2]}), - rEdge || cEdge ? 0. : getA(${t[3]})`}},GE=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=En(this.outputShape.length);let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2===1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=` + rEdge || cEdge ? 0. : getA(${t[3]})`}},W_=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length);let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2===1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=` ${r} ${a>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); @@ -1155,7 +1155,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${a>0?"}":""} `}this.userCode=` ${z7(t,this.enableShapeUniforms)} - ${this.enableShapeUniforms?q0():j0(e)} + ${this.enableShapeUniforms?q0():H0(e)} void main() { ivec3 rc = getOutputCoords(); @@ -1172,10 +1172,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } `}};function z7(e,t){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${t?tZ(["r","c","d"],"inputShape"):To(["r","c","d"],e)} + ${t?tZ(["r","c","d"],"inputShape"):So(["r","c","d"],e)} return ivec3(r, c, d); } - `}var W7=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=Zk(t,n),r=Jk(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=Yk(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===ln.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===ln.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===ln.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===ln.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===ln.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=Zk(n,a),s=Jk(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=Yk(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=H().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function B7(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function Yk(e,t,n,a,r){let s=V7(t,a),i;if(r){let[l,u]=Lu(e[0],e[1]);i=l*u}else{let[l,u]=td(e[0],e[1]);i=l*u}let o=B7(n,s);return i*o}function V7(e,t){switch(e){case ln.PACKED_2X2_FLOAT32:return J0(t);case ln.PACKED_2X2_FLOAT16:return Q0(t);case ln.UNPACKED_FLOAT32:return X0(t);case ln.UNPACKED_FLOAT16:return Y0(t);case ln.PACKED_4X1_UNSIGNED_BYTE:return Z0(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function U7(e){return H().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?ln.PACKED_2X2_FLOAT32:ln.UNPACKED_FLOAT32:e?ln.PACKED_2X2_FLOAT16:ln.UNPACKED_FLOAT16}function Zk(e,t){if(e===pa.UPLOAD)return ln.PACKED_2X2_FLOAT32;if(e===pa.RENDER||e==null)return U7(t);if(e===pa.DOWNLOAD||e===pa.PIXELS)return ln.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function Jk(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var rr=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=En(this.outputShape.length),this.userCode=` + `}var W7=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=Hk(t,n),r=qk(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=Gk(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===un.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===un.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===un.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===un.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===un.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=Hk(n,a),s=qk(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=Gk(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=H().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function B7(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function Gk(e,t,n,a,r){let s=V7(t,a),i;if(r){let[l,u]=Mu(e[0],e[1]);i=l*u}else{let[l,u]=Jc(e[0],e[1]);i=l*u}let o=B7(n,s);return i*o}function V7(e,t){switch(e){case un.PACKED_2X2_FLOAT32:return Z0(t);case un.PACKED_2X2_FLOAT16:return J0(t);case un.UNPACKED_FLOAT32:return K0(t);case un.UNPACKED_FLOAT16:return X0(t);case un.PACKED_4X1_UNSIGNED_BYTE:return Y0(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function U7(e){return H().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?un.PACKED_2X2_FLOAT32:un.UNPACKED_FLOAT32:e?un.PACKED_2X2_FLOAT16:un.UNPACKED_FLOAT16}function Hk(e,t){if(e===pa.UPLOAD)return un.PACKED_2X2_FLOAT32;if(e===pa.RENDER||e==null)return U7(t);if(e===pa.DOWNLOAD||e===pa.PIXELS)return un.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function qk(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var rr=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } @@ -1186,11 +1186,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},Ma="if (isnan(x)) return x;",G7="return x;",Qk="return abs(x);",H7="return (x >= 0.0) ? x : (exp(x) - 1.0);",j7=Ma+` + `}},Ma="if (isnan(x)) return x;",G7="return x;",jk="return abs(x);",H7="return (x >= 0.0) ? x : (exp(x) - 1.0);",q7=Ma+` return (x < 0.0) ? 0.0 : x; -`,q7=Ma+` +`,j7=Ma+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,Xr="return x;",K7="return 1.0 / (1.0 + exp(-1.0 * x));",X7="return x;",Y7=` +`,Kr="return x;",K7="return 1.0 / (1.0 + exp(-1.0 * x));",X7="return x;",Y7=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -1219,7 +1219,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,Q7="return 1.0 / (1.0 + exp(-1.0 * x));",es=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=En(this.outputShape.length),this.userCode=` +`,Q7="return 1.0 / (1.0 + exp(-1.0 * x));",Qr=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } @@ -1230,17 +1230,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},eJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=En(this.outputShape.length);let t=e.length,n=kn("rc",t),a=gt(t),r=O7(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` + `}},eJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length);let t=e.length,n=kn("rc",t),a=gt(t),r=O7(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` void main() { ${a} rc = getOutputCoords(); vec4 packedInput = getA(${r}); setOutput(getChannel(packedInput, ${i})); } - `}},tJ=hr.whereImpl,nJ=1e-7,aJ=1e-4,wb={};function rJ(e){return e in wb||(wb[e]={}),wb[e]}var sJ=H().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),iJ=600;function oJ(){return H().global.screen==null?1024:H().global.screen.height*H().global.screen.width*window.devicePixelRatio*iJ/1024/1024}var Mf=class extends sc{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!H().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof ch)t=e;else{let n=Ka(H().getNumber("WEBGL_VERSION"),e);t=new ch(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Ka(H().getNumber("WEBGL_VERSION"));t=new ch(n),this.binaryCache=rJ(H().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new W7(this.gpgpu),this.numMBBeforeWarning=oJ(),this.texData=new qh(this,Ca())}nextDataId(){return Mf.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,n,a,r,s){let i=this.makeTensorInfo(t,n),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[a,r]},o.texShape=[a,r];let l=Pp(t),u=new Xk(l,!1,s),p=this.runWebGLProgram(u,[i],n,[[a,r]]);return p.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(e,t,n){if((H().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||H().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:pa.UPLOAD,refCount:1}),a}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,a,r){if(H().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:pa.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let d;o?d=new es(i,Xr):d=new rr(i,Xr);let c=this.runWebGLProgram(d,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let p;if(a==="complex64"){let d=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);p=N.mergeRealAndImagArrays(d,c)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new es(a,Xr):h=new rr(a,Xr);let m=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(H().getBool("DEBUG")&&!H().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&H().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&H().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...th(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];p=N.mergeRealAndImagArrays(m,f)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(a);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;ce(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,p),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ca().removeDataId(e,this),this.pendingDeletes--),d}readToGPU(e,t={}){let n=this.texData.get(e),{values:a,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let c;o?c=new es(r,Xr):c=new rr(r,Xr);let h=this.runWebGLProgram(c,[{dataId:e,shape:r,dtype:i}],i),m=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),m}if(l==null)throw a!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),p=Ca().makeTensorFromTensorInfo(u),d=this.texData.get(u.dataId);return Object.assign({tensorRef:p},d.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(a=>v.decodeString(a));return Pe(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Pe(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:a,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(a,n),this.textureManager.releaseTexture(t,a,r,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=sJ){return H().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)0&&v.isString(n[0])){let r=n.map(s=>v.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){return Ca().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new eJ(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new L7(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[mi(e.shape),...fi(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[mi(t),...fi(t)],s=new GE(r,n),i=!0,o=[n],l=this.runWebGLProgram(s,[a],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:a,shape:r,dtype:s}=n;if(t!=null){let d=v.sizeFromShape(r),c=t[0]*t[1]*4;v.assert(d<=c,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Pp(r),o;a?o=new VZ(i):o=new BZ(i);let l=!0,u=[t!=null?t:th(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,n,a,r=!1,s){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===Qp.DENSE){let g=s!=null?s:th(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=H().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!ec(y.shape,g.shape)){let b=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),b.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},d=WZ(e,u,p),c=this.getAndSaveBinary(d,()=>LZ(this.gpgpu,e,u,p)),h=this.activeTimers!=null,m;h&&(m=this.startTimer()),H().get("ENGINE_COMPILE_ONLY")||zZ(this.gpgpu,c,u,p,a),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));let f=H().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let g=v.now();g-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!H().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,a,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,a,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(H().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=P(()=>{if(!H().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=H().getBool("DEBUG");H().set("DEBUG",!1);let t=this.abs(xe(1e-8)).dataSync()[0];if(H().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?nJ:aJ}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let p=t.texShape;if(p==null&&(p=cE(n,o),t.texShape=p),r!=null){let d=Pp(n),c,h=p[1],m=p[0],f=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!f)&&([h,m]=Lu(p[0],p[1])),o?c=new jZ(d,f):c=new Xk(d,f);let g=f?[m,h]:p,y=this.makeTensorInfo(g,a),b=this.texData.get(y.dataId);f?b.usage=pa.PIXELS:b.usage=pa.UPLOAD,b.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,m,r);let x=[[m,h]],w=!0,I=this.runWebGLProgram(c,[y],a,x,w),T=this.texData.get(I.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,H().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let d=this.acquireTexture(p,i,a,o);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return t!=null&&(n.values=lJ(t,a)),n.values}acquireTexture(e,t,n,a){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,a)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(a=>{try{this.checkCompletion_(t),a(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await qv(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(H0(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:a,nanLoc:r,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=kE(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=a,e.nanLoc=r,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}createTensorFromTexture(e,t,n){let{texture:a,height:r,width:s,channels:i}=e,o=Ca().backend;if(!o.gpgpu.gl.isTexture(a))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=o.writeTexture(a,t,n,r,s,i);return Ca().makeTensorFromDataId(l,t,n,o)}};Mf.nextDataId=0;function lJ(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let a=0;anew Mf,2);var pJ={forceHalfFloat:HE},t1=` + `}},tJ=hr.whereImpl,nJ=1e-7,aJ=1e-4,gb={};function rJ(e){return e in gb||(gb[e]={}),gb[e]}var sJ=H().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),iJ=600;function oJ(){return H().global.screen==null?1024:H().global.screen.height*H().global.screen.width*window.devicePixelRatio*iJ/1024/1024}var Ef=class extends ac{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!H().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof ph)t=e;else{let n=Ka(H().getNumber("WEBGL_VERSION"),e);t=new ph(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Ka(H().getNumber("WEBGL_VERSION"));t=new ph(n),this.binaryCache=rJ(H().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new W7(this.gpgpu),this.numMBBeforeWarning=oJ(),this.texData=new Hh(this,Ca())}nextDataId(){return Ef.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,n,a,r,s){let i=this.makeTensorInfo(t,n),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[a,r]},o.texShape=[a,r];let l=Fp(t),u=new Uk(l,!1,s),p=this.runWebGLProgram(u,[i],n,[[a,r]]);return p.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(e,t,n){if((H().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||H().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:pa.UPLOAD,refCount:1}),a}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,a,r){if(H().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:pa.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let d;o?d=new Qr(i,Kr):d=new rr(i,Kr);let c=this.runWebGLProgram(d,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let p;if(a==="complex64"){let d=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);p=N.mergeRealAndImagArrays(d,c)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Qr(a,Kr):h=new rr(a,Kr);let m=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(H().getBool("DEBUG")&&!H().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&H().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&H().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...eh(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];p=N.mergeRealAndImagArrays(m,f)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(a);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;ce(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,p),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ca().removeDataId(e,this),this.pendingDeletes--),d}readToGPU(e,t={}){let n=this.texData.get(e),{values:a,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let c;o?c=new Qr(r,Kr):c=new rr(r,Kr);let h=this.runWebGLProgram(c,[{dataId:e,shape:r,dtype:i}],i),m=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),m}if(l==null)throw a!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),p=Ca().makeTensorFromTensorInfo(u),d=this.texData.get(u.dataId);return Object.assign({tensorRef:p},d.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(a=>v.decodeString(a));return Le(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:a,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(a,n),this.textureManager.releaseTexture(t,a,r,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=sJ){return H().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)0&&v.isString(n[0])){let r=n.map(s=>v.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){return Ca().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new eJ(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new L7(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[hi(e.shape),...mi(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[hi(t),...mi(t)],s=new W_(r,n),i=!0,o=[n],l=this.runWebGLProgram(s,[a],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:a,shape:r,dtype:s}=n;if(t!=null){let d=v.sizeFromShape(r),c=t[0]*t[1]*4;v.assert(d<=c,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Fp(r),o;a?o=new VZ(i):o=new BZ(i);let l=!0,u=[t!=null?t:eh(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,n,a,r=!1,s){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===Zp.DENSE){let g=s!=null?s:eh(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=H().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!Jp(y.shape,g.shape)){let b=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),b.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},d=WZ(e,u,p),c=this.getAndSaveBinary(d,()=>LZ(this.gpgpu,e,u,p)),h=this.activeTimers!=null,m;h&&(m=this.startTimer()),H().get("ENGINE_COMPILE_ONLY")||zZ(this.gpgpu,c,u,p,a),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));let f=H().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let g=v.now();g-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!H().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,a,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,a,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(H().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=P(()=>{if(!H().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=H().getBool("DEBUG");H().set("DEBUG",!1);let t=this.abs(xe(1e-8)).dataSync()[0];if(H().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?nJ:aJ}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let p=t.texShape;if(p==null&&(p=o_(n,o),t.texShape=p),r!=null){let d=Fp(n),c,h=p[1],m=p[0],f=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!f)&&([h,m]=Mu(p[0],p[1])),o?c=new qZ(d,f):c=new Uk(d,f);let g=f?[m,h]:p,y=this.makeTensorInfo(g,a),b=this.texData.get(y.dataId);f?b.usage=pa.PIXELS:b.usage=pa.UPLOAD,b.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,m,r);let x=[[m,h]],w=!0,I=this.runWebGLProgram(c,[y],a,x,w),T=this.texData.get(I.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,H().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let d=this.acquireTexture(p,i,a,o);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return t!=null&&(n.values=lJ(t,a)),n.values}acquireTexture(e,t,n,a){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,a)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(a=>{try{this.checkCompletion_(t),a(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await qv(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(G0(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:a,nanLoc:r,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=b_(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=a,e.nanLoc=r,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}createTensorFromGPUData(e,t,n){e.channels=e.channels||"RGBA";let{texture:a,height:r,width:s,channels:i}=e,o=Ca().backend;if(!o.gpgpu.gl.isTexture(a))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=o.writeTexture(a,t,n,r,s,i);return Ca().makeTensorFromDataId(l,t,n,o)}};Ef.nextDataId=0;function lJ(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let a=0;anew Ef,2);var pJ={forceHalfFloat:B_},e1=` if (isnan(a)) return a; if (isnan(b)) return b; -`,kl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=En(this.outputShape.length),this.userCode=` +`,xl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=_n(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${e} } @@ -1250,12 +1250,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}},ad=` + `}},ed=` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; -`,rd=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=En(r);let s="";if(a)if(r===0||v.sizeFromShape(this.outputShape)===1)s=` +`,td=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=_n(r);let s="";if(a)if(r===0||v.sizeFromShape(this.outputShape)===1)s=` result.y = 0.; result.z = 0.; result.w = 0.; @@ -1299,13 +1299,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function ta(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var cJ={kernelName:Oi,backendName:"webgl",kernelFunc:ta};function Cs(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=ta({inputs:{x:a},backend:n}),l=ta({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var dJ={kernelName:em,backendName:"webgl",kernelFunc:Cs},jE="return (a < 0.) ? b * a : a;",qE=` + `}};function ta(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var cJ={kernelName:Pi,backendName:"webgl",kernelFunc:ta};function Ns(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=ta({inputs:{x:a},backend:n}),l=ta({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var dJ={kernelName:Qh,backendName:"webgl",kernelFunc:Ns},V_="return (a < 0.) ? b * a : a;",U_=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function hJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new rd(qE,r.shape,i.shape):new kl(jE,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var mJ={kernelName:zi,backendName:"webgl",kernelFunc:hJ},KE="return (a < 0.) ? b * a : a;",XE=` +`;function hJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new td(U_,r.shape,i.shape):new xl(V_,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var mJ={kernelName:Li,backendName:"webgl",kernelFunc:hJ},G_="return (a < 0.) ? b * a : a;",H_=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function fJ(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new rd(XE,a.shape,r.shape):new kl(KE,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var gJ={kernelName:Ji,backendName:"webgl",kernelFunc:fJ},Gu="if (isnan(x)) return x;";function Ye({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let d=o.texData.get(i.dataId),c=n(d.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=H().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new es(i.shape,t):p=new rr(i.shape,e),o.runWebGLProgram(p,[i],l)}}function pn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(a&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[w,I]=x,T={dataId:w.dataId,dtype:w.dtype,shape:l.shape},C={dataId:I.dataId,dtype:I.dtype,shape:u.shape},_=new kl(e,l.shape,u.shape);return p.runWebGLProgram(_,[T,C],ma(w.dtype,I.dtype))}),b=Cs({inputs:{real:g,imag:y},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(y),b}let d=s||ma(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(m):m,y=l.dtype==="string"?N.fromUint8ToStringArray(f):f,[b,x]=r(l.shape,u.shape,g,y,d),w=p.makeTensorInfo(x,d),I=p.texData.get(w.dataId);return I.values=b,w}let c=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new rd(t,l.shape,u.shape,n):h=new kl(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function tc(e,t=!1){if(e==="linear")return t?X7:G7;if(e==="relu")return t?Z7:j7;if(e==="elu")return t?Y7:H7;if(e==="relu6")return t?J7:q7;if(e==="prelu")return t?XE:KE;if(e==="leakyrelu")return t?qE:jE;if(e==="sigmoid")return t?Q7:K7;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var YE=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=En(this.outputShape.length);let u=a?e[1]:e[2],p=Math.ceil(u/2),d=a?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) { +`;function fJ(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new td(H_,a.shape,r.shape):new xl(G_,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var gJ={kernelName:Zi,backendName:"webgl",kernelFunc:fJ},Bu="if (isnan(x)) return x;";function Ye({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let d=o.texData.get(i.dataId),c=n(d.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=H().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new Qr(i.shape,t):p=new rr(i.shape,e),o.runWebGLProgram(p,[i],l)}}function cn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(a&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[w,I]=x,T={dataId:w.dataId,dtype:w.dtype,shape:l.shape},C={dataId:I.dataId,dtype:I.dtype,shape:u.shape},E=new xl(e,l.shape,u.shape);return p.runWebGLProgram(E,[T,C],ma(w.dtype,I.dtype))}),b=Ns({inputs:{real:g,imag:y},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(y),b}let d=s||ma(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(m):m,y=l.dtype==="string"?N.fromUint8ToStringArray(f):f,[b,x]=r(l.shape,u.shape,g,y,d),w=p.makeTensorInfo(x,d),I=p.texData.get(w.dataId);return I.values=b,w}let c=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new td(t,l.shape,u.shape,n):h=new xl(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function Qp(e,t=!1){if(e==="linear")return t?X7:G7;if(e==="relu")return t?Z7:q7;if(e==="elu")return t?Y7:H7;if(e==="relu6")return t?J7:j7;if(e==="prelu")return t?H_:G_;if(e==="leakyrelu")return t?U_:V_;if(e==="sigmoid")return t?Q7:K7;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var q_=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=_n(this.outputShape.length);let u=a?e[1]:e[2],p=Math.ceil(u/2),d=a?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:l?f=`vec4 activation(vec4 a) { @@ -1313,16 +1313,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${i} }`:f=`vec4 activation(vec4 x) { ${i} - }`,g="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let b="rc.x",x="rc.x";e[0]`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!ec(r.shape,l)&&!(p.texture!==null&&ec(p.shape,l))?bJ(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var xJ={kernelName:uu,backendName:"webgl",kernelFunc:he},aI=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${v.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%n>0&&(u=` + `}},Yk="return a * b;";function t1(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=N.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),u=new Xk(Kk.REAL,a.shape,r.shape),p=new Xk(Kk.IMAG,a.shape,r.shape),d=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Ns({inputs:{real:c,imag:h},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[u,p]=m7(a.shape,r.shape,o.values,l.values,s),d=n.makeTensorInfo(p,s),c=n.texData.get(d.dataId);return c.values=u,d}let i;return H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new td(Yk,a.shape,r.shape):i=new xl(Yk,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var yJ={kernelName:ji,backendName:"webgl",kernelFunc:t1};function bJ(e,t,n){let a=[hi(e.shape),...mi(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[hi(t),...mi(t)],i=new W_(s,a),o=!0,l=[a],u=n.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function de(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let p=i.texData.get(r.dataId);return p.isPacked&&!Jp(r.shape,l)&&!(p.texture!==null&&Jp(p.shape,l))?bJ(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var xJ={kernelName:iu,backendName:"webgl",kernelFunc:de},Zk=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let p=1/t;l=`sumValue += dot(values * ${v.isInt(p)?p.toPrecision(2):p}, ones);`}let u="";r%n>0&&(u=` if (inIdx < 0 || inIdx >= ${r}) { return 0.0; } @@ -1502,12 +1502,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${l}); } - `}};function wJ(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=N.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function Co(e,t,n,a){let r=wJ(e.shape),s=e;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],a=new Array(t);for(let r=0;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=gt(this.rank),r=UE("rc",this.rank),s=new Array(this.rank);for(let u=0;u6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],a=new Array(t);for(let r=0;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=gt(this.rank),r=z_("rc",this.rank),s=new Array(this.rank);for(let u=0;u`Error in matMul: inner shapes (${d}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let w=n?[y,d,h]:[y,h,d],I=a?[b,m,c]:[b,c,m],T=he({inputs:{x:e},backend:r,attrs:{shape:w}}),C=he({inputs:{x:t},backend:r,attrs:{shape:I}}),_=[T,C],$=Math.max(y,b),R=n?T.shape[1]:T.shape[2],F=s!=null,S=i!=null,M=l==="leakyrelu",B=l!=null?tc(l,!0):null,U=F||S||M||B!=null,G;if((h===1||m===1)&&R>ZE&&U===!1){let K=T,Z=C;n&&(K=In({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),_.push(K)),a&&(Z=In({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),_.push(Z));let J=m!==1,ee=m===1,ae=K;J&&(ae=he({inputs:{x:K},backend:r,attrs:{shape:[$,R,1]}}),_.push(ae));let te=m===1?2:1,ie=Z;ee&&(ie=he({inputs:{x:Z},backend:r,attrs:{shape:[$,1,R]}}),_.push(ie));let oe=n1({inputs:{a:ae,b:ie},backend:r});G=Of({inputs:{x:oe},backend:r,attrs:{axis:te,keepDims:!0}}),_.push(oe)}else{let K=ma(e.dtype,t.dtype),Z=new YE(w,I,[$,h,m],n,a,F,B,S,M),J=[T,C];if(s!=null&&J.push(s),S&&J.push(i),M){let ee=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));J.push(ee),_.push(ee)}G=r.runWebGLProgram(Z,J,K)}let q=he({inputs:{x:G},backend:r,attrs:{shape:x}});_.push(G);for(let K of _)r.disposeIntermediateTensorInfo(K);return q}function EJ(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a;return Vh({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var _J={kernelName:ti,backendName:"webgl",kernelFunc:EJ},rI="return abs(x);";function AJ(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=BE(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return H().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new es(a.shape,rI):r=new rr(a.shape,rI),n.runWebGLProgram(r,[a],a.dtype)}var $J={kernelName:Sl,backendName:"webgl",kernelFunc:AJ},FJ=Ma+` + `}};function Af(e,t,n){let a=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new SJ(e.shape,t):new kJ(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function TJ(e,t,n,a){let r=t,s=e.shape.length,i=v.parseAxisParam(r,e.shape),o=i,l=N.getAxesPermutation(o,s),u=l!=null,p=e;u&&(p=Af(e,l,a),o=N.getInnerMostAxes(o.length,s)),N.assertAxesAreInnerMostDims("sum",o,s);let[d,c]=N.computeOutAndReduceShapes(p.shape,o),h=d;n&&(h=N.expandShapeToKeepDim(d,i));let m=v.sizeFromShape(c),f=v.sizeFromShape(e.shape)/m,g=de({inputs:{x:p},attrs:{shape:[f,m]},backend:a}),y=_m(e.dtype),b=No(g,y,"sum",a),x=de({inputs:{x:b},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(b),u&&a.disposeIntermediateTensorInfo(p),x}function $f(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return TJ(r,s,i,n)}var NJ={kernelName:po,backendName:"webgl",kernelFunc:$f};function In(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let p=0;p`Error in matMul: inner shapes (${d}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let w=n?[y,d,h]:[y,h,d],I=a?[b,m,c]:[b,c,m],T=de({inputs:{x:e},backend:r,attrs:{shape:w}}),C=de({inputs:{x:t},backend:r,attrs:{shape:I}}),E=[T,C],$=Math.max(y,b),R=n?T.shape[1]:T.shape[2],F=s!=null,S=i!=null,M=l==="leakyrelu",B=l!=null?Qp(l,!0):null,U=F||S||M||B!=null,G;if((h===1||m===1)&&R>j_&&U===!1){let K=T,Z=C;n&&(K=In({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),E.push(K)),a&&(Z=In({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),E.push(Z));let J=m!==1,ee=m===1,ae=K;J&&(ae=de({inputs:{x:K},backend:r,attrs:{shape:[$,R,1]}}),E.push(ae));let te=m===1?2:1,re=Z;ee&&(re=de({inputs:{x:Z},backend:r,attrs:{shape:[$,1,R]}}),E.push(re));let se=t1({inputs:{a:ae,b:re},backend:r});G=$f({inputs:{x:se},backend:r,attrs:{axis:te,keepDims:!0}}),E.push(se)}else{let K=ma(e.dtype,t.dtype),Z=new q_(w,I,[$,h,m],n,a,F,B,S,M),J=[T,C];if(s!=null&&J.push(s),S&&J.push(i),M){let ee=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));J.push(ee),E.push(ee)}G=r.runWebGLProgram(Z,J,K)}let j=de({inputs:{x:G},backend:r,attrs:{shape:x}});E.push(G);for(let K of E)r.disposeIntermediateTensorInfo(K);return j}function _J(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a;return Wh({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var EJ={kernelName:Qs,backendName:"webgl",kernelFunc:_J},Jk="return abs(x);";function AJ(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=O_(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return H().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Qr(a.shape,Jk):r=new rr(a.shape,Jk),n.runWebGLProgram(r,[a],a.dtype)}var $J={kernelName:wl,backendName:"webgl",kernelFunc:AJ},FJ=Ma+` if (abs(x) > 1.) { return NAN; } return acos(x); -`,DJ=Ye({opSnippet:FJ}),RJ={kernelName:Tl,backendName:"webgl",kernelFunc:DJ},MJ=Ma+` +`,RJ=Ye({opSnippet:FJ}),DJ={kernelName:kl,backendName:"webgl",kernelFunc:RJ},MJ=Ma+` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,PJ=Ye({opSnippet:MJ}),OJ={kernelName:Nl,backendName:"webgl",kernelFunc:PJ},sI="return a + b;",LJ=pn({opSnippet:sI,packedOpSnippet:sI,supportsComplex:!0,cpuKernelImpl:KZ}),zJ={kernelName:fs,backendName:"webgl",kernelFunc:LJ},WJ=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` +return log(x + sqrt(x * x - 1.0));`,PJ=Ye({opSnippet:MJ}),OJ={kernelName:Il,backendName:"webgl",kernelFunc:PJ},Qk="return a + b;",LJ=cn({opSnippet:Qk,packedOpSnippet:Qk,supportsComplex:!0,cpuKernelImpl:KZ}),zJ={kernelName:fs,backendName:"webgl",kernelFunc:LJ},WJ=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} @@ -1547,7 +1547,7 @@ return log(x + sqrt(x * x - 1.0));`,PJ=Ye({opSnippet:MJ}),OJ={kernelName:Nl,back vec4 result = ${a}; setOutput(result); } - `}};function dh(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return ta({inputs:{x:a[0]},backend:n});if(a.length>H().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=dh({inputs:a.slice(0,o),backend:n}),u=dh({inputs:a.slice(o),backend:n});return dh({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>ma(o,l)),s=a.map(o=>o.shape),i=H().getBool("WEBGL_PACK")?new BJ(a[0].shape,s):new WJ(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var VJ={kernelName:bi,backendName:"webgl",kernelFunc:dh};function UJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=r;p!=null&&(d=In({inputs:{x:r},backend:n,attrs:{perm:p}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("all",u,o);let[c,h]=N.computeOutAndReduceShapes(d.shape,u),m=v.sizeFromShape(h),f=he({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=Co(f,f.dtype,"all",n),y;if(i){let b=N.expandShapeToKeepDim(c,l);y=he({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=he({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),y}var GJ={kernelName:Cl,backendName:"webgl",kernelFunc:UJ};function HJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=r;p!=null&&(d=In({inputs:{x:r},backend:n,attrs:{perm:p}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("any",u,o);let[c,h]=N.computeOutAndReduceShapes(d.shape,u),m=v.sizeFromShape(h),f=he({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=Co(f,f.dtype,"any",n),y;if(i){let b=N.expandShapeToKeepDim(c,l);y=he({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=he({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),y}var jJ={kernelName:El,backendName:"webgl",kernelFunc:HJ},qJ=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` + `}};function ch(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return ta({inputs:{x:a[0]},backend:n});if(a.length>H().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=ch({inputs:a.slice(0,o),backend:n}),u=ch({inputs:a.slice(o),backend:n});return ch({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>ma(o,l)),s=a.map(o=>o.shape),i=H().getBool("WEBGL_PACK")?new BJ(a[0].shape,s):new WJ(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var VJ={kernelName:yi,backendName:"webgl",kernelFunc:ch};function UJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=r;p!=null&&(d=In({inputs:{x:r},backend:n,attrs:{perm:p}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("all",u,o);let[c,h]=N.computeOutAndReduceShapes(d.shape,u),m=v.sizeFromShape(h),f=de({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=No(f,f.dtype,"all",n),y;if(i){let b=N.expandShapeToKeepDim(c,l);y=de({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=de({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),y}var GJ={kernelName:Sl,backendName:"webgl",kernelFunc:UJ};function HJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=r;p!=null&&(d=In({inputs:{x:r},backend:n,attrs:{perm:p}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("any",u,o);let[c,h]=N.computeOutAndReduceShapes(d.shape,u),m=v.sizeFromShape(h),f=de({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=No(f,f.dtype,"any",n),y;if(i){let b=N.expandShapeToKeepDim(c,l);y=de({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=de({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),y}var qJ={kernelName:Tl,backendName:"webgl",kernelFunc:HJ},jJ=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -1629,25 +1629,25 @@ return log(x + sqrt(x * x - 1.0));`,PJ=Ye({opSnippet:MJ}),OJ={kernelName:Nl,back } setOutput(bestIndex); } - `}};function JE(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=N.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new qJ(o,n,a==null),u=[t];a!=null&&u.push(a);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let d=JE(e,t,n,p);return e.disposeIntermediateTensorInfo(p),d}function QE(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=N.computeOptimalWindowSize(s),o=new KJ(r,i,n,a==null),l=a==null?[t]:[t,a],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=QE(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}return u}function e_(e,t,n,a){let r=[n];if(N.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!H().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=N.computeOutAndReduceShapes(l.shape,r),d=v.sizeFromShape(p),c=he({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});s.push(c);let h=JE(e,c,a);s.push(h);let m=he({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return QE(e,t,a)}function 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ZJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=v.parseAxisParam(s,r.shape),o=N.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=In({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=Y_(n,l,i[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var JJ={kernelName:rc,backendName:"webgl",kernelFunc:ZJ},QJ=Ma+` if (abs(x) > 1.) { return NAN; } return asin(x); -`,e9=Ye({opSnippet:QJ}),t9={kernelName:_l,backendName:"webgl",kernelFunc:e9},n9=Ma+"return log(x + sqrt(x * x + 1.0));",a9=Ye({opSnippet:n9}),r9={kernelName:Al,backendName:"webgl",kernelFunc:a9},s9=Ma+` +`,e9=Ye({opSnippet:QJ}),t9={kernelName:Nl,backendName:"webgl",kernelFunc:e9},n9=Ma+"return log(x + sqrt(x * x + 1.0));",a9=Ye({opSnippet:n9}),r9={kernelName:Cl,backendName:"webgl",kernelFunc:a9},s9=Ma+` return atan(x); 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s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,c=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let C=">=";this.userCode=` +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelName:El,backendName:"webgl",kernelFunc:h9},ec=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,c=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * 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s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,p=e.dilationHeight,d=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let b=t==="avg",x="0.0";if(b||(x="-1.0 / 1e-20"),n){let $=">=";this.userCode=` + `}},n1=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,p=e.dilationHeight,d=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let b=t==="avg",x="0.0";if(b||(x="-1.0 / 1e-20"),n){let $=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${f}, ${g}, ${y}); @@ -1850,7 +1850,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam } setOutput(float(minMaxPosition)); } - `;return}let w="max",I=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(I="avgValue / count");let T=Math.floor(s/4)*4,C=s%4,_=` + `;return}let w="max",I=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(I="avgValue / max(count, 1.0)");let T=Math.floor(s/4)*4,C=s%4,E=` if (${b}) { avgValue += dot(values, ones); } else { @@ -1915,7 +1915,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam getValue(batch, xD, xR, xC + 3 * ${d}, ch) ); - ${_} + ${E} } int xC = xCCorner + ${T}; @@ -1927,7 +1927,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam initializationValue ); - ${_} + ${E} } else if (${C===2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), @@ -1936,7 +1936,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam initializationValue ); - ${_} + ${E} } else if (${C===3}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), @@ -1945,13 +1945,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam initializationValue ); - ${_} + ${E} } } - setOutput(${I}); } + setOutput(${I}); } - `}};function f9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;zu(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;v.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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N9={kernelName:wi,backendName:"webgl",kernelFunc:T9},C9=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}};function w9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=N.computePool3DInfo(i.shape,o,l,d,u,p),h=new v9(c);return n.runWebGLProgram(h,[r],i.dtype)}var k9={kernelName:Yh,backendName:"webgl",kernelFunc:w9};function I9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;Pu([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=new x9(p);return n.runWebGLProgram(d,[r],i.dtype)}var S9={kernelName:Xh,backendName:"webgl",kernelFunc:I9};function T9(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Wh({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var N9={kernelName:vi,backendName:"webgl",kernelFunc:T9},C9=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -2059,7 +2059,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam float inv = scale * inversesqrt(variance + float(${s})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}},E9=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}},_9=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${o}; @@ -2072,7 +2072,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam setOutput((x - mean) * inv + offset); } - `}},_9=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=H().getBool("WEBGL_PACK_NORMALIZATION")?new E9(a.shape,r.shape,s.shape,p,d,l):new C9(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},A9={kernelName:Mi,backendName:"webgl",kernelFunc:_9},$9=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=gt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=F9(this.rank),a,r=e.map((s,i)=>`sourceLoc.${mx[i]} = start[${i}] + coords.${mx[i]};`);a=` + `}},E9=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=H().getBool("WEBGL_PACK_NORMALIZATION")?new _9(a.shape,r.shape,s.shape,p,d,l):new C9(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},A9={kernelName:Di,backendName:"webgl",kernelFunc:E9},$9=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=gt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=F9(this.rank),a,r=e.map((s,i)=>`sourceLoc.${lx[i]} = start[${i}] + coords.${lx[i]};`);a=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${r.join(` @@ -2082,7 +2082,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam ${a} setOutput(getSource(${n})); } - `}},mx=["x","y","z","w","u","v"];function F9(e){if(e===1)return"sourceLoc";if(e<=6)return mx.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var D9=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=gt(this.rank),n=kn("coords",this.rank),a=kn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=` + `}},lx=["x","y","z","w","u","v"];function F9(e){if(e===1)return"sourceLoc";if(e<=6)return lx.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var R9=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=gt(this.rank),n=kn("coords",this.rank),a=kn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=` result.x = ${s}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { ++${a[this.rank-1]}; @@ -2111,7 +2111,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam ${o} setOutput(result); } - `}};function R9(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=Kt.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function Hu(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=Kt.parseSliceParams(r,s,i);if(Kt.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),c=T7(d.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),p=Kt.isSliceContinous(r.shape,o,l);if(u||!p){let d=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new D9(l):new $9(l),c=[o];return n.runWebGLProgram(d,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),R9(r,o,l,n)}var M9={kernelName:hu,backendName:"webgl",kernelFunc:Hu},P9=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((b,x)=>b*x),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=[],m=he({inputs:{x:r},backend:n,attrs:{shape:l}}),f=In({inputs:{x:m},backend:n,attrs:{perm:u}}),g=he({inputs:{x:f},backend:n,attrs:{shape:p}}),y=Hu({inputs:{x:g},backend:n,attrs:{begin:d,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y},O9={kernelName:Rl,backendName:"webgl",kernelFunc:P9};function L9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),u=WE(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var z9={kernelName:Jh,backendName:"webgl",kernelFunc:L9};function W9(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.readSync(a.dataId),i=n.readSync(r.dataId),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var B9={kernelName:Qh,backendName:"webgl",kernelFunc:W9},V9="return float(a != b);",t_=pn({opSnippet:V9,cpuKernelImpl:g7,dtype:"bool"}),U9={kernelName:au,backendName:"webgl",kernelFunc:t_};function sd(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return ta({inputs:{x:r.complexTensorInfos.real},backend:n})}var G9={kernelName:km,backendName:"webgl",kernelFunc:sd},H9="return float(int(x));";function j9(e,t){let n=new rr(e.shape,H9),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function fx(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return ta({inputs:{x:r},backend:n});let i=It(r.shape),o=fx({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Cs({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=sd({inputs:{input:r},backend:n}),o=fx({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=ta({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(n.shouldExecuteOnCPU([r])){let i=n.texData.get(r.dataId).values,[o,l,u]=YZ(i,r.shape,r.dtype,s);return n.makeTensorInfo(o,l,u)}if(s==="int32")return j9(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=t_({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var q9={kernelName:ki,backendName:"webgl",kernelFunc:fx},iI="return ceil(x);",K9=Ye({opSnippet:iI,packedOpSnippet:iI,cpuKernelImpl:ZZ}),X9={kernelName:Ii,backendName:"webgl",kernelFunc:K9},Y9=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` + `}};function D9(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=Kt.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function Vu(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=Kt.parseSliceParams(r,s,i);if(Kt.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),c=T7(d.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),p=Kt.isSliceContinous(r.shape,o,l);if(u||!p){let d=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new R9(l):new $9(l),c=[o];return n.runWebGLProgram(d,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),D9(r,o,l,n)}var M9={kernelName:pu,backendName:"webgl",kernelFunc:Vu},P9=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((b,x)=>b*x),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=[],m=de({inputs:{x:r},backend:n,attrs:{shape:l}}),f=In({inputs:{x:m},backend:n,attrs:{perm:u}}),g=de({inputs:{x:f},backend:n,attrs:{shape:p}}),y=Vu({inputs:{x:g},backend:n,attrs:{begin:d,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y},O9={kernelName:$l,backendName:"webgl",kernelFunc:P9};function L9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),u=P_(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var z9={kernelName:Zh,backendName:"webgl",kernelFunc:L9};function W9(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.readSync(a.dataId),i=n.readSync(r.dataId),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var B9={kernelName:Jh,backendName:"webgl",kernelFunc:W9},V9="return float(a != b);",Z_=cn({opSnippet:V9,cpuKernelImpl:g7,dtype:"bool"}),U9={kernelName:eu,backendName:"webgl",kernelFunc:Z_};function nd(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return ta({inputs:{x:r.complexTensorInfos.real},backend:n})}var G9={kernelName:wm,backendName:"webgl",kernelFunc:nd},H9="return float(int(x));";function q9(e,t){let n=new rr(e.shape,H9),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function ux(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return ta({inputs:{x:r},backend:n});let i=It(r.shape),o=ux({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Ns({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=nd({inputs:{input:r},backend:n}),o=ux({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=ta({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(n.shouldExecuteOnCPU([r])){let i=n.texData.get(r.dataId).values,[o,l,u]=YZ(i,r.shape,r.dtype,s);return n.makeTensorInfo(o,l,u)}if(s==="int32")return q9(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=Z_({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var j9={kernelName:wi,backendName:"webgl",kernelFunc:ux},eI="return ceil(x);",K9=Ye({opSnippet:eI,packedOpSnippet:eI,cpuKernelImpl:ZZ}),X9={kernelName:ki,backendName:"webgl",kernelFunc:K9},Y9=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { float value = getAAtOutCoords(); @@ -2146,7 +2146,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function oI(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function tQ(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new eQ(a.shape),i=[oI(a,r.complexTensorInfos.real),oI(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var nQ={kernelName:lc,backendName:"webgl",kernelFunc:tQ},aQ=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s= ${o[m-1]}) { return getChannel( - getT${m}(${ah(i,l,f)}), - vec2(${ah(u,l,f)})); + getT${m}(${nh(i,l,f)}), + vec2(${nh(u,l,f)})); }`}let c=o.length,h=o[o.length-1];d+=` return getChannel( - getT${c}(${ah(i,l,h)}), - vec2(${ah(u,l,h)}));`,this.userCode=` + getT${c}(${nh(i,l,h)}), + vec2(${nh(u,l,h)}));`,this.userCode=` float getValue(${i.map(m=>"int "+m)}) { ${d} } @@ -2192,7 +2192,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam } setOutput(result); } - `}};function ah(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function Lf(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return ta({inputs:{x:r.complexTensorInfos.imag},backend:n})}var sQ={kernelName:dm,backendName:"webgl",kernelFunc:Lf};function Op(e,t,n){let a=e[0].dtype;if(a==="complex64"){let h=e.map(b=>sd({inputs:{input:b},backend:n})),m=e.map(b=>Lf({inputs:{input:b},backend:n})),f=Op(h,t,n),g=Op(m,t,n),y=Cs({inputs:{real:f,imag:g},backend:n});return h.forEach(b=>n.disposeIntermediateTensorInfo(b)),m.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),y}let r=n.shouldExecuteOnCPU(e);if(a==="string"&&(r=!0),r){let h=e.map(w=>{let I=[-1,v.sizeFromShape(w.shape.slice(t))];return he({inputs:{x:w},backend:n,attrs:{shape:I}})}),m=h.map(w=>({vals:n.readSync(w.dataId),shape:w.shape})),f=N.computeOutShape(h.map(w=>w.shape),1),g=h[0].shape[0]===1,y=JZ(m,f,a,g),b=N.computeOutShape(e.map(w=>w.shape),t),x=n.makeTensorInfo(b,a,y);return h.forEach(w=>n.disposeIntermediateTensorInfo(w)),x}let s=e.filter(h=>v.sizeFromShape(h.shape)>0),i=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let h=i?new rr(e[0].shape,Xr):new es(e[0].shape,Xr);return n.runWebGLProgram(h,e,a)}let o=H().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>o){let h=[];for(let f=0;fm.shape),t);return n.runWebGLProgram(h,s,a)}let{tensors2D:l,outShape:u}=iQ(s,t,n),p=new aQ(l.map(h=>h.shape)),d=n.runWebGLProgram(p,l,a);l.forEach(h=>n.disposeIntermediateTensorInfo(h));let c=he({inputs:{x:d},attrs:{shape:u},backend:n});return n.disposeIntermediateTensorInfo(d),c}function iQ(e,t,n){let a=N.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>he({inputs:{x:r},attrs:{shape:[-1,v.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function n_(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);N.assertParamsConsistent(i,s);let o=N.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?ta({inputs:{x:l[0]},backend:n}):Op(l,s,n)}var oQ={kernelName:Ml,backendName:"webgl",kernelFunc:n_},a_=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let 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cQ={kernelName:Si,backendName:"webgl",kernelFunc:pQ},dQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` + `}};function Bh(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function tE({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=a.texData.get(e.dataId),p=n.inChannels,d=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,y=[];if(s!=null){let b=Bh(s.shape,h);b!=null&&(s=de({inputs:{x:s},backend:a,attrs:{shape:b}}),y.push(s))}if(r!=null){let 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c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[b,x,w]=d>1?[`${(o-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` +`,AQ=Ye({opSnippet:EQ}),$Q={kernelName:Ni,backendName:"webgl",kernelFunc:AQ},FQ=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[b,x,w]=d>1?[`${(o-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` const float height_ratio = float(${f}); const float width_ratio = float(${b}); void main() { @@ -2917,20 +2917,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam setOutput(newValue); } } - `}},DQ=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,p=new FQ(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},RQ={kernelName:Ol,backendName:"webgl",kernelFunc:DQ},ac;(function(e){e.Prod="*",e.Sum="+"})(ac||(ac={}));var lI=class{constructor(e,t,n,a){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===ac.Prod?"1.0":"0.0",i=n?s:`getX(${uI(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=a?`end != ${o-1}`:"end != 0",u=a?"end + 1":"end - 1"):(l=a?`end + pow2 < ${o}`:"end >= pow2",u=a?"end + pow2":"end - pow2"),this.userCode=` + `}},RQ=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,p=new FQ(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},DQ={kernelName:Dl,backendName:"webgl",kernelFunc:RQ},tc;(function(e){e.Prod="*",e.Sum="+"})(tc||(tc={}));var nI=class{constructor(e,t,n,a){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===tc.Prod?"1.0":"0.0",i=n?s:`getX(${aI(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=a?`end != ${o-1}`:"end != 0",u=a?"end + 1":"end - 1"):(l=a?`end + pow2 < ${o}`:"end >= pow2",u=a?"end + pow2":"end - pow2"),this.userCode=` void main() { ${gt(r)} coords = getOutputCoords(); - int end = ${pI(r,"coords",this.op)}; + int end = ${rI(r,"coords",this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${l}) { int idx = ${u}; - ${pI(r,"coords",this.op)} = idx; - val ${this.op}= getX(${uI(r,"coords",this.op)}); + ${rI(r,"coords",this.op)} = idx; + val ${this.op}= getX(${aI(r,"coords",this.op)}); } setOutput(val); } - `}};function uI(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function pI(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function o_(e,t,n,a,r,s){let i=t.shape.length,o=N.getAxesPermutation([a],i),l=t;o!=null&&(l=In({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=N.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${a}`);let p=l.shape[u],d=ta({inputs:{x:l},backend:n});for(let c=0;c<=Math.ceil(Math.log2(p))-1;c++){let h=new lI(e,l.shape,!1,s),m=[[c]],f=d;d=n.runWebGLProgram(h,[d],d.dtype,m),n.disposeIntermediateTensorInfo(f)}if(r){let c=new lI(e,l.shape,r,s),h=d;d=n.runWebGLProgram(c,[d],d.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let c=N.getUndoAxesPermutation(o),h=In({inputs:{x:d},backend:n,attrs:{perm:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(l),h}return d}function MQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return o_(ac.Prod,r,n,s,i,o)}var PQ={kernelName:Pl,backendName:"webgl",kernelFunc:MQ};function OQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return o_(ac.Sum,r,n,s,i,o)}var LQ={kernelName:Ei,backendName:"webgl",kernelFunc:OQ};function zQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=WE(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=XZ(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var WQ={kernelName:rm,backendName:"webgl",kernelFunc:zQ},BQ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` + `}};function aI(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function rI(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function aE(e,t,n,a,r,s){let i=t.shape.length,o=N.getAxesPermutation([a],i),l=t;o!=null&&(l=In({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=N.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${a}`);let p=l.shape[u],d=ta({inputs:{x:l},backend:n});for(let c=0;c<=Math.ceil(Math.log2(p))-1;c++){let h=new nI(e,l.shape,!1,s),m=[[c]],f=d;d=n.runWebGLProgram(h,[d],d.dtype,m),n.disposeIntermediateTensorInfo(f)}if(r){let c=new nI(e,l.shape,r,s),h=d;d=n.runWebGLProgram(c,[d],d.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let c=N.getUndoAxesPermutation(o),h=In({inputs:{x:d},backend:n,attrs:{perm:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(l),h}return d}function MQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return aE(tc.Prod,r,n,s,i,o)}var PQ={kernelName:Rl,backendName:"webgl",kernelFunc:MQ};function OQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return aE(tc.Sum,r,n,s,i,o)}var LQ={kernelName:Ci,backendName:"webgl",kernelFunc:OQ};function zQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=P_(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=XZ(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var WQ={kernelName:am,backendName:"webgl",kernelFunc:zQ},BQ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -2949,7 +2949,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam float result = ${this.getInputSamplingString()}; setOutput(result); } - `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function VQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=new BQ(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var UQ={kernelName:Ll,backendName:"webgl",kernelFunc:VQ},l_=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=En(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(a?l=`float activation(float a) { + `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function VQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=new BQ(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var UQ={kernelName:Ml,backendName:"webgl",kernelFunc:VQ},rE=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=_n(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(a?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?l=`float activation(float a) { @@ -3002,7 +3002,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam ${u} setOutput(result); } - `}},u_=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=En(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,d=p,c=` + `}},sE=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=_n(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,d=p,c=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=N.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),c;H().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?c=new u_(d):c=new l_(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var HQ={kernelName:_i,backendName:"webgl",kernelFunc:GQ},jQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` + `}};function GQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=a,p=l;p==null&&(p=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=N.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),c;H().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?c=new sE(d):c=new rE(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var HQ={kernelName:_i,backendName:"webgl",kernelFunc:GQ},qQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -3226,7 +3226,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam } setOutput(dotProd); } - `}},qQ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` + `}},jQ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${s}, ${i}); void main() { @@ -3271,13 +3271,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam } setOutput(dotProd); } - `}};function KQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a,d=N.computeConv2DInfo(r.shape,p,i,o,l,u,!0),c=new jQ(d);return n.runWebGLProgram(c,[r,s],"float32")}var XQ={kernelName:sm,backendName:"webgl",kernelFunc:KQ};function YQ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a,d=N.computeConv2DInfo(p,s.shape,i,o,l,u,!0),c=new qQ(d);return n.runWebGLProgram(c,[r,s],"float32")}var ZQ={kernelName:im,backendName:"webgl",kernelFunc:YQ},JQ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + `}};function KQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a,d=N.computeConv2DInfo(r.shape,p,i,o,l,u,!0),c=new qQ(d);return n.runWebGLProgram(c,[r,s],"float32")}var XQ={kernelName:rm,backendName:"webgl",kernelFunc:KQ};function YQ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a,d=N.computeConv2DInfo(p,s.shape,i,o,l,u,!0),c=new jQ(d);return n.runWebGLProgram(c,[r,s],"float32")}var ZQ={kernelName:sm,backendName:"webgl",kernelFunc:YQ},JQ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); 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result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3324,12 +3324,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,lee=Ye({opSnippet:iee,packedOpSnippet:oee}),uee={kernelName:$i,backendName:"webgl",kernelFunc:lee},pee="return (b >= 1.0) ? a : a * (b + 1.0);",cee=` +`,lee=Ye({opSnippet:iee,packedOpSnippet:oee}),uee={kernelName:Ai,backendName:"webgl",kernelFunc:lee},pee="return (b >= 1.0) ? a : a * (b + 1.0);",cee=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,dee=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new rd(cee,a.shape,r.shape):new kl(pee,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},hee={kernelName:um,backendName:"webgl",kernelFunc:dee},mee=` +`,dee=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new td(cee,a.shape,r.shape):new xl(pee,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},hee={kernelName:lm,backendName:"webgl",kernelFunc:dee},mee=` return vec4(equal(a, b)); 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-`,Gte=pn({opSnippet:Vte,packedOpSnippet:Ute,dtype:"bool"}),Hte={kernelName:eu,backendName:"webgl",kernelFunc:Gte},jte=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` +`,Gte=cn({opSnippet:Vte,packedOpSnippet:Ute,dtype:"bool"}),Hte={kernelName:Zl,backendName:"webgl",kernelFunc:Gte},qte=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3564,7 +3564,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam float val = x * ${o}; setOutput(val); } - `}},qte=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` + `}},jte=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; @@ -3626,7 +3626,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam vec4 result = xAtOutputCoords * ${o}; setOutput(result); } - `}},Kte=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=H().getBool("WEBGL_PACK_NORMALIZATION")?new qte(r.shape,s,i,o,l):new jte(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},Xte={kernelName:dc,backendName:"webgl",kernelFunc:Kte},Yte=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=` + `}},Kte=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=H().getBool("WEBGL_PACK_NORMALIZATION")?new jte(r.shape,s,i,o,l):new qte(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},Xte={kernelName:pc,backendName:"webgl",kernelFunc:Kte},Yte=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3664,7 +3664,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam else if (k >= depthBegin && k < depthEnd){ float dyi = -2.0 * float(${a}) * float(${r}) - * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d) + * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { dyi += pow(norm, -1.0 * ${r}); @@ -3681,16 +3681,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam } setOutput(result); } - `}},Zte=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a,d=new Yte(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},Jte={kernelName:mm,backendName:"webgl",kernelFunc:Zte};function Qte(e,t,n,a){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=he({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Co(i,e.dtype,"max",a),l=he({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function h_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=p!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(d){if(c){let b=n.texData.get(h.dataId).values,x=new Array(o);for(let T=0;T{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a,d=new Yte(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},Jte={kernelName:hm,backendName:"webgl",kernelFunc:Zte};function Qte(e,t,n,a){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=de({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=No(i,e.dtype,"max",a),l=de({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function uE(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=p!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(d){if(c){let b=n.texData.get(h.dataId).values,x=new Array(o);for(let T=0;T`Error in maxPool: Either strides or dilations must be 1. 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Got strides ${i} and dilations '${u}'`);let p=N.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return ta({inputs:{x:r},backend:n});let d=new ec(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var ine={kernelName:Vi,backendName:"webgl",kernelFunc:sne};function one(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,p=[1,1,1],d=N.computePool3DInfo(r.shape,s,i,p,o,u,l),c=new n1(d,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var lne={kernelName:cc,backendName:"webgl",kernelFunc:one},une=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -3800,16 +3800,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam } setOutput(dotProd); } - `}};function cne(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=N.computePool3DInfo(i.shape,o,l,d,u,p),h=new a1(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new pne(c),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var dne={kernelName:gm,backendName:"webgl",kernelFunc:cne};function hne(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;zu([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=N.computePool2DInfo(o.shape,l,u,1,p,d),h=!0,m=new nc(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new une(c),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var mne={kernelName:fm,backendName:"webgl",kernelFunc:hne};function fne(e,t,n,a){let r=new nc(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new nc(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var gne={kernelName:ym,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];v.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=N.computePool2DInfo(a.shape,r,s,u,i),[d,c]=fne(a,o,p,l);return[d,c]}};function yne(e,t,n,a){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=he({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Co(i,"float32","mean",a),l=he({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var bne={kernelName:Gi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,p=N.getAxesPermutation(u,o),d=p!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(d){if(c){let x=i.texData.get(m.dataId).values,w=new Array(o);for(let C=0;C{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];v.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=N.computePool2DInfo(a.shape,r,s,u,i),[d,c]=fne(a,o,p,l);return[d,c]}};function yne(e,t,n,a){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=de({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=No(i,"float32","mean",a),l=de({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var bne={kernelName:Ui,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,p=N.getAxesPermutation(u,o),d=p!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(d){if(c){let x=i.texData.get(m.dataId).values,w=new Array(o);for(let C=0;Cu[0]+e[p]+u[1]);let a=e.length,r=gt(a),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=` +`,Ine=cn({opSnippet:wne,packedOpSnippet:kne,cpuKernelImpl:h7}),Sne={kernelName:Hi,backendName:"webgl",kernelFunc:Ine},Tne=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,p)=>u[0]+e[p]+u[1]);let a=e.length,r=gt(a),s=t.map(u=>u[0]).join(","),i=t.map((u,p)=>u[0]+e[p]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -3894,13 +3894,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam ${c} setOutput(result); } - `}},Cne=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Nne(a.shape,r,s):new Tne(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},Ene={kernelName:qi,backendName:"webgl",kernelFunc:Cne},_ne=`if (b == 0.0) return NAN; + `}},Cne=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Nne(a.shape,r,s):new Tne(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},_ne={kernelName:qi,backendName:"webgl",kernelFunc:Cne},Ene=`if (b == 0.0) return NAN; return mod(a, b);`,Ane=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); - `+ad+` + `+ed+` return result; -`,$ne=pn({opSnippet:_ne,packedOpSnippet:Ane}),Fne={kernelName:tu,backendName:"webgl",kernelFunc:$ne},Dne=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` +`,$ne=cn({opSnippet:Ene,packedOpSnippet:Ane}),Fne={kernelName:Jl,backendName:"webgl",kernelFunc:$ne},Rne=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -3920,7 +3920,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,h9=Ye({opSnippet:d9}),m9={kernelNam // If no other event happened, last event happened. setOutput(float(${t-1})); } - `}},Rne=` + `}},Dne=` if (a == b) { return 1.0; }; @@ -3942,7 +3942,7 @@ return a / b;`,Mne=` } return result; -`,m_=pn({opSnippet:Rne,packedOpSnippet:Mne,checkOutOfBounds:!0}),Pne={kernelName:Ai,backendName:"webgl",kernelFunc:m_},mI="return a - b;",f_=pn({opSnippet:mI,packedOpSnippet:mI,supportsComplex:!0,cpuKernelImpl:D7}),One={kernelName:fo,backendName:"webgl",kernelFunc:f_};function g_(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=v.parseAxisParam([s],r.shape),o=h_({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=he({inputs:{x:o},backend:n,attrs:{shape:l}}),p=f_({inputs:{a:r,b:u},backend:n}),d=p_({inputs:{x:p},backend:n}),c=Of({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=he({inputs:{x:c},backend:n,attrs:{shape:l}}),m=m_({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var Lne={kernelName:ho,backendName:"webgl",kernelFunc:g_};function zne(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:g_({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new Dne(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var Wne={kernelName:bm,backendName:"webgl",kernelFunc:zne},Bne=Ma+` +`,pE=cn({opSnippet:Dne,packedOpSnippet:Mne,checkOutOfBounds:!0}),Pne={kernelName:Ei,backendName:"webgl",kernelFunc:pE},lI="return a - b;",cE=cn({opSnippet:lI,packedOpSnippet:lI,supportsComplex:!0,cpuKernelImpl:R7}),One={kernelName:mo,backendName:"webgl",kernelFunc:cE};function dE(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=v.parseAxisParam([s],r.shape),o=uE({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=de({inputs:{x:o},backend:n,attrs:{shape:l}}),p=cE({inputs:{a:r,b:u},backend:n}),d=iE({inputs:{x:p},backend:n}),c=$f({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=de({inputs:{x:c},backend:n,attrs:{shape:l}}),m=pE({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var Lne={kernelName:co,backendName:"webgl",kernelFunc:dE};function zne(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:dE({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new Rne(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var Wne={kernelName:ym,backendName:"webgl",kernelFunc:zne},Bne=Ma+` return -x; `,Vne=` vec4 result = -x; @@ -3954,14 +3954,14 @@ return a / b;`,Mne=` result.a = isNaN.a ? x.a : result.a; return result; -`;function Une(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=f7(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return H().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new es(a.shape,Vne):r=new rr(a.shape,Bne),n.runWebGLProgram(r,[a],a.dtype)}var Gne={kernelName:nu,backendName:"webgl",kernelFunc:Une},Hne=hr.nonMaxSuppressionV3Impl;function jne(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d}=Hne(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var qne={kernelName:ru,backendName:"webgl",kernelFunc:jne},Kne=hr.nonMaxSuppressionV4Impl;function Xne(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=Kne(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Yne={kernelName:su,backendName:"webgl",kernelFunc:Xne},Zne=hr.nonMaxSuppressionV5Impl;function Jne(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=Zne(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Qne={kernelName:iu,backendName:"webgl",kernelFunc:Jne},eae=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` +`;function Une(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=f7(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return H().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Qr(a.shape,Vne):r=new rr(a.shape,Bne),n.runWebGLProgram(r,[a],a.dtype)}var Gne={kernelName:Ql,backendName:"webgl",kernelFunc:Une},Hne=hr.nonMaxSuppressionV3Impl;function qne(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d}=Hne(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var jne={kernelName:tu,backendName:"webgl",kernelFunc:qne},Kne=hr.nonMaxSuppressionV4Impl;function Xne(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=Kne(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Yne={kernelName:nu,backendName:"webgl",kernelFunc:Xne},Zne=hr.nonMaxSuppressionV5Impl;function Jne(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=Zne(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Qne={kernelName:au,backendName:"webgl",kernelFunc:Jne},eae=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${a}), float(${n}), float(index == coords.y))); } - `}},tae=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=v.sizeFromShape(r.shape),p=new eae(u,i,o,l),d=he({inputs:{x:r},backend:n,attrs:{shape:[u]}}),c=n.runWebGLProgram(p,[d],s);n.disposeIntermediateTensorInfo(d);let h=[...r.shape,i],m=he({inputs:{x:c},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(c),m},nae={kernelName:Xi,backendName:"webgl",kernelFunc:tae};function Gh(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=sd({inputs:{input:a},backend:n}),s=Gh({inputs:{x:r},backend:n}),i=Lf({inputs:{input:a},backend:n}),o=Gh({inputs:{x:i},backend:n}),l=Cs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return id({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var aae={kernelName:Su,backendName:"webgl",kernelFunc:Gh};function y_(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=sd({inputs:{input:a},backend:n}),s=y_({inputs:{x:r},backend:n}),i=Lf({inputs:{input:a},backend:n}),o=Gh({inputs:{x:i},backend:n}),l=Cs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return id({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var rae={kernelName:ou,backendName:"webgl",kernelFunc:y_};function sae(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return gx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=gx({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=n_({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var iae={kernelName:lu,backendName:"webgl",kernelFunc:sae},oae=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=gt(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=` + `}},tae=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=v.sizeFromShape(r.shape),p=new eae(u,i,o,l),d=de({inputs:{x:r},backend:n,attrs:{shape:[u]}}),c=n.runWebGLProgram(p,[d],s);n.disposeIntermediateTensorInfo(d);let h=[...r.shape,i],m=de({inputs:{x:c},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(c),m},nae={kernelName:Ki,backendName:"webgl",kernelFunc:tae};function Vh(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=nd({inputs:{input:a},backend:n}),s=Vh({inputs:{x:r},backend:n}),i=Ff({inputs:{input:a},backend:n}),o=Vh({inputs:{x:i},backend:n}),l=Ns({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return ad({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var aae={kernelName:wu,backendName:"webgl",kernelFunc:Vh};function hE(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=nd({inputs:{input:a},backend:n}),s=hE({inputs:{x:r},backend:n}),i=Ff({inputs:{input:a},backend:n}),o=Vh({inputs:{x:i},backend:n}),l=Ns({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return ad({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var rae={kernelName:ru,backendName:"webgl",kernelFunc:hE};function sae(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return px({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=px({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=J_({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var iae={kernelName:su,backendName:"webgl",kernelFunc:sae},oae=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=gt(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -4010,7 +4010,7 @@ return a / b;`,Mne=` ${h} setOutput(result); } - `}},b_=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return id({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new lae(r.shape,s,i):new oae(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},uae={kernelName:Yi,backendName:"webgl",kernelFunc:b_},pae=` + `}},mE=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return ad({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new lae(r.shape,s,i):new oae(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},uae={kernelName:Xi,backendName:"webgl",kernelFunc:mE},pae=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -4035,9 +4035,9 @@ return a / b;`,Mne=` bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); - `+ad+` + `+ed+` return result; -`,dae=pn({opSnippet:pae,packedOpSnippet:cae}),hae={kernelName:Zi,backendName:"webgl",kernelFunc:dae};function mae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=v.parseAxisParam(s,r.shape),p=u,d=N.getAxesPermutation(p,o),c=r;d!=null&&(c=In({inputs:{x:r},backend:n,attrs:{perm:d}}),p=N.getInnerMostAxes(p.length,o),l.push(c)),N.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:y}=y7(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=N.computeOutAndReduceShapes(c.shape,p),g=v.sizeFromShape(f),y=he({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),b=_m(r.dtype),x=Co(y,b,"prod",n);h=he({inputs:{x},backend:n,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(h);let m=N.expandShapeToKeepDim(h.shape,u);h=he({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var fae={kernelName:Qi,backendName:"webgl",kernelFunc:mae};function gae(e){let{inputs:t,backend:n,attrs:a}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=a,l=r.map(y=>n.readSync(y.dataId)),u=r.map(y=>y.shape),p=n.readSync(s.dataId),d=n.readSync(i.dataId),[c,h,m]=b7(l,u,p,s.shape,s.dtype,d,i.shape,o),f=c.map(y=>n.makeTensorInfo([y.length],"int32",y)),g=n.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var yae={kernelName:xm,backendName:"webgl",kernelFunc:gae};function bae(e){let{inputs:t,backend:n}=e,{starts:a,limits:r,deltas:s}=t,i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=x7(i,a.shape,a.dtype,o,r.shape,l,s.shape),d=n.makeTensorInfo([u.length],"int32",u),c=n.makeTensorInfo([p.length],a.dtype,p);return[d,c]}var xae={kernelName:vm,backendName:"webgl",kernelFunc:bae};function vae(e){let{inputs:t,backend:n,attrs:a}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=n.readSync(i.dataId),c=o.map(g=>n.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=v7(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var wae={kernelName:wm,backendName:"webgl",kernelFunc:vae},x_=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=w7(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},kae={kernelName:mc,backendName:"webgl",kernelFunc:x_},Iae="return 1.0 / x;",Sae=Ye({opSnippet:Iae}),Tae={kernelName:eo,backendName:"webgl",kernelFunc:Sae},Nae=Ma+` +`,dae=cn({opSnippet:pae,packedOpSnippet:cae}),hae={kernelName:Yi,backendName:"webgl",kernelFunc:dae};function mae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=v.parseAxisParam(s,r.shape),p=u,d=N.getAxesPermutation(p,o),c=r;d!=null&&(c=In({inputs:{x:r},backend:n,attrs:{perm:d}}),p=N.getInnerMostAxes(p.length,o),l.push(c)),N.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:y}=y7(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=N.computeOutAndReduceShapes(c.shape,p),g=v.sizeFromShape(f),y=de({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),b=_m(r.dtype),x=No(y,b,"prod",n);h=de({inputs:{x},backend:n,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(h);let m=N.expandShapeToKeepDim(h.shape,u);h=de({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var fae={kernelName:Ji,backendName:"webgl",kernelFunc:mae};function gae(e){let{inputs:t,backend:n,attrs:a}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=a,l=r.map(y=>n.readSync(y.dataId)),u=r.map(y=>y.shape),p=n.readSync(s.dataId),d=n.readSync(i.dataId),[c,h,m]=b7(l,u,p,s.shape,s.dtype,d,i.shape,o),f=c.map(y=>n.makeTensorInfo([y.length],"int32",y)),g=n.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var yae={kernelName:bm,backendName:"webgl",kernelFunc:gae};function bae(e){let{inputs:t,backend:n}=e,{starts:a,limits:r,deltas:s}=t,i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=x7(i,a.shape,a.dtype,o,r.shape,l,s.shape),d=n.makeTensorInfo([u.length],"int32",u),c=n.makeTensorInfo([p.length],a.dtype,p);return[d,c]}var xae={kernelName:xm,backendName:"webgl",kernelFunc:bae};function vae(e){let{inputs:t,backend:n,attrs:a}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=n.readSync(i.dataId),c=o.map(g=>n.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=v7(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var wae={kernelName:vm,backendName:"webgl",kernelFunc:vae},fE=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=w7(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},kae={kernelName:dc,backendName:"webgl",kernelFunc:fE},Iae="return 1.0 / x;",Sae=Ye({opSnippet:Iae}),Tae={kernelName:Qi,backendName:"webgl",kernelFunc:Sae},Nae=Ma+` return (x < 0.0) ? 0.0 : x; `,Cae=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); @@ -4049,7 +4049,7 @@ return a / b;`,Mne=` result.a = isNaN.a ? x.a : result.a; return result; -`,Eae=Ye({opSnippet:Nae,packedOpSnippet:Cae}),_ae={kernelName:to,backendName:"webgl",kernelFunc:Eae},Aae=Ma+` +`,_ae=Ye({opSnippet:Nae,packedOpSnippet:Cae}),Eae={kernelName:eo,backendName:"webgl",kernelFunc:_ae},Aae=Ma+` return (x < 0.0) ? 0.0 : min(6.0, x); `,$ae=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); @@ -4061,7 +4061,7 @@ return a / b;`,Mne=` result.a = isNaN.a ? x.a : result.a; return result; -`,Fae=Ye({opSnippet:Aae,packedOpSnippet:$ae}),Dae={kernelName:ro,backendName:"webgl",kernelFunc:Fae},Rae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,Fae=Ye({opSnippet:Aae,packedOpSnippet:$ae}),Rae={kernelName:ao,backendName:"webgl",kernelFunc:Fae},Dae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4171,7 +4171,7 @@ return a / b;`,Mne=` setOutput(newValue); } - `}};function Pae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=H().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Mae(r.shape,l,u,s,i):new Rae(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var Oae={kernelName:ao,backendName:"webgl",kernelFunc:Pae},Lae=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` + `}};function Pae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=H().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Mae(r.shape,l,u,s,i):new Dae(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var Oae={kernelName:no,backendName:"webgl",kernelFunc:Pae},Lae=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4252,7 +4252,7 @@ return a / b;`,Mne=` setOutput(accumulator); } - `}};function zae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Lae(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Wae={kernelName:Sm,backendName:"webgl",kernelFunc:zae},Bae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function zae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Lae(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Wae={kernelName:Im,backendName:"webgl",kernelFunc:zae},Bae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4315,7 +4315,7 @@ return a / b;`,Mne=` setOutput(newValue); } - `}};function Uae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=H().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Vae(r.shape,l,u,s,i):new Bae(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var Gae={kernelName:no,backendName:"webgl",kernelFunc:Uae},Hae=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` + `}};function Uae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=H().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Vae(r.shape,l,u,s,i):new Bae(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var Gae={kernelName:to,backendName:"webgl",kernelFunc:Uae},Hae=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4385,7 +4385,7 @@ return a / b;`,Mne=` setOutput(accumulator); } - `}};function jae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Hae(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var qae={kernelName:Im,backendName:"webgl",kernelFunc:jae},Kae=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` + `}};function qae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Hae(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var jae={kernelName:km,backendName:"webgl",kernelFunc:qae},Kae=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); @@ -4423,7 +4423,7 @@ return a / b;`,Mne=` } setOutput(result); } - `;function o(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function p(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((y,b)=>c(b,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function Yae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return ta({inputs:{x:r},backend:n});let l=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Xae(r.shape,o):new Kae(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var Zae={kernelName:so,backendName:"webgl",kernelFunc:Yae},Jae=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` + `;function o(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function p(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((y,b)=>c(b,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function Yae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return ta({inputs:{x:r},backend:n});let l=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Xae(r.shape,o):new Kae(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var Zae={kernelName:ro,backendName:"webgl",kernelFunc:Yae},Jae=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` vec3 fill = vec3(${t.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { @@ -4442,7 +4442,7 @@ return a / b;`,Mne=` } setOutput(outputValue); } - `}},Qae={kernelName:Tu,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new Jae(a.shape,s),[u,p]=N.getImageCenter(i,a.shape[1],a.shape[2]),d=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[a],a.dtype,d)}},ere=` + `}},Qae={kernelName:ku,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new Jae(a.shape,s),[u,p]=N.getImageCenter(i,a.shape[1],a.shape[2]),d=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[a],a.dtype,d)}},ere=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -4457,7 +4457,7 @@ return a / b;`,Mne=` return base + 1.0; } } -`,tre=Ye({opSnippet:ere}),nre={kernelName:io,backendName:"webgl",kernelFunc:tre},are="return inversesqrt(x);",rre=Ye({opSnippet:are,cpuKernelImpl:k7}),sre={kernelName:oo,backendName:"webgl",kernelFunc:rre},v_=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=gt(r.length),l=gt(s.length),u="";n===1?u="i":n===2&&(u="i, j");let p=`getIndices(${u})`,d="";a===1?d="i":a===2&&(d="i, coords[1]");let c=`getUpdates(${d})`,h=t>1?"strides[j]":"strides";this.userCode=` +`,tre=Ye({opSnippet:ere}),nre={kernelName:so,backendName:"webgl",kernelFunc:tre},are="return inversesqrt(x);",rre=Ye({opSnippet:are,cpuKernelImpl:k7}),sre={kernelName:io,backendName:"webgl",kernelFunc:rre},gE=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=gt(r.length),l=gt(s.length),u="";n===1?u="i":n===2&&(u="i, j");let p=`getIndices(${u})`,d="";a===1?d="i":a===2&&(d="i, coords[1]");let c=`getUpdates(${d})`,h=t>1?"strides[j]":"strides";this.userCode=` ${o} strides = ${o}(${r}); void main() { @@ -4477,7 +4477,7 @@ return a / b;`,Mne=` } setOutput(mix(getDefaultValue(), sum, float(found))); } - `}};function ire(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=N.calculateShapes(s,r,i),c=[d/u,u];if(d===0)return n.makeTensorInfo(i,r.dtype);let h=he({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=he({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new v_(l,o,h.shape.length,m.shape.length,p,c),y=n.runWebGLProgram(g,[m,h,f],m.dtype),b=he({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),b}var ore={kernelName:pu,backendName:"webgl",kernelFunc:ire},lre=class{constructor(e,t,n,a){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) 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i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,fre=Ye({opSnippet:mre}),gre={kernelName:du,backendName:"webgl",kernelFunc:fre},yre=Gu+` +`,fre=Ye({opSnippet:mre}),gre={kernelName:uu,backendName:"webgl",kernelFunc:fre},yre=Bu+` return 1.0 / (1.0 + exp(-1.0 * x)); `,bre=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); @@ -4530,15 +4530,15 @@ return a / b;`,Mne=` result.a = isNaN.a ? x.a : result.a; return result; -`,xre=Ye({opSnippet:yre,packedOpSnippet:bre,cpuKernelImpl:S7}),vre={kernelName:uo,backendName:"webgl",kernelFunc:xre},wre=` +`,xre=Ye({opSnippet:yre,packedOpSnippet:bre,cpuKernelImpl:S7}),vre={kernelName:lo,backendName:"webgl",kernelFunc:xre},wre=` if (isnan(x)) { return 0.0; } return sign(x); -`,kre=Ye({opSnippet:wre}),Ire={kernelName:fu,backendName:"webgl",kernelFunc:kre},Sre=Gu+` +`,kre=Ye({opSnippet:wre}),Ire={kernelName:du,backendName:"webgl",kernelFunc:kre},Sre=Bu+` return sin(x); -`,Tre=Ye({opSnippet:Sre}),Nre={kernelName:lo,backendName:"webgl",kernelFunc:Tre},Cre=` +`,Tre=Ye({opSnippet:Sre}),Nre={kernelName:oo,backendName:"webgl",kernelFunc:Tre},Cre=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; -`,Ere=Ye({opSnippet:Cre}),_re={kernelName:mu,backendName:"webgl",kernelFunc:Ere},Are=` +`,_re=Ye({opSnippet:Cre}),Ere={kernelName:cu,backendName:"webgl",kernelFunc:_re},Are=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -4558,15 +4558,15 @@ return a / b;`,Mne=` result = log(exp_x + 1.0); } return result; -`,$re=Ye({opSnippet:Are}),Fre={kernelName:gu,backendName:"webgl",kernelFunc:$re},Dre=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,b)=>y*b),l=[[0,0]];l.push(...i);for(let y=1+s.length;yn.disposeIntermediateTensorInfo(y)),g},Rre={kernelName:yu,backendName:"webgl",kernelFunc:Dre};function Mre(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: +`,$re=Ye({opSnippet:Are}),Fre={kernelName:hu,backendName:"webgl",kernelFunc:$re},Rre=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,b)=>y*b),l=[[0,0]];l.push(...i);for(let y=1+s.length;yn.disposeIntermediateTensorInfo(y)),g},Dre={kernelName:mu,backendName:"webgl",kernelFunc:Rre};function Mre(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: ${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw: ${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw: ${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw: - ${i.shape}`);let o=n.readSync(a.dataId),l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=n.readSync(i.dataId)[0],[d,c,h,m,f]=N7(o,a.shape,a.dtype,l,r.dtype,u,p);return[n.makeTensorInfo(c,a.dtype,d),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var Pre={kernelName:fc,backendName:"webgl",kernelFunc:Mre};function Ore(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),l=Array.from(n.readSync(s.dataId)),[u,p,d]=C7(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(p,a.dtype,u),n.makeTensorInfo([d.length],s.dtype,new Int32Array(d))]}var Lre={kernelName:xu,backendName:"webgl",kernelFunc:Ore};function zre(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape + ${i.shape}`);let o=n.readSync(a.dataId),l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=n.readSync(i.dataId)[0],[d,c,h,m,f]=N7(o,a.shape,a.dtype,l,r.dtype,u,p);return[n.makeTensorInfo(c,a.dtype,d),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var Pre={kernelName:hc,backendName:"webgl",kernelFunc:Mre};function Ore(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),l=Array.from(n.readSync(s.dataId)),[u,p,d]=C7(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(p,a.dtype,u),n.makeTensorInfo([d.length],s.dtype,new Int32Array(d))]}var Lre={kernelName:gu,backendName:"webgl",kernelFunc:Ore};function zre(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be 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Hre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=v.parseAxisParam(i,r.shape)[0],l=N.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),d=r.shape.slice();return l.map(c=>{let h=[...d];h[o]=c;let m=Hu({inputs:{x:r},backend:n,attrs:{begin:p,size:h}});return p[o]+=c,m})}var jre={kernelName:bu,backendName:"webgl",kernelFunc:Hre},fI="return sqrt(x);",qre=Ye({opSnippet:fI,packedOpSnippet:fI,cpuKernelImpl:E7}),Kre={kernelName:po,backendName:"webgl",kernelFunc:qre},Xre="return x * x;",Yre=Ye({opSnippet:Xre}),Zre={kernelName:bc,backendName:"webgl",kernelFunc:Yre},gI="return (a - b) * (a - b);",Jre=pn({opSnippet:gI,packedOpSnippet:gI}),Qre={kernelName:mo,backendName:"webgl",kernelFunc:Jre};function ese({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Ma+` + ${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=L_(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(p,a.dtype,u)}var Vre={kernelName:fc,backendName:"webgl",kernelFunc:Bre};function Ure(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,sliceSize:p,strides:d,outputSize:c}=N.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let y=n.bufferSync(r),b=n.bufferSync(s),x=v.decodeString(n.readSync(i.dataId)[0]),w=I7(y,b,o,c,p,u,l,d,x,h);return n.makeTensorInfo(o,w.dtype,w.values)}let m=new gE(u,l,r.shape.length,s.shape.length,d,[c,1],h),f=n.runWebGLProgram(m,[s,r,i],s.dtype),g=de({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),g}var Gre={kernelName:Tm,backendName:"webgl",kernelFunc:Ure};function Hre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=v.parseAxisParam(i,r.shape)[0],l=N.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),d=r.shape.slice();return l.map(c=>{let h=[...d];h[o]=c;let m=Vu({inputs:{x:r},backend:n,attrs:{begin:p,size:h}});return p[o]+=c,m})}var qre={kernelName:fu,backendName:"webgl",kernelFunc:Hre},uI="return sqrt(x);",jre=Ye({opSnippet:uI,packedOpSnippet:uI,cpuKernelImpl:_7}),Kre={kernelName:uo,backendName:"webgl",kernelFunc:jre},Xre="return x * x;",Yre=Ye({opSnippet:Xre}),Zre={kernelName:gc,backendName:"webgl",kernelFunc:Yre},pI="return (a - b) * (a - b);",Jre=cn({opSnippet:pI,packedOpSnippet:pI}),Qre={kernelName:ho,backendName:"webgl",kernelFunc:Jre};function ese({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Ma+` return x > 0.0 ? 1.0 : float(${t.alpha}); `,s=new rr(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var tse={kernelName:bs,backendName:"webgl",kernelFunc:ese},nse=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=gt(n.length),s=gt(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` ${r} begin = ${r}(${e}); @@ -4576,15 +4576,15 @@ return a / b;`,Mne=` ${s} coords = getOutputCoords(); setOutput(getX(${i})); } - `}};function ase(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:d,shrinkAxisMask:c}=a,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:b,end:x,strides:w}=Kt.sliceInfo(r.shape,s,i,o,l,u,p,d,c),I;if(f)I=he({inputs:{x:r},backend:n,attrs:{shape:m}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=Kt.computeOutShape(b,x,w),_=Hu({inputs:{x:r},backend:n,attrs:{begin:b,size:C}});I=he({inputs:{x:_},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(_)}else if(n.shouldExecuteOnCPU([r])){let C=n.readSync(r.dataId),_=Pe(r.shape,r.dtype,C),$=_7(h,_,w,b);I=n.makeTensorInfo(m,r.dtype,$.values)}else{let C=new nse(b,w,h);I=n.runWebGLProgram(C,[r],r.dtype)}let T=he({inputs:{x:I},backend:n,attrs:{shape:m}});return n.disposeIntermediateTensorInfo(I),T}var rse={kernelName:vu,backendName:"webgl",kernelFunc:ase};function sse(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=a,{data:p,dataSplits:d}=t,c=n.readSync(p.dataId),h=n.readSync(d.dataId),[m,f]=A7(c,h,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(d.shape,"int32",f)]}var ise={kernelName:xc,backendName:"webgl",kernelFunc:sse};function ose(e){let{inputs:t,backend:n,attrs:a}=e,{skipEmpty:r}=a,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(s.dataId),l=n.readSync(i.dataId)[0],[u,p,d]=$7(o,l,r),c=p.length;return[n.makeTensorInfo([c,2],"int32",u),n.makeTensorInfo([c],"string",p),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var lse={kernelName:vc,backendName:"webgl",kernelFunc:ose};function use(e){let{inputs:t,backend:n,attrs:a}=e,{numBuckets:r}=a,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(s.dataId),o=F7(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var pse={kernelName:wc,backendName:"webgl",kernelFunc:use},cse="return tan(x);",dse=Ye({opSnippet:cse}),hse={kernelName:go,backendName:"webgl",kernelFunc:dse},mse=` + `}};function ase(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:d,shrinkAxisMask:c}=a,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:b,end:x,strides:w}=Kt.sliceInfo(r.shape,s,i,o,l,u,p,d,c),I;if(f)I=de({inputs:{x:r},backend:n,attrs:{shape:m}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=Kt.computeOutShape(b,x,w),E=Vu({inputs:{x:r},backend:n,attrs:{begin:b,size:C}});I=de({inputs:{x:E},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let C=n.readSync(r.dataId),E=Le(r.shape,r.dtype,C),$=E7(h,E,w,b);I=n.makeTensorInfo(m,r.dtype,$.values)}else{let C=new nse(b,w,h);I=n.runWebGLProgram(C,[r],r.dtype)}let T=de({inputs:{x:I},backend:n,attrs:{shape:m}});return n.disposeIntermediateTensorInfo(I),T}var rse={kernelName:yu,backendName:"webgl",kernelFunc:ase};function sse(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=a,{data:p,dataSplits:d}=t,c=n.readSync(p.dataId),h=n.readSync(d.dataId),[m,f]=A7(c,h,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(d.shape,"int32",f)]}var ise={kernelName:yc,backendName:"webgl",kernelFunc:sse};function ose(e){let{inputs:t,backend:n,attrs:a}=e,{skipEmpty:r}=a,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(s.dataId),l=n.readSync(i.dataId)[0],[u,p,d]=$7(o,l,r),c=p.length;return[n.makeTensorInfo([c,2],"int32",u),n.makeTensorInfo([c],"string",p),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var lse={kernelName:bc,backendName:"webgl",kernelFunc:ose};function use(e){let{inputs:t,backend:n,attrs:a}=e,{numBuckets:r}=a,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(s.dataId),o=F7(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var pse={kernelName:xc,backendName:"webgl",kernelFunc:use},cse="return tan(x);",dse=Ye({opSnippet:cse}),hse={kernelName:fo,backendName:"webgl",kernelFunc:dse},mse=` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); -`,fse=Ye({opSnippet:mse}),gse={kernelName:yo,backendName:"webgl",kernelFunc:fse},yse=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(d=>v.decodeString(d)):o,u=Pe(r.shape,r.dtype,l),p=R7(u,s);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new yse(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var xse={kernelName:ys,backendName:"webgl",kernelFunc:w_},vse=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` + `}};function bse(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(d=>v.decodeString(d)):o,u=Le(r.shape,r.dtype,l),p=D7(u,s);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new yse(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var xse={kernelName:ys,backendName:"webgl",kernelFunc:yE},vse=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4658,7 +4658,7 @@ return a / b;`,Mne=` setOutput(x0 >= x1 ? float(i0) : float(i1)); } - `}};function Bs(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function yI(e){let t=1;for(;tl){let $=n.readSync(r.dataId),[R,F]=M7($,u,r.dtype,s,i);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(s===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(p===1)return[r,id({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),c=d!==null&&d.isPacked,h=c?n.unpackTensor(r):r,m=v.sizeFromShape(u)/p,f=he({inputs:{x:h},attrs:{shape:[m,p]},backend:n});c&&Bs(n,h);let g=yI(s),y=yI(p),b=null,x=()=>b===null?[f,f]:[f,b],w=($,R,F)=>{let S=x(),M=new vse(F),B=[[p],[b===null?1:0],[Number.NEGATIVE_INFINITY],[$],[R]],U=b;b=n.runWebGLProgram(M,S,"int32",B),Bs(n,U)};for(let $=1;$=1;F/=2)w(R,F,[m,y])}for(let $=y;$>g;$/=2){let R=x(),F=new wse([m,$/2]),S=[[p],[b===null?1:0],[g]],M=b;b=n.runWebGLProgram(F,R,"int32",S),Bs(n,M);let B=g/2,U=B*2;for(let G=B;G>=1;G/=2)w(U,G,b.shape)}let I=b;b=Hu({inputs:{x:b},backend:n,attrs:{begin:0,size:[m,s]}}),Bs(n,I);let T=d_({inputs:{x:f,indices:b},backend:n,attrs:{axis:1,batchDims:1}});Bs(n,f);let C=u.slice(0,-1);C.push(s),I=b,b=he({inputs:{x:b},attrs:{shape:C},backend:n}),Bs(n,I);let _=T;return T=he({inputs:{x:T},attrs:{shape:C},backend:n}),Bs(n,_),[T,b]}var Ise={kernelName:wu,backendName:"webgl",kernelFunc:kse},Sse=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=` + `}};function Bs(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function cI(e){let t=1;for(;tl){let $=n.readSync(r.dataId),[R,F]=M7($,u,r.dtype,s,i);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(s===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(p===1)return[r,ad({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),c=d!==null&&d.isPacked,h=c?n.unpackTensor(r):r,m=v.sizeFromShape(u)/p,f=de({inputs:{x:h},attrs:{shape:[m,p]},backend:n});c&&Bs(n,h);let g=cI(s),y=cI(p),b=null,x=()=>b===null?[f,f]:[f,b],w=($,R,F)=>{let S=x(),M=new vse(F),B=[[p],[b===null?1:0],[Number.NEGATIVE_INFINITY],[$],[R]],U=b;b=n.runWebGLProgram(M,S,"int32",B),Bs(n,U)};for(let $=1;$=1;F/=2)w(R,F,[m,y])}for(let $=y;$>g;$/=2){let R=x(),F=new wse([m,$/2]),S=[[p],[b===null?1:0],[g]],M=b;b=n.runWebGLProgram(F,R,"int32",S),Bs(n,M);let B=g/2,U=B*2;for(let G=B;G>=1;G/=2)w(U,G,b.shape)}let I=b;b=Vu({inputs:{x:b},backend:n,attrs:{begin:0,size:[m,s]}}),Bs(n,I);let T=lE({inputs:{x:f,indices:b},backend:n,attrs:{axis:1,batchDims:1}});Bs(n,f);let C=u.slice(0,-1);C.push(s),I=b,b=de({inputs:{x:b},attrs:{shape:C},backend:n}),Bs(n,I);let E=T;return T=de({inputs:{x:T},attrs:{shape:C},backend:n}),Bs(n,E),[T,b]}var Ise={kernelName:bu,backendName:"webgl",kernelFunc:kse},Sse=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=` float mapCoord(float outCoord, float len) { float inCoord = outCoord; if(${o} == 2) { @@ -4770,7 +4770,7 @@ return a / b;`,Mne=` } setOutput(outputValue); } - `}};function Tse(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[p,d,c,h]=r.shape,[m,f]=u!=null?u:[d,c],g=[p,m,f,h],y=new Sse(d,c,i,o,l,g);return n.runWebGLProgram(y,[r,s],"float32")}var Nse={kernelName:ku,backendName:"webgl",kernelFunc:Tse};function Cse(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;zu(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=P7(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var Ese={kernelName:Cm,backendName:"webgl",kernelFunc:Cse};function _se(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),p=0;for(let f=0;fn.disposeIntermediateTensorInfo(f)),m}var Ase={kernelName:Iu,backendName:"webgl",kernelFunc:_se},$se=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,p=n%4,d=` + `}};function Tse(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[p,d,c,h]=r.shape,[m,f]=u!=null?u:[d,c],g=[p,m,f,h],y=new Sse(d,c,i,o,l,g);return n.runWebGLProgram(y,[r,s],"float32")}var Nse={kernelName:xu,backendName:"webgl",kernelFunc:Tse};function Cse(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;Pu(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=P7(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var _se={kernelName:Nm,backendName:"webgl",kernelFunc:Cse};function Ese(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),p=0;for(let f=0;fn.disposeIntermediateTensorInfo(f)),m}var Ase={kernelName:vu,backendName:"webgl",kernelFunc:Ese},$se=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,p=n%4,d=` sumValue += dot(values, segFilter); `,c="";r%n>0&&(c=` if (inIdx < 0 || inIdx >= ${r}) { @@ -4876,7 +4876,7 @@ return a / b;`,Mne=` } setOutput(${l}); } - `}};function Fse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,p=N.getAxesPermutation([u],o),d=r;p!=null&&(d=In({inputs:{x:r},backend:n,attrs:{perm:p}}),l.push(d),u=N.getInnerMostAxes(1,o)[0]);let c=N.segment_util.computeOutShape(d.shape,u,i),h=v.sizeFromShape([d.shape[u]]),m=he({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=_m(r.dtype),g=(w,I,T,C,_)=>{let $=w.shape[0],R=w.shape[1],F=N.segment_util.segOpComputeOptimalWindowSize(R,_),S={windowSize:F,inSize:R,batchSize:$,numSegments:_},M=new $se(S,I),B=n.compileAndRun(M,[w,T],C);if(l.push(B),B.shape[1]===_)return B;let U=x_({backend:n,attrs:{start:0,stop:_,step:1,dtype:"float32"}}),G=w_({inputs:{x:U},backend:n,attrs:{reps:[R/F]}});return l.push(U),l.push(G),g(B,I,G,C,_)},y=g(m,"unsortedSegmentSum",s,f,i),b=he({inputs:{x:y},backend:n,attrs:{shape:c}}),x=b;if(p!=null){l.push(b);let w=N.getUndoAxesPermutation(p);x=In({inputs:{x},backend:n,attrs:{perm:w}})}return l.forEach(w=>n.disposeIntermediateTensorInfo(w)),x}var Dse={kernelName:kc,backendName:"webgl",kernelFunc:Fse},Rse=[_J,$J,RJ,OJ,zJ,VJ,GJ,jJ,YJ,JJ,t9,r9,o9,c9,m9,g9,b9,k9,S9,N9,A9,O9,z9,B9,q9,X9,Q9,dJ,nQ,oQ,cQ,yQ,xQ,wQ,IQ,TQ,EQ,$Q,RQ,PQ,LQ,WQ,UQ,HQ,XQ,ZQ,eee,aee,see,uee,hee,yee,vee,Iee,See,Nee,Eee,Aee,Fee,Ree,Lee,Bee,Gee,jee,Xee,Jee,nte,ite,cJ,lte,sQ,cte,mte,yte,mJ,wte,Tte,Cte,$te,Rte,Lte,Bte,Hte,Xte,Jte,ene,rne,ine,lne,dne,mne,gne,bne,vne,Sne,Ene,Fne,Wne,yJ,Gne,qne,Yne,Qne,U9,nae,rae,iae,uae,hae,gJ,fae,yae,xae,wae,kae,G9,Pne,Tae,_ae,Dae,xJ,Oae,Wae,Gae,qae,Zae,Qae,nre,sre,ore,pre,hre,gre,vre,Ire,Nre,_re,M9,Lne,Fre,Rre,Pre,Lre,Wre,Vre,Gre,jre,Kre,Zre,Qre,tse,rse,ise,lse,pse,One,NJ,hse,gse,xse,Ise,Nse,CJ,Ese,Ase,Dse,aae];for(let e of Rse)Ic(e);var _t;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(_t||(_t={}));var rc;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(rc||(rc={}));var k_;function Mse(e){k_=e.wasm.cwrap(ti,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Pse(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let _=n.dataIdMap.get(i.dataId);if(_.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${_.shape.length}.`);m=_.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=rc[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],b=u?s.shape[1]:s.shape[2],x=Nu.assertAndGetBroadcastShape(r.shape.slice(0,-2),s.shape.slice(0,-2)),w=n.makeOutput([...x,y,b],r.dtype),I=n.dataIdMap.get(w.dataId).id,T=new Uint8Array(new Int32Array(r.shape).buffer),C=new Uint8Array(new Int32Array(s.shape).buffer);return k_(c,T,r.shape.length,h,C,s.shape.length,l,u,g,m,f,d||0,I),w}var Ose={kernelName:ti,backendName:"wasm",setupFunc:Mse,kernelFunc:Pse};function Ut(e,t){let n;function a(s){n=s.wasm.cwrap(e,null,["number","number","number"])}function r(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,u=i.makeOutput(o.shape,t||o.dtype),p=i.dataIdMap.get(u.dataId).id;return v.sizeFromShape(u.shape)===0||n(l,_t[o.dtype],p),u}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:r}}var Lse=Ut(Sl);function cn(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:p}=l,d=o.dataIdMap.get(u.dataId).id,c=o.dataIdMap.get(p.dataId).id,h=n!=null?n:u.dtype,m=N.assertAndGetBroadcastShape(u.shape,p.shape),f=o.makeOutput(m,h);if(v.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(p.shape).buffer),b=o.dataIdMap.get(f.dataId).id;return a(d,g,u.shape.length,c,y,p.shape.length,_t[u.dtype],b),f}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var zse=!0,Wse=cn(fs,zse),I_;function Bse(e){I_=e.wasm.cwrap(bi,null,["array","number","number","number"])}function Vse(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return I_(s,r.length,_t[a.dtype],i),a}var Use={kernelName:bi,backendName:"wasm",setupFunc:Bse,kernelFunc:Vse};function zf(e){let{inputs:{x:t},backend:n}=e;if(t.dtype==="string")return mn(n.readSync(t.dataId),t.shape,t.dtype);let a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var Gse={kernelName:Oi,backendName:"wasm",kernelFunc:zf},S_;function Hse(e){S_=e.wasm.cwrap(Cr,null,["number","array","number","number","number","array","number"])}function ds(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=qse(t.x.shape,a.perm),i=!0;for(let m=0;m=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var Kse={kernelName:Cr,backendName:"wasm",kernelFunc:ds,setupFunc:Hse};function Es(e,t,n){let a=e.shape,r=e.shape.length,s=v.parseAxisParam(t,a),i=s,o=N.getAxesPermutation(i,r),l=null,u=!1;if(o!=null){let p=new Array(r);for(let c=0;c`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var oie={kernelName:uu,backendName:"wasm",kernelFunc:Wn},__;function lie(e){__=e.wasm.cwrap(wi,null,["number","array","number","number","array","number","number","number","number"])}function uie(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=s.shape.length,p=i?r.shape[l-2]:r.shape[l-1],d=o?s.shape[u-1]:s.shape[u-2],c=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[u-2]:s.shape[u-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=v.sizeFromShape(m),y=v.sizeFromShape(f),b=Nu.assertAndGetBroadcastShape(r.shape.slice(0,-2),s.shape.slice(0,-2)).concat([c,h]);v.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and 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gie={kernelName:Rl,backendName:"wasm",kernelFunc:fie};function ju(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var yie={kernelName:ki,backendName:"wasm",kernelFunc:ju},bie=Ut(Ii),A_;function xie(e){A_=e.wasm.cwrap(gs,null,["number","number","number","number"])}function vie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return A_(o,s,i,u),l}var wie={kernelName:gs,backendName:"wasm",setupFunc:xie,kernelFunc:vie};function $_(e){let{inputs:t,backend:n}=e,a=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=t.map(h=>h.shape);N.assertParamsConsistent(r,a);let s=N.computeOutShape(t.map(h=>h.shape),a),i=t.filter(h=>v.sizeFromShape(h.shape)>0);if(i.length===1)return zf({inputs:{x:i[0]},backend:n});let o=n.makeOutput(s,t[0].dtype);if(v.sizeFromShape(s)===0)return o;if(i[0].dtype==="string"){let h=i.map(x=>{let w=[-1,v.sizeFromShape(x.shape.slice(a))];return Wn({inputs:{x},backend:n,attrs:{shape:w}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));s=N.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,g=F0(m,s,t[0].dtype,f),y=N.computeOutShape(i.map(x=>x.shape),a);o.shape=y;let b=n.dataIdMap.get(o.dataId);return b.stringBytes=N.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),o}let l=v.sizeFromShape(i[0].shape.slice(0,a)),u=0,p=i.map(h=>{let m=v.sizeFromShape(h.shape.slice(a));return u+=m,m}),d=i.map(h=>n.typedArrayFromHeap(h)),c=n.typedArrayFromHeap(o);for(let h=0;h`cumprod does not support ${r.dtype} tensors in the WASM backend`);let u=N.getAxesPermutation([s],l),p=r;u!==null&&(p=ds({inputs:{x:r},attrs:{perm:u},backend:n}));let d=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumprod",[d],l);let c=n.makeOutput(p.shape,p.dtype),h=p.shape[d],m=n.dataIdMap.get(p.dataId).id,f=n.dataIdMap.get(c.dataId).id;M_(m,i?1:0,o?1:0,h,f,_t[r.dtype]);let g=c;if(u!==null){let y=N.getUndoAxesPermutation(u);g=ds({inputs:{x:c},attrs:{perm:y},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var Pie={kernelName:Pl,backendName:"wasm",setupFunc:Rie,kernelFunc:Mie},P_;function Oie(e){P_=e.wasm.cwrap(Ei,null,["number","number","number","number","number","number"])}function Lie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=N.getAxesPermutation([s],l),p=r;u!==null&&(p=ds({inputs:{x:r},attrs:{perm:u},backend:n}));let d=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumsum",[d],l);let c=n.makeOutput(p.shape,p.dtype),h=p.shape[d],m=n.dataIdMap.get(p.dataId).id,f=n.dataIdMap.get(c.dataId).id;P_(m,i?1:0,o?1:0,h,f,_t[r.dtype]);let g=c;if(u!==null){let y=N.getUndoAxesPermutation(u);g=ds({inputs:{x:c},attrs:{perm:y},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var zie={kernelName:Ei,backendName:"wasm",setupFunc:Oie,kernelFunc:Lie},O_;function Wie(e){O_=e.wasm.cwrap(Ll,null,["number","number","number","array","number","array","array","number","number"])}function Bie(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer),w=t.dataIdMap.get(f.dataId).id;return O_(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,x,m.length,w),f}var Vie={kernelName:Ll,backendName:"wasm",setupFunc:Wie,kernelFunc:Bie},L_;function Uie(e){L_=e.wasm.cwrap(_i,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Gie(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:d}=n,c=u==null?[1,1]:u,h=N.computeConv2DInfo(r.shape,s.shape,l,c,p,d,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,x=h.padInfo.left,w=h.dilationHeight,I=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,_=h.inChannels,$=h.outChannels,R=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not 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Joe={kernelName:Hi,backendName:"wasm",setupFunc:Yoe,kernelFunc:Zoe},Qoe=!1,ele=cn(ji,Qoe),xx;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(xx||(xx={}));var Z_;function tle(e){Z_=e.wasm.cwrap(qi,null,["number","array","number","number","array","array","number","number"])}function nle(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,mode:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=a.map(m=>m[0]),d=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(d).buffer);return Z_(i,u,t.shape.length,_t[t.dtype],c,h,xx[r],l),o}var ale={kernelName:qi,backendName:"wasm",kernelFunc:nle,setupFunc:tle},rle=!0,sle=cn(Ki,rle),ile=Ut(nu);function r1(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var J_;function ole(e){J_=e.wasm.cwrap(ru,"number",["number","number","number","number","number"])}function lle(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=J_(u,p,s,r,i),{pSelectedIndices:c,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=r1(t,d);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",c)}var ule={kernelName:ru,backendName:"wasm",setupFunc:ole,kernelFunc:lle},Q_;function ple(e){Q_=e.wasm.cwrap(su,"number",["number","number","number","number","number","bool"])}function cle(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=Q_(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=r1(t,c);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var dle={kernelName:su,backendName:"wasm",setupFunc:ple,kernelFunc:cle},eA;function hle(e){eA=e.wasm.cwrap(iu,"number",["number","number","number","number","number","number"])}function mle(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=eA(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=r1(t,c);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var fle={kernelName:iu,backendName:"wasm",setupFunc:hle,kernelFunc:mle},gle=!1,yle=cn(au,gle,"bool"),tA;function ble(e){tA=e.wasm.cwrap(Xi,null,["number","number","number","number","number"])}function xle(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=n.makeOutput([...r.shape,i],s),p=n.dataIdMap.get(u.dataId).id,d=n.dataIdMap.get(r.dataId).id;return tA(d,i,o,l,p),u}var vle={kernelName:Xi,backendName:"wasm",setupFunc:ble,kernelFunc:xle};function wle(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var kle={kernelName:ou,backendName:"wasm",kernelFunc:wle};function Ile(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return bx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching 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nc;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(nc||(nc={}));var bE;function Mse(e){bE=e.wasm.cwrap(Qs,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Pse(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);m=E.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=nc[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the 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s(i){let{backend:o,inputs:l}=i,{a:u,b:p}=l,d=o.dataIdMap.get(u.dataId).id,c=o.dataIdMap.get(p.dataId).id,h=n!=null?n:u.dtype,m=N.assertAndGetBroadcastShape(u.shape,p.shape),f=o.makeOutput(m,h);if(v.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(p.shape).buffer),b=o.dataIdMap.get(f.dataId).id;return a(d,g,u.shape.length,c,y,p.shape.length,At[u.dtype],b),f}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var zse=!0,Wse=dn(fs,zse),xE;function Bse(e){xE=e.wasm.cwrap(yi,null,["array","number","number","number"])}function Vse(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return xE(s,r.length,At[a.dtype],i),a}var Use={kernelName:yi,backendName:"wasm",setupFunc:Bse,kernelFunc:Vse};function Rf(e){let{inputs:{x:t},backend:n}=e;if(t.dtype==="string")return fn(n.readSync(t.dataId),t.shape,t.dtype);let a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var Gse={kernelName:Pi,backendName:"wasm",kernelFunc:Rf},vE;function Hse(e){vE=e.wasm.cwrap(Cr,null,["number","array","number","number","number","array","number"])}function ds(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=jse(t.x.shape,a.perm),i=!0;for(let m=0;m=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var Kse={kernelName:Cr,backendName:"wasm",kernelFunc:ds,setupFunc:Hse};function Cs(e,t,n){let a=e.shape,r=e.shape.length,s=v.parseAxisParam(t,a),i=s,o=N.getAxesPermutation(i,r),l=null,u=!1;if(o!=null){let p=new Array(r);for(let c=0;c`new shape: ${i}, old shape: ${a.shape}. 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gie={kernelName:$l,backendName:"wasm",kernelFunc:fie};function Uu(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var yie={kernelName:wi,backendName:"wasm",kernelFunc:Uu},bie=Ut(ki),NE;function xie(e){NE=e.wasm.cwrap(gs,null,["number","number","number","number"])}function vie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return NE(o,s,i,u),l}var wie={kernelName:gs,backendName:"wasm",setupFunc:xie,kernelFunc:vie};function CE(e){let{inputs:t,backend:n}=e,a=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=t.map(h=>h.shape);N.assertParamsConsistent(r,a);let s=N.computeOutShape(t.map(h=>h.shape),a),i=t.filter(h=>v.sizeFromShape(h.shape)>0);if(i.length===1)return Rf({inputs:{x:i[0]},backend:n});let o=n.makeOutput(s,t[0].dtype);if(v.sizeFromShape(s)===0)return o;if(i[0].dtype==="string"){let h=i.map(x=>{let w=[-1,v.sizeFromShape(x.shape.slice(a))];return Wn({inputs:{x},backend:n,attrs:{shape:w}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));s=N.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,g=$0(m,s,t[0].dtype,f),y=N.computeOutShape(i.map(x=>x.shape),a);o.shape=y;let b=n.dataIdMap.get(o.dataId);return b.stringBytes=N.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),o}let l=v.sizeFromShape(i[0].shape.slice(0,a)),u=0,p=i.map(h=>{let m=v.sizeFromShape(h.shape.slice(a));return u+=m,m}),d=i.map(h=>n.typedArrayFromHeap(h)),c=n.typedArrayFromHeap(o);for(let h=0;h`cumprod does not support ${r.dtype} tensors in the WASM backend`);let u=N.getAxesPermutation([s],l),p=r;u!==null&&(p=ds({inputs:{x:r},attrs:{perm:u},backend:n}));let d=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumprod",[d],l);let c=n.makeOutput(p.shape,p.dtype),h=p.shape[d],m=n.dataIdMap.get(p.dataId).id,f=n.dataIdMap.get(c.dataId).id;$E(m,i?1:0,o?1:0,h,f,At[r.dtype]);let g=c;if(u!==null){let y=N.getUndoAxesPermutation(u);g=ds({inputs:{x:c},attrs:{perm:y},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var Pie={kernelName:Rl,backendName:"wasm",setupFunc:Die,kernelFunc:Mie},FE;function Oie(e){FE=e.wasm.cwrap(Ci,null,["number","number","number","number","number","number"])}function Lie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=N.getAxesPermutation([s],l),p=r;u!==null&&(p=ds({inputs:{x:r},attrs:{perm:u},backend:n}));let d=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumsum",[d],l);let c=n.makeOutput(p.shape,p.dtype),h=p.shape[d],m=n.dataIdMap.get(p.dataId).id,f=n.dataIdMap.get(c.dataId).id;FE(m,i?1:0,o?1:0,h,f,At[r.dtype]);let g=c;if(u!==null){let y=N.getUndoAxesPermutation(u);g=ds({inputs:{x:c},attrs:{perm:y},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var zie={kernelName:Ci,backendName:"wasm",setupFunc:Oie,kernelFunc:Lie},RE;function Wie(e){RE=e.wasm.cwrap(Ml,null,["number","number","number","array","number","array","array","number","number"])}function Bie(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer),w=t.dataIdMap.get(f.dataId).id;return RE(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,x,m.length,w),f}var Vie={kernelName:Ml,backendName:"wasm",setupFunc:Wie,kernelFunc:Bie},DE;function Uie(e){DE=e.wasm.cwrap(_i,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Gie(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:d}=n,c=u==null?[1,1]:u,h=N.computeConv2DInfo(r.shape,s.shape,l,c,p,d,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,x=h.padInfo.left,w=h.dilationHeight,I=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,E=h.inChannels,$=h.outChannels,R=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let F=a.makeOutput(h.outShape,"float32"),S=a.dataIdMap.get(F.dataId).id;return DE(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,x,R,w,I,T,C,E,$,S),F}var Hie={kernelName:_i,backendName:"wasm",setupFunc:Uie,kernelFunc:Gie},qie=Ut(Ai),jie=!1,Kie=dn(Ol,jie,"bool"),Xie=Ut($i,"float32");function dx(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Wn({inputs:{x:r},backend:a,attrs:{shape:o}})}var Yie={kernelName:Ll,backendName:"wasm",kernelFunc:dx};function ME(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var Zie={kernelName:uc,backendName:"wasm",kernelFunc:ME},PE;function Jie(e){PE=e.wasm.cwrap(Wl,null,["number","number","number","number","number","number"])}function Qie(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,p]=a.shape;return PE(s,o,l,u,p,i),r}var eoe={kernelName:Wl,backendName:"wasm",kernelFunc:Qie,setupFunc:Jie},toe=Ut(Fi),noe=!1,aoe=dn(Ri,noe),OE;function roe(e){OE=e.wasm.cwrap(Di,null,["number","number","number","number","number","number","number"])}function soe(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return OE(p,d,c,h,m,r,g),f}var ioe={kernelName:Di,backendName:"wasm",setupFunc:roe,kernelFunc:soe},LE;function ooe(e){LE=e.wasm.cwrap(ei,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function loe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=nc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);w=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,$=f.padInfo.bottom,R=f.padInfo.left,F=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,G=f.padInfo.type==="SAME"?1:0,j=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return LE(y,j,K,Z,b,I,T,w,C,E,$,R,G,F,S,M,B,U,x,g,ae,m||0,ee),J}var uoe={kernelName:ei,backendName:"wasm",setupFunc:ooe,kernelFunc:loe},zE;function poe(e){zE=e.wasm.cwrap(ti,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function coe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!0),g=nc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);w=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,$=f.padInfo.bottom,R=f.padInfo.left,F=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,G=f.padInfo.type==="SAME"?1:0,j=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return zE(y,j,K,Z,b,I,T,w,C,E,$,R,G,F,S,M,B,U,x,g,ae,m||0,ee),J}var doe={kernelName:ti,backendName:"wasm",setupFunc:poe,kernelFunc:coe},WE;function hoe(e){WE=e.wasm.cwrap(Vl,null,["number","number","number","number","number","number","array","number"])}function moe(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Hv.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let p=r.shape,d=p[p.length-1],c=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return WE(c,At[a.dtype],h,i,d,o,m,f),u}var foe={kernelName:Vl,backendName:"wasm",setupFunc:hoe,kernelFunc:moe},BE;function goe(e){BE=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function yoe(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=v.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let C=0;C=0,()=>`GatherV2: the index value ${E} is not in [0, ${p-1}]`)}let d=N.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=Wn({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),m=Wn({inputs:{x:s},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),f=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(f,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let y=c.shape.length-1,b=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(m.dataId).id,w=t.dataIdMap.get(g.dataId).id,I=new Uint8Array(new Int32Array(v.computeStrides(c.shape)).buffer),T=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer);return BE(b,At[r.dtype],I,y,x,d.batchSize,T,w),t.disposeData(c.dataId),t.disposeData(m.dataId),g.shape=d.outputShape,g}var boe={kernelName:Bl,backendName:"wasm",setupFunc:goe,kernelFunc:yoe},xoe=!1,voe=dn(Ul,xoe,"bool"),woe=!1,koe=dn(Mi,woe,"bool"),Ioe=Ut(Oi,"bool"),VE;function Soe(e){VE=e.wasm.cwrap(Li,null,["number","number","number","number"])}function Toe(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;VE(r,At[t.dtype],n,i)}return s}var Noe={kernelName:Li,backendName:"wasm",setupFunc:Soe,kernelFunc:Toe},Coe=!1,_oe=dn(ql,Coe,"bool"),Eoe=!1,Aoe=dn(jl,Eoe,"bool"),$oe=Ut(zi),Foe=!1,Roe=dn(Xl,Foe,"bool"),Doe=Ut(Yl),Moe=!1,Poe=dn(Zl,Moe,"bool"),Ooe=!1,Loe=dn(FI,Ooe,"bool"),UE;function zoe(e){UE=e.wasm.cwrap(Wi,null,["number","number","number","number"])}function Woe(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:c}=Cs(i,r,t);if(c){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;N.assertAxesAreInnerMostDims("max",p,h);let[m,f]=N.computeOutAndReduceShapes(l.shape,p),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;UE(o,At[i.dtype],g,b)}if(c&&t.disposeData(u.dataId),s){let b=N.expandShapeToKeepDim(y.shape,d);y.shape=b}return y}var Boe={kernelName:Wi,backendName:"wasm",setupFunc:zoe,kernelFunc:Woe},Voe=!1,Uoe=dn(Bi,Voe),GE;function Goe(e){GE=e.wasm.cwrap(Vi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Hoe(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=N.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,y=p.dilationHeight,b=p.dilationWidth,x=p.strideHeight,w=p.strideWidth,I=p.inChannels,T=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. 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Joe={kernelName:Gi,backendName:"wasm",setupFunc:Yoe,kernelFunc:Zoe},Qoe=!1,ele=dn(Hi,Qoe),hx;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(hx||(hx={}));var jE;function tle(e){jE=e.wasm.cwrap(qi,null,["number","array","number","number","array","array","number","number"])}function nle(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,mode:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=a.map(m=>m[0]),d=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(d).buffer);return jE(i,u,t.shape.length,At[t.dtype],c,h,hx[r],l),o}var ale={kernelName:qi,backendName:"wasm",kernelFunc:nle,setupFunc:tle},rle=!0,sle=dn(ji,rle),ile=Ut(Ql);function a1(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return 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this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Ao)}};var od=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${_o(this.distance)})`:""}`}};var ld=class extends pt{constructor(n,a){super(n);this._label=a}static assertIsValidLabeledBox(n,a){if(pt.assertIsValidBox(n,a),!Za(n.label))throw new Error(`${a} - expected property label (${n.label}) to be a number`)}get label(){return this._label}};var Or=class{constructor(t,n){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(n)||n.some(a=>!(a instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=n}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(a=>new Float32Array(a));return new Or(t.label,n)}};var DA=class extends ld{constructor(n,a,r,s){super(n,a);this._score=r,this._classScore=s}static assertIsValidPredictedBox(n,a){if(ld.assertIsValidLabeledBox(n,a),!qu(n.score)||!qu(n.classScore))throw new Error(`${a} - expected properties score (${n.score}) and (${n.classScore}) to be a number between [0, 1]`)}get score(){return this._score}get classScore(){return this._classScore}};function Lr(e){return e.detection instanceof xt}function Zu(e,t){return{...e,...{detection:t}}}function u1(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser 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c1()?d1(u1()):ud()?d1(p1()):null}function Rpe(e){if(sn||h1(),!sn)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=sn.Canvas,Image:n=sn.Image}=e;sn.Canvas=t,sn.Image=n,sn.createCanvasElement=e.createCanvasElement||(()=>new t),sn.createImageElement=e.createImageElement||(()=>new n),sn.ImageData=e.ImageData||sn.ImageData,sn.Video=e.Video||sn.Video,sn.fetch=e.fetch||sn.fetch,sn.readFile=e.readFile||sn.readFile}var et={getEnv:Dpe,setEnv:d1,initialize:h1,createBrowserEnv:u1,createFileSystem:Uf,createNodejsEnv:p1,monkeyPatch:Rpe,isBrowser:c1,isNodejs:ud};h1();function Ju(e){return!et.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function aa(e){let{Canvas:t,CanvasRenderingContext2D:n}=et.getEnv();if(e instanceof n)return e;let a=Ju(e);if(!(a instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let r=a.getContext("2d");if(!r)throw new Error("resolveContext2d - canvas 2d context is 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Qu({...i,...s})}},pd=class{constructor(t,n={}){this.box=new pt(t),this.options=new Gf(n)}draw(t){let n=aa(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:u}=this.options;u&&new zr([u],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function Mpe(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof xt?a.score:Lr(a)?a.detection.score:void 0,s=a instanceof xt?a.box:Lr(a)?a.detection.box:new pt(a),i=r?`${_o(r)}`:void 0;new pd(s,{label:i}).draw(e)})}function Hf(e){let{Image:t,Video:n}=et.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function RA(e){return new Promise((t,n)=>{(e instanceof et.getEnv().Canvas||Hf(e))&&t(null);function a(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),n(s))}function 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Wr=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if(Pr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(va(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof et.getEnv().Canvas?a:jf(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return fr(this.batchSize,0,1).map((t,n)=>this.getReshapedInputDimensions(n))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let n=this.getInputWidth(t),a=this.getInputHeight(t);return l1({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,P(()=>{let a=fr(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof Te){let o=va(i)?i:Qt(i);return o=$A(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=_a.resizeBilinear(o,[t,t],!1,!1)),o.as3D(t,t,3)}if(i instanceof et.getEnv().Canvas)return bo.fromPixels(OA(i,t,n));throw new 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a=le(t.toBatchTensor(112,!0),"float32"),s=gr(a,[122.782,117.001,104.298]).div(255),i=hd(s,n.dense0,!0);return i=hd(i,n.dense1),i=hd(i,n.dense2),i=hd(i,n.dense3),i=ya(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await vt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return VA(t)}extractParams(t){return BA(t)}};function fd(e,t){return P(()=>Y(Fe(e,t.weights),t.bias))}function UA(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=$n(e),o=Xf(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function GA(e){let t=[],n=ra(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return An(e,t),{params:r,paramMappings:t}}function Qf(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var ip=class extends 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u=a(i,o,`${l}/separable_conv0`),p=a(o,o,`${l}/separable_conv1`),d=n(i,o,1,`${l}/expansion_conv`);return{separable_conv0:u,separable_conv1:p,expansion_conv:d}}function s(i,o){let l=a(i,i,`${o}/separable_conv0`),u=a(i,i,`${o}/separable_conv1`),p=a(i,i,`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:p}}return{extractConvParams:n,extractSeparableConvParams:a,extractReductionBlockParams:r,extractMainBlockParams:s}}function XA(e,t){let n=[],{extractWeights:a,getRemainingWeights:r}=$n(e),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:o,extractMainBlockParams:l}=Bpe(a,n),u=s(3,32,3,"entry_flow/conv_in"),p=o(32,64,"entry_flow/reduction_block_0"),d=o(64,128,"entry_flow/reduction_block_1"),c={conv_in:u,reduction_block_0:p,reduction_block_1:d},h={};fr(t,0,1).forEach(y=>{h[`main_block_${y}`]=l(128,`middle_flow/main_block_${y}`)});let m=o(128,256,"exit_flow/reduction_block"),f=i(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{entry_flow:c,middle_flow:h,exit_flow:g}}}function Vpe(e,t){let n=ra(e,t),a=Zf(n),r=rp(n);function s(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),p=a(`${o}/expansion_conv`);return{separable_conv0:l,separable_conv1:u,expansion_conv:p}}function i(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),p=r(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:p}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function YA(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=Vpe(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),u=s("entry_flow/reduction_block_1"),p={conv_in:o,reduction_block_0:l,reduction_block_1:u},d={};fr(t,0,1).forEach(f=>{d[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let c=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:c,separable_conv:h};return An(e,n),{params:{entry_flow:p,middle_flow:d,exit_flow:m},paramMappings:n}}function ZA(e,t,n){return Y(Ft(e,t.filters,n,"same"),t.bias)}function y1(e,t,n=!0){let a=n?Xe(e):e;return a=Gn(a,t.separable_conv0,[1,1]),a=Gn(Xe(a),t.separable_conv1,[1,1]),a=Dt(a,[3,3],[2,2],"same"),a=Y(a,ZA(e,t.expansion_conv,[2,2])),a}function Upe(e,t){let n=Gn(Xe(e),t.separable_conv0,[1,1]);return n=Gn(Xe(n),t.separable_conv1,[1,1]),n=Gn(Xe(n),t.separable_conv2,[1,1]),n=Y(n,e),n}var ag=class extends dn{constructor(n){super("TinyXception");this._numMainBlocks=n}forwardInput(n){let{params:a}=this;if(!a)throw new Error("TinyXception - load model before inference");return P(()=>{let r=le(n.toBatchTensor(112,!0),"float32"),i=gr(r,[122.782,117.001,104.298]).div(255),o=Xe(ZA(i,a.entry_flow.conv_in,[2,2]));return o=y1(o,a.entry_flow.reduction_block_0,!1),o=y1(o,a.entry_flow.reduction_block_1),fr(this._numMainBlocks,0,1).forEach(l=>{o=Upe(o,a.middle_flow[`main_block_${l}`])}),o=y1(o,a.exit_flow.reduction_block),o=Xe(Gn(o,a.exit_flow.separable_conv,[1,1])),o})}async forward(n){return this.forwardInput(await vt(n))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(n){return YA(n,this._numMainBlocks)}extractParams(n){return XA(n,this._numMainBlocks)}};function JA(e){let t=[],{extractWeights:n,getRemainingWeights:a}=$n(e),r=Xf(n,t),s=r(512,1,"fc/age"),i=r(512,2,"fc/gender");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function QA(e){let t=[],n=ra(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return An(e,t),{params:r,paramMappings:t}}var b1=(n=>(n.FEMALE="female",n.MALE="male",n))(b1||{});var rg=class extends dn{constructor(n=new ag(2)){super("AgeGenderNet");this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(n){let{params:a}=this;if(!a)throw new Error(`${this._name} - load model before inference`);return P(()=>{let r=n instanceof Wr?this.faceFeatureExtractor.forwardInput(n):n,s=ya(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),i=fd(s,a.fc.age).as1D(),o=fd(s,a.fc.gender);return{age:i,gender:o}})}forwardInput(n){return P(()=>{let{age:a,gender:r}=this.runNet(n);return{age:a,gender:Xa(r)}})}async forward(n){return this.forwardInput(await vt(n))}async predictAgeAndGender(n){let a=await vt(n),r=await this.forwardInput(a),s=ut(r.age),i=ut(r.gender),o=s.map((u,p)=>({ageTensor:u,genderTensor:i[p]})),l=await Promise.all(o.map(async({ageTensor:u,genderTensor:p})=>{let d=u.dataSync()[0],c=p.dataSync()[0],h=c>.5,m=h?"male":"female",f=h?c:1-c;return u.dispose(),p.dispose(),{age:d,gender:m,genderProbability:f}}));return r.age.dispose(),r.gender.dispose(),a.isBatchInput?l:l[0]}getDefaultModelName(){return"age_gender_model"}dispose(n=!0){this.faceFeatureExtractor.dispose(n),super.dispose(n)}loadClassifierParams(n){let{params:a,paramMappings:r}=this.extractClassifierParams(n);this._params=a,this._paramMappings=r}extractClassifierParams(n){return JA(n)}extractParamsFromWeightMap(n){let{featureExtractorMap:a,classifierMap:r}=Qf(n);return this.faceFeatureExtractor.loadFromWeightMap(a),QA(r)}extractParams(n){let r=n.slice(0,n.length-1539),s=n.slice(n.length-1539);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(s)}};var lp=class extends ip{postProcess(t,n,a){let r=a.map(({width:i,height:o})=>{let l=n/Math.max(o,i);return{width:i*l,height:o*l}}),s=r.length;return P(()=>{let i=(d,c)=>$t([yn([68],d,"float32"),yn([68],c,"float32")],1).as2D(1,136).as1D(),o=(d,c)=>{let{width:h,height:m}=r[d];return c(h,m)?Math.abs(h-m)/2:0},l=d=>o(d,(c,h)=>co(d,(c,h)=>hi(l(c),u(c))))).div($t(Array.from(Array(s),(d,c)=>i(r[c].width,r[c].height))))})}forwardInput(t){return P(()=>{let n=this.runNet(t);return this.postProcess(n,t.inputSize,t.inputDimensions.map(([a,r])=>({height:a,width:r})))})}async forward(t){return this.forwardInput(await vt(t))}async detectLandmarks(t){let n=await vt(t),a=P(()=>ut(this.forwardInput(n))),r=await Promise.all(a.map(async(s,i)=>{let o=Array.from(s.dataSync()),l=o.filter((p,d)=>Wf(d)),u=o.filter((p,d)=>!Wf(d));return new Yu(Array(68).fill(0).map((p,d)=>new Me(l[d],u[d])),{height:n.getInputHeight(i),width:n.getInputWidth(i)})}));return a.forEach(s=>s.dispose()),n.isBatchInput?r:r[0]}getClassifierChannelsOut(){return 136}};var up=class extends lp{constructor(t=new sp){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function e$(e){let t=[],{extractDenseBlock3Params:n}=Jf(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return An(e,t),{params:a,paramMappings:t}}function t$(e){let t=[],{extractWeights:n,getRemainingWeights:a}=$n(e),{extractDenseBlock3Params:r}=Yf(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}var sg=class extends dn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return P(()=>{let a=le(t.toBatchTensor(112,!0),"float32"),s=gr(a,[122.782,117.001,104.298]).div(255),i=Kf(s,n.dense0,!0);return 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l.dispose(),u.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return l$(t)}extractParams(t){return o$(t)}};function ece(e){let t=new Do;return t.extractWeights(e),t}function lSe(e){return ece(e)}var h$=class extends Do{};var m$=.4,f$=[new Me(.738768,.874946),new Me(2.42204,2.65704),new Me(4.30971,7.04493),new Me(10.246,4.59428),new Me(12.6868,11.8741)],g$=[new Me(1.603231,2.094468),new Me(6.041143,7.080126),new Me(2.882459,3.518061),new Me(4.266906,5.178857),new Me(9.041765,10.66308)],y$=[117.001,114.697,97.404],b$="tiny_yolov2_model",x$="tiny_yolov2_separable_conv_model";var lg=e=>typeof e=="number";function v$(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!lg(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>lg(t.x)&&lg(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(lg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function cp(e){return P(()=>{let t=z(e,xe(.10000000149011612));return Y(Xe(pe(e,t)),t)})}function Br(e,t){return P(()=>{let n=ba(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ft(n,t.conv.filters,[1,1],"valid"),n=pe(n,t.bn.sub),n=z(n,t.bn.truediv),n=Y(n,t.conv.bias),cp(n)})}function Vr(e,t){return P(()=>{let n=ba(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ss(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=Y(n,t.bias),cp(n)})}function tce(e,t){let n=np(e,t);function a(i,o){let l=Ke(e(i)),u=Ke(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),p=a(o,`${l}/bn`);return{conv:u,bn:p}}let s=ap(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function w$(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=$n(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=tce(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,y,b,x]=a,w=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),I=u(c,h,"conv1"),T=u(h,m,"conv2"),C=u(m,f,"conv3"),_=u(f,g,"conv4"),$=u(g,y,"conv5"),R=b?u(y,b,"conv6"):void 0,F=x?u(b,x,"conv7"):void 0,S=o(x||b||y,5*n,1,"conv8");p={conv0:w,conv1:I,conv2:T,conv3:C,conv4:_,conv5:$,conv6:R,conv7:F,conv8:S}}else{let[d,c,h,m,f,g,y,b,x]=a,w=l(d,c,"conv0"),I=l(c,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),_=l(f,g,"conv4"),$=l(g,y,"conv5"),R=l(y,b,"conv6"),F=l(b,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:w,conv1:I,conv2:T,conv3:C,conv4:_,conv5:$,conv6:R,conv7:F,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function nce(e,t){let n=ra(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=rp(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function k$(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=nce(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return An(e,n),{params:i,paramMappings:n}}var yr=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var S1=class extends dn{constructor(n){super("TinyYolov2");v$(n),this._config=n}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(n,a){let r=Br(n,a.conv0);return r=Dt(r,[2,2],[2,2],"same"),r=Br(r,a.conv1),r=Dt(r,[2,2],[2,2],"same"),r=Br(r,a.conv2),r=Dt(r,[2,2],[2,2],"same"),r=Br(r,a.conv3),r=Dt(r,[2,2],[2,2],"same"),r=Br(r,a.conv4),r=Dt(r,[2,2],[2,2],"same"),r=Br(r,a.conv5),r=Dt(r,[2,2],[1,1],"same"),r=Br(r,a.conv6),r=Br(r,a.conv7),$o(r,a.conv8,"valid",!1)}runMobilenet(n,a){let r=this.config.isFirstLayerConv2d?cp($o(n,a.conv0,"valid",!1)):Vr(n,a.conv0);return r=Dt(r,[2,2],[2,2],"same"),r=Vr(r,a.conv1),r=Dt(r,[2,2],[2,2],"same"),r=Vr(r,a.conv2),r=Dt(r,[2,2],[2,2],"same"),r=Vr(r,a.conv3),r=Dt(r,[2,2],[2,2],"same"),r=Vr(r,a.conv4),r=Dt(r,[2,2],[2,2],"same"),r=Vr(r,a.conv5),r=Dt(r,[2,2],[1,1],"same"),r=a.conv6?Vr(r,a.conv6):r,r=a.conv7?Vr(r,a.conv7):r,$o(r,a.conv8,"valid",!1)}forwardInput(n,a){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return P(()=>{let s=le(n.toBatchTensor(a,!1),"float32");return s=this.config.meanRgb?gr(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(n,a){return this.forwardInput(await vt(n),a)}async detect(n,a={}){let{inputSize:r,scoreThreshold:s}=new yr(a),i=await vt(n),o=await this.forwardInput(i,r),l=P(()=>ut(o)[0].expandDims()),u={width:i.getInputWidth(0),height:i.getInputHeight(0)},p=await this.extractBoxes(l,i.getReshapedInputDimensions(0),s);o.dispose(),l.dispose();let d=p.map(y=>y.box),c=p.map(y=>y.score),h=p.map(y=>y.classScore),m=p.map(y=>this.config.classes[y.label]);return AA(d.map(y=>y.rescale(r)),c,this.config.iouThreshold,!0).map(y=>new _s(c[y],h[y],m[y],d[y],u))}getDefaultModelName(){return""}extractParamsFromWeightMap(n){return k$(n,this.config)}extractParams(n){let a=this.config.filterSizes||S1.DEFAULT_FILTER_SIZES,r=a?a.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return w$(n,this.config,this.boxEncodingSize,a)}async extractBoxes(n,a,r){let{width:s,height:i}=a,o=Math.max(s,i),l=o/s,u=o/i,p=n.shape[1],d=this.config.anchors.length,[c,h,m]=P(()=>{let b=n.reshape([p,p,d,this.boxEncodingSize]),x=b.slice([0,0,0,0],[p,p,d,4]),w=b.slice([0,0,0,4],[p,p,d,1]),I=this.withClassScores?Xa(b.slice([0,0,0,5],[p,p,d,this.config.classes.length]),3):xe(0);return[x,w,I]}),f=[],g=await h.array(),y=await c.array();for(let b=0;br){let T=(x+Vf(y[b][x][w][0]))/p*l,C=(b+Vf(y[b][x][w][1]))/p*u,_=Math.exp(y[b][x][w][2])*this.config.anchors[w].x/p*l,$=Math.exp(y[b][x][w][3])*this.config.anchors[w].y/p*u,R=T-_/2,F=C-$/2,S={row:b,col:x,anchor:w},{classScore:M,label:B}=this.withClassScores?await this.extractPredictedClass(m,S):{classScore:1,label:0};f.push({box:new Ku(R,F,R+_,F+$),score:I,classScore:I*M,label:B,...S})}}return c.dispose(),h.dispose(),m.dispose(),f}async extractPredictedClass(n,a){let{row:r,col:s,anchor:i}=a,o=await n.array();return Array(this.config.classes.length).fill(0).map((l,u)=>o[r][s][i][u]).map((l,u)=>({classScore:l,label:u})).reduce((l,u)=>l.classScore>u.classScore?l:u)}},Ro=S1;Ro.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var dp=class extends Ro{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:m$,classes:["face"],...t?{anchors:g$,meanRgb:y$}:{anchors:f$,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new xt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?x$:b$}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function QSe(e,t=!0){let n=new dp(t);return n.extractWeights(e),n}var ug=class extends yr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var La=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function Mo(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>op(l)?r(l):l.detection),i=a||(t instanceof Te?await dd(t,s):await cd(t,s)),o=await n(i);return i.forEach(l=>l instanceof Te&&l.dispose()),o}async function hp(e,t,n,a,r){return Mo([e],t,async s=>n(s[0]),a,r)}var I$=.4,S$=[new Me(1.603231,2.094468),new Me(6.041143,7.080126),new Me(2.882459,3.518061),new Me(4.266906,5.178857),new Me(9.041765,10.66308)],T$=[117.001,114.697,97.404];var mp=class extends Ro{constructor(){let t={withSeparableConvs:!0,iouThreshold:I$,classes:["face"],anchors:S$,meanRgb:T$,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new xt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var tt={ssdMobilenetv1:new Do,tinyFaceDetector:new mp,tinyYolov2:new dp,faceLandmark68Net:new up,faceLandmark68TinyNet:new ig,faceRecognitionNet:new pp,faceExpressionNet:new eg,ageGenderNet:new rg},ace=(e,t)=>tt.ssdMobilenetv1.locateFaces(e,t),_Te=(e,t)=>tt.tinyFaceDetector.locateFaces(e,t),ATe=(e,t)=>tt.tinyYolov2.locateFaces(e,t),rce=e=>tt.faceLandmark68Net.detectLandmarks(e),$Te=e=>tt.faceLandmark68TinyNet.detectLandmarks(e),FTe=e=>tt.faceRecognitionNet.computeFaceDescriptor(e),DTe=e=>tt.faceExpressionNet.predictExpressions(e),RTe=e=>tt.ageGenderNet.predictAgeAndGender(e),sce=e=>tt.ssdMobilenetv1.load(e),MTe=e=>tt.tinyFaceDetector.load(e),PTe=e=>tt.tinyYolov2.load(e),OTe=e=>tt.faceLandmark68Net.load(e),LTe=e=>tt.faceLandmark68TinyNet.load(e),zTe=e=>tt.faceRecognitionNet.load(e),WTe=e=>tt.faceExpressionNet.load(e),BTe=e=>tt.ageGenderNet.load(e),VTe=sce,UTe=ace,GTe=rce;var pg=class extends La{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Po=class extends pg{async run(){let t=await this.parentTask,n=await Mo(t,this.input,async a=>Promise.all(a.map(r=>tt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>g1(a,n[r]))}withAgeAndGender(){return new Lo(this,this.input)}},Oo=class extends pg{async run(){let t=await this.parentTask;if(!t)return;let n=await hp(t,this.input,a=>tt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return g1(t,n)}withAgeAndGender(){return new zo(this,this.input)}},Fs=class extends Po{withAgeAndGender(){return new Rs(this,this.input)}withFaceDescriptors(){return new Ps(this,this.input)}},Ds=class extends Oo{withAgeAndGender(){return new Ms(this,this.input)}withFaceDescriptor(){return new Os(this,this.input)}};var cg=class extends La{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Lo=class extends cg{async run(){let t=await this.parentTask,n=await Mo(t,this.input,async a=>Promise.all(a.map(r=>tt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return k1(I1(a,i,o),s)})}withFaceExpressions(){return new Po(this,this.input)}},zo=class extends cg{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await hp(t,this.input,s=>tt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return k1(I1(t,a,r),n)}withFaceExpressions(){return new Oo(this,this.input)}},Rs=class extends Lo{withFaceExpressions(){return new Fs(this,this.input)}withFaceDescriptors(){return new Ps(this,this.input)}},Ms=class extends zo{withFaceExpressions(){return new Ds(this,this.input)}withFaceDescriptor(){return new Os(this,this.input)}};var dg=class extends La{constructor(n,a){super();this.parentTask=n;this.input=a}},Ps=class extends dg{async run(){let t=await this.parentTask;return(await Mo(t,this.input,a=>Promise.all(a.map(r=>tt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>w1(t[r],a))}withFaceExpressions(){return new Fs(this,this.input)}withAgeAndGender(){return new Rs(this,this.input)}},Os=class extends dg{async run(){let t=await this.parentTask;if(!t)return;let n=await hp(t,this.input,a=>tt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return w1(t,n)}withFaceExpressions(){return new Ds(this,this.input)}withAgeAndGender(){return new Ms(this,this.input)}};var hg=class extends La{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?tt.faceLandmark68TinyNet:tt.faceLandmark68Net}},mg=class extends hg{async run(){let t=await this.parentTask,n=t.map(i=>i.detection),a=this.input instanceof Te?await dd(this.input,n):await cd(this.input,n),r=await Promise.all(a.map(i=>this.landmarkNet.detectLandmarks(i)));return a.forEach(i=>i instanceof Te&&i.dispose()),t.filter((i,o)=>r[o]).map((i,o)=>gd(i,r[o]))}withFaceExpressions(){return new Fs(this,this.input)}withAgeAndGender(){return new Rs(this,this.input)}withFaceDescriptors(){return new Ps(this,this.input)}},fg=class extends hg{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Te?await dd(this.input,[n]):await cd(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Te&&s.dispose()),gd(t,r)}withFaceExpressions(){return new Ds(this,this.input)}withAgeAndGender(){return new Ms(this,this.input)}withFaceDescriptor(){return new Os(this,this.input)}};var gg=class extends La{constructor(n,a=new Oa){super();this.input=n;this.options=a}},bd=class extends gg{async run(){let{input:t,options:n}=this,a;if(n instanceof ug)a=tt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Oa)a=tt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof yr)a=tt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(a=>t(a.map(r=>Zu({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new mg(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Po(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Lo(this.runAndExtendWithFaceDetections(),this.input)}},yg=class extends gg{async run(){let t=await new bd(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?Zu({},n):void 0)})}withFaceLandmarks(t=!1){return new fg(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Oo(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new zo(this.runAndExtendWithFaceDetection(),this.input)}};function BNe(e,t=new Oa){return new yg(e,t)}function T1(e,t=new Oa){return new bd(e,t)}async function ice(e,t){return T1(e,new 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hn{constructor(n,a){super(n);this._faceFeatureExtractor=a}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(n){let{params:a}=this;if(!a)throw new Error(`${this._name} - load model before inference`);return P(()=>{let r=n instanceof Wr?this.faceFeatureExtractor.forwardInput(n):n;return dd(r.as2D(r.shape[0],-1),a.fc)})}dispose(n=!0){this.faceFeatureExtractor.dispose(n),super.dispose(n)}loadClassifierParams(n){let{params:a,paramMappings:r}=this.extractClassifierParams(n);this._params=a,this._paramMappings=r}extractClassifierParams(n){return zA(n,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(n){let{featureExtractorMap:a,classifierMap:r}=jf(n);return this.faceFeatureExtractor.loadFromWeightMap(a),WA(r)}extractParams(n){let a=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),s=r*a+r,i=n.slice(0,n.length-s),o=n.slice(n.length-s);return this.faceFeatureExtractor.extractWeights(i),this.extractClassifierParams(o)}};var BA=["neutral","happy","sad","angry","fearful","disgusted","surprised"],As=class{constructor(t){this.neutral=0;this.happy=0;this.sad=0;this.angry=0;this.fearful=0;this.disgusted=0;this.surprised=0;if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);BA.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return BA.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var Kf=class extends ap{constructor(t=new np){super("FaceExpressionNet",t)}forwardInput(t){return P(()=>Xa(this.runNet(t)))}async forward(t){return this.forwardInput(await vt(t))}async predictExpressions(t){let n=await vt(t),a=await this.forwardInput(n),r=await Promise.all(lt(a).map(async i=>{let o=i.dataSync();return i.dispose(),o}));a.dispose();let s=r.map(i=>new As(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 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expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var I1=class extends hn{constructor(n){super("TinyYolov2");g$(n),this._config=n}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(n,a){let r=Br(n,a.conv0);return r=Dt(r,[2,2],[2,2],"same"),r=Br(r,a.conv1),r=Dt(r,[2,2],[2,2],"same"),r=Br(r,a.conv2),r=Dt(r,[2,2],[2,2],"same"),r=Br(r,a.conv3),r=Dt(r,[2,2],[2,2],"same"),r=Br(r,a.conv4),r=Dt(r,[2,2],[2,2],"same"),r=Br(r,a.conv5),r=Dt(r,[2,2],[1,1],"same"),r=Br(r,a.conv6),r=Br(r,a.conv7),Ao(r,a.conv8,"valid",!1)}runMobilenet(n,a){let r=this.config.isFirstLayerConv2d?lp(Ao(n,a.conv0,"valid",!1)):Vr(n,a.conv0);return r=Dt(r,[2,2],[2,2],"same"),r=Vr(r,a.conv1),r=Dt(r,[2,2],[2,2],"same"),r=Vr(r,a.conv2),r=Dt(r,[2,2],[2,2],"same"),r=Vr(r,a.conv3),r=Dt(r,[2,2],[2,2],"same"),r=Vr(r,a.conv4),r=Dt(r,[2,2],[2,2],"same"),r=Vr(r,a.conv5),r=Dt(r,[2,2],[1,1],"same"),r=a.conv6?Vr(r,a.conv6):r,r=a.conv7?Vr(r,a.conv7):r,Ao(r,a.conv8,"valid",!1)}forwardInput(n,a){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return P(()=>{let s=ie(n.toBatchTensor(a,!1),"float32");return s=this.config.meanRgb?gr(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(n,a){return this.forwardInput(await vt(n),a)}async detect(n,a={}){let{inputSize:r,scoreThreshold:s}=new yr(a),i=await vt(n),o=await this.forwardInput(i,r),l=P(()=>lt(o)[0].expandDims()),u={width:i.getInputWidth(0),height:i.getInputHeight(0)},p=await this.extractBoxes(l,i.getReshapedInputDimensions(0),s);o.dispose(),l.dispose();let d=p.map(y=>y.box),c=p.map(y=>y.score),h=p.map(y=>y.classScore),m=p.map(y=>this.config.classes[y.label]);return NA(d.map(y=>y.rescale(r)),c,this.config.iouThreshold,!0).map(y=>new _s(c[y],h[y],m[y],d[y],u))}getDefaultModelName(){return""}extractParamsFromWeightMap(n){return b$(n,this.config)}extractParams(n){let a=this.config.filterSizes||I1.DEFAULT_FILTER_SIZES,r=a?a.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return y$(n,this.config,this.boxEncodingSize,a)}async extractBoxes(n,a,r){let{width:s,height:i}=a,o=Math.max(s,i),l=o/s,u=o/i,p=n.shape[1],d=this.config.anchors.length,[c,h,m]=P(()=>{let b=n.reshape([p,p,d,this.boxEncodingSize]),x=b.slice([0,0,0,0],[p,p,d,4]),w=b.slice([0,0,0,4],[p,p,d,1]),I=this.withClassScores?Xa(b.slice([0,0,0,5],[p,p,d,this.config.classes.length]),3):xe(0);return[x,w,I]}),f=[],g=await h.array(),y=await c.array();for(let b=0;br){let T=(x+Pf(y[b][x][w][0]))/p*l,C=(b+Pf(y[b][x][w][1]))/p*u,E=Math.exp(y[b][x][w][2])*this.config.anchors[w].x/p*l,$=Math.exp(y[b][x][w][3])*this.config.anchors[w].y/p*u,R=T-E/2,F=C-$/2,S={row:b,col:x,anchor:w},{classScore:M,label:B}=this.withClassScores?await this.extractPredictedClass(m,S):{classScore:1,label:0};f.push({box:new Hu(R,F,R+E,F+$),score:I,classScore:I*M,label:B,...S})}}return c.dispose(),h.dispose(),m.dispose(),f}async extractPredictedClass(n,a){let{row:r,col:s,anchor:i}=a,o=await n.array();return Array(this.config.classes.length).fill(0).map((l,u)=>o[r][s][i][u]).map((l,u)=>({classScore:l,label:u})).reduce((l,u)=>l.classScore>u.classScore?l:u)}},Ro=I1;Ro.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var up=class extends Ro{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:p$,classes:["face"],...t?{anchors:d$,meanRgb:h$}:{anchors:c$,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new xt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?f$:m$}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function JSe(e,t=!0){let n=new up(t);return n.extractWeights(e),n}var ag=class extends yr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var La=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function Do(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>rp(l)?r(l):l.detection),i=a||(t instanceof Ne?await ud(t,s):await ld(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ne&&l.dispose()),o}async function pp(e,t,n,a,r){return Do([e],t,async s=>n(s[0]),a,r)}var x$=.4,v$=[new Oe(1.603231,2.094468),new Oe(6.041143,7.080126),new Oe(2.882459,3.518061),new Oe(4.266906,5.178857),new Oe(9.041765,10.66308)],w$=[117.001,114.697,97.404];var cp=class extends Ro{constructor(){let t={withSeparableConvs:!0,iouThreshold:x$,classes:["face"],anchors:v$,meanRgb:w$,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new xt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var nt={ssdMobilenetv1:new Fo,tinyFaceDetector:new cp,tinyYolov2:new up,faceLandmark68Net:new ip,faceLandmark68TinyNet:new eg,faceRecognitionNet:new op,faceExpressionNet:new Kf,ageGenderNet:new Jf},ace=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),_Te=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),ETe=(e,t)=>nt.tinyYolov2.locateFaces(e,t),rce=e=>nt.faceLandmark68Net.detectLandmarks(e),ATe=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),$Te=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),FTe=e=>nt.faceExpressionNet.predictExpressions(e),RTe=e=>nt.ageGenderNet.predictAgeAndGender(e),sce=e=>nt.ssdMobilenetv1.load(e),DTe=e=>nt.tinyFaceDetector.load(e),MTe=e=>nt.tinyYolov2.load(e),PTe=e=>nt.faceLandmark68Net.load(e),OTe=e=>nt.faceLandmark68TinyNet.load(e),LTe=e=>nt.faceRecognitionNet.load(e),zTe=e=>nt.faceExpressionNet.load(e),WTe=e=>nt.ageGenderNet.load(e),BTe=sce,VTe=ace,UTe=rce;var rg=class extends La{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Mo=class extends rg{async run(){let t=await this.parentTask,n=await Do(t,this.input,async a=>Promise.all(a.map(r=>nt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>f1(a,n[r]))}withAgeAndGender(){return new Oo(this,this.input)}},Po=class extends rg{async run(){let t=await this.parentTask;if(!t)return;let n=await pp(t,this.input,a=>nt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return f1(t,n)}withAgeAndGender(){return new Lo(this,this.input)}},$s=class extends Mo{withAgeAndGender(){return new Rs(this,this.input)}withFaceDescriptors(){return new Ms(this,this.input)}},Fs=class extends Po{withAgeAndGender(){return new Ds(this,this.input)}withFaceDescriptor(){return new Ps(this,this.input)}};var sg=class extends La{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Oo=class extends sg{async run(){let t=await this.parentTask,n=await Do(t,this.input,async a=>Promise.all(a.map(r=>nt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return w1(k1(a,i,o),s)})}withFaceExpressions(){return new Mo(this,this.input)}},Lo=class extends sg{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await pp(t,this.input,s=>nt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return w1(k1(t,a,r),n)}withFaceExpressions(){return new Po(this,this.input)}},Rs=class extends Oo{withFaceExpressions(){return new $s(this,this.input)}withFaceDescriptors(){return new Ms(this,this.input)}},Ds=class extends Lo{withFaceExpressions(){return new Fs(this,this.input)}withFaceDescriptor(){return new Ps(this,this.input)}};var ig=class extends La{constructor(n,a){super();this.parentTask=n;this.input=a}},Ms=class extends ig{async run(){let t=await this.parentTask;return(await Do(t,this.input,a=>Promise.all(a.map(r=>nt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>v1(t[r],a))}withFaceExpressions(){return new $s(this,this.input)}withAgeAndGender(){return new Rs(this,this.input)}},Ps=class extends ig{async run(){let t=await this.parentTask;if(!t)return;let n=await pp(t,this.input,a=>nt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return v1(t,n)}withFaceExpressions(){return new Fs(this,this.input)}withAgeAndGender(){return new Ds(this,this.input)}};var og=class extends La{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?nt.faceLandmark68TinyNet:nt.faceLandmark68Net}},lg=class extends og{async run(){let t=await this.parentTask,n=t.map(i=>i.detection),a=this.input instanceof Ne?await ud(this.input,n):await ld(this.input,n),r=await Promise.all(a.map(i=>this.landmarkNet.detectLandmarks(i)));return a.forEach(i=>i instanceof Ne&&i.dispose()),t.filter((i,o)=>r[o]).map((i,o)=>hd(i,r[o]))}withFaceExpressions(){return new $s(this,this.input)}withAgeAndGender(){return new Rs(this,this.input)}withFaceDescriptors(){return new Ms(this,this.input)}},ug=class extends og{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ne?await ud(this.input,[n]):await ld(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ne&&s.dispose()),hd(t,r)}withFaceExpressions(){return new Fs(this,this.input)}withAgeAndGender(){return new Ds(this,this.input)}withFaceDescriptor(){return new Ps(this,this.input)}};var pg=class extends La{constructor(n,a=new Oa){super();this.input=n;this.options=a}},fd=class extends pg{async run(){let{input:t,options:n}=this,a;if(n instanceof ag)a=nt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Oa)a=nt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof yr)a=nt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(a=>t(a.map(r=>Ku({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new lg(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Mo(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Oo(this.runAndExtendWithFaceDetections(),this.input)}},cg=class extends pg{async run(){let t=await new fd(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?Ku({},n):void 0)})}withFaceLandmarks(t=!1){return new ug(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Po(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Lo(this.runAndExtendWithFaceDetection(),this.input)}};function WNe(e,t=new Oa){return new cg(e,t)}function S1(e,t=new Oa){return new fd(e,t)}async function ice(e,t){return S1(e,new Oa(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function qNe(e,t={}){return S1(e,new yr(t)).withFaceLandmarks().withFaceDescriptors()}var jNe=ice;function k$(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s*s,0))}var dg=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof Or)return i;if(i instanceof Float32Array)return new Or(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new Or(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>k$(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new rd(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>Or.fromJSON(a));return new dg(n,t.distanceThreshold)}};function c2e(e){let t=new cp;return t.extractWeights(e),t}function oce(e,t){let{width:n,height:a}=new En(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>oce(r,{width:n,height:a}));if(rp(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return hd(Ku(e,r),s)}return Lr(e)?Ku(e,e.detection.forSize(n,a)):e instanceof wa||e instanceof xt?e.forSize(n,a):e}var k2e=GA;export{Jf as AgeGenderNet,Hu as BoundingBox,ut as Box,La as ComposableTask,Ms as ComputeAllFaceDescriptorsTask,ig as ComputeFaceDescriptorsTaskBase,Ps as ComputeSingleFaceDescriptorTask,lg as DetectAllFaceLandmarksTask,fd as DetectAllFacesTask,og as DetectFaceLandmarksTaskBase,pg as DetectFacesTaskBase,ug as DetectSingleFaceLandmarksTask,cg as DetectSingleFaceTask,En as Dimensions,BA as FACE_EXPRESSION_LABELS,xt as FaceDetection,u$ as FaceDetectionNet,Kf as FaceExpressionNet,As as FaceExpressions,ip as FaceLandmark68Net,eg as FaceLandmark68TinyNet,JA as FaceLandmarkNet,wa as FaceLandmarks,_A as FaceLandmarks5,ju as FaceLandmarks68,rd as FaceMatch,dg as FaceMatcher,op as FaceRecognitionNet,y1 as Gender,sd as LabeledBox,Or as LabeledFaceDescriptors,Wr as NetInput,hn as NeuralNetwork,_s as ObjectDetection,Oe as Point,EA as PredictedBox,qu as Rect,Fo as SsdMobilenetv1,Oa as SsdMobilenetv1Options,cp as TinyFaceDetector,ag as TinyFaceDetectorOptions,up as TinyYolov2,yr as TinyYolov2Options,jNe as allFaces,ice as allFacesSsdMobilenetv1,qNe as allFacesTinyYolov2,AA as awaitMediaLoaded,$A as bufferToImage,$Te as computeFaceDescriptor,Ju as createCanvas,Wf as createCanvasFromMedia,oSe as createFaceDetectionNet,oIe as createFaceRecognitionNet,ece as createSsdMobilenetv1,c2e as createTinyFaceDetector,JSe as createTinyYolov2,S1 as detectAllFaces,rce as detectFaceLandmarks,ATe as detectFaceLandmarksTiny,UTe as detectLandmarks,WNe as detectSingleFace,UA as draw,tt as env,k$ as euclideanDistance,w1 as extendWithAge,v1 as extendWithFaceDescriptor,Ku as extendWithFaceDetection,f1 as extendWithFaceExpressions,hd as extendWithFaceLandmarks,k1 as extendWithGender,ud as extractFaceTensors,ld as extractFaces,yve as fetchImage,DA as fetchJson,kve as fetchNetWeights,Es as fetchOrThrow,_ve as fetchVideo,aa as getContext2dOrThrow,Zu as getMediaDimensions,FA as imageTensorToCanvas,RA as imageToSquare,Fye as inverseSigmoid,SA as iou,m1 as isMediaElement,zf as isMediaLoaded,cIe as isWithAge,Lr as isWithFaceDetection,VA as isWithFaceExpressions,rp as isWithFaceLandmarks,fIe as isWithGender,WTe as loadAgeGenderModel,BTe as loadFaceDetectionModel,zTe as loadFaceExpressionModel,PTe as loadFaceLandmarkModel,OTe as loadFaceLandmarkTinyModel,LTe as loadFaceRecognitionModel,sce as loadSsdMobilenetv1Model,DTe as loadTinyFaceDetectorModel,MTe as loadTinyYolov2Model,PA as loadWeightMap,VTe as locateFaces,Mve as matchDimensions,TA as minBbox,nt as nets,NA as nonMaxSuppression,gr as normalize,CA as padToSquare,RTe as predictAgeAndGender,FTe as recognizeFaceExpressions,oce as resizeResults,Xu as resolveInput,Aye as shuffleArray,Pf as sigmoid,ace as ssdMobilenetv1,ze as tf,_Te as tinyFaceDetector,ETe as tinyYolov2,vt as toNetInput,IA as utils,g$ as validateConfig,k2e as version}; //# sourceMappingURL=face-api.esm.js.map diff --git a/dist/face-api.esm.js.map b/dist/face-api.esm.js.map index 3f84c46e..e9a67e5b 100644 --- a/dist/face-api.esm.js.map +++ b/dist/face-api.esm.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["tfjs.esm.js", "../src/draw/index.ts", "../src/draw/drawContour.ts", "../src/utils/index.ts", "../src/classes/Dimensions.ts", "../src/classes/Point.ts", "../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/isNodejs.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/imageToSquare.ts", "../src/dom/NetInput.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/dom/bufferToVideo.ts", "../src/dom/fetchVideo.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/NeuralNetwork.ts", "../src/common/depthwiseSeparableConv.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/types.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMap.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeightMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", 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e=v(r,\"s0\",\"broadcastArgs\",\"int32\"),n=v(t,\"s1\",\"broadcastArgs\",\"int32\");if(e.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). 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Got strides ${e} and dilations '${s}'`),_(o===\"NDHWC\",()=>`Error in conv3d: got dataFormat of ${o} but only NDHWC is currently supported.`);let c={x:u,filter:a},p={strides:e,pad:n,dataFormat:o,dilations:s},m=k.runKernel(Rl,c,p);return l?R(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var kx=T({conv3d_:Aq});function $q(r,t,e,n,o){_(r.length===t.rank,()=>`Length of inShape (${r.length}) and rank of dy (${t.rank}) must match`);let s=r,i=t,a=!1;t.rank===4&&(a=!0,i=R(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,r[0],r[1],r[2],r[3]]);let u=s[4],l=i.shape[4];_(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),_(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),_(e.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${e.rank}`),_(u===e.shape[3],()=>`Error in conv3dDerInput: depth of input (${u}) must match input depth for filter ${e.shape[3]}.`),_(l===e.shape[4],()=>`Error in conv3dDerInput: depth of output (${l}) must match output depth for filter ${e.shape[4]}.`);let c={dy:i,filter:e},p={pad:o,strides:n,inputShape:s},m=k.runKernel(hp,c,p);return a?R(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var Ex=T({conv3DBackpropInput_:$q});function Dq(r,t,e,n,o){let s=v(r,\"x\",\"conv3dTranspose\"),i=v(t,\"filter\",\"conv3dTranspose\");return Ex(e,s,i,n,o)}var _x=T({conv3dTranspose_:Dq});function Rq(r){let e={x:v(r,\"x\",\"cos\",\"float32\")};return k.runKernel(Yo,e)}var eu=T({cos_:Rq});function Fq(r){let e={x:v(r,\"x\",\"cosh\",\"float32\")};return k.runKernel(Zo,e)}var nm=T({cosh_:Fq});function Oq(r,t=0,e=!1,n=!1){let s={x:v(r,\"x\",\"cumprod\")},i={axis:t,exclusive:e,reverse:n};return k.runKernel(ya,s,i)}var Zu=T({cumprod_:Oq});function Pq(r,t=0,e=!1,n=!1){let s={x:v(r,\"x\",\"cumsum\")},i={axis:t,exclusive:e,reverse:n};return k.runKernel(Jo,s,i)}var om=T({cumsum_:Pq});function Lq(r,t,e,n=!1){let o=v(r,\"x\",\"denseBincount\"),s=v(t,\"weights\",\"denseBincount\");_(o.dtype===\"int32\",()=>`Error in denseBincount: input dtype must be int32, but got ${o.dtype}`),_(o.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${o.rank}.`),_(e>=0,()=>`size must be non-negative, but got ${e}.`),_(s.size===o.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${o.shape}, weights shape: ${s.shape}.`);let i={x:o,weights:s},a={size:e,binaryOutput:n};return k.runKernel(gp,i,a)}var ph=T({denseBincount_:Lq});function Mq(r,t,e=\"NHWC\"){let n=v(r,\"x\",\"depthToSpace\",\"float32\"),o=e===\"NHWC\"?n.shape[1]:n.shape[2],s=e===\"NHWC\"?n.shape[2]:n.shape[3],i=e===\"NHWC\"?n.shape[3]:n.shape[1];_(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`),_(o*t>=0,()=>`Negative dimension size caused by overflow when multiplying\n ${o} and ${t} for depthToSpace with input shape\n 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m={x:l,filter:u},f={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i},d=k.runKernel(Qo,m,f);return c?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Li=T({depthwiseConv2d_:zq});function Bq(r){let e={x:v(r,\"x\",\"diag\")};return k.runKernel(bp,e)}var qE=T({diag_:Bq});function Vq(r,t,e,n,o=[1,1],s=\"NHWC\"){let i=v(r,\"x\",\"dilation2d\"),a=v(t,\"filter\",\"dilation2d\");_(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),_(a.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${a.rank}.`),_(s===\"NHWC\",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let u=i,l=!1;i.rank===3&&(u=R(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=!0);let c={x:u,filter:a},p={strides:e,pad:n,dilations:o},m=k.runKernel(Fl,c,p);return l?R(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var $x=T({dilation2d_:Vq});function Gq(r,t){let e=v(r,\"a\",\"equal\",\"string_or_numeric\"),n=v(t,\"b\",\"equal\",\"string_or_numeric\");[e,n]=Ut(e,n),Pt(e.shape,n.shape);let o={a:e,b:n};return k.runKernel(Ia,o)}var Rr=T({equal_:Gq});function Wq(r,t,e){let n=v(t,\"a\",\"where\"),o=v(e,\"b\",\"where\"),s=v(r,\"condition\",\"where\",\"bool\"),i=Pt(Pt(s.shape,n.shape),o.shape),a=Pi(s,i),u=Pi(n,i),l=Pi(o,i),c={condition:a,t:u,e:l};return k.runKernel(yi,c)}var _e=T({where_:Wq});function Uq(r){let e={x:v(r,\"x\",\"zerosLike\")};return k.runKernel(vi,e)}var It=T({zerosLike_:Uq});function Hq(r,t){let e=v(r,\"a\",\"div\"),n=v(t,\"b\",\"div\");[e,n]=Ut(e,n);let o=pt(e,n),s=It(o),i=Rr(n,s);return _e(i,s,o)}var Dx=T({divNoNan_:Hq});function qq(r,t){let e=v(r,\"t1\",\"dot\"),n=v(t,\"t2\",\"dot\");_((e.rank===1||e.rank===2)&&(n.rank===1||n.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${e.rank} and ${n.rank}.`);let o=e.rank===1?e.size:e.shape[1],s=n.rank===1?n.size:n.shape[0];if(_(o===s,()=>`Error in dot: inner 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Yq(r,t,e){_(e0(t,e),()=>`${r} supports only inner-most axes for now. 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p=v(r,\"x\",\"conv2d\",\"float32\"),m=v(t,\"filter\",\"conv2d\",\"float32\"),f=p,d=!1;p.rank===3&&(d=!0,f=R(p,[1,p.shape[0],p.shape[1],p.shape[2]])),_(f.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${f.rank}.`),_(m.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`),Ie(\"fused conv2d\",n,i);let h=o===\"NHWC\"?f.shape[3]:f.shape[1];_(m.shape[2]===h,()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${m.shape[2]}.`),_(Dr(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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e=!1;this.accumulatedGrads=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}};fu.className=\"Adagrad\";vn(fu);var du=class extends Hr{constructor(t,e,n,o=null){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=mt(e).variable(),this.accBeta2=mt(n).variable()}),o==null&&(this.epsilon=k.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=ct(1,this.accBeta1),o=ct(1,this.accBeta2);e.forEach((s,i)=>{let 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g=X(D(pt(d,X(ve(h),this.epsilon)),-this.learningRate),a);a.assign(g)}),this.accBeta1.assign(D(this.accBeta1,this.beta1)),this.accBeta2.assign(D(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Nt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Nt(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),B(()=>{this.accBeta1.assign(ln(this.beta1,this.iterations_+1)),this.accBeta2.assign(ln(this.beta2,this.iterations_+1))});let 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Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(t){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};hu.className=\"Adamax\";vn(hu);var Ui=class extends Hr{constructor(t){super(),this.learningRate=t,this.setLearningRate(t)}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=Array.isArray(t)?t[o].tensor:t[n];if(s==null)return;let i=k.registeredVariables[n];B(()=>{let a=X(D(this.c,s),i);i.assign(a)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=Ae(mt(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error(\"SGD optimizer 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this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};gu.className=\"Momentum\";vn(gu);var xu=class extends Hr{constructor(t,e=.9,n=0,o=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=n,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=k.backend.epsilon()),t==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let 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e=this.centered?t.length/3:t.length/2,n=!1;this.accumulatedMeanSquares=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedMoments=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=t.slice(e*2,e*3).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(t,e){return new t(e.learningRate,e.decay,e.momentum,e.epsilon,e.centered)}};xu.className=\"RMSProp\";vn(xu);var Hs=class{static sgd(t){return new Ui(t)}static momentum(t,e,n=!1){return new gu(t,e,n)}static rmsprop(t,e=.9,n=0,o=null,s=!1){return new xu(t,e,n,o,s)}static adam(t=.001,e=.9,n=.999,o=null){return new du(t,e,n,o)}static adadelta(t=.001,e=.95,n=null){return new mu(t,e,n)}static adamax(t=.002,e=.9,n=.999,o=null,s=0){return new hu(t,e,n,o,s)}static 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rank 3 or 4, but received ${r.rank}.`);let u=$h(r,s);if(o===\"causal\")throw new vt(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");return u=pu.conv2d({x:u,filter:t,strides:n,pad:o===\"same\"?\"same\":\"valid\",dilations:i,dataFormat:\"NHWC\",bias:e,activation:a}),s===\"channelsFirst\"&&(u=Ot(u,[0,3,1,2])),u})}function eY(r,t,e,n=[1,1,1],o=\"valid\",s,i){return B(()=>{if(s==null&&(s=dn()),Fe(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=G0(r,s);if(o===\"causal\")throw new vt(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");return a=kx(a,t,n,o===\"same\"?\"same\":\"valid\",\"NDHWC\",i),e!=null&&(a=hn(a,e)),s===\"channelsFirst\"&&(a=Ot(a,[0,4,1,2,3])),a})}var Cc=class extends Et{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",Cc.verifyArgs(e),this.rank=t,Qe(this.rank,\"rank\"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new vt(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=vu(e.kernelSize,t,\"kernelSize\"),this.strides=vu(e.strides==null?1:e.strides,t,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,mn(this.padding),this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Fe(this.dataFormat),this.activation=Zs(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=de(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Be(e.biasConstraint),this.biasRegularizer=be(e.biasRegularizer),this.activityRegularizer=be(e.activityRegularizer),this.dilationRate=vu(e.dilationRate==null?1:e.dilationRate,t,\"dilationRate\"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new z(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(io(\"kernelSize\"in t,\"required key 'kernelSize' not in config\"),typeof t.kernelSize!=\"number\"&&!Iy(t.kernelSize,\"number\",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ys(this.activation),useBias:this.useBias,biasInitializer:Te(this.biasInitializer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),biasConstraint:ze(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},Su=class extends Cc{constructor(t,e){super(t,e),this.kernel=null,Su.verifyArgs(e),this.filters=e.filters,Qe(this.filters,\"filters\"),this.kernelInitializer=de(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Be(e.kernelConstraint),this.kernelRegularizer=be(e.kernelRegularizer)}build(t){t=zt(t);let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight(\"kernel\",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=St(t);let n,o=this.bias==null?null:this.bias.read(),s=vy(this.activation.getClassName());if(s!=null&&this.rank===2)n=ID(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=tY(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=ID(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=eY(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new vt(\"convolutions greater than 3D are not implemented yet.\");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=zt(t);let e=[],n=this.dataFormat===\"channelsLast\"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},ul=class extends Su{constructor(t){super(2,t),ul.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!Iy(t.kernelSize,\"number\",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};ul.className=\"Conv2D\";Q.registerClass(ul);var cl=class extends Su{constructor(t){super(3,t),cl.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};cl.className=\"Conv3D\";Q.registerClass(cl);var tf=class extends ul{constructor(t){if(super(t),this.inputSpec=[new ye({ndim:4})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=zt(t),t.length!==4)throw new z(\"Input should have rank 4; Received input shape: \"+JSON.stringify(t));let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",o,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new ye({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a;this.dataFormat===\"channelsFirst\"?(i=2,a=3):(i=1,a=2);let u=o[i],l=o[a],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=Js(u,m,c,this.padding),h=Js(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Ot(n,[0,2,3,1]));let x=rm(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(x=Ot(x,[0,3,1,2])),this.bias!=null&&(x=hn(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(t){t=zt(t);let e=t.slice(),n,o,s;this.dataFormat===\"channelsFirst\"?(n=1,o=2,s=3):(n=3,o=1,s=2);let i=this.kernelSize[0],a=this.kernelSize[1],u=this.strides[0],l=this.strides[1];return e[n]=this.filters,e[o]=Js(e[o],u,i,this.padding),e[s]=Js(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};tf.className=\"Conv2DTranspose\";Q.registerClass(tf);var ef=class extends cl{constructor(t){if(super(t),this.inputSpec=[new ye({ndim:5})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=zt(t),t.length!==5)throw new z(\"Input should have rank 5; Received input shape: \"+JSON.stringify(t));let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",o,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new ye({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat===\"channelsFirst\"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=Js(l,h,m,this.padding),w=Js(c,g,f,this.padding),C=Js(p,x,d,this.padding),N=[s,b,w,C,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Ot(n,[0,2,3,4,1]));let E=_x(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(E=Ot(E,[0,4,1,2,3])),this.bias!==null&&(E=hn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=zt(t);let e=t.slice(),n,o,s,i;this.dataFormat===\"channelsFirst\"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=Js(e[o],c,a,this.padding),e[s]=Js(e[s],p,u,this.padding),e[i]=Js(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};ef.className=\"Conv3DTranspose\";Q.registerClass(ef);var db=class extends Su{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER=\"glorotUniform\",this.DEFAULT_POINTWISE_INITIALIZER=\"glorotUniform\",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");if(e.padding!=null&&e.padding!==\"same\"&&e.padding!==\"valid\")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=de(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=be(e.depthwiseRegularizer),this.depthwiseConstraint=Be(e.depthwiseConstraint),this.pointwiseInitializer=de(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=be(e.pointwiseRegularizer),this.pointwiseConstraint=Be(e.pointwiseConstraint)}build(t){if(t=zt(t),t.length{t=St(t);let n;if(this.rank===1)throw new vt(\"1D separable convolution is not implemented yet.\");return this.rank===2&&(this.dataFormat===\"channelsFirst\"&&(t=Ot(t,[0,2,3,1])),n=fm(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,\"NHWC\")),this.useBias&&(n=hn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat===\"channelsFirst\"&&(n=Ot(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.pointwiseInitializer=Te(this.pointwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.pointwiseRegularizer=me(this.pointwiseRegularizer),t.depthwiseConstraint=ze(this.depthwiseConstraint),t.pointwiseConstraint=ze(this.pointwiseConstraint),t}};db.className=\"SeparableConv\";var rf=class extends db{constructor(t){super(2,t)}};rf.className=\"SeparableConv2D\";Q.registerClass(rf);var Nu=class extends Su{constructor(t){super(1,t),Nu.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!Iy(t.kernelSize,\"number\",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};Nu.className=\"Conv1D\";Q.registerClass(Nu);var nf=class extends Et{constructor(t){super(t),typeof t.cropping==\"number\"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]==\"number\"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?\"channelsLast\":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat===\"channelsFirst\"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat===\"channelsLast\"){let n=Ch(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ch(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ch(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ch(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};nf.className=\"Cropping2D\";Q.registerClass(nf);var of=class extends Et{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Fe(this.dataFormat),this.interpolation=t.interpolation==null?\"nearest\":t.interpolation,$$(this.interpolation)}computeOutputShape(t){if(this.dataFormat===\"channelsFirst\"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=St(t),o=n.shape;if(this.dataFormat===\"channelsFirst\"){n=Ot(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation===\"nearest\"?so.resizeNearestNeighbor(n,[s,i]):so.resizeBilinear(n,[s,i]);return Ot(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation===\"nearest\"?so.resizeNearestNeighbor(n,[s,i]):so.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};of.className=\"UpSampling2D\";Q.registerClass(of);function rY(r,t,e=[1,1],n=\"valid\",o,s){return B(()=>{o==null&&(o=dn()),Fe(o);let i=$h(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Li(i,t,e,n===\"same\"?\"same\":\"valid\",\"NHWC\",s),o===\"channelsFirst\"&&(i=Ot(i,[0,3,1,2])),i})}var sf=class extends Cc{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=de(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Be(t.depthwiseConstraint),this.depthwiseRegularizer=be(t.depthwiseRegularizer)}build(t){if(t=zt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat===\"channelsFirst\"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{t=St(t);let n=rY(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=hn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=zt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2],o=this.dataFormat===\"channelsFirst\"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=kn(e,this.kernelSize[0],this.padding,this.strides[0]),i=kn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat===\"channelsFirst\"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.depthwiseConstraint=ze(this.depthwiseRegularizer),t}};sf.className=\"DepthwiseConv2D\";Q.registerClass(sf);function W0(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z(\"When inputs is an array, neither initialState or constants should be provided\");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function U0(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(fn(2,u));if(t=Ot(t,l),s!=null)throw new vt(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");i&&console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\"),o!=null&&(o=J(J(o,\"bool\"),\"float32\"),o.rank===u-1&&(o=sr(o,-1)),o=Ot(o,l)),n&&(t=mr(t,0),o!=null&&(o=mr(o,0)));let c=[],p,m=e,f=t.shape[0],d=dr(t),h;o!=null&&(h=dr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let C=B(()=>{let N=h[x],E=ct(wr(N),N),A=X(D(w[0],N),D(m[0],E)),$=m.map((F,P)=>X(D(w[1][P],N),D(F,E)));return{output:A,newStates:$}});p=C.output,m=C.newStates}a&&c.push(p)}let g;return a&&(g=qe(c,1)),[p,g,m]})}var En=class extends Et{constructor(t){super(t);let e;if(t.cell==null)throw new z(\"cell property is missing for the constructor of RNN.\");if(Array.isArray(t.cell)?e=new Sc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new ye({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return fn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Dy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new ye({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new Tn(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape[0];if(n==null)throw new z(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>Ne([n,o])):this.states_=[Ne([n,this.cell.stateSize])];else if(t==null)Nt(this.states_),this.keptStates!=null&&(Nt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>Ne([n,o])):this.states_[0]=Ne([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Nt(this.states_);for(let o=0;oAe(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=W0(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new ye({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof tn){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=St(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");let a={training:o},l=U0((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=Ne(t.shape);return e=ft(e,[1,2]),e=il(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?_y(e,[1,n]):e):this.cell.stateSize>1?[_y(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===En.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=yn(o,n);return new t(Object.assign(e,{cell:s}))}};En.className=\"RNN\";Q.registerClass(En);var pl=class extends Et{},Ic=class extends pl{constructor(t){super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=t.units,Qe(this.units,\"units\"),this.activation=Zs(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=be(t.kernelRegularizer),this.recurrentRegularizer=be(t.recurrentRegularizer),this.biasRegularizer=be(t.biasRegularizer),this.kernelConstraint=Be(t.kernelConstraint),this.recurrentConstraint=Be(t.recurrentConstraint),this.biasConstraint=Be(t.biasConstraint),this.dropout=uc([1,js([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=uc([1,js([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=zt(t),this.kernel=this.addWeight(\"kernel\",[t[t.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0wr(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0wr(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Eo(D(t,i),this.kernel.read()):s=Eo(t,this.kernel.read()),this.bias!=null&&(s=hn(s,this.bias.read())),a!=null&&(n=D(n,a));let u=X(s,Eo(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:Ys(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),recurrentInitializer:Te(this.recurrentInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:ze(this.kernelConstraint),recurrentConstraint:ze(this.recurrentConstraint),biasConstraint:ze(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};Ic.className=\"SimpleRNNCell\";Q.registerClass(Ic);var af=class extends En{constructor(t){t.cell=new Ic(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Nt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Nt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};af.className=\"SimpleRNN\";Q.registerClass(af);var vc=class extends pl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",t.resetAfter)throw new z(\"GRUCell does not support reset_after parameter set to true.\");this.units=t.units,Qe(this.units,\"units\"),this.activation=Zs(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=Zs(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=be(t.kernelRegularizer),this.recurrentRegularizer=be(t.recurrentRegularizer),this.biasRegularizer=be(t.biasRegularizer),this.kernelConstraint=Be(t.kernelConstraint),this.recurrentConstraint=Be(t.recurrentConstraint),this.biasConstraint=Be(t.biasConstraint),this.dropout=uc([1,js([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=uc([1,js([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=zt(t);let e=t[t.length-1];this.kernel=this.addWeight(\"kernel\",[e,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0wr(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0{this.cell.dropoutMask!=null&&(Nt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Nt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};lf.className=\"GRU\";Q.registerClass(lf);var ml=class extends pl{constructor(t){super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=t.units,Qe(this.units,\"units\"),this.activation=Zs(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=Zs(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=be(t.kernelRegularizer),this.recurrentRegularizer=be(t.recurrentRegularizer),this.biasRegularizer=be(t.biasRegularizer),this.kernelConstraint=Be(t.kernelConstraint),this.recurrentConstraint=Be(t.recurrentConstraint),this.biasConstraint=Be(t.biasConstraint),this.dropout=uc([1,js([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=uc([1,js([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=zt(t);let n=t[t.length-1];this.kernel=this.addWeight(\"kernel\",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends gn{apply(u,l){let c=s.apply([i]),p=new wu().apply([i]),m=s.apply([i*2]);return _0(_0(c,p),m)}},e.className=\"CustomInit\",e)}else o=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0wr(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Nt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Nt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};uf.className=\"LSTM\";Q.registerClass(uf);var Sc=class extends pl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{Ks(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign(Object.assign({},t),o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(yn(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return vh(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):$y(t(),e),a=()=>bu(i,t,n);return!o||o<=1?Ae(a().clone()):Array(o).fill(void 0).map(a).map(l=>Ae(l.clone()))}var nY=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols==\"function\")for(var o=0,n=Object.getOwnPropertySymbols(r);o{if(this.cell.dropoutMask!=null&&(Nt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Nt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z(\"ConvRNN2D cell does not support constants\");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=Ne(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new Tn(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ne(s)):this.states_=[Ne(s)];else if(t==null)Nt(this.states_),this.keptStates!=null&&(Nt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ne(s)):this.states_[0]=Ne(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Nt(this.states_);for(let a=0;aAe(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e===\"channelsFirst\",l=t[u?3:2],c=t[u?4:3],p=kn(l,o[0],s,i[0],a[0]),m=kn(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};hb.className=\"ConvRNN2D\";var Nc=class extends ml{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,Qe(this.filters,\"filters\"),this.kernelSize=vu(n,2,\"kernelSize\"),this.kernelSize.forEach(u=>Qe(u,\"kernelSize\")),this.strides=vu(o||1,2,\"strides\"),this.strides.forEach(u=>Qe(u,\"strides\")),this.padding=s||\"valid\",mn(this.padding),this.dataFormat=i||\"channelsLast\",Fe(this.dataFormat),this.dilationRate=vu(a||1,2,\"dilationRate\"),this.dilationRate.forEach(u=>Qe(u,\"dilationRate\"))}build(t){var e;t=zt(t);let n=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight(\"kernel\",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight(\"recurrent_kernel\",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends gn{apply(m,f){let d=l.apply([c]),h=pr([c]),g=l.apply([c*2]);return Tm([d,h,g])}},e.className=\"CustomInit\",e)}else u=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0wr(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(rt,ot,at)=>!ot||!ot[at]?rt:D(ot[at],rt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0wr(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[C,N,E,A]=fr(this.kernel.read(),a,w),[$,F,P,V]=this.useBias?fr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,C,$,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,E,P,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=fr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let j=this.recurrentActivation.apply(X(c,h)),Y=this.recurrentActivation.apply(X(p,g)),Z=X(D(Y,i),D(j,this.activation.apply(X(m,x)))),et=D(this.recurrentActivation.apply(X(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=nY(t,[\"units\"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=Sn(t,e,this.strides,o||\"valid\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\",this.dilationRate);return n?hn(s,n,this.dataFormat):s}recurrentConv(t,e){return Sn(t,e,1,\"same\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\")}};Nc.className=\"ConvLSTM2DCell\";Q.registerClass(Nc);var cf=class extends hb{constructor(t){let e=new Nc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};cf.className=\"ConvLSTM2D\";Q.registerClass(cf);var Tc=class extends Et{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);if(0$y(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Tc.className=\"Dropout\";Q.registerClass(Tc);var pf=class extends Tc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};pf.className=\"SpatialDropout1D\";Q.registerClass(pf);var mf=class extends Et{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,Qe(this.units,\"units\"),this.activation=Zs(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Be(t.kernelConstraint),this.biasConstraint=Be(t.biasConstraint),this.kernelRegularizer=be(t.kernelRegularizer),this.biasRegularizer=be(t.biasRegularizer),this.activityRegularizer=be(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=zt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight(\"kernel\",[e,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:e}}],this.built=!0}computeOutputShape(t){t=zt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=vy(this.activation.getClassName()),s;return o!=null?s=Eo(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Eo(n,this.kernel.read()),this.bias!=null&&(s=hn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:Ys(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:ze(this.kernelConstraint),biasConstraint:ze(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};mf.className=\"Dense\";Q.registerClass(mf);var ff=class extends Et{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=zt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to \"Flatten\" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);return[t[0],ko(t,1)]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(this.dataFormat===\"channelsFirst\"&&n.rank>1){let o=[0];for(let s=2;s{this.invokeCallHook(t,e);let n=St(t);return this.activation.apply(n)})}getConfig(){let t={activation:Ys(this.activation)},e=super.getConfig();return Object.assign(t,e),t}};df.className=\"Activation\";Q.registerClass(df);var hf=class extends Et{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return B(()=>(t=St(t),F$(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}};hf.className=\"RepeatVector\";Q.registerClass(hf);var gf=class extends Et{constructor(t){super(t),this.targetShape=t.targetShape;for(let e=0;e{this.invokeCallHook(t,e);let n=St(t),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return R(n,s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}};gf.className=\"Reshape\";Q.registerClass(gf);var xf=class extends Et{constructor(t){if(super(t),t.dims==null)throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${t.dims} instead.`);let e=fn(1,t.dims.length+1);if(!y.arraysEqual(t.dims.slice().sort(),e))throw new Error(\"Invalid permutation `dims`: \"+JSON.stringify(t.dims)+\" `dims` must contain consecutive integers starting from 1.\");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new ye({ndim:this.dims.length+1})]}computeOutputShape(t){t=zt(t);let e=t.slice();return this.dims.forEach((n,o)=>{e[o+1]=t[n]}),e}call(t,e){return Ot(St(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}};xf.className=\"Permute\";Q.registerClass(xf);var yf=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let n=St(t),o=-1;return ju(Ws(n,this.maskValue),o)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=-1,s=!0,i=ju(Ws(n,this.maskValue),o,s);return D(n,J(i,n.dtype))})}};yf.className=\"Masking\";Q.registerClass(yf);var bf=class extends Et{constructor(t){if(super(t),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER=\"randomUniform\",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(xe(t.inputLength))}this.inputDim=t.inputDim,Qe(this.inputDim,\"inputDim\"),this.outputDim=t.outputDim,Qe(this.outputDim,\"outputDim\"),this.embeddingsInitializer=de(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=be(t.embeddingsRegularizer),this.activityRegularizer=be(t.activityRegularizer),this.embeddingsConstraint=Be(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight(\"embeddings\",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return B(()=>this.maskZero?(t=St(t),Ws(t,It(t))):null)}computeOutputShape(t){if(t=zt(t),this.inputLength==null)return[...t,this.outputDim];let e=xe(this.inputLength);if(e.length!==t.length-1)throw new z(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${t}`);{let n=0;for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);n.dtype!==\"int32\"&&(n=Qr(n,\"int32\"));let o=Ay(this.embeddings.read(),R(n,[n.size]));return R(o,zt(this.computeOutputShape(n.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Te(this.embeddingsInitializer),embeddingsRegularizer:me(this.embeddingsRegularizer),activityRegularizer:me(this.activityRegularizer),embeddingsConstraint:ze(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}};bf.className=\"Embedding\";Q.registerClass(bf);var dl=class extends Et{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new vt}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length1)throw new z(`Can not merge tensors with different batch sizes. 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${r.shape.length}`),y.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof e==\"number\"&&(e=[e,e]),r.dtype===\"complex64\"||t.dtype===\"complex64\")throw new vt(\"batchDot is not implemented for complex64-type Tensors yet.\");let n=r.shape.length,o=t.shape.length;e==null&&(e=[n-1,o-2]);let s=e;return B(()=>{let i;if(n>o){i=n-o;let u=[];for(let l=0;ln){i=o-n;let u=[];for(let l=0;l0){let u;n>o?u=n+o-3:u=n-1;let l=[];for(let c=u;c\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let e=t[0],n=t[1];if(e.length>3||n.length>3)throw new vt(\"Dot layer does not support tensors of 4D or higher rank yet.\");let o=this.interpretAxes(e,n);if(e[o[0]]!==n[o[1]])throw new z(`Dimension incompatibility: ${e[o[0]]} !== ${n[o[1]]}`)}mergeFunction(t){if(t.length!==2)throw new z(`A \\`Dot\\` layer must be called on exactly 2 inputs, but received ${t.length} input(s).`);let e=t[0],n=t[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,i)=>Dh(s,t[i].shape.length)):o=[Dh(this.axes,e.shape.length),Dh(this.axes,n.shape.length)],this.normalize&&(e=Sh(e,o[0]),n=Sh(n,o[1])),oY(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[Dh(this.axes,t.length),Dh(this.axes,e.length)],n}computeOutputShape(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new vt(\"Dot layer does not support tensors of 4D or higher rank yet.\");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};Tf.className=\"Dot\";Q.registerClass(Tf);var kf=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return bu(()=>X(km(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};kf.className=\"GaussianNoise\";Q.registerClass(kf);var Ef=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.rate>0&&this.rate<1?bu(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return D(n,km(n.shape,1,s))},()=>n,e.training||!1):n})}};Ef.className=\"GaussianDropout\";Q.registerClass(Ef);var _f=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||St(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(t);return bu(()=>{let s=St(t),i=1.6732632423543772,a=1.0507009873554805,u=-i*a,l=un(Gi(n),this.rate);l=Qr(l,\"float32\");let c=((1-this.rate)*(1+this.rate*u**2))**-.5,p=-c*u*this.rate,m=X(D(s,l),D(X(l,-1),u));return X(D(m,c),p)},()=>St(t),e.training||!1)}return t})}};_f.className=\"AlphaDropout\";Q.registerClass(_f);function Rh(r,t,e,n,o,s=.001){let i;if(r.rank===2)i=yx(r,t,e,n,o,s);else if(r.rank===3)i=bx(r,t,e,n,o,s);else if(r.rank===4)i=wx(r,t,e,n,o,s);else throw new vt(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return i}function sY(r,t,e,n,o=.001){return B(()=>{let s=Qu(r,n),i=s.mean,a=s.variance;return[Rh(r,i,a,e,t,o),i,a]})}function iY(r,t,e,n,o=.001){return B(()=>{let s=Qu(r,n),i=s.mean,a=s.variance,u=[];for(let d of fn(0,r.rank))n.indexOf(d)!==-1?u.push(1):u.push(r.shape[d]);let l=R(i,u),c=R(a,u),p=t==null?null:R(t,u),m=e==null?null:R(e,u);return[Rh(r,l,c,m,p,o),i,a]})}function aY(r,t,e,n,o=.001){return y.arraysEqual(n.slice().sort(),fn(0,r.rank-1))?sY(r,t,e,n,o):iY(r,t,e,n,o)}var Af=class extends Et{constructor(t){t==null&&(t={}),super(t),this.supportsMasking=!0,this.axis=t.axis==null?-1:t.axis,this.momentum=t.momentum==null?.99:t.momentum,this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=de(t.betaInitializer||\"zeros\"),this.gammaInitializer=de(t.gammaInitializer||\"ones\"),this.movingMeanInitializer=de(t.movingMeanInitializer||\"zeros\"),this.movingVarianceInitializer=de(t.movingVarianceInitializer||\"ones\"),this.betaConstraint=Be(t.betaConstraint),this.gammaConstraint=Be(t.gammaConstraint),this.betaRegularizer=be(t.betaRegularizer),this.gammaRegularizer=be(t.gammaRegularizer)}build(t){t=zt(t);let e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new ye({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight(\"gamma\",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight(\"beta\",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight(\"moving_mean\",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight(\"moving_variance\",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=fn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=So(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,fn(0,i).slice(0,i-1)),m=()=>{if(p){let b=R(this.movingMean.read(),l),w=R(this.movingVariance.read(),l),C=this.center?R(this.beta.read(),l):null,N=this.scale?R(this.gamma.read(),l):null;return Rh(o,b,w,C,N,this.epsilon)}else return Rh(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return m();let[f,d,h]=aY(o,this.gamma.read(),this.beta.read(),a,this.epsilon),g=(b,w,C)=>{B(()=>{let N=1-C,E=b.read(),A=D(ct(E,w),N);b.write(ct(E,A))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Te(this.betaInitializer),gammaInitializer:Te(this.gammaInitializer),movingMeanInitializer:Te(this.movingMeanInitializer),movingVarianceInitializer:Te(this.movingVarianceInitializer),betaRegularizer:me(this.betaRegularizer),gammaRegularizer:me(this.gammaRegularizer),betaConstraint:ze(this.betaConstraint),gammaConstraint:ze(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};Af.className=\"BatchNormalization\";Q.registerClass(Af);var $f=class extends Et{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.axis,typeof this.axis==\"number\"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=de(t.betaInitializer||\"zeros\"),this.gammaInitializer=de(t.gammaInitializer||\"ones\"),this.betaRegularizer=be(t.betaRegularizer),this.gammaRegularizer=be(t.gammaRegularizer),this.supportsMasking=!0}build(t){t=zt(t);let e=t.length;typeof this.axis==\"number\"&&(this.axis=[this.axis]);for(let s=0;s=e)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==To(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight(\"gamma\",n,\"float32\",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight(\"beta\",n,\"float32\",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let n=St(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=Qu(n,this.axis,!0),l=So(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new z(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");if(e==null&&(e=dn()),e!==\"channelsLast\"&&e!==\"channelsFirst\")throw new z(`Unknown data format: ${e}. 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length-${t.padding[1].length} array.`);n=t.padding[1]}this.padding=[e,n]}this.inputSpec=[new ye({ndim:4})]}computeOutputShape(t){t=zt(t);let e,n;return this.dataFormat===\"channelsFirst\"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?n=t[3]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],t[1],e,n]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?n=t[2]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],e,n,t[3]])}call(t,e){return B(()=>lY(St(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};Df.className=\"ZeroPadding2D\";Q.registerClass(Df);function Cb(r,t,e,n,o,s){return B(()=>{Fe(o),N0(s),mn(n),e==null&&(e=[1,1]),n==null&&(n=\"valid\"),o==null&&(o=dn()),s==null&&(s=\"max\"),r=$h(r,o);let i,a=n===\"same\"?\"same\":\"valid\";return s===\"max\"?i=su(r,t,e,a):i=Ql(r,t,e,a),o===\"channelsFirst\"&&(i=Ot(i,[0,3,1,2])),i})}function vD(r,t,e,n,o,s){return B(()=>{Fe(o),N0(s),mn(n),e==null&&(e=[1,1,1]),n==null&&(n=\"valid\"),o==null&&(o=dn()),s==null&&(s=\"max\"),r=G0(r,o);let i,a=n===\"same\"?\"same\":\"valid\";return s===\"max\"?i=qx(r,t,e,a):i=xx(r,t,e,a),o===\"channelsFirst\"&&(i=Ot(i,[0,4,1,2,3])),i})}var gb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize==\"number\")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]==\"number\")this.poolSize=t.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(Qe(this.poolSize,\"poolSize\"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides==\"number\")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]==\"number\")this.strides=t.strides;else throw new z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);Qe(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,mn(this.padding),this.inputSpec=[new ye({ndim:3})]}computeOutputShape(t){t=zt(t);let e=kn(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return B(()=>{this.invokeCallHook(t,e),t=il(St(t),2);let n=this.poolingFunction(St(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,\"channelsLast\");return Bn(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Rf=class extends gb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Fe(s),mn(o),Cb(t,e,n,o,s,\"max\")}};Rf.className=\"MaxPooling1D\";Q.registerClass(Rf);var Ff=class extends gb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Fe(s),mn(o),Cb(t,e,n,o,s,\"avg\")}};Ff.className=\"AveragePooling1D\";Q.registerClass(Ff);var xb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];Qe(this.poolSize,\"poolSize\"),Qe(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Fe(this.dataFormat),mn(this.padding),this.inputSpec=[new ye({ndim:4})]}computeOutputShape(t){t=zt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2];return e=kn(e,this.poolSize[0],this.padding,this.strides[0]),n=kn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat===\"channelsFirst\"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Of=class extends xb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Fe(s),mn(o),Cb(t,e,n,o,s,\"max\")}};Of.className=\"MaxPooling2D\";Q.registerClass(Of);var Pf=class extends xb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Fe(s),mn(o),Cb(t,e,n,o,s,\"avg\")}};Pf.className=\"AveragePooling2D\";Q.registerClass(Pf);var yb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];Qe(this.poolSize,\"poolSize\"),Qe(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Fe(this.dataFormat),mn(this.padding),this.inputSpec=[new ye({ndim:5})]}computeOutputShape(t){t=zt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2],o=this.dataFormat===\"channelsFirst\"?t[4]:t[3];return e=kn(e,this.poolSize[0],this.padding,this.strides[0]),n=kn(n,this.poolSize[1],this.padding,this.strides[1]),o=kn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat===\"channelsFirst\"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Lf=class extends yb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Fe(s),mn(o),vD(t,e,n,o,s,\"max\")}};Lf.className=\"MaxPooling3D\";Q.registerClass(Lf);var Mf=class extends yb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Fe(s),mn(o),vD(t,e,n,o,s,\"avg\")}};Mf.className=\"AveragePooling3D\";Q.registerClass(Mf);var bb=class extends Et{constructor(t){super(t),this.inputSpec=[new ye({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new vt}},zf=class extends bb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Se(n,1)})}};zf.className=\"GlobalAveragePooling1D\";Q.registerClass(zf);var Bf=class extends bb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Sr(n,1)})}};Bf.className=\"GlobalMaxPooling1D\";Q.registerClass(Bf);var wb=class extends Et{constructor(t){super(t),this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Fe(this.dataFormat),this.inputSpec=[new ye({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat===\"channelsLast\"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new vt}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Vf=class extends wb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat===\"channelsLast\"?Se(n,[1,2]):Se(n,[2,3])})}};Vf.className=\"GlobalAveragePooling2D\";Q.registerClass(Vf);var Gf=class extends wb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat===\"channelsLast\"?Sr(n,[1,2]):Sr(n,[2,3])})}};Gf.className=\"GlobalMaxPooling2D\";Q.registerClass(Gf);var Ib=class extends Et{constructor(t){super(t),this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,n={}){let o=e.layer,s=yn(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},Wf=class extends Ib{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=zt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=zt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=St(t),U0((i,a)=>[St(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};Wf.className=\"TimeDistributed\";Q.registerClass(Wf);function uY(r){Ki(_$,\"BidirectionalMergeMode\",r)}var cY=\"concat\",Uf=class extends Ib{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=yn(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=yn(o),this.forwardLayer.name=\"forward_\"+this.forwardLayer.name,this.backwardLayer.name=\"backward_\"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?cY:t.mergeMode,uY(this.mergeMode),t.weights)throw new vt(\"weights support is not implemented for Bidirectional layer yet.\");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let n,o,s;return this.returnState&&(s=e.slice(1)),n=e[0],n=n,this.mergeMode===\"concat\"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):Tr(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=W0(t,n,o,this.numConstants);if(t=s.inputs,n=s.initialState,o=s.constants,Array.isArray(t)&&(n=t.slice(1),t=t[0]),(n==null||n.length===0)&&o==null)return super.apply(t,e);let i=[],a=[];if(n!=null){let l=n.length;if(l%2>0)throw new z(\"When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.\");e.initialState=n,i.push(...n);let c=n.map(p=>new ye({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),a.push(...c)}if(o!=null)throw new vt(\"Support for constants in Bidirectional layers is not implemented yet.\");let u=i[0]instanceof tn;for(let l of i)if(l instanceof tn!==u)throw new z(\"The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors\");if(u){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e.initialState,o,s;if(n==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let u=n.slice(0,n.length/2),l=n.slice(n.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:u})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:l}))}let i;this.returnState&&(Array.isArray(o)&&(i=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=mr(s,1));let a;return this.mergeMode===\"concat\"?a=Tm([o,s]):this.mergeMode===\"sum\"?a=X(o,s):this.mergeMode===\"ave\"?a=D(.5,X(o,s)):this.mergeMode===\"mul\"?a=D(o,s):this.mergeMode==null&&(a=[o,s]),this.returnState?this.mergeMode==null?a.concat(i):[a].concat(i):a})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){Ks(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),Ks(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[e,e]:n=e:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(i=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let n=yn(e.layer);if(delete e.layer,e.numConstants!=null)throw new vt(\"Deserialization of a Bidirectional layer with numConstants present is not supported yet.\");let o=e;return o.layer=n,new t(o)}};Uf.className=\"Bidirectional\";Q.registerClass(Uf);var Hf=class extends Et{constructor(t){super(t),this.scale=t.scale,t.offset?this.offset=t.offset:this.offset=0}getConfig(){let t={scale:this.scale,offset:this.offset},e=super.getConfig();return 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n=I(\"tensorArrayId\",r,t,e),o=I(\"indices\",r,t,e),s=I(\"dtype\",r,t,e);return[e.getTensorArray(n.id).gather(o,s)]}case\"TensorArrayScatterV3\":{let n=I(\"tensorArrayId\",r,t,e),o=I(\"indices\",r,t,e),s=I(\"tensor\",r,t,e),i=e.getTensorArray(n.id);return i.scatter(o,s),[i.idTensor]}case\"TensorArrayConcatV3\":{let n=I(\"tensorArrayId\",r,t,e),o=e.getTensorArray(n.id),s=I(\"dtype\",r,t,e);return[o.concat(s)]}case\"TensorArraySplitV3\":{let n=I(\"tensorArrayId\",r,t,e),o=I(\"tensor\",r,t,e),s=I(\"lengths\",r,t,e),i=e.getTensorArray(n.id);return i.split(s,o),[i.idTensor]}case\"TensorArraySizeV3\":{let n=I(\"tensorArrayId\",r,t,e),o=e.getTensorArray(n.id);return[mt(o.size(),\"int32\")]}case\"TensorArrayCloseV3\":{let n=I(\"tensorArrayId\",r,t,e),o=e.getTensorArray(n.id);return o.clearAndClose(),[o.idTensor]}case\"TensorListSetItem\":{let n=I(\"tensorListId\",r,t,e),o=I(\"index\",r,t,e),s=I(\"tensor\",r,t,e),i=e.getTensorList(n.id);return i.setItem(o,s),[i.idTensor]}case\"TensorListGetItem\":{let n=I(\"tensorListId\",r,t,e),o=I(\"index\",r,t,e),s=I(\"elementShape\",r,t,e),i=I(\"elementDType\",r,t,e);return[e.getTensorList(n.id).getItem(o,s,i)]}case\"TensorListScatterV2\":case\"TensorListScatter\":{let n=I(\"indices\",r,t,e),o=I(\"tensor\",r,t,e),s=I(\"elementShape\",r,t,e),i=I(\"numElements\",r,t,e),a=HD(o,n,s,i);return e.addTensorList(a),[a.idTensor]}case\"TensorListReserve\":case\"EmptyTensorList\":{let n=I(\"elementShape\",r,t,e),o=I(\"elementDType\",r,t,e),s;r.op===\"TensorListReserve\"?s=\"numElements\":s=\"maxNumElements\";let i=I(s,r,t,e),a=r.op===\"TensorListReserve\"?-1:i,u=UD(n,o,i,a);return e.addTensorList(u),[u.idTensor]}case\"TensorListGather\":{let n=I(\"tensorListId\",r,t,e),o=I(\"indices\",r,t,e),s=I(\"elementShape\",r,t,e),i=I(\"elementDType\",r,t,e);return[e.getTensorList(n.id).gather(o,i,s)]}case\"TensorListStack\":{let n=I(\"tensorListId\",r,t,e),o=I(\"elementShape\",r,t,e),s=I(\"elementDType\",r,t,e),i=I(\"numElements\",r,t,e);return[e.getTensorList(n.id).stack(o,s,i)]}case\"TensorListFromTensor\":{let n=I(\"tensor\",r,t,e),o=I(\"elementShape\",r,t,e),s=I(\"elementDType\",r,t,e),i=WD(n,o,s);return e.addTensorList(i),[i.idTensor]}case\"TensorListConcat\":case\"TensorListConcatV2\":{let n=I(\"tensorListId\",r,t,e),o=e.getTensorList(n.id),s=I(\"dtype\",r,t,e),i=I(\"elementShape\",r,t,e);return[o.concat(s,i)]}case\"TensorListPushBack\":{let n=I(\"tensorListId\",r,t,e),o=I(\"tensor\",r,t,e),s=e.getTensorList(n.id);return s.pushBack(o),[s.idTensor]}case\"TensorListPopBack\":{let n=I(\"tensorListId\",r,t,e),o=I(\"elementShape\",r,t,e),s=I(\"elementDType\",r,t,e);return[e.getTensorList(n.id).popBack(o,s)]}case\"TensorListSplit\":{let n=I(\"tensor\",r,t,e),o=I(\"elementShape\",r,t,e),s=I(\"lengths\",r,t,e),i=qD(n,s,o);return e.addTensorList(i),[i.idTensor]}case\"TensorListLength\":{let n=I(\"tensorListId\",r,t,e),o=e.getTensorList(n.id);return[mt(o.size(),\"int32\")]}case\"TensorListResize\":{let n=I(\"tensorListId\",r,t,e),o=I(\"size\",r,t,e),i=e.getTensorList(n.id).resize(o);return e.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function jD(r,t,e){let[n,o]=I(\"fusedOps\",r,t,e),s=n===\"biasadd\",i=!s,a=o===\"prelu\",u=n===\"fusedbatchnorm\",l=I(\"numArgs\",r,t,e);if(s){if(a&&l!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!a&&s&&l!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(u)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=I(\"strides\",r,t,e),p=Fh(r,t,e),m=I(\"dataFormat\",r,t,e).toUpperCase(),f=I(\"dilations\",r,t,e),[d,h]=I(\"args\",r,t,e);i&&(h=d,d=void 0);let g=I(\"leakyreluAlpha\",r,t,e);return{stride:c,pad:p,dataFormat:m,dilations:f,biasArg:d,preluArg:h,activationFunc:o,leakyreluAlpha:g}}var XD=(r,t,e,n=se)=>{switch(r.op){case\"Conv1D\":{let o=I(\"stride\",r,t,e),s=I(\"pad\",r,t,e),i=I(\"dataFormat\",r,t,e).toUpperCase(),a=I(\"dilation\",r,t,e);return[n.conv1d(I(\"x\",r,t,e),I(\"filter\",r,t,e),o,s,i,a)]}case\"Conv2D\":{let o=I(\"strides\",r,t,e),s=Fh(r,t,e),i=I(\"dataFormat\",r,t,e).toUpperCase(),a=I(\"dilations\",r,t,e);return[n.conv2d(I(\"x\",r,t,e),I(\"filter\",r,t,e),[o[1],o[2]],s,i,[a[1],a[2]])]}case\"_FusedConv2D\":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=jD(r,t,e);return[n.fused.conv2d({x:I(\"x\",r,t,e),filter:I(\"filter\",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case\"FusedDepthwiseConv2dNative\":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=jD(r,t,e);return[n.fused.depthwiseConv2d({x:I(\"x\",r,t,e),filter:I(\"filter\",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let o=I(\"outputShape\",r,t,e),s=I(\"strides\",r,t,e),i=Fh(r,t,e);return[n.conv2dTranspose(I(\"x\",r,t,e),I(\"filter\",r,t,e),o,[s[1],s[2]],i)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let o=I(\"strides\",r,t,e),s=Fh(r,t,e),i=I(\"dilations\",r,t,e),a=I(\"dataFormat\",r,t,e).toUpperCase();return[n.depthwiseConv2d(I(\"input\",r,t,e),I(\"filter\",r,t,e),[o[1],o[2]],s,a,[i[1],i[2]])]}case\"Conv3D\":{let o=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),i=I(\"dataFormat\",r,t,e).toUpperCase(),a=I(\"dilations\",r,t,e);return[n.conv3d(I(\"x\",r,t,e),I(\"filter\",r,t,e),[o[1],o[2],o[3]],s,i,[a[1],a[2],a[3]])]}case\"AvgPool\":{let o=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),i=I(\"kernelSize\",r,t,e);return[n.avgPool(I(\"x\",r,t,e),[i[1],i[2]],[o[1],o[2]],s)]}case\"MaxPool\":{let o=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),i=I(\"kernelSize\",r,t,e);return[n.maxPool(I(\"x\",r,t,e),[i[1],i[2]],[o[1],o[2]],s)]}case\"MaxPoolWithArgmax\":{let o=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),i=I(\"kernelSize\",r,t,e),a=I(\"includeBatchInIndex\",r,t,e),{result:u,indexes:l}=n.maxPoolWithArgmax(I(\"x\",r,t,e),[i[1],i[2]],[o[1],o[2]],s,a);return[u,l]}case\"AvgPool3D\":{let o=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),i=I(\"kernelSize\",r,t,e);return[n.avgPool3d(I(\"x\",r,t,e),[i[1],i[2],i[3]],[o[1],o[2],o[3]],s)]}case\"MaxPool3D\":{let o=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),i=I(\"kernelSize\",r,t,e);return[n.maxPool3d(I(\"x\",r,t,e),[i[1],i[2],i[3]],[o[1],o[2],o[3]],s)]}case\"Dilation2D\":{let o=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),i=I(\"dilations\",r,t,e),a=o[1],u=o[2],l=i[1],c=i[2];return[n.dilation2d(I(\"x\",r,t,e),I(\"filter\",r,t,e),[a,u],s,[l,c],\"NHWC\")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var YD=(r,t,e,n=se)=>{switch(r.op){case\"Fill\":{let o=I(\"shape\",r,t,e),s=I(\"dtype\",r,t,e),i=I(\"value\",r,t,e);return[n.fill(o,i,s)]}case\"LinSpace\":{let o=I(\"start\",r,t,e),s=I(\"stop\",r,t,e),i=I(\"num\",r,t,e);return[n.linspace(o,s,i)]}case\"Multinomial\":{let o=I(\"logits\",r,t,e),s=I(\"numSamples\",r,t,e),i=I(\"seed\",r,t,e);return[n.multinomial(o,s,i)]}case\"OneHot\":{let o=I(\"indices\",r,t,e),s=I(\"depth\",r,t,e),i=I(\"onValue\",r,t,e),a=I(\"offValue\",r,t,e),u=I(\"dtype\",r,t,e);return[n.oneHot(o,s,i,a,u)]}case\"Ones\":return[n.ones(I(\"shape\",r,t,e),I(\"dtype\",r,t,e))];case\"OnesLike\":return[n.onesLike(I(\"x\",r,t,e))];case\"RandomStandardNormal\":return[n.randomStandardNormal(I(\"shape\",r,t,e),I(\"dtype\",r,t,e),I(\"seed\",r,t,e))];case\"RandomUniform\":return[n.randomUniform(I(\"shape\",r,t,e),I(\"minval\",r,t,e),I(\"maxval\",r,t,e),I(\"dtype\",r,t,e))];case\"Range\":{let o=I(\"start\",r,t,e),s=I(\"stop\",r,t,e),i=I(\"step\",r,t,e);return[n.range(o,s,i,I(\"dtype\",r,t,e))]}case\"TruncatedNormal\":{let o=I(\"shape\",r,t,e),s=I(\"mean\",r,t,e),i=I(\"stdDev\",r,t,e),a=I(\"seed\",r,t,e);return[n.truncatedNormal(o,s,i,I(\"dtype\",r,t,e),a)]}case\"Zeros\":return[n.zeros(I(\"shape\",r,t,e),I(\"dtype\",r,t,e))];case\"ZerosLike\":return[n.zerosLike(I(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function hN(r,t,e){let n=I(\"boxes\",r,t,e),o=I(\"scores\",r,t,e),s=I(\"maxOutputSize\",r,t,e),i=I(\"iouThreshold\",r,t,e),a=I(\"scoreThreshold\",r,t,e),u=I(\"softNmsSigma\",r,t,e);return{boxes:n,scores:o,maxOutputSize:s,iouThreshold:i,scoreThreshold:a,softNmsSigma:u}}var ZD=async(r,t,e,n,o=se)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l,softNmsSigma:c}=hN(r,t,e),p=await 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o=I(\"x\",r,t,e),s=I(\"k\",r,t,e),i=I(\"sorted\",r,t,e),a=n.topk(o,s,i);return[a.values,a.indices]}case\"UpperBound\":{let o=I(\"sortedSequence\",r,t,e),s=I(\"values\",r,t,e);return[n.upperBound(o,s)]}case\"Unique\":{let o=I(\"x\",r,t,e),s=n.unique(o);return[s.values,s.indices]}case\"UniqueV2\":{let o=I(\"x\",r,t,e),s=I(\"axis\",r,t,e),i=n.unique(o,s);return[i.values,i.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var QD=(r,t,e,n=se)=>{switch(r.op){case\"Const\":return t[r.name];case\"PlaceholderWithDefault\":let o=I(\"default\",r,t,e);return[Cr(r.name,t,e)||o];case\"Placeholder\":return[Cr(r.name,t,e)];case\"Identity\":case\"StopGradient\":case\"FakeQuantWithMinMaxVars\":{let c=I(\"x\",r,t,e);return[ti(c)]}case\"IdentityN\":return I(\"x\",r,t,e).map(c=>ti(c));case\"Snapshot\":let s=I(\"x\",r,t,e);return[ti(s)];case\"Shape\":return[n.tensor1d(I(\"x\",r,t,e).shape,\"int32\")];case\"ShapeN\":return I(\"x\",r,t,e).map(c=>n.tensor1d(c.shape));case\"Size\":return[n.scalar(I(\"x\",r,t,e).size,\"int32\")];case\"Rank\":return[n.scalar(I(\"x\",r,t,e).rank,\"int32\")];case\"NoOp\":return[n.scalar(1)];case\"Print\":let i=I(\"x\",r,t,e),a=I(\"data\",r,t,e),u=I(\"message\",r,t,e),l=I(\"summarize\",r,t,e);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(u);for(let c=0;ct.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return mt(this.size(),\"int32\")}async import(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),B(()=>{let o=dr(e),s=n.length,i=o.length;y.assert(s===i,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${i} elements.`);for(let a=0;a{let o=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let o=n.getHashTableHandleByName(r.name);if(o!=null)return[o];{let s=I(\"keyDType\",r,t,e),i=I(\"valueDType\",r,t,e),a=new Vb(s,i);return n.addHashTable(r.name,a),[a.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let o=I(\"tableHandle\",r,t,e,n),s=I(\"keys\",r,t,e),i=I(\"values\",r,t,e);return[await n.getHashTableById(o.id).import(s,i)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let o=I(\"tableHandle\",r,t,e,n),s=I(\"keys\",r,t,e),i=I(\"defaultValue\",r,t,e);return[await n.getHashTableById(o.id).find(s,i)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let o=I(\"tableHandle\",r,t,e,n);return[n.getHashTableById(o.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var eR=(r,t,e,n=se)=>{switch(r.op){case\"ResizeBilinear\":{let o=I(\"images\",r,t,e),s=I(\"size\",r,t,e),i=I(\"alignCorners\",r,t,e),a=I(\"halfPixelCenters\",r,t,e);return[n.image.resizeBilinear(o,[s[0],s[1]],i,a)]}case\"ResizeNearestNeighbor\":{let o=I(\"images\",r,t,e),s=I(\"size\",r,t,e),i=I(\"alignCorners\",r,t,e),a=I(\"halfPixelCenters\",r,t,e);return[n.image.resizeNearestNeighbor(o,[s[0],s[1]],i,a)]}case\"CropAndResize\":{let o=I(\"image\",r,t,e),s=I(\"boxes\",r,t,e),i=I(\"boxInd\",r,t,e),a=I(\"cropSize\",r,t,e),u=I(\"method\",r,t,e),l=I(\"extrapolationValue\",r,t,e);return[n.image.cropAndResize(o,s,i,a,u,l)]}case\"ImageProjectiveTransformV3\":{let o=I(\"images\",r,t,e),s=I(\"transforms\",r,t,e),i=I(\"outputShape\",r,t,e),a=I(\"fillValue\",r,t,e),u=I(\"interpolation\",r,t,e),l=I(\"fillMode\",r,t,e);return[n.image.transform(o,s,u.toLowerCase(),l.toLowerCase(),a,i)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var rR=(r,t,e,n=se)=>{switch(r.op){case\"Equal\":return[n.equal(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"NotEqual\":return[n.notEqual(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"Greater\":return[n.greater(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"GreaterEqual\":return[n.greaterEqual(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"Less\":return[n.less(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"LessEqual\":return[n.lessEqual(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"LogicalAnd\":return[n.logicalAnd(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"LogicalNot\":return[n.logicalNot(I(\"a\",r,t,e))];case\"LogicalOr\":return[n.logicalOr(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"Select\":case\"SelectV2\":return[n.where(I(\"condition\",r,t,e),I(\"a\",r,t,e),I(\"b\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var nR=(r,t,e,n=se)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[n.matMul(I(\"a\",r,t,e),I(\"b\",r,t,e),I(\"transposeA\",r,t,e),I(\"transposeB\",r,t,e))];case\"Einsum\":return[n.einsum(I(\"equation\",r,t,e),...I(\"tensors\",r,t,e))];case\"Transpose\":return[n.transpose(I(\"x\",r,t,e),I(\"perm\",r,t,e))];case\"_FusedMatMul\":let[o,s]=I(\"fusedOps\",r,t,e),i=o===\"biasadd\",a=s===\"prelu\",u=I(\"numArgs\",r,t,e),l=I(\"leakyreluAlpha\",r,t,e);if(i){if(a&&u!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!a&&u!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,p]=I(\"args\",r,t,e);return[n.fused.matMul({a:I(\"a\",r,t,e),b:I(\"b\",r,t,e),transposeA:I(\"transposeA\",r,t,e),transposeB:I(\"transposeB\",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var oR=(r,t,e,n=se)=>{switch(r.op){case\"EuclideanNorm\":return[n.euclideanNorm(I(\"x\",r,t,e),I(\"axis\",r,t,e),I(\"keepDims\",r,t,e))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[n.batchNorm(I(\"x\",r,t,e),I(\"mean\",r,t,e),I(\"variance\",r,t,e),I(\"offset\",r,t,e),I(\"scale\",r,t,e),I(\"epsilon\",r,t,e))];case\"FusedBatchNormV3\":return[n.batchNorm(I(\"x\",r,t,e),I(\"mean\",r,t,e),I(\"variance\",r,t,e),I(\"offset\",r,t,e),I(\"scale\",r,t,e),I(\"epsilon\",r,t,e))];case\"LRN\":return[n.localResponseNormalization(I(\"x\",r,t,e),I(\"radius\",r,t,e),I(\"bias\",r,t,e),I(\"alpha\",r,t,e),I(\"beta\",r,t,e))];case\"Softmax\":return[n.softmax(I(\"x\",r,t,e))];case\"LogSoftmax\":return[n.logSoftmax(I(\"x\",r,t,e))];case\"SparseToDense\":return[n.sparseToDense(I(\"sparseIndices\",r,t,e),I(\"outputShape\",r,t,e),I(\"sparseValues\",r,t,e),I(\"defaultValue\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var sR=(r,t,e,n=se)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(I(\"paramsNestedSplits\",r,t,e),I(\"paramsDenseValues\",r,t,e),I(\"indices\",r,t,e),I(\"outputRaggedRank\",r,t,e));return o.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(I(\"starts\",r,t,e),I(\"limits\",r,t,e),I(\"splits\",r,t,e));return[o,s]}case\"RaggedTensorToTensor\":return[n.raggedTensorToTensor(I(\"shape\",r,t,e),I(\"values\",r,t,e),I(\"defaultValue\",r,t,e),I(\"rowPartitionTensors\",r,t,e),I(\"rowPartitionTypes\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var iR=(r,t,e,n=se)=>{switch(r.op){case\"Max\":{let a=I(\"axis\",r,t,e),u=I(\"keepDims\",r,t,e);return[n.max(I(\"x\",r,t,e),a,u)]}case\"Mean\":{let a=I(\"axis\",r,t,e),u=I(\"keepDims\",r,t,e);return[n.mean(I(\"x\",r,t,e),a,u)]}case\"Min\":{let a=I(\"axis\",r,t,e),u=I(\"keepDims\",r,t,e);return[n.min(I(\"x\",r,t,e),a,u)]}case\"Sum\":{let a=I(\"axis\",r,t,e),u=I(\"keepDims\",r,t,e);return[n.sum(I(\"x\",r,t,e),a,u)]}case\"All\":{let a=I(\"axis\",r,t,e),u=I(\"keepDims\",r,t,e);return[n.all(I(\"x\",r,t,e),a,u)]}case\"Any\":{let a=I(\"axis\",r,t,e),u=I(\"keepDims\",r,t,e);return[n.any(I(\"x\",r,t,e),a,u)]}case\"ArgMax\":{let a=I(\"axis\",r,t,e);return[n.argMax(I(\"x\",r,t,e),a)]}case\"ArgMin\":{let a=I(\"axis\",r,t,e);return[n.argMin(I(\"x\",r,t,e),a)]}case\"Prod\":{let a=I(\"axis\",r,t,e),u=I(\"keepDims\",r,t,e);return[n.prod(I(\"x\",r,t,e),a,u)]}case\"Cumprod\":{let a=I(\"axis\",r,t,e),u=I(\"exclusive\",r,t,e),l=I(\"reverse\",r,t,e);return[n.cumprod(I(\"x\",r,t,e),a,u,l)]}case\"Cumsum\":{let a=I(\"axis\",r,t,e),u=I(\"exclusive\",r,t,e),l=I(\"reverse\",r,t,e);return[n.cumsum(I(\"x\",r,t,e),a,u,l)]}case\"Bincount\":let o=I(\"x\",r,t,e),s=I(\"weights\",r,t,e),i=I(\"size\",r,t,e);return[n.bincount(o,s,i)];case\"DenseBincount\":{let a=I(\"x\",r,t,e),u=I(\"weights\",r,t,e),l=I(\"size\",r,t,e),c=I(\"binaryOutput\",r,t,e);return[n.denseBincount(a,u,l,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var aR=(r,t,e,n=se)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let o=I(\"n\",r,t,e),s=I(\"axis\",r,t,e),i=I(\"tensors\",r,t,e);return i=i.slice(0,o),[n.concat(i,s)]}case\"Gather\":{let o=I(\"x\",r,t,e),s=I(\"indices\",r,t,e);return[n.gather(o,n.cast(s,\"int32\"),0)]}case\"GatherV2\":{let o=I(\"axis\",r,t,e),s=I(\"batchDims\",r,t,e),i=I(\"x\",r,t,e),a=I(\"indices\",r,t,e);return[n.gather(i,n.cast(a,\"int32\"),o,s)]}case\"Reverse\":{let o=I(\"dims\",r,t,e),s=[];for(let a=0;a{let o=I(\"axis\",r,t,e),s=I(\"tensors\",r,t,e),i=s[0].shape,a=n.squeeze(s[0]).shape,u=s.map(l=>{let c=y.arraysEqual(l.shape,i);if(!c&&!y.arraysEqual(n.squeeze(l).shape,a))throw new Error(\"the input tensors shape does not match\");return c?l:n.reshape(l,i)});return[n.stack(u,o)]});case\"Unpack\":{let o=I(\"axis\",r,t,e),s=I(\"tensor\",r,t,e);return n.unstack(s,o)}case\"Tile\":{let o=I(\"reps\",r,t,e);return[n.tile(I(\"x\",r,t,e),o)]}case\"Split\":case\"SplitV\":{let o=I(\"axis\",r,t,e),s=I(\"numOrSizeSplits\",r,t,e),i=I(\"x\",r,t,e);return n.split(i,s,o)}case\"ScatterNd\":{let o=I(\"indices\",r,t,e),s=I(\"values\",r,t,e),i=I(\"shape\",r,t,e);return[n.scatterND(o,s,i)]}case\"GatherNd\":{let o=I(\"x\",r,t,e),s=I(\"indices\",r,t,e);return[n.gatherND(o,s)]}case\"SparseToDense\":{let o=I(\"sparseIndices\",r,t,e),s=I(\"outputShape\",r,t,e),i=I(\"sparseValues\",r,t,e),a=I(\"defaultValue\",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var lR=(r,t,e,n=se)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(I(\"indices\",r,t,e),I(\"values\",r,t,e),I(\"denseShape\",r,t,e),I(\"defaultValue\",r,t,e));return[o,s,i,a]}case\"SparseReshape\":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(I(\"inputIndices\",r,t,e),I(\"inputShape\",r,t,e),I(\"newShape\",r,t,e));return[o,s]}case\"SparseSegmentMean\":return[n.sparse.sparseSegmentMean(I(\"data\",r,t,e),I(\"indices\",r,t,e),I(\"segmentIds\",r,t,e))];case\"SparseSegmentSum\":return[n.sparse.sparseSegmentSum(I(\"data\",r,t,e),I(\"indices\",r,t,e),I(\"segmentIds\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var uR=(r,t,e,n=se)=>{switch(r.op){case\"FFT\":return[n.fft(I(\"x\",r,t,e))];case\"IFFT\":return[n.ifft(I(\"x\",r,t,e))];case\"RFFT\":return[n.rfft(I(\"x\",r,t,e))];case\"IRFFT\":return[n.irfft(I(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var cR=(r,t,e,n=se)=>{switch(r.op){case\"StringNGrams\":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(I(\"data\",r,t,e),I(\"dataSplits\",r,t,e),I(\"separator\",r,t,e),I(\"nGramWidths\",r,t,e),I(\"leftPad\",r,t,e),I(\"rightPad\",r,t,e),I(\"padWidth\",r,t,e),I(\"preserveShortSequences\",r,t,e));return[o,s]}case\"StringSplit\":{let{indices:o,values:s,shape:i}=n.string.stringSplit(I(\"input\",r,t,e),I(\"delimiter\",r,t,e),I(\"skipEmpty\",r,t,e));return[o,s,i]}case\"StringToHashBucketFast\":return[n.string.stringToHashBucketFast(I(\"input\",r,t,e),I(\"numBuckets\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var pR=(r,t,e,n=se)=>{switch(r.op){case\"Cast\":return[n.cast(I(\"x\",r,t,e),I(\"dtype\",r,t,e))];case\"ExpandDims\":{let o=I(\"axis\",r,t,e);return[n.expandDims(I(\"x\",r,t,e),o)]}case\"Squeeze\":{let o=I(\"axis\",r,t,e);return[n.squeeze(I(\"x\",r,t,e),o)]}case\"Reshape\":return[n.reshape(I(\"x\",r,t,e),I(\"shape\",r,t,e))];case\"MirrorPad\":return[n.mirrorPad(I(\"x\",r,t,e),I(\"padding\",r,t,e),I(\"mode\",r,t,e))];case\"PadV2\":case\"Pad\":return[n.pad(I(\"x\",r,t,e),I(\"padding\",r,t,e),I(\"constantValue\",r,t,e))];case\"SpaceToBatchND\":{let o=I(\"blockShape\",r,t,e),s=I(\"paddings\",r,t,e);return[n.spaceToBatchND(I(\"x\",r,t,e),o,s)]}case\"BatchToSpaceND\":{let o=I(\"blockShape\",r,t,e),s=I(\"crops\",r,t,e);return[n.batchToSpaceND(I(\"x\",r,t,e),o,s)]}case\"DepthToSpace\":{let o=I(\"blockSize\",r,t,e),s=I(\"dataFormat\",r,t,e).toUpperCase();return[n.depthToSpace(I(\"x\",r,t,e),o,s)]}case\"BroadcastTo\":return[n.broadcastTo(I(\"x\",r,t,e),I(\"shape\",r,t,e))];case\"BroadcastArgs\":return[n.broadcastArgs(I(\"s0\",r,t,e),I(\"s1\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function gN(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case\"arithmetic\":return o(()=>BD(i,a,u));case\"basic_math\":return o(()=>VD(i,a,u));case\"control\":return KD(i,a,u);case\"convolution\":return o(()=>XD(i,a,u));case\"creation\":return o(()=>YD(i,a,u));case\"dynamic\":return ZD(i,a,u);case\"evaluation\":return o(()=>JD(i,a,u));case\"image\":return o(()=>eR(i,a,u));case\"graph\":return o(()=>QD(i,a,u));case\"logical\":return o(()=>rR(i,a,u));case\"matrices\":return o(()=>nR(i,a,u));case\"normalization\":return o(()=>oR(i,a,u));case\"ragged\":return o(()=>sR(i,a,u));case\"reduction\":return o(()=>iR(i,a,u));case\"slice_join\":return o(()=>aR(i,a,u));case\"sparse\":return o(()=>lR(i,a,u));case\"spectral\":return o(()=>uR(i,a,u));case\"string\":return o(()=>cR(i,a,u));case\"transformation\":return o(()=>pR(i,a,u));case\"hash_table\":return tR(i,a,u,n);case\"custom\":let l=Tb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Mb(i,a,u));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Ph=class{constructor(t={},e={},n={},o={}){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=n,this.functionMap=o,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;ee.id===0&&e.iterationId===0?\"\":`${e.frameName}-${e.iterationId}`).join(\"/\"):\"\"}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function xN(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=Object.keys(r).map(m=>bn(m)[0]),c=[];n!=null&&(c=n.map(m=>bn(m.name)[0]));let p=[...t];for(;p.length>0;){let m=p.pop();if((yN(m)||G7(m)||W7(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&l.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function mR(r,t,e){let{usedNodes:n,inputs:o}=e,s=[],i=Object.keys(o).map(c=>bn(c)[0]).map(c=>r.nodes[c]),a=r.initNodes;i.forEach(c=>{n.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{n.has(c.name)&&s.push(c)}),a!=null&&a.forEach(c=>{n.has(c.name)&&s.push(c)});let u=new Set,l=[];for(;s.length>0;){let c=s.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&n.has(p.name)&&p.inputs.every(m=>u.has(m.name))&&s.push(p)})}return l}var z7=[\"Switch\",\"Merge\",\"Enter\",\"Exit\",\"NextIteration\",\"StatelessIf\",\"StatelessWhile\",\"if\",\"While\"],B7=[\"NonMaxSuppressionV2\",\"NonMaxSuppressionV3\",\"NonMaxSuppressionV5\",\"Where\"],V7=[\"HashTable\",\"HashTableV2\",\"LookupTableImport\",\"LookupTableImportV2\",\"LookupTableFind\",\"LookupTableFindV2\",\"LookupTableSize\",\"LookupTableSizeV2\"];function yN(r){return z7.indexOf(r.op)>=0}function G7(r){return B7.indexOf(r.op)>=0}function W7(r){return V7.indexOf(r.op)>=0}var kc=class{constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new kc(t.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(t){let e=Object.keys(t).map(n=>t[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPERATOR)+\"--\"+o.join(this.SEPERATOR)}compile(t,e){let n=xN(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let a=e.map(l=>l.name),u=Object.keys(t);throw new Error(`Cannot compute the outputs [${a}] from the provided inputs [${u}]. Missing the following inputs: [${o}]`)}return mR(this.graph,this.weightMap,n)}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return Ae(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(p=>this.graph.nodes[bn(p)[0]]),s=e.map(p=>bn(p)[0]),i=s.map(p=>this.graph.nodes[p]);i.length===0&&(i=this._outputs);let a=this.getCompilationKey(o,i),u=this.compiledMap.get(a);u==null&&(u=this.compile(t,i),this.compiledMap.set(a,u));try{this.keepIntermediateTensors=M().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(p){this.keepIntermediateTensors=!1,console.warn(p.message)}let l={},c={};return B(()=>{let p=new Ph(this.weightMap,l,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(h=>{let[g,x]=bn(h),b=[];b[x]=t[h],m[g]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[g]=this.cloneTensorList(b))});let f=this.getFrozenTensorIds(m),d={};for(let h=0;hCr(h,m,p))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,n,o,s,i,a){e.category===\"control\"||i.indexOf(t)!==-1||(n[t].forEach(u=>{u!=null&&(a[u.id]=(a[u.id]||0)+e.children.length)}),e.inputs.forEach(u=>{if(u.category!==\"control\"){let l=PD(u.name,n,o);l!=null&&l.forEach(c=>{if(c&&!c.kept&&!s.has(c.id)){let p=a[c.id];p===1?(c.dispose(),delete a[c.id]):p!=null&&a[c.id]--}})}}))}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){!this.clonedTensorsMap||(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,n=!1,o={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=M().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let i=new Ph(this.weightMap,o,s,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let a=await this.executeWithControlFlow(t,i,e,n),u=e.map(m=>Cr(m,a,i)),l=u.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),p=new Set([...l,...c,...this.weightIds]);return Object.values(a).forEach(m=>{m.forEach(f=>{f&&!f.isDisposed&&!p.has(f.id)&&f.dispose()})}),this.parent==null&&i.dispose(p),u}async executeFunctionAsync(t,e,n){let o=t.reduce((s,i,a)=>(s[this.inputs[a].name]=i,s),{});return this._executeAsync(o,this.outputNodes,!0,e,n)}async executeWithControlFlow(t,e,n,o){let s=Object.keys(t),i=s.map(w=>this.graph.nodes[bn(w)[0]]),a=n.map(w=>bn(w)[0]),u=a.map(w=>this.graph.nodes[w]);u.length===0&&(u=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:p,syncInputs:m}=xN(t,u,this.weightMap,this._initNodes),f=[...i,...this.graph.weights,...this._initNodes||[]].map(w=>({node:w,contexts:e.currentContext})),d=Object.assign({},this.weightMap);Object.keys(t).forEach(w=>{let[C,N]=bn(w),E=[];E[N]=t[w],d[C]=E});let h={},g=this.getFrozenTensorIds(d),x={};for(;f.length>0;){let w=this.processStack(i,f,e,d,x,g,a,h,l);await Promise.all(w)}p==null&&!o&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let b=u.filter(w=>!yN(w)&&!Cr(w.name,d,e)).map(w=>w.name);if(b.length>0){let w=\"\";throw p!=null&&(w=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${w}`)}return d}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m=\"\";if(p.node.op===\"Enter\"&&I(\"isConstant\",p.node,o,n)&&([m]=Qs(p.node.name,n)),o[p.node.name]==null){let f=gN(p.node,o,n,this._resourceManager);m||([m]=Qs(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(f)),this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=Qs(a.name,n);s[u]||!i.has(a.name)||(a.op===\"Merge\"?a.inputNames.some(l=>!!Cr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!Cr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=bn(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=bn(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var n,o;let s=(o=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||o===void 0?void 0:o[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[n]=bn(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var Gb=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var U7=\"?tfjs-format=file\",H7=\"model.json\",Lh=class{constructor(t,e={},n=$r){this.modelUrl=t,this.loadOptions=e,this.version=\"n/a\",this.io=n,e==null&&(this.loadOptions={}),this.resourceManager=new Gb}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else 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RR={};Wt(RR,{CSVDataset:()=>Jf,Dataset:()=>ei,FileDataSource:()=>nd,TextLineDataset:()=>Zf,URLDataSource:()=>od,array:()=>vR,csv:()=>ER,func:()=>_R,generator:()=>AR,microphone:()=>DR,version_data:()=>zN,webcam:()=>$R,zip:()=>SR});var IR=_l(dh());var bR=_l(dh());function dR(r,t){return Wb(r,t)}function Wb(r,t,e=new Map,n=new Set){if(r==null)return null;if(typeof Blob==\"function\"&&r instanceof Blob)return r.slice();if(n.has(r))throw new Error(\"Circular references are not supported.\");if(e.has(r))return e.get(r);let o=t(r);if(o.recurse&&o.value!==null)throw new Error(\"A deep map function may not return both a value and recurse=true.\");if(o.recurse)if(Tu(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let a=r[i],u=Wb(a,t,e,n);s[i]=u}return n.delete(r),r.__proto__&&(s.__proto__=r.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return e.set(r,o.value),o.value}function hR(r,t=wN){return gR(r,t)}function gR(r,t,e=new Set){let 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Yf{constructor(){super(Ec.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,e=new Array(t),n=this.length();for(let o=0;oe===!0)}rowMajorBatch(t,e=!0){return new TN(this,t,e)}columnMajorBatch(t,e=!0,n=wN){return this.rowMajorBatch(t,e).map(s=>hR(s,n))}concatenate(t,e){return new qb(RN([this,t]),e)}take(t){return t<0||t==null?this:new NN(this,t)}skip(t){return t<0||t==null?this:new SN(this,t)}prefetch(t){return new Kb(this,t)}shuffle(t,e){return new DN(this,t,e)}serial(){return new vN(this)}},CN=class extends tr{constructor(t){super(),this.items=t,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let t=this.items[this.trav];return this.trav++,{value:yR(t),done:!1}}},IN=class extends tr{constructor(t){super(),this.nextFn=t}summary(){return\"Function call\"}async 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tr{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},kN=class extends tr{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Nt(t.value)}}},EN=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=yo.getTensorsInContainer(t.value),n=this.transform(t.value),o=yo.getTensorsInContainer(n);for(let s of e)yo.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},_N=class extends tr{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},Hb=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=yo.getTensorsInContainer(t.value),n=await this.transform(t.value),o=yo.getTensorsInContainer(n);for(let s of e)yo.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},_c=class extends tr{constructor(){super(),this.outputQueue=new Ec,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},AN=class extends _c{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=yo.getTensorsInContainer(t.value),n=this.transform(t.value),o=yo.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)yo.isTensorInList(s,o)||s.dispose();return!0}},qb=class extends tr{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return\"TODO: fill in upstream of chained summaries 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At least one type of data should be returned.\")}summary(){return\"microphone\"}static async create(t={}){if(!M().get(\"IS_BROWSER\"))throw new Error(\"microphone API is only supported in browser environment.\");let e=new Qf(t);return await e.start(),e}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error(\"Could not obtain audio from microphone.\");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error(\"Can not convert infinite audio stream to array.\")}getSampleRate(){return this.sampleRateHz}flattenQueue(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),nr(n,e)}};var td=class extends tr{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ke([0],\"int32\"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=Us([i,s,u,a],[1,4])}else this.cropBox=Us([0,0,1,1],[1,4])}summary(){return\"webcam\"}static async create(t,e={}){if(!M().get(\"IS_BROWSER\"))throw new Error(\"tf.data.webcam is only supported in browser environment.\");if(!t){if(t=document.createElement(\"video\"),!e.resizeWidth||!e.resizeHeight)throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new td(t,e);return await n.start(),n}async start(){this.webcamConfig.facingMode&&y.assert(this.webcamConfig.facingMode===\"user\"||this.webcamConfig.facingMode===\"environment\",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:\"user\",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error(\"Could not obtain video from webcam.\");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=ox.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=sr(J(t,\"float32\"),0),n;n=so.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,\"bilinear\");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error(\"Can not convert infinite video stream to array.\")}};var ed=class{};var Bh=class extends tr{split(t){return new ON(this,t)}},ON=class extends Bh{constructor(t,e){super(),this.upstream=t,this.impl=new PN(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},PN=class extends _c{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=\"\"}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===\"\"?!1:(this.outputQueue.push(this.carryover),this.carryover=\"\",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var Yb=class extends tr{decodeUTF8(){return new LN(this)}},LN=class extends Bh{constructor(t){super(),this.upstream=t,this.impl=new MN(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},MN=class extends _c{constructor(t){if(super(),this.upstream=t,M().get(\"IS_BROWSER\"))this.decoder=new TextDecoder(\"utf-8\");else{let{StringDecoder:e}=bN();this.decoder=new e(\"utf8\")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let n;return M().get(\"IS_BROWSER\")?n=this.decoder.decode(e,{stream:!0}):n=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(n),!0}};var rd=class extends Yb{constructor(t,e={}){super(),this.file=t,this.options=e,y.assert(t instanceof Uint8Array||(M().get(\"IS_BROWSER\")?t instanceof File||t instanceof Blob:!1),()=>\"FileChunkIterator only supports File, Blob and Uint8Array right now.\"),this.offset=e.offset||0,this.chunkSize=e.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new 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e.makeTensorInfo([a.length],\"int32\",Int32Array.from(a))}var UF={kernelName:pp,backendName:\"cpu\",kernelFunc:ZJ};var JJ=_t(po,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;lh.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Xr({inputs:{x:u[0]},backend:e});if(u[0].dtype===\"complex64\"){let h=u.map(C=>$o({inputs:{input:C},backend:e})),g=u.map(C=>Ji({inputs:{input:C},backend:e})),x=Eu({inputs:h,backend:e,attrs:{axis:s}}),b=Eu({inputs:g,backend:e,attrs:{axis:s}}),w=Ir({inputs:{real:x,imag:b},backend:e});return 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p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat===\"channelsLast\",C=new pe(m.outShape,o.dtype),N=y.computeStrides(o.shape),E=y.computeStrides(s.shape),A=N[0],$=w?N[1]:N[2],F=w?N[2]:1,P=w?1:N[1],V=C.strides[0],G=w?C.strides[1]:C.strides[2],W=w?C.strides[2]:1,q=w?1:C.strides[1],H=e.data.get(o.dataId).values,j=e.data.get(s.dataId).values,Y=C.values;for(let Z=0;Z=m.inHeight)continue;let gt=st*E[0],bt=et+dt*$;for(let kt=0;kt=m.inWidth)continue;let he=gt+Zt*E[1],jt=bt+ce*F,ke=he;for(let fe=0;fe=l.inDepth)continue;let Z=j*F[0],et=V+Y*$[1];for(let rt=0;rt=l.inHeight)continue;let dt=Z+nt*F[1],gt=et+st*$[2];for(let bt=0;bt=l.inWidth)continue;let ce=dt+Vt*F[2],he=gt+Zt*l.inChannels,jt=ce;for(let ke=0;keMath.cos(r)),eO={kernelName:Yo,backendName:\"cpu\",kernelFunc:sQ};var iQ=_t(Zo,r=>Math.cosh(r)),rO={kernelName:Zo,backendName:\"cpu\",kernelFunc:iQ};function aQ(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=wt([d,h,g,f],\"float32\"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,C=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),E=y.computeStrides(x.shape);for(let A=0;A=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-P)*(m-1)/(g-1):0;for(let j=0;j1?F*(p-1)+j*q:.5*(F+V)*(p-1);if(Y<0||Y>p-1){for(let Z=0;Z1?P*(m-1)+ot*H:.5*(P+G)*(m-1);if(at<0||at>m-1){for(let gt=0;gt1?P*(m-1)+Z*H:.5*(P+G)*(m-1);if(et<0||et>m-1){for(let at=0;atx+d-b-1:(x,b)=>x+b;for(let x=0;xx+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. 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gO={kernelName:wp,backendName:\"cpu\",kernelFunc:hQ};function gQ(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t;tt([n,o],\"eluGrad\");let s=new Float32Array(y.sizeFromShape(o.shape)),i=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values;for(let u=0;u=1?s[u]=a[u]:s[u]=a[u]*(l+1)}return e.makeTensorInfo(o.shape,\"float32\",s)}var xO={kernelName:Cp,backendName:\"cpu\",kernelFunc:gQ};var xQ=S.ERF_P,yQ=S.ERF_A1,bQ=S.ERF_A2,wQ=S.ERF_A3,CQ=S.ERF_A4,IQ=S.ERF_A5,vQ=_t(Ca,r=>{let t=Math.sign(r),e=Math.abs(r),n=1/(1+xQ*e);return t*(1-((((IQ*n+CQ)*n+wQ)*n+bQ)*n+yQ)*n*Math.exp(-e*e))}),yO={kernelName:Ca,backendName:\"cpu\",kernelFunc:vQ};function md(r){let{inputs:t,backend:e,attrs:n}=r,{input:o}=t,{dim:s}=n,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),Yt({inputs:{x:o},backend:e,attrs:{shape:a}})}var bO={kernelName:mi,backendName:\"cpu\",kernelFunc:md};var SQ=Qt((r,t)=>r/t),Uh=ie(ts,SQ),Hh={kernelName:ts,backendName:\"cpu\",kernelFunc:Uh};function gw(r,t,e){let n=r.shape,o=n[0],s=n[1],i=e.data.get(r.dataId),a=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[o,s],c=y.sizeFromShape(l),p=y.getTypedArrayFromDType(\"float32\",c),m=y.getTypedArrayFromDType(\"float32\",c);for(let g=0;g{let{image:n}=r,o=e,s=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(n.shape)),[i,a,u,l]=n.shape,c=o.data.get(n.dataId).values;for(let m=0;m=0&&wMath.floor(r/t)),$Q=ie(os,AQ,null,\"int32\"),vO={kernelName:os,backendName:\"cpu\",kernelFunc:$Q};function DQ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=xT({inputs:{x:o,filter:s},backend:e,attrs:{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m}});if(i){let g=h;if(c===\"NCHW\"&&i.shape.length===1&&i.shape[0]!==1){let x=Yt({inputs:{x:i},backend:e,attrs:{shape:[i.shape[0],1,1]}});h=Zi({inputs:{a:h,b:x},backend:e}),e.disposeIntermediateTensorInfo(x)}else h=Zi({inputs:{a:h,b:i},backend:e});e.disposeIntermediateTensorInfo(g)}if(f){let g=h;if(c===\"NCHW\"&&f===\"prelu\"&&a.shape.length===1&&a.shape[0]!==1){let x=Yt({inputs:{x:a},backend:e,attrs:{shape:[a.shape[0],1,1]}});h=Lc(e,h,f,x,d),e.disposeIntermediateTensorInfo(x)}else h=Lc(e,h,f,a,d);e.disposeIntermediateTensorInfo(g)}return h}var SO={kernelName:Ni,backendName:\"cpu\",kernelFunc:DQ};function RQ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=yT({inputs:{x:o,filter:s},backend:e,attrs:{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m}});if(i){let g=h;h=Zi({inputs:{a:h,b:i},backend:e}),e.disposeIntermediateTensorInfo(g)}if(f){let g=h;h=Lc(e,h,f,a,d),e.disposeIntermediateTensorInfo(g)}return h}var NO={kernelName:Ti,backendName:\"cpu\",kernelFunc:RQ};function FQ(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=y.sizeFromShape(n.shape),i=o.shape,a=i[i.length-1],[u,l,c,p]=S.prepareAndValidate(n,o);if(l===0)return e.makeTensorInfo(u,n.dtype,[]);let m=e.data.get(o.dataId).values,f=e.bufferSync(n),d=tw(m,f,n.dtype,l,a,c,p,n.shape,s);return e.makeTensorInfo(u,n.dtype,d.values)}var TO={kernelName:Na,backendName:\"cpu\",kernelFunc:FQ};function OQ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,indices:s}=t,{axis:i,batchDims:a}=n;tt([o,s],\"gatherV2\");let u=y.parseAxisParam(i,o.shape)[0],l=e.data.get(s.dataId).values,c=o.shape[u];for(let C=0;C=0,()=>`GatherV2: the index value ${N} is not in [0, ${c-1}]`)}let p=a;a==null&&(p=0);let m=y.sizeFromShape(s.shape),f=S.segment_util.collectGatherOpShapeInfo(o,s,u,p),d=Yt({inputs:{x:o},backend:e,attrs:{shape:[f.batchSize,f.outerSize,f.dimSize,f.sliceSize]}}),h=Yt({inputs:{x:s},backend:e,attrs:{shape:[f.batchSize,m/f.batchSize]}}),g=[f.batchSize,f.outerSize,m/f.batchSize,f.sliceSize],x=e.bufferSync(h),b=e.bufferSync(d),w=ew(b,x,g);return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.makeTensorInfo(f.outputShape,w.dtype,w.values)}var kO={kernelName:fi,backendName:\"cpu\",kernelFunc:OQ};function PQ(r){let{inputs:t,backend:e}=r,{input:n}=t,o=y.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=o/s,a=Yt({inputs:{x:n},backend:e,attrs:{shape:[i,s]}}),u=gw(a,!0,e),l=Yt({inputs:{x:u},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(u),l}var EO={kernelName:vp,backendName:\"cpu\",kernelFunc:PQ};var LQ=_t(ka,r=>Number.isFinite(r)?1:0,\"bool\"),_O={kernelName:ka,backendName:\"cpu\",kernelFunc:LQ};var MQ=_t(Ea,r=>Math.abs(r)===1/0?1:0,\"bool\"),AO={kernelName:Ea,backendName:\"cpu\",kernelFunc:MQ};var zQ=_t(as,r=>Number.isNaN(r)?1:0,\"bool\"),$O={kernelName:as,backendName:\"cpu\",kernelFunc:zQ};function BQ(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=rw(n,o,s);return t.makeTensorInfo([i.length],\"float32\",i)}var DO={kernelName:Np,backendName:\"cpu\",kernelFunc:BQ};var VQ=_t($a,r=>Math.log1p(r)),RO={kernelName:$a,backendName:\"cpu\",kernelFunc:VQ};var GQ=Qt((r,t)=>r&&t),WQ=ie(Da,GQ,null,\"bool\"),FO={kernelName:Da,backendName:\"cpu\",kernelFunc:WQ};var UQ=_t(Ra,r=>r?0:1,\"bool\"),OO={kernelName:Ra,backendName:\"cpu\",kernelFunc:UQ};var HQ=Qt((r,t)=>r||t),qQ=ie(Fa,HQ,null,\"bool\"),PO={kernelName:Fa,backendName:\"cpu\",kernelFunc:qQ};function KQ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;tt(o,\"LRN\");let 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Tt=M();Tt.registerFlag(\"HAS_WEBGL\",()=>Tt.getNumber(\"WEBGL_VERSION\")>0);Tt.registerFlag(\"WEBGL_VERSION\",()=>Nw(2)?2:Nw(1)?1:0);Tt.registerFlag(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\",()=>!1);Tt.registerFlag(\"WEBGL_BUFFER_SUPPORTED\",()=>Tt.get(\"WEBGL_VERSION\")===2);Tt.registerFlag(\"WEBGL_CPU_FORWARD\",()=>!0);Tt.registerFlag(\"WEBGL_FORCE_F16_TEXTURES\",()=>!1);Tt.registerFlag(\"WEBGL_PACK\",()=>Tt.getBool(\"HAS_WEBGL\"));Tt.registerFlag(\"WEBGL_PACK_NORMALIZATION\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_PACK_CLIP\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_PACK_DEPTHWISECONV\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_PACK_BINARY_OPERATIONS\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_PACK_UNARY_OPERATIONS\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_PACK_ARRAY_OPERATIONS\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_PACK_IMAGE_OPERATIONS\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_PACK_REDUCE\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_LAZILY_UNPACK\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_CONV_IM2COL\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_MAX_TEXTURE_SIZE\",()=>BT(Tt.getNumber(\"WEBGL_VERSION\")));Tt.registerFlag(\"WEBGL_MAX_TEXTURES_IN_SHADER\",()=>VT(Tt.getNumber(\"WEBGL_VERSION\")));Tt.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\",()=>{let r=Tt.getNumber(\"WEBGL_VERSION\");return r===0?0:GT(r)});Tt.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\",()=>Tt.getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0&&!Yl.isMobile());Tt.registerFlag(\"WEBGL_RENDER_FLOAT32_CAPABLE\",()=>WT(Tt.getNumber(\"WEBGL_VERSION\")));Tt.registerFlag(\"WEBGL_RENDER_FLOAT32_ENABLED\",()=>Tt.getBool(\"WEBGL_FORCE_F16_TEXTURES\")?!1:Tt.getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\"));Tt.registerFlag(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\",()=>UT(Tt.getNumber(\"WEBGL_VERSION\")));Tt.registerFlag(\"WEBGL_FENCE_API_ENABLED\",()=>HT(Tt.getNumber(\"WEBGL_VERSION\")));Tt.registerFlag(\"WEBGL_SIZE_UPLOAD_UNIFORM\",()=>Tt.getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?4:0);Tt.registerFlag(\"WEBGL_DELETE_TEXTURE_THRESHOLD\",()=>-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});Tt.registerFlag(\"WEBGL_FLUSH_THRESHOLD\",()=>Yl.isMobile()?1:-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});Tt.registerFlag(\"CPU_HANDOFF_SIZE_THRESHOLD\",()=>128);Tt.registerFlag(\"WEBGL_USE_SHAPES_UNIFORMS\",()=>!1);Tt.registerFlag(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e5);Tt.registerFlag(\"TOPK_K_CPU_HANDOFF_THRESHOLD\",()=>128);Tt.registerFlag(\"WEBGL_EXP_CONV\",()=>!1);Tt.registerFlag(\"SOFTWARE_WEBGL_ENABLED\",()=>Tt.getBool(\"IS_TEST\"));Tt.registerFlag(\"WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE\",()=>1/0);Tt.registerFlag(\"WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE\",()=>!1);Tt.registerFlag(\"WEBGL2_ISNAN_CUSTOM\",()=>!1);Tt.registerFlag(\"ENGINE_COMPILE_ONLY\",()=>!1);function Ge(){let r,t,e,n,o,s,i,a,u,l;return M().getNumber(\"WEBGL_VERSION\")===2?(r=\"#version 300 es\",t=\"in\",e=\"out\",n=\"in\",o=\"texture\",s=\"outputColor\",i=\"out vec4 outputColor;\",a=M().getBool(\"WEBGL2_ISNAN_CUSTOM\")?`\n bool isnan_custom(float val) {\n uint floatToUint = floatBitsToUint(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan_custom(val.x),\n isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n }\n\n #define isnan(value) isnan_custom(value)\n `:\"\",u=\"\",l=`\n #define round(value) newRound(value)\n int newRound(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 newRound(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `):(r=\"\",t=\"attribute\",e=\"varying\",n=\"varying\",o=\"texture2D\",s=\"gl_FragColor\",i=\"\",a=`\n #define isnan(value) isnan_custom(value)\n bool isnan_custom(float val) {\n return (val > 0. || val < 1. || val == 0.) ? false : true;\n }\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n }\n `,u=`\n uniform float INFINITY;\n\n bool isinf(float val) {\n return abs(val) == INFINITY;\n }\n bvec4 isinf(vec4 val) {\n return equal(abs(val), vec4(INFINITY));\n }\n `,l=`\n int round(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 round(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function ni(r,t,e=\"index\"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join(\"\")}function Bc(r,t,e=\"index\"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join(\"\")}function Stt(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function oL(r,t,e=\"index\"){let n=r.map((s,i)=>i),o=Stt(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join(\"\")}function xd(r){let t=y.computeStrides(r).map(e=>e.toString());return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;\n }\n`}function yd(){return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n }\n`}var Tw=`\n const float FLOAT_MAX = 1.70141184e38;\n const float FLOAT_MIN = 1.17549435e-38;\n\n lowp vec4 encode_float(highp float v) {\n if (isnan(v)) {\n return vec4(255, 255, 255, 255);\n }\n\n highp float av = abs(v);\n\n if(av < FLOAT_MIN) {\n return vec4(0.0, 0.0, 0.0, 0.0);\n } else if(v > FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n } else if(v < -FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;\n }\n\n highp vec4 c = vec4(0,0,0,0);\n\n highp float e = floor(log2(av));\n highp float m = exp2(fract(log2(av))) - 1.0;\n\n c[2] = floor(128.0 * m);\n m -= c[2] / 128.0;\n c[1] = floor(32768.0 * m);\n m -= c[1] / 32768.0;\n c[0] = floor(8388608.0 * m);\n\n highp float ebias = e + 127.0;\n c[3] = floor(ebias / 2.0);\n ebias -= c[3] * 2.0;\n c[2] += floor(ebias) * 128.0;\n\n c[3] += 128.0 * step(0.0, -v);\n\n return c / 255.0;\n }\n`;var{getBroadcastDims:sL}=S;function iL(r,t,e){let n=[];if(r.forEach(f=>{let d=y.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:\"\"};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=kw(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push(\"uniform int outShape;\");break;case 2:n.push(\"uniform ivec2 outShape;\"),n.push(\"uniform int outShapeStrides;\");break;case 3:n.push(\"uniform ivec3 outShape;\"),n.push(\"uniform ivec2 outShapeStrides;\");break;case 4:n.push(\"uniform ivec4 outShape;\"),n.push(\"uniform ivec3 outShapeStrides;\");break;default:break}n.push(\"uniform ivec2 outTexShape;\")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:\"\"};`)});let o=n.join(`\n`),s=r.map(f=>Ntt(f,t,e.packedInputs,e.enableShapeUniforms)).join(`\n`),i=t.texShape,a=Ge(),u=Ett(a),l,c,p=$tt(a);return t.isPacked?(l=Ttt(t.logicalShape,i,e.enableShapeUniforms),c=Att(a)):(l=ktt(t.logicalShape,i,e.enableShapeUniforms),c=_tt(a)),e.packedInputs&&(p+=Ott),[p,u,c,o,l,s,e.userCode].join(`\n`)}function wd(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return Ktt(r,t);case 1:return Xtt(r,t);case 2:return Ztt(r,t);case 3:return Qtt(r,t);case 4:return eet(r,t);case 5:return ret(r);case 6:return net(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function aL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return qtt(r);case 1:return jtt(r,t);case 2:return Ytt(r,t);case 3:return Jtt(r,t);default:return tet(r,t)}}function Ntt(r,t,e=!1,n){let o=\"\";e?o+=aL(r,n):o+=wd(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=oet(r,t):o+=set(r,t)),o}function Ttt(r,t,e){switch(r.length){case 0:return lL();case 1:return Ptt(r,t,e);case 2:return Utt(r,t,e);case 3:return Mtt(r,t,e);default:return Btt(r,t,e)}}function ktt(r,t,e){switch(r.length){case 0:return lL();case 1:return Ltt(r,t,e);case 2:return Htt(r,t,e);case 3:return ztt(r,t,e);case 4:return Vtt(r,t,e);case 5:return Gtt(r,t);case 6:return Wtt(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function Ett(r){return`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${r.texture2D}(textureSampler, uv).r;\n }\n `}function _tt(r){return`\n void setOutput(float val) {\n ${r.output} = vec4(val, 0, 0, 0);\n }\n `}function Att(r){return`\n void setOutput(vec4 val) {\n ${r.output} = val;\n }\n `}function $tt(r){return`${r.version}\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n ${r.varyingFs} vec2 resultUV;\n ${r.defineOutput}\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n uniform float NAN;\n ${r.defineSpecialNaN}\n ${r.defineSpecialInf}\n ${r.defineRound}\n\n int imod(int x, int y) {\n return x - y * (x / y);\n }\n\n int idiv(int a, int b, float sign) {\n int res = a / b;\n int mod = imod(a, b);\n if (sign < 0. && mod != 0) {\n res -= 1;\n }\n return res;\n }\n\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n #define HASHSCALE1 443.8975\n float random(float seed){\n vec2 p = resultUV * seed;\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 += dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${Dtt}\n ${Rtt}\n ${Ftt}\n `}var Dtt=`\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n int texelIndex = index / 2;\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,Rtt=`\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n int texNumC, int row, int col) {\n int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,Ftt=`\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n int texelsInBatch, int texelsInLogicalRow, int b,\n int row, int col) {\n int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,Ott=`\n float getChannel(vec4 frag, vec2 innerDims) {\n vec2 modCoord = mod(innerDims, 2.);\n return modCoord.x == 0. ?\n (modCoord.y == 0. ? frag.r : frag.g) :\n (modCoord.y == 0. ? frag.b : frag.a);\n }\n float getChannel(vec4 frag, int dim) {\n float modCoord = mod(float(dim), 2.);\n return modCoord == 0. ? frag.r : frag.g;\n }\n`;function lL(){return`\n int getOutputCoords() {\n return 0;\n }\n `}function Ptt(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?e?`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ${n[1]}.0);\n }\n `:n[1]===1?e?`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ${n[0]}.0);\n }\n `:e?`\n int getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);\n }\n `}function Ltt(r,t,e){return t[0]===1?e?`\n int getOutputCoords() {\n return int(resultUV.x * float(outTexShape[1]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.x * ${t[1]}.0);\n }\n `:t[1]===1?e?`\n int getOutputCoords() {\n return int(resultUV.y * float(outTexShape[0]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.y * ${t[0]}.0);\n }\n `:e?`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n return resTexRC.x * outTexShape[1] + resTexRC.y;\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n return resTexRC.x * ${t[1]} + resTexRC.y;\n }\n `}function Mtt(r,t,e){if(e)return`\n ivec3 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec3(b, r, c);\n }\n `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[2]/2),s=o*Math.ceil(r[1]/2);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n int index = resTexRC.x * ${n[1]} + resTexRC.y;\n\n int b = index / ${s};\n index -= b * ${s};\n\n int r = 2 * (index / ${o});\n int c = imod(index, ${o}) * 2;\n\n return ivec3(b, r, c);\n }\n `}function ztt(r,t,e){if(e)return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n ${Bc([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n`;let n=ni([\"r\",\"c\",\"d\"],r);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n int index = resTexRC.x * ${t[1]} + resTexRC.y;\n ${n}\n return ivec3(r, c, d);\n }\n `}function Btt(r,t,e){if(e)return`\n ivec4 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatchN = texelsInBatch * outShape[1];\n\n int b2 = index / texelsInBatchN;\n index -= b2 * texelsInBatchN;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec4(b2, b, r, c);\n }\n `;let 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ht(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,a,r.STREAM_READ)),ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,0)),ht(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),o}function sk(r,t,e){let n=r,o=new Float32Array(e);return n.bindBuffer(n.PIXEL_PACK_BUFFER,t),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,o),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),o}function ik(r,t,e,n){let[o,s]=zc(t,e),i=4,a=new Uint8Array(JP(t*e,i));return ht(r,()=>r.readPixels(0,0,o,s,n.downloadTextureFormat,r.UNSIGNED_BYTE,a)),new Float32Array(a.buffer)}function ak(r,t,e,n,o,s,i,a){let u=r,l=new Float32Array(QP(s,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function lk(r,t,e){let n=new Float32Array(t*e*4);return ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,n)),n}var Gc=class{constructor(t){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let e=M().getNumber(\"WEBGL_VERSION\");if(t!=null?(this.gl=t,vT(e,t)):this.gl=Un(e),t=this.gl,M().getNumber(\"WEBGL_VERSION\")===2){let s=t;this.createVertexArray=()=>ht(s,()=>s.createVertexArray()),this.bindVertexArray=i=>ht(s,()=>s.bindVertexArray(i)),this.deleteVertexArray=i=>ht(s,()=>s.deleteVertexArray(i)),this.getVertexArray=()=>ht(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(t!=null){let s=t.getExtension(\"OES_vertex_array_object\");if(s==null)throw new Error(\"All WebGL1 implementations are expected to offer OES_vertex_array_object.\");this.createVertexArray=()=>ht(t,()=>s.createVertexArrayOES()),this.bindVertexArray=i=>ht(t,()=>s.bindVertexArrayOES(i)),this.deleteVertexArray=i=>ht(t,()=>s.deleteVertexArrayOES(i)),this.getVertexArray=()=>ht(t,()=>t.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let n=\"WEBGL_color_buffer_float\",o=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),M().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",i=\"OES_texture_half_float\";if(this.textureFloatExtension=fd(this.gl,s),Hn(this.gl,i))this.textureHalfFloatExtension=fd(this.gl,i);else if(M().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Hn(this.gl,o))this.colorBufferHalfFloatExtension=fd(this.gl,o);else if(M().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(n=\"EXT_color_buffer_float\",Hn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Hn(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=jT(this.gl),this.indexBuffer=XT(this.gl),this.framebuffer=OT(this.gl),this.textureConfig=Yh(this.gl,this.textureHalfFloatExtension)}get debug(){return M().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\"),this.outputTexture!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");let t=this.gl;ht(t,()=>t.finish()),ht(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),ht(t,()=>t.deleteFramebuffer(this.framebuffer)),ht(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),ht(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),ht(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),YT(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),ZT(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),JT(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),nk(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),rk(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),tk(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),QT(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(Sw(this.gl,this.framebuffer),this.outputTexture=null),ht(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,n){return this.downloadMatrixDriver(t,()=>ik(this.gl,e,n,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,n,o,s,i){return ak(this.gl,t,e,n,o,s,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return sk(this.gl,t,e)}createBufferFromTexture(t,e,n){this.bindTextureToFrameBuffer(t);let o=ok(this.gl,e,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,n;if(M().getBool(\"WEBGL_FENCE_API_ENABLED\")){let o=t,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),n=()=>{let i=o.clientWaitSync(s,0,0);return i===o.ALREADY_SIGNALED||i===o.CONDITION_SATISFIED},e=s}else M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(e=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(e,M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))):n=()=>!0;return{query:e,isFencePassed:n}}downloadMatrixFromPackedTexture(t,e,n){return this.downloadMatrixDriver(t,()=>lk(this.gl,e,n))}createProgram(t){this.throwIfDisposed();let e=this.gl;this.vertexShader==null&&(this.vertexShader=KT(e));let n=_T(e);ht(e,()=>e.attachShader(n,this.vertexShader)),ht(e,()=>e.attachShader(n,t)),AT(e,n);let o;return o=Object.assign(n,{vao:this.createVertexArray()}),this.bindVertexArray(o.vao),ht(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),console.assert(ek(e,o,this.vertexBuffer),\"gpgpu_util.bindVertexProgramAttributeStreams not fully successful.\"),this.debug&&Zh(e,o),this.setProgram(o),o}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ht(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&(this.bindVertexArray(this.program.vao),this.debug&&Zh(this.gl,this.program)),ht(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,n=!0){return this.throwIfDisposed(),n?PT(this.gl,t,e):LT(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ht(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),MT(this.gl,t,e,n)}setOutputMatrixTexture(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)}setOutputPackedMatrixTexture(t,e,n){this.throwIfDisposed();let[o,s]=Qi(e,n);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,n,o){this.setOutputMatrixWriteRegionDriver(n,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,n,o){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&Zh(this.gl,this.program),dd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;if(this.debug){let e=this.getVertexArray();console.assert(e===this.program.vao,\"VAO changed between setProgram and executeProgram!\"),this.debugValidate()}ht(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ht(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=fd(this.gl,M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(t,M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))),this.getQueryTime(t,M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(t,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(t,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=cet(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:n}=this.itemsToPoll[e];n()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;let n;\"setTimeoutCustom\"in M().platform&&(n=M().platform.setTimeoutCustom.bind(M().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(t){this.throwIfDisposed(),Jh(this.gl,t,this.framebuffer),this.debug&&dd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Jh(this.gl,this.outputTexture,this.framebuffer),this.debug&&dd(this.gl)):Sw(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let n=e();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(t,e,n){this.throwIfDisposed();let o=this.gl;Jh(o,t,this.framebuffer),this.debug&&dd(o),this.outputTexture=t,ht(o,()=>o.viewport(0,0,e,n)),ht(o,()=>o.scissor(0,0,e,n))}setOutputMatrixWriteRegionDriver(t,e,n,o){this.throwIfDisposed(),ht(this.gl,()=>this.gl.scissor(t,e,n,o))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function cet(r){let t=0;for(;t`${r}.${e}`)}function er(r,t){return t===1?[r]:ck(r,t)}function rM(r,t){if(r===1)return\"rc\";let e=\"\";for(let n=0;n ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let e=\"\";for(let n=this.rank-2;n= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n= ${n};\n bool rEdge = rp1 >= ${o};\n `}getOutput(t){let e=this.getSourceCoordsArr(t);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}),\n cEdge ? 0. : getA(${e[1]}),\n rEdge ? 0. : getA(${e[2]}),\n rEdge || cEdge ? 0. : getA(${e[3]})`}};var Sd=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=t,this.enableShapeUniforms=we(this.outputShape.length);let n=\"\";for(let o=0;o<4;o++){let s=\"thisRC = rc;\";o%2===1&&(s+=\"thisRC.z += 1;\"),o>1&&(s+=\"thisRC.y += 1;\"),n+=`\n ${s}\n ${o>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${o}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${o>0?\"}\":\"\"}\n `}this.userCode=`\n ${pet(e,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?yd():xd(t)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":t[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":t[2]};\n\n ${n}\n\n setOutput(result);\n }\n `}};function pet(r,t){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${t?oL([\"r\",\"c\",\"d\"],\"inputShape\"):ni([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var Gw=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(t,e,n){let o=oM(e,n),s=sM(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=nM(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].shift();return this.usedTextures[s].push(u),u}let a;return o===Lr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Lr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Lr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=oM(n,o),i=sM(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=nM(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=M().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l.indexOf(t);if(c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function met(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function nM(r,t,e,n,o){let s=fet(t,n),i;if(o){let[u,l]=Qi(r[0],r[1]);i=u*l}else{let[u,l]=zc(r[0],r[1]);i=u*l}let a=met(e,s);return i*a}function fet(r,t){switch(r){case Lr.PACKED_2X2_FLOAT32:return Pw(t);case Lr.PACKED_2X2_FLOAT16:return Lw(t);case Lr.UNPACKED_FLOAT32:return Rw(t);case Lr.UNPACKED_FLOAT16:return Fw(t);case Lr.PACKED_4X1_UNSIGNED_BYTE:return Ow(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function det(r){return M().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?Lr.PACKED_2X2_FLOAT32:Lr.UNPACKED_FLOAT32:r?Lr.PACKED_2X2_FLOAT16:Lr.UNPACKED_FLOAT16}function oM(r,t){if(r===Yr.UPLOAD)return Lr.PACKED_2X2_FLOAT32;if(r===Yr.RENDER||r==null)return det(t);if(r===Yr.DOWNLOAD||r===Yr.PIXELS)return Lr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function sM(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var Mr=class{constructor(t,e){this.variableNames=[\"A\"],this.outputShape=t,this.enableShapeUniforms=we(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${e}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},hr=\"if (isnan(x)) return x;\",iM=\"return x;\",pk=\"return abs(x);\";var aM=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",lM=hr+`\n return (x < 0.0) ? 0.0 : x;\n`,uM=hr+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,ta=\"return x;\",cM=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var mM=\"return x;\",fM=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,dM=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,hM=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,gM=\"return 1.0 / (1.0 + exp(-1.0 * x));\",An=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=we(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${e}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var Ww=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=we(this.outputShape.length);let e=t.length,n=er(\"rc\",e),o=Bt(e),s=rM(e,n),i=n.slice(-2),a=e<=1?\"rc\":`vec2(${i.join(\",\")})`;this.userCode=`\n void main() {\n ${o} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${a}));\n }\n `}};var get=qr.whereImpl,xet=1e-7,yet=1e-4,Uw={};function bet(r){return r in Uw||(Uw[r]={}),Uw[r]}var wet=M().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),Cet=600;function Iet(){return M().global.screen==null?1024:M().global.screen.height*M().global.screen.width*window.devicePixelRatio*Cet/1024/1024}var $u=class extends Bo{constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!M().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let e;if(t!=null){if(t instanceof Gc)e=t;else{let n=Un(M().getNumber(\"WEBGL_VERSION\"),t);e=new Gc(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Un(M().getNumber(\"WEBGL_VERSION\"));e=new Gc(n),this.binaryCache=bet(M().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Gw(this.gpgpu),this.numMBBeforeWarning=Iet(),this.texData=new aa(this,Mn())}nextDataId(){return $u.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=hd(e),c=new Qh(l,!1,i),p=this.runWebGLProgram(c,[a],n,[[o,s]]);return p.shape=e,u.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(t,e,n){if((M().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||M().getBool(\"DEBUG\"))&&this.checkNumericalProblems(t),n===\"complex64\"&&t!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Yr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(M().getBool(\"DEBUG\")&&this.checkNumericalProblems(e),o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Yr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new An(a,ta):m=new Mr(a,ta);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o===\"string\")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o===\"complex64\"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new An(o,ta):d=new Mr(o,ta);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(M().getBool(\"DEBUG\")&&!M().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&M().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let l=null,c;if(i!==\"complex64\"&&M().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...Xh(o))}this.pendingRead.set(t,[]),i!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let p;if(i===\"complex64\"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Mn().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(i!=null){let f;u?f=new An(s,ta):f=new Mr(s,ta);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(t,e.customTexShape),p=Mn().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype===\"string\")try{let n=e.map(o=>y.decodeString(o));return wt(t.shape,t.dtype,n)}catch(n){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return wt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(\", \")}else a.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=wet){return M().getBool(\"WEBGL_CPU_FORWARD\")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Mn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new Ww(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new Vw(t.shape),n=!0;return this.runWebGLProgram(e,[t],t.dtype,null,n)}packedReshape(t,e){let n=[wl(t.shape),...Cl(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[wl(e),...Cl(e)],i=new Sd(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let a=hd(s),u;o?u=new _w(a):u=new Ew(a);let l=!0,c=[e!=null?e:Xh(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===_u.DENSE){let x=i!=null?i:Xh(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=M().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Au(b.shape,x.shape)){let w=x,C=x.shape;x.shape=b.shape,x=this.packedReshape(x,C),l.push(x),b=this.texData.get(x.dataId),w.shape=C}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=mL(t,c,p),f=this.getAndSaveBinary(m,()=>cL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),M().get(\"ENGINE_COMPILE_ONLY\")||pL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=M().get(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!M().getBool(\"WEBGL_LAZILY_UNPACK\")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(M().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!M().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let t=M().getBool(\"DEBUG\");M().set(\"DEBUG\",!1);let e=this.abs(mt(1e-8)).dataSync()[0];if(M().set(\"DEBUG\",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?xet:yet}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=zT(n,u),e.texShape=p),s!=null){let m=hd(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=Qi(p[0],p[1])),u?f=new Dw(m,g):f=new Qh(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Yr.PIXELS:w.usage=Yr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let C=[[h,d]],N=!0,E=this.runWebGLProgram(f,[b],o,C,N),A=this.texData.get(E.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,M().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(E.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=vet(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await xh(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Iw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let[,t]of Object.entries(this.binaryCache)){let{uniformLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,inShapesLocations:i,inTexShapesLocations:a,outShapeLocation:u,outShapeStridesLocation:l,outTexShapeLocation:c}=qT(this.gpgpu,t.program,t.webGLProgram);t.uniformLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.inShapesLocations=i,t.inTexShapesLocations=a,t.outShapeLocation=u,t.outShapeStridesLocation=l,t.outTexShapeLocation=c}}createTensorFromTexture(t,e,n){let{texture:o,height:s,width:i,channels:a}=t,u=Mn().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let l=u.writeTexture(o,e,n,s,i,a);return Mn().makeTensorFromDataId(l,e,n,u)}};$u.nextDataId=0;function vet(r,t){if(t===\"float32\"||t===\"complex64\")return r;if(t===\"int32\"||t===\"bool\"){let e=t===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;nnew $u,2);var b1e={forceHalfFloat:yM};var Nd=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var lo=class{constructor(t,e,n){this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=we(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${t}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var ea=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var Po=class{constructor(t,e,n,o=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(e,n);let s=this.outputShape.length;this.enableShapeUniforms=we(s);let i=\"\";if(o)if(s===0||y.sizeFromShape(this.outputShape)===1)i=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(i=`\n ${Bt(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?i+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:i+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let u=er(\"coords\",s);this.enableShapeUniforms?i+=`\n bool nextRowOutOfBounds =\n (${u[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${u[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:i+=`\n bool nextRowOutOfBounds =\n (${u[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${u[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${t}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${i}\n\n setOutput(result);\n }\n `}};function rr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var bM={kernelName:mo,backendName:\"webgl\",kernelFunc:rr};function $n(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,\"complex64\"),i=e.texData.get(s.dataId),a=rr({inputs:{x:n},backend:e}),u=rr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var wM={kernelName:mp,backendName:\"webgl\",kernelFunc:$n};var mk=\"return (a < 0.) ? b * a : a;\",fk=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function Net(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),a=M().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Po(fk,o.shape,i.shape):new lo(mk,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],\"float32\");return e.disposeIntermediateTensorInfo(i),u}var CM={kernelName:ls,backendName:\"webgl\",kernelFunc:Net};var dk=\"return (a < 0.) ? b * a : a;\",hk=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function Tet(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=M().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Po(hk,n.shape,o.shape):new lo(dk,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],\"float32\")}var IM={kernelName:Cs,backendName:\"webgl\",kernelFunc:Tet};var Lo=\"if (isnan(x)) return x;\";function Ct({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=M().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&t!=null,c;return l?c=new An(i.shape,t):c=new Mr(i.shape,r),a.runWebGLProgram(c,[i],u)}}function le({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype===\"complex64\"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[C,N]=w,E={dataId:C.dataId,dtype:C.dtype,shape:u.shape},A={dataId:N.dataId,dtype:N.dtype,shape:l.shape},$=new lo(r,u.shape,l.shape);return c.runWebGLProgram($,[E,A],ar(C.dtype,N.dtype))}),b=$n({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||ar(u.dtype,l.dtype);if((u.dtype===\"string\"||l.dtype===\"string\"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype===\"string\"?S.fromUint8ToStringArray(d):d,x=u.dtype===\"string\"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,x,p),C=c.makeTensorInfo(w,p),N=c.texData.get(C.dataId);return N.values=b,C}let m=M().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&t!=null,f;return m?f=new Po(t,u.shape,l.shape,e):f=new lo(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Il(r,t=!1){if(r===\"linear\")return t?mM:iM;if(r===\"relu\")return t?dM:lM;if(r===\"elu\")return t?fM:aM;if(r===\"relu6\")return t?hM:uM;if(r===\"prelu\")return t?hk:dk;if(r===\"leakyrelu\")return t?fk:mk;if(r===\"sigmoid\")return t?gM:cM;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Td=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=we(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?\"i * 2, rc.y\":\"rc.y, i * 2\",f=s?\"rc.z, i * 2\":\"i * 2, rc.z\",d=o?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";a&&(u?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${a}\n }`:l?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${a}\n }`:g=`vec4 activation(vec4 x) {\n ${a}\n }`,x=\"result = activation(result);\");let b=i?\"result += getBiasAtOutCoords();\":\"\";i&&this.variableNames.push(\"bias\"),u&&this.variableNames.push(\"preluActivationWeights\"),l&&this.variableNames.push(\"leakyreluAlpha\");let w=\"rc.x\",C=\"rc.x\";t[0]`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!Au(o.shape,u)&&!(c.texture!==null&&Au(c.shape,u))?NM(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var TM={kernelName:xi,backendName:\"webgl\",kernelFunc:it};var ng=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l=\"sumValue += dot(values, ones);\";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c=\"\";s%n>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${a}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${l}\n }\n\n int inIdx = inOffset + ${a};\n if (${u===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${l}\n } else if (${u===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${l}\n } else if (${u===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${l}\n }\n setOutput(sumValue);\n }\n `}};var Hw=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=\"0.0\",u=\"\";e===\"prod\"?a=\"1.0\":e===\"min\"?(a=\"1.0 / 1e-20\",u=\"min\"):e===\"max\"&&(a=\"-1.0 / 1e-20\",u=\"max\");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"sum\"?l=\"sumValue\":e===\"prod\"?l=\"prodValue\":e===\"all\"?l=\"allValue\":e===\"any\"&&(l=\"anyValue\");let c=Math.floor(n/4)*4,p=n%4,m=`\n if (${e===\"sum\"}) {\n sumValue += dot(values, ones);\n } else if (${e===\"prod\"}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${u}(values, minMaxValue);\n if (${e===\"min\"} || ${e===\"max\"}) {\n minMaxValue = ${u}(values, minMaxValue);\n bvec4 isNaN = isnan(values);\n if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n minMaxValue = vec4(NAN);\n }\n }\n }\n `,f=\"vec4\";e===\"all\"?(a=\"1.0\",m=`\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n `,f=\"bvec4\"):e===\"any\"&&(a=\"0.0\",m=`\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n `,f=\"bvec4\");let d=\"\";s%n>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `),this.userCode=`\n const float initializationValue = ${a};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${d}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n vec4 minMaxValue = vec4(${a});\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${p===1}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${p===2}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${p===3}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${l});\n }\n `}};function Eet(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],n=S.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:n,outSize:Math.ceil(e/n)})}return t}function qn(r,t,e,n){let o=Eet(r.shape),s=r;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],n=new Array(t);for(let o=0;o6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=Bt(this.rank),s=ck(\"rc\",this.rank),i=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let N=e?[x,p,f]:[x,f,p],E=n?[b,d,m]:[b,m,d],A=it({inputs:{x:r},backend:o,attrs:{shape:N}}),$=it({inputs:{x:t},backend:o,attrs:{shape:E}}),F=[A,$],P=Math.max(x,b),V=e?A.shape[1]:A.shape[2],G=s!=null,W=i!=null,q=u===\"leakyrelu\",H=u!=null?Il(u,!0):null,j=G||W||q||H!=null,Y;if((f===1||d===1)&&V>xk&&j===!1){let et=A,rt=$;e&&(et=Oe({inputs:{x:A},backend:o,attrs:{perm:[0,2,1]}}),F.push(et)),n&&(rt=Oe({inputs:{x:$},backend:o,attrs:{perm:[0,2,1]}}),F.push(rt));let ot=d!==1,at=d===1,nt=et;ot&&(nt=it({inputs:{x:et},backend:o,attrs:{shape:[P,V,1]}}),F.push(nt));let st=d===1?2:1,dt=rt;at&&(dt=it({inputs:{x:rt},backend:o,attrs:{shape:[P,1,V]}}),F.push(dt));let gt=rg({inputs:{a:nt,b:dt},backend:o});Y=Uc({inputs:{x:gt},backend:o,attrs:{axis:st,keepDims:!0}}),F.push(gt)}else{let et=ar(r.dtype,t.dtype),rt=new Td(N,E,[P,f,d],e,n,G,H,W,q),ot=[A,$];if(s!=null&&ot.push(s),W&&ot.push(i),q){let at=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));ot.push(at),F.push(at)}Y=o.runWebGLProgram(rt,ot,et)}let Z=it({inputs:{x:Y},backend:o,attrs:{shape:C}});F.push(Y);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function Aet(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return Hc({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var AM={kernelName:Si,backendName:\"webgl\",kernelFunc:Aet};var $M=\"return abs(x);\";function $et(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!==\"complex64\"){let s=e.texData.get(n.dataId),i=zw(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return M().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?o=new An(n.shape,$M):o=new Mr(n.shape,$M),e.runWebGLProgram(o,[n],n.dtype)}var DM={kernelName:ui,backendName:\"webgl\",kernelFunc:$et};var Det=hr+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n`,Ret=Ct({opSnippet:Det}),RM={kernelName:ua,backendName:\"webgl\",kernelFunc:Ret};var Fet=hr+`\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,Oet=Ct({opSnippet:Fet}),FM={kernelName:ca,backendName:\"webgl\",kernelFunc:Oet};var OM=\"return a + b;\",Pet=le({opSnippet:OM,packedOpSnippet:OM,supportsComplex:!0,cpuKernelImpl:fL}),PM={kernelName:Qn,backendName:\"webgl\",kernelFunc:Pet};var jw=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${n.join(`\n `)}\n\n float result = ${o};\n setOutput(result);\n }\n `}};var Xw=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${n.join(`\n `)}\n\n vec4 result = ${o};\n setOutput(result);\n }\n `}};function Yw(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return rr({inputs:{x:n[0]},backend:e});if(n.length>M().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let u=Math.floor(n.length/2),l=Yw({inputs:n.slice(0,u),backend:e}),c=Yw({inputs:n.slice(u),backend:e});return Yw({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>ar(u,l)),s=n.map(u=>u.shape),a=M().getBool(\"WEBGL_PACK\")?new Xw(n[0].shape,s):new jw(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var LM={kernelName:Wo,backendName:\"webgl\",kernelFunc:Yw};function Let(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Oe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims(\"all\",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=it({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=qn(h,h.dtype,\"all\",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=it({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=it({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var MM={kernelName:pa,backendName:\"webgl\",kernelFunc:Let};function Met(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Oe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims(\"any\",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=it({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=qn(h,h.dtype,\"any\",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=it({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=it({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var zM={kernelName:ma,backendName:\"webgl\",kernelFunc:Met};var Zw=class{constructor(t,e,n){this.variableNames=[\"A\"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[s,i];let a=e===\"max\"?\">\":\"<\",u=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${o}; i++) {\n int inIdx = ${u};\n float candidate = getA(batch, inIdx);\n if (candidate ${a} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var Jw=class{constructor(t,e,n,o){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push(\"bestIndicesA\");let a=this.outputShape,u=a.length,l=Bt(u),c=er(\"coords\",u),p,m;if(i===1){m=u+1;let $=Bt(m);p=`\n ${$} sourceLocR = ${$}(${c.join()}, 0);\n ++${c[u-1]};\n ${$} sourceLocG = ${$}(${c.join()}, 0);\n ++${c[u-2]};\n ${$} sourceLocA = ${$}(${c.join()}, 0);\n --${c[u-1]};\n ${$} sourceLocB = ${$}(${c.join()}, 0);\n --${c[u-2]};`}else m=u,p=`\n ${l} sourceLocR = coords;\n ++${c[u-1]};\n ${l} sourceLocG = coords;\n ++${c[u-2]};\n ${l} sourceLocA = coords;\n --${c[u-1]};\n ${l} sourceLocB = coords;\n --${c[u-2]};`;let f=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,m),d=\".\"+f[m-1],h=f.map($=>\"int \"+$),g=er(\"sourceLocR\",m-1).concat(\"inIdx.r\"),x=er(\"sourceLocG\",m-1).concat(\"inIdx.g\"),b=er(\"sourceLocB\",m-1).concat(\"inIdx.b\"),w=er(\"sourceLocA\",m-1).concat(\"inIdx.a\"),C=n===\"max\"?\"greaterThan\":\"lessThan\",N=o?\"\":`\n inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${x.join()}),\n getBestIndicesAChannel(${b.join()}),\n getBestIndicesAChannel(${w.join()})));`,E=`vec4(\n getAChannel(${g.join()}),\n hasNextCol ? getAChannel(${x.join()}) : 0.,\n hasNextRow ? getAChannel(${b.join()}) : 0.,\n hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,A=o?\"\":`\n float getBestIndicesAChannel(${h.join()}) {\n return getChannel(getBestIndicesA(${f.join()}),\n vec2(${f.slice(-2).join()}));\n }`;this.userCode=`\n float getAChannel(${h.join()}) {\n return getChannel(getA(${f.join()}),\n vec2(${f.slice(-2).join()}));\n }\n ${A}\n void main() {\n ${l} coords = getOutputCoords();\n bool hasNextCol = ${c[u-1]} < ${a[u-1]-1};\n bool hasNextRow = ${c[u-2]} < ${a[u-2]-1};\n ${p}\n ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},\n sourceLocB${d}, sourceLocA${d}) * ${e};\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = ${E};\n\n for (int i = 0; i < ${e}; i++) {\n inIdx = srcIdx;\n ${N}\n vec4 candidate = ${E};\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4(${C}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n `}};function BM(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new Zw(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,\"int32\");if(c.shape[1]===1)return c;let p=BM(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function VM(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new Jw(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,\"int32\");if(l.shape.length===t.shape.length){let c=VM(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function Qw(r,t,e,n){let o=[e];if(S.assertAxesAreInnerMostDims(\"arg\"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!M().getBool(\"WEBGL_PACK_REDUCE\")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,o),p=y.sizeFromShape(c),m=it({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=BM(r,m,n);s.push(f);let d=it({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return VM(r,t,n)}function zet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Oe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=Qw(e,u,i[0],\"max\");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var GM={kernelName:Uo,backendName:\"webgl\",kernelFunc:zet};function Bet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Oe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=Qw(e,u,i[0],\"min\");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var WM={kernelName:Al,backendName:\"webgl\",kernelFunc:Bet};var Vet=hr+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,Get=Ct({opSnippet:Vet}),UM={kernelName:fa,backendName:\"webgl\",kernelFunc:Get};var Wet=hr+\"return log(x + sqrt(x * x + 1.0));\",Uet=Ct({opSnippet:Wet}),HM={kernelName:da,backendName:\"webgl\",kernelFunc:Uet};var Het=hr+`\n return atan(x);\n`,qet=Ct({opSnippet:Het}),qM={kernelName:ha,backendName:\"webgl\",kernelFunc:qet};var Ket=Nd+`\n return atan(a, b);\n`,jet=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+ea+`\n return result;\n`,Xet=le({opSnippet:Ket,packedOpSnippet:jet}),KM={kernelName:xa,backendName:\"webgl\",kernelFunc:Xet};var Yet=hr+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelName:ga,backendName:\"webgl\",kernelFunc:Zet};var oi=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e===\"avg\",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),n){let $=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${a}, ${u});\n const ivec2 pads = ivec2(${f}, ${d});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${p};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${$} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${o?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let w=\"max\",C=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(C=\"avgValue / count\");let N=Math.floor(i/4)*4,E=i%4,A=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${w}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${a}, ${u});\n const ivec2 pads = ivec2(${f}, ${d});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${p};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${N}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${A}\n }\n\n int xC = xCCorner + ${N};\n if (${E===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${A}\n } else if (${E===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${A}\n } else if (${E===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${A}\n }\n }\n setOutput(${C});\n }\n `}},Ru=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e===\"avg\",C=\"0.0\";if(w||(C=\"-1.0 / 1e-20\"),n){let P=\">=\";this.userCode=`\n const ivec3 strides =\n ivec3(${a}, ${u}, ${l});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${f};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${p}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${P} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${o?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${d} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let N=\"max\",E=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(E=\"avgValue / count\");let A=Math.floor(i/4)*4,$=i%4,F=`\n if (${w}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${N}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${a}, ${u}, ${l});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${C};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${C});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${f};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${p}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${A}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${F}\n }\n\n int xC = xCCorner + ${A};\n if (${$===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${F}\n } else if (${$===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${F}\n } else if (${$===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${F}\n }\n }\n setOutput(${E});\n }\n }\n `}};function Jet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;ri(o,\"avgPool\");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new oi(c,\"avg\",!1);return e.runWebGLProgram(p,[o],\"float32\")}var XM={kernelName:Ho,backendName:\"webgl\",kernelFunc:Jet};function Qet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new Ru(p,\"avg\",!1);return e.runWebGLProgram(m,[o],\"float32\")}var YM={kernelName:$l,backendName:\"webgl\",kernelFunc:Qet};var tC=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${p});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${u};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${l};\n wC+= ${a}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},eC=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=`\n const ivec3 pads = ivec3(${d}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${u}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${l}) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${f};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function trt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new eC(m);return e.runWebGLProgram(f,[o],i.dtype)}var ZM={kernelName:up,backendName:\"webgl\",kernelFunc:trt};function ert(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;ri([o,s],\"avgPoolGrad\");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new tC(c);return e.runWebGLProgram(p,[o],i.dtype)}var JM={kernelName:lp,backendName:\"webgl\",kernelFunc:ert};function rrt(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return Hc({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var QM={kernelName:qo,backendName:\"webgl\",kernelFunc:rrt};var rC=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a=\"0.0\";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push(\"offset\"),a=\"getOffsetAtOutCoords()\");let u=\"1.0\";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),u=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${a};\n float scale = ${u};\n float inv = scale * inversesqrt(variance + float(${i}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}};var nC=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a=\"vec4(0.0)\";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push(\"offset\"),a=\"getOffsetAtOutCoords()\");let u=\"vec4(1.0)\";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),u=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n vec4 offset = ${a};\n vec4 scale = ${u};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${i}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}};var nrt=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(i==null||o.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(a==null||o.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=M().getBool(\"WEBGL_PACK_NORMALIZATION\")?new nC(n.shape,o.shape,s.shape,c,p,u):new rC(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},tz={kernelName:ss,backendName:\"webgl\",kernelFunc:nrt};var oC=class{constructor(t){this.variableNames=[\"source\"],this.outputShape=t,this.rank=t.length;let e=Bt(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let n=ort(this.rank),o,s=t.map((i,a)=>`sourceLoc.${yk[a]} = start[${a}] + coords.${yk[a]};`);o=`\n ${e} sourceLoc;\n ${e} coords = getOutputCoords();\n ${s.join(`\n`)}\n `,this.userCode=`\n void main() {\n ${o}\n setOutput(getSource(${n}));\n }\n `}},yk=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function ort(r){if(r===1)return\"sourceLoc\";if(r<=6)return yk.slice(0,r).map(t=>\"sourceLoc.\"+t).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var sC=class{constructor(t){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let e=Bt(this.rank),n=er(\"coords\",this.rank),o=er(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=`\n result.x = ${i};\n if (++${n[this.rank-1]} < ${t[this.rank-1]}) {\n ++${o[this.rank-1]};\n result.y = ${i};\n --${o[this.rank-1]};\n }\n `,u=this.rank===1?\"\":`\n --${n[this.rank-1]};\n if (++${n[this.rank-2]} < ${t[this.rank-2]}) {\n ++${o[this.rank-2]};\n result.z = ${i};\n if (++${n[this.rank-1]} < ${t[this.rank-1]}) {\n ++${o[this.rank-1]};\n result.w = ${i};\n }\n }\n `,l=this.rank<=4?`sourceLoc = coords +\n ${e}(${t.map((c,p)=>`start[${p}]`).join()});`:t.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(`\n`);this.userCode=`\n void main() {\n ${e} coords = getOutputCoords();\n ${e} sourceLoc;\n ${l}\n vec4 result = vec4(0.);\n ${a}\n ${u}\n setOutput(result);\n }\n `}};function srt(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=Me.computeFlatOffset(t,y.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function si(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=Me.parseSliceParams(o,s,i);if(Me.assertParamsValid(o,a,u),y.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype===\"string\"){let p=e.texData.get(o.dataId),m=UL(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=Me.isSliceContinous(o.shape,a,u);if(l||!c){let p=M().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new sC(u):new oC(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),srt(o,a,u,e)}var ez={kernelName:bi,backendName:\"webgl\",kernelFunc:si};var irt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;y.assert(o.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=it({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Oe({inputs:{x:d},backend:e,attrs:{perm:l}}),g=it({inputs:{x:h},backend:e,attrs:{shape:c}}),x=si({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},rz={kernelName:ci,backendName:\"webgl\",kernelFunc:irt};function art(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=Mw(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var nz={kernelName:cp,backendName:\"webgl\",kernelFunc:art};function lrt(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],\"int32\",Int32Array.from(a))}var oz={kernelName:pp,backendName:\"webgl\",kernelFunc:lrt};var urt=\"return float(a != b);\",bk=le({opSnippet:urt,cpuKernelImpl:OL,dtype:\"bool\"}),sz={kernelName:Pa,backendName:\"webgl\",kernelFunc:bk};function vl(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.real},backend:e})}var iz={kernelName:Fp,backendName:\"webgl\",kernelFunc:vl};var crt=\"return float(int(x));\";function az(r,t){let e=new Mr(r.shape,crt),n=t.runWebGLProgram(e,[r],\"int32\");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function wk(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dtype:s}=n;if(s===\"complex64\"){if(o.dtype===\"complex64\")return rr({inputs:{x:o},backend:e});let i=Ne(o.shape),a=wk({inputs:{x:o},backend:e,attrs:{dtype:\"float32\"}}),u=$n({inputs:{real:a,imag:i},backend:e});return i.dispose(),e.disposeIntermediateTensorInfo(a),u}if(o.dtype===\"complex64\"){let 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iC=class{constructor(t){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `}};var aC=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `}};function mrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;M().getBool(\"WEBGL_PACK_CLIP\")?a=new aC(o.shape):a=new iC(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var pz={kernelName:po,backendName:\"webgl\",kernelFunc:mrt};var lC=class{constructor(t){this.variableNames=[\"real\",\"imag\"],this.outputShape=t,this.userCode=`\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n `}};function mz(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function frt(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new lC(n.shape),i=[mz(n,o.complexTensorInfos.real),mz(n,o.complexTensorInfos.imag)];return e.runWebGLProgram(s,i,i[0].dtype)}var fz={kernelName:Dl,backendName:\"webgl\",kernelFunc:frt};var uC=class{constructor(t){this.outputShape=[],this.outputShape=S.computeOutShape(t,1),this.variableNames=t.map((i,a)=>`T${a}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let i=1;i`T${g}`);let u=new Array(t.length-1);u[0]=t[0][e];for(let h=1;h= ${u[h-1]}) {\n return getChannel(\n getT${h}(${cC(a,l,g)}),\n vec2(${cC(c,l,g)}));\n }`}let f=u.length,d=u[u.length-1];m+=`\n return getChannel(\n getT${f}(${cC(a,l,d)}),\n vec2(${cC(c,l,d)}));`,this.userCode=`\n float getValue(${a.map(h=>\"int \"+h)}) {\n ${m}\n }\n\n void main() {\n ${s} coords = getOutputCoords();\n vec4 result = vec4(getValue(${i}), 0., 0., 0.);\n\n ${i[o-1]} = ${i[o-1]} + 1;\n if (${i[o-1]} < ${n[o-1]}) {\n result.g = getValue(${i});\n }\n\n ${i[o-2]} = ${i[o-2]} + 1;\n if (${i[o-2]} < ${n[o-2]}) {\n result.a = getValue(${i});\n }\n\n ${i[o-1]} = ${i[o-1]} - 1;\n if (${i[o-2]} < ${n[o-2]} &&\n ${i[o-1]} < ${n[o-1]}) {\n result.b = getValue(${i});\n }\n setOutput(result);\n }\n `}};function cC(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function qc(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var dz={kernelName:Sp,backendName:\"webgl\",kernelFunc:qc};function kd(r,t,e){let n=r[0].dtype;if(n===\"complex64\"){let f=r.map(b=>vl({inputs:{input:b},backend:e})),d=r.map(b=>qc({inputs:{input:b},backend:e})),h=kd(f,t,e),g=kd(d,t,e),x=$n({inputs:{real:h,imag:g},backend:e});return f.forEach(b=>e.disposeIntermediateTensorInfo(b)),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let o=e.shouldExecuteOnCPU(r);if(n===\"string\"&&(o=!0),o){let f=r.map(C=>{let E=[-1,y.sizeFromShape(C.shape.slice(t))];return it({inputs:{x:C},backend:e,attrs:{shape:E}})}),d=f.map(C=>({vals:e.readSync(C.dataId),shape:C.shape})),h=S.computeOutShape(f.map(C=>C.shape),1),g=f[0].shape[0]===1,x=xL(d,h,n,g),b=S.computeOutShape(r.map(C=>C.shape),t),w=e.makeTensorInfo(b,n,x);return f.forEach(C=>e.disposeIntermediateTensorInfo(C)),w}let s=r.filter(f=>y.sizeFromShape(f.shape)>0),i=M().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let f=i?new Mr(r[0].shape,ta):new An(r[0].shape,ta);return e.runWebGLProgram(f,r,n)}let a=M().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>a){let f=[];for(let h=0;hd.shape),t);return e.runWebGLProgram(f,s,n)}let{tensors2D:u,outShape:l}=drt(s,t,e),c=new uC(u.map(f=>f.shape)),p=e.runWebGLProgram(c,u,n);u.forEach(f=>e.disposeIntermediateTensorInfo(f));let m=it({inputs:{x:p},attrs:{shape:l},backend:e});return e.disposeIntermediateTensorInfo(p),m}function drt(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>it({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function Ck(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(l=>l.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(l=>l.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(l=>y.sizeFromShape(l.shape)>0);return u.length===1?rr({inputs:{x:u[0]},backend:e}):kd(u,s,e)}var hz={kernelName:pi,backendName:\"webgl\",kernelFunc:Ck};var Ed=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,w=g?3:1,C=\"\",N=\"\";n&&(o?C=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:s?C=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:C=`\n float activation(float x) {\n ${n}\n }\n `,N=\"result = activation(result);\");let E=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${C}\n\n const ivec2 strides = ivec2(${u}, ${l});\n const ivec2 pads = ivec2(${i}, ${a});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${w}];\n\n ivec2 xRCCorner =\n ivec2(coords[${x}], coords[${b}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f}; wC++) {\n int xC = xCCorner + wC * ${p};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${d}) *\n getW(wR, wC, ${d}, d2);\n } else {\n dotProd +=\n getX(batch, ${d}, xR, xC) *\n getW(wR, wC, ${d}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2),\n getW(wR, wC, ${d} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1),\n getX(batch, xR, xC, ${d} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC),\n getX(batch, ${d} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${E}\n ${N}\n setOutput(result);\n }\n `}},mC=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${i}, ${a});\n const ivec3 pads = ivec3(${e}, ${n}, ${o});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${p}; wF++) {\n int xF = xFCorner + wF * ${u};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${l};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${d}) *\n getW(wF, wR, wC, ${d}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1),\n getX(batch, xF, xR, xC, ${d} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2),\n getW(wF, wR, wC, ${d} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var _d=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=we(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(p+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*u};\n `,a===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,u===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,u>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${a}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+p}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+p}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${l}\n\n ${o.output} = result;\n }\n `}};function dC(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function hC({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat===\"channelsLast\",d=!1,h=!1,g,x=[];if(s!=null){let C=dC(s.shape,f);C!=null&&(s=it({inputs:{x:s},backend:n,attrs:{shape:C}}),x.push(s))}if(o!=null){let C=dC(o.shape,f);C!=null&&(o=it({inputs:{x:o},backend:n,attrs:{shape:C}}),x.push(o))}if(!((p===1||m===1)&&c>xk)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&y.arraysEqual(l.shape.slice(-3),u.slice(-3))){let C=u[0]*u[1]*(u[2]+1),N={dataId:r.dataId,shape:[1,C,e.inChannels],dtype:r.dtype},E=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,y.assert(Au(l.shape,N.shape),()=>`packed reshape ${l.shape} to ${N.shape} isn't free`);let A=it({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(A);let $=Hc({a:N,b:A,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get($.dataId);y.assert(F.isPacked,()=>\"batchMatMul result is expected to be packed\"),l.shape=E,F.shape=e.outShape,g=rr({inputs:{x:$},backend:n}),g.shape=e.outShape,x.push($)}else{let C=e.outHeight*e.outWidth,N=it({inputs:{x:r},backend:n,attrs:{shape:f?[e.batchSize,C,e.inChannels]:[e.batchSize,e.inChannels,C]}}),E=it({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),A=Hc({a:f?N:E,b:f?E:N,transposeA:!f,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=it({inputs:{x:A},backend:n,attrs:{shape:e.outShape}}),x.push(N),x.push(E),x.push(A)}for(let C of x)n.disposeIntermediateTensorInfo(C);return g}function gC({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f===\"channelsLast\",h=u*l*c,g=m*p,x=[e.batchSize,h,g],b=!0,w=!1,C=[];if(s!=null){let Z=dC(s.shape,d);Z!=null&&(s=it({inputs:{x:s},backend:n,attrs:{shape:Z}}),C.push(s))}if(o!=null){let Z=dC(o.shape,d);Z!=null&&(o=it({inputs:{x:o},backend:n,attrs:{shape:Z}}),C.push(o))}let N=it({inputs:{x:t},backend:n,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});C.push(N);let E=new fC(x,e),A=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],$=n.runWebGLProgram(E,[r],\"float32\",A),F=it({inputs:{x:$},backend:n,attrs:{shape:x}});C.push($),C.push(F);let P=o!=null,V=s!=null,G=a===\"leakyrelu\",W=a?Il(a,!0):null,q=new Td(d?F.shape:N.shape,d?N.shape:F.shape,d?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,w,P,W,V,G),H=d?[F,N]:[N,F];if(o&&H.push(o),V&&H.push(s),G){let Z=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));H.push(Z),C.push(Z)}let j=n.runWebGLProgram(q,H,\"float32\"),Y=it({inputs:{x:j},backend:n,attrs:{shape:e.outShape}});C.push(j);for(let Z of C)n.disposeIntermediateTensorInfo(Z);return Y}function hrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))f=hC({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p===\"channelsLast\"&&M().getBool(\"WEBGL_EXP_CONV\")){let h=new _d(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];f=e.runWebGLProgram(h,[o,s],\"float32\",g)}else if(M().getBool(\"WEBGL_CONV_IM2COL\"))f=gC({x:o,filter:s,convInfo:m,backend:e});else{let h=new Ed(m);f=e.runWebGLProgram(h,[o,s],\"float32\")}let d=it({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var gz={kernelName:jo,backendName:\"webgl\",kernelFunc:hrt};var xC=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${o};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n if (${i}) {\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n } else {\n float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},yC=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat===\"channelsLast\",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${u});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${p}];\n\n ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n\n if (${i}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},bC=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yF = 0; yF < ${t.outDepth}; yF++) {\n int xF = wF + yF * ${e} - ${s};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${n} - ${i};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${a};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},wC=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${u}, ${l}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${e}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${e} - 1 - wF;\n\n for (int wR = 0; wR < ${n}; wR++) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${n} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function grt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new xC(m);return e.runWebGLProgram(f,[o,s],\"float32\")}var xz={kernelName:fp,backendName:\"webgl\",kernelFunc:grt};function xrt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p),f=new yC(m);return e.runWebGLProgram(f,[o,s],\"float32\")}var yz={kernelName:Xo,backendName:\"webgl\",kernelFunc:xrt};function yrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new mC(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var bz={kernelName:Rl,backendName:\"webgl\",kernelFunc:yrt};function brt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new bC(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var wz={kernelName:dp,backendName:\"webgl\",kernelFunc:brt};function wrt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new wC(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var Cz={kernelName:hp,backendName:\"webgl\",kernelFunc:wrt};var Crt=Lo+`\n return cos(x);\n`,Irt=Ct({opSnippet:Crt}),Iz={kernelName:Yo,backendName:\"webgl\",kernelFunc:Irt};var vrt=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,Srt=Ct({opSnippet:vrt}),vz={kernelName:Zo,backendName:\"webgl\",kernelFunc:Srt};var CC=class{constructor(t,e,n,o,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o===\"bilinear\"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,x,b]=p>1?[`${(a-1)/(p-1)}`,\"(y2-y1) * height_ratio\",`y1*${d} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${d}`],[w,C,N]=m>1?[`${(u-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${w});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${i}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${C};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${d} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${N};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${f} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var Nrt=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new CC(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],\"float32\")},Sz={kernelName:ba,backendName:\"webgl\",kernelFunc:Nrt};var Kc;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Kc||(Kc={}));var og=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,i=this.op===Kc.Prod?\"1.0\":\"0.0\",a=n?i:`getX(${Nz(s,\"coords\",this.op)})`,u=this.outputShape[this.outputShape.length-1],l=\"\",c=\"\";n?(l=o?`end != ${u-1}`:\"end != 0\",c=o?\"end + 1\":\"end - 1\"):(l=o?`end + pow2 < ${u}`:\"end >= pow2\",c=o?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${Bt(s)} coords = getOutputCoords();\n int end = ${Tz(s,\"coords\",this.op)};\n float val = ${a};\n int pow2 = int(pow(2.0, index));\n if (${l}) {\n int idx = ${c};\n ${Tz(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${Nz(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function Nz(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function Tz(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function IC(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=Oe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=rr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new og(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new og(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Oe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function Trt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return IC(Kc.Prod,o,e,s,i,a)}var kz={kernelName:ya,backendName:\"webgl\",kernelFunc:Trt};function krt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return IC(Kc.Sum,o,e,s,i,a)}var Ez={kernelName:Jo,backendName:\"webgl\",kernelFunc:krt};function Ert(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=Mw(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=dL(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var _z={kernelName:gp,backendName:\"webgl\",kernelFunc:Ert};var vC=class{constructor(t,e,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${e};\n int offset_h = imod(h, ${e});\n int in_w = w / ${e};\n int offset_w = imod(w, ${e});\n int offset_d = (offset_h * ${e} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function _rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i===\"NHWC\"?o.shape[1]:o.shape[2],l=i===\"NHWC\"?o.shape[2]:o.shape[3],c=i===\"NHWC\"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i===\"NHWC\"?[a,p,m,f]:[a,f,p,m],h=new vC(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var Az={kernelName:wa,backendName:\"webgl\",kernelFunc:_rt};var Ad=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=we(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l=\"\",c=\"\";n&&(o?l=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:s?l=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:l=`\n float activation(float x) {\n ${n}\n }\n `,c=\"result = activation(result);\");let p=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${l}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${u};\n int q = d2 - d1 * ${u};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${i}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${a}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${p}\n ${c}\n setOutput(result);\n }\n `}};var $d=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=we(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(f+=`\n xC = xCCorner + ${b*l};\n `,u===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,l===1&&b>0?f+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:f+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):f+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,l>1?f+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:f+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):w===1?f+=`\n xC${b+1} = xTexelC${b};\n `:f+=`\n xCOffset = xC + ${w};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(f+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;M().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new $d(p):m=new Ad(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],\"float32\",f)}var $z={kernelName:Qo,backendName:\"webgl\",kernelFunc:Art};var SC=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${i} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${o};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},NC=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${a});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${u}; dm++) {\n int d2 = d1 * ${u} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function $rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new SC(p);return e.runWebGLProgram(m,[o,s],\"float32\")}var Dz={kernelName:xp,backendName:\"webgl\",kernelFunc:$rt};function Drt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new NC(p);return e.runWebGLProgram(m,[o,s],\"float32\")}var Rz={kernelName:yp,backendName:\"webgl\",kernelFunc:Drt};var TC=class{constructor(t){this.variableNames=[\"X\"],this.outputShape=[t,t],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function Rrt(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=it({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new TC(s),u=e.runWebGLProgram(a,[i],i.dtype),l=it({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var Fz={kernelName:bp,backendName:\"webgl\",kernelFunc:Rrt};var kC=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${i});\n const ivec2 pads = ivec2(${p}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${a}; h++) {\n int hIn = hBeg + h * ${l};\n\n if (hIn >= 0 && hIn < ${e}) {\n for (int w = 0; w < ${u}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${n}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function Frt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,\"NHWC\",u),c,p=new kC(l);c=e.runWebGLProgram(p,[o,s],\"float32\");let m=it({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var Oz={kernelName:Fl,backendName:\"webgl\",kernelFunc:Frt};function Ort(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h=0&&(m=Uc({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var Pz={kernelName:wp,backendName:\"webgl\",kernelFunc:Ort};var Prt=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",Lrt=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,Mrt=Ct({opSnippet:Prt,packedOpSnippet:Lrt}),Lz={kernelName:es,backendName:\"webgl\",kernelFunc:Mrt};var zrt=\"return (b >= 1.0) ? a : a * (b + 1.0);\",Brt=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,Vrt=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=M().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Po(Brt,n.shape,o.shape):new lo(zrt,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},Mz={kernelName:Cp,backendName:\"webgl\",kernelFunc:Vrt};var Grt=`\n return vec4(equal(a, b));\n`,Wrt=\"return float(a == b);\",Urt=le({opSnippet:Wrt,packedOpSnippet:Grt,dtype:\"bool\",cpuKernelImpl:yL}),zz={kernelName:Ia,backendName:\"webgl\",kernelFunc:Urt};var Hrt=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${S.ERF_P};\n float a1 = ${S.ERF_A1};\n float a2 = ${S.ERF_A2};\n float a3 = ${S.ERF_A3};\n float a4 = ${S.ERF_A4};\n float a5 = ${S.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,qrt=Ct({opSnippet:Hrt}),Bz={kernelName:Ca,backendName:\"webgl\",kernelFunc:qrt};var Krt=Lo+`\n return exp(x);\n`,jrt=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Ik=Ct({opSnippet:Krt,packedOpSnippet:jrt,cpuKernelImpl:bL,dtype:\"float32\"}),Vz={kernelName:rs,backendName:\"webgl\",kernelFunc:Ik};function EC(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),it({inputs:{x:s},backend:n,attrs:{shape:a}})}var Gz={kernelName:mi,backendName:\"webgl\",kernelFunc:EC};var Wz=\"return exp(x) - 1.0;\",Xrt=Ct({opSnippet:Wz,packedOpSnippet:Wz,cpuKernelImpl:wL}),Uz={kernelName:va,backendName:\"webgl\",kernelFunc:Xrt};var sg=class{constructor(t,e,n){this.variableNames=[\"real\",\"imag\"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:\"1.0\",a;if(t===\"real\")a=\"return real * expR - imag * expI;\";else if(t===\"imag\")a=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${t}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${a}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${o});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${o}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${i};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function _C(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=it({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new sg(\"real\",u,t),c=new sg(\"imag\",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,\"float32\"),f=e.runWebGLProgram(c,p,\"float32\"),d=$n({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=it({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function Yrt(r){let{inputs:t,backend:e}=r,{input:n}=t;return _C(n,!1,e)}var Hz={kernelName:Ip,backendName:\"webgl\",kernelFunc:Yrt};var AC=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=t,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function Sl(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||y.inferDtype(o),s===\"string\"){let i=y.getArrayFromDType(s,y.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new AC(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var qz={kernelName:Ol,backendName:\"webgl\",kernelFunc:Sl};var $C=class{constructor(t){this.variableNames=[\"Image\"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${e} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${e}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var Kz={kernelName:Sa,backendName:\"webgl\",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new $C(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var jz=\"return floor(x);\",Zrt=Ct({opSnippet:jz,packedOpSnippet:jz,cpuKernelImpl:CL}),Xz={kernelName:ns,backendName:\"webgl\",kernelFunc:Zrt};var Jrt=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,Qrt=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,tnt=le({opSnippet:Jrt,packedOpSnippet:Qrt,dtype:\"int32\"}),Yz={kernelName:os,backendName:\"webgl\",kernelFunc:tnt};var DC=class{constructor(t){this.variableNames=[\"A\"];let e=Ge(),[n,o]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);\n\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var RC=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let e=Ge(),[n,o]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${o}.0, ${n}.0);\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${e.output} = result;\n }\n `}};var Zz={kernelName:Zd,backendName:\"webgl\",kernelFunc:ent},Dd,vk=M().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function ent(r){let{inputs:t,backend:e,attrs:n}=r,{pixels:o}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!=\"undefined\"&&o instanceof HTMLVideoElement,a=typeof HTMLImageElement!=\"undefined\"&&o instanceof HTMLImageElement,[u,l]=i?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[l,u],p=[l,u,s];if(a||i){let h=M().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Dd==null||h!==vk)&&(vk=h,Dd=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:vk})),Dd.canvas.width=u,Dd.canvas.height=l,Dd.drawImage(o,0,0,u,l),o=Dd.canvas}let m=e.makeTensorInfo(c,\"int32\");e.texData.get(m.dataId).usage=Yr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),o);let f=M().getBool(\"WEBGL_PACK\")?new RC(p):new DC(p),d=e.runWebGLProgram(f,[m],\"int32\");return e.disposeData(m.dataId),d}function rnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(o.shape,s.shape,u,p,l,m,!1,h),x,b=[],w=i!=null,C=a!=null,N=f===\"leakyrelu\",E=()=>{let $=[o,s],F=(P,V)=>{if(V===\"NCHW\"&&P.shape.length===1&&P.shape[0]!==1){let G=it({inputs:{x:P},backend:e,attrs:{shape:[P.shape[0],1,1]}});return b.push(G),G}return P};if(w&&$.push(F(i,c)),C&&$.push(F(a,c)),N){let P=e.makeTensorInfo([],\"float32\",y.createScalarValue(d,\"float32\"));$.push(P),b.push(P)}return $};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))x=hC({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else if(g.strideWidth<=2&&h===\"channelsLast\"&&M().getBool(\"WEBGL_EXP_CONV\")){let $=f?Il(f,!0):null,F=new _d(g,w,$,C,N),P=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],V=E();x=e.runWebGLProgram(F,V,\"float32\",P)}else if(M().getBool(\"WEBGL_CONV_IM2COL\"))x=gC({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else{let $=f?Il(f,!1):null,F=new Ed(g,w,$,C,N),P=E();x=e.runWebGLProgram(F,P,\"float32\")}let A=it({inputs:{x},backend:e,attrs:{shape:g.outShape}});return b.push(x),b.forEach($=>e.disposeIntermediateTensorInfo($)),A}var Jz={kernelName:Ni,backendName:\"webgl\",kernelFunc:rnt};function nnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let g=S.computeConv2DInfo(o.shape,s.shape,u,h,l,p,!0),x=M().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?Il(m,x):null,w=[o,s],C=i!=null,N=a!=null,E=m===\"leakyrelu\";if(C&&w.push(i),N&&w.push(a),E){let P=e.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));w.push(P),d.push(P)}let A;x?A=new $d(g,C,b,N,E):A=new Ad(g,C,b,N,E);let $=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=e.runWebGLProgram(A,w,\"float32\",$);return d.forEach(P=>e.disposeIntermediateTensorInfo(P)),F}var Qz={kernelName:Ti,backendName:\"webgl\",kernelFunc:nnt};var FC=class{constructor(t,e,n,o){this.sliceDim=t,this.strides=e,this.paramsShape=o,this.variableNames=[\"x\",\"indices\"],this.outputShape=n;let s=Bt(n.length),i=`\n int index;`;for(let a=0;a= ${this.paramsShape[a]};\n flattenIndex += index * ${this.strides[a]};`;this.userCode=`\n void main() {\n ${s} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n\n ${i}\n\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}};function ont(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=o.shape,i=s[s.length-1],a=y.sizeFromShape(n.shape),[u,l,c,p]=S.prepareAndValidate(n,o),m=it({inputs:{x:o},backend:e,attrs:{shape:[l,i]}}),f=it({inputs:{x:n},backend:e,attrs:{shape:[y.sizeFromShape(n.shape)/c,c]}});if(e.shouldExecuteOnCPU([n,o])||n.dtype===\"string\"){let x=e.readSync(o.dataId),b=e.bufferSync(n),w=IL(x,b,n.dtype,l,i,c,p,n.shape,a);return e.makeTensorInfo(u,n.dtype,w.values)}let d=new FC(i,p,[l,c],n.shape),h=e.runWebGLProgram(d,[f,m],f.dtype),g=it({inputs:{x:h},backend:e,attrs:{shape:u}});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),g}var t3={kernelName:Na,backendName:\"webgl\",kernelFunc:ont};var OC=class{constructor(t,e){this.variableNames=[\"A\",\"indices\"],this.outputShape=e,this.rank=e.length;let n=Bt(this.rank),o=snt(t,2);this.userCode=`\n void main() {\n ${n} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${t[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${o}));\n }\n `}};function snt(r,t){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],n=[];for(let o=0;o=0,()=>`GatherV2: the index value ${N} is not in [0, ${w-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),c=y.sizeFromShape(s.shape),p=[],m=it({inputs:{x:o},backend:e,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=it({inputs:{x:s},backend:e,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(m),p.push(f);let d=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(e.shouldExecuteOnCPU([o,s])||o.dtype===\"string\"){let b=e.bufferSync(f),w=e.bufferSync(m),C=vL(w,b,d);return p.forEach(N=>e.disposeIntermediateTensorInfo(N)),e.makeTensorInfo(l.outputShape,C.dtype,C.values)}let h=new OC(m.shape,d),g=e.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=it({inputs:{x:g},backend:e,attrs:{shape:l.outputShape}});return p.forEach(b=>e.disposeIntermediateTensorInfo(b)),x}var e3={kernelName:fi,backendName:\"webgl\",kernelFunc:Sk};var int=\"return float(a > b);\",ant=`\n return vec4(greaterThan(a, b));\n`,lnt=le({opSnippet:int,packedOpSnippet:ant,cpuKernelImpl:SL,dtype:\"bool\"}),r3={kernelName:Ta,backendName:\"webgl\",kernelFunc:lnt};var unt=\"return float(a >= b);\",cnt=`\n return vec4(greaterThanEqual(a, b));\n`,pnt=le({opSnippet:unt,packedOpSnippet:cnt,dtype:\"bool\",cpuKernelImpl:NL}),n3={kernelName:is,backendName:\"webgl\",kernelFunc:pnt};function mnt(r){let{inputs:t,backend:e}=r,{input:n}=t;return _C(n,!0,e)}var o3={kernelName:vp,backendName:\"webgl\",kernelFunc:mnt};var fnt=\"return float(!isnan(x) && !isinf(x));\",dnt=Ct({opSnippet:fnt,dtype:\"bool\"}),s3={kernelName:ka,backendName:\"webgl\",kernelFunc:dnt};var hnt=\"return float(isinf(x));\",gnt=Ct({opSnippet:hnt,dtype:\"bool\"}),i3={kernelName:Ea,backendName:\"webgl\",kernelFunc:gnt};var xnt=\"return float(isnan(x));\",ynt=Ct({opSnippet:xnt,dtype:\"bool\"}),a3={kernelName:as,backendName:\"webgl\",kernelFunc:ynt};var bnt=\"return float(a < b);\",wnt=`\n return vec4(lessThan(a, b));\n`,Cnt=le({opSnippet:bnt,packedOpSnippet:wnt,cpuKernelImpl:TL,dtype:\"bool\"}),l3={kernelName:_a,backendName:\"webgl\",kernelFunc:Cnt};var Int=\"return float(a <= b);\",vnt=`\n return vec4(lessThanEqual(a, b));\n`,Snt=le({opSnippet:Int,packedOpSnippet:vnt,cpuKernelImpl:kL,dtype:\"bool\"}),u3={kernelName:Aa,backendName:\"webgl\",kernelFunc:Snt};function Nnt(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=EL(n,o,s);return t.makeTensorInfo([i.length],\"float32\",i)}var c3={kernelName:Np,backendName:\"webgl\",kernelFunc:Nnt};var Tnt=Lo+`\n return x < 0.0 ? 0./0. : log(x);\n`,knt=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,Ent=Ct({opSnippet:Tnt,packedOpSnippet:knt,cpuKernelImpl:_L}),p3={kernelName:us,backendName:\"webgl\",kernelFunc:Ent};var _nt=Lo+`\n return log(1.0 + x);\n`,Ant=Ct({opSnippet:_nt}),m3={kernelName:$a,backendName:\"webgl\",kernelFunc:Ant};var $nt=\"return float(a >= 1.0 && b >= 1.0);\",Dnt=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,Rnt=le({opSnippet:$nt,packedOpSnippet:Dnt,dtype:\"bool\"}),f3={kernelName:Da,backendName:\"webgl\",kernelFunc:Rnt};var Fnt=\"return float(!(x >= 1.0));\",Ont=Ct({opSnippet:Fnt}),d3={kernelName:Ra,backendName:\"webgl\",kernelFunc:Ont};var Pnt=\"return float(a >= 1.0 || b >= 1.0);\",Lnt=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,Mnt=le({opSnippet:Pnt,packedOpSnippet:Lnt,dtype:\"bool\"}),h3={kernelName:Fa,backendName:\"webgl\",kernelFunc:Mnt};var PC=class{constructor(t,e,n,o,s){this.variableNames=[\"x\"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${i}; j <= ${i}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${a}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${u};\n setOutput(val);\n }\n `}};var LC=class{constructor(t,e,n,o,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${i};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${i}; j <= ${i}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${a}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${u};\n setOutput(result);\n }\n `}};var znt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=M().getBool(\"WEBGL_PACK_NORMALIZATION\")?new LC(o.shape,s,i,a,u):new PC(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},g3={kernelName:Pl,backendName:\"webgl\",kernelFunc:znt};var MC=class{constructor(t,e,n,o,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${e})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${e} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${o}) * norm + float(${n});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${o})\n * float(${s})\n * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var Bnt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new MC(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},x3={kernelName:Tp,backendName:\"webgl\",kernelFunc:Bnt};function y3(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=it({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=qn(a,r.dtype,\"max\",n),l=it({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function Nk(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,C=new Array(a);for(let A=0;A`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new oi(c,\"max\",!1);return e.runWebGLProgram(p,[o],o.dtype)}var C3={kernelName:ms,backendName:\"webgl\",kernelFunc:Unt};function Hnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new Ru(p,\"max\",!1);return e.runWebGLProgram(m,[o],o.dtype)}var I3={kernelName:Ll,backendName:\"webgl\",kernelFunc:Hnt};var zC=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${u});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${o}) {\n float dyR = float(dyRCorner + wR) / ${e}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${i}; wC++) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${i} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},BC=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${m}, ${f});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${u};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${e}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${l};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${a}) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${d} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${l} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function qnt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new Ru(m,\"max\",!0),d=e.runWebGLProgram(f,[i],i.dtype),h=new BC(m),g=e.runWebGLProgram(h,[o,d],i.dtype);return e.disposeIntermediateTensorInfo(d),g}var v3={kernelName:Ep,backendName:\"webgl\",kernelFunc:qnt};function Knt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;ri([s,i],\"maxPoolGrad\");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=!0,d=new oi(m,\"max\",f),h=e.runWebGLProgram(d,[a],a.dtype),g=new zC(m),x=e.runWebGLProgram(g,[o,h],a.dtype);return e.disposeIntermediateTensorInfo(h),x}var S3={kernelName:kp,backendName:\"webgl\",kernelFunc:Knt};function N3(r,t,e,n){let o=new oi(e,\"max\",!1),s=n.runWebGLProgram(o,[r],\"float32\");o=new oi(e,\"max\",!0,!0,t);let i=n.runWebGLProgram(o,[r],\"float32\");return[s,i]}var T3={kernelName:_p,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${l}'`);let c=S.computePool2DInfo(n.shape,o,s,l,i),[p,m]=N3(n,a,c,u);return[p,m]}};function k3(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=it({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=qn(a,\"float32\",\"mean\",n),l=it({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}var E3={kernelName:fs,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{keepDims:o,axis:s}=t,i=e,a=n.shape.length,u=y.parseAxisParam(s,n.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=i.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let C=i.texData.get(d.dataId).values,N=new Array(a);for(let $=0;$c[0]+t[p]+c[1]);let o=t.length,s=Bt(o),i=e.map(c=>c[0]).join(\",\"),a=e.map((c,p)=>c[0]+t[p]).join(\",\"),u=[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,o),l=n===\"reflect\"?0:1;if(o===1){this.userCode=`\n int start = ${i};\n int end = ${a};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start) {\n outC = start * 2 - outC - ${l};\n } else if(outC >= end) {\n outC = (end - 1) * 2 - outC + ${l};\n }\n setOutput(getX(outC - start));\n }\n `;return}this.userCode=`\n ${s} start = ${s}(${i});\n ${s} end = ${s}(${a});\n\n void main() {\n ${s} outC = getOutputCoords();\n for (int i = 0; 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i < ${e-1}; i++) {\n cdf += getProbs(batch, i);\n\n if (r < cdf) {\n setOutput(float(i));\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutput(float(${e-1}));\n }\n `}};var rot=`\nif (a == b) {\n return 1.0;\n};\nreturn a / b;`,not=`\n // vec4 one = vec4(equal(a, b));\n // return one + (vec4(1.0) - one) * a / b;\n vec4 result = a / b;\n if(a.x == b.x) {\n result.x = 1.;\n }\n if(a.y == b.y) {\n result.y = 1.;\n }\n if(a.z == b.z) {\n result.z = 1.;\n }\n if(a.w == b.w) {\n result.w = 1.;\n }\n\n return result;\n`,Tk=le({opSnippet:rot,packedOpSnippet:not,checkOutOfBounds:!0}),R3={kernelName:ts,backendName:\"webgl\",kernelFunc:Tk};var F3=\"return a - b;\",kk=le({opSnippet:F3,packedOpSnippet:F3,supportsComplex:!0,cpuKernelImpl:JL}),O3={kernelName:Ls,backendName:\"webgl\",kernelFunc:kk};function Ek(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{dim:s}=n,i=y.parseAxisParam([s],o.shape),a=Nk({inputs:{x:o},backend:e,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(a.shape,i),l=it({inputs:{x:a},backend:e,attrs:{shape:u}}),c=kk({inputs:{a:o,b:l},backend:e}),p=Ik({inputs:{x:c},backend:e}),m=Uc({inputs:{x:p},backend:e,attrs:{axis:i,keepDims:!1}}),f=it({inputs:{x:m},backend:e,attrs:{shape:u}}),d=Tk({inputs:{a:p,b:f},backend:e});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}var P3={kernelName:Os,backendName:\"webgl\",kernelFunc:Ek};function oot(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n,u=a?o:Ek({inputs:{logits:o},backend:e,attrs:{dim:o.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new WC(l,c,s),m=[[i]],f=e.runWebGLProgram(p,[u],\"int32\",m);return a||e.disposeIntermediateTensorInfo(u),f}var L3={kernelName:Ap,backendName:\"webgl\",kernelFunc:oot};var sot=hr+`\n return -x;\n`,iot=`\n vec4 result = -x;\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`;function aot(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])){let s=e.texData.get(n.dataId),[i,a]=FL(s.values,n.shape,n.dtype);return e.makeTensorInfo(a,n.dtype,i)}let o;return M().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?o=new An(n.shape,iot):o=new Mr(n.shape,sot),e.runWebGLProgram(o,[n],n.dtype)}var M3={kernelName:di,backendName:\"webgl\",kernelFunc:aot};var lot=qr.nonMaxSuppressionV3Impl;function uot(r){S.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),{selectedIndices:p}=lot(l,c,i,a,u);return e.makeTensorInfo([p.length],\"int32\",new Int32Array(p))}var z3={kernelName:La,backendName:\"webgl\",kernelFunc:uot};var cot=qr.nonMaxSuppressionV4Impl;function pot(r){S.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. 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UC(l,i,a,u),p=it({inputs:{x:o},backend:e,attrs:{shape:[l]}}),m=e.runWebGLProgram(c,[p],s);e.disposeIntermediateTensorInfo(p);let f=[...o.shape,i],d=it({inputs:{x:m},backend:e,attrs:{shape:f}});return e.disposeIntermediateTensorInfo(m),d},G3={kernelName:ys,backendName:\"webgl\",kernelFunc:dot};function ig(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype===\"complex64\"){let o=vl({inputs:{input:n},backend:e}),s=ig({inputs:{x:o},backend:e}),i=qc({inputs:{input:n},backend:e}),a=ig({inputs:{x:i},backend:e}),u=$n({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Sl({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype===\"string\"?\"\":0},backend:e})}var W3={kernelName:vi,backendName:\"webgl\",kernelFunc:ig};function U3(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype===\"string\")throw new Error(\"onesLike is not supported under string 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a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var q3={kernelName:gi,backendName:\"webgl\",kernelFunc:hot};var HC=class{constructor(t,e,n){this.variableNames=[\"x\"],this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=e.map((l,c)=>l[0]+t[c]+l[1]);let o=t.length,s=Bt(o),i=e.map(l=>l[0]).join(\",\"),a=e.map((l,c)=>l[0]+t[c]).join(\",\"),u=[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,o);if(o===1){this.userCode=`\n int start = ${i};\n int end = ${a};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start || outC >= end) {\n setOutput(value);\n } else {\n setOutput(getX(outC - start));\n }\n }\n `;return}this.userCode=`\n ${s} start = ${s}(${i});\n ${s} end = ${s}(${a});\n\n void main() {\n ${s} outC = getOutputCoords();\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n setOutput(value);\n } else {\n ${s} coords = outC - start;\n setOutput(getX(${u}));\n }\n }\n `}};var qC=class{constructor(t,e,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=Bt(o),i=e.map(h=>h[0]).join(\",\"),a=e.map((h,g)=>h[0]+t[g]).join(\",\"),u=er(\"rc\",o),l=er(\"source\",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?\"source\":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1;\n if(${c}) {\n `,o===1?\"\":`}\n rc = outputLoc;\n ${u[o-2]} += 1;\n if(${u[o-2]} < ${this.outputShape[o-2]}) {`,o===1?\"\":` ${u[o-1]} += 1;\n if(${c}) {`],f=o===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",d=\"\";for(let h=0,g=o===1?2:4;h{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(y.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Sl({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=M().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new qC(o.shape,s,i):new HC(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},K3={kernelName:bs,backendName:\"webgl\",kernelFunc:_k};var got=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,xot=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = lessThan(floor(b), b);\n bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);\n `+ea+`\n return result;\n`,yot=le({opSnippet:got,packedOpSnippet:xot}),j3={kernelName:ws,backendName:\"webgl\",kernelFunc:yot};function bot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=[],l=y.parseAxisParam(s,o.shape),c=l,p=S.getAxesPermutation(c,a),m=o;p!=null&&(m=Oe({inputs:{x:o},backend:e,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,a),u.push(m)),S.assertAxesAreInnerMostDims(\"prod\",c,a);let f;if(e.shouldExecuteOnCPU([m])){let d=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=PL(m.shape,m.dtype,d,c);f=e.makeTensorInfo(g,x,h)}else{let[d,h]=S.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=it({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=Hu(o.dtype),w=qn(x,b,\"prod\",e);f=it({inputs:{x:w},backend:e,attrs:{shape:d}}),u.push(x),u.push(w)}if(i){u.push(f);let d=S.expandShapeToKeepDim(f.shape,l);f=it({inputs:{x:f},backend:e,attrs:{shape:d}})}return u.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var X3={kernelName:Is,backendName:\"webgl\",kernelFunc:bot};function wot(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=LL(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],\"int32\",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var Y3={kernelName:$p,backendName:\"webgl\",kernelFunc:wot};function Cot(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=ML(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],\"int32\",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var Z3={kernelName:Dp,backendName:\"webgl\",kernelFunc:Cot};function Iot(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),p=e.readSync(i.dataId),m=a.map(g=>e.readSync(g.dataId)),f=a.map(g=>g.shape),[d,h]=zL(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var J3={kernelName:Rp,backendName:\"webgl\",kernelFunc:Iot};var Ak=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=BL(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},Q3={kernelName:Ml,backendName:\"webgl\",kernelFunc:Ak};var vot=\"return 1.0 / x;\",Sot=Ct({opSnippet:vot}),tB={kernelName:vs,backendName:\"webgl\",kernelFunc:Sot};var Not=hr+`\n return (x < 0.0) ? 0.0 : x;\n`,Tot=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,kot=Ct({opSnippet:Not,packedOpSnippet:Tot}),eB={kernelName:Ss,backendName:\"webgl\",kernelFunc:kot};var Eot=hr+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,_ot=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Aot=Ct({opSnippet:Eot,packedOpSnippet:_ot}),rB={kernelName:ks,backendName:\"webgl\",kernelFunc:Aot};var KC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/p[0]},\n ${c[1]/p[1]});\n const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var jC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/p[0]},\n ${c[1]/p[1]},\n ${c[1]/p[1]});\n const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,\n ${u}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${l-1};\n bool hasNextRow = coords.z < ${n-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function $ot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=M().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new jC(o.shape,u,l,s,i):new KC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],\"float32\")}var nB={kernelName:Ts,backendName:\"webgl\",kernelFunc:$ot};var XC=class{constructor(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${p});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${f});\n\n const int winHeight = int(${d});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${a}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${o-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function Dot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new XC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var oB={kernelName:Pp,backendName:\"webgl\",kernelFunc:Dot};var YC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?\"0.5\":\"0.0\",f;s?f=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":f=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/p[0]},\n ${c[1]/p[1]});\n const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${f};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var ZC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?\"0.5\":\"0.0\",f;s?f=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":f=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/p[0]},\n ${c[1]/p[1]},\n ${c[1]/p[1]});\n const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,\n ${u}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${f};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${l-1};\n bool hasNextRow = coords.z < ${n-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function Rot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=M().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new ZC(o.shape,u,l,s,i):new YC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var sB={kernelName:Ns,backendName:\"webgl\",kernelFunc:Rot};var JC=class{constructor(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${p});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${f});\n\n const int winHeight = int(${d});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${a}) {\n continue;\n }\n\n float sourceFracRow =\n float(${u[0]}) *\n (float(dyR) / float(${l[0]}));\n\n float sourceFracCol =\n float(${u[1]}) *\n (float(dyC) / float(${l[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${o}) - 1),\n ${n} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${n} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function Fot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new JC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var iB={kernelName:Op,backendName:\"webgl\",kernelFunc:Fot};var QC=class{constructor(t,e){this.variableNames=[\"x\"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${t[0]} - coord - 1));\n }\n `;return}let o=a=>e.indexOf(a)!==-1&&t[a]!==1?`${t[a]} - coords[${a}] - 1`:`coords[${a}]`,s=t.map((a,u)=>o(u)).join(\",\"),i=Bt(n);this.userCode=`\n void main() {\n ${i} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var tI=class{constructor(t,e){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=er(\"rc\",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=Bt(n);n===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${t[0]} - rc - 1),\n ${t[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${t[0]} - (rc + 1) - 1),\n ${t[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${a} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${u(o.slice())};\n if(${s}){\n result.g = ${l(o.slice())};\n }\n if(${i}) {\n result.b = ${c(o.slice())};\n if(${s}) {\n result.a = ${p(o.slice())};\n }\n }\n setOutput(result);\n }\n `;function u(d){return m(d)}function l(d){return d[n-1]=\"(\"+d[n-1]+\" + 1)\",m(d)}function c(d){return d[n-2]=\"(\"+d[n-2]+\" + 1)\",m(d)}function p(d){return d[n-1]=\"(\"+d[n-1]+\" + 1)\",d[n-2]=\"(\"+d[n-2]+\" + 1)\",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function Oot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=o.shape.length,a=y.parseAxisParam(s,o.shape);if(i===0)return rr({inputs:{x:o},backend:e});let u=M().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new tI(o.shape,a):new QC(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var aB={kernelName:Es,backendName:\"webgl\",kernelFunc:Oot};var eI=class{constructor(t,e){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let n=t[1],o=t[2];this.outputShape=t;let s=\"\";typeof e==\"number\"?s=`float outputValue = ${e.toFixed(2)};`:s=`\n vec3 fill = vec3(${e.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${n}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var lB={kernelName:Xa,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new eI(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var Pot=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n 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Bot(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=[p/l,l];if(p===0)return e.makeTensorInfo(i,o.dtype);let f=it({inputs:{x:o},backend:e,attrs:{shape:[u,a]}}),d=it({inputs:{x:s},backend:e,attrs:{shape:[u,l]}}),h=e.makeTensorInfo([],\"float32\",new Float32Array([0])),g=new Rd(u,a,f.shape.length,d.shape.length,c,m),x=e.runWebGLProgram(g,[d,f,h],d.dtype),b=it({inputs:{x},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var pB={kernelName:Ba,backendName:\"webgl\",kernelFunc:Bot};var rI=class{constructor(t,e,n,o){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[t,n];let s=\"while (left < right) {\",i=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) 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supported`);if(n===1)s=\"resRC\",o=\"resRC\";else{let a=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],u=[],l=[];for(let c=0;c= 1.0) {\n setOutput(getA(${s}));\n } else {\n setOutput(getB(${s}));\n }\n }\n `}};function Got(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t,i=new nI(n.shape.length,o.shape,o.shape.length);return e.runWebGLProgram(i,[n,o,s],ar(o.dtype,s.dtype))}var fB={kernelName:yi,backendName:\"webgl\",kernelFunc:Got};var Wot=`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${S.SELU_SCALEALPHA};\n float scale = ${S.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,Uot=Ct({opSnippet:Wot}),dB={kernelName:Va,backendName:\"webgl\",kernelFunc:Uot};var Hot=Lo+`\n return 1.0 / (1.0 + exp(-1.0 * x));\n`,qot=`\n vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Kot=Ct({opSnippet:Hot,packedOpSnippet:qot,cpuKernelImpl:WL}),hB={kernelName:Ds,backendName:\"webgl\",kernelFunc:Kot};var jot=`\n if (isnan(x)) { return 0.0; }\n return sign(x);\n`,Xot=Ct({opSnippet:jot}),gB={kernelName:Wa,backendName:\"webgl\",kernelFunc:Xot};var Yot=Lo+`\n return sin(x);\n`,Zot=Ct({opSnippet:Yot}),xB={kernelName:$s,backendName:\"webgl\",kernelFunc:Zot};var Jot=`\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`,Qot=Ct({opSnippet:Jot}),yB={kernelName:Ga,backendName:\"webgl\",kernelFunc:Qot};var tst=`\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return 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a=e.readSync(n.dataId),u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=e.readSync(i.dataId)[0],[p,m,f,d,h]=HL(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],\"bool\",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var CB={kernelName:zl,backendName:\"webgl\",kernelFunc:nst};function ost(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.readSync(o.dataId)),a=e.readSync(n.dataId),u=Array.from(e.readSync(s.dataId)),[l,c,p]=qL(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var IB={kernelName:Ha,backendName:\"webgl\",kernelFunc:ost};function sst(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=Bw(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var vB={kernelName:Bl,backendName:\"webgl\",kernelFunc:sst};function ist(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error(\"Data should be at 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e.disposeIntermediateTensorInfo(h),g}var NB={kernelName:Mp,backendName:\"webgl\",kernelFunc:ast};function lst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=o.shape.length,c=new Array(l).fill(0),p=o.shape.slice();return u.map(m=>{let f=[...p];f[a]=m;let d=si({inputs:{x:o},backend:e,attrs:{begin:c,size:f}});return c[a]+=m,d})}var TB={kernelName:Ci,backendName:\"webgl\",kernelFunc:lst};var kB=\"return sqrt(x);\",ust=Ct({opSnippet:kB,packedOpSnippet:kB,cpuKernelImpl:KL}),EB={kernelName:Rs,backendName:\"webgl\",kernelFunc:ust};var cst=\"return x * x;\",pst=Ct({opSnippet:cst}),_B={kernelName:Gl,backendName:\"webgl\",kernelFunc:pst};var AB=\"return (a - b) * (a - b);\",mst=le({opSnippet:AB,packedOpSnippet:AB}),$B={kernelName:Ps,backendName:\"webgl\",kernelFunc:mst};function fst({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=hr+`\n return x > 0.0 ? 1.0 : float(${t.alpha});\n `,s=new Mr(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var DB={kernelName:fo,backendName:\"webgl\",kernelFunc:fst};var oI=class{constructor(t,e,n){this.variableNames=[\"x\"],this.outputShape=n;let o=n.length,s=Bt(n.length),i=Bt(n.length),a=\"\";if(o===1)a=\"coords * strides + begin\";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${u-1}] * strides[${c}] + begin[${c}]`)).join(\",\")}this.userCode=`\n ${s} begin = ${s}(${t});\n ${s} strides = ${s}(${e});\n\n void main() {\n ${i} coords = getOutputCoords();\n setOutput(getX(${a}));\n }\n `}};function dst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:C}=Me.sliceInfo(o.shape,s,i,a,u,l,c,p,m),N;if(h)N=it({inputs:{x:o},backend:e,attrs:{shape:d}});else if(g||x){y.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: 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iI=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=t,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. 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When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n `}};function jc(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function BB(r){let t=1;for(;tu){let P=e.readSync(o.dataId),[V,G]=tM(P,l,o.dtype,s,i);return[e.makeTensorInfo(V.shape,V.dtype,V.values),e.makeTensorInfo(G.shape,G.dtype,G.values)]}if(s===0)return l[l.length-1]=0,[e.makeTensorInfo(l,o.dtype,[]),e.makeTensorInfo(l,\"int32\",[])];if(c===1)return[o,Sl({attrs:{shape:l,dtype:\"int32\",value:0},backend:e})];let p=e.texData.get(o.dataId),m=p!==null&&p.isPacked,f=m?e.unpackTensor(o):o,h=y.sizeFromShape(l)/c,g=it({inputs:{x:f},attrs:{shape:[h,c]},backend:e});m&&jc(e,f);let x=BB(s),b=BB(c),w=null,C=()=>w===null?[g,g]:[g,w],N=(P,V,G)=>{let W=C(),q=new iI(G),j=[[c],[w===null?1:0],[Number.NEGATIVE_INFINITY],[P],[V]],Y=w;w=e.runWebGLProgram(q,W,\"int32\",j),jc(e,Y)};for(let P=1;P=1;G/=2)N(V,G,[h,b])}for(let P=b;P>x;P/=2){let V=C(),G=new aI([h,P/2]),q=[[c],[w===null?1:0],[x]],H=w;w=e.runWebGLProgram(G,V,\"int32\",q),jc(e,H);let j=x/2,Y=j*2;for(let Z=j;Z>=1;Z/=2)N(Y,Z,w.shape)}let E=w;w=si({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),jc(e,E);let A=Sk({inputs:{x:g,indices:w},backend:e,attrs:{axis:1,batchDims:1}});jc(e,g);let $=l.slice(0,-1);$.push(s),E=w,w=it({inputs:{x:w},attrs:{shape:$},backend:e}),jc(e,E);let F=A;return A=it({inputs:{x:A},attrs:{shape:$},backend:e}),jc(e,F),[A,w]}var VB={kernelName:Ka,backendName:\"webgl\",kernelFunc:vst};var lI=class{constructor(t,e,n,o,s,i){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=i;let a=n===\"nearest\"?1:2,u;switch(o){case\"constant\":u=1;break;case\"reflect\":u=2;break;case\"wrap\":u=3;break;case\"nearest\":u=4;break;default:u=1;break}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${u} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${u} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${u} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${t} && 0 <= coordX && coordX < ${e}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = getTransforms(batch, 2);\n float b1 = getTransforms(batch, 3);\n float b2 = getTransforms(batch, 4);\n float b3 = getTransforms(batch, 5);\n float c1 = getTransforms(batch, 6);\n float c2 = getTransforms(batch, 7);\n float projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = float(${s});\n } else {\n float inX = (a1 * xf + a2 * yf + a3) / projection;\n float inY = (b1 * xf + b2 * yf + b3) / projection;\n float mapX = mapCoord(inX, float(${e}));\n float mapY = mapCoord(inY, float(${t}));\n\n if (${a} == 1) {\n int coordY = int(round(mapY));\n int coordX = int(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n float yFloor = floor(mapY);\n float xFloor = floor(mapX);\n float yCeil = yFloor + 1.0;\n float xCeil = xFloor + 1.0;\n float valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n float valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}};function Sst(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,transforms:s}=t,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=n,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],x=new lI(p,m,i,a,u,g);return e.runWebGLProgram(x,[o,s],\"float32\")}var GB={kernelName:ja,backendName:\"webgl\",kernelFunc:Sst};function Nst(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;ri(s,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let i=n.readSync(s.dataId),{outputValues:a,outputShape:u,indices:l}=eM(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],\"int32\",l)]}var WB={kernelName:zp,backendName:\"webgl\",kernelFunc:Nst};function Tst(r){let{inputs:t,backend:e,attrs:n}=r,{value:o}=t,{axis:s}=n;s<0&&(s+=o.shape.length);let i=o,a=i.shape.length,u=o.shape[s],l=new Array(a-1),c=0;for(let h=0;he.disposeIntermediateTensorInfo(h)),d}var UB={kernelName:Ii,backendName:\"webgl\",kernelFunc:Tst};var uI=class{constructor(t,e){this.variableNames=[\"x\",\"segmentIds\"];let n=t.windowSize,o=t.batchSize,s=t.inSize,i=t.numSegments,a=i*Math.ceil(s/n);this.outputShape=[o,a];let u=\"0.0\",l=\"sumValue\",c=Math.floor(n/4)*4,p=n%4,m=`\n sumValue += dot(values, segFilter);\n `,f=\"\";s%n>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let d=\"\";s%n>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${u};\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${d}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${i})) * float(${n}));\n int currentSeg = int(mod(float(outIdx), float(${i})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${p===1}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${l});\n }\n `}};function kst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,segmentIds:s}=t,{numSegments:i}=n,a=o.shape.length,u=[],l=0,c=S.getAxesPermutation([l],a),p=o;c!=null&&(p=Oe({inputs:{x:o},backend:e,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,a)[0]);let m=S.segment_util.computeOutShape(p.shape,l,i),f=y.sizeFromShape([p.shape[l]]),d=it({inputs:{x:p},backend:e,attrs:{shape:[-1,f]}});u.push(d);let h=Hu(o.dtype),g=(C,N,E,A,$)=>{let F=C.shape[0],P=C.shape[1],V=S.segment_util.segOpComputeOptimalWindowSize(P,$),G={windowSize:V,inSize:P,batchSize:F,numSegments:$},W=new uI(G,N),q=e.compileAndRun(W,[C,E],A);if(u.push(q),q.shape[1]===$)return q;let H=Ak({backend:e,attrs:{start:0,stop:$,step:1,dtype:\"float32\"}}),j=$k({inputs:{x:H},backend:e,attrs:{reps:[P/V]}});return u.push(H),u.push(j),g(q,N,j,A,$)},x=g(d,\"unsortedSegmentSum\",s,h,i),b=it({inputs:{x},backend:e,attrs:{shape:m}}),w=b;if(c!=null){u.push(b);let C=S.getUndoAxesPermutation(c);w=Oe({inputs:{x:w},backend:e,attrs:{perm:C}})}return u.forEach(C=>e.disposeIntermediateTensorInfo(C)),w}var HB={kernelName:ql,backendName:\"webgl\",kernelFunc:kst};var Est=[AM,DM,RM,FM,PM,LM,MM,zM,GM,WM,UM,HM,qM,KM,jM,XM,YM,ZM,JM,QM,tz,rz,nz,oz,lz,cz,pz,wM,fz,hz,gz,xz,yz,bz,wz,Cz,Iz,vz,Sz,kz,Ez,_z,Az,$z,Dz,Rz,Fz,Oz,Pz,Lz,Mz,zz,Bz,Vz,Gz,Uz,Hz,qz,Kz,Xz,Yz,Zz,Jz,Qz,t3,e3,r3,n3,bM,o3,dz,s3,i3,a3,CM,l3,u3,c3,p3,m3,f3,d3,h3,g3,x3,b3,w3,C3,I3,v3,S3,T3,E3,_3,A3,$3,D3,L3,SM,M3,z3,B3,V3,sz,G3,H3,q3,K3,j3,IM,X3,Y3,Z3,J3,Q3,iz,R3,tB,eB,rB,TM,nB,oB,sB,iB,aB,lB,uB,cB,pB,mB,fB,dB,hB,gB,xB,yB,ez,P3,bB,wB,CB,IB,vB,SB,NB,TB,EB,_B,$B,DB,RB,FB,OB,PB,O3,EM,LB,MB,zB,VB,GB,_M,WB,UB,HB,W3];for(let r of Est)zu(r);var qt;(function(r){r[r.float32=0]=\"float32\",r[r.int32=1]=\"int32\",r[r.bool=2]=\"bool\",r[r.string=3]=\"string\",r[r.complex64=4]=\"complex64\"})(qt||(qt={}));var Fu;(function(r){r[r.linear=0]=\"linear\",r[r.relu=1]=\"relu\",r[r.relu6=2]=\"relu6\",r[r.prelu=3]=\"prelu\",r[r.leakyrelu=4]=\"leakyrelu\",r[r.sigmoid=5]=\"sigmoid\",r[r.elu=6]=\"elu\"})(Fu||(Fu={}));var qB;function _st(r){qB=r.wasm.cwrap(Si,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Ast(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t;if(o.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n,m=e.dataIdMap.get(o.dataId).id,f=e.dataIdMap.get(s.dataId).id,d=0;if(i!=null){let $=e.dataIdMap.get(i.dataId);if($.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${$.shape.length}.`);d=$.id}let h=a==null?0:e.dataIdMap.get(a.dataId).id,g=Fu[c];if(g==null)throw new Error(`${c} 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o(i){n=i.wasm.cwrap(r,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function s(i){let{backend:a,inputs:u}=i,{a:l,b:c}=u,p=a.dataIdMap.get(l.dataId).id,m=a.dataIdMap.get(c.dataId).id,f=e!=null?e:l.dtype,d=S.assertAndGetBroadcastShape(l.shape,c.shape),h=a.makeOutput(d,f);if(y.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(l.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=a.dataIdMap.get(h.dataId).id;return(()=>n(p,g,l.shape.length,m,x,c.shape.length,qt[l.dtype],b))(),h}return{kernelName:r,backendName:\"wasm\",setupFunc:o,kernelFunc:s}}var $st=!0,XB=ue(Qn,$st);var YB;function Dst(r){YB=r.wasm.cwrap(Wo,null,[\"array\",\"number\",\"number\",\"number\"])}function Rst(r){let{inputs:t,backend:e}=r,n=e.makeOutput(t[0].shape,t[0].dtype);if(y.sizeFromShape(n.shape)===0)return n;let o=t.map(a=>e.dataIdMap.get(a.dataId).id),s=new Uint8Array(new Int32Array(o).buffer),i=e.dataIdMap.get(n.dataId).id;return YB(s,o.length,qt[n.dtype],i),n}var ZB={kernelName:Wo,backendName:\"wasm\",setupFunc:Dst,kernelFunc:Rst};function Xc(r){let{inputs:{x:t},backend:e}=r;if(t.dtype===\"string\")return nr(e.readSync(t.dataId),t.shape,t.dtype);let n=e.makeOutput(t.shape,t.dtype),o=e.typedArrayFromHeap(t);return e.typedArrayFromHeap(n).set(o),n}var JB={kernelName:mo,backendName:\"wasm\",kernelFunc:Xc};var QB;function Fst(r){QB=r.wasm.cwrap(eo,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function uo(r){let{inputs:t,backend:e,attrs:n}=r,[o,s]=Pst(t.x.shape,n.perm),i=!0;for(let d=0;d=o&&(s===-1||n[s]>n[i])&&(s=i);n[s]=o}return[e,n]}var tV={kernelName:eo,backendName:\"wasm\",kernelFunc:uo,setupFunc:Fst};function Cn(r,t,e){let n=r.shape,o=r.shape.length,s=y.parseAxisParam(t,n),i=s,a=S.getAxesPermutation(i,o),u=null,l=!1;if(a!=null){let c=new Array(o);for(let f=0;f`new shape: ${i}, old shape: ${n.shape}. 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t.dtype===\"string\"?p.stringBytes=u.slice(d,d+y.sizeFromShape(i)):o.typedArrayFromHeap(l).set(u.subarray(d,d+y.sizeFromShape(i))),l}if(t.dtype===\"string\"){let d=Rc(u,s,i,t.shape,t.dtype);return p.stringBytes=d,l}let m=o.typedArrayFromHeap(l),f=t.shape.length;if(f===2)Kst(u,c[0],m,s,i);else if(f===3)jst(u,c[0],c[1],m,s,i);else if(f===4)Xst(u,c[0],c[1],c[2],m,s,i);else{let d=Rc(u,s,i,t.shape,t.dtype);m.set(d)}return l}function Kst(r,t,e,n,o){let s=0,i=n[0],a=n[1],u=i+o[0];for(let l=i;lx*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=ur({inputs:{x:o},backend:e,attrs:{shape:u}}),d=uo({inputs:{x:f},backend:e,attrs:{perm:l}}),h=ur({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Mo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeData(f.dataId),e.disposeData(d.dataId),e.disposeData(f.dataId),g}var 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Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. 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Promise.all([M().getAsync(\"WASM_HAS_SIMD_SUPPORT\"),M().getAsync(\"WASM_HAS_MULTITHREAD_SUPPORT\")]);return new Promise((e,n)=>{let o={};o.locateFile=(a,u)=>{if(a.endsWith(\".worker.js\")){let l=tU.wasmWorkerContents.replace(/\\n/g,\"\\\\n\"),c=new Blob([l],{type:\"application/javascript\"});return URL.createObjectURL(c)}return a.endsWith(\".wasm\")?QW(r,t,lg!=null?lg:u):u+a},Uk&&(o.instantiateWasm=ilt(QW(r,t,lg!=null?lg:\"\")));let s=!1;o.onAbort=()=>{if(s||cg)return;cg=!0,n({message:\"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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BroadcastArgs,d1 as BroadcastTo,Sb as Callback,Ly as CallbackList,co as Cast,Ko as Ceil,po as ClipByValue,mp as Complex,Dl as ComplexAbs,pi as Concat,jo as Conv2D,fp as Conv2DBackpropFilter,Xo as Conv2DBackpropInput,Rl as Conv3D,dp as Conv3DBackpropFilterV2,hp as Conv3DBackpropInputV2,Yo as Cos,Zo as Cosh,ba as CropAndResize,ya as Cumprod,Jo as Cumsum,zy as CustomCallback,aa as DataStorage,gp as DenseBincount,wa as DepthToSpace,Qo as DepthwiseConv2dNative,xp as DepthwiseConv2dNativeBackpropFilter,yp as DepthwiseConv2dNativeBackpropInput,bp as Diag,Fl as Dilation2D,Yd as Dilation2DBackpropFilter,Xd as Dilation2DBackpropInput,uS as ENV,Nb as EarlyStopping,wp as Einsum,es as Elu,Cp as EluGrad,Kd as Environment,Ia as Equal,Ca as Erf,rs as Exp,mi as ExpandDims,va as Expm1,Ip as FFT,Ol as Fill,Sa as FlipLeftRight,ns as Floor,os as FloorDiv,Zd as FromPixels,ss as FusedBatchNorm,Ni as FusedConv2D,Ti as FusedDepthwiseConv2D,Gc as GPGPUContext,Na as GatherNd,fi as GatherV2,Lh as GraphModel,Ta as Greater,is as GreaterEqual,My as History,vp as IFFT,mo as Identity,Sp as Imag,ye as InputSpec,ka as IsFinite,Ea as IsInf,as as IsNan,Bo as KernelBackend,Pl as LRN,Tp as LRNGrad,Ih as LayerVariable,Gn as LayersModel,ls as LeakyRelu,_a as Less,Aa as LessEqual,Np as LinSpace,us as Log,$a as Log1p,g1 as LogSoftmax,Da as LogicalAnd,Ra as LogicalNot,Fa as LogicalOr,h1 as LogicalXor,Nlt as LowerBound,$u as MathBackendWebGL,cs as Max,ms as MaxPool,Ll as MaxPool3D,Ep as MaxPool3DGrad,kp as MaxPoolGrad,_p as MaxPoolWithArgmax,ps as Maximum,fs as Mean,ds as Min,hs as Minimum,gs as MirrorPad,Oa as Mod,gu as MomentumOptimizer,Ap as Multinomial,xs as Multiply,di as Neg,La as NonMaxSuppressionV3,Ma as NonMaxSuppressionV4,za as NonMaxSuppressionV5,Pa as NotEqual,AS as OP_SCOPE_SUFFIX,ys as OneHot,hi as OnesLike,Hr as Optimizer,Hs as OptimizerConstructors,gi as Pack,bs as PadV2,Tlt as Pool,ws as Pow,Cs as Prelu,Is as Prod,xu as RMSPropOptimizer,En as RNN,$p as RaggedGather,Dp as RaggedRange,Rp as RaggedTensorToTensor,Ml as Range,wS as Rank,Fp as Real,ts as RealDiv,vs as Reciprocal,Ze as Reduction,Ss as Relu,ks as Relu6,xi as Reshape,Ts as ResizeBilinear,Pp as ResizeBilinearGrad,Ns as ResizeNearestNeighbor,Op as ResizeNearestNeighborGrad,Es as Reverse,Xa as RotateWithOffset,_s as Round,As as Rsqrt,Ui as SGDOptimizer,Ba as ScatterNd,Lp as SearchSorted,yi as Select,Va as Selu,Yi as Sequential,Ds as Sigmoid,Wa as Sign,$s as Sin,Ga as Sinh,bi as Slice,Os as Softmax,Ua as Softplus,wi as SpaceToBatchND,zl as SparseFillEmptyRows,Ha as SparseReshape,Bl as SparseSegmentMean,Vl as SparseSegmentSum,Mp as SparseToDense,Ci as SplitV,Rs as Sqrt,Gl as Square,Ps as SquaredDifference,fo as Step,qa as StridedSlice,Wl as StringNGrams,Ul as StringSplit,Hl as StringToHashBucketFast,Ls as Sub,Fs as Sum,tn as SymbolicTensor,Ms as Tan,zs as Tanh,Ft as Tensor,pe as TensorBuffer,to as Tile,Ka as TopK,ja as Transform,eo as Transpose,zp as Unique,Ii as Unpack,ql as UnsortedSegmentSum,klt as UpperBound,Ya as Variable,vi as ZerosLike,Si as _FusedMatMul,Ee as abs,lx as acos,ux as acosh,X as add,BE as addN,Jp as all,ju as any,Ri as argMax,cx as argMin,px as asin,mx as asinh,fx as atan,dx as atan2,hx as atanh,Ql as avgPool,xx as avgPool3d,bE as backend,S as backend_util,WE as basicLSTMCell,Oi as batchNorm,yx as batchNorm2d,bx as batchNorm3d,wx as batchNorm4d,tu as batchToSpaceND,Cx as bincount,l6 as booleanMaskAsync,HE as broadcastArgs,Pi as broadcastTo,Wr as broadcast_util,ox as browser,wt as buffer,jZ as callbacks,J as cast,Ix as ceil,vr as clipByValue,an as clone,In as complex,oe as concat,vx as concat1d,Sx as concat2d,Nx as concat3d,Tx as concat4d,Y$ as constraints,tm as conv1d,Sn as conv2d,rm as conv2dTranspose,kx as conv3d,_x as conv3dTranspose,Flt as copyRegisteredKernels,eu as cos,nm as cosh,gh as cosineWindow,Zu as cumprod,om as cumsum,cn as customGrad,RR as data,ph as denseBincount,qS as deprecationWarn,Ax as depthToSpace,Li as depthwiseConv2d,JZ as deregisterOp,Yl as device_util,qE as diag,$x as dilation2d,Spt as disableDeprecationWarnings,Nt as dispose,Npt as disposeVariables,pt as div,Dx as divNoNan,Rx as dot,p0 as dropout,KE as einsum,Mi as elu,vpt as enableDebugMode,Ipt as enableProdMode,m0 as enclosingPowerOfTwo,Mn as engine,M as env,Rr as equal,Fx as erf,Ox as euclideanNorm,or as exp,sr as expandDims,Px as expm1,Ju as eye,uu as fft,bo as fill,$pt as findBackend,Dpt as findBackendFactory,zi as floor,Zp as floorDiv,yM as forceHalfFloat,pu as fused,Bi as gather,x6 as gatherND,sx as gather_util,_pt as getBackend,mS as getGradient,Qd as getKernel,Bg as getKernelsForBackend,mlt as getThreadsCount,uk as gpgpu_util,SK as grad,NK as grads,Re as greater,un as greaterEqual,nl as ifft,Jl as imag,so as image,w6 as inTopKAsync,Z$ as initializers,z0 as input,$r as io,ym as irfft,Lx as isFinite,Mx as isInf,zx as isNaN,Ae as keep,qr as kernel_impls,AD as layers,ru as leakyRelu,sm as less,zn as lessEqual,d0 as linalg,YE as linspace,q7 as loadGraphModel,K7 as loadGraphModelSync,xD as loadLayersModel,Bx as localResponseNormalization,Nr as log,nu as log1p,Wx as logSigmoid,im as logSoftmax,am as logSumExp,Or as logicalAnd,ou as logicalNot,lm as logicalOr,Ux as logicalXor,wX as losses,ZE as lowerBound,Lt as matMul,CE as math,Sr as max,su as maxPool,qx as maxPool3d,JE as maxPoolWithArgmax,Nn as maximum,Se as mean,lh as memory,QE as meshgrid,$D as metrics,el as min,Vi as minimum,Kx as mirrorPad,jx as mod,Y8 as model,DD as models,Qu as moments,c6 as movingAverage,D as mul,t_ as multiRNNCell,e_ as multinomial,Ht as neg,xh as nextFrame,rl as norm,Ws as notEqual,$i as oneHot,pr as ones,wr as onesLike,T as op,r_ as outerProduct,pn as pad,n_ as pad1d,o_ as pad2d,s_ as pad3d,i_ as pad4d,Xx as pool,ln as pow,au as prelu,Qg as print,Yx as prod,Tpt as profile,a_ as raggedGather,l_ as raggedRange,u_ as raggedTensorToTensor,c_ as rand,k_ as randomGamma,rc as randomNormal,E_ as randomStandardNormal,Gi as randomUniform,Wi as range,Ept as ready,tl as real,ey as reciprocal,Yp as registerBackend,J8 as registerCallbackConstructor,y1 as registerGradient,zu as registerKernel,ZZ as registerOp,RD as regularizers,Pr as relu,um as relu6,Apt as removeBackend,R as reshape,mr as reverse,__ as reverse1d,A_ as reverse2d,$_ as reverse3d,D_ as reverse4d,cu as rfft,cm as round,pm as rsqrt,mt as scalar,m6 as scatterND,uh as scatter_util,fh as searchSorted,mm as selu,fm as separableConv2d,Z8 as sequential,Q as serialization,sH as setBackend,Rpt as setPlatform,plt as setThreadsCount,ult as setWasmPath,clt as setWasmPaths,vT as setWebGLContext,R_ as setdiff1dAsync,Jr as sigmoid,ry as sign,bX as signal,dm as sin,hm as sinh,Rt as slice,gm as slice1d,hh as slice2d,xm as slice3d,nc as slice4d,Me as slice_util,lu as softmax,Gs as softplus,iu as spaceToBatchND,CX as sparse,h6 as sparseToDense,yX as spectral,fr as split,ve as sqrt,Mt as square,bm as squaredDifference,Bn as squeeze,qe as stack,Co as step,ny as stridedSlice,IX as string,ct as sub,ft as sum,Hu as sumOutType,oy as tan,Fi as tanh,nr as tensor,Ke as tensor1d,Us as tensor2d,nx as tensor3d,F_ as tensor4d,O_ as tensor5d,P_ as tensor6d,yo as tensor_util,ME as test_util,B as tidy,Fr as tile,kpt as time,sy as topk,lc as train,Ot as transpose,wm as truncatedNormal,iy as unique,Rlt as unregisterGradient,Dlt as unregisterKernel,Cm as unsortedSegmentSum,dr as unstack,ar as upcastType,L_ as upperBound,y as util,TK as valueAndGrad,kK as valueAndGrads,ay as variable,Vx as variableGrads,blt as version,fR as version_converter,zE as version_core,Hm as version_layers,flt as version_wasm,xM as version_webgl,b1e as webgl,gd as webgl_util,_e as where,uy as whereAsync,Ne as zeros,It as zerosLike};\n", "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n ctx: CanvasRenderingContext2D,\n points: Point[],\n isClosed = false,\n) {\n ctx.beginPath();\n\n points.slice(1).forEach(({ x, y }, prevIdx) => {\n const from = points[prevIdx];\n ctx.moveTo(from.x, from.y);\n ctx.lineTo(x, y);\n });\n\n if (isClosed) {\n const from = points[points.length - 1];\n const to = points[0];\n if (!from || !to) {\n return;\n }\n\n ctx.moveTo(from.x, from.y);\n ctx.lineTo(to.x, to.y);\n }\n\n ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n return num % 2 === 0;\n}\n\nexport function round(num: number, prec = 2) {\n const f = 10 ** prec;\n return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n const scale = inputSize / Math.max(height, width);\n return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n width: number\n height: number\n}\n\nexport class Dimensions implements IDimensions {\n private _width: number;\n\n private _height: number;\n\n constructor(width: number, height: number) {\n if (!isValidNumber(width) || !isValidNumber(height)) {\n throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n }\n\n this._width = width;\n this._height = height;\n }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public reverse(): Dimensions {\n return new Dimensions(1 / this.width, 1 / this.height);\n }\n}\n", "export interface IPoint {\n x: number\n y: number\n}\n\nexport class Point implements IPoint {\n private _x: number;\n\n private _y: number;\n\n constructor(x: number, y: number) {\n this._x = x;\n this._y = y;\n }\n\n get x(): number { return this._x; }\n\n get y(): number { return this._y; }\n\n public add(pt: IPoint): Point {\n return new Point(this.x + pt.x, this.y + pt.y);\n }\n\n public sub(pt: IPoint): Point {\n return new Point(this.x - pt.x, this.y - pt.y);\n }\n\n public mul(pt: IPoint): Point {\n return new Point(this.x * pt.x, this.y * pt.y);\n }\n\n public div(pt: IPoint): Point {\n return new Point(this.x / pt.x, this.y / pt.y);\n }\n\n public abs(): Point {\n return new Point(Math.abs(this.x), Math.abs(this.y));\n }\n\n public magnitude(): number {\n return Math.sqrt((this.x ** 2) + (this.y ** 2));\n }\n\n public floor(): Point {\n return new Point(Math.floor(this.x), Math.floor(this.y));\n }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box implements IBoundingBox, IRect {\n public static isRect(rect: any): boolean {\n return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n }\n\n public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions = false) {\n if (!Box.isRect(box)) {\n throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n }\n\n if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n }\n }\n\n private _x: number;\n\n private _y: number;\n\n private _width: number;\n\n private _height: number;\n\n constructor(_box: IBoundingBox | IRect, allowNegativeDimensions = true) {\n const box = (_box || {}) as any;\n\n const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n if (!isRect && !isBbox) {\n throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n }\n\n const [x, y, width, height] = isRect\n ? [box.x, box.y, box.width, box.height]\n : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n Box.assertIsValidBox({\n x, y, width, height,\n }, 'Box.constructor', allowNegativeDimensions);\n\n this._x = x;\n this._y = y;\n this._width = width;\n this._height = height;\n }\n\n public get x(): number { return this._x; }\n\n public get y(): number { return this._y; }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public get left(): number { return this.x; }\n\n public get top(): number { return this.y; }\n\n public get right(): number { return this.x + this.width; }\n\n public get bottom(): number { return this.y + this.height; }\n\n public get area(): number { return this.width * this.height; }\n\n public get topLeft(): Point { return new Point(this.left, this.top); }\n\n public get topRight(): Point { return new Point(this.right, this.top); }\n\n public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n public round(): Box {\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n .map((val) => Math.round(val));\n return new Box({\n x, y, width, height,\n });\n }\n\n public floor(): Box {\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n .map((val) => Math.floor(val));\n return new Box({\n x, y, width, height,\n });\n }\n\n public toSquare(): Box {\n let {\n x, y, width, height,\n } = this;\n const diff = Math.abs(width - height);\n if (width < height) {\n x -= (diff / 2);\n width += diff;\n }\n if (height < width) {\n y -= (diff / 2);\n height += diff;\n }\n\n return new Box({ x, y, width, height });\n }\n\n public rescale(s: IDimensions | number): Box {\n const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n return new Box({\n x: this.x * scaleX,\n y: this.y * scaleY,\n width: this.width * scaleX,\n height: this.height * scaleY,\n });\n }\n\n public pad(padX: number, padY: number): Box {\n const [x, y, width, height] = [\n this.x - (padX / 2),\n this.y - (padY / 2),\n this.width + padX,\n this.height + padY,\n ];\n return new Box({ x, y, width, height });\n }\n\n public clipAtImageBorders(imgWidth: number, imgHeight: number): Box {\n const { x, y, right, bottom } = this;\n const clippedX = Math.max(x, 0);\n const clippedY = Math.max(y, 0);\n\n const newWidth = right - clippedX;\n const newHeight = bottom - clippedY;\n const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight })).floor();\n }\n\n public shift(sx: number, sy: number): Box {\n const { width, height } = this;\n const x = this.x + sx;\n const y = this.y + sy;\n\n return new Box({ x, y, width, height });\n }\n\n public padAtBorders(imageHeight: number, imageWidth: number) {\n const w = this.width + 1;\n const h = this.height + 1;\n\n const dx = 1;\n const dy = 1;\n let edx = w;\n let edy = h;\n\n let x = this.left;\n let y = this.top;\n let ex = this.right;\n let ey = this.bottom;\n\n if (ex > imageWidth) {\n edx = -ex + imageWidth + w;\n ex = imageWidth;\n }\n if (ey > imageHeight) {\n edy = -ey + imageHeight + h;\n ey = imageHeight;\n }\n if (x < 1) {\n edy = 2 - x;\n x = 1;\n }\n if (y < 1) {\n edy = 2 - y;\n y = 1;\n }\n\n return { dy, edy, dx, edx, y, ey, x, ex, w, h };\n }\n\n public calibrate(region: Box) {\n return new Box({\n left: this.left + (region.left * this.width),\n top: this.top + (region.top * this.height),\n right: this.right + (region.right * this.width),\n bottom: this.bottom + (region.bottom * this.height),\n }).toSquare().round();\n }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n left: number\n top: number\n right: number\n bottom: number\n}\n\nexport class BoundingBox extends Box implements IBoundingBox {\n constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions = false) {\n super({ left, top, right, bottom }, allowNegativeDimensions);\n }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n private _score: number;\n\n private _classScore: number;\n\n private _className: string;\n\n private _box: Rect;\n\n private _imageDims: Dimensions;\n\n constructor(\n score: number,\n classScore: number,\n className: string,\n relativeBox: IRect,\n imageDims: IDimensions,\n ) {\n this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n this._score = score;\n this._classScore = classScore;\n this._className = className;\n this._box = new Box(relativeBox).rescale(this._imageDims);\n }\n\n public get score(): number { return this._score; }\n\n public get classScore(): number { return this._classScore; }\n\n public get className(): string { return this._className; }\n\n public get box(): Box { return this._box; }\n\n public get imageDims(): Dimensions { return this._imageDims; }\n\n public get imageWidth(): number { return this.imageDims.width; }\n\n public get imageHeight(): number { return this.imageDims.height; }\n\n public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n public forSize(width: number, height: number): ObjectDetection {\n return new ObjectDetection(\n this.score,\n this.classScore,\n this.className,\n this.relativeBox,\n { width, height },\n );\n }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n score: number\n box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n constructor(\n score: number,\n relativeBox: Rect,\n imageDims: IDimensions,\n ) {\n super(score, score, '', relativeBox, imageDims);\n }\n\n public override forSize(width: number, height: number): FaceDetection {\n const { score, relativeBox, imageDims } = super.forSize(width, height);\n return new FaceDetection(score, relativeBox, imageDims);\n }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU = true) {\n const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n const interSection = width * height;\n\n return isIOU\n ? interSection / (box1.area + box2.area - interSection)\n : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n const xs = pts.map((pt) => pt.x);\n const ys = pts.map((pt) => pt.y);\n const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n boxes: Box[],\n scores: number[],\n iouThreshold: number,\n isIOU = true,\n): number[] {\n let indicesSortedByScore = scores\n .map((score, boxIndex) => ({ score, boxIndex }))\n .sort((c1, c2) => c1.score - c2.score)\n .map((c) => c.boxIndex);\n\n const pick: number[] = [];\n\n while (indicesSortedByScore.length > 0) {\n const curr = indicesSortedByScore.pop() as number;\n pick.push(curr);\n\n const indices = indicesSortedByScore;\n\n const outputs: number[] = [];\n for (let i = 0; i < indices.length; i++) {\n const idx = indices[i];\n\n const currBox = boxes[curr];\n const idxBox = boxes[idx];\n\n outputs.push(iou(currBox, idxBox, isIOU));\n }\n\n indicesSortedByScore = indicesSortedByScore.filter(\n (_, j) => outputs[j] <= iouThreshold,\n );\n }\n\n return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n return tf.tidy(() => {\n const [r, g, b] = meanRgb;\n const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n return tf.sub(x, avg_rgb);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(imgTensor: tf.Tensor4D, isCenterImage = false): tf.Tensor4D {\n return tf.tidy(() => {\n const [height, width] = imgTensor.shape.slice(1);\n if (height === width) return imgTensor;\n const dimDiff = Math.abs(height - width);\n const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n const paddingAxis = height > width ? 2 : 1;\n const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n const paddingTensorShape = imgTensor.shape.slice();\n paddingTensorShape[paddingAxis] = paddingAmountLocal;\n return tf.fill(paddingTensorShape, 0, 'float32');\n };\n const paddingTensorAppend = createPaddingTensor(paddingAmount);\n const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n const paddingTensorPrepend = isCenterImage && remainingPaddingAmount ? createPaddingTensor(remainingPaddingAmount) : null;\n const tensorsToStack = [paddingTensorPrepend, imgTensor, paddingTensorAppend]\n .filter((t) => !!t)\n .map((t) => tf.cast(t as tf.Tensor4D, 'float32')) as tf.Tensor4D[];\n return tf.concat(tensorsToStack, paddingAxis);\n });\n}\n", "export function shuffleArray(inputArray: any[]) {\n const array = inputArray.slice();\n for (let i = array.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n const x = array[i];\n array[i] = array[j];\n array[j] = x;\n }\n return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n x: number\n y: number\n width: number\n height: number\n}\n\nexport class Rect extends Box implements IRect {\n constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions = false) {\n super({ x, y, width, height }, allowNegativeDimensions);\n }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n positions: Point[]\n shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n protected _shift: Point;\n\n protected _positions: Point[];\n\n protected _imgDims: Dimensions;\n\n constructor(\n relativeFaceLandmarkPositions: Point[],\n imgDims: IDimensions,\n shift: Point = new Point(0, 0),\n ) {\n const { width, height } = imgDims;\n this._imgDims = new Dimensions(width, height);\n this._shift = shift;\n this._positions = relativeFaceLandmarkPositions.map(\n (pt) => pt.mul(new Point(width, height)).add(shift),\n );\n }\n\n public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n public get imageWidth(): number { return this._imgDims.width; }\n\n public get imageHeight(): number { return this._imgDims.height; }\n\n public get positions(): Point[] { return this._positions; }\n\n public get relativePositions(): Point[] {\n return this._positions.map(\n (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n );\n }\n\n public forSize(width: number, height: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n { width, height },\n );\n }\n\n public shiftBy(x: number, y: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n this._imgDims,\n new Point(x, y),\n );\n }\n\n public shiftByPoint(pt: Point): T {\n return this.shiftBy(pt.x, pt.y);\n }\n\n /**\n * Aligns the face landmarks after face detection from the relative positions of the faces\n * bounding box, or it's current shift. This function should be used to align the face images\n * after face detection has been performed, before they are passed to the face recognition net.\n * This will make the computed face descriptor more accurate.\n *\n * @param detection (optional) The bounding box of the face or the face detection result. If\n * no argument was passed the position of the face landmarks are assumed to be relative to\n * it's current shift.\n * @returns The bounding box of the aligned face.\n */\n public align(\n detection?: FaceDetection | IRect | IBoundingBox | null,\n options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n ): Box {\n if (detection) {\n const box = detection instanceof FaceDetection\n ? detection.box.floor()\n : new Box(detection);\n\n return this.shiftBy(box.x, box.y).align(null, options);\n }\n\n const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n if (useDlibAlignment) {\n return this.alignDlib();\n }\n\n return this.alignMinBbox(minBoxPadding);\n }\n\n private alignDlib(): Box {\n const centers = this.getRefPointsForAlignment();\n\n const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n const size = Math.floor(eyeToMouthDist / relScale);\n\n const refPoint = getCenterPoint(centers);\n // TODO: pad in case rectangle is out of image bounds\n const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n }\n\n private alignMinBbox(padding: number): Box {\n const box = minBbox(this.positions);\n return box.pad(box.width * padding, box.height * padding);\n }\n\n protected getRefPointsForAlignment(): Point[] {\n throw new Error('getRefPointsForAlignment not implemented by base class');\n }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n protected override getRefPointsForAlignment(): Point[] {\n const pts = this.positions;\n return [\n pts[0],\n pts[1],\n getCenterPoint([pts[3], pts[4]]),\n ];\n }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n public getJawOutline(): Point[] {\n return this.positions.slice(0, 17);\n }\n\n public getLeftEyeBrow(): Point[] {\n return this.positions.slice(17, 22);\n }\n\n public getRightEyeBrow(): Point[] {\n return this.positions.slice(22, 27);\n }\n\n public getNose(): Point[] {\n return this.positions.slice(27, 36);\n }\n\n public getLeftEye(): Point[] {\n return this.positions.slice(36, 42);\n }\n\n public getRightEye(): Point[] {\n return this.positions.slice(42, 48);\n }\n\n public getMouth(): Point[] {\n return this.positions.slice(48, 68);\n }\n\n protected override getRefPointsForAlignment(): Point[] {\n return [\n this.getLeftEye(),\n this.getRightEye(),\n this.getMouth(),\n ].map(getCenterPoint);\n }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n label: string\n distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n private _label: string;\n private _distance: number;\n\n constructor(label: string, distance: number) {\n this._label = label;\n this._distance = distance;\n }\n\n public get label(): string { return this._label; }\n\n public get distance(): number { return this._distance; }\n\n public toString(withDistance = true): string {\n return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box {\n public static assertIsValidLabeledBox(box: any, callee: string) {\n Box.assertIsValidBox(box, callee);\n if (!isValidNumber(box.label)) {\n throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n }\n }\n\n private _label: number;\n\n constructor(box: IBoundingBox | IRect | any, label: number) {\n super(box);\n this._label = label;\n }\n\n public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n private _label: string;\n\n private _descriptors: Float32Array[];\n\n constructor(label: string, descriptors: Float32Array[]) {\n if (!(typeof label === 'string')) {\n throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n }\n\n if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n }\n\n this._label = label;\n this._descriptors = descriptors;\n }\n\n public get label(): string { return this._label; }\n\n public get descriptors(): Float32Array[] { return this._descriptors; }\n\n public toJSON(): any {\n return {\n label: this.label,\n descriptors: this.descriptors.map((d) => Array.from(d)),\n };\n }\n\n public static fromJSON(json: any): LabeledFaceDescriptors {\n const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n return new LabeledFaceDescriptors(json.label, descriptors);\n }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n public static assertIsValidPredictedBox(box: any, callee: string) {\n LabeledBox.assertIsValidLabeledBox(box, callee);\n\n if (\n !isValidProbablitiy(box.score)\n || !isValidProbablitiy(box.classScore)\n ) {\n throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n }\n }\n\n private _score: number;\n\n private _classScore: number;\n\n constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n super(box, label);\n this._score = score;\n this._classScore = classScore;\n }\n\n public get score(): number { return this._score; }\n\n public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection = TSource & {\n detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection(sourceObj: TSource, detection: FaceDetection): WithFaceDetection {\n const extension = { detection };\n return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n const fetch = window.fetch;\n if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n const readFile = () => {\n throw new Error('readFile - filesystem not available for browser environment');\n };\n\n return {\n Canvas: HTMLCanvasElement,\n CanvasRenderingContext2D,\n Image: HTMLImageElement,\n ImageData,\n Video: HTMLVideoElement,\n createCanvasElement: () => document.createElement('canvas'),\n createImageElement: () => document.createElement('img'),\n createVideoElement: () => document.createElement('video'),\n fetch,\n readFile,\n };\n}\n", "export function isNodejs(): boolean {\n return typeof global === 'object'\n && typeof process !== 'undefined'\n && process.versions != null\n && process.versions.node != null;\n}\n", "import { FileSystem } from './types';\nimport { isNodejs } from './isNodejs';\n\nexport function createFileSystem(fs?: any): FileSystem {\n let requireFsError = '';\n if (!fs && isNodejs()) {\n try {\n // eslint-disable-next-line global-require\n fs = require('fs');\n } catch (err) {\n requireFsError = (err as any).toString();\n }\n }\n\n const readFile = fs\n ? (filePath: string) => new Promise((resolve, reject) => { fs.readFile(filePath, (err: any, buffer) => (err ? reject(err) : resolve(buffer))); })\n : () => { throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`); };\n return { readFile };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n // eslint-disable-next-line dot-notation\n const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n const Image = global.Image || global.HTMLImageElement;\n // eslint-disable-next-line dot-notation\n const Video = global['Video'] || global.HTMLVideoElement;\n\n const createCanvasElement = () => {\n if (Canvas) return new Canvas();\n throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n };\n\n const createImageElement = () => {\n if (Image) return new Image();\n throw new Error('createImageElement - missing Image implementation for nodejs environment');\n };\n\n const createVideoElement = () => {\n if (Video) return new Video();\n throw new Error('createVideoElement - missing Video implementation for nodejs environment');\n };\n\n const fetch = global.fetch;\n // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n const fileSystem = createFileSystem();\n\n return {\n Canvas: Canvas || class {},\n CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n Image: Image || class {},\n ImageData: global.ImageData || class {},\n Video: global.HTMLVideoElement || class {},\n createCanvasElement,\n createImageElement,\n createVideoElement,\n fetch,\n ...fileSystem,\n };\n}\n", "export function isBrowser(): boolean {\n return typeof window === 'object'\n && typeof document !== 'undefined'\n && typeof HTMLImageElement !== 'undefined'\n && typeof HTMLCanvasElement !== 'undefined'\n && typeof HTMLVideoElement !== 'undefined'\n && typeof ImageData !== 'undefined'\n && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n if (!environment) {\n throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n }\n return environment;\n}\n\nfunction setEnv(env: Environment) {\n environment = env;\n}\n\nfunction initialize() {\n // check for isBrowser() first to prevent electron renderer process\n // to be initialized with wrong environment due to isNodejs() returning true\n if (isBrowser()) return setEnv(createBrowserEnv());\n if (isNodejs()) return setEnv(createNodejsEnv());\n return null;\n}\n\nfunction monkeyPatch(env: Partial) {\n if (!environment) {\n initialize();\n }\n\n if (!environment) {\n throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n }\n\n const { Canvas = environment.Canvas, Image = environment.Image } = env;\n environment.Canvas = Canvas;\n environment.Image = Image;\n environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n environment.createImageElement = env.createImageElement || (() => new Image());\n\n environment.ImageData = env.ImageData || environment.ImageData;\n environment.Video = env.Video || environment.Video;\n environment.fetch = env.fetch || environment.fetch;\n environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n getEnv,\n setEnv,\n initialize,\n createBrowserEnv,\n createFileSystem,\n createNodejsEnv,\n monkeyPatch,\n isBrowser,\n isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n if (!env.isNodejs() && typeof arg === 'string') {\n return document.getElementById(arg);\n }\n return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n if (canvasArg instanceof CanvasRenderingContext2D) {\n return canvasArg;\n }\n\n const canvas = resolveInput(canvasArg);\n\n if (!(canvas instanceof Canvas)) {\n throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n }\n\n const ctx = canvas.getContext('2d');\n if (!ctx) {\n throw new Error('resolveContext2d - canvas 2d context is null');\n }\n\n return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n // eslint-disable-next-line no-unused-vars\n TOP_LEFT = 'TOP_LEFT',\n // eslint-disable-next-line no-unused-vars\n TOP_RIGHT = 'TOP_RIGHT',\n // eslint-disable-next-line no-unused-vars\n BOTTOM_LEFT = 'BOTTOM_LEFT',\n // eslint-disable-next-line no-unused-vars\n BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n anchorPosition?: AnchorPosition\n backgroundColor?: string\n fontColor?: string\n fontSize?: number\n fontStyle?: string\n padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n public anchorPosition: AnchorPosition;\n\n public backgroundColor: string;\n\n public fontColor: string;\n\n public fontSize: number;\n\n public fontStyle: string;\n\n public padding: number;\n\n constructor(options: IDrawTextFieldOptions = {}) {\n const {\n anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n } = options;\n this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n this.fontSize = fontSize || 14;\n this.fontStyle = fontStyle || 'Georgia';\n this.padding = padding || 4;\n }\n}\n\nexport class DrawTextField {\n public text: string[];\n\n public anchor : IPoint;\n\n public options: DrawTextFieldOptions;\n\n constructor(\n text: string | string[] | DrawTextField,\n anchor: IPoint,\n options: IDrawTextFieldOptions = {},\n ) {\n // eslint-disable-next-line no-nested-ternary\n this.text = typeof text === 'string'\n ? [text]\n : (text instanceof DrawTextField ? text.text : text);\n this.anchor = anchor;\n this.options = new DrawTextFieldOptions(options);\n }\n\n measureWidth(ctx: CanvasRenderingContext2D): number {\n const { padding } = this.options;\n return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n }\n\n measureHeight(): number {\n const { fontSize, padding } = this.options;\n return this.text.length * fontSize + (2 * padding);\n }\n\n getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n const { anchorPosition } = this.options;\n const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n const textFieldWidth = this.measureWidth(ctx);\n const textFieldHeight = this.measureHeight();\n const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n // adjust anchor if text box exceeds canvas borders\n if (canvasDims) {\n const { width, height } = canvasDims;\n const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n return { x: newX, y: newY };\n }\n return { x, y };\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const canvas = resolveInput(canvasArg);\n const ctx = getContext2dOrThrow(canvas);\n\n const {\n backgroundColor, fontColor, fontSize, fontStyle, padding,\n } = this.options;\n\n ctx.font = `${fontSize}px ${fontStyle}`;\n const maxTextWidth = this.measureWidth(ctx);\n const textHeight = this.measureHeight();\n\n ctx.fillStyle = backgroundColor;\n const upperLeft = this.getUpperLeft(ctx, canvas);\n ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n ctx.fillStyle = fontColor;\n this.text.forEach((textLine, i) => {\n const x = padding + upperLeft.x;\n const y = padding + upperLeft.y + ((i + 1) * fontSize);\n ctx.fillText(textLine, x, y);\n });\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n boxColor?: string\n lineWidth?: number\n drawLabelOptions?: IDrawTextFieldOptions\n label?: string\n}\n\nexport class DrawBoxOptions {\n public boxColor: string;\n\n public lineWidth: number;\n\n public drawLabelOptions: DrawTextFieldOptions;\n\n public label?: string;\n\n constructor(options: IDrawBoxOptions = {}) {\n const {\n boxColor, lineWidth, label, drawLabelOptions,\n } = options;\n this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n this.lineWidth = lineWidth || 2;\n this.label = label;\n\n const defaultDrawLabelOptions = {\n anchorPosition: AnchorPosition.BOTTOM_LEFT,\n backgroundColor: this.boxColor,\n };\n this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n }\n}\n\nexport class DrawBox {\n public box: Box;\n\n public options: DrawBoxOptions;\n\n constructor(\n box: IBoundingBox | IRect,\n options: IDrawBoxOptions = {},\n ) {\n this.box = new Box(box);\n this.options = new DrawBoxOptions(options);\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const ctx = getContext2dOrThrow(canvasArg);\n\n const { boxColor, lineWidth } = this.options;\n\n const {\n x, y, width, height,\n } = this.box;\n ctx.strokeStyle = boxColor;\n ctx.lineWidth = lineWidth;\n ctx.strokeRect(x, y, width, height);\n\n const { label } = this.options;\n if (label) {\n new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n }\n }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n canvasArg: string | HTMLCanvasElement,\n detections: TDrawDetectionsInput | Array,\n) {\n const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n detectionsArray.forEach((det) => {\n // eslint-disable-next-line no-nested-ternary\n const score = det instanceof FaceDetection\n ? det.score\n : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n // eslint-disable-next-line no-nested-ternary\n const box = det instanceof FaceDetection\n ? det.box\n : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n const label = score ? `${round(score)}` : undefined;\n new DrawBox(box, { label }).draw(canvasArg);\n });\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n const { Image, Video } = env.getEnv();\n\n return (media instanceof Image && media.complete)\n || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n // eslint-disable-next-line consistent-return\n return new Promise((resolve, reject) => {\n if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) resolve(null);\n\n function onError(e: Event) {\n if (!e.currentTarget) return;\n // eslint-disable-next-line no-use-before-define\n e.currentTarget.removeEventListener('load', onLoad);\n e.currentTarget.removeEventListener('error', onError);\n reject(e);\n }\n\n function onLoad(e: Event) {\n if (!e.currentTarget) return;\n e.currentTarget.removeEventListener('load', onLoad);\n e.currentTarget.removeEventListener('error', onError);\n resolve(e);\n }\n\n media.addEventListener('load', onLoad);\n media.addEventListener('error', onError);\n });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise {\n return new Promise((resolve, reject) => {\n if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n const reader = new FileReader();\n reader.onload = () => {\n if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n const img = env.getEnv().createImageElement();\n img.onload = () => resolve(img);\n img.onerror = reject;\n img.src = reader.result as string;\n };\n reader.onerror = reject;\n reader.readAsDataURL(buf);\n });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n const { Image, Video } = env.getEnv();\n\n if (input instanceof Image) {\n return new Dimensions(input.naturalWidth, input.naturalHeight);\n }\n if (input instanceof Video) {\n return new Dimensions(input.videoWidth, input.videoHeight);\n }\n return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n const { createCanvasElement } = env.getEnv();\n const canvas = createCanvasElement();\n canvas.width = width;\n canvas.height = height;\n return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n const { ImageData } = env.getEnv();\n\n if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n throw new Error('createCanvasFromMedia - media has not finished loading yet');\n }\n\n const { width, height } = dims || getMediaDimensions(media);\n const canvas = createCanvas({ width, height });\n\n if (media instanceof ImageData) {\n getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n } else {\n getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n }\n return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n imgTensor: tf.Tensor,\n canvas?: HTMLCanvasElement,\n): Promise {\n const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n await tf['browser'].toPixels(imgTensor3D, targetCanvas);\n\n imgTensor3D.dispose();\n\n return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n const { Image, Canvas, Video } = env.getEnv();\n\n return input instanceof Image\n || input instanceof Canvas\n || input instanceof Video;\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage = false) {\n const { Image, Canvas } = env.getEnv();\n\n if (!(input instanceof Image || input instanceof Canvas)) {\n throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n }\n\n if (inputSize <= 0) return createCanvas({ width: 1, height: 1 });\n const dims = getMediaDimensions(input);\n const scale = inputSize / Math.max(dims.height, dims.width);\n const width = scale * dims.width;\n const height = scale * dims.height;\n\n const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n const offset = Math.abs(width - height) / 2;\n const dx = centerImage && width < height ? offset : 0;\n const dy = centerImage && height < width ? offset : 0;\n if (inputCanvas.width > 0 && inputCanvas.height > 0) getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n return targetCanvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n private _imageTensors: Array = [];\n\n private _canvases: HTMLCanvasElement[] = [];\n\n private _batchSize: number;\n\n private _treatAsBatchInput = false;\n\n private _inputDimensions: number[][] = [];\n\n private _inputSize = 0;\n\n constructor(inputs: Array, treatAsBatchInput = false) {\n if (!Array.isArray(inputs)) {\n throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n }\n\n this._treatAsBatchInput = treatAsBatchInput;\n this._batchSize = inputs.length;\n\n inputs.forEach((input, idx) => {\n if (isTensor3D(input)) {\n this._imageTensors[idx] = input;\n this._inputDimensions[idx] = input.shape;\n return;\n }\n\n if (isTensor4D(input)) {\n const batchSize = (input as any).shape[0];\n if (batchSize !== 1) {\n throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n\n this._imageTensors[idx] = input;\n this._inputDimensions[idx] = (input as any).shape.slice(1);\n return;\n }\n\n // @ts-ignore\n const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n this._canvases[idx] = canvas as HTMLCanvasElement;\n this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n });\n }\n\n public get imageTensors(): Array {\n return this._imageTensors;\n }\n\n public get canvases(): HTMLCanvasElement[] {\n return this._canvases;\n }\n\n public get isBatchInput(): boolean {\n return this.batchSize > 1 || this._treatAsBatchInput;\n }\n\n public get batchSize(): number {\n return this._batchSize;\n }\n\n public get inputDimensions(): number[][] {\n return this._inputDimensions;\n }\n\n public get inputSize(): number | undefined {\n return this._inputSize;\n }\n\n public get reshapedInputDimensions(): Dimensions[] {\n return range(this.batchSize, 0, 1).map(\n (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n );\n }\n\n public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n }\n\n public getInputDimensions(batchIdx: number): number[] {\n return this._inputDimensions[batchIdx];\n }\n\n public getInputHeight(batchIdx: number): number {\n return this._inputDimensions[batchIdx][0];\n }\n\n public getInputWidth(batchIdx: number): number {\n return this._inputDimensions[batchIdx][1];\n }\n\n public getReshapedInputDimensions(batchIdx: number): Dimensions {\n if (typeof this.inputSize !== 'number') {\n throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n }\n\n const width = this.getInputWidth(batchIdx);\n const height = this.getInputHeight(batchIdx);\n return computeReshapedDimensions({ width, height }, this.inputSize);\n }\n\n /**\n * Create a batch tensor from all input canvases and tensors\n * with size [batchSize, inputSize, inputSize, 3].\n *\n * @param inputSize Height and width of the tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The batch tensor.\n */\n public toBatchTensor(inputSize: number, isCenterInputs = true): tf.Tensor4D {\n this._inputSize = inputSize;\n\n return tf.tidy(() => {\n const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n const input = this.getInput(batchIdx);\n\n if (input instanceof tf.Tensor) {\n let imgTensor = isTensor4D(input) ? input : tf.expandDims(input);\n imgTensor = padToSquare(imgTensor as tf.Tensor4D, isCenterInputs);\n\n if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n imgTensor = tf['image'].resizeBilinear(imgTensor as tf.Tensor4D, [inputSize, inputSize], false, false);\n }\n\n return imgTensor.as3D(inputSize, inputSize, 3);\n }\n\n if (input instanceof env.getEnv().Canvas) {\n return tf['browser'].fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n }\n\n throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n });\n\n const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n // const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))) as tf.Tensor4D;\n\n return batchTensor;\n });\n }\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise {\n if (inputs instanceof NetInput) return inputs;\n const inputArgArray = Array.isArray(inputs) ? inputs : [inputs];\n if (!inputArgArray.length) throw new Error('toNetInput - empty array passed as input');\n const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n const inputArray = inputArgArray.map(resolveInput);\n inputArray.forEach((input, i) => {\n if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n if (typeof inputArgArray[i] === 'string') throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n }\n if (isTensor4D(input)) {\n // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n const batchSize = input.shape[0];\n if (batchSize !== 1) throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n });\n // wait for all media elements being loaded\n await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)));\n return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(input: TNetInput, detections: Array): Promise {\n const { Canvas } = env.getEnv();\n let canvas = input as HTMLCanvasElement;\n if (!(input instanceof Canvas)) {\n const netInput = await toNetInput(input);\n if (netInput.batchSize > 1) throw new Error('extractFaces - batchSize > 1 not supported');\n const tensorOrCanvas = netInput.getInput(0);\n canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas);\n }\n const ctx = getContext2dOrThrow(canvas);\n const boxes = detections\n .map((det) => (det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det))\n .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n return boxes.map(({ x, y, width, height }) => {\n const faceImg = createCanvas({ width, height });\n if (width > 0 && height > 0) getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n return faceImg;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(imageTensor: tf.Tensor3D | tf.Tensor4D, detections: Array): Promise {\n if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n }\n\n if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n throw new Error('extractFaceTensors - batchSize > 1 not supported');\n }\n\n return tf.tidy(() => {\n const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n const boxes = detections.map((det) => (det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det))\n .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n const faceTensors = boxes\n .filter((box) => box.width > 0 && box.height > 0)\n .map(({ x, y, width, height }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n return faceTensors;\n });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n url: string,\n // eslint-disable-next-line no-undef\n init?: RequestInit,\n): Promise {\n const { fetch } = env.getEnv();\n const res = await fetch(url, init);\n if (!(res.status < 400)) {\n throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n }\n return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise {\n const res = await fetchOrThrow(uri);\n const blob = await (res).blob();\n\n if (!blob.type.startsWith('image/')) {\n throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n }\n return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson(uri: string): Promise {\n return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise {\n return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "import { env } from '../env/index';\n\nexport function bufferToVideo(buf: Blob): Promise {\n return new Promise((resolve, reject) => {\n if (!(buf instanceof Blob)) reject(new Error('bufferToVideo - expected buf to be of type: Blob'));\n\n const video = env.getEnv().createVideoElement();\n video.oncanplay = () => resolve(video);\n video.onerror = reject;\n video.playsInline = true;\n video.muted = true;\n video.src = URL.createObjectURL(buf);\n video.play();\n });\n}\n", "import { bufferToVideo } from './bufferToVideo';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchVideo(uri: string): Promise {\n const res = await fetchOrThrow(uri);\n const blob = await (res).blob();\n\n if (!blob.type.startsWith('video/')) {\n throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${blob.type}, for url: ${res.url}`);\n }\n return bufferToVideo(blob);\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n if (!uri) {\n return {\n modelBaseUri: '',\n manifestUri: defaultManifestFilename,\n };\n }\n\n if (uri === '/') {\n return {\n modelBaseUri: '/',\n manifestUri: `/${defaultManifestFilename}`,\n };\n }\n // eslint-disable-next-line no-nested-ternary\n const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n uri = uri.replace(protocol, '');\n\n const parts = uri.split('/').filter((s) => s);\n\n const manifestFile = uri.endsWith('.json')\n ? parts[parts.length - 1]\n : defaultManifestFilename;\n\n let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n return {\n modelBaseUri,\n manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n uri: string | undefined,\n defaultModelName: string,\n): Promise {\n const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n // @ts-ignore\n const manifest = await fetchJson(manifestUri);\n // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n return tf['io'].loadWeights(manifest, modelBaseUri);\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions = false) {\n const { width, height } = useMediaDimensions\n ? getMediaDimensions(reference)\n : reference;\n input.width = width;\n input.height = height;\n return { width, height };\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork {\n constructor(name: string) {\n this._name = name;\n }\n\n protected _params: TNetParams | undefined = undefined;\n\n protected _paramMappings: ParamMapping[] = [];\n\n public _name: any;\n\n public get params(): TNetParams | undefined { return this._params; }\n\n public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n public get isLoaded(): boolean { return !!this.params; }\n\n public getParamFromPath(paramPath: string): tf.Tensor {\n const { obj, objProp } = this.traversePropertyPath(paramPath);\n return obj[objProp];\n }\n\n public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n const { obj, objProp } = this.traversePropertyPath(paramPath);\n obj[objProp].dispose();\n obj[objProp] = tensor;\n }\n\n public getParamList() {\n return this._paramMappings.map(({ paramPath }) => ({\n path: paramPath,\n tensor: this.getParamFromPath(paramPath),\n }));\n }\n\n public getTrainableParams() {\n return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n }\n\n public getFrozenParams() {\n return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n }\n\n public variable() {\n this.getFrozenParams().forEach(({ path, tensor }) => {\n this.reassignParamFromPath(path, tensor.variable());\n });\n }\n\n public freeze() {\n this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n const tensor = tf.tensor(variable.dataSync());\n variable.dispose();\n this.reassignParamFromPath(path, tensor);\n });\n }\n\n public dispose(throwOnRedispose = true) {\n this.getParamList().forEach((param) => {\n if (throwOnRedispose && param.tensor.isDisposed) {\n throw new Error(`param tensor has already been disposed for path ${param.path}`);\n }\n param.tensor.dispose();\n });\n this._params = undefined;\n }\n\n public serializeParams(): Float32Array {\n return new Float32Array(\n this.getParamList()\n .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n .reduce((flat, arr) => flat.concat(arr)),\n );\n }\n\n public async load(weightsOrUrl: Float32Array | string | undefined): Promise {\n if (weightsOrUrl instanceof Float32Array) {\n this.extractWeights(weightsOrUrl);\n return;\n }\n await this.loadFromUri(weightsOrUrl);\n }\n\n public async loadFromUri(uri: string | undefined) {\n if (uri && typeof uri !== 'string') {\n throw new Error(`${this._name}.loadFromUri - expected model uri`);\n }\n const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n this.loadFromWeightMap(weightMap);\n }\n\n public async loadFromDisk(filePath: string | undefined) {\n if (filePath && typeof filePath !== 'string') {\n throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n }\n const { readFile } = env.getEnv();\n const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)));\n const loadWeights = tf['io'].weightsLoaderFactory(fetchWeightsFromDisk);\n const manifest = JSON.parse((await readFile(manifestUri)).toString());\n const weightMap = await loadWeights(manifest, modelBaseUri);\n this.loadFromWeightMap(weightMap);\n }\n\n public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n public extractWeights(weights: Float32Array) {\n const { paramMappings, params } = this.extractParams(weights);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n private traversePropertyPath(paramPath: string) {\n if (!this.params) {\n throw new Error('traversePropertyPath - model has no loaded params');\n }\n\n const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n // eslint-disable-next-line no-prototype-builtins\n if (!res.nextObj.hasOwnProperty(objProp)) {\n throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n }\n return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n }, { nextObj: this.params });\n\n const { obj, objProp } = result;\n if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n }\n\n return { obj, objProp };\n }\n\n protected abstract getDefaultModelName(): string\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n x: tf.Tensor4D,\n params: SeparableConvParams,\n stride: [number, number],\n): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n out = tf.add(out, params.bias);\n return out;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock3Params,\n isFirstLayer = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out1 = tf.relu(\n isFirstLayer\n ? tf.add(\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n denseBlockParams.conv0.bias,\n )\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n ) as tf.Tensor4D;\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n });\n}\n\nexport function denseBlock4(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock4Params,\n isFirstLayer = false,\n isScaleDown = true,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out1 = tf.relu(\n isFirstLayer\n ? tf.add(\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n denseBlockParams.conv0.bias,\n )\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n ) as tf.Tensor4D;\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n x: tf.Tensor4D,\n params: ConvParams,\n padding: 'valid' | 'same' = 'same',\n withRelu = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out = tf.add(\n tf.conv2d(x, params.filters, [1, 1], padding),\n params.bias,\n ) as tf.Tensor4D;\n\n return withRelu ? tf.relu(out) : out;\n });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n Object.keys(weightMap).forEach((path) => {\n if (!paramMappings.some((pm) => pm.originalPath === path)) {\n weightMap[path].dispose();\n }\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvParams => {\n const filters = tf.tensor4d(\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n [filterSize, filterSize, channelsIn, channelsOut],\n );\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return { filters, bias };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (\n channelsIn: number,\n channelsOut: number,\n mappedPrefix: string,\n ): FCParams => {\n const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/weights` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return {\n weights: fc_weights,\n bias: fc_bias,\n };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n originalPath?: string\n paramPath: string\n}\n\nexport type ConvParams = {\n filters: tf.Tensor4D\n bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n weights: tf.Tensor2D\n bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n // eslint-disable-next-line no-useless-constructor\n constructor(\n // eslint-disable-next-line no-unused-vars\n public depthwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public pointwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public bias: tf.Tensor1D,\n // eslint-disable-next-line no-empty-function\n ) {}\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/depthwise_filter` },\n { paramPath: `${mappedPrefix}/pointwise_filter` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n\nexport function loadSeparableConvParamsFactory(\n // eslint-disable-next-line no-unused-vars\n extractWeightEntry: (originalPath: string, paramRank: number) => T,\n) {\n return (prefix: string): SeparableConvParams => {\n const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4);\n const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n return (originalPath: string, paramRank: number, mappedPath?: string) => {\n const tensor = weightMap[originalPath];\n\n if (!isTensor(tensor, paramRank)) {\n throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n }\n\n paramMappings.push(\n { originalPath, paramPath: mappedPath || originalPath },\n );\n\n return tensor;\n };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n let remainingWeights = weights;\n\n function extractWeights(numWeights: number): Float32Array {\n const ret = remainingWeights.slice(0, numWeights);\n remainingWeights = remainingWeights.slice(numWeights);\n return ret;\n }\n\n function getRemainingWeights(): Float32Array {\n return remainingWeights;\n }\n\n return {\n extractWeights,\n getRemainingWeights,\n };\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer = false): DenseBlock4Params {\n const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n return {\n conv0, conv1, conv2, conv3,\n };\n }\n\n return {\n extractDenseBlock3Params,\n extractDenseBlock4Params,\n };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractDenseBlock4Params,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: {\n dense0, dense1, dense2, dense3,\n },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: (originalPath: string, paramRank: number) => T) {\n return (prefix: string): ConvParams => {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return { filters, bias };\n };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractDenseBlock3Params(prefix: string, isFirstLayer = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(prefix: string, isFirstLayer = false): DenseBlock4Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n return {\n conv0, conv1, conv2, conv3,\n };\n }\n\n return {\n extractDenseBlock3Params,\n extractDenseBlock4Params,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractDenseBlock4Params,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const params = {\n dense0: extractDenseBlock4Params('dense0', true),\n dense1: extractDenseBlock4Params('dense1'),\n dense2: extractDenseBlock4Params('dense2'),\n dense3: extractDenseBlock4Params('dense3'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork implements IFaceFeatureExtractor {\n constructor() {\n super('FaceFeatureExtractor');\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('FaceFeatureExtractor - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n let out = denseBlock4(normalized, params.dense0, true);\n out = denseBlock4(out, params.dense1);\n out = denseBlock4(out, params.dense2);\n out = denseBlock4(out, params.dense3);\n out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'face_feature_extractor_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n x: tf.Tensor2D,\n params: FCParams,\n): tf.Tensor2D {\n return tf.tidy(() => tf.add(\n tf.matMul(x, params.weights),\n params.bias,\n ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: extractFcParams('fc'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n const featureExtractorMap: tf.NamedTensorMap = {};\n const classifierMap: tf.NamedTensorMap = {};\n\n Object.keys(weightMap).forEach((key) => {\n const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n map[key] = weightMap[key];\n });\n\n return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends NeuralNetwork {\n protected _faceFeatureExtractor: IFaceFeatureExtractor;\n\n constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor) {\n super(_name);\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): IFaceFeatureExtractor {\n return this._faceFeatureExtractor;\n }\n\n protected abstract override getDefaultModelName(): string\n\n protected abstract getClassifierChannelsIn(): number\n\n protected abstract getClassifierChannelsOut(): number\n\n public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${this._name} - load model before inference`);\n }\n\n return tf.tidy(() => {\n const bottleneckFeatures = input instanceof NetInput\n ? this.faceFeatureExtractor.forwardInput(input)\n : input;\n return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n });\n }\n\n public override dispose(throwOnRedispose = true) {\n this.faceFeatureExtractor.dispose(throwOnRedispose);\n super.dispose(throwOnRedispose);\n }\n\n public loadClassifierParams(weights: Float32Array) {\n const { params, paramMappings } = this.extractClassifierParams(weights);\n this._params = params;\n this._paramMappings = paramMappings;\n }\n\n public extractClassifierParams(weights: Float32Array) {\n return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n return extractParamsFromWeightMap(classifierMap);\n }\n\n protected extractParams(weights: Float32Array) {\n const cIn = this.getClassifierChannelsIn();\n const cOut = this.getClassifierChannelsOut();\n const classifierWeightSize = (cOut * cIn) + cOut;\n\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n return this.extractClassifierParams(classifierWeights);\n }\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n public neutral = 0;\n public happy = 0;\n public sad = 0;\n public angry = 0;\n public fearful = 0;\n public disgusted = 0;\n public surprised = 0;\n\n constructor(probabilities: number[] | Float32Array) {\n if (probabilities.length !== 7) {\n throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n }\n\n FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n this[expression] = probabilities[idx];\n });\n }\n\n asSortedArray() {\n return FACE_EXPRESSION_LABELS\n .map((expression) => ({ expression, probability: this[expression] as number }))\n .sort((e0, e1) => e1.probability - e0.probability);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor {\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n super('FaceExpressionNet', faceFeatureExtractor);\n }\n\n public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n return tf.tidy(() => tf.softmax(this.runNet(input)));\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictExpressions(input: TNetInput) {\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput);\n const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n const data = t.dataSync();\n t.dispose();\n return data;\n }));\n out.dispose();\n\n const predictionsByBatch = probabilitesByBatch\n .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n return netInput.isBatchInput\n ? predictionsByBatch\n : predictionsByBatch[0];\n }\n\n protected getDefaultModelName(): string {\n return 'face_expression_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 256;\n }\n\n protected getClassifierChannelsOut(): number {\n return 7;\n }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions = TSource & { expressions: FaceExpressions }\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions(sourceObj: TSource, expressions: FaceExpressions): WithFaceExpressions {\n const extension = { expressions };\n return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(canvasArg: string | HTMLCanvasElement, faceExpressions: DrawFaceExpressionsInput | Array, minConfidence = 0.1, textFieldAnchor?: IPoint) {\n const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n faceExpressionsArray.forEach((e) => {\n // eslint-disable-next-line no-nested-ternary\n const expr = e instanceof FaceExpressions\n ? e\n : (isWithFaceExpressions(e) ? e.expressions : undefined);\n if (!expr) {\n throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n }\n\n const sorted = expr.asSortedArray();\n const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n const anchor = isWithFaceDetection(e)\n ? e.detection.box.bottomLeft\n : (textFieldAnchor || new Point(0, 0));\n\n const drawTextField = new DrawTextField(\n resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n anchor,\n );\n drawTextField.draw(canvasArg);\n });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n TSource extends WithFaceDetection<{}>,\n TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n landmarks: TFaceLandmarks;\n unshiftedLandmarks: TFaceLandmarks;\n alignedRect: FaceDetection;\n angle: {\n roll: number | undefined;\n pitch: number | undefined;\n yaw: number | undefined;\n };\n};\n\nexport function isWithFaceLandmarks(\n obj: any,\n): obj is WithFaceLandmarks, FaceLandmarks> {\n return (\n isWithFaceDetection(obj)\n // eslint-disable-next-line dot-notation\n && obj['landmarks'] instanceof FaceLandmarks\n // eslint-disable-next-line dot-notation\n && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n // eslint-disable-next-line dot-notation\n && obj['alignedRect'] instanceof FaceDetection\n );\n}\n\nfunction calculateFaceAngle(mesh) {\n // Helper to convert radians to degrees\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const degrees = (radians) => (radians * 180) / Math.PI;\n const calcLengthBetweenTwoPoints = (a, b) => Math.sqrt((a._x - b._x) ** 2 + (a._y - b._y) ** 2);\n\n const angle = {\n roll: undefined,\n pitch: undefined,\n yaw: undefined,\n };\n\n const calcYaw = (leftPoint, midPoint, rightPoint) => {\n // Calc x-distance from left side of the face (\"ear\") to facial midpoint (\"nose\")\n const leftToMidpoint = Math.floor(leftPoint._x - midPoint._x);\n // Calc x-distance from facial midpoint (\"nose\") to the right side of the face (\"ear\")\n const rightToMidpoint = Math.floor(midPoint._x - rightPoint._x);\n // Difference in distances coincidentally approximates to angles\n return leftToMidpoint - rightToMidpoint;\n };\n\n const calcRoll = (lever, pivot) => {\n // When rolling, the head seems to pivot from the nose/lips/chin area.\n // So, we'll choose any two points from the facial midline, where the first point should be the pivot, and the other \"lever\"\n // Plan/Execution: get the hypotenuse & opposite sides of a 90deg triangle ==> Calculate angle in radians\n const hypotenuse = Math.hypot(pivot._x - lever._x, pivot._y - lever._y);\n const opposite = pivot._y - lever._y;\n const angleInRadians = Math.asin(opposite / hypotenuse);\n const angleInDegrees = degrees(angleInRadians);\n const normalizeAngle = Math.floor(90 - angleInDegrees);\n // If lever more to the left of the pivot, then we're tilting left\n // \"-\" is negative direction. \"+\", or absence of a sign is positive direction\n const tiltDirection = pivot._x - lever._x < 0 ? -1 : 1;\n const result = normalizeAngle * tiltDirection;\n return result;\n };\n\n const calcPitch = (leftPoint, midPoint, rightPoint) => {\n // Theory: While pitching, the nose is the most salient point --> That's what we'll use to make a trianle.\n // The \"base\" is between point that don't move when we pitch our head (i.e. an imaginary line running ear to ear through the nose).\n // Executuin: Get the opposite & adjacent lengths of the triangle from the ear's perspective. Use it to get angle.\n\n const base = calcLengthBetweenTwoPoints(leftPoint, rightPoint);\n // adjecent is base/2 technically.\n const baseCoords = {\n _x: (leftPoint._x + rightPoint._x) / 2,\n _y: (leftPoint._y + rightPoint._y) / 2,\n };\n const midToBaseLength = calcLengthBetweenTwoPoints(midPoint, baseCoords);\n const angleInRadians = Math.atan(midToBaseLength / base);\n const angleInDegrees = Math.floor(degrees(angleInRadians));\n // Account for directionality.\n // pitch forwards (_i.e. tilting your head forwards) is positive (or no sign); backward is negative.\n const direction = baseCoords._y - midPoint._y < 0 ? -1 : 1;\n const result = angleInDegrees * direction;\n return result;\n };\n\n if (!mesh || !mesh._positions || mesh._positions.length !== 68) return angle;\n const pt = mesh._positions;\n angle.roll = calcRoll(pt[27], pt[66]);\n angle.pitch = calcPitch(pt[14], pt[30], pt[2]);\n angle.yaw = calcYaw(pt[14], pt[33], pt[2]);\n return angle;\n}\n\nexport function extendWithFaceLandmarks, TFaceLandmarks extends FaceLandmarks = FaceLandmarks68>(\n sourceObj: TSource,\n unshiftedLandmarks: TFaceLandmarks,\n): WithFaceLandmarks {\n const { box: shift } = sourceObj.detection;\n const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);\n const rect = landmarks.align();\n const { imageDims } = sourceObj.detection;\n const alignedRect = new FaceDetection(\n sourceObj.detection.score,\n rect.rescale(imageDims.reverse()),\n imageDims,\n );\n const angle = calculateFaceAngle(unshiftedLandmarks);\n const extension = { landmarks, unshiftedLandmarks, alignedRect, angle };\n return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n drawLines?: boolean\n drawPoints?: boolean\n lineWidth?: number\n pointSize?: number\n lineColor?: string\n pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n public drawLines: boolean;\n\n public drawPoints: boolean;\n\n public lineWidth: number;\n\n public pointSize: number;\n\n public lineColor: string;\n\n public pointColor: string;\n\n constructor(options: IDrawFaceLandmarksOptions = {}) {\n const {\n drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n } = options;\n this.drawLines = drawLines;\n this.drawPoints = drawPoints;\n this.lineWidth = lineWidth || 1;\n this.pointSize = pointSize || 2;\n this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n }\n}\n\nexport class DrawFaceLandmarks {\n public faceLandmarks: FaceLandmarks;\n\n public options: DrawFaceLandmarksOptions;\n\n constructor(\n faceLandmarks: FaceLandmarks,\n options: IDrawFaceLandmarksOptions = {},\n ) {\n this.faceLandmarks = faceLandmarks;\n this.options = new DrawFaceLandmarksOptions(options);\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const ctx = getContext2dOrThrow(canvasArg);\n\n const {\n drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n } = this.options;\n\n if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n ctx.strokeStyle = lineColor;\n ctx.lineWidth = lineWidth;\n drawContour(ctx, this.faceLandmarks.getJawOutline());\n drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n drawContour(ctx, this.faceLandmarks.getNose());\n drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n drawContour(ctx, this.faceLandmarks.getMouth(), true);\n }\n\n if (drawPoints) {\n ctx.strokeStyle = pointColor;\n ctx.fillStyle = pointColor;\n\n const drawPoint = (pt: IPoint) => {\n ctx.beginPath();\n ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n ctx.fill();\n };\n this.faceLandmarks.positions.forEach(drawPoint);\n }\n }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks>\n\nexport function drawFaceLandmarks(\n canvasArg: string | HTMLCanvasElement,\n faceLandmarks: DrawFaceLandmarksInput | Array,\n) {\n const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n faceLandmarksArray.forEach((f) => {\n // eslint-disable-next-line no-nested-ternary\n const landmarks = f instanceof FaceLandmarks\n ? f\n : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n if (!landmarks) {\n throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof');\n }\n\n new DrawFaceLandmarks(landmarks).draw(canvasArg);\n });\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n return { separable_conv0, separable_conv1, expansion_conv };\n }\n\n function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n });\n\n const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n const exit_flow = {\n reduction_block: exit_flow_reduction_block,\n separable_conv: exit_flow_separable_conv,\n };\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { entry_flow, middle_flow, exit_flow },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n return { separable_conv0, separable_conv1, expansion_conv };\n }\n\n function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n });\n\n const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n const exit_flow = {\n reduction_block: exit_flow_reduction_block,\n separable_conv: exit_flow_separable_conv,\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput = true): tf.Tensor4D {\n let out = isActivateInput ? tf.relu(x) : x;\n out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n out = tf.add(out, x);\n return out;\n}\n\nexport class TinyXception extends NeuralNetwork {\n private _numMainBlocks: number;\n\n constructor(numMainBlocks: number) {\n super('TinyXception');\n this._numMainBlocks = numMainBlocks;\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n if (!params) {\n throw new Error('TinyXception - load model before inference');\n }\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n out = reductionBlock(out, params.entry_flow.reduction_block_1);\n range(this._numMainBlocks, 0, 1).forEach((idx) => {\n out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n });\n out = reductionBlock(out, params.exit_flow.reduction_block);\n out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'tiny_xception_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap, this._numMainBlocks);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights, this._numMainBlocks);\n }\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n const age = extractFCParams(512, 1, 'fc/age');\n const gender = extractFCParams(512, 2, 'fc/gender');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc: { age, gender } },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: {\n age: extractFcParams('fc/age'),\n gender: extractFcParams('fc/gender'),\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n // eslint-disable-next-line no-unused-vars\n FEMALE = 'female',\n // eslint-disable-next-line no-unused-vars\n MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n age: number\n gender: Gender\n genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n fc: {\n age: FCParams\n gender: FCParams\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork {\n private _faceFeatureExtractor: TinyXception;\n\n constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n super('AgeGenderNet');\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): TinyXception {\n return this._faceFeatureExtractor;\n }\n\n public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${this._name} - load model before inference`);\n }\n\n return tf.tidy(() => {\n const bottleneckFeatures = input instanceof NetInput\n ? this.faceFeatureExtractor.forwardInput(input)\n : input;\n\n const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n const gender = fullyConnectedLayer(pooled, params.fc.gender);\n return { age, gender };\n });\n }\n\n public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n return tf.tidy(() => {\n const { age, gender } = this.runNet(input);\n return { age, gender: tf.softmax(gender) };\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictAgeAndGender(input: TNetInput): Promise {\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput);\n\n const ages = tf.unstack(out.age);\n const genders = tf.unstack(out.gender);\n const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n ageTensor,\n genderTensor: genders[i],\n }));\n\n const predictionsByBatch = await Promise.all(\n ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n const age = (ageTensor.dataSync())[0];\n const probMale = (genderTensor.dataSync())[0];\n const isMale = probMale > 0.5;\n const gender = isMale ? Gender.MALE : Gender.FEMALE;\n const genderProbability = isMale ? probMale : (1 - probMale);\n\n ageTensor.dispose();\n genderTensor.dispose();\n return { age, gender, genderProbability };\n }),\n );\n out.age.dispose();\n out.gender.dispose();\n\n return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n }\n\n protected getDefaultModelName(): string {\n return 'age_gender_model';\n }\n\n public override dispose(throwOnRedispose = true) {\n this.faceFeatureExtractor.dispose(throwOnRedispose);\n super.dispose(throwOnRedispose);\n }\n\n public loadClassifierParams(weights: Float32Array) {\n const { params, paramMappings } = this.extractClassifierParams(weights);\n this._params = params;\n this._paramMappings = paramMappings;\n }\n\n public extractClassifierParams(weights: Float32Array) {\n return extractParams(weights);\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n return extractParamsFromWeightMap(classifierMap);\n }\n\n protected extractParams(weights: Float32Array) {\n const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n return this.extractClassifierParams(classifierWeights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends FaceProcessor {\n public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n const inputDimensions = originalDimensions.map(({ width, height }) => {\n const scale = inputSize / Math.max(height, width);\n return {\n width: width * scale,\n height: height * scale,\n };\n });\n\n const batchSize = inputDimensions.length;\n\n return tf.tidy(() => {\n const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n // eslint-disable-next-line no-unused-vars\n const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n const { width, height } = inputDimensions[batchIdx];\n return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n };\n\n const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n const landmarkTensors = output\n .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n getPaddingX(batchIdx),\n getPaddingY(batchIdx),\n ))))\n .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n inputDimensions[batchIdx].width,\n inputDimensions[batchIdx].height,\n ))));\n\n return landmarkTensors as tf.Tensor2D;\n });\n }\n\n public forwardInput(input: NetInput): tf.Tensor2D {\n return tf.tidy(() => {\n const out = this.runNet(input);\n return this.postProcess(\n out,\n input.inputSize as number,\n input.inputDimensions.map(([height, width]) => ({ height, width })),\n );\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async detectLandmarks(input: TNetInput): Promise {\n const netInput = await toNetInput(input);\n const landmarkTensors = tf.tidy(\n () => tf.unstack(this.forwardInput(netInput)),\n );\n\n const landmarksForBatch = await Promise.all(landmarkTensors.map(\n async (landmarkTensor, batchIdx) => {\n const landmarksArray = Array.from(landmarkTensor.dataSync());\n const xCoords = landmarksArray.filter((_, i) => isEven(i));\n const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n return new FaceLandmarks68(\n Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n {\n height: netInput.getInputHeight(batchIdx),\n width: netInput.getInputWidth(batchIdx),\n },\n );\n },\n ));\n\n landmarkTensors.forEach((t) => t.dispose());\n\n return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n }\n\n protected getClassifierChannelsOut(): number {\n return 136;\n }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase {\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n super('FaceLandmark68Net', faceFeatureExtractor);\n }\n\n protected getDefaultModelName(): string {\n return 'face_landmark_68_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 256;\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMapTiny(\n weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractDenseBlock3Params,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const params = {\n dense0: extractDenseBlock3Params('dense0', true),\n dense1: extractDenseBlock3Params('dense1'),\n dense2: extractDenseBlock3Params('dense2'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractDenseBlock3Params,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { dense0, dense1, dense2 },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeightMapTiny } from './extractParamsFromWeightMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork implements IFaceFeatureExtractor {\n constructor() {\n super('TinyFaceFeatureExtractor');\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('TinyFaceFeatureExtractor - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n let out = denseBlock3(normalized, params.dense0, true);\n out = denseBlock3(out, params.dense1);\n out = denseBlock3(out, params.dense2);\n out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'face_feature_extractor_tiny_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMapTiny(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParamsTiny(weights);\n }\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase {\n constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n super('FaceLandmark68TinyNet', faceFeatureExtractor);\n }\n\n protected getDefaultModelName(): string {\n return 'face_landmark_68_tiny_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 128;\n }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n x: tf.Tensor4D,\n params: ConvLayerParams,\n strides: [number, number],\n withRelu: boolean,\n padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n const { filters, bias } = params.conv;\n\n let out = tf.conv2d(x, filters, strides, padding);\n out = tf.add(out, bias);\n out = scale(out, params.scale);\n return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { isFloat } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n const weights = extractWeights(numFilterValues);\n const depth = weights.length / (numFilters * filterSize * filterSize);\n\n if (isFloat(depth)) {\n throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n }\n\n return tf.tidy(\n () => tf.transpose(\n tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n [2, 3, 1, 0],\n ),\n );\n }\n\n function extractConvParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvParams {\n const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n const bias = tf.tensor1d(extractWeights(numFilters));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return { filters, bias };\n }\n\n function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n const weights = tf.tensor1d(extractWeights(numWeights));\n const biases = tf.tensor1d(extractWeights(numWeights));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/weights` },\n { paramPath: `${mappedPrefix}/biases` },\n );\n\n return {\n weights,\n biases,\n };\n }\n\n function extractConvLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvLayerParams {\n const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n return { conv, scale };\n }\n\n function extractResidualLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n isDown = false,\n ): ResidualLayerParams {\n const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n return { conv1, conv2 };\n }\n\n return {\n extractConvLayerParams,\n extractResidualLayerParams,\n };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvLayerParams,\n extractResidualLayerParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n const fc = tf.tidy(\n () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n );\n paramMappings.push({ paramPath: 'fc' });\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n const params = {\n conv32_down,\n conv32_1,\n conv32_2,\n conv32_3,\n conv64_down,\n conv64_1,\n conv64_2,\n conv64_3,\n conv128_down,\n conv128_1,\n conv128_2,\n conv256_down,\n conv256_1,\n conv256_2,\n conv256_down_out,\n fc,\n };\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n return { weights, biases };\n }\n\n function extractConvLayerParams(prefix: string): ConvLayerParams {\n const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n const scale = extractScaleLayerParams(prefix);\n\n return { conv: { filters, bias }, scale };\n }\n\n function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n return {\n conv1: extractConvLayerParams(`${prefix}/conv1`),\n conv2: extractConvLayerParams(`${prefix}/conv2`),\n };\n }\n\n return {\n extractConvLayerParams,\n extractResidualLayerParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvLayerParams,\n extractResidualLayerParams,\n } = extractorsFactory(weightMap, paramMappings);\n\n const conv32_down = extractConvLayerParams('conv32_down');\n const conv32_1 = extractResidualLayerParams('conv32_1');\n const conv32_2 = extractResidualLayerParams('conv32_2');\n const conv32_3 = extractResidualLayerParams('conv32_3');\n\n const conv64_down = extractResidualLayerParams('conv64_down');\n const conv64_1 = extractResidualLayerParams('conv64_1');\n const conv64_2 = extractResidualLayerParams('conv64_2');\n const conv64_3 = extractResidualLayerParams('conv64_3');\n\n const conv128_down = extractResidualLayerParams('conv128_down');\n const conv128_1 = extractResidualLayerParams('conv128_1');\n const conv128_2 = extractResidualLayerParams('conv128_2');\n\n const conv256_down = extractResidualLayerParams('conv256_down');\n const conv256_1 = extractResidualLayerParams('conv256_1');\n const conv256_2 = extractResidualLayerParams('conv256_2');\n const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n const { fc } = weightMap;\n paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n if (!isTensor2D(fc)) {\n throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n }\n\n const params = {\n conv32_down,\n conv32_1,\n conv32_2,\n conv32_3,\n conv64_down,\n conv64_1,\n conv64_2,\n conv64_3,\n conv128_down,\n conv128_1,\n conv128_2,\n conv256_down,\n conv256_1,\n conv256_2,\n conv256_down_out,\n fc,\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n let out = conv(x, params.conv1);\n out = convNoRelu(out, params.conv2);\n out = tf.add(out, x);\n out = tf.relu(out);\n return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n let out = convDown(x, params.conv1);\n out = convNoRelu(out, params.conv2);\n\n let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n const zeros = tf.zeros(pooled.shape);\n const isPad = pooled.shape[3] !== out.shape[3];\n const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n if (isAdjustShape) {\n const padShapeX = [...out.shape] as [number, number, number, number];\n padShapeX[1] = 1;\n const zerosW = tf.zeros(padShapeX);\n out = tf.concat([out, zerosW], 1);\n\n const padShapeY = [...out.shape] as [number, number, number, number];\n padShapeY[2] = 1;\n const zerosH = tf.zeros(padShapeY);\n out = tf.concat([out, zerosH], 2);\n }\n\n pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n out = tf.add(pooled, out) as tf.Tensor4D;\n\n out = tf.relu(out);\n return out;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork {\n constructor() {\n super('FaceRecognitionNet');\n }\n\n public forwardInput(input: NetInput): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error('FaceRecognitionNet - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n let out = convDown(normalized, params.conv32_down);\n out = tf.maxPool(out, 3, 2, 'valid');\n\n out = residual(out, params.conv32_1);\n out = residual(out, params.conv32_2);\n out = residual(out, params.conv32_3);\n\n out = residualDown(out, params.conv64_down);\n out = residual(out, params.conv64_1);\n out = residual(out, params.conv64_2);\n out = residual(out, params.conv64_3);\n\n out = residualDown(out, params.conv128_down);\n out = residual(out, params.conv128_1);\n out = residual(out, params.conv128_2);\n\n out = residualDown(out, params.conv256_down);\n out = residual(out, params.conv256_1);\n out = residual(out, params.conv256_2);\n out = residualDown(out, params.conv256_down_out);\n\n const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n return fullyConnected as tf.Tensor2D;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async computeFaceDescriptor(input: TNetInput): Promise {\n // @ts-ignore\n if (input?.shape?.some((dim) => dim <= 0)) return new Float32Array(128);\n const netInput = await toNetInput(input);\n const faceDescriptorTensors = tf.tidy(() => tf.unstack(this.forwardInput(netInput)));\n const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())) as Float32Array[];\n faceDescriptorTensors.forEach((t) => t.dispose());\n return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];\n }\n\n protected getDefaultModelName(): string {\n return 'face_recognition_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n const net = new FaceRecognitionNet();\n net.extractWeights(weights);\n return net;\n}\n", "export type WithFaceDescriptor = TSource & {\n descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n TSource\n>(\n sourceObj: TSource,\n descriptor: Float32Array,\n): WithFaceDescriptor {\n const extension = { descriptor };\n return { ...sourceObj, ...extension };\n}\n", "export type WithAge = TSource & {\n age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n TSource\n>(\n sourceObj: TSource,\n age: number,\n): WithAge {\n const extension = { age };\n return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender = TSource & {\n gender: Gender\n genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n TSource\n>(\n sourceObj: TSource,\n gender: Gender,\n genderProbability: number,\n): WithGender {\n const extension = { gender, genderProbability };\n return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common/index';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/batch_norm_scale` },\n { paramPath: `${mappedPrefix}/batch_norm_offset` },\n { paramPath: `${mappedPrefix}/batch_norm_mean` },\n { paramPath: `${mappedPrefix}/batch_norm_variance` },\n );\n\n return {\n filters,\n batch_norm_scale,\n batch_norm_offset,\n batch_norm_mean,\n batch_norm_variance,\n };\n }\n\n function extractConvParams(\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n isPointwiseConv?: boolean,\n ): ConvParams {\n const filters = tf.tensor4d(\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n [filterSize, filterSize, channelsIn, channelsOut],\n );\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n );\n\n return { filters, bias };\n }\n\n function extractPointwiseConvParams(\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n ): PointwiseConvParams {\n const {\n filters,\n bias,\n } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n return {\n filters,\n batch_norm_offset: bias,\n };\n }\n\n function extractConvPairParams(\n channelsIn: number,\n channelsOut: number,\n mappedPrefix: string,\n ): MobileNetV1.ConvPairParams {\n const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n return { depthwise_conv, pointwise_conv };\n }\n\n function extractMobilenetV1Params(): MobileNetV1.Params {\n const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n return {\n conv_0,\n conv_1,\n conv_2,\n conv_3,\n conv_4,\n conv_5,\n conv_6,\n conv_7,\n conv_8,\n conv_9,\n conv_10,\n conv_11,\n conv_12,\n conv_13,\n };\n }\n\n function extractPredictionLayerParams(): PredictionLayerParams {\n const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n const box_predictor_0 = {\n box_encoding_predictor: box_encoding_0_predictor,\n class_predictor: class_predictor_0,\n };\n const box_predictor_1 = {\n box_encoding_predictor: box_encoding_1_predictor,\n class_predictor: class_predictor_1,\n };\n const box_predictor_2 = {\n box_encoding_predictor: box_encoding_2_predictor,\n class_predictor: class_predictor_2,\n };\n const box_predictor_3 = {\n box_encoding_predictor: box_encoding_3_predictor,\n class_predictor: class_predictor_3,\n };\n const box_predictor_4 = {\n box_encoding_predictor: box_encoding_4_predictor,\n class_predictor: class_predictor_4,\n };\n const box_predictor_5 = {\n box_encoding_predictor: box_encoding_5_predictor,\n class_predictor: class_predictor_5,\n };\n return {\n conv_0,\n conv_1,\n conv_2,\n conv_3,\n conv_4,\n conv_5,\n conv_6,\n conv_7,\n box_predictor_0,\n box_predictor_1,\n box_predictor_2,\n box_predictor_3,\n box_predictor_4,\n box_predictor_5,\n };\n }\n\n return {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n const {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n } = extractorsFactory(extractWeights, paramMappings);\n const mobilenetv1 = extractMobilenetV1Params();\n const prediction_layer = extractPredictionLayerParams();\n const extra_dim = tf.tensor3d(\n extractWeights(5118 * 4),\n [1, 5118, 4],\n );\n const output_layer = {\n extra_dim,\n };\n paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n params: {\n mobilenetv1,\n prediction_layer,\n output_layer,\n },\n paramMappings,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n return { filters, batch_norm_offset };\n }\n\n function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n const mappedPrefix = `mobilenetv1/conv_${idx}`;\n const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n return {\n depthwise_conv: {\n filters,\n batch_norm_scale,\n batch_norm_offset,\n batch_norm_mean,\n batch_norm_variance,\n },\n pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n };\n }\n\n function extractMobilenetV1Params(): MobileNetV1.Params {\n return {\n conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n conv_1: extractConvPairParams(1),\n conv_2: extractConvPairParams(2),\n conv_3: extractConvPairParams(3),\n conv_4: extractConvPairParams(4),\n conv_5: extractConvPairParams(5),\n conv_6: extractConvPairParams(6),\n conv_7: extractConvPairParams(7),\n conv_8: extractConvPairParams(8),\n conv_9: extractConvPairParams(9),\n conv_10: extractConvPairParams(10),\n conv_11: extractConvPairParams(11),\n conv_12: extractConvPairParams(12),\n conv_13: extractConvPairParams(13),\n };\n }\n\n function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n return { filters, bias };\n }\n\n function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n const box_encoding_predictor = extractConvParams(\n `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n );\n const class_predictor = extractConvParams(\n `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n `prediction_layer/box_predictor_${idx}/class_predictor`,\n );\n return { box_encoding_predictor, class_predictor };\n }\n\n function extractPredictionLayerParams(): PredictionLayerParams {\n return {\n conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n box_predictor_0: extractBoxPredictorParams(0),\n box_predictor_1: extractBoxPredictorParams(1),\n box_predictor_2: extractBoxPredictorParams(2),\n box_predictor_3: extractBoxPredictorParams(3),\n box_predictor_4: extractBoxPredictorParams(4),\n box_predictor_5: extractBoxPredictorParams(5),\n };\n }\n\n return {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n const {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n } = extractorsFactory(weightMap, paramMappings);\n const extra_dim = weightMap['Output/extra_dim'];\n paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n if (!isTensor3D(extra_dim)) {\n throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n }\n\n const params = {\n mobilenetv1: extractMobilenetV1Params(),\n prediction_layer: extractPredictionLayerParams(),\n output_layer: {\n extra_dim,\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]) {\n return tf.tidy(() => {\n let out = tf.conv2d(x, params.filters, strides, 'same');\n out = tf.add(out, params.batch_norm_offset);\n return tf.clipByValue(out, 0, 6);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(x: tf.Tensor4D, params: MobileNetV1.DepthwiseConvParams, strides: [number, number]) {\n return tf.tidy(() => {\n let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n out = tf.batchNorm(\n out,\n params.batch_norm_mean,\n params.batch_norm_variance,\n params.batch_norm_offset,\n params.batch_norm_scale,\n epsilon,\n );\n return tf.clipByValue(out, 0, 6);\n });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n return tf.tidy(() => {\n let conv11;\n let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n const convPairParams = [\n params.conv_1,\n params.conv_2,\n params.conv_3,\n params.conv_4,\n params.conv_5,\n params.conv_6,\n params.conv_7,\n params.conv_8,\n params.conv_9,\n params.conv_10,\n params.conv_11,\n params.conv_12,\n params.conv_13,\n ];\n\n convPairParams.forEach((param, i) => {\n const layerIdx = i + 1;\n const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n if (layerIdx === 11) conv11 = out;\n });\n\n if (conv11 === null) {\n throw new Error('mobileNetV1 - output of conv layer 11 is null');\n }\n\n return {\n out,\n conv11: conv11 as any,\n };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n const boxesData = boxes.arraySync();\n const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n if (areaI <= 0 || areaJ <= 0) return 0.0;\n const intersectionYmin = Math.max(yminI, yminJ);\n const intersectionXmin = Math.max(xminI, xminJ);\n const intersectionYmax = Math.min(ymaxI, ymaxJ);\n const intersectionXmax = Math.min(xmaxI, xmaxJ);\n const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) * Math.max(intersectionXmax - intersectionXmin, 0.0);\n return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n boxes: tf.Tensor2D,\n scores: number[],\n maxOutputSize: number,\n iouThreshold: number,\n scoreThreshold: number,\n): number[] {\n const numBoxes = boxes.shape[0];\n const outputSize = Math.min(maxOutputSize, numBoxes);\n\n const candidates = scores\n .map((score, boxIndex) => ({ score, boxIndex }))\n .filter((c) => c.score > scoreThreshold)\n .sort((c1, c2) => c2.score - c1.score);\n\n const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n const selected: number[] = [];\n\n candidates.forEach((c) => {\n if (selected.length >= outputSize) return;\n const originalScore = c.score;\n for (let j = selected.length - 1; j >= 0; --j) {\n const iou = IOU(boxes, c.boxIndex, selected[j]);\n if (iou === 0.0) continue;\n c.score *= suppressFunc(iou);\n if (c.score <= scoreThreshold) break;\n }\n if (originalScore === c.score) {\n selected.push(c.boxIndex);\n }\n });\n return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n const sizes = [\n tf.sub(vec[2], vec[0]),\n tf.sub(vec[3], vec[1]),\n ];\n const centers = [\n tf.add(vec[0], tf.div(sizes[0], 2)),\n tf.add(vec[1], tf.div(sizes[1], 2)),\n ];\n return { sizes, centers };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n const { sizes, centers } = getCenterCoordinatesAndSizesLayer(x0);\n\n const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], 5)), sizes[0]), 2);\n const add0_out = tf.add(tf.mul(tf.div(vec[0], 10), sizes[0]), centers[0]);\n const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], 5)), sizes[1]), 2);\n const add1_out = tf.add(tf.mul(tf.div(vec[1], 10), sizes[1]), centers[1]);\n\n return tf.transpose(\n tf.stack([\n tf.sub(add0_out, div0_out),\n tf.sub(add1_out, div1_out),\n tf.add(add0_out, div0_out),\n tf.add(add1_out, div1_out),\n ]),\n [1, 0],\n );\n}\n\nexport function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams) {\n return tf.tidy(() => {\n const batchSize = boxPredictions.shape[0];\n\n let boxes = decodeBoxesLayer(\n tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n );\n boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);\n\n const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n scores = tf.reshape(scores, [batchSize, scores.shape[1] as number]);\n\n const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n return { boxes: boxesByBatch, scores: scoresByBatch };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n x: tf.Tensor4D,\n params: BoxPredictionParams,\n) {\n return tf.tidy(() => {\n const batchSize = x.shape[0];\n const boxPredictionEncoding = tf.reshape(\n convLayer(x, params.box_encoding_predictor),\n [batchSize, -1, 1, 4],\n );\n const classPrediction = tf.reshape(\n convLayer(x, params.class_predictor),\n [batchSize, -1, 3],\n );\n return { boxPredictionEncoding, classPrediction };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n x: tf.Tensor4D,\n conv11: tf.Tensor4D,\n params: PredictionLayerParams,\n) {\n return tf.tidy(() => {\n const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n const boxPredictions = tf.concat([\n boxPrediction0.boxPredictionEncoding,\n boxPrediction1.boxPredictionEncoding,\n boxPrediction2.boxPredictionEncoding,\n boxPrediction3.boxPredictionEncoding,\n boxPrediction4.boxPredictionEncoding,\n boxPrediction5.boxPredictionEncoding,\n ], 1) as tf.Tensor4D;\n\n const classPredictions = tf.concat([\n boxPrediction0.classPrediction,\n boxPrediction1.classPrediction,\n boxPrediction2.classPrediction,\n boxPrediction3.classPrediction,\n boxPrediction4.classPrediction,\n boxPrediction5.classPrediction,\n ], 1) as tf.Tensor4D;\n\n return {\n boxPredictions,\n classPredictions,\n };\n });\n}\n", "export interface ISsdMobilenetv1Options {\n minConfidence?: number\n maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n protected _name = 'SsdMobilenetv1Options';\n\n private _minConfidence: number;\n\n private _maxResults: number;\n\n constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n this._minConfidence = minConfidence || 0.5;\n this._maxResults = maxResults || 100;\n\n if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n }\n\n if (typeof this._maxResults !== 'number') {\n throw new Error(`${this._name} - expected maxResults to be a number`);\n }\n }\n\n get minConfidence(): number { return this._minConfidence; }\n\n get maxResults(): number { return this._maxResults; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork {\n constructor() {\n super('SsdMobilenetv1');\n }\n\n public forwardInput(input: NetInput) {\n const { params } = this;\n if (!params) throw new Error('SsdMobilenetv1 - load model before inference');\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n const x = tf.sub(tf.div(batchTensor, 127.5), 1) as tf.Tensor4D; // input is normalized -1..1\n const features = mobileNetV1(x, params.mobilenetv1);\n const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n return outputLayer(boxPredictions, classPredictions, params.output_layer);\n });\n }\n\n public async forward(input: TNetInput) {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async locateFaces(input: TNetInput, options: ISsdMobilenetv1Options = {}): Promise {\n const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n const netInput = await toNetInput(input);\n const { boxes: _boxes, scores: _scores } = this.forwardInput(netInput);\n const boxes = _boxes[0];\n const scores = _scores[0];\n for (let i = 1; i < _boxes.length; i++) {\n _boxes[i].dispose();\n _scores[i].dispose();\n }\n const scoresData = Array.from(scores.dataSync());\n const iouThreshold = 0.5;\n const indices = nonMaxSuppression(boxes, scoresData as number[], maxResults, iouThreshold, minConfidence);\n const reshapedDims = netInput.getReshapedInputDimensions(0);\n const inputSize = netInput.inputSize as number;\n const padX = inputSize / reshapedDims.width;\n const padY = inputSize / reshapedDims.height;\n const boxesData = boxes.arraySync();\n const results = indices\n .map((idx) => {\n const [top, bottom] = [\n Math.max(0, boxesData[idx][0]),\n Math.min(1.0, boxesData[idx][2]),\n ].map((val) => val * padY);\n const [left, right] = [\n Math.max(0, boxesData[idx][1]),\n Math.min(1.0, boxesData[idx][3]),\n ].map((val) => val * padX);\n return new FaceDetection(\n scoresData[idx] as number,\n new Rect(left, top, right - left, bottom - top),\n { height: netInput.getInputHeight(0), width: netInput.getInputWidth(0) },\n );\n });\n boxes.dispose();\n scores.dispose();\n return results;\n }\n\n protected getDefaultModelName(): string {\n return 'ssd_mobilenetv1_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n const net = new SsdMobilenetv1();\n net.extractWeights(weights);\n return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n new Point(0.738768, 0.874946),\n new Point(2.42204, 2.65704),\n new Point(4.30971, 7.04493),\n new Point(10.246, 4.59428),\n new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n new Point(1.603231, 2.094468),\n new Point(6.041143, 7.080126),\n new Point(2.882459, 3.518061),\n new Point(4.266906, 5.178857),\n new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n withSeparableConvs: boolean\n iouThreshold: number\n anchors: Point[]\n classes: string[]\n meanRgb?: [number, number, number]\n withClassScores?: boolean,\n filterSizes?: number[]\n isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n if (!config) {\n throw new Error(`invalid config: ${config}`);\n }\n\n if (typeof config.withSeparableConvs !== 'boolean') {\n throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n }\n\n if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n }\n\n if (\n !Array.isArray(config.classes)\n || !config.classes.length\n || !config.classes.every((c: any) => typeof c === 'string')\n ) {\n throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n }\n\n if (\n !Array.isArray(config.anchors)\n || !config.anchors.length\n || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n ) {\n throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n }\n\n if (config.meanRgb && (\n !Array.isArray(config.meanRgb)\n || config.meanRgb.length !== 3\n || !config.meanRgb.every(isNumber)\n )) {\n throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n return tf.tidy(() => {\n const min = tf.mul(x, tf.scalar(0.10000000149011612));\n return tf.add(tf.relu(tf.sub(x, min)), min);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n out = tf.sub(out, params.bn.sub);\n out = tf.mul(out, params.bn.truediv);\n out = tf.add(out, params.conv.bias);\n return leaky(out);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n out = tf.add(out, params.bias);\n return leaky(out);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n const sub = tf.tensor1d(extractWeights(size));\n const truediv = tf.tensor1d(extractWeights(size));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/sub` },\n { paramPath: `${mappedPrefix}/truediv` },\n );\n return { sub, truediv };\n }\n\n function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n return { conv, bn };\n }\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n return {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n };\n}\n\nexport function extractParams(\n weights: Float32Array,\n config: TinyYolov2Config,\n boxEncodingSize: number,\n filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const paramMappings: ParamMapping[] = [];\n const {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n } = extractorsFactory(extractWeights, paramMappings);\n let params: TinyYolov2NetParams;\n\n if (config.withSeparableConvs) {\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n const conv0 = config.isFirstLayerConv2d\n ? extractConvParams(s0, s1, 3, 'conv0')\n : extractSeparableConvParams(s0, s1, 'conv0');\n const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n params = {\n conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n };\n } else {\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n params = {\n conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n };\n }\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractBatchNormParams(prefix: string): BatchNorm {\n const sub = extractWeightEntry(`${prefix}/sub`, 1);\n const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n return { sub, truediv };\n }\n\n function extractConvParams(prefix: string): ConvParams {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { filters, bias };\n }\n\n function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n const conv = extractConvParams(`${prefix}/conv`);\n const bn = extractBatchNormParams(`${prefix}/bn`);\n return { conv, bn };\n }\n\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n return {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n } = extractorsFactory(weightMap, paramMappings);\n\n let params: TinyYolov2NetParams;\n\n if (config.withSeparableConvs) {\n // eslint-disable-next-line no-mixed-operators\n const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n params = {\n conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n conv1: extractSeparableConvParams('conv1'),\n conv2: extractSeparableConvParams('conv2'),\n conv3: extractSeparableConvParams('conv3'),\n conv4: extractSeparableConvParams('conv4'),\n conv5: extractSeparableConvParams('conv5'),\n conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n conv8: extractConvParams('conv8'),\n };\n } else {\n params = {\n conv0: extractConvWithBatchNormParams('conv0'),\n conv1: extractConvWithBatchNormParams('conv1'),\n conv2: extractConvWithBatchNormParams('conv2'),\n conv3: extractConvWithBatchNormParams('conv3'),\n conv4: extractConvWithBatchNormParams('conv4'),\n conv5: extractConvWithBatchNormParams('conv5'),\n conv6: extractConvWithBatchNormParams('conv6'),\n conv7: extractConvWithBatchNormParams('conv7'),\n conv8: extractConvParams('conv8'),\n };\n }\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n inputSize?: number\n scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n protected _name = 'TinyYolov2Options';\n\n private _inputSize: number;\n\n private _scoreThreshold: number;\n\n constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n this._inputSize = inputSize || 416;\n this._scoreThreshold = scoreThreshold || 0.5;\n\n if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n }\n\n if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n }\n }\n\n get inputSize(): number { return this._inputSize; }\n\n get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork {\n public static DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024];\n\n private _config: TinyYolov2Config;\n\n constructor(config: TinyYolov2Config) {\n super('TinyYolov2');\n validateConfig(config);\n this._config = config;\n }\n\n public get config(): TinyYolov2Config {\n return this._config;\n }\n\n public get withClassScores(): boolean {\n return this.config.withClassScores || this.config.classes.length > 1;\n }\n\n public get boxEncodingSize(): number {\n return 5 + (this.withClassScores ? this.config.classes.length : 0);\n }\n\n public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n let out = convWithBatchNorm(x, params.conv0);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv1);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv2);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv3);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv4);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv5);\n out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n out = convWithBatchNorm(out, params.conv6);\n out = convWithBatchNorm(out, params.conv7);\n return convLayer(out, params.conv8, 'valid', false);\n }\n\n public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n let out = this.config.isFirstLayerConv2d\n ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv1);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv2);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv3);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv4);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv5);\n out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n return convLayer(out, params.conv8, 'valid', false);\n }\n\n public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('TinyYolov2 - load model before inference');\n }\n\n return tf.tidy(() => {\n let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n batchTensor = this.config.meanRgb\n ? normalize(batchTensor, this.config.meanRgb)\n : batchTensor;\n batchTensor = batchTensor.div(255) as tf.Tensor4D;\n return this.config.withSeparableConvs\n ? this.runMobilenet(batchTensor, params as MobilenetParams)\n : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n });\n }\n\n public async forward(input: TNetInput, inputSize: number): Promise {\n return this.forwardInput(await toNetInput(input), inputSize);\n }\n\n public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise {\n const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput, inputSize);\n const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n const inputDimensions = {\n width: netInput.getInputWidth(0),\n height: netInput.getInputHeight(0),\n };\n\n const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n out.dispose();\n out0.dispose();\n\n const boxes = results.map((res) => res.box);\n const scores = results.map((res) => res.score);\n const classScores = results.map((res) => res.classScore);\n const classNames = results.map((res) => this.config.classes[res.label]);\n\n const indices = nonMaxSuppression(\n boxes.map((box) => box.rescale(inputSize)),\n scores,\n this.config.iouThreshold,\n true,\n );\n\n const detections = indices.map((idx) => new ObjectDetection(\n scores[idx],\n classScores[idx],\n classNames[idx],\n boxes[idx],\n inputDimensions,\n ));\n return detections;\n }\n\n protected getDefaultModelName(): string {\n return '';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap, this.config);\n }\n\n protected extractParams(weights: Float32Array) {\n const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n const numFilters = filterSizes ? filterSizes.length : undefined;\n if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n }\n return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n }\n\n protected async extractBoxes(\n outputTensor: tf.Tensor4D,\n inputBlobDimensions: Dimensions,\n scoreThreshold?: number,\n ) {\n const { width, height } = inputBlobDimensions;\n const inputSize = Math.max(width, height);\n const correctionFactorX = inputSize / width;\n const correctionFactorY = inputSize / height;\n\n const numCells = outputTensor.shape[1];\n const numBoxes = this.config.anchors.length;\n\n const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n const classScores = this.withClassScores\n ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n : tf.scalar(0);\n return [boxes, scores, classScores];\n });\n\n const results = [] as any;\n const scoresData = await scoresTensor.array();\n const boxesData = await boxesTensor.array();\n for (let row = 0; row < numCells; row++) {\n for (let col = 0; col < numCells; col++) {\n for (let anchor = 0; anchor < numBoxes; anchor++) {\n const score = sigmoid(scoresData[row][col][anchor][0]);\n if (!scoreThreshold || score > scoreThreshold) {\n const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n const x = (ctX - (widthLocal / 2));\n const y = (ctY - (heightLocal / 2));\n const pos = { row, col, anchor };\n const { classScore, label } = this.withClassScores\n ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n : { classScore: 1, label: 0 };\n results.push({\n box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n score,\n classScore: score * classScore,\n label,\n ...pos,\n });\n }\n }\n }\n }\n\n boxesTensor.dispose();\n scoresTensor.dispose();\n classScoresTensor.dispose();\n return results;\n }\n\n private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n const { row, col, anchor } = pos;\n const classesData = await classesTensor.array();\n return Array(this.config.classes.length).fill(0)\n .map((_, i) => classesData[row][col][anchor][i])\n .map((classScore, label) => ({\n classScore,\n label,\n }))\n .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n BOX_ANCHORS,\n BOX_ANCHORS_SEPARABLE,\n DEFAULT_MODEL_NAME,\n DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n IOU_THRESHOLD,\n MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n constructor(withSeparableConvs = true) {\n const config = {\n withSeparableConvs,\n iouThreshold: IOU_THRESHOLD,\n classes: ['face'],\n ...(withSeparableConvs\n ? {\n anchors: BOX_ANCHORS_SEPARABLE,\n meanRgb: MEAN_RGB_SEPARABLE,\n }\n : {\n anchors: BOX_ANCHORS,\n withClassScores: true,\n }),\n };\n\n super(config);\n }\n\n public get withSeparableConvs(): boolean {\n return this.config.withSeparableConvs;\n }\n\n public get anchors(): Point[] {\n return this.config.anchors;\n }\n\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise {\n const objectDetections = await this.detect(input, forwardParams);\n return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n }\n\n protected override getDefaultModelName(): string {\n return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n }\n\n protected override extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n return super.extractParamsFromWeightMap(weightMap);\n }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\nexport * from './config';\nexport * from './types';\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs = true) {\n const net = new TinyYolov2(withSeparableConvs);\n net.extractWeights(weights);\n return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport type ITinyFaceDetectorOptions = ITinyYolov2Options\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n protected override _name = 'TinyFaceDetectorOptions';\n}\n", "export class ComposableTask {\n // eslint-disable-next-line no-unused-vars\n public async then(onfulfilled: (value: T) => T | PromiseLike): Promise {\n return onfulfilled(await this.run());\n }\n\n public async run(): Promise {\n throw new Error('ComposableTask - run is not implemented');\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults, TResult>(\n parentResults: TSource[],\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResults: (faces: Array) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment: (parentResult: WithFaceLandmarks) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n ? getRectForAlignment(parentResult)\n : parentResult.detection));\n const faces: Array = extractedFaces || (\n input instanceof tf.Tensor\n ? await extractFaceTensors(input, faceBoxes)\n : await extractFaces(input, faceBoxes)\n );\n const results = await computeResults(faces);\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n return results;\n}\n\nexport async function extractSingleFaceAndComputeResult, TResult>(\n parentResult: TSource,\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment?: (parentResultLocal: WithFaceLandmarks) => FaceDetection,\n) {\n return extractAllFacesAndComputeResults(\n [parentResult],\n input,\n async (faces) => computeResult(faces[0]),\n extractedFaces,\n getRectForAlignment,\n );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n new Point(1.603231, 2.094468),\n new Point(6.041143, 7.080126),\n new Point(2.882459, 3.518061),\n new Point(4.266906, 5.178857),\n new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n constructor() {\n const config = {\n withSeparableConvs: true,\n iouThreshold: IOU_THRESHOLD,\n classes: ['face'],\n anchors: BOX_ANCHORS,\n meanRgb: MEAN_RGB,\n isFirstLayerConv2d: true,\n filterSizes: [3, 16, 32, 64, 128, 256, 512],\n };\n\n super(config);\n }\n\n public get anchors(): Point[] {\n return this.config.anchors;\n }\n\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise {\n const objectDetections = await this.detect(input, forwardParams);\n return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n }\n\n protected override getDefaultModelName(): string {\n return 'tiny_face_detector_model';\n }\n\n protected override extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n return super.extractParamsFromWeightMap(weightMap);\n }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n ssdMobilenetv1: new SsdMobilenetv1(),\n tinyFaceDetector: new TinyFaceDetector(),\n tinyYolov2: new TinyYolov2(),\n faceLandmark68Net: new FaceLandmark68Net(),\n faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n faceRecognitionNet: new FaceRecognitionNet(),\n faceExpressionNet: new FaceExpressionNet(),\n ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllFaceExpressionsTask> extends PredictFaceExpressionsTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n\n const faceExpressionsByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(\n faces.map((face) => nets.faceExpressionNet.predictExpressions(face) as Promise),\n ),\n this.extractedFaces,\n );\n\n return parentResults.map(\n (parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]),\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsTask> extends PredictFaceExpressionsTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n\n const faceExpressions = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceExpressionNet.predictExpressions(face) as Promise,\n this.extractedFaces,\n );\n\n return extendWithFaceExpressions(parentResult, faceExpressions);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask>> extends PredictAllFaceExpressionsTask {\n override withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask>> extends PredictSingleFaceExpressionsTask {\n override withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllFaceExpressionsTask, PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllAgeAndGenderTask> extends PredictAgeAndGenderTaskBase>[], TSource[]> {\n public override async run(): Promise>[]> {\n const parentResults = await this.parentTask;\n const ageAndGenderByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise)),\n this.extractedFaces,\n );\n return parentResults.map((parentResult, i) => {\n const { age, gender, genderProbability } = ageAndGenderByFace[i];\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n });\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictSingleAgeAndGenderTask> extends PredictAgeAndGenderTaskBase> | undefined, TSource | undefined> {\n public override async run(): Promise> | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) return undefined;\n const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise,\n this.extractedFaces,\n );\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask>> extends PredictAllAgeAndGenderTask {\n override withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask>> extends PredictSingleAgeAndGenderTask {\n override withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n ) {\n super();\n }\n}\n\nexport class ComputeAllFaceDescriptorsTask>> extends ComputeFaceDescriptorsTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const descriptors = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise)),\n null,\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor));\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n}\n\nexport class ComputeSingleFaceDescriptorTask>> extends ComputeFaceDescriptorsTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) return undefined;\n const descriptor = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise,\n null,\n // eslint-disable-next-line no-shadow, @typescript-eslint/no-shadow\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n return extendWithFaceDescriptor(parentResult, descriptor);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected useTinyLandmarkNet: boolean,\n ) {\n super();\n }\n\n protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n return this.useTinyLandmarkNet\n ? nets.faceLandmark68TinyNet\n : nets.faceLandmark68Net;\n }\n}\n\nexport class DetectAllFaceLandmarksTask> extends DetectFaceLandmarksTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const detections = parentResults.map((res) => res.detection);\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, detections)\n : await extractFaces(this.input, detections);\n const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face))) as FaceLandmarks68[];\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n const result = parentResults\n .filter((_parentResult, i) => faceLandmarksByFace[i])\n .map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));\n return result;\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class DetectSingleFaceLandmarksTask> extends DetectFaceLandmarksTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n const { detection } = parentResult;\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, [detection])\n : await extractFaces(this.input, [detection]);\n const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n return extendWithFaceLandmarks(parentResult, landmarks);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase extends ComposableTask {\n // eslint-disable-next-line no-unused-vars\n constructor(protected input: TNetInput, protected options: FaceDetectionOptions = new SsdMobilenetv1Options()) {\n super();\n }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase {\n public override async run(): Promise {\n const { input, options } = this;\n let result;\n if (options instanceof TinyFaceDetectorOptions) result = nets.tinyFaceDetector.locateFaces(input, options);\n else if (options instanceof SsdMobilenetv1Options) result = nets.ssdMobilenetv1.locateFaces(input, options);\n else if (options instanceof TinyYolov2Options) result = nets.tinyYolov2.locateFaces(input, options);\n else throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options');\n return result;\n }\n\n private runAndExtendWithFaceDetections(): Promise[]> {\n return new Promise[]>((resolve, reject) => {\n this.run()\n .then((detections) => resolve(detections.map((detection) => extendWithFaceDetection({}, detection))))\n .catch((err) => reject(err));\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet = false) {\n return new DetectAllFaceLandmarksTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n useTinyLandmarkNet,\n );\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n );\n }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase {\n public override async run(): Promise {\n const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n let faceDetectionWithHighestScore = faceDetections[0];\n faceDetections.forEach((faceDetection) => {\n if (faceDetection.score > faceDetectionWithHighestScore.score) faceDetectionWithHighestScore = faceDetection;\n });\n return faceDetectionWithHighestScore;\n }\n\n private runAndExtendWithFaceDetection(): Promise | undefined> {\n // eslint-disable-next-line no-async-promise-executor\n return new Promise | undefined>(async (resolve) => {\n const detection = await this.run();\n resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet = false) {\n return new DetectSingleFaceLandmarksTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n useTinyLandmarkNet,\n );\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n );\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n );\n }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectSingleFaceTask {\n return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectAllFacesTask {\n return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\nexport async function allFacesSsdMobilenetv1(input: TNetInput, minConfidence?: number): Promise>>[]> {\n return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise>>[]> {\n return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n const desc1 = Array.from(arr1);\n const desc2 = Array.from(arr2);\n return Math.sqrt(\n desc1\n .map((val, i) => val - desc2[i])\n .reduce((res, diff) => res + (diff * diff), 0),\n );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n private _labeledDescriptors: LabeledFaceDescriptors[];\n private _distanceThreshold: number;\n\n constructor(inputs: LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>, distanceThreshold = 0.6) {\n this._distanceThreshold = distanceThreshold;\n const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n if (!inputArray.length) throw new Error('FaceRecognizer.constructor - expected atleast one input');\n let count = 1;\n const createUniqueLabel = () => `person ${count++}`;\n this._labeledDescriptors = inputArray.map((desc) => {\n if (desc instanceof LabeledFaceDescriptors) return desc;\n if (desc instanceof Float32Array) return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n if (desc.descriptor && desc.descriptor instanceof Float32Array) return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>');\n });\n }\n\n public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n public get distanceThreshold(): number { return this._distanceThreshold; }\n\n public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n return descriptors\n .map((d) => euclideanDistance(d, queryDescriptor))\n .reduce((d1, d2) => d1 + d2, 0) / (descriptors.length || 1);\n }\n\n public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n return this.labeledDescriptors\n .map(({ descriptors, label }) => new FaceMatch(label, this.computeMeanDistance(queryDescriptor, descriptors)))\n .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n }\n\n public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n const bestMatch = this.matchDescriptor(queryDescriptor);\n return (bestMatch.distance < this._distanceThreshold) ? bestMatch : new FaceMatch('unknown', bestMatch.distance);\n }\n\n public toJSON(): any {\n return {\n distanceThreshold: this._distanceThreshold,\n labeledDescriptors: this._labeledDescriptors.map((ld) => ld.toJSON()),\n };\n }\n\n public static fromJSON(json: any): FaceMatcher {\n const labeledDescriptors = json.labeledDescriptors.map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n const net = new TinyFaceDetector();\n net.extractWeights(weights);\n return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults(results: T, dimensions: IDimensions): T {\n const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n if (width <= 0 || height <= 0) {\n throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n }\n\n if (Array.isArray(results)) {\n // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n return (results as Array).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n }\n\n if (isWithFaceLandmarks(results)) {\n const resizedDetection = results.detection.forSize(width, height);\n const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n }\n\n if (isWithFaceDetection(results)) {\n return extendWithFaceDetection(results, results.detection.forSize(width, height));\n }\n\n if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n return (results as any).forSize(width, height);\n }\n\n return results;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nexport const version = pkg.version as string;\n\n// set webgl defaults\n// if (browser) tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n"], - "mappings": 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Ft{constructor(t,e,n,o){super(t.shape,t.dtype,t.dataId,o),this.trainable=e,this.name=n}assign(t){if(t.dtype!==this.dtype)throw new Error(`dtype of the new value (${t.dtype}) and previous value (${this.dtype}) must match`);if(!Fn(t.shape,this.shape))throw new Error(`shape of the new value (${t.shape}) and previous value (${this.shape}) must match`);Bs().disposeTensor(this),this.dataId=t.dataId,Bs().incRef(this,null)}dispose(){Bs().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Xa,Symbol.hasInstance,{value:r=>r instanceof Ft&&r.assign!=null&&r.assign instanceof Function});var xo={};Ut(xo,{assertTypesMatch:()=>N0,getTensorsInContainer:()=>rh,isTensorInList:()=>I4,makeTypesMatch:()=>Ht});var w0;(function(r){r.R0=\"R0\",r.R1=\"R1\",r.R2=\"R2\",r.R3=\"R3\",r.R4=\"R4\",r.R5=\"R5\",r.R6=\"R6\"})(w0||(w0={}));var C0;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"int32\",r.complex64=\"complex64\"})(C0||(C0={}));var I0;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"bool\",r.complex64=\"complex64\"})(I0||(I0={}));var v0;(function(r){r.float32=\"float32\",r.int32=\"float32\",r.bool=\"float32\",r.complex64=\"complex64\"})(v0||(v0={}));var S0;(function(r){r.float32=\"complex64\",r.int32=\"complex64\",r.bool=\"complex64\",r.complex64=\"complex64\"})(S0||(S0={}));var C4={float32:v0,int32:C0,bool:I0,complex64:S0};function lr(r,t){if(r===\"string\"||t===\"string\"){if(r===\"string\"&&t===\"string\")return\"string\";throw new Error(`Can not upcast ${r} with ${t}`)}return C4[r][t]}function Bu(r){return lr(r,\"int32\")}function Ht(r,t){if(r.dtype===t.dtype)return[r,t];let e=lr(r.dtype,t.dtype);return[r.cast(e),t.cast(e)]}function N0(r,t){E(r.dtype===t.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${t.dtype}) input must match`)}function I4(r,t){return t.some(e=>e.id===r.id)}function rh(r){let t=[];return M1(r,t,new Set),t}function M1(r,t,e){if(r==null)return;if(r instanceof Ft){t.push(r);return}if(!v4(r))return;let n=r;for(let o in n){let s=n[o];e.has(s)||(e.add(s),M1(s,t,e))}}function v4(r){return Array.isArray(r)||typeof r==\"object\"}function T0(r){return r.kernelName!=null}var Hg=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(t=>t.name)))}}}dispose(){for(let t in this.registeredVariables)this.registeredVariables[t].dispose()}},jl=class{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Hg}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let t=this.getSortedBackends();for(let e=0;e{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(t){Bg(t).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[t])})}initializeBackend(t){let e=this.registryFactory[t];if(e==null)throw new Error(`Cannot initialize backend ${t}, no registration found.`);try{let n=e.factory();if(n&&!(n instanceof zo)&&typeof n.then==\"function\"){let o=++this.pendingBackendInitId,s=n.then(i=>o(othis.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;ethis.startScope(n),()=>this.endScope(o),()=>(o=e(),o instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),o))}scopedRun(t,e,n){t();try{let o=n();return e(),o}catch(o){throw e(),o}}nextTensorId(){return jl.nextTensorId++}nextVariableId(){return jl.nextVariableId++}clone(t){let e=k.runKernel(po,{x:t}),n={x:t},o=i=>({x:()=>{let a=\"float32\",u={x:i},l={dtype:a};return k.runKernel(uo,u,l)}}),s=[];return this.addTapeNode(this.state.activeScope.name,n,[e],o,s,{}),e}runKernel(t,e,n){if(this.backendName==null&&this.backend,!(Zd(t,this.backendName)!=null))throw new Error(`Kernel '${t}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:t,inputs:e,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(t,e,n){let o=this.backend.numDataIds(),s=0;n.forEach(u=>{s+=u.dtype===\"complex64\"?3:1});let i=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=o-e-s-i;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${t}'`)}runKernelFunc(t){let e,n=[],o=this.isTapeOn(),s=this.state.numBytes,i=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let a;this.backendName==null&&this.backend;let u,l=T0(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(T0(t)){let{kernelName:d,inputs:h,attrs:g}=t;this.backendName==null&&this.backend;let x=Zd(d,this.backendName);E(x!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),a=()=>{let b=this.backend.numDataIds();u=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let w=Array.isArray(u)?u:[u];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,b,w);let C=w.map(N=>N.rank!=null?N:this.makeTensorFromTensorInfo(N));if(o){let N=this.getTensorsForGradient(d,h,C);n=this.saveTensorsForBackwardMode(N)}return C}}else{let{forwardFunc:d}=t,h=g=>{!o||(n=g.map(x=>this.keep(this.clone(x))))};a=()=>{let g=this.backend.numDataIds();u=this.tidy(()=>d(this.backend,h));let x=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,g,x),x}}let{inputs:c,attrs:p}=t,m=T0(t)?null:t.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?e=a():(f=this.profiler.profileKernel(l,c,()=>a()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(f),e=f.outputs)}),o&&this.addTapeNode(l,c,e,m,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-i,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:e.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(u)?e:e[0]}saveTensorsForBackwardMode(t){return t.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(t,e,n){let o=m0(t);if(o!=null){let s=o.inputsToSave||[],i=o.outputsToSave||[],a;o.saveAllInputs?(E(Array.isArray(e),()=>\"saveAllInputs is true, expected inputs to be an array.\"),a=Object.keys(e).map(l=>e[l])):a=s.map(l=>e[l]);let u=n.filter((l,c)=>i[c]);return a.concat(u)}return[]}makeTensor(t,e,n,o){if(t==null)throw new Error(\"Values passed to engine.makeTensor() are null\");n=n||\"float32\",o=o||this.backend;let s=t;n===\"string\"&&Bo(t[0])&&(s=t.map(u=>Kl(u)));let i=o.write(s,e,n),a=new Ft(e,n,i,this.nextTensorId());if(this.trackTensor(a,o),n===\"string\"){let u=this.state.tensorInfo.get(i),l=l0(s);this.state.numBytes+=l-u.bytes,u.bytes=l}return a}makeTensorFromDataId(t,e,n,o){n=n||\"float32\";let s={dataId:t,shape:e,dtype:n};return this.makeTensorFromTensorInfo(s,o)}makeTensorFromTensorInfo(t,e){let{dataId:n,shape:o,dtype:s}=t,i=new Ft(o,s,n,this.nextTensorId());return this.trackTensor(i,e),i}makeVariable(t,e=!0,n,o){n=n||this.nextVariableId().toString(),o!=null&&o!==t.dtype&&(t=t.cast(o));let s=new Xa(t,e,n,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(t,e){this.state.numTensors++,t.dtype===\"string\"&&this.state.numStringTensors++;let n=0;t.dtype!==\"complex64\"&&t.dtype!==\"string\"&&(n=t.size*op(t.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(t.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:n})),t instanceof Xa||this.track(t)}incRef(t,e){this.trackTensor(t,e),this.backend.incRef(t.dataId)}removeDataId(t,e){this.state.tensorInfo.has(t)&&this.state.tensorInfo.get(t).backend===e&&(this.state.tensorInfo.delete(t),this.state.numDataBuffers--)}disposeTensor(t){if(!this.state.tensorInfo.has(t.dataId))return;let e=this.state.tensorInfo.get(t.dataId);if(this.state.numTensors--,t.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=e.bytes),t.dtype!==\"complex64\"&&t.dtype!==\"string\"){let n=t.size*op(t.dtype);this.state.numBytes-=n}e.backend.disposeData(t.dataId)&&this.removeDataId(t.dataId,e.backend)}disposeVariables(){for(let t in this.state.registeredVariables){let e=this.state.registeredVariables[t];this.disposeVariable(e)}}disposeVariable(t){this.disposeTensor(t),this.state.registeredVariables[t.name]!=null&&delete this.state.registeredVariables[t.name]}memory(){let t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,t.reasons==null&&(t.reasons=[]),t.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),t}async profile(t){this.state.profiling=!0;let e=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(o=>o.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let o of this.state.activeProfile.kernels)o.kernelTimeMs=await o.kernelTimeMs,o.extraInfo=await o.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(t,e,n,o,s,i){let a={id:this.state.nextTapeNodeId++,kernelName:t,inputs:e,outputs:n,saved:s},u=m0(t);u!=null&&(o=u.gradFunc),o!=null&&(a.gradient=l=>(l=l.map((c,p)=>{if(c==null){let m=n[p],f=ap(m.size,m.dtype);return this.makeTensor(f,m.shape,m.dtype)}return c}),o(l.length>1?l:l[0],s,i))),this.state.activeTape.push(a)}keep(t){return t.kept=!0,t}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(t){let e={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e}endScope(t){let e=rh(t),n=new Set(e.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===o.id&&this.track(s)})}gradients(t,e,n,o=!1){if(E(e.length>0,()=>\"gradients() received an empty list of xs.\"),n!=null&&n.dtype!==\"float32\")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy(\"forward\",t));E(s instanceof Ft,()=>\"The result y returned by f() must be a tensor.\");let i=A1(this.state.activeTape,e,s);if(!o&&i.length===0&&e.length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");return this.tidy(\"backward\",()=>{let a={};a[s.id]=n==null?S4(s.shape):n,$1(a,i,l=>this.tidy(l),N4);let u=e.map(l=>a[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let c of l.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:u}})}customGrad(t){return E(ii(t),()=>\"The f passed in customGrad(f) must be a function.\"),(...e)=>{E(e.every(a=>a instanceof Ft),()=>\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");let n,o={};e.forEach((a,u)=>{o[u]=a});let s=(a,u)=>(n=t(...e,u),E(n.value instanceof Ft,()=>\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),E(ii(n.gradFunc),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),n.value),i=(a,u)=>{let l=n.gradFunc(a,u),c=Array.isArray(l)?l:[l];E(c.length===e.length,()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\"),E(c.every(m=>m instanceof Ft),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\");let p={};return c.forEach((m,f)=>{p[f]=()=>m}),p};return this.runKernelFunc({forwardFunc:s,backwardsFunc:i,inputs:o})}}readSync(t){return this.state.tensorInfo.get(t).backend.readSync(t)}read(t){return this.state.tensorInfo.get(t).backend.read(t)}readToGPU(t,e){return this.state.tensorInfo.get(t).backend.readToGPU(t,e)}async time(t){let e=zu(),n=await this.backend.time(t);return n.wallMs=zu()-e,n}track(t){return this.state.activeScope!=null&&(t.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(t)),t}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new Hg;for(let t in this.registry)this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};jl.nextTensorId=0;jl.nextVariableId=0;function S4(r){let t=Ud(Jt(r),\"float32\");return k.makeTensor(t,r,\"float32\")}function k0(){let r=p0();if(r._tfengine==null){let t=new Hd(r);r._tfengine=new jl(t)}return c1(r._tfengine.ENV),O1(()=>r._tfengine),r._tfengine}var k=k0();function N4(r,t){let e={a:r,b:t};return k.runKernel(Jn,e)}var Xl={};Ut(Xl,{isBrowser:()=>_0,isMobile:()=>E4,mockIsMobile:()=>k4});function T4(){return typeof navigator!=\"undefined\"&&navigator!=null}var E0;function k4(r){E0=r}function E4(r){if(E0!==void 0)return E0;if(r||T4()){if(r||(r=navigator),r.product===\"ReactNative\")return!0;let t=r.userAgent||r.vendor||(typeof window!=\"undefined\"?window.opera:\"\");if(!t){let e=r;return 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be rank 4 or rank 1 but got rank ${a.rank}.`),E(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),l!=null&&E(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&E(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),Ri(i,a,u,c,l,s)}var px=T({batchNorm4d_:$H});function DH(r,t,e){let n=v(r,\"x\",\"bincount\"),o=v(t,\"weights\",\"bincount\");E(n.dtype===\"int32\",()=>`Error in bincount: input dtype must be int32, but got ${n.dtype}`),E(e>=0,()=>`size must be non-negative, but got ${e}.`),E(o.size===n.size||o.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${n.shape}, weights shape: ${o.shape}.`);let s={x:n,weights:o},i={size:e};return k.runKernel(cp,s,i)}var mx=T({bincount_:DH});function RH(r,t){let e=v(r,\"s0\",\"broadcastArgs\",\"int32\"),n=v(t,\"s1\",\"broadcastArgs\",\"int32\");if(e.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${e.rank}`);if(n.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${n.rank}`);let o={s0:e,s1:n};return k.runKernel(pp,o)}var dE=T({broadcastArgs_:RH});function FH(r,t){let e=v(r,\"broadcastTo\",\"x\"),n=e.shape;if(Le(t),t.lengthe.rank){let l=e.shape.slice();for(;l.length=0;l--)if(o[l]===t[l])s[l]=1;else if(e.shape[l]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${t}].`);if(s.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return an(e);let a={x:e},u={reps:s};return k.runKernel(Qn,a,u)}var Fi=T({broadcastTo_:FH});function OH(r){let e={x:v(r,\"x\",\"ceil\",\"float32\")};return k.runKernel(Ho,e)}var fx=T({ceil_:OH});function yo(r,t,e){Le(r);let n={shape:r,value:t,dtype:e};return k.runKernel(Fl,{},n)}function PH(r,t,e){let n=v(r,\"x\",\"clipByValue\");if(E(t<=e,()=>`Error in clip: min (${t}) must be less than or equal to max (${e}).`),t===e)return yo(n.shape,t,n.dtype);let o={x:n},s={clipValueMin:t,clipValueMax:e};return k.runKernel(co,o,s)}var vr=T({clipByValue_:PH});function LH(r){return oe(r,0)}var dx=T({concat1d_:LH});function MH(r,t){return oe(r,t)}var hx=T({concat2d_:MH});function zH(r,t){return oe(r,t)}var gx=T({concat3d_:zH});function BH(r,t){return oe(r,t)}var xx=T({concat4d_:BH});function VH(r,t,e,n,o=\"NHWC\",s=[1,1],i){let a=v(r,\"x\",\"conv2d\",\"float32\"),u=v(t,\"filter\",\"conv2d\",\"float32\"),l=a,c=!1;a.rank===3&&(c=!0,l=R(a,[1,a.shape[0],a.shape[1],a.shape[2]])),E(l.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${l.rank}.`),E(u.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`),ve(\"conv2d\",n,i);let p=o===\"NHWC\"?l.shape[3]:l.shape[1];E(p===u.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`),E($r(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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s={x:v(r,\"x\",\"cumsum\")},i={axis:t,exclusive:e,reverse:n};return k.runKernel(Yo,s,i)}var om=T({cumsum_:ZH});function JH(r,t,e,n=!1){let o=v(r,\"x\",\"denseBincount\"),s=v(t,\"weights\",\"denseBincount\");E(o.dtype===\"int32\",()=>`Error in denseBincount: input dtype must be int32, but got ${o.dtype}`),E(o.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${o.rank}.`),E(e>=0,()=>`size must be non-negative, but got ${e}.`),E(s.size===o.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${o.shape}, weights shape: ${s.shape}.`);let i={x:o,weights:s},a={size:e,binaryOutput:n};return k.runKernel(gp,i,a)}var ah=T({denseBincount_:JH});function QH(r,t,e=\"NHWC\"){let n=v(r,\"x\",\"depthToSpace\",\"float32\"),o=e===\"NHWC\"?n.shape[1]:n.shape[2],s=e===\"NHWC\"?n.shape[2]:n.shape[3],i=e===\"NHWC\"?n.shape[3]:n.shape[1];E(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: 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number of input channels (${p}) must match the inChannels dimension in filter ${u.shape[2]}.`),ve(\"depthwiseConv2d\",n,i);let m={x:l,filter:u},f={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i},d=k.runKernel(Zo,m,f);return c?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Oi=T({depthwiseConv2d_:tq});function eq(r){let e={x:v(r,\"x\",\"diag\")};return k.runKernel(bp,e)}var hE=T({diag_:eq});function rq(r,t,e,n,o=[1,1],s=\"NHWC\"){let i=v(r,\"x\",\"dilation2d\"),a=v(t,\"filter\",\"dilation2d\");E(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),E(a.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${a.rank}.`),E(s===\"NHWC\",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let u=i,l=!1;i.rank===3&&(u=R(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=!0),E(u.shape[3]===a.shape[2],()=>`Error in dilation2d: input and filter must have the same depth: ${u.shape[3]} vs 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o=v(r,\"labels\",\"meanSquaredError\"),s=v(t,\"predictions\",\"meanSquaredError\"),i=null;e!=null&&(i=v(e,\"weights\",\"meanSquaredError\")),Re(o.shape,s.shape,\"Error in meanSquaredError: \");let a=bm(o,s);return Ur(a,i,n)}var W_=T({meanSquaredError_:F6});function O6(r,t){let e=v(r,\"labels\",\"sigmoidCrossEntropyWithLogits\"),n=v(t,\"logits\",\"sigmoidCrossEntropyWithLogits\");Re(e.shape,n.shape,\"Error in sigmoidCrossEntropyWithLogits: \");let o=Or(n),s=D(n,e),i=ru(sr(qt(Ee(n))));return Y(lt(o,s),i)}function P6(r,t,e,n=0,o=Ze.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"multiClassLabels\",\"sigmoidCrossEntropy\"),i=v(t,\"logits\",\"sigmoidCrossEntropy\"),a=null;if(e!=null&&(a=v(e,\"weights\",\"sigmoidCrossEntropy\")),Re(s.shape,i.shape,\"Error in sigmoidCrossEntropy: \"),n>0){let l=pt(n),c=pt(1),p=pt(.5);s=Y(D(s,lt(c,l)),D(p,l))}let u=O6(s,i);return Ur(u,a,o)}var U_=T({sigmoidCrossEntropy_:P6});function L6(r,t,e=-1){if(e===-1&&(e=t.rank-1),e!==t.rank-1)throw Error(`Softmax cross entropy 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i={inputIndices:n,inputShape:o,newShape:s},a=k.runKernel(Ua,i);return{outputIndices:a[0],outputShape:a[1]}}var K_=T({sparseReshape_:B6});function V6(r,t,e){let n=v(r,\"data\",\"sparseSegmentMean\"),o=v(t,\"indices\",\"sparseSegmentMean\",\"int32\"),s=v(e,\"segmentIds\",\"sparseSegmentMean\",\"int32\");if(n.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${o.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let i={data:n,indices:o,segmentIds:s};return k.runKernel(zl,i)}var j_=T({sparseSegmentMean_:V6});function G6(r,t,e){let n=v(r,\"data\",\"sparseSegmentSum\"),o=v(t,\"indices\",\"sparseSegmentSum\",\"int32\"),s=v(e,\"segmentIds\",\"sparseSegmentSum\",\"int32\");if(n.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but 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q6={fft:lu,ifft:el,rfft:uu,irfft:ym},K6={hammingWindow:y_,hannWindow:iy,frame:ay,stft:b_},oo={flipLeftRight:C_,grayscaleToRGB:I_,resizeNearestNeighbor:my,resizeBilinear:py,rotateWithOffset:v_,cropAndResize:w_,nonMaxSuppression:S_,nonMaxSuppressionAsync:k_,nonMaxSuppressionWithScore:E_,nonMaxSuppressionWithScoreAsync:__,nonMaxSuppressionPadded:A_,nonMaxSuppressionPaddedAsync:$_,threshold:D_,transform:R_},iS={bandPart:F_,gramSchmidt:O_,qr:L_},j6={absoluteDifference:M_,computeWeightedLoss:Ur,cosineDistance:z_,hingeLoss:B_,huberLoss:V_,logLoss:G_,meanSquaredError:W_,sigmoidCrossEntropy:U_,softmaxCrossEntropy:H_},X6={sparseFillEmptyRows:q_,sparseReshape:K_,sparseSegmentMean:j_,sparseSegmentSum:X_},Y6={stringNGrams:Y_,stringSplit:Z_,stringToHashBucketFast:J_};var Q={};Ut(Q,{Serializable:()=>dh,SerializationMap:()=>Wi,registerClass:()=>aS});var dh=class{getClassName(){return this.constructor.className}static fromConfig(t,e){return new t(e)}},Wi=class{constructor(){this.classNameMap={}}static getMap(){return Wi.instance==null&&(Wi.instance=new Wi),Wi.instance}static register(t){Wi.getMap().classNameMap[t.className]=[t,t.fromConfig]}};function aS(r){E(r.className!=null,()=>\"Class being registered does not have the static className property defined.\"),E(typeof r.className==\"string\",()=>\"className is required to be a string, but got type \"+typeof r.className),E(r.className.length>0,()=>\"Class being registered has an empty-string as its className, which is disallowed.\"),Wi.register(r)}var Hr=class extends dh{minimize(t,e=!1,n){let{value:o,grads:s}=this.computeGradients(t,n);if(n!=null){let i=n.map(a=>({name:a.name,tensor:s[a.name]}));this.applyGradients(i)}else this.applyGradients(s);return Nt(s),e?o:(o.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(t,e){return Dx(t,e)}dispose(){this.iterations_!=null&&Nt(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:\"iter\",tensor:pt(this.iterations_,\"int32\")}}async getWeights(){throw new Error(\"getWeights() is not implemented for this optimizer yet.\")}async setWeights(t){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(t){return this.iterations_=(await t[0].tensor.data())[0],t.slice(1)}};Object.defineProperty(Hr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var ec=class extends Hr{constructor(t,e,n=null){super(),this.learningRate=t,this.rho=e,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=k.backend.epsilon())}static get className(){return\"Adadelta\"}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let 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setWeights(t){t=await this.extractIterations(t);let e=t.length/2,n=!1;this.accumulatedGrads=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedUpdates=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}};var rc=class extends Hr{constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}static get className(){return\"Adagrad\"}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=k.registeredVariables[n];this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:B(()=>yo(s.shape,this.initialAccumulatorValue).variable(!1))});let i=Array.isArray(t)?t[o].tensor:t[n];if(i==null)return;let a=this.accumulatedGrads[o].variable;B(()=>{let u=Y(a,zt(i));a.assign(u);let l=Y(D(ut(i,Se(Y(u,k.backend.epsilon()))),-this.learningRate),s);s.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Nt(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}};var nc=class extends Hr{constructor(t,e,n,o=null){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=pt(e).variable(),this.accBeta2=pt(n).variable()}),o==null&&(this.epsilon=k.backend.epsilon())}static get className(){return\"Adam\"}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=lt(1,this.accBeta1),o=lt(1,this.accBeta2);e.forEach((s,i)=>{let a=k.registeredVariables[s],u=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${s}/m`,variable:B(()=>It(a).variable(u))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${s}/v`,variable:B(()=>It(a).variable(u))});let l=Array.isArray(t)?t[i].tensor:t[s];if(l==null)return;let c=this.accumulatedFirstMoment[i].variable,p=this.accumulatedSecondMoment[i].variable,m=Y(D(c,this.beta1),D(l,1-this.beta1)),f=Y(D(p,this.beta2),D(zt(l),1-this.beta2)),d=ut(m,n),h=ut(f,o);c.assign(m),p.assign(f);let g=Y(D(ut(d,Y(Se(h),this.epsilon)),-this.learningRate),a);a.assign(g)}),this.accBeta1.assign(D(this.accBeta1,this.beta1)),this.accBeta2.assign(D(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Nt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Nt(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),B(()=>{this.accBeta1.assign(ln(this.beta1,this.iterations_+1)),this.accBeta2.assign(ln(this.beta2,this.iterations_+1))});let e=t.length/2,n=!1;this.accumulatedFirstMoment=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}};var oc=class extends Hr{constructor(t,e,n,o=null,s=0){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],B(()=>{this.iteration=pt(0).variable(),this.accBeta1=pt(e).variable()}),o==null&&(this.epsilon=k.backend.epsilon())}static get className(){return\"Adamax\"}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=lt(1,this.accBeta1),o=ut(-this.learningRate,Y(D(this.iteration,this.decay),1));e.forEach((s,i)=>{let 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Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(t){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};var rl=class extends Hr{constructor(t){super(),this.learningRate=t,this.setLearningRate(t)}static get className(){return\"SGD\"}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=Array.isArray(t)?t[o].tensor:t[n];if(s==null)return;let i=k.registeredVariables[n];B(()=>{let a=Y(D(this.c,s),i);i.assign(a)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=De(pt(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error(\"SGD optimizer does not have settable weights.\")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(t,e){return new t(e.learningRate)}};var sc=class extends rl{constructor(t,e,n=!1){super(t),this.learningRate=t,this.momentum=e,this.useNesterov=n,this.accumulations=[],this.m=pt(this.momentum)}static get className(){return\"Momentum\"}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=k.registeredVariables[n];this.accumulations[o]==null&&(this.accumulations[o]={originalName:`${n}/momentum`,variable:B(()=>It(s).variable(!1))});let i=this.accumulations[o].variable,a=Array.isArray(t)?t[o].tensor:t[n];a!=null&&B(()=>{let u,l=Y(D(this.m,i),a);this.useNesterov?u=Y(D(this.c,Y(a,D(l,this.m))),s):u=Y(D(this.c,l),s),i.assign(l),s.assign(u)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Nt(this.accumulations.map(t=>t.variable))}setMomentum(t){this.momentum=t}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};var ic=class extends Hr{constructor(t,e=.9,n=0,o=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=n,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=k.backend.epsilon()),t==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}static get className(){return\"RMSProp\"}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=k.registeredVariables[n],i=!1;this.accumulatedMeanSquares[o]==null&&(this.accumulatedMeanSquares[o]={originalName:`${n}/rms`,variable:B(()=>It(s).variable(i))}),this.accumulatedMoments[o]==null&&(this.accumulatedMoments[o]={originalName:`${n}/momentum`,variable:B(()=>It(s).variable(i))}),this.accumulatedMeanGrads[o]==null&&this.centered&&(this.accumulatedMeanGrads[o]={originalName:`${n}/mg`,variable:B(()=>It(s).variable(i))});let a=Array.isArray(t)?t[o].tensor:t[n];if(a==null)return;let u=this.accumulatedMeanSquares[o].variable,l=this.accumulatedMoments[o].variable;B(()=>{let c=Y(D(u,this.decay),D(zt(a),1-this.decay));if(this.centered){let p=this.accumulatedMeanGrads[o].variable,m=Y(D(p,this.decay),D(a,1-this.decay)),f=ut(D(a,this.learningRate),Se(lt(c,Y(zt(m),this.epsilon)))),d=Y(D(l,this.momentum),f);u.assign(c),p.assign(m),l.assign(d);let h=lt(s,d);s.assign(h)}else{let 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a=this.weightDataAnchor==null?document.createElement(\"a\"):this.weightDataAnchor;a.download=this.weightDataFileName,a.href=e,await tA(()=>a.dispatchEvent(new MouseEvent(\"click\")))}return{modelArtifactsInfo:ki(t)}}}};nl.URL_SCHEME=\"downloads://\";var lS=class{constructor(t){if(t==null||t.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${t}`);this.jsonFile=t[0],this.weightsFiles=t.slice(1)}async load(){return new Promise((t,e)=>{let n=new FileReader;n.onload=o=>{let s=JSON.parse(o.target.result),i=s.modelTopology;if(i==null){e(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));return}if(s.weightsManifest==null){e(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));return}if(this.weightsFiles.length===0){t({modelTopology:i});return}let u=Kp(s,l=>this.loadWeights(l));t(u)},n.onerror=o=>e(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. 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used.\");return this.model.fitDataset(t,e)}async trainOnBatch(t,e){return this.model.trainOnBatch(t,e)}static fromConfig(t,e,n={},o=!1){let s,i={};if(e instanceof Array){if(e[0].className==null||e[0].className===\"Merge\")throw new z(\"Legacy serialization format not supported yet.\");s=e}else y.assert(e.layers!=null,()=>\"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.\"),s=e.layers,delete e.layers,i=e;let a=new t(i);if(!(a instanceof Xi))throw new vt(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(let u of s){let c=yn(u,void 0,o);o&&c.setFastWeightInitDuringBuild(!0),a.add(c)}return a}set stopTraining(t){if(this.model==null)throw new z(\"Cannot set the stopTraining property of a sequential model before it is compiled.\");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new z(\"Cannot get the stopTraining property of a sequential model before it is compiled.\");return 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extends rn{apply(t){return L$(t)}};sb.className=\"hardSigmoid\";Q.registerClass(sb);var ib=class extends rn{apply(t){return Vs(t)}};ib.className=\"softplus\";Q.registerClass(ib);var ab=class extends rn{apply(t){return P$(t)}};ab.className=\"softsign\";Q.registerClass(ab);var lb=class extends rn{apply(t){return Di(t)}};lb.className=\"tanh\";Q.registerClass(lb);var Km=class extends rn{apply(t,e=-1){return au(t,e)}};Km.className=\"softmax\";Q.registerClass(Km);var ub=class extends rn{apply(t,e=-1){return im(t,e)}};ub.className=\"logSoftmax\";Q.registerClass(ub);var cb=class extends rn{apply(t,e=1){return B(()=>D(Qr(D(t,e)),t))}};cb.className=\"swish\";Q.registerClass(cb);var pb=class extends rn{apply(t){return B(()=>D(t,Di(Vs(t))))}};pb.className=\"mish\";Q.registerClass(pb);function js(r){return r.getClassName()}function VS(r,t={}){return Hi(r,Q.SerializationMap.getMap().classNameMap,t,\"activation\")}function Xs(r){if(r==null){let t={};return t.className=\"linear\",t.config={},VS(t)}if(typeof r==\"string\"){let t={};return t.className=r,t.config={},VS(t)}else return r instanceof rn?r:VS(r)}function GS(r){if(r!=null&&typeof r!=\"object\")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var mb=class extends Q.Serializable{},hu=class extends mb{constructor(t){super(),GS(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=Te([1]);return this.hasL1&&(e=Y(e,mt(D(this.l1,Ee(t))))),this.hasL2&&(e=Y(e,mt(D(this.l2,cc(t))))),R(e,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}};hu.className=\"L1L2\";Q.registerClass(hu);function yD(r){return GS(r),new hu({l1:r!=null?r.l1:null,l2:0})}function bD(r){return GS(r),new hu({l2:r!=null?r.l2:null,l1:0})}var gD={l1l2:\"L1L2\"};function me(r){return Sm(r)}function xD(r,t={}){return Hi(r,Q.SerializationMap.getMap().classNameMap,t,\"regularizer\")}function we(r){if(r==null)return null;if(typeof r==\"string\"){let e={className:r in gD?gD[r]:r,config:{}};return xD(e)}else return r instanceof mb?r:xD(r)}var jm=class extends kt{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=St(t);let n=Or(t);return this.maxValue!=null&&(n=vr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};jm.className=\"ReLU\";Q.registerClass(jm);var Xm=class extends kt{constructor(t){super(t==null?{}:t),this.DEFAULT_ALPHA=.3,t==null&&(t={}),this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=St(t);return eu(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};Xm.className=\"LeakyReLU\";Q.registerClass(Xm);var Ym=class extends kt{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA_INITIALIZER=\"zeros\",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=de(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=we(t.alphaRegularizer),this.alphaConstraint=Ve(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes==\"number\")this.sharedAxes=[t.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Bt(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)e[o-1]=1;this.alpha=this.addWeight(\"alpha\",e,\"float32\",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o(Oe(t),t===\"channelsFirst\"?Pt(r,[0,2,3,1]):r))}function WS(r,t){return B(()=>(Oe(t),t===\"channelsFirst\"?Pt(r,[0,2,3,4,1]):r))}function tY(r,t,e,n=1,o=\"valid\",s,i=1){return B(()=>{if(s==null&&(s=dn()),Oe(s),r.shape.length!==3)throw new z(`The input of a conv1dWithBias operation should be 3, but is ${r.shape.length} instead.`);if(t.shape.length!==3)throw new z(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(e!=null&&e.shape.length!==1)throw new z(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s===\"channelsFirst\"&&(r=Pt(r,[0,2,1])),o===\"causal\")throw new vt(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");let a=tm(r,t,n,o===\"same\"?\"same\":\"valid\",\"NWC\",i);return e!=null&&(a=hn(a,e)),a})}function wD(r,t,e,n=[1,1],o=\"valid\",s,i,a=null){return B(()=>{if(s==null&&(s=dn()),Oe(s),r.rank!==3&&r.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=Dh(r,s);if(o===\"causal\")throw new vt(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");return u=cu.conv2d({x:u,filter:t,strides:n,pad:o===\"same\"?\"same\":\"valid\",dilations:i,dataFormat:\"NHWC\",bias:e,activation:a}),s===\"channelsFirst\"&&(u=Pt(u,[0,3,1,2])),u})}function eY(r,t,e,n=[1,1,1],o=\"valid\",s,i){return B(()=>{if(s==null&&(s=dn()),Oe(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=WS(r,s);if(o===\"causal\")throw new vt(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");return a=yx(a,t,n,o===\"same\"?\"same\":\"valid\",\"NDHWC\",i),e!=null&&(a=hn(a,e)),s===\"channelsFirst\"&&(a=Pt(a,[0,4,1,2,3])),a})}var Cc=class extends kt{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",Cc.verifyArgs(e),this.rank=t,Qe(this.rank,\"rank\"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new vt(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=gu(e.kernelSize,t,\"kernelSize\"),this.strides=gu(e.strides==null?1:e.strides,t,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,mn(this.padding),this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Oe(this.dataFormat),this.activation=Xs(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=de(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ve(e.biasConstraint),this.biasRegularizer=we(e.biasRegularizer),this.activityRegularizer=we(e.activityRegularizer),this.dilationRate=gu(e.dilationRate==null?1:e.dilationRate,t,\"dilationRate\"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new z(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(so(\"kernelSize\"in t,\"required key 'kernelSize' not in config\"),typeof t.kernelSize!=\"number\"&&!Cy(t.kernelSize,\"number\",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:js(this.activation),useBias:this.useBias,biasInitializer:ke(this.biasInitializer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),biasConstraint:Be(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},xu=class extends Cc{constructor(t,e){super(t,e),this.kernel=null,xu.verifyArgs(e),this.filters=e.filters,Qe(this.filters,\"filters\"),this.kernelInitializer=de(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ve(e.kernelConstraint),this.kernelRegularizer=we(e.kernelRegularizer)}build(t){t=Bt(t);let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight(\"kernel\",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=St(t);let n,o=this.bias==null?null:this.bias.read(),s=Iy(this.activation.getClassName());if(s!=null&&this.rank===2)n=wD(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=tY(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=wD(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=eY(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new vt(\"convolutions greater than 3D are not implemented yet.\");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Bt(t);let e=[],n=this.dataFormat===\"channelsLast\"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},ul=class extends xu{constructor(t){super(2,t),ul.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!Cy(t.kernelSize,\"number\",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};ul.className=\"Conv2D\";Q.registerClass(ul);var cl=class extends xu{constructor(t){super(3,t),cl.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};cl.className=\"Conv3D\";Q.registerClass(cl);var tf=class extends ul{constructor(t){if(super(t),this.inputSpec=[new be({ndim:4})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Bt(t),t.length!==4)throw new z(\"Input should have rank 4; Received input shape: \"+JSON.stringify(t));let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",o,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new be({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a;this.dataFormat===\"channelsFirst\"?(i=2,a=3):(i=1,a=2);let u=o[i],l=o[a],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=Ys(u,m,c,this.padding),h=Ys(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Pt(n,[0,2,3,1]));let x=rm(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(x=Pt(x,[0,3,1,2])),this.bias!=null&&(x=hn(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(t){t=Bt(t);let e=t.slice(),n,o,s;this.dataFormat===\"channelsFirst\"?(n=1,o=2,s=3):(n=3,o=1,s=2);let i=this.kernelSize[0],a=this.kernelSize[1],u=this.strides[0],l=this.strides[1];return e[n]=this.filters,e[o]=Ys(e[o],u,i,this.padding),e[s]=Ys(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};tf.className=\"Conv2DTranspose\";Q.registerClass(tf);var ef=class extends cl{constructor(t){if(super(t),this.inputSpec=[new be({ndim:5})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Bt(t),t.length!==5)throw new z(\"Input should have rank 5; Received input shape: \"+JSON.stringify(t));let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",o,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new be({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat===\"channelsFirst\"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=Ys(l,h,m,this.padding),w=Ys(c,g,f,this.padding),C=Ys(p,x,d,this.padding),N=[s,b,w,C,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Pt(n,[0,2,3,4,1]));let _=wx(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(_=Pt(_,[0,4,1,2,3])),this.bias!==null&&(_=hn(_,this.bias.read(),this.dataFormat)),this.activation!==null&&(_=this.activation.apply(_)),_})}computeOutputShape(t){t=Bt(t);let e=t.slice(),n,o,s,i;this.dataFormat===\"channelsFirst\"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=Ys(e[o],c,a,this.padding),e[s]=Ys(e[s],p,u,this.padding),e[i]=Ys(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};ef.className=\"Conv3DTranspose\";Q.registerClass(ef);var fb=class extends xu{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER=\"glorotUniform\",this.DEFAULT_POINTWISE_INITIALIZER=\"glorotUniform\",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");if(e.padding!=null&&e.padding!==\"same\"&&e.padding!==\"valid\")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=de(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=we(e.depthwiseRegularizer),this.depthwiseConstraint=Ve(e.depthwiseConstraint),this.pointwiseInitializer=de(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=we(e.pointwiseRegularizer),this.pointwiseConstraint=Ve(e.pointwiseConstraint)}build(t){if(t=Bt(t),t.length{t=St(t);let n;if(this.rank===1)throw new vt(\"1D separable convolution is not implemented yet.\");return this.rank===2&&(this.dataFormat===\"channelsFirst\"&&(t=Pt(t,[0,2,3,1])),n=fm(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,\"NHWC\")),this.useBias&&(n=hn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat===\"channelsFirst\"&&(n=Pt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=ke(this.depthwiseInitializer),t.pointwiseInitializer=ke(this.pointwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.pointwiseRegularizer=me(this.pointwiseRegularizer),t.depthwiseConstraint=Be(this.depthwiseConstraint),t.pointwiseConstraint=Be(this.pointwiseConstraint),t}};fb.className=\"SeparableConv\";var rf=class extends fb{constructor(t){super(2,t)}};rf.className=\"SeparableConv2D\";Q.registerClass(rf);var yu=class extends xu{constructor(t){super(1,t),yu.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!Cy(t.kernelSize,\"number\",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};yu.className=\"Conv1D\";Q.registerClass(yu);var nf=class extends kt{constructor(t){super(t),typeof t.cropping==\"number\"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]==\"number\"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?\"channelsLast\":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat===\"channelsFirst\"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat===\"channelsLast\"){let n=Ih(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ih(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ih(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ih(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};nf.className=\"Cropping2D\";Q.registerClass(nf);var of=class extends kt{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Oe(this.dataFormat),this.interpolation=t.interpolation==null?\"nearest\":t.interpolation,_$(this.interpolation)}computeOutputShape(t){if(this.dataFormat===\"channelsFirst\"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=St(t),o=n.shape;if(this.dataFormat===\"channelsFirst\"){n=Pt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation===\"nearest\"?oo.resizeNearestNeighbor(n,[s,i]):oo.resizeBilinear(n,[s,i]);return Pt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation===\"nearest\"?oo.resizeNearestNeighbor(n,[s,i]):oo.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};of.className=\"UpSampling2D\";Q.registerClass(of);function rY(r,t,e=[1,1],n=\"valid\",o,s){return B(()=>{o==null&&(o=dn()),Oe(o);let i=Dh(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Oi(i,t,e,n===\"same\"?\"same\":\"valid\",\"NHWC\",s),o===\"channelsFirst\"&&(i=Pt(i,[0,3,1,2])),i})}var sf=class extends Cc{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=de(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ve(t.depthwiseConstraint),this.depthwiseRegularizer=we(t.depthwiseRegularizer)}build(t){if(t=Bt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat===\"channelsFirst\"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{t=St(t);let n=rY(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=hn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Bt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2],o=this.dataFormat===\"channelsFirst\"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=Tn(e,this.kernelSize[0],this.padding,this.strides[0]),i=Tn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat===\"channelsFirst\"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=ke(this.depthwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.depthwiseConstraint=Be(this.depthwiseRegularizer),t}};sf.className=\"DepthwiseConv2D\";Q.registerClass(sf);function US(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z(\"When inputs is an array, neither initialState or constants should be provided\");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function HS(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(fn(2,u));if(t=Pt(t,l),s!=null)throw new vt(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");i&&console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\"),o!=null&&(o=J(J(o,\"bool\"),\"float32\"),o.rank===u-1&&(o=ir(o,-1)),o=Pt(o,l)),n&&(t=dr(t,0),o!=null&&(o=dr(o,0)));let c=[],p,m=e,f=t.shape[0],d=gr(t),h;o!=null&&(h=gr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let C=B(()=>{let N=h[x],_=lt(wr(N),N),A=Y(D(w[0],N),D(m[0],_)),$=m.map((F,P)=>Y(D(w[1][P],N),D(F,_)));return{output:A,newStates:$}});p=C.output,m=C.newStates}a&&c.push(p)}let g;return a&&(g=qe(c,1)),[p,g,m]})}var kn=class extends kt{constructor(t){super(t);let e;if(t.cell==null)throw new z(\"cell property is missing for the constructor of RNN.\");if(Array.isArray(t.cell)?e=new Sc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new be({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return fn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){$y(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new be({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new Nn(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape[0];if(n==null)throw new z(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>Te([n,o])):this.states_=[Te([n,this.cell.stateSize])];else if(t==null)Nt(this.states_),this.keptStates!=null&&(Nt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>Te([n,o])):this.states_[0]=Te([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Nt(this.states_);for(let o=0;oDe(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=US(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new be({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof en){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=St(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");let a={training:o},l=HS((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=Te(t.shape);return e=mt(e,[1,2]),e=il(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Ey(e,[1,n]):e):this.cell.stateSize>1?[Ey(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===kn.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=yn(o,n);return new t(Object.assign(e,{cell:s}))}};kn.className=\"RNN\";Q.registerClass(kn);var pl=class extends kt{},Ic=class extends pl{constructor(t){super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=t.units,Qe(this.units,\"units\"),this.activation=Xs(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=we(t.kernelRegularizer),this.recurrentRegularizer=we(t.recurrentRegularizer),this.biasRegularizer=we(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=uc([1,qs([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=uc([1,qs([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Bt(t),this.kernel=this.addWeight(\"kernel\",[t[t.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0wr(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0wr(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=ko(D(t,i),this.kernel.read()):s=ko(t,this.kernel.read()),this.bias!=null&&(s=hn(s,this.bias.read())),a!=null&&(n=D(n,a));let u=Y(s,ko(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:js(this.activation),useBias:this.useBias,kernelInitializer:ke(this.kernelInitializer),recurrentInitializer:ke(this.recurrentInitializer),biasInitializer:ke(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),recurrentConstraint:Be(this.recurrentConstraint),biasConstraint:Be(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};Ic.className=\"SimpleRNNCell\";Q.registerClass(Ic);var af=class extends kn{constructor(t){t.cell=new Ic(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Nt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Nt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};af.className=\"SimpleRNN\";Q.registerClass(af);var vc=class extends pl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",t.resetAfter)throw new z(\"GRUCell does not support reset_after parameter set to true.\");this.units=t.units,Qe(this.units,\"units\"),this.activation=Xs(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=Xs(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=we(t.kernelRegularizer),this.recurrentRegularizer=we(t.recurrentRegularizer),this.biasRegularizer=we(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=uc([1,qs([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=uc([1,qs([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Bt(t);let e=t[t.length-1];this.kernel=this.addWeight(\"kernel\",[e,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0wr(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0{this.cell.dropoutMask!=null&&(Nt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Nt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};lf.className=\"GRU\";Q.registerClass(lf);var ml=class extends pl{constructor(t){super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=t.units,Qe(this.units,\"units\"),this.activation=Xs(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=Xs(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=we(t.kernelRegularizer),this.recurrentRegularizer=we(t.recurrentRegularizer),this.biasRegularizer=we(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=uc([1,qs([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=uc([1,qs([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Bt(t);let n=t[t.length-1];this.kernel=this.addWeight(\"kernel\",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends gn{apply(u,l){let c=s.apply([i]),p=new fu().apply([i]),m=s.apply([i*2]);return AS(AS(c,p),m)}},e.className=\"CustomInit\",e)}else o=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0wr(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Nt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Nt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};uf.className=\"LSTM\";Q.registerClass(uf);var Sc=class extends pl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{Hs(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign(Object.assign({},t),o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(yn(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return Sh(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):Ay(t(),e),a=()=>mu(i,t,n);return!o||o<=1?De(a().clone()):Array(o).fill(void 0).map(a).map(l=>De(l.clone()))}var nY=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols==\"function\")for(var o=0,n=Object.getOwnPropertySymbols(r);o{if(this.cell.dropoutMask!=null&&(Nt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Nt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z(\"ConvRNN2D cell does not support constants\");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=Te(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new Nn(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Te(s)):this.states_=[Te(s)];else if(t==null)Nt(this.states_),this.keptStates!=null&&(Nt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Te(s)):this.states_[0]=Te(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Nt(this.states_);for(let a=0;aDe(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e===\"channelsFirst\",l=t[u?3:2],c=t[u?4:3],p=Tn(l,o[0],s,i[0],a[0]),m=Tn(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};db.className=\"ConvRNN2D\";var Nc=class extends ml{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,Qe(this.filters,\"filters\"),this.kernelSize=gu(n,2,\"kernelSize\"),this.kernelSize.forEach(u=>Qe(u,\"kernelSize\")),this.strides=gu(o||1,2,\"strides\"),this.strides.forEach(u=>Qe(u,\"strides\")),this.padding=s||\"valid\",mn(this.padding),this.dataFormat=i||\"channelsLast\",Oe(this.dataFormat),this.dilationRate=gu(a||1,2,\"dilationRate\"),this.dilationRate.forEach(u=>Qe(u,\"dilationRate\"))}build(t){var e;t=Bt(t);let n=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight(\"kernel\",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight(\"recurrent_kernel\",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends gn{apply(m,f){let d=l.apply([c]),h=fr([c]),g=l.apply([c*2]);return Tm([d,h,g])}},e.className=\"CustomInit\",e)}else u=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0wr(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(rt,ot,at)=>!ot||!ot[at]?rt:D(ot[at],rt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0wr(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[C,N,_,A]=hr(this.kernel.read(),a,w),[$,F,P,V]=this.useBias?hr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,C,$,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,_,P,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=hr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let K=this.recurrentActivation.apply(Y(c,h)),X=this.recurrentActivation.apply(Y(p,g)),Z=Y(D(X,i),D(K,this.activation.apply(Y(m,x)))),et=D(this.recurrentActivation.apply(Y(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=nY(t,[\"units\"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=vn(t,e,this.strides,o||\"valid\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\",this.dilationRate);return n?hn(s,n,this.dataFormat):s}recurrentConv(t,e){return vn(t,e,1,\"same\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\")}};Nc.className=\"ConvLSTM2DCell\";Q.registerClass(Nc);var cf=class extends db{constructor(t){let e=new Nc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};cf.className=\"ConvLSTM2D\";Q.registerClass(cf);var Tc=class extends kt{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);if(0Ay(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Tc.className=\"Dropout\";Q.registerClass(Tc);var pf=class extends Tc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};pf.className=\"SpatialDropout1D\";Q.registerClass(pf);var mf=class extends kt{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,Qe(this.units,\"units\"),this.activation=Xs(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ve(t.kernelConstraint),this.biasConstraint=Ve(t.biasConstraint),this.kernelRegularizer=we(t.kernelRegularizer),this.biasRegularizer=we(t.biasRegularizer),this.activityRegularizer=we(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Bt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight(\"kernel\",[e,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:e}}],this.built=!0}computeOutputShape(t){t=Bt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=Iy(this.activation.getClassName()),s;return o!=null?s=ko(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=ko(n,this.kernel.read()),this.bias!=null&&(s=hn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:js(this.activation),useBias:this.useBias,kernelInitializer:ke(this.kernelInitializer),biasInitializer:ke(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),biasConstraint:Be(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};mf.className=\"Dense\";Q.registerClass(mf);var ff=class extends kt{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Bt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to \"Flatten\" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);return[t[0],To(t,1)]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(this.dataFormat===\"channelsFirst\"&&n.rank>1){let o=[0];for(let s=2;s{this.invokeCallHook(t,e);let n=St(t);return this.activation.apply(n)})}getConfig(){let t={activation:js(this.activation)},e=super.getConfig();return Object.assign(t,e),t}};df.className=\"Activation\";Q.registerClass(df);var hf=class extends kt{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return B(()=>(t=St(t),D$(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}};hf.className=\"RepeatVector\";Q.registerClass(hf);var gf=class extends kt{constructor(t){super(t),this.targetShape=t.targetShape;for(let e=0;e{this.invokeCallHook(t,e);let n=St(t),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return R(n,s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}};gf.className=\"Reshape\";Q.registerClass(gf);var xf=class extends kt{constructor(t){if(super(t),t.dims==null)throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${t.dims} instead.`);let e=fn(1,t.dims.length+1);if(!y.arraysEqual(t.dims.slice().sort(),e))throw new Error(\"Invalid permutation `dims`: \"+JSON.stringify(t.dims)+\" `dims` must contain consecutive integers starting from 1.\");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new be({ndim:this.dims.length+1})]}computeOutputShape(t){t=Bt(t);let e=t.slice();return this.dims.forEach((n,o)=>{e[o+1]=t[n]}),e}call(t,e){return Pt(St(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}};xf.className=\"Permute\";Q.registerClass(xf);var yf=class extends kt{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let n=St(t),o=-1;return Gu(Gs(n,this.maskValue),o)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=-1,s=!0,i=Gu(Gs(n,this.maskValue),o,s);return D(n,J(i,n.dtype))})}};yf.className=\"Masking\";Q.registerClass(yf);var bf=class extends kt{constructor(t){if(super(t),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER=\"randomUniform\",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(ye(t.inputLength))}this.inputDim=t.inputDim,Qe(this.inputDim,\"inputDim\"),this.outputDim=t.outputDim,Qe(this.outputDim,\"outputDim\"),this.embeddingsInitializer=de(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=we(t.embeddingsRegularizer),this.activityRegularizer=we(t.activityRegularizer),this.embeddingsConstraint=Ve(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight(\"embeddings\",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return B(()=>this.maskZero?(t=St(t),Gs(t,It(t))):null)}computeOutputShape(t){if(t=Bt(t),this.inputLength==null)return[...t,this.outputDim];let e=ye(this.inputLength);if(e.length!==t.length-1)throw new z(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${t}`);{let n=0;for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);n.dtype!==\"int32\"&&(n=tn(n,\"int32\"));let o=_y(this.embeddings.read(),R(n,[n.size]));return R(o,Bt(this.computeOutputShape(n.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:ke(this.embeddingsInitializer),embeddingsRegularizer:me(this.embeddingsRegularizer),activityRegularizer:me(this.activityRegularizer),embeddingsConstraint:Be(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}};bf.className=\"Embedding\";Q.registerClass(bf);var dl=class extends kt{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new vt}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length1)throw new z(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(t)}.`);let n=t[0]==null?null:t[0].slice(1);for(let s=1;ss.length);t.indexOf(null)===-1&&No(o).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(t,e){return B(()=>{if(t=t,this.reshapeRequired){let n=[],o=t.map(s=>s.rank);if(o.indexOf(null)===-1){let s=qs(o);for(let i of t){let a=i.rank;for(let u=0;u1){let c=fn(1,l).concat([0]);n.push(Pt(u,c)),s=!0}else n.push(u)}let i=this.mergeFunction(n),a=i.rank;if(s){if(a==null){let u=i.shape,l=u.length,c=u[l-1],p=[c].concat(u.slice(0,u.length-1));i=R(Pt(R(i,[-1,c]),[1,0]),p)}else if(a>1){let u=[a-1].concat(fn(0,a-1));i=Pt(i,u)}}return i}}else return this.mergeFunction(t)})}computeOutputShape(t){t=t;let e;t[0]==null?e=null:e=t[0].slice(1);for(let o=1;o{if(e==null)return null;if(!Array.isArray(e))throw new z(\"`mask` should be an Array\");if(!Array.isArray(t))throw new z(\"`inputs` should be an Array\");if(e.length!==t.length)throw new z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${t.length} vs ${e.length})`);if(e.every(o=>o==null))return null;e=e.map(o=>o==null?o:ir(o,0));let n=e[0];for(let o=1;o{let e=t[0].clone();for(let n=1;n{let e=t[0].clone();for(let n=1;n{let e=t[0].clone();for(let n=1;n{let e=t[0];for(let n=1;n{let e=t[0];for(let n=1;n1)throw new z(\"A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: \"+JSON.stringify(t))}mergeFunction(t){return B(()=>Tm(t,this.axis))}computeOutputShape(t){if(!(Array.isArray(t)&&Array.isArray(t[0])))throw new z(\"A `Concatenate` layer should be called on a list of inputs.\");let e=t,n=e[0].slice(),o=this.axis<0?n.length+this.axis:this.axis;for(let s of e.slice(1)){if(n[o]==null||s[o]==null){n[o]=null;break}n[o]+=s[o]}return n}computeMask(t,e){if(e==null)return null;if(!Array.isArray(e))throw new z(\"`mask` should be an array for Concatenate\");if(!Array.isArray(t))throw new z(\"`inputs` should be an array for Concatenate\");if(e.length!==t.length)throw new z(`Mismatch in the length of mask (${e.length}) and the legnth of inputs (${t.length})`);return B(()=>{let n=!0;if(e.forEach(i=>{if(i!=null){n=!1;return}}),n)return null;let o=[];for(let i=0;i3||t.shape.length>3)throw new vt(\"batchDot is not implemented for tensors of 4D or higher rank yet\");if(y.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),y.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof e==\"number\"&&(e=[e,e]),r.dtype===\"complex64\"||t.dtype===\"complex64\")throw new vt(\"batchDot is not implemented for complex64-type Tensors yet.\");let n=r.shape.length,o=t.shape.length;e==null&&(e=[n-1,o-2]);let s=e;return B(()=>{let i;if(n>o){i=n-o;let u=[];for(let l=0;ln){i=o-n;let u=[];for(let l=0;l0){let u;n>o?u=n+o-3:u=n-1;let l=[];for(let c=u;c\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let e=t[0],n=t[1];if(e.length>3||n.length>3)throw new vt(\"Dot layer does not support tensors of 4D or higher rank yet.\");let o=this.interpretAxes(e,n);if(e[o[0]]!==n[o[1]])throw new z(`Dimension incompatibility: ${e[o[0]]} !== ${n[o[1]]}`)}mergeFunction(t){if(t.length!==2)throw new z(`A \\`Dot\\` layer must be called on exactly 2 inputs, but received ${t.length} input(s).`);let e=t[0],n=t[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,i)=>Rh(s,t[i].shape.length)):o=[Rh(this.axes,e.shape.length),Rh(this.axes,n.shape.length)],this.normalize&&(e=Nh(e,o[0]),n=Nh(n,o[1])),oY(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[Rh(this.axes,t.length),Rh(this.axes,e.length)],n}computeOutputShape(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new vt(\"Dot layer does not support tensors of 4D or higher rank yet.\");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};Tf.className=\"Dot\";Q.registerClass(Tf);var kf=class extends kt{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return mu(()=>Y(km(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};kf.className=\"GaussianNoise\";Q.registerClass(kf);var Ef=class extends kt{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.rate>0&&this.rate<1?mu(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return D(n,km(n.shape,1,s))},()=>n,e.training||!1):n})}};Ef.className=\"GaussianDropout\";Q.registerClass(Ef);var _f=class extends kt{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||St(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(t);return mu(()=>{let s=St(t),i=1.6732632423543772,a=1.0507009873554805,u=-i*a,l=un(Vi(n),this.rate);l=tn(l,\"float32\");let c=((1-this.rate)*(1+this.rate*u**2))**-.5,p=-c*u*this.rate,m=Y(D(s,l),D(Y(l,-1),u));return Y(D(m,c),p)},()=>St(t),e.training||!1)}return t})}};_f.className=\"AlphaDropout\";Q.registerClass(_f);function Fh(r,t,e,n,o,s=.001){let i;if(r.rank===2)i=ux(r,t,e,n,o,s);else if(r.rank===3)i=cx(r,t,e,n,o,s);else if(r.rank===4)i=px(r,t,e,n,o,s);else throw new vt(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return i}function sY(r,t,e,n,o=.001){return B(()=>{let s=qu(r,n),i=s.mean,a=s.variance;return[Fh(r,i,a,e,t,o),i,a]})}function iY(r,t,e,n,o=.001){return B(()=>{let s=qu(r,n),i=s.mean,a=s.variance,u=[];for(let d of fn(0,r.rank))n.indexOf(d)!==-1?u.push(1):u.push(r.shape[d]);let l=R(i,u),c=R(a,u),p=t==null?null:R(t,u),m=e==null?null:R(e,u);return[Fh(r,l,c,m,p,o),i,a]})}function aY(r,t,e,n,o=.001){return y.arraysEqual(n.slice().sort(),fn(0,r.rank-1))?sY(r,t,e,n,o):iY(r,t,e,n,o)}var Af=class extends kt{constructor(t){t==null&&(t={}),super(t),this.supportsMasking=!0,this.axis=t.axis==null?-1:t.axis,this.momentum=t.momentum==null?.99:t.momentum,this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=de(t.betaInitializer||\"zeros\"),this.gammaInitializer=de(t.gammaInitializer||\"ones\"),this.movingMeanInitializer=de(t.movingMeanInitializer||\"zeros\"),this.movingVarianceInitializer=de(t.movingVarianceInitializer||\"ones\"),this.betaConstraint=Ve(t.betaConstraint),this.gammaConstraint=Ve(t.gammaConstraint),this.betaRegularizer=we(t.betaRegularizer),this.gammaRegularizer=we(t.gammaRegularizer)}build(t){t=Bt(t);let e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new be({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight(\"gamma\",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight(\"beta\",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight(\"moving_mean\",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight(\"moving_variance\",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=fn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=vo(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,fn(0,i).slice(0,i-1)),m=()=>{if(p){let b=R(this.movingMean.read(),l),w=R(this.movingVariance.read(),l),C=this.center?R(this.beta.read(),l):null,N=this.scale?R(this.gamma.read(),l):null;return Fh(o,b,w,C,N,this.epsilon)}else return Fh(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return m();let[f,d,h]=aY(o,this.gamma.read(),this.beta.read(),a,this.epsilon),g=(b,w,C)=>{B(()=>{let N=1-C,_=b.read(),A=D(lt(_,w),N);b.write(lt(_,A))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:ke(this.betaInitializer),gammaInitializer:ke(this.gammaInitializer),movingMeanInitializer:ke(this.movingMeanInitializer),movingVarianceInitializer:ke(this.movingVarianceInitializer),betaRegularizer:me(this.betaRegularizer),gammaRegularizer:me(this.gammaRegularizer),betaConstraint:Be(this.betaConstraint),gammaConstraint:Be(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};Af.className=\"BatchNormalization\";Q.registerClass(Af);var $f=class extends kt{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.axis,typeof this.axis==\"number\"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=de(t.betaInitializer||\"zeros\"),this.gammaInitializer=de(t.gammaInitializer||\"ones\"),this.betaRegularizer=we(t.betaRegularizer),this.gammaRegularizer=we(t.gammaRegularizer),this.supportsMasking=!0}build(t){t=Bt(t);let e=t.length;typeof this.axis==\"number\"&&(this.axis=[this.axis]);for(let s=0;s=e)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==No(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight(\"gamma\",n,\"float32\",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight(\"beta\",n,\"float32\",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let n=St(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=qu(n,this.axis,!0),l=vo(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new z(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");if(e==null&&(e=dn()),e!==\"channelsLast\"&&e!==\"channelsFirst\")throw new z(`Unknown data format: ${e}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return e===\"channelsFirst\"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],pn(r,n)})}var Df=class extends kt{constructor(t){if(t==null&&(t={}),super(t),this.dataFormat=t.dataFormat==null?dn():t.dataFormat,t.padding==null)this.padding=[[1,1],[1,1]];else if(typeof t.padding==\"number\")this.padding=[[t.padding,t.padding],[t.padding,t.padding]];else{if(t.padding=t.padding,t.padding.length!==2)throw new z(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${t.padding.length} array.`);let e,n;if(typeof t.padding[0]==\"number\")e=[t.padding[0],t.padding[0]],n=[t.padding[1],t.padding[1]];else{if(t.padding=t.padding,t.padding[0].length!==2)throw new z(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${t.padding[0].length} array.`);if(e=t.padding[0],t.padding[1].length!==2)throw new z(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${t.padding[1].length} array.`);n=t.padding[1]}this.padding=[e,n]}this.inputSpec=[new be({ndim:4})]}computeOutputShape(t){t=Bt(t);let e,n;return this.dataFormat===\"channelsFirst\"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?n=t[3]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],t[1],e,n]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?n=t[2]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],e,n,t[3]])}call(t,e){return B(()=>lY(St(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};Df.className=\"ZeroPadding2D\";Q.registerClass(Df);function wb(r,t,e,n,o,s){return B(()=>{Oe(o),TS(s),mn(n),e==null&&(e=[1,1]),n==null&&(n=\"valid\"),o==null&&(o=dn()),s==null&&(s=\"max\"),r=Dh(r,o);let i,a=n===\"same\"?\"same\":\"valid\";return s===\"max\"?i=ou(r,t,e,a):i=Zl(r,t,e,a),o===\"channelsFirst\"&&(i=Pt(i,[0,3,1,2])),i})}function CD(r,t,e,n,o,s){return B(()=>{Oe(o),TS(s),mn(n),e==null&&(e=[1,1,1]),n==null&&(n=\"valid\"),o==null&&(o=dn()),s==null&&(s=\"max\"),r=WS(r,o);let i,a=n===\"same\"?\"same\":\"valid\";return s===\"max\"?i=Lx(r,t,e,a):i=lx(r,t,e,a),o===\"channelsFirst\"&&(i=Pt(i,[0,4,1,2,3])),i})}var hb=class extends kt{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize==\"number\")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]==\"number\")this.poolSize=t.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(Qe(this.poolSize,\"poolSize\"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides==\"number\")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]==\"number\")this.strides=t.strides;else throw new z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);Qe(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,mn(this.padding),this.inputSpec=[new be({ndim:3})]}computeOutputShape(t){t=Bt(t);let e=Tn(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return B(()=>{this.invokeCallHook(t,e),t=il(St(t),2);let n=this.poolingFunction(St(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,\"channelsLast\");return zn(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Rf=class extends hb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),mn(o),wb(t,e,n,o,s,\"max\")}};Rf.className=\"MaxPooling1D\";Q.registerClass(Rf);var Ff=class extends hb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),mn(o),wb(t,e,n,o,s,\"avg\")}};Ff.className=\"AveragePooling1D\";Q.registerClass(Ff);var gb=class extends kt{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];Qe(this.poolSize,\"poolSize\"),Qe(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Oe(this.dataFormat),mn(this.padding),this.inputSpec=[new be({ndim:4})]}computeOutputShape(t){t=Bt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat===\"channelsFirst\"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Of=class extends gb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),mn(o),wb(t,e,n,o,s,\"max\")}};Of.className=\"MaxPooling2D\";Q.registerClass(Of);var Pf=class extends gb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),mn(o),wb(t,e,n,o,s,\"avg\")}};Pf.className=\"AveragePooling2D\";Q.registerClass(Pf);var xb=class extends kt{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];Qe(this.poolSize,\"poolSize\"),Qe(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Oe(this.dataFormat),mn(this.padding),this.inputSpec=[new be({ndim:5})]}computeOutputShape(t){t=Bt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2],o=this.dataFormat===\"channelsFirst\"?t[4]:t[3];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),o=Tn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat===\"channelsFirst\"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Lf=class extends xb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),mn(o),CD(t,e,n,o,s,\"max\")}};Lf.className=\"MaxPooling3D\";Q.registerClass(Lf);var Mf=class extends xb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),mn(o),CD(t,e,n,o,s,\"avg\")}};Mf.className=\"AveragePooling3D\";Q.registerClass(Mf);var yb=class extends kt{constructor(t){super(t),this.inputSpec=[new be({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new vt}},zf=class extends yb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Ne(n,1)})}};zf.className=\"GlobalAveragePooling1D\";Q.registerClass(zf);var Bf=class extends yb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Sr(n,1)})}};Bf.className=\"GlobalMaxPooling1D\";Q.registerClass(Bf);var bb=class extends kt{constructor(t){super(t),this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Oe(this.dataFormat),this.inputSpec=[new be({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat===\"channelsLast\"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new vt}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Vf=class extends bb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat===\"channelsLast\"?Ne(n,[1,2]):Ne(n,[2,3])})}};Vf.className=\"GlobalAveragePooling2D\";Q.registerClass(Vf);var Gf=class extends bb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat===\"channelsLast\"?Sr(n,[1,2]):Sr(n,[2,3])})}};Gf.className=\"GlobalMaxPooling2D\";Q.registerClass(Gf);var Cb=class extends kt{constructor(t){super(t),this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,n={}){let o=e.layer,s=yn(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},Wf=class extends Cb{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=Bt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Bt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=St(t),HS((i,a)=>[St(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};Wf.className=\"TimeDistributed\";Q.registerClass(Wf);function uY(r){qi(k$,\"BidirectionalMergeMode\",r)}var cY=\"concat\",Uf=class extends Cb{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=yn(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=yn(o),this.forwardLayer.name=\"forward_\"+this.forwardLayer.name,this.backwardLayer.name=\"backward_\"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?cY:t.mergeMode,uY(this.mergeMode),t.weights)throw new vt(\"weights support is not implemented for Bidirectional layer yet.\");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let n,o,s;return this.returnState&&(s=e.slice(1)),n=e[0],n=n,this.mergeMode===\"concat\"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):Tr(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=US(t,n,o,this.numConstants);if(t=s.inputs,n=s.initialState,o=s.constants,Array.isArray(t)&&(n=t.slice(1),t=t[0]),(n==null||n.length===0)&&o==null)return super.apply(t,e);let i=[],a=[];if(n!=null){let l=n.length;if(l%2>0)throw new z(\"When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.\");e.initialState=n,i.push(...n);let c=n.map(p=>new be({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),a.push(...c)}if(o!=null)throw new vt(\"Support for constants in Bidirectional layers is not implemented yet.\");let u=i[0]instanceof en;for(let l of i)if(l instanceof en!==u)throw new z(\"The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors\");if(u){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e.initialState,o,s;if(n==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let u=n.slice(0,n.length/2),l=n.slice(n.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:u})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:l}))}let i;this.returnState&&(Array.isArray(o)&&(i=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=dr(s,1));let a;return this.mergeMode===\"concat\"?a=Tm([o,s]):this.mergeMode===\"sum\"?a=Y(o,s):this.mergeMode===\"ave\"?a=D(.5,Y(o,s)):this.mergeMode===\"mul\"?a=D(o,s):this.mergeMode==null&&(a=[o,s]),this.returnState?this.mergeMode==null?a.concat(i):[a].concat(i):a})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){Hs(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),Hs(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[e,e]:n=e:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(i=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let n=yn(e.layer);if(delete e.layer,e.numConstants!=null)throw new vt(\"Deserialization of a Bidirectional layer with numConstants present is not supported yet.\");let o=e;return o.layer=n,new t(o)}};Uf.className=\"Bidirectional\";Q.registerClass(Uf);var Hf=class extends kt{constructor(t){super(t),this.scale=t.scale,t.offset?this.offset=t.offset:this.offset=0}getConfig(){let t={scale:this.scale,offset:this.offset},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>(t=St(t),t.dtype!==\"float32\"&&(t=tn(t,\"float32\")),Y(D(t,this.scale),this.offset)))}};Hf.className=\"Rescaling\";Q.registerClass(Hf);var{resizeBilinear:pY,cropAndResize:mY}=oo,qf=class extends kt{constructor(t){super(t),this.height=t.height,this.width=t.width}centerCrop(t,e,n,o,s,i,a,u){return B(()=>{let l,c=!1,p=e/i,m=n/a,f=(o+e)/i,d=(s+n)/a,h=[p,m,f,d],g=[];t.rank===3?(c=!0,l=qe([t])):l=t;for(let N=0;N{let s=pY(t,[e,n]);return tn(s,o)})}call(t,e){return B(()=>{let n=St(t),o=n.dtype,s=n.shape,i=s[s.length-3],a=s[s.length-2],u=0;i!==this.height&&(u=Math.floor((i-this.height)/2));let l=0;return a!==this.width&&(l=Math.floor((a-this.width)/2),l===0&&(l=1)),u>=0&&l>=0?this.centerCrop(n,u,l,this.height,this.width,i,a,o):this.upsize(t,this.height,this.width,o)})}getConfig(){let t={height:this.height,width:this.width},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){t=Bt(t);let e=t.length-3,n=t.length-2;return t[e]=this.height,t[n]=this.width,t}};qf.className=\"CenterCrop\";Q.registerClass(qf);function ID(r,t,e,n){let o=St(r);if(o.dtype!==\"int32\"&&(o=tn(o,\"int32\")),t===\"int\")return o;let s=o.shape;if(o.rank===0&&(o=ir(o,-1)),t===\"oneHot\"&&o.shape[o.shape.length-1]!==1&&(o=ir(o,-1)),o.rank>2)throw new z(`When outputMode is not int, maximum output rank is 2 Received outputMode ${t} and input shape ${s} which would result in output rank ${o.rank}.`);let i=[\"multiHot\",\"oneHot\"].includes(t),a=o,u;if(typeof n!=\"undefined\"&&t===\"count\"?u=ah(a,n,e,i):u=ah(a,[],e,i),t!==\"tfIdf\")return u;if(n)return D(u,n);throw new z(\"When outputMode is 'tfIdf', weights must be provided.\")}var Kf=class extends kt{constructor(t){super(t),this.numTokens=t.numTokens,t.outputMode?this.outputMode=t.outputMode:this.outputMode=\"multiHot\"}getConfig(){let t={numTokens:this.numTokens,outputMode:this.outputMode},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){return t=Bt(t),t==null?[this.numTokens]:this.outputMode===\"oneHot\"&&t[t.length-1]!==1?(t.push(this.numTokens),t):(t[t.length-1]=this.numTokens,t)}call(t,e){return B(()=>{t=St(t),t.dtype!==\"int32\"&&(t=tn(t,\"int32\"));let n;if(typeof e.countWeights!=\"undefined\"){if(this.outputMode!==\"count\")throw new z(`countWeights is not used when outputMode !== count.\n Received countWeights=${e.countWeights}`);n=St(e.countWeights)}let o=Sr(t),s=Ja(t),i=Fe(this.numTokens,o).bufferSync().get(0),a=un(s,0).bufferSync().get(0);if(!(i&&a))throw new z(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return ID(t,this.outputMode,this.numTokens,n)})}};Kf.className=\"CategoryEncoding\";Q.registerClass(Kf);var dY=[\"bilinear\",\"nearest\"],vD=new Set(dY),jf=class extends kt{constructor(t){if(super(t),this.height=t.height,this.width=t.width,t.interpolation)if(vD.has(t.interpolation))this.interpolation=t.interpolation;else throw new z(`Invalid interpolation parameter: 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i.setItem(o,s),[i.idTensor]}case\"TensorListGetItem\":{let n=I(\"tensorListId\",r,t,e),o=I(\"index\",r,t,e),s=I(\"elementShape\",r,t,e),i=I(\"elementDType\",r,t,e);return[e.getTensorList(n.id).getItem(o,s,i)]}case\"TensorListScatterV2\":case\"TensorListScatter\":{let n=I(\"indices\",r,t,e),o=I(\"tensor\",r,t,e),s=I(\"elementShape\",r,t,e),i=I(\"numElements\",r,t,e),a=WD(o,n,s,i);return e.addTensorList(a),[a.idTensor]}case\"TensorListReserve\":case\"EmptyTensorList\":{let n=I(\"elementShape\",r,t,e),o=I(\"elementDType\",r,t,e),s;r.op===\"TensorListReserve\"?s=\"numElements\":s=\"maxNumElements\";let i=I(s,r,t,e),a=r.op===\"TensorListReserve\"?-1:i,u=GD(n,o,i,a);return e.addTensorList(u),[u.idTensor]}case\"TensorListGather\":{let n=I(\"tensorListId\",r,t,e),o=I(\"indices\",r,t,e),s=I(\"elementShape\",r,t,e),i=I(\"elementDType\",r,t,e);return[e.getTensorList(n.id).gather(o,i,s)]}case\"TensorListStack\":{let n=I(\"tensorListId\",r,t,e),o=I(\"elementShape\",r,t,e),s=I(\"elementDType\",r,t,e),i=I(\"numElements\",r,t,e);return[e.getTensorList(n.id).stack(o,s,i)]}case\"TensorListFromTensor\":{let n=I(\"tensor\",r,t,e),o=I(\"elementShape\",r,t,e),s=I(\"elementDType\",r,t,e),i=VD(n,o,s);return e.addTensorList(i),[i.idTensor]}case\"TensorListConcat\":case\"TensorListConcatV2\":{let n=I(\"tensorListId\",r,t,e),o=e.getTensorList(n.id),s=I(\"dtype\",r,t,e),i=I(\"elementShape\",r,t,e);return[o.concat(s,i)]}case\"TensorListPushBack\":{let n=I(\"tensorListId\",r,t,e),o=I(\"tensor\",r,t,e),s=e.getTensorList(n.id);return s.pushBack(o),[s.idTensor]}case\"TensorListPopBack\":{let n=I(\"tensorListId\",r,t,e),o=I(\"elementShape\",r,t,e),s=I(\"elementDType\",r,t,e);return[e.getTensorList(n.id).popBack(o,s)]}case\"TensorListSplit\":{let n=I(\"tensor\",r,t,e),o=I(\"elementShape\",r,t,e),s=I(\"lengths\",r,t,e),i=UD(n,s,o);return e.addTensorList(i),[i.idTensor]}case\"TensorListLength\":{let n=I(\"tensorListId\",r,t,e),o=e.getTensorList(n.id);return[pt(o.size(),\"int32\")]}case\"TensorListResize\":{let n=I(\"tensorListId\",r,t,e),o=I(\"size\",r,t,e),i=e.getTensorList(n.id).resize(o);return e.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function qD(r,t,e){let[n,o]=I(\"fusedOps\",r,t,e),s=n===\"biasadd\",i=!s,a=o===\"prelu\",u=n===\"fusedbatchnorm\",l=I(\"numArgs\",r,t,e);if(s){if(a&&l!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!a&&s&&l!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(u)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=I(\"strides\",r,t,e),p=Oh(r,t,e),m=I(\"dataFormat\",r,t,e).toUpperCase(),f=I(\"dilations\",r,t,e),[d,h]=I(\"args\",r,t,e);i&&(h=d,d=void 0);let g=I(\"leakyreluAlpha\",r,t,e);return{stride:c,pad:p,dataFormat:m,dilations:f,biasArg:d,preluArg:h,activationFunc:o,leakyreluAlpha:g}}var KD=(r,t,e,n=se)=>{switch(r.op){case\"Conv1D\":{let o=I(\"stride\",r,t,e),s=I(\"pad\",r,t,e),i=I(\"dataFormat\",r,t,e).toUpperCase(),a=I(\"dilation\",r,t,e);return[n.conv1d(I(\"x\",r,t,e),I(\"filter\",r,t,e),o,s,i,a)]}case\"Conv2D\":{let o=I(\"strides\",r,t,e),s=Oh(r,t,e),i=I(\"dataFormat\",r,t,e).toUpperCase(),a=I(\"dilations\",r,t,e);return[n.conv2d(I(\"x\",r,t,e),I(\"filter\",r,t,e),[o[1],o[2]],s,i,[a[1],a[2]])]}case\"_FusedConv2D\":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=qD(r,t,e);return[n.fused.conv2d({x:I(\"x\",r,t,e),filter:I(\"filter\",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case\"FusedDepthwiseConv2dNative\":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=qD(r,t,e);return[n.fused.depthwiseConv2d({x:I(\"x\",r,t,e),filter:I(\"filter\",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let o=I(\"outputShape\",r,t,e),s=I(\"strides\",r,t,e),i=Oh(r,t,e);return[n.conv2dTranspose(I(\"x\",r,t,e),I(\"filter\",r,t,e),o,[s[1],s[2]],i)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let o=I(\"strides\",r,t,e),s=Oh(r,t,e),i=I(\"dilations\",r,t,e),a=I(\"dataFormat\",r,t,e).toUpperCase();return[n.depthwiseConv2d(I(\"input\",r,t,e),I(\"filter\",r,t,e),[o[1],o[2]],s,a,[i[1],i[2]])]}case\"Conv3D\":{let o=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),i=I(\"dataFormat\",r,t,e).toUpperCase(),a=I(\"dilations\",r,t,e);return[n.conv3d(I(\"x\",r,t,e),I(\"filter\",r,t,e),[o[1],o[2],o[3]],s,i,[a[1],a[2],a[3]])]}case\"AvgPool\":{let o=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),i=I(\"kernelSize\",r,t,e);return[n.avgPool(I(\"x\",r,t,e),[i[1],i[2]],[o[1],o[2]],s)]}case\"MaxPool\":{let o=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),i=I(\"kernelSize\",r,t,e);return[n.maxPool(I(\"x\",r,t,e),[i[1],i[2]],[o[1],o[2]],s)]}case\"MaxPoolWithArgmax\":{let o=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),i=I(\"kernelSize\",r,t,e),a=I(\"includeBatchInIndex\",r,t,e),{result:u,indexes:l}=n.maxPoolWithArgmax(I(\"x\",r,t,e),[i[1],i[2]],[o[1],o[2]],s,a);return[u,l]}case\"AvgPool3D\":{let o=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),i=I(\"kernelSize\",r,t,e);return[n.avgPool3d(I(\"x\",r,t,e),[i[1],i[2],i[3]],[o[1],o[2],o[3]],s)]}case\"MaxPool3D\":{let o=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),i=I(\"kernelSize\",r,t,e);return[n.maxPool3d(I(\"x\",r,t,e),[i[1],i[2],i[3]],[o[1],o[2],o[3]],s)]}case\"Dilation2D\":{let o=I(\"strides\",r,t,e),s=I(\"pad\",r,t,e),i=I(\"dilations\",r,t,e),a=o[1],u=o[2],l=i[1],c=i[2];return[n.dilation2d(I(\"x\",r,t,e),I(\"filter\",r,t,e),[a,u],s,[l,c],\"NHWC\")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var jD=(r,t,e,n=se)=>{switch(r.op){case\"Fill\":{let o=I(\"shape\",r,t,e),s=I(\"dtype\",r,t,e),i=I(\"value\",r,t,e);return[n.fill(o,i,s)]}case\"LinSpace\":{let o=I(\"start\",r,t,e),s=I(\"stop\",r,t,e),i=I(\"num\",r,t,e);return[n.linspace(o,s,i)]}case\"Multinomial\":{let o=I(\"logits\",r,t,e),s=I(\"numSamples\",r,t,e),i=I(\"seed\",r,t,e);return[n.multinomial(o,s,i)]}case\"OneHot\":{let o=I(\"indices\",r,t,e),s=I(\"depth\",r,t,e),i=I(\"onValue\",r,t,e),a=I(\"offValue\",r,t,e),u=I(\"dtype\",r,t,e);return[n.oneHot(o,s,i,a,u)]}case\"Ones\":return[n.ones(I(\"shape\",r,t,e),I(\"dtype\",r,t,e))];case\"OnesLike\":return[n.onesLike(I(\"x\",r,t,e))];case\"RandomStandardNormal\":return[n.randomStandardNormal(I(\"shape\",r,t,e),I(\"dtype\",r,t,e),I(\"seed\",r,t,e))];case\"RandomUniform\":return[n.randomUniform(I(\"shape\",r,t,e),I(\"minval\",r,t,e),I(\"maxval\",r,t,e),I(\"dtype\",r,t,e))];case\"Range\":{let o=I(\"start\",r,t,e),s=I(\"stop\",r,t,e),i=I(\"step\",r,t,e);return[n.range(o,s,i,I(\"dtype\",r,t,e))]}case\"TruncatedNormal\":{let o=I(\"shape\",r,t,e),s=I(\"mean\",r,t,e),i=I(\"stdDev\",r,t,e),a=I(\"seed\",r,t,e);return[n.truncatedNormal(o,s,i,I(\"dtype\",r,t,e),a)]}case\"Zeros\":return[n.zeros(I(\"shape\",r,t,e),I(\"dtype\",r,t,e))];case\"ZerosLike\":return[n.zerosLike(I(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function gN(r,t,e){let n=I(\"boxes\",r,t,e),o=I(\"scores\",r,t,e),s=I(\"maxOutputSize\",r,t,e),i=I(\"iouThreshold\",r,t,e),a=I(\"scoreThreshold\",r,t,e),u=I(\"softNmsSigma\",r,t,e);return{boxes:n,scores:o,maxOutputSize:s,iouThreshold:i,scoreThreshold:a,softNmsSigma:u}}var XD=async(r,t,e,n,o=se)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l,softNmsSigma:c}=gN(r,t,e),p=await o.image.nonMaxSuppressionWithScoreAsync(s,i,a,u,l,c);return[p.selectedIndices,p.selectedScores]}case\"NonMaxSuppressionV4\":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l}=gN(r,t,e),c=I(\"padToMaxOutputSize\",r,t,e),p=await o.image.nonMaxSuppressionPaddedAsync(s,i,a,u,l,c);return[p.selectedIndices,p.validOutputs]}case\"NonMaxSuppressionV3\":case\"NonMaxSuppressionV2\":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l}=gN(r,t,e);return[await o.image.nonMaxSuppressionAsync(s,i,a,u,l)]}case\"Where\":{let s=o.cast(I(\"condition\",r,t,e),\"bool\"),i=[await o.whereAsync(s)];return s.dispose(),i}case\"ListDiff\":return o.setdiff1dAsync(I(\"x\",r,t,e),I(\"y\",r,t,e));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var YD=(r,t,e,n=se)=>{switch(r.op){case\"LowerBound\":{let o=I(\"sortedSequence\",r,t,e),s=I(\"values\",r,t,e);return[n.lowerBound(o,s)]}case\"TopKV2\":{let o=I(\"x\",r,t,e),s=I(\"k\",r,t,e),i=I(\"sorted\",r,t,e),a=n.topk(o,s,i);return[a.values,a.indices]}case\"UpperBound\":{let o=I(\"sortedSequence\",r,t,e),s=I(\"values\",r,t,e);return[n.upperBound(o,s)]}case\"Unique\":{let o=I(\"x\",r,t,e),s=n.unique(o);return[s.values,s.indices]}case\"UniqueV2\":{let o=I(\"x\",r,t,e),s=I(\"axis\",r,t,e),i=n.unique(o,s);return[i.values,i.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ZD=(r,t,e,n=se)=>{switch(r.op){case\"Const\":return t[r.name];case\"PlaceholderWithDefault\":let o=I(\"default\",r,t,e);return[Cr(r.name,t,e)||o];case\"Placeholder\":return[Cr(r.name,t,e)];case\"Identity\":case\"StopGradient\":case\"FakeQuantWithMinMaxVars\":{let c=I(\"x\",r,t,e);return[Js(c)]}case\"IdentityN\":return I(\"x\",r,t,e).map(c=>Js(c));case\"Snapshot\":let s=I(\"x\",r,t,e);return[Js(s)];case\"Shape\":return[n.tensor1d(I(\"x\",r,t,e).shape,\"int32\")];case\"ShapeN\":return I(\"x\",r,t,e).map(c=>n.tensor1d(c.shape));case\"Size\":return[n.scalar(I(\"x\",r,t,e).size,\"int32\")];case\"Rank\":return[n.scalar(I(\"x\",r,t,e).rank,\"int32\")];case\"NoOp\":return[n.scalar(1)];case\"Print\":let i=I(\"x\",r,t,e),a=I(\"data\",r,t,e),u=I(\"message\",r,t,e),l=I(\"summarize\",r,t,e);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(u);for(let c=0;ct.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return pt(this.size(),\"int32\")}async import(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),B(()=>{let o=gr(e),s=n.length,i=o.length;y.assert(s===i,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${i} elements.`);for(let a=0;a{let o=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let o=n.getHashTableHandleByName(r.name);if(o!=null)return[o];{let s=I(\"keyDType\",r,t,e),i=I(\"valueDType\",r,t,e),a=new Bb(s,i);return n.addHashTable(r.name,a),[a.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let o=I(\"tableHandle\",r,t,e,n),s=I(\"keys\",r,t,e),i=I(\"values\",r,t,e);return[await n.getHashTableById(o.id).import(s,i)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let o=I(\"tableHandle\",r,t,e,n),s=I(\"keys\",r,t,e),i=I(\"defaultValue\",r,t,e);return[await n.getHashTableById(o.id).find(s,i)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let o=I(\"tableHandle\",r,t,e,n);return[n.getHashTableById(o.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var QD=(r,t,e,n=se)=>{switch(r.op){case\"ResizeBilinear\":{let o=I(\"images\",r,t,e),s=I(\"size\",r,t,e),i=I(\"alignCorners\",r,t,e),a=I(\"halfPixelCenters\",r,t,e);return[n.image.resizeBilinear(o,[s[0],s[1]],i,a)]}case\"ResizeNearestNeighbor\":{let o=I(\"images\",r,t,e),s=I(\"size\",r,t,e),i=I(\"alignCorners\",r,t,e),a=I(\"halfPixelCenters\",r,t,e);return[n.image.resizeNearestNeighbor(o,[s[0],s[1]],i,a)]}case\"CropAndResize\":{let o=I(\"image\",r,t,e),s=I(\"boxes\",r,t,e),i=I(\"boxInd\",r,t,e),a=I(\"cropSize\",r,t,e),u=I(\"method\",r,t,e),l=I(\"extrapolationValue\",r,t,e);return[n.image.cropAndResize(o,s,i,a,u,l)]}case\"ImageProjectiveTransformV3\":{let o=I(\"images\",r,t,e),s=I(\"transforms\",r,t,e),i=I(\"outputShape\",r,t,e),a=I(\"fillValue\",r,t,e),u=I(\"interpolation\",r,t,e),l=I(\"fillMode\",r,t,e);return[n.image.transform(o,s,u.toLowerCase(),l.toLowerCase(),a,i)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var tR=(r,t,e,n=se)=>{switch(r.op){case\"Equal\":return[n.equal(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"NotEqual\":return[n.notEqual(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"Greater\":return[n.greater(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"GreaterEqual\":return[n.greaterEqual(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"Less\":return[n.less(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"LessEqual\":return[n.lessEqual(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"LogicalAnd\":return[n.logicalAnd(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"LogicalNot\":return[n.logicalNot(I(\"a\",r,t,e))];case\"LogicalOr\":return[n.logicalOr(I(\"a\",r,t,e),I(\"b\",r,t,e))];case\"Select\":case\"SelectV2\":return[n.where(I(\"condition\",r,t,e),I(\"a\",r,t,e),I(\"b\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var eR=(r,t,e,n=se)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[n.matMul(I(\"a\",r,t,e),I(\"b\",r,t,e),I(\"transposeA\",r,t,e),I(\"transposeB\",r,t,e))];case\"Einsum\":return[n.einsum(I(\"equation\",r,t,e),...I(\"tensors\",r,t,e))];case\"Transpose\":return[n.transpose(I(\"x\",r,t,e),I(\"perm\",r,t,e))];case\"_FusedMatMul\":let[o,s]=I(\"fusedOps\",r,t,e),i=o===\"biasadd\",a=s===\"prelu\",u=I(\"numArgs\",r,t,e),l=I(\"leakyreluAlpha\",r,t,e);if(i){if(a&&u!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!a&&u!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,p]=I(\"args\",r,t,e);return[n.fused.matMul({a:I(\"a\",r,t,e),b:I(\"b\",r,t,e),transposeA:I(\"transposeA\",r,t,e),transposeB:I(\"transposeB\",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var rR=(r,t,e,n=se)=>{switch(r.op){case\"EuclideanNorm\":return[n.euclideanNorm(I(\"x\",r,t,e),I(\"axis\",r,t,e),I(\"keepDims\",r,t,e))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[n.batchNorm(I(\"x\",r,t,e),I(\"mean\",r,t,e),I(\"variance\",r,t,e),I(\"offset\",r,t,e),I(\"scale\",r,t,e),I(\"epsilon\",r,t,e))];case\"FusedBatchNormV3\":return[n.batchNorm(I(\"x\",r,t,e),I(\"mean\",r,t,e),I(\"variance\",r,t,e),I(\"offset\",r,t,e),I(\"scale\",r,t,e),I(\"epsilon\",r,t,e))];case\"LRN\":return[n.localResponseNormalization(I(\"x\",r,t,e),I(\"radius\",r,t,e),I(\"bias\",r,t,e),I(\"alpha\",r,t,e),I(\"beta\",r,t,e))];case\"Softmax\":return[n.softmax(I(\"x\",r,t,e))];case\"LogSoftmax\":return[n.logSoftmax(I(\"x\",r,t,e))];case\"SparseToDense\":return[n.sparseToDense(I(\"sparseIndices\",r,t,e),I(\"outputShape\",r,t,e),I(\"sparseValues\",r,t,e),I(\"defaultValue\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var nR=(r,t,e,n=se)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(I(\"paramsNestedSplits\",r,t,e),I(\"paramsDenseValues\",r,t,e),I(\"indices\",r,t,e),I(\"outputRaggedRank\",r,t,e));return o.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(I(\"starts\",r,t,e),I(\"limits\",r,t,e),I(\"splits\",r,t,e));return[o,s]}case\"RaggedTensorToTensor\":return[n.raggedTensorToTensor(I(\"shape\",r,t,e),I(\"values\",r,t,e),I(\"defaultValue\",r,t,e),I(\"rowPartitionTensors\",r,t,e),I(\"rowPartitionTypes\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var oR=(r,t,e,n=se)=>{switch(r.op){case\"Max\":{let a=I(\"axis\",r,t,e),u=I(\"keepDims\",r,t,e);return[n.max(I(\"x\",r,t,e),a,u)]}case\"Mean\":{let a=I(\"axis\",r,t,e),u=I(\"keepDims\",r,t,e);return[n.mean(I(\"x\",r,t,e),a,u)]}case\"Min\":{let a=I(\"axis\",r,t,e),u=I(\"keepDims\",r,t,e);return[n.min(I(\"x\",r,t,e),a,u)]}case\"Sum\":{let a=I(\"axis\",r,t,e),u=I(\"keepDims\",r,t,e);return[n.sum(I(\"x\",r,t,e),a,u)]}case\"All\":{let a=I(\"axis\",r,t,e),u=I(\"keepDims\",r,t,e);return[n.all(I(\"x\",r,t,e),a,u)]}case\"Any\":{let a=I(\"axis\",r,t,e),u=I(\"keepDims\",r,t,e);return[n.any(I(\"x\",r,t,e),a,u)]}case\"ArgMax\":{let a=I(\"axis\",r,t,e);return[n.argMax(I(\"x\",r,t,e),a)]}case\"ArgMin\":{let a=I(\"axis\",r,t,e);return[n.argMin(I(\"x\",r,t,e),a)]}case\"Prod\":{let a=I(\"axis\",r,t,e),u=I(\"keepDims\",r,t,e);return[n.prod(I(\"x\",r,t,e),a,u)]}case\"Cumprod\":{let a=I(\"axis\",r,t,e),u=I(\"exclusive\",r,t,e),l=I(\"reverse\",r,t,e);return[n.cumprod(I(\"x\",r,t,e),a,u,l)]}case\"Cumsum\":{let a=I(\"axis\",r,t,e),u=I(\"exclusive\",r,t,e),l=I(\"reverse\",r,t,e);return[n.cumsum(I(\"x\",r,t,e),a,u,l)]}case\"Bincount\":let o=I(\"x\",r,t,e),s=I(\"weights\",r,t,e),i=I(\"size\",r,t,e);return[n.bincount(o,s,i)];case\"DenseBincount\":{let a=I(\"x\",r,t,e),u=I(\"weights\",r,t,e),l=I(\"size\",r,t,e),c=I(\"binaryOutput\",r,t,e);return[n.denseBincount(a,u,l,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var sR=(r,t,e,n=se)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let o=I(\"n\",r,t,e),s=I(\"axis\",r,t,e),i=I(\"tensors\",r,t,e);return i=i.slice(0,o),[n.concat(i,s)]}case\"Gather\":{let o=I(\"x\",r,t,e),s=I(\"indices\",r,t,e);return[n.gather(o,n.cast(s,\"int32\"),0)]}case\"GatherV2\":{let o=I(\"axis\",r,t,e),s=I(\"batchDims\",r,t,e),i=I(\"x\",r,t,e),a=I(\"indices\",r,t,e);return[n.gather(i,n.cast(a,\"int32\"),o,s)]}case\"Reverse\":{let o=I(\"dims\",r,t,e),s=[];for(let a=0;a{let o=I(\"axis\",r,t,e),s=I(\"tensors\",r,t,e),i=s[0].shape,a=n.squeeze(s[0]).shape,u=s.map(l=>{let c=y.arraysEqual(l.shape,i);if(!c&&!y.arraysEqual(n.squeeze(l).shape,a))throw new Error(\"the input tensors shape does not match\");return c?l:n.reshape(l,i)});return[n.stack(u,o)]});case\"Unpack\":{let o=I(\"axis\",r,t,e),s=I(\"tensor\",r,t,e);return n.unstack(s,o)}case\"Tile\":{let o=I(\"reps\",r,t,e);return[n.tile(I(\"x\",r,t,e),o)]}case\"Split\":case\"SplitV\":{let o=I(\"axis\",r,t,e),s=I(\"numOrSizeSplits\",r,t,e),i=I(\"x\",r,t,e);return n.split(i,s,o)}case\"ScatterNd\":{let o=I(\"indices\",r,t,e),s=I(\"values\",r,t,e),i=I(\"shape\",r,t,e);return[n.scatterND(o,s,i)]}case\"GatherNd\":{let o=I(\"x\",r,t,e),s=I(\"indices\",r,t,e);return[n.gatherND(o,s)]}case\"SparseToDense\":{let o=I(\"sparseIndices\",r,t,e),s=I(\"outputShape\",r,t,e),i=I(\"sparseValues\",r,t,e),a=I(\"defaultValue\",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var iR=(r,t,e,n=se)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(I(\"indices\",r,t,e),I(\"values\",r,t,e),I(\"denseShape\",r,t,e),I(\"defaultValue\",r,t,e));return[o,s,i,a]}case\"SparseReshape\":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(I(\"inputIndices\",r,t,e),I(\"inputShape\",r,t,e),I(\"newShape\",r,t,e));return[o,s]}case\"SparseSegmentMean\":return[n.sparse.sparseSegmentMean(I(\"data\",r,t,e),I(\"indices\",r,t,e),I(\"segmentIds\",r,t,e))];case\"SparseSegmentSum\":return[n.sparse.sparseSegmentSum(I(\"data\",r,t,e),I(\"indices\",r,t,e),I(\"segmentIds\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var aR=(r,t,e,n=se)=>{switch(r.op){case\"FFT\":return[n.fft(I(\"x\",r,t,e))];case\"IFFT\":return[n.ifft(I(\"x\",r,t,e))];case\"RFFT\":return[n.rfft(I(\"x\",r,t,e))];case\"IRFFT\":return[n.irfft(I(\"x\",r,t,e))];default:throw TypeError(`Node type 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o=I(\"axis\",r,t,e);return[n.squeeze(I(\"x\",r,t,e),o)]}case\"Reshape\":return[n.reshape(I(\"x\",r,t,e),I(\"shape\",r,t,e))];case\"MirrorPad\":return[n.mirrorPad(I(\"x\",r,t,e),I(\"padding\",r,t,e),I(\"mode\",r,t,e))];case\"PadV2\":case\"Pad\":return[n.pad(I(\"x\",r,t,e),I(\"padding\",r,t,e),I(\"constantValue\",r,t,e))];case\"SpaceToBatchND\":{let o=I(\"blockShape\",r,t,e),s=I(\"paddings\",r,t,e);return[n.spaceToBatchND(I(\"x\",r,t,e),o,s)]}case\"BatchToSpaceND\":{let o=I(\"blockShape\",r,t,e),s=I(\"crops\",r,t,e);return[n.batchToSpaceND(I(\"x\",r,t,e),o,s)]}case\"DepthToSpace\":{let o=I(\"blockSize\",r,t,e),s=I(\"dataFormat\",r,t,e).toUpperCase();return[n.depthToSpace(I(\"x\",r,t,e),o,s)]}case\"BroadcastTo\":return[n.broadcastTo(I(\"x\",r,t,e),I(\"shape\",r,t,e))];case\"BroadcastArgs\":return[n.broadcastArgs(I(\"s0\",r,t,e),I(\"s1\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function xN(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case\"arithmetic\":return o(()=>MD(i,a,u));case\"basic_math\":return o(()=>zD(i,a,u));case\"control\":return HD(i,a,u);case\"convolution\":return o(()=>KD(i,a,u));case\"creation\":return o(()=>jD(i,a,u));case\"dynamic\":return XD(i,a,u);case\"evaluation\":return o(()=>YD(i,a,u));case\"image\":return o(()=>QD(i,a,u));case\"graph\":return o(()=>ZD(i,a,u));case\"logical\":return o(()=>tR(i,a,u));case\"matrices\":return o(()=>eR(i,a,u));case\"normalization\":return o(()=>rR(i,a,u));case\"ragged\":return o(()=>nR(i,a,u));case\"reduction\":return o(()=>oR(i,a,u));case\"slice_join\":return o(()=>sR(i,a,u));case\"sparse\":return o(()=>iR(i,a,u));case\"spectral\":return o(()=>aR(i,a,u));case\"string\":return o(()=>lR(i,a,u));case\"transformation\":return o(()=>uR(i,a,u));case\"hash_table\":return JD(i,a,u,n);case\"custom\":let l=Nb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Lb(i,a,u));throw TypeError(`Custom op ${i.op} is not 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this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function yN(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=Object.keys(r).map(m=>bn(m)[0]),c=[];n!=null&&(c=n.map(m=>bn(m.name)[0]));let p=[...t];for(;p.length>0;){let m=p.pop();if((bN(m)||G7(m)||W7(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&l.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function cR(r,t,e){let{usedNodes:n,inputs:o}=e,s=[],i=Object.keys(o).map(c=>bn(c)[0]).map(c=>r.nodes[c]),a=r.initNodes;i.forEach(c=>{n.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{n.has(c.name)&&s.push(c)}),a!=null&&a.forEach(c=>{n.has(c.name)&&s.push(c)});let u=new Set,l=[];for(;s.length>0;){let c=s.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&n.has(p.name)&&p.inputs.every(m=>u.has(m.name))&&s.push(p)})}return l}var z7=[\"Switch\",\"Merge\",\"Enter\",\"Exit\",\"NextIteration\",\"StatelessIf\",\"StatelessWhile\",\"if\",\"While\"],B7=[\"NonMaxSuppressionV2\",\"NonMaxSuppressionV3\",\"NonMaxSuppressionV5\",\"Where\"],V7=[\"HashTable\",\"HashTableV2\",\"LookupTableImport\",\"LookupTableImportV2\",\"LookupTableFind\",\"LookupTableFindV2\",\"LookupTableSize\",\"LookupTableSizeV2\"];function bN(r){return z7.indexOf(r.op)>=0}function G7(r){return B7.indexOf(r.op)>=0}function W7(r){return V7.indexOf(r.op)>=0}var kc=class{constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new kc(t.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(t){let e=Object.keys(t).map(n=>t[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPERATOR)+\"--\"+o.join(this.SEPERATOR)}compile(t,e){let n=yN(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let a=e.map(l=>l.name),u=Object.keys(t);throw new Error(`Cannot compute the outputs [${a}] from the provided inputs [${u}]. Missing the following inputs: [${o}]`)}return cR(this.graph,this.weightMap,n)}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return De(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(p=>this.graph.nodes[bn(p)[0]]),s=e.map(p=>bn(p)[0]),i=s.map(p=>this.graph.nodes[p]);i.length===0&&(i=this._outputs);let a=this.getCompilationKey(o,i),u=this.compiledMap.get(a);u==null&&(u=this.compile(t,i),this.compiledMap.set(a,u));try{this.keepIntermediateTensors=M().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(p){this.keepIntermediateTensors=!1,console.warn(p.message)}let l={},c={};return B(()=>{let p=new Lh(this.weightMap,l,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(h=>{let[g,x]=bn(h),b=[];b[x]=t[h],m[g]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[g]=this.cloneTensorList(b))});let f=this.getFrozenTensorIds(m),d={};for(let h=0;hCr(h,m,p))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,n,o,s,i,a){e.category===\"control\"||i.indexOf(t)!==-1||(n[t].forEach(u=>{u!=null&&(a[u.id]=(a[u.id]||0)+e.children.length)}),e.inputs.forEach(u=>{if(u.category!==\"control\"){let l=FD(u.name,n,o);l!=null&&l.forEach(c=>{if(c&&!c.kept&&!s.has(c.id)){let p=a[c.id];p===1?(c.dispose(),delete a[c.id]):p!=null&&a[c.id]--}})}}))}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){!this.clonedTensorsMap||(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,n=!1,o={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=M().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let i=new Lh(this.weightMap,o,s,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let a=await this.executeWithControlFlow(t,i,e,n),u=e.map(m=>Cr(m,a,i)),l=u.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),p=new Set([...l,...c,...this.weightIds]);return Object.values(a).forEach(m=>{m.forEach(f=>{f&&!f.isDisposed&&!p.has(f.id)&&f.dispose()})}),this.parent==null&&i.dispose(p),u}async executeFunctionAsync(t,e,n){let o=t.reduce((s,i,a)=>(s[this.inputs[a].name]=i,s),{});return this._executeAsync(o,this.outputNodes,!0,e,n)}async executeWithControlFlow(t,e,n,o){let s=Object.keys(t),i=s.map(w=>this.graph.nodes[bn(w)[0]]),a=n.map(w=>bn(w)[0]),u=a.map(w=>this.graph.nodes[w]);u.length===0&&(u=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:p,syncInputs:m}=yN(t,u,this.weightMap,this._initNodes),f=[...i,...this.graph.weights,...this._initNodes||[]].map(w=>({node:w,contexts:e.currentContext})),d=Object.assign({},this.weightMap);Object.keys(t).forEach(w=>{let[C,N]=bn(w),_=[];_[N]=t[w],d[C]=_});let h={},g=this.getFrozenTensorIds(d),x={};for(;f.length>0;){let w=this.processStack(i,f,e,d,x,g,a,h,l);await Promise.all(w)}p==null&&!o&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let b=u.filter(w=>!bN(w)&&!Cr(w.name,d,e)).map(w=>w.name);if(b.length>0){let w=\"\";throw p!=null&&(w=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${w}`)}return d}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m=\"\";if(p.node.op===\"Enter\"&&I(\"isConstant\",p.node,o,n)&&([m]=Zs(p.node.name,n)),o[p.node.name]==null){let f=xN(p.node,o,n,this._resourceManager);m||([m]=Zs(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(f)),this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=Zs(a.name,n);s[u]||!i.has(a.name)||(a.op===\"Merge\"?a.inputNames.some(l=>!!Cr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!Cr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=bn(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=bn(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var n,o;let s=(o=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||o===void 0?void 0:o[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[n]=bn(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var Vb=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in 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s=this.artifacts.userDefinedMetadata;s.signature!=null&&(n=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=n,this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let o=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new kc(Ph.Instance.transformGraph(e,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(o),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Ph.Instance.transformGraph(t.modelInitializer);this.initializer=new kc(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t==\"string\"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(n.length>1)throw new Error(`Found more than 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Yf{constructor(){super(Ec.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,e=new Array(t),n=this.length();for(let o=0;oe===!0)}rowMajorBatch(t,e=!0){return new kN(this,t,e)}columnMajorBatch(t,e=!0,n=CN){return this.rowMajorBatch(t,e).map(s=>fR(s,n))}concatenate(t,e){return new Hb(FN([this,t]),e)}take(t){return t<0||t==null?this:new TN(this,t)}skip(t){return t<0||t==null?this:new NN(this,t)}prefetch(t){return new qb(this,t)}shuffle(t,e){return new RN(this,t,e)}serial(){return new SN(this)}},IN=class extends tr{constructor(t){super(),this.items=t,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let t=this.items[this.trav];return this.trav++,{value:gR(t),done:!1}}},vN=class extends tr{constructor(t){super(),this.nextFn=t}summary(){return\"Function call\"}async next(){try{return this.nextFn()}catch(t){throw t.message=`Error thrown while iterating through a dataset: ${t.message}`,t}}},SN=class extends tr{constructor(t){super(),this.upstream=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},NN=class extends tr{constructor(t,e){super(),this.upstream=t,this.maxCount=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},kN=class extends tr{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},EN=class extends tr{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Nt(t.value)}}},_N=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=xo.getTensorsInContainer(t.value),n=this.transform(t.value),o=xo.getTensorsInContainer(n);for(let s of e)xo.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},AN=class extends tr{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},Ub=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=xo.getTensorsInContainer(t.value),n=await this.transform(t.value),o=xo.getTensorsInContainer(n);for(let s of e)xo.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},_c=class extends tr{constructor(){super(),this.outputQueue=new Ec,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},$N=class extends _c{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=xo.getTensorsInContainer(t.value),n=this.transform(t.value),o=xo.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)xo.isTensorInList(s,o)||s.dispose();return!0}},Hb=class extends tr{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return\"TODO: fill in upstream of chained summaries 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Wb(this.iterators,o);if(e===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case gl.FAIL:throw new Error(`Zipped streams should have the same length. 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At least one type of data should be returned.\")}summary(){return\"microphone\"}static async create(t={}){if(!M().get(\"IS_BROWSER\"))throw new Error(\"microphone API is only supported in browser environment.\");let e=new Qf(t);return await e.start(),e}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error(\"Could not obtain audio from microphone.\");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error(\"Can not convert infinite audio stream to array.\")}getSampleRate(){return this.sampleRateHz}flattenQueue(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),or(n,e)}};var td=class extends tr{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ke([0],\"int32\"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=Ws([i,s,u,a],[1,4])}else this.cropBox=Ws([0,0,1,1],[1,4])}summary(){return\"webcam\"}static async create(t,e={}){if(!M().get(\"IS_BROWSER\"))throw new Error(\"tf.data.webcam is only supported in browser environment.\");if(!t){if(t=document.createElement(\"video\"),!e.resizeWidth||!e.resizeHeight)throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new td(t,e);return await n.start(),n}async start(){this.webcamConfig.facingMode&&y.assert(this.webcamConfig.facingMode===\"user\"||this.webcamConfig.facingMode===\"environment\",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:\"user\",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error(\"Could not obtain video from webcam.\");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=gy.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=ir(J(t,\"float32\"),0),n;n=oo.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,\"bilinear\");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error(\"Can not convert infinite video stream to array.\")}};var ed=class{};var Vh=class extends tr{split(t){return new PN(this,t)}},PN=class extends Vh{constructor(t,e){super(),this.upstream=t,this.impl=new LN(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},LN=class extends _c{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=\"\"}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===\"\"?!1:(this.outputQueue.push(this.carryover),this.carryover=\"\",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var Xb=class extends tr{decodeUTF8(){return new MN(this)}},MN=class extends Vh{constructor(t){super(),this.upstream=t,this.impl=new zN(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},zN=class extends _c{constructor(t){if(super(),this.upstream=t,M().get(\"IS_BROWSER\"))this.decoder=new TextDecoder(\"utf-8\");else{let{StringDecoder:e}=wN();this.decoder=new e(\"utf8\")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let n;return M().get(\"IS_BROWSER\")?n=this.decoder.decode(e,{stream:!0}):n=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(n),!0}};var rd=class extends Xb{constructor(t,e={}){super(),this.file=t,this.options=e,y.assert(t instanceof Uint8Array||(M().get(\"IS_BROWSER\")?t instanceof File||t instanceof Blob:!1),()=>\"FileChunkIterator only supports File, Blob and Uint8Array right now.\"),this.offset=e.offset||0,this.chunkSize=e.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new 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c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,d=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,w=c.dilationWidth,C=c.effectiveFilterDepth,N=c.effectiveFilterHeight,_=c.effectiveFilterWidth,A=C-1-c.padInfo.front,$=_-1-c.padInfo.left,F=N-1-c.padInfo.top,P=bt(s.shape,\"float32\"),V=1/(d*h*g),G=e.bufferSync(o);for(let W=0;W=c.outDepth||Math.floor(nt)!==nt))for(let st=0;st=c.outHeight||Math.floor(ft)!==ft))for(let gt=0;gt<_;gt+=w){let Ct=(rt+gt)/f;if(Ct<0||Ct>=c.outWidth||Math.floor(Ct)!==Ct)continue;let $t=G.get(W,nt,ft,Ct,q);ot+=$t}}}P.set(ot*V,W,H,K,X,q)}return e.makeTensorInfo(P.shape,P.dtype,P.values)}var LF={kernelName:up,backendName:\"cpu\",kernelFunc:qJ};function KJ(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;tt([o,s],\"avgPoolGrad\");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,C=x-1-c.padInfo.top,N=bt(i.shape,\"float32\"),_=1/(f*d),A=e.data.get(o.dataId).values,$=bt(o.shape,\"float32\",A);for(let F=0;F=c.outHeight||Math.floor(X)!==X))for(let Z=0;Z=c.outWidth||Math.floor(et)!==et)continue;let rt=$.get(F,X,et,P);H+=rt}}N.set(H*_,F,V,G,P)}return e.makeTensorInfo(N.shape,N.dtype,N.values)}var MF={kernelName:lp,backendName:\"cpu\",kernelFunc:KJ};function jJ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,scale:s,offset:i,mean:a,variance:u}=t;y.assert(a.shape.length===u.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(i==null||a.shape.length===i.shape.length,()=>\"Batch 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a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=Zt({inputs:{x:o},backend:e,attrs:{shape:u}}),d=Ge({inputs:{x:f},backend:e,attrs:{perm:l}}),h=Zt({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Fo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var BF={kernelName:ui,backendName:\"cpu\",kernelFunc:XJ};function YJ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=ad(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var VF={kernelName:cp,backendName:\"cpu\",kernelFunc:YJ};function ZJ(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],\"int32\",Int32Array.from(a))}var GF={kernelName:pp,backendName:\"cpu\",kernelFunc:ZJ};var JJ=Et(co,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;lh.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Xr({inputs:{x:u[0]},backend:e});if(u[0].dtype===\"complex64\"){let h=u.map(C=>Ao({inputs:{input:C},backend:e})),g=u.map(C=>Zi({inputs:{input:C},backend:e})),x=Cu({inputs:h,backend:e,attrs:{axis:s}}),b=Cu({inputs:g,backend:e,attrs:{axis:s}}),w=Ir({inputs:{real:x,imag:b},backend:e});return h.forEach(C=>e.disposeIntermediateTensorInfo(C)),g.forEach(C=>e.disposeIntermediateTensorInfo(C)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),w}let l=u.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return Zt({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));a=S.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=Ac(c,a,t[0].dtype,p),f=S.computeOutShape(u.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var qF={kernelName:ci,backendName:\"cpu\",kernelFunc:Cu};function yT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;tt([o,s],\"conv2d\");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat===\"channelsLast\",C=new pe(m.outShape,o.dtype),N=y.computeStrides(o.shape),_=y.computeStrides(s.shape),A=N[0],$=w?N[1]:N[2],F=w?N[2]:1,P=w?1:N[1],V=C.strides[0],G=w?C.strides[1]:C.strides[2],W=w?C.strides[2]:1,q=w?1:C.strides[1],H=e.data.get(o.dataId).values,K=e.data.get(s.dataId).values,X=C.values;for(let Z=0;Z=m.inHeight)continue;let gt=st*_[0],Ct=et+ft*$;for(let $t=0;$t=m.inWidth)continue;let he=gt+Wt*_[1],ee=Ct+ue*F,ge=he;for(let fe=0;fe=l.inDepth)continue;let Z=K*F[0],et=V+X*$[1];for(let rt=0;rt=l.inHeight)continue;let ft=Z+nt*F[1],gt=et+st*$[2];for(let Ct=0;Ct=l.inWidth)continue;let ue=ft+Gt*F[2],he=gt+Wt*l.inChannels,ee=ue;for(let ge=0;geMath.cos(r)),QF={kernelName:jo,backendName:\"cpu\",kernelFunc:sQ};var iQ=Et(Xo,r=>Math.cosh(r)),tO={kernelName:Xo,backendName:\"cpu\",kernelFunc:iQ};function aQ(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=bt([d,h,g,f],\"float32\"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,C=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),_=y.computeStrides(x.shape);for(let A=0;A=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-P)*(m-1)/(g-1):0;for(let K=0;K1?F*(p-1)+K*q:.5*(F+V)*(p-1);if(X<0||X>p-1){for(let Z=0;Z1?P*(m-1)+ot*H:.5*(P+G)*(m-1);if(at<0||at>m-1){for(let gt=0;gt1?P*(m-1)+Z*H:.5*(P+G)*(m-1);if(et<0||et>m-1){for(let at=0;atx+d-b-1:(x,b)=>x+b;for(let x=0;xx+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let a=o.shape[0],u=o.shape[1],l=o.shape[2],c=o.shape[3],p=u*s,m=l*s,f=c/(s*s),d=e.data.get(o.dataId).values,h=new Float32Array(a*p*m*f),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${m}'`);let f=S.computeConv2DInfo(o.shape,s.shape,i,m,a,l,!0),{filterHeight:d,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=f,w=b.left,C=b.top,N=f.outChannels/f.inChannels,_=new pe(f.outShape,o.dtype),A=e.data.get(o.dataId).values,$=e.data.get(s.dataId).values,F=_.values;for(let P=0;P=f.inHeight)continue;let Z=K*p[0],et=V+X*c[1];for(let rt=0;rt=f.inWidth)continue;let ft=Z+nt*p[1],gt=et+st*f.inChannels,Ct=ot,$t=ft;for(let _t=0;_t{let{x:n,filter:o}=r,{strides:s,pad:i,dilations:a}=e,u=t,l=u.data.get(n.dataId).values,c=n.shape.length,p=u.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:C,strideWidth:N,filterHeight:_,filterWidth:A,dilationHeight:$,dilationWidth:F,outShape:P}=S.computeDilation2DInfo(n.shape,o.shape,s,i,\"NHWC\",a),V=y.sizeFromShape(P),G=P.length,W=y.getArrayFromDType(n.dtype,V);for(let H=0;H=0&&st=0&>ot&&(ot=_t)}}}let at=y.locToIndex([H,K,Z,rt],G,y.computeStrides(P));W[at]=ot}}}return{dataId:u.write(y.toTypedArray(W,n.dtype),P,n.dtype),shape:P,dtype:n.dtype}}};var pO={kernelName:jd,backendName:\"cpu\",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:C,filterHeight:N,filterWidth:_,dilationHeight:A,dilationWidth:$,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,\"NHWC\",u);y.assert(s.rank===F.length,()=>`Error in ${jd}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let W=0;W=0&&nt=0&&ftet&&(et=gt,rt=at,ot=st)}}}V[rt][ot][Z]+=P[W][q][K][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};var mO={kernelName:Kd,backendName:\"cpu\",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:C,filterHeight:N,filterWidth:_,dilationHeight:A,dilationWidth:$,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,\"NHWC\",u);y.assert(s.rank===F.length,()=>`Error in ${Kd}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let W=0;W=0&&nt=0&&ftet&&(et=gt,rt=nt,ot=ft)}}}V[W][rt][ot][Z]+=P[W][q][K][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function yl(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,\"sum\");let a;o.dtype===\"bool\"?a=$o({inputs:{x:o},backend:e,attrs:{dtype:\"int32\"}}):a=Xr({inputs:{x:o},backend:e});let u=a.shape.length,l=y.parseAxisParam(s,a.shape),c=S.getAxesPermutation(l,u),p=l,m=a;c!=null&&(m=Ge({inputs:{x:a},backend:e,attrs:{perm:c}}),p=S.getInnerMostAxes(p.length,u)),S.assertAxesAreInnerMostDims(\"sum\",p,m.shape.length);let[f,d]=S.computeOutAndReduceShapes(m.shape,p),h=S.upcastType(m.dtype,\"int32\"),g=sd(e,f,h),x=y.sizeFromShape(d),b=e.data.get(g.dataId).values,w=e.data.get(m.dataId).values;for(let C=0;C=0&&(m=yl({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var dO={kernelName:wp,backendName:\"cpu\",kernelFunc:hQ};function gQ(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t;tt([n,o],\"eluGrad\");let s=new Float32Array(y.sizeFromShape(o.shape)),i=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values;for(let u=0;u=1?s[u]=a[u]:s[u]=a[u]*(l+1)}return e.makeTensorInfo(o.shape,\"float32\",s)}var hO={kernelName:Cp,backendName:\"cpu\",kernelFunc:gQ};var xQ=S.ERF_P,yQ=S.ERF_A1,bQ=S.ERF_A2,wQ=S.ERF_A3,CQ=S.ERF_A4,IQ=S.ERF_A5,vQ=Et(wa,r=>{let t=Math.sign(r),e=Math.abs(r),n=1/(1+xQ*e);return t*(1-((((IQ*n+CQ)*n+wQ)*n+bQ)*n+yQ)*n*Math.exp(-e*e))}),gO={kernelName:wa,backendName:\"cpu\",kernelFunc:vQ};function md(r){let{inputs:t,backend:e,attrs:n}=r,{input:o}=t,{dim:s}=n,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),Zt({inputs:{x:o},backend:e,attrs:{shape:a}})}var xO={kernelName:pi,backendName:\"cpu\",kernelFunc:md};var SQ=Qt((r,t)=>r/t),Hh=ie(Jo,SQ),qh={kernelName:Jo,backendName:\"cpu\",kernelFunc:Hh};function hw(r,t,e){let n=r.shape,o=n[0],s=n[1],i=e.data.get(r.dataId),a=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[o,s],c=y.sizeFromShape(l),p=y.getTypedArrayFromDType(\"float32\",c),m=y.getTypedArrayFromDType(\"float32\",c);for(let g=0;g{let{image:n}=r,o=e,s=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(n.shape)),[i,a,u,l]=n.shape,c=o.data.get(n.dataId).values;for(let m=0;m=0&&wMath.floor(r/t)),$Q=ie(rs,AQ,null,\"int32\"),CO={kernelName:rs,backendName:\"cpu\",kernelFunc:$Q};function DQ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=yT({inputs:{x:o,filter:s},backend:e,attrs:{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m}});if(i){let g=h;if(c===\"NCHW\"&&i.shape.length===1&&i.shape[0]!==1){let x=Zt({inputs:{x:i},backend:e,attrs:{shape:[i.shape[0],1,1]}});h=Yi({inputs:{a:h,b:x},backend:e}),e.disposeIntermediateTensorInfo(x)}else h=Yi({inputs:{a:h,b:i},backend:e});e.disposeIntermediateTensorInfo(g)}if(f){let g=h;if(c===\"NCHW\"&&f===\"prelu\"&&a.shape.length===1&&a.shape[0]!==1){let x=Zt({inputs:{x:a},backend:e,attrs:{shape:[a.shape[0],1,1]}});h=Lc(e,h,f,x,d),e.disposeIntermediateTensorInfo(x)}else h=Lc(e,h,f,a,d);e.disposeIntermediateTensorInfo(g)}return h}var IO={kernelName:Si,backendName:\"cpu\",kernelFunc:DQ};function RQ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=bT({inputs:{x:o,filter:s},backend:e,attrs:{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m}});if(i){let g=h;h=Yi({inputs:{a:h,b:i},backend:e}),e.disposeIntermediateTensorInfo(g)}if(f){let g=h;h=Lc(e,h,f,a,d),e.disposeIntermediateTensorInfo(g)}return h}var vO={kernelName:Ni,backendName:\"cpu\",kernelFunc:RQ};function FQ(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=y.sizeFromShape(n.shape),i=o.shape,a=i[i.length-1],[u,l,c,p]=S.prepareAndValidate(n,o);if(l===0)return e.makeTensorInfo(u,n.dtype,[]);let m=e.data.get(o.dataId).values,f=e.bufferSync(n),d=Qb(m,f,n.dtype,l,a,c,p,n.shape,s);return e.makeTensorInfo(u,n.dtype,d.values)}var SO={kernelName:Sa,backendName:\"cpu\",kernelFunc:FQ};function OQ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,indices:s}=t,{axis:i,batchDims:a}=n;tt([o,s],\"gatherV2\");let u=y.parseAxisParam(i,o.shape)[0],l=e.data.get(s.dataId).values,c=o.shape[u];for(let C=0;C=0,()=>`GatherV2: the index value ${N} is not in [0, ${c-1}]`)}let p=a;a==null&&(p=0);let m=y.sizeFromShape(s.shape),f=S.segment_util.collectGatherOpShapeInfo(o,s,u,p),d=Zt({inputs:{x:o},backend:e,attrs:{shape:[f.batchSize,f.outerSize,f.dimSize,f.sliceSize]}}),h=Zt({inputs:{x:s},backend:e,attrs:{shape:[f.batchSize,m/f.batchSize]}}),g=[f.batchSize,f.outerSize,m/f.batchSize,f.sliceSize],x=e.bufferSync(h),b=e.bufferSync(d),w=tw(b,x,g);return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.makeTensorInfo(f.outputShape,w.dtype,w.values)}var NO={kernelName:mi,backendName:\"cpu\",kernelFunc:OQ};function PQ(r){let{inputs:t,backend:e}=r,{input:n}=t,o=y.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=o/s,a=Zt({inputs:{x:n},backend:e,attrs:{shape:[i,s]}}),u=hw(a,!0,e),l=Zt({inputs:{x:u},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(u),l}var TO={kernelName:vp,backendName:\"cpu\",kernelFunc:PQ};var LQ=Et(Ta,r=>Number.isFinite(r)?1:0,\"bool\"),kO={kernelName:Ta,backendName:\"cpu\",kernelFunc:LQ};var MQ=Et(ka,r=>Math.abs(r)===1/0?1:0,\"bool\"),EO={kernelName:ka,backendName:\"cpu\",kernelFunc:MQ};var zQ=Et(ss,r=>Number.isNaN(r)?1:0,\"bool\"),_O={kernelName:ss,backendName:\"cpu\",kernelFunc:zQ};function BQ(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=ew(n,o,s);return t.makeTensorInfo([i.length],\"float32\",i)}var AO={kernelName:Np,backendName:\"cpu\",kernelFunc:BQ};var VQ=Et(Aa,r=>Math.log1p(r)),$O={kernelName:Aa,backendName:\"cpu\",kernelFunc:VQ};var GQ=Qt((r,t)=>r&&t),WQ=ie($a,GQ,null,\"bool\"),DO={kernelName:$a,backendName:\"cpu\",kernelFunc:WQ};var UQ=Et(Da,r=>r?0:1,\"bool\"),RO={kernelName:Da,backendName:\"cpu\",kernelFunc:UQ};var HQ=Qt((r,t)=>r||t),qQ=ie(Ra,HQ,null,\"bool\"),FO={kernelName:Ra,backendName:\"cpu\",kernelFunc:qQ};function KQ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;tt(o,\"LRN\");let l=o.shape[3],c=l-1,p=e.data.get(o.dataId).values,m=y.sizeFromShape(o.shape),f=new Float32Array(m);function d(h){let g=h%l,x=h-g+Math.max(0,g-s),b=h-g+Math.min(g+s,c),w=0;for(;x<=b;x++){let C=p[x];w+=C*C}return w}for(let h=0;h`Error in maxPool: Either strides or dilations must be 1. 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Tt=M();Tt.registerFlag(\"HAS_WEBGL\",()=>Tt.getNumber(\"WEBGL_VERSION\")>0);Tt.registerFlag(\"WEBGL_VERSION\",()=>Sw(2)?2:Sw(1)?1:0);Tt.registerFlag(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\",()=>!1);Tt.registerFlag(\"WEBGL_BUFFER_SUPPORTED\",()=>Tt.get(\"WEBGL_VERSION\")===2);Tt.registerFlag(\"WEBGL_CPU_FORWARD\",()=>!0);Tt.registerFlag(\"WEBGL_FORCE_F16_TEXTURES\",()=>!1);Tt.registerFlag(\"WEBGL_PACK\",()=>Tt.getBool(\"HAS_WEBGL\"));Tt.registerFlag(\"WEBGL_PACK_NORMALIZATION\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_PACK_CLIP\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_PACK_DEPTHWISECONV\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_PACK_BINARY_OPERATIONS\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_PACK_UNARY_OPERATIONS\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_PACK_ARRAY_OPERATIONS\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_PACK_IMAGE_OPERATIONS\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_PACK_REDUCE\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_LAZILY_UNPACK\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_CONV_IM2COL\",()=>Tt.getBool(\"WEBGL_PACK\"));Tt.registerFlag(\"WEBGL_MAX_TEXTURE_SIZE\",()=>VT(Tt.getNumber(\"WEBGL_VERSION\")));Tt.registerFlag(\"WEBGL_MAX_TEXTURES_IN_SHADER\",()=>GT(Tt.getNumber(\"WEBGL_VERSION\")));Tt.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\",()=>{let r=Tt.getNumber(\"WEBGL_VERSION\");return r===0?0:WT(r)});Tt.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\",()=>Tt.getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0&&!Xl.isMobile());Tt.registerFlag(\"WEBGL_RENDER_FLOAT32_CAPABLE\",()=>UT(Tt.getNumber(\"WEBGL_VERSION\")));Tt.registerFlag(\"WEBGL_RENDER_FLOAT32_ENABLED\",()=>Tt.getBool(\"WEBGL_FORCE_F16_TEXTURES\")?!1:Tt.getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\"));Tt.registerFlag(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\",()=>HT(Tt.getNumber(\"WEBGL_VERSION\")));Tt.registerFlag(\"WEBGL_FENCE_API_ENABLED\",()=>qT(Tt.getNumber(\"WEBGL_VERSION\")));Tt.registerFlag(\"WEBGL_SIZE_UPLOAD_UNIFORM\",()=>Tt.getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?4:0);Tt.registerFlag(\"WEBGL_DELETE_TEXTURE_THRESHOLD\",()=>-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});Tt.registerFlag(\"WEBGL_FLUSH_THRESHOLD\",()=>Xl.isMobile()?1:-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});Tt.registerFlag(\"CPU_HANDOFF_SIZE_THRESHOLD\",()=>128);Tt.registerFlag(\"WEBGL_USE_SHAPES_UNIFORMS\",()=>!1);Tt.registerFlag(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e5);Tt.registerFlag(\"TOPK_K_CPU_HANDOFF_THRESHOLD\",()=>128);Tt.registerFlag(\"WEBGL_EXP_CONV\",()=>!1);Tt.registerFlag(\"SOFTWARE_WEBGL_ENABLED\",()=>Tt.getBool(\"IS_TEST\"));Tt.registerFlag(\"WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE\",()=>1/0);Tt.registerFlag(\"WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE\",()=>!1);Tt.registerFlag(\"WEBGL2_ISNAN_CUSTOM\",()=>!1);Tt.registerFlag(\"ENGINE_COMPILE_ONLY\",()=>!1);function We(){let r,t,e,n,o,s,i,a,u,l;return M().getNumber(\"WEBGL_VERSION\")===2?(r=\"#version 300 es\",t=\"in\",e=\"out\",n=\"in\",o=\"texture\",s=\"outputColor\",i=\"out vec4 outputColor;\",a=M().getBool(\"WEBGL2_ISNAN_CUSTOM\")?`\n bool isnan_custom(float val) {\n uint floatToUint = floatBitsToUint(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan_custom(val.x),\n isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n }\n\n #define isnan(value) isnan_custom(value)\n `:\"\",u=\"\",l=`\n #define round(value) newRound(value)\n int newRound(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 newRound(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `):(r=\"\",t=\"attribute\",e=\"varying\",n=\"varying\",o=\"texture2D\",s=\"gl_FragColor\",i=\"\",a=`\n #define isnan(value) isnan_custom(value)\n bool isnan_custom(float val) {\n return (val > 0. || val < 1. || val == 0.) ? false : true;\n }\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n }\n `,u=`\n uniform float INFINITY;\n\n bool isinf(float val) {\n return abs(val) == INFINITY;\n }\n bvec4 isinf(vec4 val) {\n return equal(abs(val), vec4(INFINITY));\n }\n `,l=`\n int round(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 round(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function ei(r,t,e=\"index\"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join(\"\")}function Bc(r,t,e=\"index\"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join(\"\")}function Stt(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function rL(r,t,e=\"index\"){let n=r.map((s,i)=>i),o=Stt(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join(\"\")}function xd(r){let t=y.computeStrides(r).map(e=>e.toString());return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;\n }\n`}function yd(){return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n }\n`}var Nw=`\n const float FLOAT_MAX = 1.70141184e38;\n const float FLOAT_MIN = 1.17549435e-38;\n\n lowp vec4 encode_float(highp float v) {\n if (isnan(v)) {\n return vec4(255, 255, 255, 255);\n }\n\n highp float av = abs(v);\n\n if(av < FLOAT_MIN) {\n return vec4(0.0, 0.0, 0.0, 0.0);\n } else if(v > FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n } else if(v < -FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;\n }\n\n highp vec4 c = vec4(0,0,0,0);\n\n highp float e = floor(log2(av));\n highp float m = exp2(fract(log2(av))) - 1.0;\n\n c[2] = floor(128.0 * m);\n m -= c[2] / 128.0;\n c[1] = floor(32768.0 * m);\n m -= c[1] / 32768.0;\n c[0] = floor(8388608.0 * m);\n\n highp float ebias = e + 127.0;\n c[3] = floor(ebias / 2.0);\n ebias -= c[3] * 2.0;\n c[2] += floor(ebias) * 128.0;\n\n c[3] += 128.0 * step(0.0, -v);\n\n return c / 255.0;\n }\n`;var{getBroadcastDims:nL}=S;function oL(r,t,e){let n=[];if(r.forEach(f=>{let d=y.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:\"\"};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=Tw(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push(\"uniform int outShape;\");break;case 2:n.push(\"uniform ivec2 outShape;\"),n.push(\"uniform int outShapeStrides;\");break;case 3:n.push(\"uniform ivec3 outShape;\"),n.push(\"uniform ivec2 outShapeStrides;\");break;case 4:n.push(\"uniform ivec4 outShape;\"),n.push(\"uniform ivec3 outShapeStrides;\");break;default:break}n.push(\"uniform ivec2 outTexShape;\")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:\"\"};`)});let o=n.join(`\n`),s=r.map(f=>Ntt(f,t,e.packedInputs,e.enableShapeUniforms)).join(`\n`),i=t.texShape,a=We(),u=Ett(a),l,c,p=$tt(a);return t.isPacked?(l=Ttt(t.logicalShape,i,e.enableShapeUniforms),c=Att(a)):(l=ktt(t.logicalShape,i,e.enableShapeUniforms),c=_tt(a)),e.packedInputs&&(p+=Ott),[p,u,c,o,l,s,e.userCode].join(`\n`)}function wd(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return Ktt(r,t);case 1:return Xtt(r,t);case 2:return Ztt(r,t);case 3:return Qtt(r,t);case 4:return eet(r,t);case 5:return ret(r);case 6:return net(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function sL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return qtt(r);case 1:return jtt(r,t);case 2:return Ytt(r,t);case 3:return Jtt(r,t);default:return tet(r,t)}}function Ntt(r,t,e=!1,n){let o=\"\";e?o+=sL(r,n):o+=wd(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=oet(r,t):o+=set(r,t)),o}function Ttt(r,t,e){switch(r.length){case 0:return iL();case 1:return Ptt(r,t,e);case 2:return Utt(r,t,e);case 3:return Mtt(r,t,e);default:return Btt(r,t,e)}}function ktt(r,t,e){switch(r.length){case 0:return iL();case 1:return Ltt(r,t,e);case 2:return Htt(r,t,e);case 3:return ztt(r,t,e);case 4:return Vtt(r,t,e);case 5:return Gtt(r,t);case 6:return Wtt(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function Ett(r){return`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${r.texture2D}(textureSampler, uv).r;\n }\n `}function _tt(r){return`\n void setOutput(float val) {\n ${r.output} = vec4(val, 0, 0, 0);\n }\n `}function Att(r){return`\n void setOutput(vec4 val) {\n ${r.output} = val;\n }\n `}function $tt(r){return`${r.version}\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n ${r.varyingFs} vec2 resultUV;\n ${r.defineOutput}\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n uniform float NAN;\n ${r.defineSpecialNaN}\n ${r.defineSpecialInf}\n ${r.defineRound}\n\n int imod(int x, int y) {\n return x - y * (x / y);\n }\n\n int idiv(int a, int b, float sign) {\n int res = a / b;\n int mod = imod(a, b);\n if (sign < 0. && mod != 0) {\n res -= 1;\n }\n return res;\n }\n\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n #define HASHSCALE1 443.8975\n float random(float seed){\n vec2 p = resultUV * seed;\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 += dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${Dtt}\n ${Rtt}\n ${Ftt}\n `}var Dtt=`\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n int texelIndex = index / 2;\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,Rtt=`\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n int texNumC, int row, int col) {\n int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,Ftt=`\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n int texelsInBatch, int texelsInLogicalRow, int b,\n int row, int col) {\n int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,Ott=`\n float getChannel(vec4 frag, vec2 innerDims) {\n vec2 modCoord = mod(innerDims, 2.);\n return modCoord.x == 0. ?\n (modCoord.y == 0. ? frag.r : frag.g) :\n (modCoord.y == 0. ? frag.b : frag.a);\n }\n float getChannel(vec4 frag, int dim) {\n float modCoord = mod(float(dim), 2.);\n return modCoord == 0. ? frag.r : frag.g;\n }\n`;function iL(){return`\n int getOutputCoords() {\n return 0;\n }\n `}function Ptt(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?e?`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ${n[1]}.0);\n }\n `:n[1]===1?e?`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ${n[0]}.0);\n }\n `:e?`\n int getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);\n }\n `}function Ltt(r,t,e){return t[0]===1?e?`\n int getOutputCoords() {\n return int(resultUV.x * float(outTexShape[1]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.x * ${t[1]}.0);\n }\n `:t[1]===1?e?`\n int getOutputCoords() {\n return int(resultUV.y * float(outTexShape[0]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.y * ${t[0]}.0);\n }\n `:e?`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n return resTexRC.x * outTexShape[1] + resTexRC.y;\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n return resTexRC.x * ${t[1]} + resTexRC.y;\n }\n `}function Mtt(r,t,e){if(e)return`\n ivec3 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec3(b, r, c);\n }\n `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[2]/2),s=o*Math.ceil(r[1]/2);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n int index = resTexRC.x * ${n[1]} + resTexRC.y;\n\n int b = index / ${s};\n index -= b * ${s};\n\n int r = 2 * (index / ${o});\n int c = imod(index, ${o}) * 2;\n\n return ivec3(b, r, c);\n }\n `}function ztt(r,t,e){if(e)return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n ${Bc([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n`;let n=ei([\"r\",\"c\",\"d\"],r);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n int index = resTexRC.x * ${t[1]} + resTexRC.y;\n ${n}\n return ivec3(r, c, d);\n }\n `}function Btt(r,t,e){if(e)return`\n ivec4 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatchN = texelsInBatch * outShape[1];\n\n int b2 = index / texelsInBatchN;\n index -= b2 * texelsInBatchN;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec4(b2, b, r, c);\n }\n `;let 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ht(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,a,r.STREAM_READ)),ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,0)),ht(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),o}function ik(r,t,e){let n=r,o=new Float32Array(e);return n.bindBuffer(n.PIXEL_PACK_BUFFER,t),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,o),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),o}function ak(r,t,e,n){let[o,s]=zc(t,e),i=4,a=new Uint8Array(YP(t*e,i));return ht(r,()=>r.readPixels(0,0,o,s,n.downloadTextureFormat,r.UNSIGNED_BYTE,a)),new Float32Array(a.buffer)}function lk(r,t,e,n,o,s,i,a){let u=r,l=new Float32Array(ZP(s,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function uk(r,t,e){let n=new Float32Array(t*e*4);return ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,n)),n}var Gc=class{constructor(t){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let e=M().getNumber(\"WEBGL_VERSION\");if(t!=null?(this.gl=t,ST(e,t)):this.gl=Wn(e),t=this.gl,M().getNumber(\"WEBGL_VERSION\")===2){let s=t;this.createVertexArray=()=>ht(s,()=>s.createVertexArray()),this.bindVertexArray=i=>ht(s,()=>s.bindVertexArray(i)),this.deleteVertexArray=i=>ht(s,()=>s.deleteVertexArray(i)),this.getVertexArray=()=>ht(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(t!=null){let s=t.getExtension(\"OES_vertex_array_object\");if(s==null)throw new Error(\"All WebGL1 implementations are expected to offer OES_vertex_array_object.\");this.createVertexArray=()=>ht(t,()=>s.createVertexArrayOES()),this.bindVertexArray=i=>ht(t,()=>s.bindVertexArrayOES(i)),this.deleteVertexArray=i=>ht(t,()=>s.deleteVertexArrayOES(i)),this.getVertexArray=()=>ht(t,()=>t.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let n=\"WEBGL_color_buffer_float\",o=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),M().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",i=\"OES_texture_half_float\";if(this.textureFloatExtension=fd(this.gl,s),Un(this.gl,i))this.textureHalfFloatExtension=fd(this.gl,i);else if(M().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Un(this.gl,o))this.colorBufferHalfFloatExtension=fd(this.gl,o);else if(M().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(n=\"EXT_color_buffer_float\",Un(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Un(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=XT(this.gl),this.indexBuffer=YT(this.gl),this.framebuffer=PT(this.gl),this.textureConfig=Zh(this.gl,this.textureHalfFloatExtension)}get debug(){return M().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\"),this.outputTexture!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");let t=this.gl;ht(t,()=>t.finish()),ht(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),ht(t,()=>t.deleteFramebuffer(this.framebuffer)),ht(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),ht(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),ht(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),ZT(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),JT(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),QT(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),ok(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),nk(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),ek(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),tk(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(vw(this.gl,this.framebuffer),this.outputTexture=null),ht(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,n){return this.downloadMatrixDriver(t,()=>ak(this.gl,e,n,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,n,o,s,i){return lk(this.gl,t,e,n,o,s,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return ik(this.gl,t,e)}createBufferFromTexture(t,e,n){this.bindTextureToFrameBuffer(t);let o=sk(this.gl,e,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,n;if(M().getBool(\"WEBGL_FENCE_API_ENABLED\")){let o=t,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),n=()=>{let i=o.clientWaitSync(s,0,0);return i===o.ALREADY_SIGNALED||i===o.CONDITION_SATISFIED},e=s}else M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(e=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(e,M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))):n=()=>!0;return{query:e,isFencePassed:n}}downloadMatrixFromPackedTexture(t,e,n){return this.downloadMatrixDriver(t,()=>uk(this.gl,e,n))}createProgram(t){this.throwIfDisposed();let e=this.gl;this.vertexShader==null&&(this.vertexShader=jT(e));let n=AT(e);ht(e,()=>e.attachShader(n,this.vertexShader)),ht(e,()=>e.attachShader(n,t)),$T(e,n);let o;return o=Object.assign(n,{vao:this.createVertexArray()}),this.bindVertexArray(o.vao),ht(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),console.assert(rk(e,o,this.vertexBuffer),\"gpgpu_util.bindVertexProgramAttributeStreams not fully successful.\"),this.debug&&Jh(e,o),this.setProgram(o),o}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ht(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&(this.bindVertexArray(this.program.vao),this.debug&&Jh(this.gl,this.program)),ht(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,n=!0){return this.throwIfDisposed(),n?LT(this.gl,t,e):MT(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ht(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),zT(this.gl,t,e,n)}setOutputMatrixTexture(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)}setOutputPackedMatrixTexture(t,e,n){this.throwIfDisposed();let[o,s]=Ji(e,n);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,n,o){this.setOutputMatrixWriteRegionDriver(n,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,n,o){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&Jh(this.gl,this.program),dd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;if(this.debug){let e=this.getVertexArray();console.assert(e===this.program.vao,\"VAO changed between setProgram and executeProgram!\"),this.debugValidate()}ht(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ht(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=fd(this.gl,M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(t,M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))),this.getQueryTime(t,M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(t,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(t,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=cet(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:n}=this.itemsToPoll[e];n()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;let n;\"setTimeoutCustom\"in M().platform&&(n=M().platform.setTimeoutCustom.bind(M().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(t){this.throwIfDisposed(),Qh(this.gl,t,this.framebuffer),this.debug&&dd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Qh(this.gl,this.outputTexture,this.framebuffer),this.debug&&dd(this.gl)):vw(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let n=e();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(t,e,n){this.throwIfDisposed();let o=this.gl;Qh(o,t,this.framebuffer),this.debug&&dd(o),this.outputTexture=t,ht(o,()=>o.viewport(0,0,e,n)),ht(o,()=>o.scissor(0,0,e,n))}setOutputMatrixWriteRegionDriver(t,e,n,o){this.throwIfDisposed(),ht(this.gl,()=>this.gl.scissor(t,e,n,o))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function cet(r){let t=0;for(;t`${r}.${e}`)}function er(r,t){return t===1?[r]:pk(r,t)}function tM(r,t){if(r===1)return\"rc\";let e=\"\";for(let n=0;n ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let e=\"\";for(let n=this.rank-2;n= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n= ${n};\n bool rEdge = rp1 >= ${o};\n `}getOutput(t){let e=this.getSourceCoordsArr(t);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}),\n cEdge ? 0. : getA(${e[1]}),\n rEdge ? 0. : getA(${e[2]}),\n rEdge || cEdge ? 0. : getA(${e[3]})`}};var Sd=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=t,this.enableShapeUniforms=Ce(this.outputShape.length);let n=\"\";for(let o=0;o<4;o++){let s=\"thisRC = rc;\";o%2===1&&(s+=\"thisRC.z += 1;\"),o>1&&(s+=\"thisRC.y += 1;\"),n+=`\n ${s}\n ${o>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${o}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${o>0?\"}\":\"\"}\n `}this.userCode=`\n ${pet(e,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?yd():xd(t)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":t[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":t[2]};\n\n ${n}\n\n setOutput(result);\n }\n `}};function pet(r,t){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${t?rL([\"r\",\"c\",\"d\"],\"inputShape\"):ei([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var Vw=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(t,e,n){let o=rM(e,n),s=nM(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=eM(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].shift();return this.usedTextures[s].push(u),u}let a;return o===Lr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Lr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Lr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=rM(n,o),i=nM(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=eM(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=M().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l.indexOf(t);if(c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function met(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function eM(r,t,e,n,o){let s=fet(t,n),i;if(o){let[u,l]=Ji(r[0],r[1]);i=u*l}else{let[u,l]=zc(r[0],r[1]);i=u*l}let a=met(e,s);return i*a}function fet(r,t){switch(r){case Lr.PACKED_2X2_FLOAT32:return Ow(t);case Lr.PACKED_2X2_FLOAT16:return Pw(t);case Lr.UNPACKED_FLOAT32:return Dw(t);case Lr.UNPACKED_FLOAT16:return Rw(t);case Lr.PACKED_4X1_UNSIGNED_BYTE:return Fw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function det(r){return M().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?Lr.PACKED_2X2_FLOAT32:Lr.UNPACKED_FLOAT32:r?Lr.PACKED_2X2_FLOAT16:Lr.UNPACKED_FLOAT16}function rM(r,t){if(r===Yr.UPLOAD)return Lr.PACKED_2X2_FLOAT32;if(r===Yr.RENDER||r==null)return det(t);if(r===Yr.DOWNLOAD||r===Yr.PIXELS)return Lr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function nM(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var Mr=class{constructor(t,e){this.variableNames=[\"A\"],this.outputShape=t,this.enableShapeUniforms=Ce(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${e}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},xr=\"if (isnan(x)) return x;\",oM=\"return x;\",mk=\"return abs(x);\";var sM=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",iM=xr+`\n return (x < 0.0) ? 0.0 : x;\n`,aM=xr+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Qi=\"return x;\",lM=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var cM=\"return x;\",pM=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,mM=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,fM=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,dM=\"return 1.0 / (1.0 + exp(-1.0 * x));\",_n=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=Ce(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${e}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var Gw=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=Ce(this.outputShape.length);let e=t.length,n=er(\"rc\",e),o=Vt(e),s=tM(e,n),i=n.slice(-2),a=e<=1?\"rc\":`vec2(${i.join(\",\")})`;this.userCode=`\n void main() {\n ${o} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${a}));\n }\n `}};var get=qr.whereImpl,xet=1e-7,yet=1e-4,Ww={};function bet(r){return r in Ww||(Ww[r]={}),Ww[r]}var wet=M().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),Cet=600;function Iet(){return M().global.screen==null?1024:M().global.screen.height*M().global.screen.width*window.devicePixelRatio*Cet/1024/1024}var Su=class extends zo{constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!M().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let e;if(t!=null){if(t instanceof Gc)e=t;else{let n=Wn(M().getNumber(\"WEBGL_VERSION\"),t);e=new Gc(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Wn(M().getNumber(\"WEBGL_VERSION\"));e=new Gc(n),this.binaryCache=bet(M().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Vw(this.gpgpu),this.numMBBeforeWarning=Iet(),this.texData=new ia(this,Ln())}nextDataId(){return Su.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=hd(e),c=new tg(l,!1,i),p=this.runWebGLProgram(c,[a],n,[[o,s]]);return p.shape=e,u.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(t,e,n){if((M().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||M().getBool(\"DEBUG\"))&&this.checkNumericalProblems(t),n===\"complex64\"&&t!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Yr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(M().getBool(\"DEBUG\")&&this.checkNumericalProblems(e),o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Yr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new _n(a,Qi):m=new Mr(a,Qi);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o===\"string\")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o===\"complex64\"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new _n(o,Qi):d=new Mr(o,Qi);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(M().getBool(\"DEBUG\")&&!M().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&M().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let l=null,c;if(i!==\"complex64\"&&M().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...Yh(o))}this.pendingRead.set(t,[]),i!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let p;if(i===\"complex64\"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Ln().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(i!=null){let f;u?f=new _n(s,Qi):f=new Mr(s,Qi);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(t,e.customTexShape),p=Ln().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype===\"string\")try{let n=e.map(o=>y.decodeString(o));return bt(t.shape,t.dtype,n)}catch(n){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return bt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(\", \")}else a.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(M().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=wet){return M().getBool(\"WEBGL_CPU_FORWARD\")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Ln().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new Gw(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new Bw(t.shape),n=!0;return this.runWebGLProgram(e,[t],t.dtype,null,n)}packedReshape(t,e){let n=[wl(t.shape),...Cl(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[wl(e),...Cl(e)],i=new Sd(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let a=hd(s),u;o?u=new Ew(a):u=new kw(a);let l=!0,c=[e!=null?e:Yh(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===Iu.DENSE){let x=i!=null?i:Yh(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=M().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!vu(b.shape,x.shape)){let w=x,C=x.shape;x.shape=b.shape,x=this.packedReshape(x,C),l.push(x),b=this.texData.get(x.dataId),w.shape=C}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=cL(t,c,p),f=this.getAndSaveBinary(m,()=>lL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),M().get(\"ENGINE_COMPILE_ONLY\")||uL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=M().get(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!M().getBool(\"WEBGL_LAZILY_UNPACK\")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(M().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!M().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let t=M().getBool(\"DEBUG\");M().set(\"DEBUG\",!1);let e=this.abs(pt(1e-8)).dataSync()[0];if(M().set(\"DEBUG\",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?xet:yet}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=BT(n,u),e.texShape=p),s!=null){let m=hd(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=Ji(p[0],p[1])),u?f=new $w(m,g):f=new tg(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Yr.PIXELS:w.usage=Yr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let C=[[h,d]],N=!0,_=this.runWebGLProgram(f,[b],o,C,N),A=this.texData.get(_.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,M().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(_.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=vet(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await yh(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Cw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let[,t]of Object.entries(this.binaryCache)){let{uniformLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,inShapesLocations:i,inTexShapesLocations:a,outShapeLocation:u,outShapeStridesLocation:l,outTexShapeLocation:c}=KT(this.gpgpu,t.program,t.webGLProgram);t.uniformLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.inShapesLocations=i,t.inTexShapesLocations=a,t.outShapeLocation=u,t.outShapeStridesLocation=l,t.outTexShapeLocation=c}}createTensorFromGPUData(t,e,n){t.channels=t.channels||\"RGBA\";let{texture:o,height:s,width:i,channels:a}=t,u=Ln().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let l=u.writeTexture(o,e,n,s,i,a);return Ln().makeTensorFromDataId(l,e,n,u)}};Su.nextDataId=0;function vet(r,t){if(t===\"float32\"||t===\"complex64\")return r;if(t===\"int32\"||t===\"bool\"){let e=t===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;nnew Su,2);var g1e={forceHalfFloat:gM};var Nd=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var ao=class{constructor(t,e,n){this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=Ce(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${t}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var ta=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var Oo=class{constructor(t,e,n,o=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(e,n);let s=this.outputShape.length;this.enableShapeUniforms=Ce(s);let i=\"\";if(o)if(s===0||y.sizeFromShape(this.outputShape)===1)i=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(i=`\n ${Vt(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?i+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:i+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let u=er(\"coords\",s);this.enableShapeUniforms?i+=`\n bool nextRowOutOfBounds =\n (${u[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${u[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:i+=`\n bool nextRowOutOfBounds =\n (${u[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${u[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${t}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${i}\n\n setOutput(result);\n }\n `}};function rr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var xM={kernelName:po,backendName:\"webgl\",kernelFunc:rr};function An(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,\"complex64\"),i=e.texData.get(s.dataId),a=rr({inputs:{x:n},backend:e}),u=rr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var yM={kernelName:mp,backendName:\"webgl\",kernelFunc:An};var fk=\"return (a < 0.) ? b * a : a;\",dk=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function Net(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),a=M().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Oo(dk,o.shape,i.shape):new ao(fk,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],\"float32\");return e.disposeIntermediateTensorInfo(i),u}var bM={kernelName:is,backendName:\"webgl\",kernelFunc:Net};var hk=\"return (a < 0.) ? b * a : a;\",gk=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function Tet(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=M().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Oo(gk,n.shape,o.shape):new ao(hk,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],\"float32\")}var wM={kernelName:bs,backendName:\"webgl\",kernelFunc:Tet};var Po=\"if (isnan(x)) return x;\";function wt({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=M().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&t!=null,c;return l?c=new _n(i.shape,t):c=new Mr(i.shape,r),a.runWebGLProgram(c,[i],u)}}function ae({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype===\"complex64\"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[C,N]=w,_={dataId:C.dataId,dtype:C.dtype,shape:u.shape},A={dataId:N.dataId,dtype:N.dtype,shape:l.shape},$=new ao(r,u.shape,l.shape);return c.runWebGLProgram($,[_,A],lr(C.dtype,N.dtype))}),b=An({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||lr(u.dtype,l.dtype);if((u.dtype===\"string\"||l.dtype===\"string\"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype===\"string\"?S.fromUint8ToStringArray(d):d,x=u.dtype===\"string\"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,x,p),C=c.makeTensorInfo(w,p),N=c.texData.get(C.dataId);return N.values=b,C}let m=M().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&t!=null,f;return m?f=new Oo(t,u.shape,l.shape,e):f=new ao(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Il(r,t=!1){if(r===\"linear\")return t?cM:oM;if(r===\"relu\")return t?mM:iM;if(r===\"elu\")return t?pM:sM;if(r===\"relu6\")return t?fM:aM;if(r===\"prelu\")return t?gk:hk;if(r===\"leakyrelu\")return t?dk:fk;if(r===\"sigmoid\")return t?dM:lM;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Td=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Ce(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?\"i * 2, rc.y\":\"rc.y, i * 2\",f=s?\"rc.z, i * 2\":\"i * 2, rc.z\",d=o?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";a&&(u?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${a}\n }`:l?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${a}\n }`:g=`vec4 activation(vec4 x) {\n ${a}\n }`,x=\"result = activation(result);\");let b=i?\"result += getBiasAtOutCoords();\":\"\";i&&this.variableNames.push(\"bias\"),u&&this.variableNames.push(\"preluActivationWeights\"),l&&this.variableNames.push(\"leakyreluAlpha\");let w=\"rc.x\",C=\"rc.x\";t[0]`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!vu(o.shape,u)&&!(c.texture!==null&&vu(c.shape,u))?vM(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var SM={kernelName:gi,backendName:\"webgl\",kernelFunc:it};var og=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l=\"sumValue += dot(values, ones);\";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c=\"\";s%n>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${a}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${l}\n }\n\n int inIdx = inOffset + ${a};\n if (${u===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${l}\n } else if (${u===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${l}\n } else if (${u===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${l}\n }\n setOutput(sumValue);\n }\n `}};var Uw=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=\"0.0\",u=\"\";e===\"prod\"?a=\"1.0\":e===\"min\"?(a=\"1.0 / 1e-20\",u=\"min\"):e===\"max\"&&(a=\"-1.0 / 1e-20\",u=\"max\");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"sum\"?l=\"sumValue\":e===\"prod\"?l=\"prodValue\":e===\"all\"?l=\"allValue\":e===\"any\"&&(l=\"anyValue\");let c=Math.floor(n/4)*4,p=n%4,m=`\n if (${e===\"sum\"}) {\n sumValue += dot(values, ones);\n } else if (${e===\"prod\"}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${u}(values, minMaxValue);\n if (${e===\"min\"} || ${e===\"max\"}) {\n minMaxValue = ${u}(values, minMaxValue);\n bvec4 isNaN = isnan(values);\n if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n minMaxValue = vec4(NAN);\n }\n }\n }\n `,f=\"vec4\";e===\"all\"?(a=\"1.0\",m=`\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n `,f=\"bvec4\"):e===\"any\"&&(a=\"0.0\",m=`\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || 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);\n\n ${m}\n } else if (${p===2}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${p===3}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${l});\n }\n `}};function Eet(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],n=S.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:n,outSize:Math.ceil(e/n)})}return t}function Hn(r,t,e,n){let o=Eet(r.shape),s=r;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],n=new Array(t);for(let o=0;o6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=Vt(this.rank),s=pk(\"rc\",this.rank),i=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let N=e?[x,p,f]:[x,f,p],_=n?[b,d,m]:[b,m,d],A=it({inputs:{x:r},backend:o,attrs:{shape:N}}),$=it({inputs:{x:t},backend:o,attrs:{shape:_}}),F=[A,$],P=Math.max(x,b),V=e?A.shape[1]:A.shape[2],G=s!=null,W=i!=null,q=u===\"leakyrelu\",H=u!=null?Il(u,!0):null,K=G||W||q||H!=null,X;if((f===1||d===1)&&V>yk&&K===!1){let et=A,rt=$;e&&(et=Pe({inputs:{x:A},backend:o,attrs:{perm:[0,2,1]}}),F.push(et)),n&&(rt=Pe({inputs:{x:$},backend:o,attrs:{perm:[0,2,1]}}),F.push(rt));let ot=d!==1,at=d===1,nt=et;ot&&(nt=it({inputs:{x:et},backend:o,attrs:{shape:[P,V,1]}}),F.push(nt));let st=d===1?2:1,ft=rt;at&&(ft=it({inputs:{x:rt},backend:o,attrs:{shape:[P,1,V]}}),F.push(ft));let gt=ng({inputs:{a:nt,b:ft},backend:o});X=Uc({inputs:{x:gt},backend:o,attrs:{axis:st,keepDims:!0}}),F.push(gt)}else{let et=lr(r.dtype,t.dtype),rt=new Td(N,_,[P,f,d],e,n,G,H,W,q),ot=[A,$];if(s!=null&&ot.push(s),W&&ot.push(i),q){let at=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));ot.push(at),F.push(at)}X=o.runWebGLProgram(rt,ot,et)}let Z=it({inputs:{x:X},backend:o,attrs:{shape:C}});F.push(X);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function Aet(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return Hc({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var EM={kernelName:vi,backendName:\"webgl\",kernelFunc:Aet};var _M=\"return abs(x);\";function $et(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!==\"complex64\"){let s=e.texData.get(n.dataId),i=Mw(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return M().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?o=new _n(n.shape,_M):o=new Mr(n.shape,_M),e.runWebGLProgram(o,[n],n.dtype)}var AM={kernelName:li,backendName:\"webgl\",kernelFunc:$et};var Det=xr+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n`,Ret=wt({opSnippet:Det}),$M={kernelName:la,backendName:\"webgl\",kernelFunc:Ret};var Fet=xr+`\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,Oet=wt({opSnippet:Fet}),DM={kernelName:ua,backendName:\"webgl\",kernelFunc:Oet};var RM=\"return a + b;\",Pet=ae({opSnippet:RM,packedOpSnippet:RM,supportsComplex:!0,cpuKernelImpl:pL}),FM={kernelName:Jn,backendName:\"webgl\",kernelFunc:Pet};var Kw=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${n.join(`\n `)}\n\n float result = ${o};\n setOutput(result);\n }\n `}};var jw=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${n.join(`\n `)}\n\n vec4 result = ${o};\n setOutput(result);\n }\n `}};function Xw(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return rr({inputs:{x:n[0]},backend:e});if(n.length>M().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let u=Math.floor(n.length/2),l=Xw({inputs:n.slice(0,u),backend:e}),c=Xw({inputs:n.slice(u),backend:e});return Xw({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>lr(u,l)),s=n.map(u=>u.shape),a=M().getBool(\"WEBGL_PACK\")?new jw(n[0].shape,s):new Kw(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var OM={kernelName:Vo,backendName:\"webgl\",kernelFunc:Xw};function Let(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims(\"all\",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=it({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Hn(h,h.dtype,\"all\",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=it({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=it({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var PM={kernelName:ca,backendName:\"webgl\",kernelFunc:Let};function Met(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims(\"any\",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=it({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Hn(h,h.dtype,\"any\",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=it({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=it({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var LM={kernelName:pa,backendName:\"webgl\",kernelFunc:Met};var Yw=class{constructor(t,e,n){this.variableNames=[\"A\"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[s,i];let a=e===\"max\"?\">\":\"<\",u=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${o}; i++) {\n int inIdx = ${u};\n float candidate = getA(batch, inIdx);\n if (candidate ${a} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var Zw=class{constructor(t,e,n,o){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push(\"bestIndicesA\");let a=this.outputShape,u=a.length,l=Vt(u),c=er(\"coords\",u),p,m;if(i===1){m=u+1;let $=Vt(m);p=`\n ${$} sourceLocR = ${$}(${c.join()}, 0);\n ++${c[u-1]};\n ${$} sourceLocG = ${$}(${c.join()}, 0);\n ++${c[u-2]};\n ${$} sourceLocA = ${$}(${c.join()}, 0);\n --${c[u-1]};\n ${$} sourceLocB = ${$}(${c.join()}, 0);\n --${c[u-2]};`}else m=u,p=`\n ${l} sourceLocR = coords;\n ++${c[u-1]};\n ${l} sourceLocG = coords;\n ++${c[u-2]};\n ${l} sourceLocA = coords;\n --${c[u-1]};\n ${l} sourceLocB = coords;\n --${c[u-2]};`;let f=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,m),d=\".\"+f[m-1],h=f.map($=>\"int \"+$),g=er(\"sourceLocR\",m-1).concat(\"inIdx.r\"),x=er(\"sourceLocG\",m-1).concat(\"inIdx.g\"),b=er(\"sourceLocB\",m-1).concat(\"inIdx.b\"),w=er(\"sourceLocA\",m-1).concat(\"inIdx.a\"),C=n===\"max\"?\"greaterThan\":\"lessThan\",N=o?\"\":`\n inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${x.join()}),\n getBestIndicesAChannel(${b.join()}),\n getBestIndicesAChannel(${w.join()})));`,_=`vec4(\n getAChannel(${g.join()}),\n hasNextCol ? getAChannel(${x.join()}) : 0.,\n hasNextRow ? getAChannel(${b.join()}) : 0.,\n hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,A=o?\"\":`\n float getBestIndicesAChannel(${h.join()}) {\n return getChannel(getBestIndicesA(${f.join()}),\n vec2(${f.slice(-2).join()}));\n }`;this.userCode=`\n float getAChannel(${h.join()}) {\n return getChannel(getA(${f.join()}),\n vec2(${f.slice(-2).join()}));\n }\n ${A}\n void main() {\n ${l} coords = getOutputCoords();\n bool hasNextCol = ${c[u-1]} < ${a[u-1]-1};\n bool hasNextRow = ${c[u-2]} < ${a[u-2]-1};\n ${p}\n ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},\n sourceLocB${d}, sourceLocA${d}) * ${e};\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = ${_};\n\n for (int i = 0; i < ${e}; i++) {\n inIdx = srcIdx;\n ${N}\n vec4 candidate = ${_};\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4(${C}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n `}};function MM(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new Yw(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,\"int32\");if(c.shape[1]===1)return c;let p=MM(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function zM(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new Zw(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,\"int32\");if(l.shape.length===t.shape.length){let c=zM(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function Jw(r,t,e,n){let o=[e];if(S.assertAxesAreInnerMostDims(\"arg\"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!M().getBool(\"WEBGL_PACK_REDUCE\")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,o),p=y.sizeFromShape(c),m=it({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=MM(r,m,n);s.push(f);let d=it({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return zM(r,t,n)}function zet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=Jw(e,u,i[0],\"max\");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var BM={kernelName:Go,backendName:\"webgl\",kernelFunc:zet};function Bet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=Jw(e,u,i[0],\"min\");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var VM={kernelName:_l,backendName:\"webgl\",kernelFunc:Bet};var Vet=xr+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,Get=wt({opSnippet:Vet}),GM={kernelName:ma,backendName:\"webgl\",kernelFunc:Get};var Wet=xr+\"return log(x + sqrt(x * x + 1.0));\",Uet=wt({opSnippet:Wet}),WM={kernelName:fa,backendName:\"webgl\",kernelFunc:Uet};var Het=xr+`\n return atan(x);\n`,qet=wt({opSnippet:Het}),UM={kernelName:da,backendName:\"webgl\",kernelFunc:qet};var Ket=Nd+`\n return atan(a, b);\n`,jet=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+ta+`\n return result;\n`,Xet=ae({opSnippet:Ket,packedOpSnippet:jet}),HM={kernelName:ga,backendName:\"webgl\",kernelFunc:Xet};var Yet=xr+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=wt({opSnippet:Yet}),qM={kernelName:ha,backendName:\"webgl\",kernelFunc:Zet};var ri=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e===\"avg\",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),n){let $=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${a}, ${u});\n const ivec2 pads = ivec2(${f}, ${d});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${p};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${$} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${o?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let w=\"max\",C=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(C=\"avgValue / max(count, 1.0)\");let N=Math.floor(i/4)*4,_=i%4,A=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${w}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${a}, ${u});\n const ivec2 pads = ivec2(${f}, ${d});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${p};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${N}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${A}\n }\n\n int xC = xCCorner + ${N};\n if (${_===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${A}\n } else if (${_===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${A}\n } else if (${_===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${A}\n }\n }\n setOutput(${C});\n }\n `}},Tu=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e===\"avg\",C=\"0.0\";if(w||(C=\"-1.0 / 1e-20\"),n){let P=\">=\";this.userCode=`\n const ivec3 strides =\n ivec3(${a}, ${u}, ${l});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${f};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${p}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${P} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${o?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${d} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let N=\"max\",_=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(_=\"avgValue / max(count, 1.0)\");let A=Math.floor(i/4)*4,$=i%4,F=`\n if (${w}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${N}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${a}, ${u}, ${l});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${C};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${C});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${f};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${p}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${A}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${F}\n }\n\n int xC = xCCorner + ${A};\n if (${$===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${F}\n } else if (${$===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${F}\n } else if (${$===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${F}\n }\n }\n }\n setOutput(${_});\n }\n `}};function Jet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;ti(o,\"avgPool\");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new ri(c,\"avg\",!1);return e.runWebGLProgram(p,[o],\"float32\")}var KM={kernelName:Wo,backendName:\"webgl\",kernelFunc:Jet};function Qet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new Tu(p,\"avg\",!1);return e.runWebGLProgram(m,[o],\"float32\")}var jM={kernelName:Al,backendName:\"webgl\",kernelFunc:Qet};var Qw=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${p});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${u};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${l};\n wC+= ${a}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},tC=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=`\n const ivec3 pads = ivec3(${d}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${u}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${l}) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${f};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function trt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new tC(m);return e.runWebGLProgram(f,[o],i.dtype)}var XM={kernelName:up,backendName:\"webgl\",kernelFunc:trt};function ert(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;ti([o,s],\"avgPoolGrad\");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new Qw(c);return e.runWebGLProgram(p,[o],i.dtype)}var YM={kernelName:lp,backendName:\"webgl\",kernelFunc:ert};function rrt(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return Hc({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var ZM={kernelName:Uo,backendName:\"webgl\",kernelFunc:rrt};var eC=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a=\"0.0\";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push(\"offset\"),a=\"getOffsetAtOutCoords()\");let u=\"1.0\";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),u=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${a};\n float scale = ${u};\n float inv = scale * inversesqrt(variance + float(${i}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}};var rC=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a=\"vec4(0.0)\";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push(\"offset\"),a=\"getOffsetAtOutCoords()\");let u=\"vec4(1.0)\";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),u=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n vec4 offset = ${a};\n vec4 scale = ${u};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${i}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}};var nrt=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(i==null||o.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(a==null||o.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=M().getBool(\"WEBGL_PACK_NORMALIZATION\")?new rC(n.shape,o.shape,s.shape,c,p,u):new eC(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},JM={kernelName:ns,backendName:\"webgl\",kernelFunc:nrt};var nC=class{constructor(t){this.variableNames=[\"source\"],this.outputShape=t,this.rank=t.length;let e=Vt(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let n=ort(this.rank),o,s=t.map((i,a)=>`sourceLoc.${bk[a]} = start[${a}] + coords.${bk[a]};`);o=`\n ${e} sourceLoc;\n ${e} coords = getOutputCoords();\n ${s.join(`\n`)}\n `,this.userCode=`\n void main() {\n ${o}\n setOutput(getSource(${n}));\n }\n `}},bk=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function ort(r){if(r===1)return\"sourceLoc\";if(r<=6)return bk.slice(0,r).map(t=>\"sourceLoc.\"+t).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var oC=class{constructor(t){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let e=Vt(this.rank),n=er(\"coords\",this.rank),o=er(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=`\n result.x = ${i};\n if (++${n[this.rank-1]} < ${t[this.rank-1]}) {\n ++${o[this.rank-1]};\n result.y = ${i};\n --${o[this.rank-1]};\n }\n `,u=this.rank===1?\"\":`\n --${n[this.rank-1]};\n if (++${n[this.rank-2]} < ${t[this.rank-2]}) {\n ++${o[this.rank-2]};\n result.z = ${i};\n if (++${n[this.rank-1]} < ${t[this.rank-1]}) {\n ++${o[this.rank-1]};\n result.w = ${i};\n }\n }\n `,l=this.rank<=4?`sourceLoc = coords +\n ${e}(${t.map((c,p)=>`start[${p}]`).join()});`:t.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(`\n`);this.userCode=`\n void main() {\n ${e} coords = getOutputCoords();\n ${e} sourceLoc;\n ${l}\n vec4 result = vec4(0.);\n ${a}\n ${u}\n setOutput(result);\n }\n `}};function srt(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=ze.computeFlatOffset(t,y.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function ni(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=ze.parseSliceParams(o,s,i);if(ze.assertParamsValid(o,a,u),y.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype===\"string\"){let p=e.texData.get(o.dataId),m=GL(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=ze.isSliceContinous(o.shape,a,u);if(l||!c){let p=M().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new oC(u):new nC(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),srt(o,a,u,e)}var QM={kernelName:yi,backendName:\"webgl\",kernelFunc:ni};var irt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;y.assert(o.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=it({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Pe({inputs:{x:d},backend:e,attrs:{perm:l}}),g=it({inputs:{x:h},backend:e,attrs:{shape:c}}),x=ni({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},tz={kernelName:ui,backendName:\"webgl\",kernelFunc:irt};function art(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=Lw(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var ez={kernelName:cp,backendName:\"webgl\",kernelFunc:art};function lrt(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],\"int32\",Int32Array.from(a))}var rz={kernelName:pp,backendName:\"webgl\",kernelFunc:lrt};var urt=\"return float(a != b);\",wk=ae({opSnippet:urt,cpuKernelImpl:RL,dtype:\"bool\"}),nz={kernelName:Oa,backendName:\"webgl\",kernelFunc:wk};function vl(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.real},backend:e})}var oz={kernelName:Fp,backendName:\"webgl\",kernelFunc:vl};var crt=\"return float(int(x));\";function sz(r,t){let e=new Mr(r.shape,crt),n=t.runWebGLProgram(e,[r],\"int32\");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function Ck(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dtype:s}=n;if(s===\"complex64\"){if(o.dtype===\"complex64\")return rr({inputs:{x:o},backend:e});let i=Te(o.shape),a=Ck({inputs:{x:o},backend:e,attrs:{dtype:\"float32\"}}),u=An({inputs:{real:a,imag:i},backend:e});return i.dispose(),e.disposeIntermediateTensorInfo(a),u}if(o.dtype===\"complex64\"){let i=vl({inputs:{input:o},backend:e}),a=Ck({inputs:{x:i},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(i),a}if(!y.hasEncodingLoss(o.dtype,s)){let i=rr({inputs:{x:o},backend:e});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(e.shouldExecuteOnCPU([o])){let i=e.texData.get(o.dataId).values,[a,u,l]=fL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s===\"int32\")return sz(o,e);if(s===\"bool\"){let i=e.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),u=wk({inputs:{a:o,b:i},backend:e});return e.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var iz={kernelName:uo,backendName:\"webgl\",kernelFunc:Ck};var az=\"return ceil(x);\",prt=wt({opSnippet:az,packedOpSnippet:az,cpuKernelImpl:dL}),lz={kernelName:Ho,backendName:\"webgl\",kernelFunc:prt};var sC=class{constructor(t){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `}};var iC=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `}};function mrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;M().getBool(\"WEBGL_PACK_CLIP\")?a=new iC(o.shape):a=new sC(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var uz={kernelName:co,backendName:\"webgl\",kernelFunc:mrt};var aC=class{constructor(t){this.variableNames=[\"real\",\"imag\"],this.outputShape=t,this.userCode=`\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n `}};function cz(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function frt(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new aC(n.shape),i=[cz(n,o.complexTensorInfos.real),cz(n,o.complexTensorInfos.imag)];return e.runWebGLProgram(s,i,i[0].dtype)}var pz={kernelName:$l,backendName:\"webgl\",kernelFunc:frt};var lC=class{constructor(t){this.outputShape=[],this.outputShape=S.computeOutShape(t,1),this.variableNames=t.map((i,a)=>`T${a}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let i=1;i`T${g}`);let u=new Array(t.length-1);u[0]=t[0][e];for(let h=1;h= ${u[h-1]}) {\n return getChannel(\n getT${h}(${uC(a,l,g)}),\n vec2(${uC(c,l,g)}));\n }`}let f=u.length,d=u[u.length-1];m+=`\n return getChannel(\n getT${f}(${uC(a,l,d)}),\n vec2(${uC(c,l,d)}));`,this.userCode=`\n float getValue(${a.map(h=>\"int \"+h)}) {\n ${m}\n }\n\n void main() {\n ${s} coords = getOutputCoords();\n vec4 result = vec4(getValue(${i}), 0., 0., 0.);\n\n ${i[o-1]} = ${i[o-1]} + 1;\n if (${i[o-1]} < ${n[o-1]}) {\n result.g = getValue(${i});\n }\n\n ${i[o-2]} = ${i[o-2]} + 1;\n if (${i[o-2]} < ${n[o-2]}) {\n result.a = getValue(${i});\n }\n\n ${i[o-1]} = ${i[o-1]} - 1;\n if (${i[o-2]} < ${n[o-2]} &&\n ${i[o-1]} < ${n[o-1]}) {\n result.b = getValue(${i});\n }\n setOutput(result);\n }\n `}};function uC(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function qc(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var mz={kernelName:Sp,backendName:\"webgl\",kernelFunc:qc};function kd(r,t,e){let n=r[0].dtype;if(n===\"complex64\"){let f=r.map(b=>vl({inputs:{input:b},backend:e})),d=r.map(b=>qc({inputs:{input:b},backend:e})),h=kd(f,t,e),g=kd(d,t,e),x=An({inputs:{real:h,imag:g},backend:e});return f.forEach(b=>e.disposeIntermediateTensorInfo(b)),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let o=e.shouldExecuteOnCPU(r);if(n===\"string\"&&(o=!0),o){let f=r.map(C=>{let _=[-1,y.sizeFromShape(C.shape.slice(t))];return it({inputs:{x:C},backend:e,attrs:{shape:_}})}),d=f.map(C=>({vals:e.readSync(C.dataId),shape:C.shape})),h=S.computeOutShape(f.map(C=>C.shape),1),g=f[0].shape[0]===1,x=hL(d,h,n,g),b=S.computeOutShape(r.map(C=>C.shape),t),w=e.makeTensorInfo(b,n,x);return f.forEach(C=>e.disposeIntermediateTensorInfo(C)),w}let s=r.filter(f=>y.sizeFromShape(f.shape)>0),i=M().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let f=i?new Mr(r[0].shape,Qi):new _n(r[0].shape,Qi);return e.runWebGLProgram(f,r,n)}let a=M().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>a){let f=[];for(let h=0;hd.shape),t);return e.runWebGLProgram(f,s,n)}let{tensors2D:u,outShape:l}=drt(s,t,e),c=new lC(u.map(f=>f.shape)),p=e.runWebGLProgram(c,u,n);u.forEach(f=>e.disposeIntermediateTensorInfo(f));let m=it({inputs:{x:p},attrs:{shape:l},backend:e});return e.disposeIntermediateTensorInfo(p),m}function drt(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>it({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function Ik(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(l=>l.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(l=>l.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(l=>y.sizeFromShape(l.shape)>0);return u.length===1?rr({inputs:{x:u[0]},backend:e}):kd(u,s,e)}var fz={kernelName:ci,backendName:\"webgl\",kernelFunc:Ik};var Ed=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,w=g?3:1,C=\"\",N=\"\";n&&(o?C=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:s?C=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:C=`\n float activation(float x) {\n ${n}\n }\n `,N=\"result = activation(result);\");let _=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${C}\n\n const ivec2 strides = ivec2(${u}, ${l});\n const ivec2 pads = ivec2(${i}, ${a});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${w}];\n\n ivec2 xRCCorner =\n ivec2(coords[${x}], coords[${b}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f}; wC++) {\n int xC = xCCorner + wC * ${p};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${d}) *\n getW(wR, wC, ${d}, d2);\n } else {\n dotProd +=\n getX(batch, ${d}, xR, xC) *\n getW(wR, wC, ${d}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2),\n getW(wR, wC, ${d} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1),\n getX(batch, xR, xC, ${d} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC),\n getX(batch, ${d} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${_}\n ${N}\n setOutput(result);\n }\n `}},pC=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${i}, ${a});\n const ivec3 pads = ivec3(${e}, ${n}, ${o});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${p}; wF++) {\n int xF = xFCorner + wF * ${u};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${l};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${d}) *\n getW(wF, wR, wC, ${d}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1),\n getX(batch, xF, xR, xC, ${d} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2),\n getW(wF, wR, wC, ${d} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var _d=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=Ce(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(p+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*u};\n `,a===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,u===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,u>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${a}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+p}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+p}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${l}\n\n ${o.output} = result;\n }\n `}};function fC(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function dC({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat===\"channelsLast\",d=!1,h=!1,g,x=[];if(s!=null){let C=fC(s.shape,f);C!=null&&(s=it({inputs:{x:s},backend:n,attrs:{shape:C}}),x.push(s))}if(o!=null){let C=fC(o.shape,f);C!=null&&(o=it({inputs:{x:o},backend:n,attrs:{shape:C}}),x.push(o))}if(!((p===1||m===1)&&c>yk)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&y.arraysEqual(l.shape.slice(-3),u.slice(-3))){let C=u[0]*u[1]*(u[2]+1),N={dataId:r.dataId,shape:[1,C,e.inChannels],dtype:r.dtype},_=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,y.assert(vu(l.shape,N.shape),()=>`packed reshape ${l.shape} to ${N.shape} isn't free`);let A=it({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(A);let $=Hc({a:N,b:A,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get($.dataId);y.assert(F.isPacked,()=>\"batchMatMul result is expected to be packed\"),l.shape=_,F.shape=e.outShape,g=rr({inputs:{x:$},backend:n}),g.shape=e.outShape,x.push($)}else{let C=e.outHeight*e.outWidth,N=it({inputs:{x:r},backend:n,attrs:{shape:f?[e.batchSize,C,e.inChannels]:[e.batchSize,e.inChannels,C]}}),_=it({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),A=Hc({a:f?N:_,b:f?_:N,transposeA:!f,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=it({inputs:{x:A},backend:n,attrs:{shape:e.outShape}}),x.push(N),x.push(_),x.push(A)}for(let C of x)n.disposeIntermediateTensorInfo(C);return g}function hC({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f===\"channelsLast\",h=u*l*c,g=m*p,x=[e.batchSize,h,g],b=!0,w=!1,C=[];if(s!=null){let Z=fC(s.shape,d);Z!=null&&(s=it({inputs:{x:s},backend:n,attrs:{shape:Z}}),C.push(s))}if(o!=null){let Z=fC(o.shape,d);Z!=null&&(o=it({inputs:{x:o},backend:n,attrs:{shape:Z}}),C.push(o))}let N=it({inputs:{x:t},backend:n,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});C.push(N);let _=new mC(x,e),A=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],$=n.runWebGLProgram(_,[r],\"float32\",A),F=it({inputs:{x:$},backend:n,attrs:{shape:x}});C.push($),C.push(F);let P=o!=null,V=s!=null,G=a===\"leakyrelu\",W=a?Il(a,!0):null,q=new Td(d?F.shape:N.shape,d?N.shape:F.shape,d?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,w,P,W,V,G),H=d?[F,N]:[N,F];if(o&&H.push(o),V&&H.push(s),G){let Z=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));H.push(Z),C.push(Z)}let K=n.runWebGLProgram(q,H,\"float32\"),X=it({inputs:{x:K},backend:n,attrs:{shape:e.outShape}});C.push(K);for(let Z of C)n.disposeIntermediateTensorInfo(Z);return X}function hrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))f=dC({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p===\"channelsLast\"&&M().getBool(\"WEBGL_EXP_CONV\")){let h=new _d(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];f=e.runWebGLProgram(h,[o,s],\"float32\",g)}else if(M().getBool(\"WEBGL_CONV_IM2COL\"))f=hC({x:o,filter:s,convInfo:m,backend:e});else{let h=new Ed(m);f=e.runWebGLProgram(h,[o,s],\"float32\")}let d=it({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var dz={kernelName:qo,backendName:\"webgl\",kernelFunc:hrt};var gC=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${o};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n if (${i}) {\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n } else {\n float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},xC=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat===\"channelsLast\",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${u});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${p}];\n\n ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n\n if (${i}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},yC=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yF = 0; yF < ${t.outDepth}; yF++) {\n int xF = wF + yF * ${e} - ${s};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${n} - ${i};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${a};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},bC=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${u}, ${l}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${e}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${e} - 1 - wF;\n\n for (int wR = 0; wR < ${n}; wR++) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${n} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function grt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new gC(m);return e.runWebGLProgram(f,[o,s],\"float32\")}var hz={kernelName:fp,backendName:\"webgl\",kernelFunc:grt};function xrt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p),f=new xC(m);return e.runWebGLProgram(f,[o,s],\"float32\")}var gz={kernelName:Ko,backendName:\"webgl\",kernelFunc:xrt};function yrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new pC(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var xz={kernelName:Dl,backendName:\"webgl\",kernelFunc:yrt};function brt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new yC(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var yz={kernelName:dp,backendName:\"webgl\",kernelFunc:brt};function wrt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new bC(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var bz={kernelName:hp,backendName:\"webgl\",kernelFunc:wrt};var Crt=Po+`\n return cos(x);\n`,Irt=wt({opSnippet:Crt}),wz={kernelName:jo,backendName:\"webgl\",kernelFunc:Irt};var vrt=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,Srt=wt({opSnippet:vrt}),Cz={kernelName:Xo,backendName:\"webgl\",kernelFunc:Srt};var wC=class{constructor(t,e,n,o,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o===\"bilinear\"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,x,b]=p>1?[`${(a-1)/(p-1)}`,\"(y2-y1) * height_ratio\",`y1*${d} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${d}`],[w,C,N]=m>1?[`${(u-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${w});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${i}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${C};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${d} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${N};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${f} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var Nrt=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new wC(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],\"float32\")},Iz={kernelName:ya,backendName:\"webgl\",kernelFunc:Nrt};var Kc;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Kc||(Kc={}));var sg=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,i=this.op===Kc.Prod?\"1.0\":\"0.0\",a=n?i:`getX(${vz(s,\"coords\",this.op)})`,u=this.outputShape[this.outputShape.length-1],l=\"\",c=\"\";n?(l=o?`end != ${u-1}`:\"end != 0\",c=o?\"end + 1\":\"end - 1\"):(l=o?`end + pow2 < ${u}`:\"end >= pow2\",c=o?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${Vt(s)} coords = getOutputCoords();\n int end = ${Sz(s,\"coords\",this.op)};\n float val = ${a};\n int pow2 = int(pow(2.0, index));\n if (${l}) {\n int idx = ${c};\n ${Sz(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${vz(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function vz(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function Sz(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function CC(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=Pe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=rr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new sg(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new sg(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Pe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function Trt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return CC(Kc.Prod,o,e,s,i,a)}var Nz={kernelName:xa,backendName:\"webgl\",kernelFunc:Trt};function krt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return CC(Kc.Sum,o,e,s,i,a)}var Tz={kernelName:Yo,backendName:\"webgl\",kernelFunc:krt};function Ert(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=Lw(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=mL(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var kz={kernelName:gp,backendName:\"webgl\",kernelFunc:Ert};var IC=class{constructor(t,e,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${e};\n int offset_h = imod(h, ${e});\n int in_w = w / ${e};\n int offset_w = imod(w, ${e});\n int offset_d = (offset_h * ${e} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function _rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i===\"NHWC\"?o.shape[1]:o.shape[2],l=i===\"NHWC\"?o.shape[2]:o.shape[3],c=i===\"NHWC\"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i===\"NHWC\"?[a,p,m,f]:[a,f,p,m],h=new IC(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var Ez={kernelName:ba,backendName:\"webgl\",kernelFunc:_rt};var Ad=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=Ce(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l=\"\",c=\"\";n&&(o?l=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:s?l=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:l=`\n float activation(float x) {\n ${n}\n }\n `,c=\"result = activation(result);\");let p=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${l}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${u};\n int q = d2 - d1 * ${u};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${i}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${a}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${p}\n ${c}\n setOutput(result);\n }\n `}};var $d=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=Ce(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(f+=`\n xC = xCCorner + ${b*l};\n `,u===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,l===1&&b>0?f+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:f+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):f+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,l>1?f+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:f+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):w===1?f+=`\n xC${b+1} = xTexelC${b};\n `:f+=`\n xCOffset = xC + ${w};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(f+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;M().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new $d(p):m=new Ad(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],\"float32\",f)}var _z={kernelName:Zo,backendName:\"webgl\",kernelFunc:Art};var vC=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${i} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${o};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},SC=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${a});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${u}; dm++) {\n int d2 = d1 * ${u} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function $rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new vC(p);return e.runWebGLProgram(m,[o,s],\"float32\")}var Az={kernelName:xp,backendName:\"webgl\",kernelFunc:$rt};function Drt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new SC(p);return e.runWebGLProgram(m,[o,s],\"float32\")}var $z={kernelName:yp,backendName:\"webgl\",kernelFunc:Drt};var NC=class{constructor(t){this.variableNames=[\"X\"],this.outputShape=[t,t],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function Rrt(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=it({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new NC(s),u=e.runWebGLProgram(a,[i],i.dtype),l=it({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var Dz={kernelName:bp,backendName:\"webgl\",kernelFunc:Rrt};var TC=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${i});\n const ivec2 pads = ivec2(${p}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${a}; h++) {\n int hIn = hBeg + h * ${l};\n\n if (hIn >= 0 && hIn < ${e}) {\n for (int w = 0; w < ${u}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${n}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function Frt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,\"NHWC\",u),c,p=new TC(l);c=e.runWebGLProgram(p,[o,s],\"float32\");let m=it({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var Rz={kernelName:Rl,backendName:\"webgl\",kernelFunc:Frt};function Ort(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h=0&&(m=Uc({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var Fz={kernelName:wp,backendName:\"webgl\",kernelFunc:Ort};var Prt=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",Lrt=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,Mrt=wt({opSnippet:Prt,packedOpSnippet:Lrt}),Oz={kernelName:Qo,backendName:\"webgl\",kernelFunc:Mrt};var zrt=\"return (b >= 1.0) ? a : a * (b + 1.0);\",Brt=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,Vrt=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=M().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Oo(Brt,n.shape,o.shape):new ao(zrt,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},Pz={kernelName:Cp,backendName:\"webgl\",kernelFunc:Vrt};var Grt=`\n return vec4(equal(a, b));\n`,Wrt=\"return float(a == b);\",Urt=ae({opSnippet:Wrt,packedOpSnippet:Grt,dtype:\"bool\",cpuKernelImpl:gL}),Lz={kernelName:Ca,backendName:\"webgl\",kernelFunc:Urt};var Hrt=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${S.ERF_P};\n float a1 = ${S.ERF_A1};\n float a2 = ${S.ERF_A2};\n float a3 = ${S.ERF_A3};\n float a4 = ${S.ERF_A4};\n float a5 = ${S.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,qrt=wt({opSnippet:Hrt}),Mz={kernelName:wa,backendName:\"webgl\",kernelFunc:qrt};var Krt=Po+`\n return exp(x);\n`,jrt=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,vk=wt({opSnippet:Krt,packedOpSnippet:jrt,cpuKernelImpl:xL,dtype:\"float32\"}),zz={kernelName:ts,backendName:\"webgl\",kernelFunc:vk};function kC(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),it({inputs:{x:s},backend:n,attrs:{shape:a}})}var Bz={kernelName:pi,backendName:\"webgl\",kernelFunc:kC};var Vz=\"return exp(x) - 1.0;\",Xrt=wt({opSnippet:Vz,packedOpSnippet:Vz,cpuKernelImpl:yL}),Gz={kernelName:Ia,backendName:\"webgl\",kernelFunc:Xrt};var ig=class{constructor(t,e,n){this.variableNames=[\"real\",\"imag\"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:\"1.0\",a;if(t===\"real\")a=\"return real * expR - imag * expI;\";else if(t===\"imag\")a=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${t}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${a}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${o});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${o}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${i};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function EC(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=it({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new ig(\"real\",u,t),c=new ig(\"imag\",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,\"float32\"),f=e.runWebGLProgram(c,p,\"float32\"),d=An({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=it({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function Yrt(r){let{inputs:t,backend:e}=r,{input:n}=t;return EC(n,!1,e)}var Wz={kernelName:Ip,backendName:\"webgl\",kernelFunc:Yrt};var _C=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=t,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function Sl(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||y.inferDtype(o),s===\"string\"){let i=y.getArrayFromDType(s,y.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new _C(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var Uz={kernelName:Fl,backendName:\"webgl\",kernelFunc:Sl};var AC=class{constructor(t){this.variableNames=[\"Image\"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${e} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${e}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var Hz={kernelName:va,backendName:\"webgl\",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new AC(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var qz=\"return floor(x);\",Zrt=wt({opSnippet:qz,packedOpSnippet:qz,cpuKernelImpl:bL}),Kz={kernelName:es,backendName:\"webgl\",kernelFunc:Zrt};var Jrt=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,Qrt=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,tnt=ae({opSnippet:Jrt,packedOpSnippet:Qrt,dtype:\"int32\"}),jz={kernelName:rs,backendName:\"webgl\",kernelFunc:tnt};var $C=class{constructor(t){this.variableNames=[\"A\"];let e=We(),[n,o]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);\n\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var DC=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let e=We(),[n,o]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${o}.0, ${n}.0);\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${e.output} = result;\n }\n `}};var Xz={kernelName:Xd,backendName:\"webgl\",kernelFunc:ent},Dd,Sk=M().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function ent(r){let{inputs:t,backend:e,attrs:n}=r,{pixels:o}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!=\"undefined\"&&o instanceof HTMLVideoElement,a=typeof HTMLImageElement!=\"undefined\"&&o instanceof HTMLImageElement,[u,l]=i?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[l,u],p=[l,u,s];if(a||i){let h=M().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Dd==null||h!==Sk)&&(Sk=h,Dd=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:Sk})),Dd.canvas.width=u,Dd.canvas.height=l,Dd.drawImage(o,0,0,u,l),o=Dd.canvas}let m=e.makeTensorInfo(c,\"int32\");e.texData.get(m.dataId).usage=Yr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),o);let f=M().getBool(\"WEBGL_PACK\")?new DC(p):new $C(p),d=e.runWebGLProgram(f,[m],\"int32\");return e.disposeData(m.dataId),d}function rnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(o.shape,s.shape,u,p,l,m,!1,h),x,b=[],w=i!=null,C=a!=null,N=f===\"leakyrelu\",_=()=>{let $=[o,s],F=(P,V)=>{if(V===\"NCHW\"&&P.shape.length===1&&P.shape[0]!==1){let G=it({inputs:{x:P},backend:e,attrs:{shape:[P.shape[0],1,1]}});return b.push(G),G}return P};if(w&&$.push(F(i,c)),C&&$.push(F(a,c)),N){let P=e.makeTensorInfo([],\"float32\",y.createScalarValue(d,\"float32\"));$.push(P),b.push(P)}return $};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))x=dC({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else if(g.strideWidth<=2&&h===\"channelsLast\"&&M().getBool(\"WEBGL_EXP_CONV\")){let $=f?Il(f,!0):null,F=new _d(g,w,$,C,N),P=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],V=_();x=e.runWebGLProgram(F,V,\"float32\",P)}else if(M().getBool(\"WEBGL_CONV_IM2COL\"))x=hC({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else{let $=f?Il(f,!1):null,F=new Ed(g,w,$,C,N),P=_();x=e.runWebGLProgram(F,P,\"float32\")}let A=it({inputs:{x},backend:e,attrs:{shape:g.outShape}});return b.push(x),b.forEach($=>e.disposeIntermediateTensorInfo($)),A}var Yz={kernelName:Si,backendName:\"webgl\",kernelFunc:rnt};function nnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let g=S.computeConv2DInfo(o.shape,s.shape,u,h,l,p,!0),x=M().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?Il(m,x):null,w=[o,s],C=i!=null,N=a!=null,_=m===\"leakyrelu\";if(C&&w.push(i),N&&w.push(a),_){let P=e.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));w.push(P),d.push(P)}let A;x?A=new $d(g,C,b,N,_):A=new Ad(g,C,b,N,_);let $=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=e.runWebGLProgram(A,w,\"float32\",$);return d.forEach(P=>e.disposeIntermediateTensorInfo(P)),F}var Zz={kernelName:Ni,backendName:\"webgl\",kernelFunc:nnt};var RC=class{constructor(t,e,n,o){this.sliceDim=t,this.strides=e,this.paramsShape=o,this.variableNames=[\"x\",\"indices\"],this.outputShape=n;let s=Vt(n.length),i=`\n int index;`;for(let a=0;a= ${this.paramsShape[a]};\n flattenIndex += index * ${this.strides[a]};`;this.userCode=`\n void main() {\n ${s} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n\n ${i}\n\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}};function ont(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=o.shape,i=s[s.length-1],a=y.sizeFromShape(n.shape),[u,l,c,p]=S.prepareAndValidate(n,o),m=it({inputs:{x:o},backend:e,attrs:{shape:[l,i]}}),f=it({inputs:{x:n},backend:e,attrs:{shape:[y.sizeFromShape(n.shape)/c,c]}});if(e.shouldExecuteOnCPU([n,o])||n.dtype===\"string\"){let x=e.readSync(o.dataId),b=e.bufferSync(n),w=wL(x,b,n.dtype,l,i,c,p,n.shape,a);return e.makeTensorInfo(u,n.dtype,w.values)}let d=new RC(i,p,[l,c],n.shape),h=e.runWebGLProgram(d,[f,m],f.dtype),g=it({inputs:{x:h},backend:e,attrs:{shape:u}});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),g}var Jz={kernelName:Sa,backendName:\"webgl\",kernelFunc:ont};var FC=class{constructor(t,e){this.variableNames=[\"A\",\"indices\"],this.outputShape=e,this.rank=e.length;let n=Vt(this.rank),o=snt(t,2);this.userCode=`\n void main() {\n ${n} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${t[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${o}));\n }\n `}};function snt(r,t){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],n=[];for(let o=0;o=0,()=>`GatherV2: the index value ${N} is not in [0, ${w-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),c=y.sizeFromShape(s.shape),p=[],m=it({inputs:{x:o},backend:e,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=it({inputs:{x:s},backend:e,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(m),p.push(f);let d=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(e.shouldExecuteOnCPU([o,s])||o.dtype===\"string\"){let b=e.bufferSync(f),w=e.bufferSync(m),C=CL(w,b,d);return p.forEach(N=>e.disposeIntermediateTensorInfo(N)),e.makeTensorInfo(l.outputShape,C.dtype,C.values)}let h=new FC(m.shape,d),g=e.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=it({inputs:{x:g},backend:e,attrs:{shape:l.outputShape}});return p.forEach(b=>e.disposeIntermediateTensorInfo(b)),x}var Qz={kernelName:mi,backendName:\"webgl\",kernelFunc:Nk};var int=\"return float(a > b);\",ant=`\n return vec4(greaterThan(a, b));\n`,lnt=ae({opSnippet:int,packedOpSnippet:ant,cpuKernelImpl:IL,dtype:\"bool\"}),t3={kernelName:Na,backendName:\"webgl\",kernelFunc:lnt};var unt=\"return float(a >= b);\",cnt=`\n return vec4(greaterThanEqual(a, b));\n`,pnt=ae({opSnippet:unt,packedOpSnippet:cnt,dtype:\"bool\",cpuKernelImpl:vL}),e3={kernelName:os,backendName:\"webgl\",kernelFunc:pnt};function mnt(r){let{inputs:t,backend:e}=r,{input:n}=t;return EC(n,!0,e)}var r3={kernelName:vp,backendName:\"webgl\",kernelFunc:mnt};var fnt=\"return float(!isnan(x) && !isinf(x));\",dnt=wt({opSnippet:fnt,dtype:\"bool\"}),n3={kernelName:Ta,backendName:\"webgl\",kernelFunc:dnt};var hnt=\"return float(isinf(x));\",gnt=wt({opSnippet:hnt,dtype:\"bool\"}),o3={kernelName:ka,backendName:\"webgl\",kernelFunc:gnt};var xnt=\"return float(isnan(x));\",ynt=wt({opSnippet:xnt,dtype:\"bool\"}),s3={kernelName:ss,backendName:\"webgl\",kernelFunc:ynt};var bnt=\"return float(a < b);\",wnt=`\n return vec4(lessThan(a, b));\n`,Cnt=ae({opSnippet:bnt,packedOpSnippet:wnt,cpuKernelImpl:SL,dtype:\"bool\"}),i3={kernelName:Ea,backendName:\"webgl\",kernelFunc:Cnt};var Int=\"return float(a <= b);\",vnt=`\n return vec4(lessThanEqual(a, b));\n`,Snt=ae({opSnippet:Int,packedOpSnippet:vnt,cpuKernelImpl:NL,dtype:\"bool\"}),a3={kernelName:_a,backendName:\"webgl\",kernelFunc:Snt};function Nnt(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=TL(n,o,s);return t.makeTensorInfo([i.length],\"float32\",i)}var l3={kernelName:Np,backendName:\"webgl\",kernelFunc:Nnt};var Tnt=Po+`\n return x < 0.0 ? 0./0. : log(x);\n`,knt=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,Ent=wt({opSnippet:Tnt,packedOpSnippet:knt,cpuKernelImpl:kL}),u3={kernelName:as,backendName:\"webgl\",kernelFunc:Ent};var _nt=Po+`\n return log(1.0 + x);\n`,Ant=wt({opSnippet:_nt}),c3={kernelName:Aa,backendName:\"webgl\",kernelFunc:Ant};var $nt=\"return float(a >= 1.0 && b >= 1.0);\",Dnt=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,Rnt=ae({opSnippet:$nt,packedOpSnippet:Dnt,dtype:\"bool\"}),p3={kernelName:$a,backendName:\"webgl\",kernelFunc:Rnt};var Fnt=\"return float(!(x >= 1.0));\",Ont=wt({opSnippet:Fnt}),m3={kernelName:Da,backendName:\"webgl\",kernelFunc:Ont};var Pnt=\"return float(a >= 1.0 || b >= 1.0);\",Lnt=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,Mnt=ae({opSnippet:Pnt,packedOpSnippet:Lnt,dtype:\"bool\"}),f3={kernelName:Ra,backendName:\"webgl\",kernelFunc:Mnt};var OC=class{constructor(t,e,n,o,s){this.variableNames=[\"x\"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${i}; j <= ${i}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${a}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${u};\n setOutput(val);\n }\n `}};var PC=class{constructor(t,e,n,o,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${i};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${i}; j <= ${i}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${a}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${u};\n setOutput(result);\n }\n `}};var znt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=M().getBool(\"WEBGL_PACK_NORMALIZATION\")?new PC(o.shape,s,i,a,u):new OC(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},d3={kernelName:Ol,backendName:\"webgl\",kernelFunc:znt};var LC=class{constructor(t,e,n,o,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${e})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${e} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${o}) * norm + float(${n});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${o})\n * float(${s})\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var Bnt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new LC(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},h3={kernelName:Tp,backendName:\"webgl\",kernelFunc:Bnt};function g3(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=it({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Hn(a,r.dtype,\"max\",n),l=it({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function Tk(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,C=new Array(a);for(let A=0;A`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new ri(c,\"max\",!1);return e.runWebGLProgram(p,[o],o.dtype)}var b3={kernelName:cs,backendName:\"webgl\",kernelFunc:Unt};function Hnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new Tu(p,\"max\",!1);return e.runWebGLProgram(m,[o],o.dtype)}var w3={kernelName:Pl,backendName:\"webgl\",kernelFunc:Hnt};var MC=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${u});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${o}) {\n float dyR = float(dyRCorner + wR) / ${e}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${i}; wC++) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${i} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},zC=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${m}, ${f});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${u};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${e}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${l};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${a}) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${d} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${l} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function qnt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new Tu(m,\"max\",!0),d=e.runWebGLProgram(f,[i],i.dtype),h=new zC(m),g=e.runWebGLProgram(h,[o,d],i.dtype);return e.disposeIntermediateTensorInfo(d),g}var C3={kernelName:Ep,backendName:\"webgl\",kernelFunc:qnt};function Knt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;ti([s,i],\"maxPoolGrad\");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=!0,d=new ri(m,\"max\",f),h=e.runWebGLProgram(d,[a],a.dtype),g=new MC(m),x=e.runWebGLProgram(g,[o,h],a.dtype);return e.disposeIntermediateTensorInfo(h),x}var I3={kernelName:kp,backendName:\"webgl\",kernelFunc:Knt};function v3(r,t,e,n){let o=new ri(e,\"max\",!1),s=n.runWebGLProgram(o,[r],\"float32\");o=new ri(e,\"max\",!0,!0,t);let i=n.runWebGLProgram(o,[r],\"float32\");return[s,i]}var S3={kernelName:_p,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${l}'`);let c=S.computePool2DInfo(n.shape,o,s,l,i),[p,m]=v3(n,a,c,u);return[p,m]}};function N3(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=it({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Hn(a,\"float32\",\"mean\",n),l=it({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}var T3={kernelName:ps,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{keepDims:o,axis:s}=t,i=e,a=n.shape.length,u=y.parseAxisParam(s,n.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=i.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let C=i.texData.get(d.dataId).values,N=new Array(a);for(let $=0;$c[0]+t[p]+c[1]);let o=t.length,s=Vt(o),i=e.map(c=>c[0]).join(\",\"),a=e.map((c,p)=>c[0]+t[p]).join(\",\"),u=[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,o),l=n===\"reflect\"?0:1;if(o===1){this.userCode=`\n int start = ${i};\n int end = ${a};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start) {\n outC = start * 2 - outC - ${l};\n } else if(outC >= end) {\n outC = (end - 1) * 2 - outC + ${l};\n }\n setOutput(getX(outC - start));\n }\n `;return}this.userCode=`\n ${s} start = ${s}(${i});\n ${s} end = ${s}(${a});\n\n void main() {\n ${s} outC = getOutputCoords();\n for (int i = 0; i < ${o}; i++) {\n if (outC[i] < start[i]) {\n outC[i] = start[i] * 2 - outC[i] - ${l};\n } else if(outC[i] >= end[i]) {\n outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};\n }\n }\n ${s} coords = outC - start;\n setOutput(getX(${u}));\n }\n `}};var VC=class{constructor(t,e,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((d,h)=>d[0]+t[h]+d[1]);let o=t.length,s=Vt(o),i=e.map(d=>d[0]).join(\",\"),a=e.map((d,h)=>d[0]+t[h]).join(\",\"),u=er(\"rc\",o),l=er(\"source\",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?\"source\":`vec2(${l.slice(-2).join()})`,m=n===\"reflect\"?0:1,f=\"\";if(o===1){let d=`\n ${s} source = rc;\n if (source < start) {\n source = start * 2 - source - ${m};\n } else if (source >= end) {\n source = (end - 1) * 2 - source + ${m};\n }\n source -= start;\n `;f=`\n ${s} rc = outputLoc;\n ${d}\n result[0] = getChannel(getX(${l.join()}), ${p});\n ${u[o-1]} += 1;\n if(${c}) {\n ${d}\n result[1] = getChannel(getX(${l.join()}), ${p});\n }\n `}else{let d=`\n ${s} source = rc;\n ${s} lt = ${s}(lessThan(source, start));\n ${s} gte = ${s}(greaterThanEqual(source, end));\n ${s} orig = 1 - (lt + gte);\n source = orig * source +\n lt * (start * 2 - source - ${m}) +\n gte * ((end - 1) * 2 - source + ${m});\n source -= start;\n `;f=`\n ${s} rc = outputLoc;\n ${d}\n result[0] = getChannel(getX(${l.join()}), ${p});\n ${u[o-1]} += 1;\n if(${c}) {\n ${d}\n result[1] = getChannel(getX(${l.join()}), ${p});\n }\n rc = outputLoc;\n ${u[o-2]} += 1;\n if(${u[o-2]} < ${this.outputShape[o-2]}) {\n ${d}\n result[2] = getChannel(getX(${l.join()}), ${p});\n ${u[o-1]} += 1;\n if(${c}) {\n ${d}\n result[3] = getChannel(getX(${l.join()}), ${p});\n }\n }\n `}this.userCode=`\n const ${s} start = ${s}(${i});\n const ${s} end = ${s}(${a});\n\n void main() {\n ${s} outputLoc = getOutputCoords();\n vec4 result = vec4(0.);\n ${f}\n setOutput(result);\n }\n `}};var Jnt=({inputs:r,backend:t,attrs:e})=>{let{x:n}=r,{paddings:o,mode:s}=e,i=M().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new VC(n.shape,o,s):new BC(n.shape,o,s);return t.runWebGLProgram(i,[n],n.dtype)},_3={kernelName:ds,backendName:\"webgl\",kernelFunc:Jnt};var Qnt=`if (b == 0.0) return NAN;\n return mod(a, b);`,tot=`\n vec4 result = mod(a, b);\n bvec4 isNaN = equal(b, vec4(0.0));\n `+ta+`\n return result;\n`,eot=ae({opSnippet:Qnt,packedOpSnippet:tot}),A3={kernelName:Fa,backendName:\"webgl\",kernelFunc:eot};var GC=class{constructor(t,e,n){this.variableNames=[\"probs\"],this.customUniforms=[{name:\"seed\",type:\"float\"}],this.outputShape=[t,n],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n\n float r = random(seed);\n float cdf = 0.0;\n\n for (int i = 0; i < ${e-1}; i++) {\n cdf += getProbs(batch, i);\n\n if (r < cdf) {\n setOutput(float(i));\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutput(float(${e-1}));\n }\n `}};var rot=`\nif (a == b) {\n return 1.0;\n};\nreturn a / b;`,not=`\n // vec4 one = vec4(equal(a, b));\n // return one + (vec4(1.0) - one) * a / b;\n vec4 result = a / b;\n if(a.x == b.x) {\n result.x = 1.;\n }\n if(a.y == b.y) {\n result.y = 1.;\n }\n if(a.z == b.z) {\n result.z = 1.;\n }\n if(a.w == b.w) {\n result.w = 1.;\n }\n\n return result;\n`,kk=ae({opSnippet:rot,packedOpSnippet:not,checkOutOfBounds:!0}),$3={kernelName:Jo,backendName:\"webgl\",kernelFunc:kk};var D3=\"return a - b;\",Ek=ae({opSnippet:D3,packedOpSnippet:D3,supportsComplex:!0,cpuKernelImpl:YL}),R3={kernelName:Os,backendName:\"webgl\",kernelFunc:Ek};function _k(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{dim:s}=n,i=y.parseAxisParam([s],o.shape),a=Tk({inputs:{x:o},backend:e,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(a.shape,i),l=it({inputs:{x:a},backend:e,attrs:{shape:u}}),c=Ek({inputs:{a:o,b:l},backend:e}),p=vk({inputs:{x:c},backend:e}),m=Uc({inputs:{x:p},backend:e,attrs:{axis:i,keepDims:!1}}),f=it({inputs:{x:m},backend:e,attrs:{shape:u}}),d=kk({inputs:{a:p,b:f},backend:e});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}var F3={kernelName:Rs,backendName:\"webgl\",kernelFunc:_k};function oot(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n,u=a?o:_k({inputs:{logits:o},backend:e,attrs:{dim:o.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new GC(l,c,s),m=[[i]],f=e.runWebGLProgram(p,[u],\"int32\",m);return a||e.disposeIntermediateTensorInfo(u),f}var O3={kernelName:Ap,backendName:\"webgl\",kernelFunc:oot};var sot=xr+`\n return -x;\n`,iot=`\n vec4 result = -x;\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`;function aot(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])){let s=e.texData.get(n.dataId),[i,a]=DL(s.values,n.shape,n.dtype);return e.makeTensorInfo(a,n.dtype,i)}let o;return M().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?o=new _n(n.shape,iot):o=new Mr(n.shape,sot),e.runWebGLProgram(o,[n],n.dtype)}var P3={kernelName:fi,backendName:\"webgl\",kernelFunc:aot};var lot=qr.nonMaxSuppressionV3Impl;function uot(r){S.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),{selectedIndices:p}=lot(l,c,i,a,u);return e.makeTensorInfo([p.length],\"int32\",new Int32Array(p))}var L3={kernelName:Pa,backendName:\"webgl\",kernelFunc:uot};var cot=qr.nonMaxSuppressionV4Impl;function pot(r){S.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,padToMaxOutputSize:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=cot(c,p,i,a,u,l);return[e.makeTensorInfo([m.length],\"int32\",new Int32Array(m)),e.makeTensorInfo([],\"int32\",new Int32Array([f]))]}var M3={kernelName:La,backendName:\"webgl\",kernelFunc:pot};var mot=qr.nonMaxSuppressionV5Impl;function fot(r){S.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,softNmsSigma:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),m=i,f=a,d=u,h=l,{selectedIndices:g,selectedScores:x}=mot(c,p,m,f,d,h);return[e.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),e.makeTensorInfo([x.length],\"float32\",new Float32Array(x))]}var z3={kernelName:Ma,backendName:\"webgl\",kernelFunc:fot};var WC=class{constructor(t,e,n,o){this.variableNames=[\"indices\"],this.outputShape=[t,e],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int index = round(getIndices(coords.x));\n setOutput(mix(float(${o}), float(${n}),\n float(index == coords.y)));\n }\n `}};var dot=r=>{let{inputs:t,backend:e,attrs:n}=r,{indices:o}=t,{dtype:s,depth:i,onValue:a,offValue:u}=n,l=y.sizeFromShape(o.shape),c=new WC(l,i,a,u),p=it({inputs:{x:o},backend:e,attrs:{shape:[l]}}),m=e.runWebGLProgram(c,[p],s);e.disposeIntermediateTensorInfo(p);let f=[...o.shape,i],d=it({inputs:{x:m},backend:e,attrs:{shape:f}});return e.disposeIntermediateTensorInfo(m),d},B3={kernelName:gs,backendName:\"webgl\",kernelFunc:dot};function ag(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype===\"complex64\"){let o=vl({inputs:{input:n},backend:e}),s=ag({inputs:{x:o},backend:e}),i=qc({inputs:{input:n},backend:e}),a=ag({inputs:{x:i},backend:e}),u=An({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Sl({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype===\"string\"?\"\":0},backend:e})}var V3={kernelName:Ii,backendName:\"webgl\",kernelFunc:ag};function G3(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype===\"string\")throw new Error(\"onesLike is not supported under string 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a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var U3={kernelName:hi,backendName:\"webgl\",kernelFunc:hot};var UC=class{constructor(t,e,n){this.variableNames=[\"x\"],this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=e.map((l,c)=>l[0]+t[c]+l[1]);let o=t.length,s=Vt(o),i=e.map(l=>l[0]).join(\",\"),a=e.map((l,c)=>l[0]+t[c]).join(\",\"),u=[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,o);if(o===1){this.userCode=`\n int start = ${i};\n int end = ${a};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start || outC >= end) {\n setOutput(value);\n } else {\n setOutput(getX(outC - start));\n }\n }\n `;return}this.userCode=`\n ${s} start = ${s}(${i});\n ${s} end = ${s}(${a});\n\n void main() {\n ${s} outC = getOutputCoords();\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n setOutput(value);\n } else {\n ${s} coords = outC - start;\n setOutput(getX(${u}));\n }\n }\n `}};var HC=class{constructor(t,e,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=Vt(o),i=e.map(h=>h[0]).join(\",\"),a=e.map((h,g)=>h[0]+t[g]).join(\",\"),u=er(\"rc\",o),l=er(\"source\",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?\"source\":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1;\n if(${c}) {\n `,o===1?\"\":`}\n rc = outputLoc;\n ${u[o-2]} += 1;\n if(${u[o-2]} < ${this.outputShape[o-2]}) {`,o===1?\"\":` ${u[o-1]} += 1;\n if(${c}) {`],f=o===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",d=\"\";for(let h=0,g=o===1?2:4;h{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(y.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Sl({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=M().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new HC(o.shape,s,i):new UC(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},H3={kernelName:xs,backendName:\"webgl\",kernelFunc:Ak};var got=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,xot=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = lessThan(floor(b), b);\n bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);\n `+ta+`\n return result;\n`,yot=ae({opSnippet:got,packedOpSnippet:xot}),q3={kernelName:ys,backendName:\"webgl\",kernelFunc:yot};function bot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=[],l=y.parseAxisParam(s,o.shape),c=l,p=S.getAxesPermutation(c,a),m=o;p!=null&&(m=Pe({inputs:{x:o},backend:e,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,a),u.push(m)),S.assertAxesAreInnerMostDims(\"prod\",c,a);let f;if(e.shouldExecuteOnCPU([m])){let d=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=FL(m.shape,m.dtype,d,c);f=e.makeTensorInfo(g,x,h)}else{let[d,h]=S.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=it({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=Bu(o.dtype),w=Hn(x,b,\"prod\",e);f=it({inputs:{x:w},backend:e,attrs:{shape:d}}),u.push(x),u.push(w)}if(i){u.push(f);let d=S.expandShapeToKeepDim(f.shape,l);f=it({inputs:{x:f},backend:e,attrs:{shape:d}})}return u.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var K3={kernelName:ws,backendName:\"webgl\",kernelFunc:bot};function wot(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=OL(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],\"int32\",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var j3={kernelName:$p,backendName:\"webgl\",kernelFunc:wot};function Cot(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=PL(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],\"int32\",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var X3={kernelName:Dp,backendName:\"webgl\",kernelFunc:Cot};function Iot(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),p=e.readSync(i.dataId),m=a.map(g=>e.readSync(g.dataId)),f=a.map(g=>g.shape),[d,h]=LL(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var Y3={kernelName:Rp,backendName:\"webgl\",kernelFunc:Iot};var $k=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=ML(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},Z3={kernelName:Ll,backendName:\"webgl\",kernelFunc:$k};var vot=\"return 1.0 / x;\",Sot=wt({opSnippet:vot}),J3={kernelName:Cs,backendName:\"webgl\",kernelFunc:Sot};var Not=xr+`\n return (x < 0.0) ? 0.0 : x;\n`,Tot=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,kot=wt({opSnippet:Not,packedOpSnippet:Tot}),Q3={kernelName:Is,backendName:\"webgl\",kernelFunc:kot};var Eot=xr+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,_ot=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Aot=wt({opSnippet:Eot,packedOpSnippet:_ot}),tB={kernelName:Ns,backendName:\"webgl\",kernelFunc:Aot};var qC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/p[0]},\n ${c[1]/p[1]});\n const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var KC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/p[0]},\n ${c[1]/p[1]},\n ${c[1]/p[1]});\n const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,\n ${u}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${l-1};\n bool hasNextRow = coords.z < ${n-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function $ot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=M().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new KC(o.shape,u,l,s,i):new qC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],\"float32\")}var eB={kernelName:Ss,backendName:\"webgl\",kernelFunc:$ot};var jC=class{constructor(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${p});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${f});\n\n const int winHeight = int(${d});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${a}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${o-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function Dot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new jC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var rB={kernelName:Pp,backendName:\"webgl\",kernelFunc:Dot};var XC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?\"0.5\":\"0.0\",f;s?f=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":f=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/p[0]},\n ${c[1]/p[1]});\n const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${f};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var YC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?\"0.5\":\"0.0\",f;s?f=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":f=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/p[0]},\n ${c[1]/p[1]},\n ${c[1]/p[1]});\n const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,\n ${u}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${f};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${l-1};\n bool hasNextRow = coords.z < ${n-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function Rot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=M().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new YC(o.shape,u,l,s,i):new XC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var nB={kernelName:vs,backendName:\"webgl\",kernelFunc:Rot};var ZC=class{constructor(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${p});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${f});\n\n const int winHeight = int(${d});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${a}) {\n continue;\n }\n\n float sourceFracRow =\n float(${u[0]}) *\n (float(dyR) / float(${l[0]}));\n\n float sourceFracCol =\n float(${u[1]}) *\n (float(dyC) / float(${l[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${o}) - 1),\n ${n} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${n} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function Fot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new ZC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var oB={kernelName:Op,backendName:\"webgl\",kernelFunc:Fot};var JC=class{constructor(t,e){this.variableNames=[\"x\"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${t[0]} - coord - 1));\n }\n `;return}let o=a=>e.indexOf(a)!==-1&&t[a]!==1?`${t[a]} - coords[${a}] - 1`:`coords[${a}]`,s=t.map((a,u)=>o(u)).join(\",\"),i=Vt(n);this.userCode=`\n void main() {\n ${i} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var QC=class{constructor(t,e){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=er(\"rc\",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=Vt(n);n===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${t[0]} - rc - 1),\n ${t[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${t[0]} - (rc + 1) - 1),\n ${t[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${a} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${u(o.slice())};\n if(${s}){\n result.g = ${l(o.slice())};\n }\n if(${i}) {\n result.b = ${c(o.slice())};\n if(${s}) {\n result.a = ${p(o.slice())};\n }\n }\n setOutput(result);\n }\n `;function u(d){return m(d)}function l(d){return d[n-1]=\"(\"+d[n-1]+\" + 1)\",m(d)}function c(d){return d[n-2]=\"(\"+d[n-2]+\" + 1)\",m(d)}function p(d){return d[n-1]=\"(\"+d[n-1]+\" + 1)\",d[n-2]=\"(\"+d[n-2]+\" + 1)\",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function Oot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=o.shape.length,a=y.parseAxisParam(s,o.shape);if(i===0)return rr({inputs:{x:o},backend:e});let u=M().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new QC(o.shape,a):new JC(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var sB={kernelName:Ts,backendName:\"webgl\",kernelFunc:Oot};var tI=class{constructor(t,e){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let n=t[1],o=t[2];this.outputShape=t;let s=\"\";typeof e==\"number\"?s=`float outputValue = ${e.toFixed(2)};`:s=`\n vec3 fill = vec3(${e.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${n}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var iB={kernelName:ja,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new tI(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var Pot=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n`,Lot=wt({opSnippet:Pot}),aB={kernelName:ks,backendName:\"webgl\",kernelFunc:Lot};var Mot=\"return inversesqrt(x);\",zot=wt({opSnippet:Mot,cpuKernelImpl:zL}),lB={kernelName:Es,backendName:\"webgl\",kernelFunc:zot};var Rd=class{constructor(t,e,n,o,s,i,a=!0){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=i;let u=Vt(s.length),l=Vt(i.length),c=\"\";n===1?c=\"i\":n===2&&(c=\"i, j\");let p=`getIndices(${c})`,m=\"\";o===1?m=\"i\":o===2&&(m=\"i, coords[1]\");let f=`getUpdates(${m})`,d=e>1?\"strides[j]\":\"strides\";this.userCode=`\n ${u} strides = ${u}(${s});\n\n void main() {\n ${l} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${t}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${e}; j++) {\n int index = round(${p});\n flattenedIndex += index * ${d};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${f};\n found = true;\n }\n }\n setOutput(mix(getDefaultValue(), sum, float(found)));\n }\n `}};function Bot(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=[p/l,l];if(p===0)return e.makeTensorInfo(i,o.dtype);let f=it({inputs:{x:o},backend:e,attrs:{shape:[u,a]}}),d=it({inputs:{x:s},backend:e,attrs:{shape:[u,l]}}),h=e.makeTensorInfo([],\"float32\",new Float32Array([0])),g=new Rd(u,a,f.shape.length,d.shape.length,c,m),x=e.runWebGLProgram(g,[d,f,h],d.dtype),b=it({inputs:{x},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var uB={kernelName:za,backendName:\"webgl\",kernelFunc:Bot};var eI=class{constructor(t,e,n,o){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[t,n];let s=\"while (left < right) {\",i=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) 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supported`);if(n===1)s=\"resRC\",o=\"resRC\";else{let a=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],u=[],l=[];for(let c=0;c= 1.0) {\n setOutput(getA(${s}));\n } else {\n setOutput(getB(${s}));\n }\n }\n `}};function Got(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t,i=new rI(n.shape.length,o.shape,o.shape.length);return e.runWebGLProgram(i,[n,o,s],lr(o.dtype,s.dtype))}var pB={kernelName:xi,backendName:\"webgl\",kernelFunc:Got};var Wot=`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${S.SELU_SCALEALPHA};\n float scale = ${S.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,Uot=wt({opSnippet:Wot}),mB={kernelName:Ba,backendName:\"webgl\",kernelFunc:Uot};var Hot=Po+`\n return 1.0 / (1.0 + exp(-1.0 * x));\n`,qot=`\n vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Kot=wt({opSnippet:Hot,packedOpSnippet:qot,cpuKernelImpl:VL}),fB={kernelName:As,backendName:\"webgl\",kernelFunc:Kot};var jot=`\n if (isnan(x)) { return 0.0; }\n return sign(x);\n`,Xot=wt({opSnippet:jot}),dB={kernelName:Ga,backendName:\"webgl\",kernelFunc:Xot};var Yot=Po+`\n return sin(x);\n`,Zot=wt({opSnippet:Yot}),hB={kernelName:_s,backendName:\"webgl\",kernelFunc:Zot};var Jot=`\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`,Qot=wt({opSnippet:Jot}),gB={kernelName:Va,backendName:\"webgl\",kernelFunc:Qot};var tst=`\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n`,est=wt({opSnippet:tst}),xB={kernelName:Wa,backendName:\"webgl\",kernelFunc:est};var rst=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n;y.assert(o.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let a=s.reduce((x,b)=>x*b),u=[[0,0]];u.push(...i);for(let x=1+s.length;xe.disposeIntermediateTensorInfo(x)),g},yB={kernelName:bi,backendName:\"webgl\",kernelFunc:rst};function nst(r){let{inputs:t,backend:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n ${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw:\n ${o.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n ${i.shape}`);let a=e.readSync(n.dataId),u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=e.readSync(i.dataId)[0],[p,m,f,d,h]=WL(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],\"bool\",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var bB={kernelName:Ml,backendName:\"webgl\",kernelFunc:nst};function ost(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.readSync(o.dataId)),a=e.readSync(n.dataId),u=Array.from(e.readSync(s.dataId)),[l,c,p]=UL(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var wB={kernelName:Ua,backendName:\"webgl\",kernelFunc:ost};function sst(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=zw(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var CB={kernelName:zl,backendName:\"webgl\",kernelFunc:sst};function ist(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error(\"Data should be at 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e.disposeIntermediateTensorInfo(h),g}var vB={kernelName:Mp,backendName:\"webgl\",kernelFunc:ast};function lst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=o.shape.length,c=new Array(l).fill(0),p=o.shape.slice();return u.map(m=>{let f=[...p];f[a]=m;let d=ni({inputs:{x:o},backend:e,attrs:{begin:c,size:f}});return c[a]+=m,d})}var SB={kernelName:wi,backendName:\"webgl\",kernelFunc:lst};var NB=\"return sqrt(x);\",ust=wt({opSnippet:NB,packedOpSnippet:NB,cpuKernelImpl:HL}),TB={kernelName:$s,backendName:\"webgl\",kernelFunc:ust};var cst=\"return x * x;\",pst=wt({opSnippet:cst}),kB={kernelName:Vl,backendName:\"webgl\",kernelFunc:pst};var EB=\"return (a - b) * (a - b);\",mst=ae({opSnippet:EB,packedOpSnippet:EB}),_B={kernelName:Fs,backendName:\"webgl\",kernelFunc:mst};function fst({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=xr+`\n return x > 0.0 ? 1.0 : float(${t.alpha});\n `,s=new Mr(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var AB={kernelName:mo,backendName:\"webgl\",kernelFunc:fst};var nI=class{constructor(t,e,n){this.variableNames=[\"x\"],this.outputShape=n;let o=n.length,s=Vt(n.length),i=Vt(n.length),a=\"\";if(o===1)a=\"coords * strides + begin\";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${u-1}] * strides[${c}] + begin[${c}]`)).join(\",\")}this.userCode=`\n ${s} begin = ${s}(${t});\n ${s} strides = ${s}(${e});\n\n void main() {\n ${i} coords = getOutputCoords();\n setOutput(getX(${a}));\n }\n `}};function dst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:C}=ze.sliceInfo(o.shape,s,i,a,u,l,c,p,m),N;if(h)N=it({inputs:{x:o},backend:e,attrs:{shape:d}});else if(g||x){y.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: 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tan(x);\",bst=wt({opSnippet:yst}),OB={kernelName:Ps,backendName:\"webgl\",kernelFunc:bst};var wst=`\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,Cst=wt({opSnippet:wst}),PB={kernelName:Ls,backendName:\"webgl\",kernelFunc:Cst};var oI=class{constructor(t,e){this.variableNames=[\"A\"];let n=new Array(t.length);for(let i=0;i5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${r[0]})`;let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],n=[];for(let o=0;o5){let u=e.readSync(o.dataId),l=o.dtype===\"string\"?u.map(m=>y.decodeString(m)):u,c=bt(o.shape,o.dtype,l),p=ZL(c,s);return e.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new oI(o.shape,s);return e.runWebGLProgram(i,[o],o.dtype)}var LB={kernelName:Qn,backendName:\"webgl\",kernelFunc:Dk};var sI=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=t,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. 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When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n `}};function jc(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function MB(r){let t=1;for(;tu){let P=e.readSync(o.dataId),[V,G]=JL(P,l,o.dtype,s,i);return[e.makeTensorInfo(V.shape,V.dtype,V.values),e.makeTensorInfo(G.shape,G.dtype,G.values)]}if(s===0)return l[l.length-1]=0,[e.makeTensorInfo(l,o.dtype,[]),e.makeTensorInfo(l,\"int32\",[])];if(c===1)return[o,Sl({attrs:{shape:l,dtype:\"int32\",value:0},backend:e})];let p=e.texData.get(o.dataId),m=p!==null&&p.isPacked,f=m?e.unpackTensor(o):o,h=y.sizeFromShape(l)/c,g=it({inputs:{x:f},attrs:{shape:[h,c]},backend:e});m&&jc(e,f);let x=MB(s),b=MB(c),w=null,C=()=>w===null?[g,g]:[g,w],N=(P,V,G)=>{let W=C(),q=new sI(G),K=[[c],[w===null?1:0],[Number.NEGATIVE_INFINITY],[P],[V]],X=w;w=e.runWebGLProgram(q,W,\"int32\",K),jc(e,X)};for(let P=1;P=1;G/=2)N(V,G,[h,b])}for(let P=b;P>x;P/=2){let V=C(),G=new iI([h,P/2]),q=[[c],[w===null?1:0],[x]],H=w;w=e.runWebGLProgram(G,V,\"int32\",q),jc(e,H);let K=x/2,X=K*2;for(let Z=K;Z>=1;Z/=2)N(X,Z,w.shape)}let _=w;w=ni({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),jc(e,_);let A=Nk({inputs:{x:g,indices:w},backend:e,attrs:{axis:1,batchDims:1}});jc(e,g);let $=l.slice(0,-1);$.push(s),_=w,w=it({inputs:{x:w},attrs:{shape:$},backend:e}),jc(e,_);let F=A;return A=it({inputs:{x:A},attrs:{shape:$},backend:e}),jc(e,F),[A,w]}var zB={kernelName:qa,backendName:\"webgl\",kernelFunc:vst};var aI=class{constructor(t,e,n,o,s,i){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=i;let a=n===\"nearest\"?1:2,u;switch(o){case\"constant\":u=1;break;case\"reflect\":u=2;break;case\"wrap\":u=3;break;case\"nearest\":u=4;break;default:u=1;break}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${u} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${u} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${u} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${t} && 0 <= coordX && coordX < ${e}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = 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valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}};function Sst(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,transforms:s}=t,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=n,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],x=new aI(p,m,i,a,u,g);return e.runWebGLProgram(x,[o,s],\"float32\")}var BB={kernelName:Ka,backendName:\"webgl\",kernelFunc:Sst};function Nst(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;ti(s,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let i=n.readSync(s.dataId),{outputValues:a,outputShape:u,indices:l}=QL(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],\"int32\",l)]}var VB={kernelName:zp,backendName:\"webgl\",kernelFunc:Nst};function Tst(r){let{inputs:t,backend:e,attrs:n}=r,{value:o}=t,{axis:s}=n;s<0&&(s+=o.shape.length);let i=o,a=i.shape.length,u=o.shape[s],l=new Array(a-1),c=0;for(let h=0;he.disposeIntermediateTensorInfo(h)),d}var GB={kernelName:Ci,backendName:\"webgl\",kernelFunc:Tst};var lI=class{constructor(t,e){this.variableNames=[\"x\",\"segmentIds\"];let n=t.windowSize,o=t.batchSize,s=t.inSize,i=t.numSegments,a=i*Math.ceil(s/n);this.outputShape=[o,a];let u=\"0.0\",l=\"sumValue\",c=Math.floor(n/4)*4,p=n%4,m=`\n sumValue += dot(values, segFilter);\n `,f=\"\";s%n>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let d=\"\";s%n>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${u};\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${d}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${i})) * float(${n}));\n int currentSeg = int(mod(float(outIdx), float(${i})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${p===1}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${l});\n }\n `}};function kst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,segmentIds:s}=t,{numSegments:i}=n,a=o.shape.length,u=[],l=0,c=S.getAxesPermutation([l],a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,a)[0]);let m=S.segment_util.computeOutShape(p.shape,l,i),f=y.sizeFromShape([p.shape[l]]),d=it({inputs:{x:p},backend:e,attrs:{shape:[-1,f]}});u.push(d);let h=Bu(o.dtype),g=(C,N,_,A,$)=>{let F=C.shape[0],P=C.shape[1],V=S.segment_util.segOpComputeOptimalWindowSize(P,$),G={windowSize:V,inSize:P,batchSize:F,numSegments:$},W=new lI(G,N),q=e.compileAndRun(W,[C,_],A);if(u.push(q),q.shape[1]===$)return q;let H=$k({backend:e,attrs:{start:0,stop:$,step:1,dtype:\"float32\"}}),K=Dk({inputs:{x:H},backend:e,attrs:{reps:[P/V]}});return u.push(H),u.push(K),g(q,N,K,A,$)},x=g(d,\"unsortedSegmentSum\",s,h,i),b=it({inputs:{x},backend:e,attrs:{shape:m}}),w=b;if(c!=null){u.push(b);let C=S.getUndoAxesPermutation(c);w=Pe({inputs:{x:w},backend:e,attrs:{perm:C}})}return 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ku;(function(r){r[r.linear=0]=\"linear\",r[r.relu=1]=\"relu\",r[r.relu6=2]=\"relu6\",r[r.prelu=3]=\"prelu\",r[r.leakyrelu=4]=\"leakyrelu\",r[r.sigmoid=5]=\"sigmoid\",r[r.elu=6]=\"elu\"})(ku||(ku={}));var UB;function _st(r){UB=r.wasm.cwrap(vi,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Ast(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t;if(o.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n,m=e.dataIdMap.get(o.dataId).id,f=e.dataIdMap.get(s.dataId).id,d=0;if(i!=null){let $=e.dataIdMap.get(i.dataId);if($.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${$.shape.length}.`);d=$.id}let h=a==null?0:e.dataIdMap.get(a.dataId).id,g=ku[c];if(g==null)throw new Error(`${c} 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o(i){n=i.wasm.cwrap(r,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function s(i){let{backend:a,inputs:u}=i,{a:l,b:c}=u,p=a.dataIdMap.get(l.dataId).id,m=a.dataIdMap.get(c.dataId).id,f=e!=null?e:l.dtype,d=S.assertAndGetBroadcastShape(l.shape,c.shape),h=a.makeOutput(d,f);if(y.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(l.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=a.dataIdMap.get(h.dataId).id;return(()=>n(p,g,l.shape.length,m,x,c.shape.length,Kt[l.dtype],b))(),h}return{kernelName:r,backendName:\"wasm\",setupFunc:o,kernelFunc:s}}var $st=!0,KB=le(Jn,$st);var jB;function Dst(r){jB=r.wasm.cwrap(Vo,null,[\"array\",\"number\",\"number\",\"number\"])}function Rst(r){let{inputs:t,backend:e}=r,n=e.makeOutput(t[0].shape,t[0].dtype);if(y.sizeFromShape(n.shape)===0)return n;let o=t.map(a=>e.dataIdMap.get(a.dataId).id),s=new Uint8Array(new Int32Array(o).buffer),i=e.dataIdMap.get(n.dataId).id;return jB(s,o.length,Kt[n.dtype],i),n}var XB={kernelName:Vo,backendName:\"wasm\",setupFunc:Dst,kernelFunc:Rst};function Xc(r){let{inputs:{x:t},backend:e}=r;if(t.dtype===\"string\")return or(e.readSync(t.dataId),t.shape,t.dtype);let n=e.makeOutput(t.shape,t.dtype),o=e.typedArrayFromHeap(t);return e.typedArrayFromHeap(n).set(o),n}var YB={kernelName:po,backendName:\"wasm\",kernelFunc:Xc};var ZB;function Fst(r){ZB=r.wasm.cwrap(to,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function lo(r){let{inputs:t,backend:e,attrs:n}=r,[o,s]=Pst(t.x.shape,n.perm),i=!0;for(let d=0;d=o&&(s===-1||n[s]>n[i])&&(s=i);n[s]=o}return[e,n]}var JB={kernelName:to,backendName:\"wasm\",kernelFunc:lo,setupFunc:Fst};function Cn(r,t,e){let n=r.shape,o=r.shape.length,s=y.parseAxisParam(t,n),i=s,a=S.getAxesPermutation(i,o),u=null,l=!1;if(a!=null){let c=new Array(o);for(let f=0;f`new shape: ${i}, old shape: ${n.shape}. 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Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. 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Please use 'NHWC'.`);let rt=n.makeOutput(h.outShape,\"float32\"),ot=n.dataIdMap.get(rt.dataId).id,at=a==null?0:n.dataIdMap.get(a.dataId).id;return UV(x,X,Z,et,b,N,_,C,A,$,F,P,K,V,G,W,q,H,w,g,at,d||0,ot),rt}var HV={kernelName:Si,backendName:\"wasm\",setupFunc:bit,kernelFunc:wit};var qV;function Cit(r){qV=r.wasm.cwrap(Ni,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Iit(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m,!0),g=ku[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,C=0;if(i!=null){let nt=n.dataIdMap.get(i.dataId);if(nt.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${nt.shape.length}.`);if(nt.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${nt.shape}) does not match the number of output channels (${w})`);C=nt.id}let N=h.filterHeight,_=h.filterWidth,A=h.padInfo.top,$=h.padInfo.right,F=h.padInfo.bottom,P=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,K=h.padInfo.type===\"SAME\"?1:0,X=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let rt=n.makeOutput(h.outShape,\"float32\"),ot=n.dataIdMap.get(rt.dataId).id,at=a==null?0:n.dataIdMap.get(a.dataId).id;return qV(x,X,Z,et,b,N,_,C,A,$,F,P,K,V,G,W,q,H,w,g,at,d||0,ot),rt}var KV={kernelName:Ni,backendName:\"wasm\",setupFunc:Cit,kernelFunc:Iit};var jV;function vit(r){jV=r.wasm.cwrap(Sa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Sit(r){let{backend:t,inputs:e}=r,{params:n,indices:o}=e,[s,i,a,u]=xy.prepareAndValidate(n,o),l=t.makeOutput(s,n.dtype);if(i===0)return l;let c=o.shape,p=c[c.length-1],f=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(o.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),x=t.dataIdMap.get(l.dataId).id;return jV(f,Kt[n.dtype],h,i,p,a,g,x),l}var XV={kernelName:Sa,backendName:\"wasm\",setupFunc:vit,kernelFunc:Sit};var YV;function Nit(r){YV=r.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Tit(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,indices:s}=e,{axis:i,batchDims:a}=n,u=y.parseAxisParam(i,o.shape)[0],l=t.readSync(s.dataId),c=o.shape[u];for(let F=0;F=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),m=cr({inputs:{x:o},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),f=y.sizeFromShape(s.shape),d=cr({inputs:{x:s},attrs:{shape:[p.batchSize,f/p.batchSize]},backend:t}),h=[p.batchSize,p.outerSize,f/p.batchSize,p.sliceSize],g=t.makeOutput(h,o.dtype);if(y.sizeFromShape(o.shape)===0)return g;let x=m.shape.length-1,w=t.dataIdMap.get(m.dataId).id,N=t.dataIdMap.get(d.dataId).id,_=t.dataIdMap.get(g.dataId).id,A=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),$=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return YV(w,Kt[o.dtype],A,x,N,p.batchSize,$,_),t.disposeData(m.dataId),t.disposeData(d.dataId),g.shape=p.outputShape,g}var ZV={kernelName:mi,backendName:\"wasm\",setupFunc:Nit,kernelFunc:Tit};var kit=!1,JV=le(Na,kit,\"bool\");var Eit=!1,QV=le(os,Eit,\"bool\");var tG=te(ss,\"bool\");var eG;function _it(r){eG=r.wasm.cwrap(is,null,[\"number\",\"number\",\"number\",\"number\"])}function Ait(r){let{inputs:{x:t},attrs:{alpha:e},backend:n}=r,o=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,\"float32\");if(y.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;eG(o,Kt[t.dtype],e,i)}return s}var rG={kernelName:is,backendName:\"wasm\",setupFunc:_it,kernelFunc:Ait};var $it=!1,nG=le(Ea,$it,\"bool\");var Dit=!1,oG=le(_a,Dit,\"bool\");var sG=te(as);var Rit=!1,iG=le($a,Rit,\"bool\");var aG=te(Da);var Fit=!1,lG=le(Ra,Fit,\"bool\");var Oit=!1,uG=le(m1,Oit,\"bool\");var cG;function Pit(r){cG=r.wasm.cwrap(ls,null,[\"number\",\"number\",\"number\",\"number\"])}function Lit(r){let{backend:t,inputs:e,attrs:n}=r,{reductionIndices:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims(\"max\",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;cG(u,Kt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var pG={kernelName:ls,backendName:\"wasm\",setupFunc:Pit,kernelFunc:Lit};var Mit=!1,mG=le(us,Mit);var fG;function zit(r){fG=r.wasm.cwrap(cs,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Bit(r){let{inputs:t,attrs:e,backend:n}=r,o=t.x,s=n.dataIdMap.get(o.dataId).id;y.assert(o.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. 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Greater,os as GreaterEqual,Ly as History,vp as IFFT,po as Identity,Sp as Imag,be as InputSpec,Ta as IsFinite,ka as IsInf,ss as IsNan,zo as KernelBackend,Ol as LRN,Tp as LRNGrad,vh as LayerVariable,Vn as LayersModel,is as LeakyRelu,Ea as Less,_a as LessEqual,Np as LinSpace,as as Log,Aa as Log1p,f1 as LogSoftmax,$a as LogicalAnd,Da as LogicalNot,Ra as LogicalOr,m1 as LogicalXor,Nlt as LowerBound,Su as MathBackendWebGL,ls as Max,cs as MaxPool,Pl as MaxPool3D,Ep as MaxPool3DGrad,kp as MaxPoolGrad,_p as MaxPoolWithArgmax,us as Maximum,ps as Mean,ms as Min,fs as Minimum,ds as MirrorPad,Fa as Mod,sc as MomentumOptimizer,Ap as Multinomial,hs as Multiply,fi as Neg,Pa as NonMaxSuppressionV3,La as NonMaxSuppressionV4,Ma as NonMaxSuppressionV5,Oa as NotEqual,A0 as OP_SCOPE_SUFFIX,gs as OneHot,di as OnesLike,Hr as Optimizer,xh as OptimizerConstructors,hi as Pack,xs as PadV2,Tlt as Pool,ys as Pow,bs as Prelu,ws as Prod,ic as RMSPropOptimizer,kn as RNN,$p as RaggedGather,Dp as RaggedRange,Rp as 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device_util,hE as diag,Ix as dilation2d,Bct as disableDeprecationWarnings,Nt as dispose,Vct as disposeVariables,ut as div,vx as divNoNan,Sx as dot,nS as dropout,xE as einsum,Pi as elu,zct as enableDebugMode,Mct as enableProdMode,oS as enclosingPowerOfTwo,Ln as engine,M as env,Dr as equal,Nx as erf,Tx as euclideanNorm,sr as exp,ir as expandDims,kx as expm1,Hu as eye,lu as fft,yo as fill,Kct as findBackend,jct as findBackendFactory,Li as floor,Zp as floorDiv,gM as forceHalfFloat,cu as fused,Mi as gather,Hj as gatherND,xy as gather_util,Hct as getBackend,m0 as getGradient,Zd as getKernel,Bg as getKernelsForBackend,mlt as getThreadsCount,ck as gpgpu_util,Bq as grad,Vq as grads,Fe as greater,un as greaterEqual,el as ifft,tu as imag,oo as image,jj as inTopKAsync,X$ as initializers,BS as input,Pr as io,ym as irfft,Ex as isFinite,_x as isInf,Ax as isNaN,De as keep,qr as kernel_impls,ED as layers,eu as leakyRelu,sm as less,Mn as lessEqual,iS as linalg,wE as linspace,q7 as loadGraphModel,K7 as loadGraphModelSync,hD as loadLayersModel,$x as localResponseNormalization,Nr as log,ru as log1p,Fx as logSigmoid,im as logSoftmax,am as logSumExp,Fr as logicalAnd,nu as logicalNot,lm as logicalOr,Ox as logicalXor,j6 as losses,CE as lowerBound,Mt as matMul,uA as math,Sr as max,ou as maxPool,Lx as maxPool3d,IE as maxPoolWithArgmax,Sn as maximum,Ne as mean,oh as memory,vE as meshgrid,_D as metrics,Ja as min,zi as minimum,Mx as mirrorPad,zx as mod,Y8 as model,AD as models,qu as moments,Mj as movingAverage,D as mul,SE as multiRNNCell,NE as multinomial,qt as neg,yh as nextFrame,Qa as norm,Gs as notEqual,Bi as oneHot,fr as ones,wr as onesLike,T as op,TE as outerProduct,pn as pad,kE as pad1d,EE as pad2d,_E as pad3d,AE as pad4d,Bx as pool,ln as pow,iu as prelu,Qg as print,Vx as prod,Gct as profile,$E as raggedGather,DE as raggedRange,RE as raggedTensorToTensor,FE as rand,e_ as randomGamma,Xu as randomNormal,r_ as randomStandardNormal,Vi as randomUniform,Gi as range,Uct as ready,tl as real,Kx as reciprocal,Yp as registerBackend,J8 as registerCallbackConstructor,h1 as registerGradient,Fu as registerKernel,ZZ as registerOp,$D as regularizers,Or as relu,um as relu6,qct as removeBackend,R as reshape,dr as reverse,n_ as reverse1d,o_ as reverse2d,s_ as reverse3d,i_ as reverse4d,uu as rfft,cm as round,pm as rsqrt,pt as scalar,Bj as scatterND,mh as scatter_util,uh as searchSorted,mm as selu,fm as separableConv2d,Z8 as sequential,Q as serialization,j4 as setBackend,Xct as setPlatform,plt as setThreadsCount,ult as setWasmPath,clt as setWasmPaths,ST as setWebGLContext,a_ as setdiff1dAsync,Qr as sigmoid,jx as sign,K6 as signal,dm as sin,hm as sinh,Rt as slice,gm as slice1d,ph as slice2d,xm as slice3d,Yu as slice4d,ze as slice_util,au as softmax,Vs as softplus,su as spaceToBatchND,X6 as sparse,Wj as sparseToDense,q6 as spectral,hr as split,Se as sqrt,zt as square,bm as squaredDifference,zn as squeeze,qe as stack,wo as step,Xx as stridedSlice,Y6 as string,lt as sub,mt as sum,Bu as sumOutType,Yx as tan,Di as tanh,or as tensor,Ke as tensor1d,Ws as tensor2d,Zx as tensor3d,l_ as tensor4d,u_ as tensor5d,c_ as tensor6d,xo as tensor_util,t_ as test_util,B as tidy,Rr as tile,Wct as time,Jx as topk,lc as train,Pt as transpose,wm as truncatedNormal,Qx as unique,Rlt as unregisterGradient,Dlt as unregisterKernel,Cm as unsortedSegmentSum,gr as unstack,lr as upcastType,p_ as upperBound,y as util,Gq as valueAndGrad,Wq as valueAndGrads,ty as variable,Dx as variableGrads,blt as version,pR as version_converter,CA as version_core,Hm as version_layers,flt as version_wasm,hM as version_webgl,g1e as webgl,gd as webgl_util,_e as where,ry as whereAsync,Te as zeros,It as zerosLike};\n", "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n ctx: CanvasRenderingContext2D,\n points: Point[],\n isClosed = false,\n) {\n ctx.beginPath();\n\n points.slice(1).forEach(({ x, y }, prevIdx) => {\n const from = points[prevIdx];\n ctx.moveTo(from.x, from.y);\n ctx.lineTo(x, y);\n });\n\n if (isClosed) {\n const from = points[points.length - 1];\n const to = points[0];\n if (!from || !to) {\n return;\n }\n\n ctx.moveTo(from.x, from.y);\n ctx.lineTo(to.x, to.y);\n }\n\n ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n return num % 2 === 0;\n}\n\nexport function round(num: number, prec = 2) {\n const f = 10 ** prec;\n return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n const scale = inputSize / Math.max(height, width);\n return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n width: number\n height: number\n}\n\nexport class Dimensions implements IDimensions {\n private _width: number;\n\n private _height: number;\n\n constructor(width: number, height: number) {\n if (!isValidNumber(width) || !isValidNumber(height)) {\n throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n }\n\n this._width = width;\n this._height = height;\n }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public reverse(): Dimensions {\n return new Dimensions(1 / this.width, 1 / this.height);\n }\n}\n", "export interface IPoint {\n x: number\n y: number\n}\n\nexport class Point implements IPoint {\n private _x: number;\n\n private _y: number;\n\n constructor(x: number, y: number) {\n this._x = x;\n this._y = y;\n }\n\n get x(): number { return this._x; }\n\n get y(): number { return this._y; }\n\n public add(pt: IPoint): Point {\n return new Point(this.x + pt.x, this.y + pt.y);\n }\n\n public sub(pt: IPoint): Point {\n return new Point(this.x - pt.x, this.y - pt.y);\n }\n\n public mul(pt: IPoint): Point {\n return new Point(this.x * pt.x, this.y * pt.y);\n }\n\n public div(pt: IPoint): Point {\n return new Point(this.x / pt.x, this.y / pt.y);\n }\n\n public abs(): Point {\n return new Point(Math.abs(this.x), Math.abs(this.y));\n }\n\n public magnitude(): number {\n return Math.sqrt((this.x ** 2) + (this.y ** 2));\n }\n\n public floor(): Point {\n return new Point(Math.floor(this.x), Math.floor(this.y));\n }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box implements IBoundingBox, IRect {\n public static isRect(rect: any): boolean {\n return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n }\n\n public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions = false) {\n if (!Box.isRect(box)) {\n throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n }\n\n if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n }\n }\n\n private _x: number;\n\n private _y: number;\n\n private _width: number;\n\n private _height: number;\n\n constructor(_box: IBoundingBox | IRect, allowNegativeDimensions = true) {\n const box = (_box || {}) as any;\n\n const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n if (!isRect && !isBbox) {\n throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n }\n\n const [x, y, width, height] = isRect\n ? [box.x, box.y, box.width, box.height]\n : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n Box.assertIsValidBox({\n x, y, width, height,\n }, 'Box.constructor', allowNegativeDimensions);\n\n this._x = x;\n this._y = y;\n this._width = width;\n this._height = height;\n }\n\n public get x(): number { return this._x; }\n\n public get y(): number { return this._y; }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public get left(): number { return this.x; }\n\n public get top(): number { return this.y; }\n\n public get right(): number { return this.x + this.width; }\n\n public get bottom(): number { return this.y + this.height; }\n\n public get area(): number { return this.width * this.height; }\n\n public get topLeft(): Point { return new Point(this.left, this.top); }\n\n public get topRight(): Point { return new Point(this.right, this.top); }\n\n public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n public round(): Box {\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n .map((val) => Math.round(val));\n return new Box({\n x, y, width, height,\n });\n }\n\n public floor(): Box {\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n .map((val) => Math.floor(val));\n return new Box({\n x, y, width, height,\n });\n }\n\n public toSquare(): Box {\n let {\n x, y, width, height,\n } = this;\n const diff = Math.abs(width - height);\n if (width < height) {\n x -= (diff / 2);\n width += diff;\n }\n if (height < width) {\n y -= (diff / 2);\n height += diff;\n }\n\n return new Box({ x, y, width, height });\n }\n\n public rescale(s: IDimensions | number): Box {\n const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n return new Box({\n x: this.x * scaleX,\n y: this.y * scaleY,\n width: this.width * scaleX,\n height: this.height * scaleY,\n });\n }\n\n public pad(padX: number, padY: number): Box {\n const [x, y, width, height] = [\n this.x - (padX / 2),\n this.y - (padY / 2),\n this.width + padX,\n this.height + padY,\n ];\n return new Box({ x, y, width, height });\n }\n\n public clipAtImageBorders(imgWidth: number, imgHeight: number): Box {\n const { x, y, right, bottom } = this;\n const clippedX = Math.max(x, 0);\n const clippedY = Math.max(y, 0);\n\n const newWidth = right - clippedX;\n const newHeight = bottom - clippedY;\n const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight })).floor();\n }\n\n public shift(sx: number, sy: number): Box {\n const { width, height } = this;\n const x = this.x + sx;\n const y = this.y + sy;\n\n return new Box({ x, y, width, height });\n }\n\n public padAtBorders(imageHeight: number, imageWidth: number) {\n const w = this.width + 1;\n const h = this.height + 1;\n\n const dx = 1;\n const dy = 1;\n let edx = w;\n let edy = h;\n\n let x = this.left;\n let y = this.top;\n let ex = this.right;\n let ey = this.bottom;\n\n if (ex > imageWidth) {\n edx = -ex + imageWidth + w;\n ex = imageWidth;\n }\n if (ey > imageHeight) {\n edy = -ey + imageHeight + h;\n ey = imageHeight;\n }\n if (x < 1) {\n edy = 2 - x;\n x = 1;\n }\n if (y < 1) {\n edy = 2 - y;\n y = 1;\n }\n\n return { dy, edy, dx, edx, y, ey, x, ex, w, h };\n }\n\n public calibrate(region: Box) {\n return new Box({\n left: this.left + (region.left * this.width),\n top: this.top + (region.top * this.height),\n right: this.right + (region.right * this.width),\n bottom: this.bottom + (region.bottom * this.height),\n }).toSquare().round();\n }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n left: number\n top: number\n right: number\n bottom: number\n}\n\nexport class BoundingBox extends Box implements IBoundingBox {\n constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions = false) {\n super({ left, top, right, bottom }, allowNegativeDimensions);\n }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n private _score: number;\n\n private _classScore: number;\n\n private _className: string;\n\n private _box: Rect;\n\n private _imageDims: Dimensions;\n\n constructor(\n score: number,\n classScore: number,\n className: string,\n relativeBox: IRect,\n imageDims: IDimensions,\n ) {\n this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n this._score = score;\n this._classScore = classScore;\n this._className = className;\n this._box = new Box(relativeBox).rescale(this._imageDims);\n }\n\n public get score(): number { return this._score; }\n\n public get classScore(): number { return this._classScore; }\n\n public get className(): string { return this._className; }\n\n public get box(): Box { return this._box; }\n\n public get imageDims(): Dimensions { return this._imageDims; }\n\n public get imageWidth(): number { return this.imageDims.width; }\n\n public get imageHeight(): number { return this.imageDims.height; }\n\n public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n public forSize(width: number, height: number): ObjectDetection {\n return new ObjectDetection(\n this.score,\n this.classScore,\n this.className,\n this.relativeBox,\n { width, height },\n );\n }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n score: number\n box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n constructor(\n score: number,\n relativeBox: Rect,\n imageDims: IDimensions,\n ) {\n super(score, score, '', relativeBox, imageDims);\n }\n\n public override forSize(width: number, height: number): FaceDetection {\n const { score, relativeBox, imageDims } = super.forSize(width, height);\n return new FaceDetection(score, relativeBox, imageDims);\n }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU = true) {\n const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n const interSection = width * height;\n\n return isIOU\n ? interSection / (box1.area + box2.area - interSection)\n : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n const xs = pts.map((pt) => pt.x);\n const ys = pts.map((pt) => pt.y);\n const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n boxes: Box[],\n scores: number[],\n iouThreshold: number,\n isIOU = true,\n): number[] {\n let indicesSortedByScore = scores\n .map((score, boxIndex) => ({ score, boxIndex }))\n .sort((c1, c2) => c1.score - c2.score)\n .map((c) => c.boxIndex);\n\n const pick: number[] = [];\n\n while (indicesSortedByScore.length > 0) {\n const curr = indicesSortedByScore.pop() as number;\n pick.push(curr);\n\n const indices = indicesSortedByScore;\n\n const outputs: number[] = [];\n for (let i = 0; i < indices.length; i++) {\n const idx = indices[i];\n\n const currBox = boxes[curr];\n const idxBox = boxes[idx];\n\n outputs.push(iou(currBox, idxBox, isIOU));\n }\n\n indicesSortedByScore = indicesSortedByScore.filter(\n (_, j) => outputs[j] <= iouThreshold,\n );\n }\n\n return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n return tf.tidy(() => {\n const [r, g, b] = meanRgb;\n const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n return tf.sub(x, avg_rgb);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(imgTensor: tf.Tensor4D, isCenterImage = false): tf.Tensor4D {\n return tf.tidy(() => {\n const [height, width] = imgTensor.shape.slice(1);\n if (height === width) return imgTensor;\n const dimDiff = Math.abs(height - width);\n const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n const paddingAxis = height > width ? 2 : 1;\n const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n const paddingTensorShape = imgTensor.shape.slice();\n paddingTensorShape[paddingAxis] = paddingAmountLocal;\n return tf.fill(paddingTensorShape, 0, 'float32');\n };\n const paddingTensorAppend = createPaddingTensor(paddingAmount);\n const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n const paddingTensorPrepend = isCenterImage && remainingPaddingAmount ? createPaddingTensor(remainingPaddingAmount) : null;\n const tensorsToStack = [paddingTensorPrepend, imgTensor, paddingTensorAppend]\n .filter((t) => !!t)\n .map((t) => tf.cast(t as tf.Tensor4D, 'float32')) as tf.Tensor4D[];\n return tf.concat(tensorsToStack, paddingAxis);\n });\n}\n", "export function shuffleArray(inputArray: any[]) {\n const array = inputArray.slice();\n for (let i = array.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n const x = array[i];\n array[i] = array[j];\n array[j] = x;\n }\n return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n x: number\n y: number\n width: number\n height: number\n}\n\nexport class Rect extends Box implements IRect {\n constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions = false) {\n super({ x, y, width, height }, allowNegativeDimensions);\n }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n positions: Point[]\n shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n protected _shift: Point;\n\n protected _positions: Point[];\n\n protected _imgDims: Dimensions;\n\n constructor(\n relativeFaceLandmarkPositions: Point[],\n imgDims: IDimensions,\n shift: Point = new Point(0, 0),\n ) {\n const { width, height } = imgDims;\n this._imgDims = new Dimensions(width, height);\n this._shift = shift;\n this._positions = relativeFaceLandmarkPositions.map(\n (pt) => pt.mul(new Point(width, height)).add(shift),\n );\n }\n\n public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n public get imageWidth(): number { return this._imgDims.width; }\n\n public get imageHeight(): number { return this._imgDims.height; }\n\n public get positions(): Point[] { return this._positions; }\n\n public get relativePositions(): Point[] {\n return this._positions.map(\n (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n );\n }\n\n public forSize(width: number, height: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n { width, height },\n );\n }\n\n public shiftBy(x: number, y: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n this._imgDims,\n new Point(x, y),\n );\n }\n\n public shiftByPoint(pt: Point): T {\n return this.shiftBy(pt.x, pt.y);\n }\n\n /**\n * Aligns the face landmarks after face detection from the relative positions of the faces\n * bounding box, or it's current shift. This function should be used to align the face images\n * after face detection has been performed, before they are passed to the face recognition net.\n * This will make the computed face descriptor more accurate.\n *\n * @param detection (optional) The bounding box of the face or the face detection result. If\n * no argument was passed the position of the face landmarks are assumed to be relative to\n * it's current shift.\n * @returns The bounding box of the aligned face.\n */\n public align(\n detection?: FaceDetection | IRect | IBoundingBox | null,\n options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n ): Box {\n if (detection) {\n const box = detection instanceof FaceDetection\n ? detection.box.floor()\n : new Box(detection);\n\n return this.shiftBy(box.x, box.y).align(null, options);\n }\n\n const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n if (useDlibAlignment) {\n return this.alignDlib();\n }\n\n return this.alignMinBbox(minBoxPadding);\n }\n\n private alignDlib(): Box {\n const centers = this.getRefPointsForAlignment();\n\n const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n const size = Math.floor(eyeToMouthDist / relScale);\n\n const refPoint = getCenterPoint(centers);\n // TODO: pad in case rectangle is out of image bounds\n const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n }\n\n private alignMinBbox(padding: number): Box {\n const box = minBbox(this.positions);\n return box.pad(box.width * padding, box.height * padding);\n }\n\n protected getRefPointsForAlignment(): Point[] {\n throw new Error('getRefPointsForAlignment not implemented by base class');\n }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n protected override getRefPointsForAlignment(): Point[] {\n const pts = this.positions;\n return [\n pts[0],\n pts[1],\n getCenterPoint([pts[3], pts[4]]),\n ];\n }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n public getJawOutline(): Point[] {\n return this.positions.slice(0, 17);\n }\n\n public getLeftEyeBrow(): Point[] {\n return this.positions.slice(17, 22);\n }\n\n public getRightEyeBrow(): Point[] {\n return this.positions.slice(22, 27);\n }\n\n public getNose(): Point[] {\n return this.positions.slice(27, 36);\n }\n\n public getLeftEye(): Point[] {\n return this.positions.slice(36, 42);\n }\n\n public getRightEye(): Point[] {\n return this.positions.slice(42, 48);\n }\n\n public getMouth(): Point[] {\n return this.positions.slice(48, 68);\n }\n\n protected override getRefPointsForAlignment(): Point[] {\n return [\n this.getLeftEye(),\n this.getRightEye(),\n this.getMouth(),\n ].map(getCenterPoint);\n }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n label: string\n distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n private _label: string;\n private _distance: number;\n\n constructor(label: string, distance: number) {\n this._label = label;\n this._distance = distance;\n }\n\n public get label(): string { return this._label; }\n\n public get distance(): number { return this._distance; }\n\n public toString(withDistance = true): string {\n return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box {\n public static assertIsValidLabeledBox(box: any, callee: string) {\n Box.assertIsValidBox(box, callee);\n if (!isValidNumber(box.label)) {\n throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n }\n }\n\n private _label: number;\n\n constructor(box: IBoundingBox | IRect | any, label: number) {\n super(box);\n this._label = label;\n }\n\n public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n private _label: string;\n\n private _descriptors: Float32Array[];\n\n constructor(label: string, descriptors: Float32Array[]) {\n if (!(typeof label === 'string')) {\n throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n }\n\n if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n }\n\n this._label = label;\n this._descriptors = descriptors;\n }\n\n public get label(): string { return this._label; }\n\n public get descriptors(): Float32Array[] { return this._descriptors; }\n\n public toJSON(): any {\n return {\n label: this.label,\n descriptors: this.descriptors.map((d) => Array.from(d)),\n };\n }\n\n public static fromJSON(json: any): LabeledFaceDescriptors {\n const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n return new LabeledFaceDescriptors(json.label, descriptors);\n }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n public static assertIsValidPredictedBox(box: any, callee: string) {\n LabeledBox.assertIsValidLabeledBox(box, callee);\n\n if (\n !isValidProbablitiy(box.score)\n || !isValidProbablitiy(box.classScore)\n ) {\n throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n }\n }\n\n private _score: number;\n\n private _classScore: number;\n\n constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n super(box, label);\n this._score = score;\n this._classScore = classScore;\n }\n\n public get score(): number { return this._score; }\n\n public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection = TSource & {\n detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection(sourceObj: TSource, detection: FaceDetection): WithFaceDetection {\n const extension = { detection };\n return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n const fetch = window.fetch;\n if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n const readFile = () => {\n throw new Error('readFile - filesystem not available for browser environment');\n };\n\n return {\n Canvas: HTMLCanvasElement,\n CanvasRenderingContext2D,\n Image: HTMLImageElement,\n ImageData,\n Video: HTMLVideoElement,\n createCanvasElement: () => document.createElement('canvas'),\n createImageElement: () => document.createElement('img'),\n createVideoElement: () => document.createElement('video'),\n fetch,\n readFile,\n };\n}\n", "export function isNodejs(): boolean {\n return typeof global === 'object'\n && typeof process !== 'undefined'\n && process.versions != null\n && process.versions.node != null;\n}\n", "import { FileSystem } from './types';\nimport { isNodejs } from './isNodejs';\n\nexport function createFileSystem(fs?: any): FileSystem {\n let requireFsError = '';\n if (!fs && isNodejs()) {\n try {\n // eslint-disable-next-line global-require\n fs = require('fs');\n } catch (err) {\n requireFsError = (err as any).toString();\n }\n }\n\n const readFile = fs\n ? (filePath: string) => new Promise((resolve, reject) => { fs.readFile(filePath, (err: any, buffer) => (err ? reject(err) : resolve(buffer))); })\n : () => { throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`); };\n return { readFile };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n // eslint-disable-next-line dot-notation\n const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n const Image = global.Image || global.HTMLImageElement;\n // eslint-disable-next-line dot-notation\n const Video = global['Video'] || global.HTMLVideoElement;\n\n const createCanvasElement = () => {\n if (Canvas) return new Canvas();\n throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n };\n\n const createImageElement = () => {\n if (Image) return new Image();\n throw new Error('createImageElement - missing Image implementation for nodejs environment');\n };\n\n const createVideoElement = () => {\n if (Video) return new Video();\n throw new Error('createVideoElement - missing Video implementation for nodejs environment');\n };\n\n const fetch = global.fetch;\n // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n const fileSystem = createFileSystem();\n\n return {\n Canvas: Canvas || class {},\n CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n Image: Image || class {},\n ImageData: global.ImageData || class {},\n Video: global.HTMLVideoElement || class {},\n createCanvasElement,\n createImageElement,\n createVideoElement,\n fetch,\n ...fileSystem,\n };\n}\n", "export function isBrowser(): boolean {\n return typeof window === 'object'\n && typeof document !== 'undefined'\n && typeof HTMLImageElement !== 'undefined'\n && typeof HTMLCanvasElement !== 'undefined'\n && typeof HTMLVideoElement !== 'undefined'\n && typeof ImageData !== 'undefined'\n && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n if (!environment) {\n throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n }\n return environment;\n}\n\nfunction setEnv(env: Environment) {\n environment = env;\n}\n\nfunction initialize() {\n // check for isBrowser() first to prevent electron renderer process\n // to be initialized with wrong environment due to isNodejs() returning true\n if (isBrowser()) return setEnv(createBrowserEnv());\n if (isNodejs()) return setEnv(createNodejsEnv());\n return null;\n}\n\nfunction monkeyPatch(env: Partial) {\n if (!environment) {\n initialize();\n }\n\n if (!environment) {\n throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n }\n\n const { Canvas = environment.Canvas, Image = environment.Image } = env;\n environment.Canvas = Canvas;\n environment.Image = Image;\n environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n environment.createImageElement = env.createImageElement || (() => new Image());\n\n environment.ImageData = env.ImageData || environment.ImageData;\n environment.Video = env.Video || environment.Video;\n environment.fetch = env.fetch || environment.fetch;\n environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n getEnv,\n setEnv,\n initialize,\n createBrowserEnv,\n createFileSystem,\n createNodejsEnv,\n monkeyPatch,\n isBrowser,\n isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n if (!env.isNodejs() && typeof arg === 'string') {\n return document.getElementById(arg);\n }\n return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n\n if (canvasArg instanceof CanvasRenderingContext2D) {\n return canvasArg;\n }\n\n const canvas = resolveInput(canvasArg);\n\n if (!(canvas instanceof Canvas)) {\n throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n }\n\n const ctx = canvas.getContext('2d');\n if (!ctx) {\n throw new Error('resolveContext2d - canvas 2d context is null');\n }\n\n return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n // eslint-disable-next-line no-unused-vars\n TOP_LEFT = 'TOP_LEFT',\n // eslint-disable-next-line no-unused-vars\n TOP_RIGHT = 'TOP_RIGHT',\n // eslint-disable-next-line no-unused-vars\n BOTTOM_LEFT = 'BOTTOM_LEFT',\n // eslint-disable-next-line no-unused-vars\n BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n anchorPosition?: AnchorPosition\n backgroundColor?: string\n fontColor?: string\n fontSize?: number\n fontStyle?: string\n padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n public anchorPosition: AnchorPosition;\n\n public backgroundColor: string;\n\n public fontColor: string;\n\n public fontSize: number;\n\n public fontStyle: string;\n\n public padding: number;\n\n constructor(options: IDrawTextFieldOptions = {}) {\n const {\n anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n } = options;\n this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n this.fontSize = fontSize || 14;\n this.fontStyle = fontStyle || 'Georgia';\n this.padding = padding || 4;\n }\n}\n\nexport class DrawTextField {\n public text: string[];\n\n public anchor : IPoint;\n\n public options: DrawTextFieldOptions;\n\n constructor(\n text: string | string[] | DrawTextField,\n anchor: IPoint,\n options: IDrawTextFieldOptions = {},\n ) {\n // eslint-disable-next-line no-nested-ternary\n this.text = typeof text === 'string'\n ? [text]\n : (text instanceof DrawTextField ? text.text : text);\n this.anchor = anchor;\n this.options = new DrawTextFieldOptions(options);\n }\n\n measureWidth(ctx: CanvasRenderingContext2D): number {\n const { padding } = this.options;\n return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n }\n\n measureHeight(): number {\n const { fontSize, padding } = this.options;\n return this.text.length * fontSize + (2 * padding);\n }\n\n getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n const { anchorPosition } = this.options;\n const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n const textFieldWidth = this.measureWidth(ctx);\n const textFieldHeight = this.measureHeight();\n const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n // adjust anchor if text box exceeds canvas borders\n if (canvasDims) {\n const { width, height } = canvasDims;\n const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n return { x: newX, y: newY };\n }\n return { x, y };\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const canvas = resolveInput(canvasArg);\n const ctx = getContext2dOrThrow(canvas);\n\n const {\n backgroundColor, fontColor, fontSize, fontStyle, padding,\n } = this.options;\n\n ctx.font = `${fontSize}px ${fontStyle}`;\n const maxTextWidth = this.measureWidth(ctx);\n const textHeight = this.measureHeight();\n\n ctx.fillStyle = backgroundColor;\n const upperLeft = this.getUpperLeft(ctx, canvas);\n ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n ctx.fillStyle = fontColor;\n this.text.forEach((textLine, i) => {\n const x = padding + upperLeft.x;\n const y = padding + upperLeft.y + ((i + 1) * fontSize);\n ctx.fillText(textLine, x, y);\n });\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n boxColor?: string\n lineWidth?: number\n drawLabelOptions?: IDrawTextFieldOptions\n label?: string\n}\n\nexport class DrawBoxOptions {\n public boxColor: string;\n\n public lineWidth: number;\n\n public drawLabelOptions: DrawTextFieldOptions;\n\n public label?: string;\n\n constructor(options: IDrawBoxOptions = {}) {\n const {\n boxColor, lineWidth, label, drawLabelOptions,\n } = options;\n this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n this.lineWidth = lineWidth || 2;\n this.label = label;\n\n const defaultDrawLabelOptions = {\n anchorPosition: AnchorPosition.BOTTOM_LEFT,\n backgroundColor: this.boxColor,\n };\n this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n }\n}\n\nexport class DrawBox {\n public box: Box;\n\n public options: DrawBoxOptions;\n\n constructor(\n box: IBoundingBox | IRect,\n options: IDrawBoxOptions = {},\n ) {\n this.box = new Box(box);\n this.options = new DrawBoxOptions(options);\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const ctx = getContext2dOrThrow(canvasArg);\n\n const { boxColor, lineWidth } = this.options;\n\n const {\n x, y, width, height,\n } = this.box;\n ctx.strokeStyle = boxColor;\n ctx.lineWidth = lineWidth;\n ctx.strokeRect(x, y, width, height);\n\n const { label } = this.options;\n if (label) {\n new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n }\n }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n canvasArg: string | HTMLCanvasElement,\n detections: TDrawDetectionsInput | Array,\n) {\n const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n detectionsArray.forEach((det) => {\n // eslint-disable-next-line no-nested-ternary\n const score = det instanceof FaceDetection\n ? det.score\n : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n // eslint-disable-next-line no-nested-ternary\n const box = det instanceof FaceDetection\n ? det.box\n : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n const label = score ? `${round(score)}` : undefined;\n new DrawBox(box, { label }).draw(canvasArg);\n });\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n const { Image, Video } = env.getEnv();\n\n return (media instanceof Image && media.complete)\n || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n // eslint-disable-next-line consistent-return\n return new Promise((resolve, reject) => {\n if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) resolve(null);\n\n function onError(e: Event) {\n if (!e.currentTarget) return;\n // eslint-disable-next-line no-use-before-define\n e.currentTarget.removeEventListener('load', onLoad);\n e.currentTarget.removeEventListener('error', onError);\n reject(e);\n }\n\n function onLoad(e: Event) {\n if (!e.currentTarget) return;\n e.currentTarget.removeEventListener('load', onLoad);\n e.currentTarget.removeEventListener('error', onError);\n resolve(e);\n }\n\n media.addEventListener('load', onLoad);\n media.addEventListener('error', onError);\n });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise {\n return new Promise((resolve, reject) => {\n if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n const reader = new FileReader();\n reader.onload = () => {\n if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n const img = env.getEnv().createImageElement();\n img.onload = () => resolve(img);\n img.onerror = reject;\n img.src = reader.result as string;\n };\n reader.onerror = reject;\n reader.readAsDataURL(buf);\n });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n const { Image, Video } = env.getEnv();\n\n if (input instanceof Image) {\n return new Dimensions(input.naturalWidth, input.naturalHeight);\n }\n if (input instanceof Video) {\n return new Dimensions(input.videoWidth, input.videoHeight);\n }\n return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n const { createCanvasElement } = env.getEnv();\n const canvas = createCanvasElement();\n canvas.width = width;\n canvas.height = height;\n return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n const { ImageData } = env.getEnv();\n\n if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n throw new Error('createCanvasFromMedia - media has not finished loading yet');\n }\n\n const { width, height } = dims || getMediaDimensions(media);\n const canvas = createCanvas({ width, height });\n\n if (media instanceof ImageData) {\n getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n } else {\n getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n }\n return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n imgTensor: tf.Tensor,\n canvas?: HTMLCanvasElement,\n): Promise {\n const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n await tf['browser'].toPixels(imgTensor3D, targetCanvas);\n\n imgTensor3D.dispose();\n\n return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n const { Image, Canvas, Video } = env.getEnv();\n\n return input instanceof Image\n || input instanceof Canvas\n || input instanceof Video;\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage = false) {\n const { Image, Canvas } = env.getEnv();\n\n if (!(input instanceof Image || input instanceof Canvas)) {\n throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n }\n\n if (inputSize <= 0) return createCanvas({ width: 1, height: 1 });\n const dims = getMediaDimensions(input);\n const scale = inputSize / Math.max(dims.height, dims.width);\n const width = scale * dims.width;\n const height = scale * dims.height;\n\n const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n const offset = Math.abs(width - height) / 2;\n const dx = centerImage && width < height ? offset : 0;\n const dy = centerImage && height < width ? offset : 0;\n if (inputCanvas.width > 0 && inputCanvas.height > 0) getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n return targetCanvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n private _imageTensors: Array = [];\n\n private _canvases: HTMLCanvasElement[] = [];\n\n private _batchSize: number;\n\n private _treatAsBatchInput = false;\n\n private _inputDimensions: number[][] = [];\n\n private _inputSize = 0;\n\n constructor(inputs: Array, treatAsBatchInput = false) {\n if (!Array.isArray(inputs)) {\n throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n }\n\n this._treatAsBatchInput = treatAsBatchInput;\n this._batchSize = inputs.length;\n\n inputs.forEach((input, idx) => {\n if (isTensor3D(input)) {\n this._imageTensors[idx] = input;\n this._inputDimensions[idx] = input.shape;\n return;\n }\n\n if (isTensor4D(input)) {\n const batchSize = (input as any).shape[0];\n if (batchSize !== 1) {\n throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n\n this._imageTensors[idx] = input;\n this._inputDimensions[idx] = (input as any).shape.slice(1);\n return;\n }\n\n // @ts-ignore\n const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n this._canvases[idx] = canvas as HTMLCanvasElement;\n this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n });\n }\n\n public get imageTensors(): Array {\n return this._imageTensors;\n }\n\n public get canvases(): HTMLCanvasElement[] {\n return this._canvases;\n }\n\n public get isBatchInput(): boolean {\n return this.batchSize > 1 || this._treatAsBatchInput;\n }\n\n public get batchSize(): number {\n return this._batchSize;\n }\n\n public get inputDimensions(): number[][] {\n return this._inputDimensions;\n }\n\n public get inputSize(): number | undefined {\n return this._inputSize;\n }\n\n public get reshapedInputDimensions(): Dimensions[] {\n return range(this.batchSize, 0, 1).map(\n (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n );\n }\n\n public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n }\n\n public getInputDimensions(batchIdx: number): number[] {\n return this._inputDimensions[batchIdx];\n }\n\n public getInputHeight(batchIdx: number): number {\n return this._inputDimensions[batchIdx][0];\n }\n\n public getInputWidth(batchIdx: number): number {\n return this._inputDimensions[batchIdx][1];\n }\n\n public getReshapedInputDimensions(batchIdx: number): Dimensions {\n if (typeof this.inputSize !== 'number') {\n throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n }\n\n const width = this.getInputWidth(batchIdx);\n const height = this.getInputHeight(batchIdx);\n return computeReshapedDimensions({ width, height }, this.inputSize);\n }\n\n /**\n * Create a batch tensor from all input canvases and tensors\n * with size [batchSize, inputSize, inputSize, 3].\n *\n * @param inputSize Height and width of the tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The batch tensor.\n */\n public toBatchTensor(inputSize: number, isCenterInputs = true): tf.Tensor4D {\n this._inputSize = inputSize;\n\n return tf.tidy(() => {\n const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n const input = this.getInput(batchIdx);\n\n if (input instanceof tf.Tensor) {\n let imgTensor = isTensor4D(input) ? input : tf.expandDims(input);\n imgTensor = padToSquare(imgTensor as tf.Tensor4D, isCenterInputs);\n\n if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n imgTensor = tf['image'].resizeBilinear(imgTensor as tf.Tensor4D, [inputSize, inputSize], false, false);\n }\n\n return imgTensor.as3D(inputSize, inputSize, 3);\n }\n\n if (input instanceof env.getEnv().Canvas) {\n return tf['browser'].fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n }\n\n throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n });\n\n const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n // const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))) as tf.Tensor4D;\n\n return batchTensor;\n });\n }\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise {\n if (inputs instanceof NetInput) return inputs;\n const inputArgArray = Array.isArray(inputs) ? inputs : [inputs];\n if (!inputArgArray.length) throw new Error('toNetInput - empty array passed as input');\n const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n const inputArray = inputArgArray.map(resolveInput);\n inputArray.forEach((input, i) => {\n if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n if (typeof inputArgArray[i] === 'string') throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n }\n if (isTensor4D(input)) {\n // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n const batchSize = input.shape[0];\n if (batchSize !== 1) throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n });\n // wait for all media elements being loaded\n await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)));\n return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(input: TNetInput, detections: Array): Promise {\n const { Canvas } = env.getEnv();\n let canvas = input as HTMLCanvasElement;\n if (!(input instanceof Canvas)) {\n const netInput = await toNetInput(input);\n if (netInput.batchSize > 1) throw new Error('extractFaces - batchSize > 1 not supported');\n const tensorOrCanvas = netInput.getInput(0);\n canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas);\n }\n const ctx = getContext2dOrThrow(canvas);\n const boxes = detections\n .map((det) => (det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det))\n .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n return boxes.map(({ x, y, width, height }) => {\n const faceImg = createCanvas({ width, height });\n if (width > 0 && height > 0) getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n return faceImg;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(imageTensor: tf.Tensor3D | tf.Tensor4D, detections: Array): Promise {\n if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n }\n\n if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n throw new Error('extractFaceTensors - batchSize > 1 not supported');\n }\n\n return tf.tidy(() => {\n const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n const boxes = detections.map((det) => (det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det))\n .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n const faceTensors = boxes\n .filter((box) => box.width > 0 && box.height > 0)\n .map(({ x, y, width, height }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n return faceTensors;\n });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n url: string,\n // eslint-disable-next-line no-undef\n init?: RequestInit,\n): Promise {\n const { fetch } = env.getEnv();\n const res = await fetch(url, init);\n if (!(res.status < 400)) {\n throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n }\n return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise {\n const res = await fetchOrThrow(uri);\n const blob = await (res).blob();\n\n if (!blob.type.startsWith('image/')) {\n throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n }\n return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson(uri: string): Promise {\n return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise {\n return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "import { env } from '../env/index';\n\nexport function bufferToVideo(buf: Blob): Promise {\n return new Promise((resolve, reject) => {\n if (!(buf instanceof Blob)) reject(new Error('bufferToVideo - expected buf to be of type: Blob'));\n\n const video = env.getEnv().createVideoElement();\n video.oncanplay = () => resolve(video);\n video.onerror = reject;\n video.playsInline = true;\n video.muted = true;\n video.src = URL.createObjectURL(buf);\n video.play();\n });\n}\n", "import { bufferToVideo } from './bufferToVideo';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchVideo(uri: string): Promise {\n const res = await fetchOrThrow(uri);\n const blob = await (res).blob();\n\n if (!blob.type.startsWith('video/')) {\n throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${blob.type}, for url: ${res.url}`);\n }\n return bufferToVideo(blob);\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n if (!uri) {\n return {\n modelBaseUri: '',\n manifestUri: defaultManifestFilename,\n };\n }\n\n if (uri === '/') {\n return {\n modelBaseUri: '/',\n manifestUri: `/${defaultManifestFilename}`,\n };\n }\n // eslint-disable-next-line no-nested-ternary\n const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n uri = uri.replace(protocol, '');\n\n const parts = uri.split('/').filter((s) => s);\n\n const manifestFile = uri.endsWith('.json')\n ? parts[parts.length - 1]\n : defaultManifestFilename;\n\n let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n return {\n modelBaseUri,\n manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n uri: string | undefined,\n defaultModelName: string,\n): Promise {\n const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n // @ts-ignore\n const manifest = await fetchJson(manifestUri);\n // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n return tf['io'].loadWeights(manifest, modelBaseUri);\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions = false) {\n const { width, height } = useMediaDimensions\n ? getMediaDimensions(reference)\n : reference;\n input.width = width;\n input.height = height;\n return { width, height };\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork {\n constructor(name: string) {\n this._name = name;\n }\n\n protected _params: TNetParams | undefined = undefined;\n\n protected _paramMappings: ParamMapping[] = [];\n\n public _name: any;\n\n public get params(): TNetParams | undefined { return this._params; }\n\n public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n public get isLoaded(): boolean { return !!this.params; }\n\n public getParamFromPath(paramPath: string): tf.Tensor {\n const { obj, objProp } = this.traversePropertyPath(paramPath);\n return obj[objProp];\n }\n\n public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n const { obj, objProp } = this.traversePropertyPath(paramPath);\n obj[objProp].dispose();\n obj[objProp] = tensor;\n }\n\n public getParamList() {\n return this._paramMappings.map(({ paramPath }) => ({\n path: paramPath,\n tensor: this.getParamFromPath(paramPath),\n }));\n }\n\n public getTrainableParams() {\n return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n }\n\n public getFrozenParams() {\n return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n }\n\n public variable() {\n this.getFrozenParams().forEach(({ path, tensor }) => {\n this.reassignParamFromPath(path, tensor.variable());\n });\n }\n\n public freeze() {\n this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n const tensor = tf.tensor(variable.dataSync());\n variable.dispose();\n this.reassignParamFromPath(path, tensor);\n });\n }\n\n public dispose(throwOnRedispose = true) {\n this.getParamList().forEach((param) => {\n if (throwOnRedispose && param.tensor.isDisposed) {\n throw new Error(`param tensor has already been disposed for path ${param.path}`);\n }\n param.tensor.dispose();\n });\n this._params = undefined;\n }\n\n public serializeParams(): Float32Array {\n return new Float32Array(\n this.getParamList()\n .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n .reduce((flat, arr) => flat.concat(arr)),\n );\n }\n\n public async load(weightsOrUrl: Float32Array | string | undefined): Promise {\n if (weightsOrUrl instanceof Float32Array) {\n this.extractWeights(weightsOrUrl);\n return;\n }\n await this.loadFromUri(weightsOrUrl);\n }\n\n public async loadFromUri(uri: string | undefined) {\n if (uri && typeof uri !== 'string') {\n throw new Error(`${this._name}.loadFromUri - expected model uri`);\n }\n const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n this.loadFromWeightMap(weightMap);\n }\n\n public async loadFromDisk(filePath: string | undefined) {\n if (filePath && typeof filePath !== 'string') {\n throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n }\n const { readFile } = env.getEnv();\n const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => buf.buffer)));\n const loadWeights = tf['io'].weightsLoaderFactory(fetchWeightsFromDisk);\n const manifest = JSON.parse((await readFile(manifestUri)).toString());\n const weightMap = await loadWeights(manifest, modelBaseUri);\n this.loadFromWeightMap(weightMap);\n }\n\n public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n public extractWeights(weights: Float32Array) {\n const { paramMappings, params } = this.extractParams(weights);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n private traversePropertyPath(paramPath: string) {\n if (!this.params) {\n throw new Error('traversePropertyPath - model has no loaded params');\n }\n\n const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n // eslint-disable-next-line no-prototype-builtins\n if (!res.nextObj.hasOwnProperty(objProp)) {\n throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n }\n return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n }, { nextObj: this.params });\n\n const { obj, objProp } = result;\n if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n }\n\n return { obj, objProp };\n }\n\n protected abstract getDefaultModelName(): string\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n x: tf.Tensor4D,\n params: SeparableConvParams,\n stride: [number, number],\n): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n out = tf.add(out, params.bias);\n return out;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock3Params,\n isFirstLayer = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out1 = tf.relu(\n isFirstLayer\n ? tf.add(\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n denseBlockParams.conv0.bias,\n )\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n ) as tf.Tensor4D;\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n });\n}\n\nexport function denseBlock4(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock4Params,\n isFirstLayer = false,\n isScaleDown = true,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out1 = tf.relu(\n isFirstLayer\n ? tf.add(\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n denseBlockParams.conv0.bias,\n )\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n ) as tf.Tensor4D;\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n x: tf.Tensor4D,\n params: ConvParams,\n padding: 'valid' | 'same' = 'same',\n withRelu = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out = tf.add(\n tf.conv2d(x, params.filters, [1, 1], padding),\n params.bias,\n ) as tf.Tensor4D;\n\n return withRelu ? tf.relu(out) : out;\n });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n Object.keys(weightMap).forEach((path) => {\n if (!paramMappings.some((pm) => pm.originalPath === path)) {\n weightMap[path].dispose();\n }\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvParams => {\n const filters = tf.tensor4d(\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n [filterSize, filterSize, channelsIn, channelsOut],\n );\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return { filters, bias };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (\n channelsIn: number,\n channelsOut: number,\n mappedPrefix: string,\n ): FCParams => {\n const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/weights` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return {\n weights: fc_weights,\n bias: fc_bias,\n };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n originalPath?: string\n paramPath: string\n}\n\nexport type ConvParams = {\n filters: tf.Tensor4D\n bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n weights: tf.Tensor2D\n bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n // eslint-disable-next-line no-useless-constructor\n constructor(\n // eslint-disable-next-line no-unused-vars\n public depthwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public pointwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public bias: tf.Tensor1D,\n // eslint-disable-next-line no-empty-function\n ) {}\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/depthwise_filter` },\n { paramPath: `${mappedPrefix}/pointwise_filter` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n\nexport function loadSeparableConvParamsFactory(\n // eslint-disable-next-line no-unused-vars\n extractWeightEntry: (originalPath: string, paramRank: number) => T,\n) {\n return (prefix: string): SeparableConvParams => {\n const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4);\n const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n return (originalPath: string, paramRank: number, mappedPath?: string) => {\n const tensor = weightMap[originalPath];\n\n if (!isTensor(tensor, paramRank)) {\n throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n }\n\n paramMappings.push(\n { originalPath, paramPath: mappedPath || originalPath },\n );\n\n return tensor;\n };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n let remainingWeights = weights;\n\n function extractWeights(numWeights: number): Float32Array {\n const ret = remainingWeights.slice(0, numWeights);\n remainingWeights = remainingWeights.slice(numWeights);\n return ret;\n }\n\n function getRemainingWeights(): Float32Array {\n return remainingWeights;\n }\n\n return {\n extractWeights,\n getRemainingWeights,\n };\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer = false): DenseBlock4Params {\n const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n return {\n conv0, conv1, conv2, conv3,\n };\n }\n\n return {\n extractDenseBlock3Params,\n extractDenseBlock4Params,\n };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractDenseBlock4Params,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: {\n dense0, dense1, dense2, dense3,\n },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: (originalPath: string, paramRank: number) => T) {\n return (prefix: string): ConvParams => {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return { filters, bias };\n };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractDenseBlock3Params(prefix: string, isFirstLayer = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(prefix: string, isFirstLayer = false): DenseBlock4Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n return {\n conv0, conv1, conv2, conv3,\n };\n }\n\n return {\n extractDenseBlock3Params,\n extractDenseBlock4Params,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractDenseBlock4Params,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const params = {\n dense0: extractDenseBlock4Params('dense0', true),\n dense1: extractDenseBlock4Params('dense1'),\n dense2: extractDenseBlock4Params('dense2'),\n dense3: extractDenseBlock4Params('dense3'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork implements IFaceFeatureExtractor {\n constructor() {\n super('FaceFeatureExtractor');\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('FaceFeatureExtractor - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n let out = denseBlock4(normalized, params.dense0, true);\n out = denseBlock4(out, params.dense1);\n out = denseBlock4(out, params.dense2);\n out = denseBlock4(out, params.dense3);\n out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'face_feature_extractor_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n x: tf.Tensor2D,\n params: FCParams,\n): tf.Tensor2D {\n return tf.tidy(() => tf.add(\n tf.matMul(x, params.weights),\n params.bias,\n ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: extractFcParams('fc'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n const featureExtractorMap: tf.NamedTensorMap = {};\n const classifierMap: tf.NamedTensorMap = {};\n\n Object.keys(weightMap).forEach((key) => {\n const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n map[key] = weightMap[key];\n });\n\n return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends NeuralNetwork {\n protected _faceFeatureExtractor: IFaceFeatureExtractor;\n\n constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor) {\n super(_name);\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): IFaceFeatureExtractor {\n return this._faceFeatureExtractor;\n }\n\n protected abstract override getDefaultModelName(): string\n\n protected abstract getClassifierChannelsIn(): number\n\n protected abstract getClassifierChannelsOut(): number\n\n public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${this._name} - load model before inference`);\n }\n\n return tf.tidy(() => {\n const bottleneckFeatures = input instanceof NetInput\n ? this.faceFeatureExtractor.forwardInput(input)\n : input;\n return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n });\n }\n\n public override dispose(throwOnRedispose = true) {\n this.faceFeatureExtractor.dispose(throwOnRedispose);\n super.dispose(throwOnRedispose);\n }\n\n public loadClassifierParams(weights: Float32Array) {\n const { params, paramMappings } = this.extractClassifierParams(weights);\n this._params = params;\n this._paramMappings = paramMappings;\n }\n\n public extractClassifierParams(weights: Float32Array) {\n return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n return extractParamsFromWeightMap(classifierMap);\n }\n\n protected extractParams(weights: Float32Array) {\n const cIn = this.getClassifierChannelsIn();\n const cOut = this.getClassifierChannelsOut();\n const classifierWeightSize = (cOut * cIn) + cOut;\n\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n return this.extractClassifierParams(classifierWeights);\n }\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\n\nexport class FaceExpressions {\n public neutral = 0;\n public happy = 0;\n public sad = 0;\n public angry = 0;\n public fearful = 0;\n public disgusted = 0;\n public surprised = 0;\n\n constructor(probabilities: number[] | Float32Array) {\n if (probabilities.length !== 7) {\n throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n }\n\n FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n this[expression] = probabilities[idx];\n });\n }\n\n asSortedArray() {\n return FACE_EXPRESSION_LABELS\n .map((expression) => ({ expression, probability: this[expression] as number }))\n .sort((e0, e1) => e1.probability - e0.probability);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor {\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n super('FaceExpressionNet', faceFeatureExtractor);\n }\n\n public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n return tf.tidy(() => tf.softmax(this.runNet(input)));\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictExpressions(input: TNetInput) {\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput);\n const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n const data = t.dataSync();\n t.dispose();\n return data;\n }));\n out.dispose();\n\n const predictionsByBatch = probabilitesByBatch\n .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n return netInput.isBatchInput\n ? predictionsByBatch\n : predictionsByBatch[0];\n }\n\n protected getDefaultModelName(): string {\n return 'face_expression_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 256;\n }\n\n protected getClassifierChannelsOut(): number {\n return 7;\n }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions = TSource & { expressions: FaceExpressions }\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions(sourceObj: TSource, expressions: FaceExpressions): WithFaceExpressions {\n const extension = { expressions };\n return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(canvasArg: string | HTMLCanvasElement, faceExpressions: DrawFaceExpressionsInput | Array, minConfidence = 0.1, textFieldAnchor?: IPoint) {\n const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n faceExpressionsArray.forEach((e) => {\n // eslint-disable-next-line no-nested-ternary\n const expr = e instanceof FaceExpressions\n ? e\n : (isWithFaceExpressions(e) ? e.expressions : undefined);\n if (!expr) {\n throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n }\n\n const sorted = expr.asSortedArray();\n const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n const anchor = isWithFaceDetection(e)\n ? e.detection.box.bottomLeft\n : (textFieldAnchor || new Point(0, 0));\n\n const drawTextField = new DrawTextField(\n resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n anchor,\n );\n drawTextField.draw(canvasArg);\n });\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n TSource extends WithFaceDetection<{}>,\n TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n landmarks: TFaceLandmarks;\n unshiftedLandmarks: TFaceLandmarks;\n alignedRect: FaceDetection;\n angle: {\n roll: number | undefined;\n pitch: number | undefined;\n yaw: number | undefined;\n };\n};\n\nexport function isWithFaceLandmarks(\n obj: any,\n): obj is WithFaceLandmarks, FaceLandmarks> {\n return (\n isWithFaceDetection(obj)\n // eslint-disable-next-line dot-notation\n && obj['landmarks'] instanceof FaceLandmarks\n // eslint-disable-next-line dot-notation\n && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n // eslint-disable-next-line dot-notation\n && obj['alignedRect'] instanceof FaceDetection\n );\n}\n\nfunction calculateFaceAngle(mesh) {\n // Helper to convert radians to degrees\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const degrees = (radians) => (radians * 180) / Math.PI;\n const calcLengthBetweenTwoPoints = (a, b) => Math.sqrt((a._x - b._x) ** 2 + (a._y - b._y) ** 2);\n\n const angle = {\n roll: undefined,\n pitch: undefined,\n yaw: undefined,\n };\n\n const calcYaw = (leftPoint, midPoint, rightPoint) => {\n // Calc x-distance from left side of the face (\"ear\") to facial midpoint (\"nose\")\n const leftToMidpoint = Math.floor(leftPoint._x - midPoint._x);\n // Calc x-distance from facial midpoint (\"nose\") to the right side of the face (\"ear\")\n const rightToMidpoint = Math.floor(midPoint._x - rightPoint._x);\n // Difference in distances coincidentally approximates to angles\n return leftToMidpoint - rightToMidpoint;\n };\n\n const calcRoll = (lever, pivot) => {\n // When rolling, the head seems to pivot from the nose/lips/chin area.\n // So, we'll choose any two points from the facial midline, where the first point should be the pivot, and the other \"lever\"\n // Plan/Execution: get the hypotenuse & opposite sides of a 90deg triangle ==> Calculate angle in radians\n const hypotenuse = Math.hypot(pivot._x - lever._x, pivot._y - lever._y);\n const opposite = pivot._y - lever._y;\n const angleInRadians = Math.asin(opposite / hypotenuse);\n const angleInDegrees = degrees(angleInRadians);\n const normalizeAngle = Math.floor(90 - angleInDegrees);\n // If lever more to the left of the pivot, then we're tilting left\n // \"-\" is negative direction. \"+\", or absence of a sign is positive direction\n const tiltDirection = pivot._x - lever._x < 0 ? -1 : 1;\n const result = normalizeAngle * tiltDirection;\n return result;\n };\n\n const calcPitch = (leftPoint, midPoint, rightPoint) => {\n // Theory: While pitching, the nose is the most salient point --> That's what we'll use to make a trianle.\n // The \"base\" is between point that don't move when we pitch our head (i.e. an imaginary line running ear to ear through the nose).\n // Executuin: Get the opposite & adjacent lengths of the triangle from the ear's perspective. Use it to get angle.\n\n const base = calcLengthBetweenTwoPoints(leftPoint, rightPoint);\n // adjecent is base/2 technically.\n const baseCoords = {\n _x: (leftPoint._x + rightPoint._x) / 2,\n _y: (leftPoint._y + rightPoint._y) / 2,\n };\n const midToBaseLength = calcLengthBetweenTwoPoints(midPoint, baseCoords);\n const angleInRadians = Math.atan(midToBaseLength / base);\n const angleInDegrees = Math.floor(degrees(angleInRadians));\n // Account for directionality.\n // pitch forwards (_i.e. tilting your head forwards) is positive (or no sign); backward is negative.\n const direction = baseCoords._y - midPoint._y < 0 ? -1 : 1;\n const result = angleInDegrees * direction;\n return result;\n };\n\n if (!mesh || !mesh._positions || mesh._positions.length !== 68) return angle;\n const pt = mesh._positions;\n angle.roll = calcRoll(pt[27], pt[66]);\n angle.pitch = calcPitch(pt[14], pt[30], pt[2]);\n angle.yaw = calcYaw(pt[14], pt[33], pt[2]);\n return angle;\n}\n\nexport function extendWithFaceLandmarks, TFaceLandmarks extends FaceLandmarks = FaceLandmarks68>(\n sourceObj: TSource,\n unshiftedLandmarks: TFaceLandmarks,\n): WithFaceLandmarks {\n const { box: shift } = sourceObj.detection;\n const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);\n const rect = landmarks.align();\n const { imageDims } = sourceObj.detection;\n const alignedRect = new FaceDetection(\n sourceObj.detection.score,\n rect.rescale(imageDims.reverse()),\n imageDims,\n );\n const angle = calculateFaceAngle(unshiftedLandmarks);\n const extension = { landmarks, unshiftedLandmarks, alignedRect, angle };\n return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n drawLines?: boolean\n drawPoints?: boolean\n lineWidth?: number\n pointSize?: number\n lineColor?: string\n pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n public drawLines: boolean;\n\n public drawPoints: boolean;\n\n public lineWidth: number;\n\n public pointSize: number;\n\n public lineColor: string;\n\n public pointColor: string;\n\n constructor(options: IDrawFaceLandmarksOptions = {}) {\n const {\n drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n } = options;\n this.drawLines = drawLines;\n this.drawPoints = drawPoints;\n this.lineWidth = lineWidth || 1;\n this.pointSize = pointSize || 2;\n this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n }\n}\n\nexport class DrawFaceLandmarks {\n public faceLandmarks: FaceLandmarks;\n\n public options: DrawFaceLandmarksOptions;\n\n constructor(\n faceLandmarks: FaceLandmarks,\n options: IDrawFaceLandmarksOptions = {},\n ) {\n this.faceLandmarks = faceLandmarks;\n this.options = new DrawFaceLandmarksOptions(options);\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const ctx = getContext2dOrThrow(canvasArg);\n\n const {\n drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n } = this.options;\n\n if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n ctx.strokeStyle = lineColor;\n ctx.lineWidth = lineWidth;\n drawContour(ctx, this.faceLandmarks.getJawOutline());\n drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n drawContour(ctx, this.faceLandmarks.getNose());\n drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n drawContour(ctx, this.faceLandmarks.getMouth(), true);\n }\n\n if (drawPoints) {\n ctx.strokeStyle = pointColor;\n ctx.fillStyle = pointColor;\n\n const drawPoint = (pt: IPoint) => {\n ctx.beginPath();\n ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n ctx.fill();\n };\n this.faceLandmarks.positions.forEach(drawPoint);\n }\n }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks>\n\nexport function drawFaceLandmarks(\n canvasArg: string | HTMLCanvasElement,\n faceLandmarks: DrawFaceLandmarksInput | Array,\n) {\n const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n faceLandmarksArray.forEach((f) => {\n // eslint-disable-next-line no-nested-ternary\n const landmarks = f instanceof FaceLandmarks\n ? f\n : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n if (!landmarks) {\n throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof');\n }\n\n new DrawFaceLandmarks(landmarks).draw(canvasArg);\n });\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n return { separable_conv0, separable_conv1, expansion_conv };\n }\n\n function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n });\n\n const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n const exit_flow = {\n reduction_block: exit_flow_reduction_block,\n separable_conv: exit_flow_separable_conv,\n };\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { entry_flow, middle_flow, exit_flow },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n return { separable_conv0, separable_conv1, expansion_conv };\n }\n\n function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n });\n\n const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n const exit_flow = {\n reduction_block: exit_flow_reduction_block,\n separable_conv: exit_flow_separable_conv,\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput = true): tf.Tensor4D {\n let out = isActivateInput ? tf.relu(x) : x;\n out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n out = tf.add(out, x);\n return out;\n}\n\nexport class TinyXception extends NeuralNetwork {\n private _numMainBlocks: number;\n\n constructor(numMainBlocks: number) {\n super('TinyXception');\n this._numMainBlocks = numMainBlocks;\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n if (!params) {\n throw new Error('TinyXception - load model before inference');\n }\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n out = reductionBlock(out, params.entry_flow.reduction_block_1);\n range(this._numMainBlocks, 0, 1).forEach((idx) => {\n out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n });\n out = reductionBlock(out, params.exit_flow.reduction_block);\n out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'tiny_xception_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap, this._numMainBlocks);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights, this._numMainBlocks);\n }\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n const age = extractFCParams(512, 1, 'fc/age');\n const gender = extractFCParams(512, 2, 'fc/gender');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc: { age, gender } },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: {\n age: extractFcParams('fc/age'),\n gender: extractFcParams('fc/gender'),\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n // eslint-disable-next-line no-unused-vars\n FEMALE = 'female',\n // eslint-disable-next-line no-unused-vars\n MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n age: number\n gender: Gender\n genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n fc: {\n age: FCParams\n gender: FCParams\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork {\n private _faceFeatureExtractor: TinyXception;\n\n constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n super('AgeGenderNet');\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): TinyXception {\n return this._faceFeatureExtractor;\n }\n\n public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${this._name} - load model before inference`);\n }\n\n return tf.tidy(() => {\n const bottleneckFeatures = input instanceof NetInput\n ? this.faceFeatureExtractor.forwardInput(input)\n : input;\n\n const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n const gender = fullyConnectedLayer(pooled, params.fc.gender);\n return { age, gender };\n });\n }\n\n public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n return tf.tidy(() => {\n const { age, gender } = this.runNet(input);\n return { age, gender: tf.softmax(gender) };\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictAgeAndGender(input: TNetInput): Promise {\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput);\n\n const ages = tf.unstack(out.age);\n const genders = tf.unstack(out.gender);\n const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n ageTensor,\n genderTensor: genders[i],\n }));\n\n const predictionsByBatch = await Promise.all(\n ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n const age = (ageTensor.dataSync())[0];\n const probMale = (genderTensor.dataSync())[0];\n const isMale = probMale > 0.5;\n const gender = isMale ? Gender.MALE : Gender.FEMALE;\n const genderProbability = isMale ? probMale : (1 - probMale);\n\n ageTensor.dispose();\n genderTensor.dispose();\n return { age, gender, genderProbability };\n }),\n );\n out.age.dispose();\n out.gender.dispose();\n\n return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n }\n\n protected getDefaultModelName(): string {\n return 'age_gender_model';\n }\n\n public override dispose(throwOnRedispose = true) {\n this.faceFeatureExtractor.dispose(throwOnRedispose);\n super.dispose(throwOnRedispose);\n }\n\n public loadClassifierParams(weights: Float32Array) {\n const { params, paramMappings } = this.extractClassifierParams(weights);\n this._params = params;\n this._paramMappings = paramMappings;\n }\n\n public extractClassifierParams(weights: Float32Array) {\n return extractParams(weights);\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n return extractParamsFromWeightMap(classifierMap);\n }\n\n protected extractParams(weights: Float32Array) {\n const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n return this.extractClassifierParams(classifierWeights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends FaceProcessor {\n public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n const inputDimensions = originalDimensions.map(({ width, height }) => {\n const scale = inputSize / Math.max(height, width);\n return {\n width: width * scale,\n height: height * scale,\n };\n });\n\n const batchSize = inputDimensions.length;\n\n return tf.tidy(() => {\n const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n // eslint-disable-next-line no-unused-vars\n const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n const { width, height } = inputDimensions[batchIdx];\n return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n };\n\n const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n const landmarkTensors = output\n .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n getPaddingX(batchIdx),\n getPaddingY(batchIdx),\n ))))\n .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n inputDimensions[batchIdx].width,\n inputDimensions[batchIdx].height,\n ))));\n\n return landmarkTensors as tf.Tensor2D;\n });\n }\n\n public forwardInput(input: NetInput): tf.Tensor2D {\n return tf.tidy(() => {\n const out = this.runNet(input);\n return this.postProcess(\n out,\n input.inputSize as number,\n input.inputDimensions.map(([height, width]) => ({ height, width })),\n );\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async detectLandmarks(input: TNetInput): Promise {\n const netInput = await toNetInput(input);\n const landmarkTensors = tf.tidy(\n () => tf.unstack(this.forwardInput(netInput)),\n );\n\n const landmarksForBatch = await Promise.all(landmarkTensors.map(\n async (landmarkTensor, batchIdx) => {\n const landmarksArray = Array.from(landmarkTensor.dataSync());\n const xCoords = landmarksArray.filter((_, i) => isEven(i));\n const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n return new FaceLandmarks68(\n Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n {\n height: netInput.getInputHeight(batchIdx),\n width: netInput.getInputWidth(batchIdx),\n },\n );\n },\n ));\n\n landmarkTensors.forEach((t) => t.dispose());\n\n return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n }\n\n protected getClassifierChannelsOut(): number {\n return 136;\n }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase {\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n super('FaceLandmark68Net', faceFeatureExtractor);\n }\n\n protected getDefaultModelName(): string {\n return 'face_landmark_68_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 256;\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMapTiny(\n weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractDenseBlock3Params,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const params = {\n dense0: extractDenseBlock3Params('dense0', true),\n dense1: extractDenseBlock3Params('dense1'),\n dense2: extractDenseBlock3Params('dense2'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractDenseBlock3Params,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { dense0, dense1, dense2 },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeightMapTiny } from './extractParamsFromWeightMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork implements IFaceFeatureExtractor {\n constructor() {\n super('TinyFaceFeatureExtractor');\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('TinyFaceFeatureExtractor - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n let out = denseBlock3(normalized, params.dense0, true);\n out = denseBlock3(out, params.dense1);\n out = denseBlock3(out, params.dense2);\n out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'face_feature_extractor_tiny_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMapTiny(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParamsTiny(weights);\n }\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase {\n constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n super('FaceLandmark68TinyNet', faceFeatureExtractor);\n }\n\n protected getDefaultModelName(): string {\n return 'face_landmark_68_tiny_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 128;\n }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n x: tf.Tensor4D,\n params: ConvLayerParams,\n strides: [number, number],\n withRelu: boolean,\n padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n const { filters, bias } = params.conv;\n\n let out = tf.conv2d(x, filters, strides, padding);\n out = tf.add(out, bias);\n out = scale(out, params.scale);\n return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { isFloat } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n const weights = extractWeights(numFilterValues);\n const depth = weights.length / (numFilters * filterSize * filterSize);\n\n if (isFloat(depth)) {\n throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n }\n\n return tf.tidy(\n () => tf.transpose(\n tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n [2, 3, 1, 0],\n ),\n );\n }\n\n function extractConvParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvParams {\n const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n const bias = tf.tensor1d(extractWeights(numFilters));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return { filters, bias };\n }\n\n function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n const weights = tf.tensor1d(extractWeights(numWeights));\n const biases = tf.tensor1d(extractWeights(numWeights));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/weights` },\n { paramPath: `${mappedPrefix}/biases` },\n );\n\n return {\n weights,\n biases,\n };\n }\n\n function extractConvLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvLayerParams {\n const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n return { conv, scale };\n }\n\n function extractResidualLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n isDown = false,\n ): ResidualLayerParams {\n const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n return { conv1, conv2 };\n }\n\n return {\n extractConvLayerParams,\n extractResidualLayerParams,\n };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvLayerParams,\n extractResidualLayerParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n const fc = tf.tidy(\n () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n );\n paramMappings.push({ paramPath: 'fc' });\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n const params = {\n conv32_down,\n conv32_1,\n conv32_2,\n conv32_3,\n conv64_down,\n conv64_1,\n conv64_2,\n conv64_3,\n conv128_down,\n conv128_1,\n conv128_2,\n conv256_down,\n conv256_1,\n conv256_2,\n conv256_down_out,\n fc,\n };\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n return { weights, biases };\n }\n\n function extractConvLayerParams(prefix: string): ConvLayerParams {\n const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n const scale = extractScaleLayerParams(prefix);\n\n return { conv: { filters, bias }, scale };\n }\n\n function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n return {\n conv1: extractConvLayerParams(`${prefix}/conv1`),\n conv2: extractConvLayerParams(`${prefix}/conv2`),\n };\n }\n\n return {\n extractConvLayerParams,\n extractResidualLayerParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvLayerParams,\n extractResidualLayerParams,\n } = extractorsFactory(weightMap, paramMappings);\n\n const conv32_down = extractConvLayerParams('conv32_down');\n const conv32_1 = extractResidualLayerParams('conv32_1');\n const conv32_2 = extractResidualLayerParams('conv32_2');\n const conv32_3 = extractResidualLayerParams('conv32_3');\n\n const conv64_down = extractResidualLayerParams('conv64_down');\n const conv64_1 = extractResidualLayerParams('conv64_1');\n const conv64_2 = extractResidualLayerParams('conv64_2');\n const conv64_3 = extractResidualLayerParams('conv64_3');\n\n const conv128_down = extractResidualLayerParams('conv128_down');\n const conv128_1 = extractResidualLayerParams('conv128_1');\n const conv128_2 = extractResidualLayerParams('conv128_2');\n\n const conv256_down = extractResidualLayerParams('conv256_down');\n const conv256_1 = extractResidualLayerParams('conv256_1');\n const conv256_2 = extractResidualLayerParams('conv256_2');\n const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n const { fc } = weightMap;\n paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n if (!isTensor2D(fc)) {\n throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n }\n\n const params = {\n conv32_down,\n conv32_1,\n conv32_2,\n conv32_3,\n conv64_down,\n conv64_1,\n conv64_2,\n conv64_3,\n conv128_down,\n conv128_1,\n conv128_2,\n conv256_down,\n conv256_1,\n conv256_2,\n conv256_down_out,\n fc,\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n let out = conv(x, params.conv1);\n out = convNoRelu(out, params.conv2);\n out = tf.add(out, x);\n out = tf.relu(out);\n return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n let out = convDown(x, params.conv1);\n out = convNoRelu(out, params.conv2);\n\n let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n const zeros = tf.zeros(pooled.shape);\n const isPad = pooled.shape[3] !== out.shape[3];\n const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n if (isAdjustShape) {\n const padShapeX = [...out.shape] as [number, number, number, number];\n padShapeX[1] = 1;\n const zerosW = tf.zeros(padShapeX);\n out = tf.concat([out, zerosW], 1);\n\n const padShapeY = [...out.shape] as [number, number, number, number];\n padShapeY[2] = 1;\n const zerosH = tf.zeros(padShapeY);\n out = tf.concat([out, zerosH], 2);\n }\n\n pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n out = tf.add(pooled, out) as tf.Tensor4D;\n\n out = tf.relu(out);\n return out;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork {\n constructor() {\n super('FaceRecognitionNet');\n }\n\n public forwardInput(input: NetInput): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error('FaceRecognitionNet - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n let out = convDown(normalized, params.conv32_down);\n out = tf.maxPool(out, 3, 2, 'valid');\n\n out = residual(out, params.conv32_1);\n out = residual(out, params.conv32_2);\n out = residual(out, params.conv32_3);\n\n out = residualDown(out, params.conv64_down);\n out = residual(out, params.conv64_1);\n out = residual(out, params.conv64_2);\n out = residual(out, params.conv64_3);\n\n out = residualDown(out, params.conv128_down);\n out = residual(out, params.conv128_1);\n out = residual(out, params.conv128_2);\n\n out = residualDown(out, params.conv256_down);\n out = residual(out, params.conv256_1);\n out = residual(out, params.conv256_2);\n out = residualDown(out, params.conv256_down_out);\n\n const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n return fullyConnected as tf.Tensor2D;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async computeFaceDescriptor(input: TNetInput): Promise {\n // @ts-ignore\n if (input?.shape?.some((dim) => dim <= 0)) return new Float32Array(128);\n const netInput = await toNetInput(input);\n const faceDescriptorTensors = tf.tidy(() => tf.unstack(this.forwardInput(netInput)));\n const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())) as Float32Array[];\n faceDescriptorTensors.forEach((t) => t.dispose());\n return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];\n }\n\n protected getDefaultModelName(): string {\n return 'face_recognition_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n const net = new FaceRecognitionNet();\n net.extractWeights(weights);\n return net;\n}\n", "export type WithFaceDescriptor = TSource & {\n descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n TSource\n>(\n sourceObj: TSource,\n descriptor: Float32Array,\n): WithFaceDescriptor {\n const extension = { descriptor };\n return { ...sourceObj, ...extension };\n}\n", "export type WithAge = TSource & {\n age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n TSource\n>(\n sourceObj: TSource,\n age: number,\n): WithAge {\n const extension = { age };\n return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender = TSource & {\n gender: Gender\n genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n TSource\n>(\n sourceObj: TSource,\n gender: Gender,\n genderProbability: number,\n): WithGender {\n const extension = { gender, genderProbability };\n return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common/index';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/batch_norm_scale` },\n { paramPath: `${mappedPrefix}/batch_norm_offset` },\n { paramPath: `${mappedPrefix}/batch_norm_mean` },\n { paramPath: `${mappedPrefix}/batch_norm_variance` },\n );\n\n return {\n filters,\n batch_norm_scale,\n batch_norm_offset,\n batch_norm_mean,\n batch_norm_variance,\n };\n }\n\n function extractConvParams(\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n isPointwiseConv?: boolean,\n ): ConvParams {\n const filters = tf.tensor4d(\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n [filterSize, filterSize, channelsIn, channelsOut],\n );\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n );\n\n return { filters, bias };\n }\n\n function extractPointwiseConvParams(\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n ): PointwiseConvParams {\n const {\n filters,\n bias,\n } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n return {\n filters,\n batch_norm_offset: bias,\n };\n }\n\n function extractConvPairParams(\n channelsIn: number,\n channelsOut: number,\n mappedPrefix: string,\n ): MobileNetV1.ConvPairParams {\n const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n return { depthwise_conv, pointwise_conv };\n }\n\n function extractMobilenetV1Params(): MobileNetV1.Params {\n const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n return {\n conv_0,\n conv_1,\n conv_2,\n conv_3,\n conv_4,\n conv_5,\n conv_6,\n conv_7,\n conv_8,\n conv_9,\n conv_10,\n conv_11,\n conv_12,\n conv_13,\n };\n }\n\n function extractPredictionLayerParams(): PredictionLayerParams {\n const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n const box_predictor_0 = {\n box_encoding_predictor: box_encoding_0_predictor,\n class_predictor: class_predictor_0,\n };\n const box_predictor_1 = {\n box_encoding_predictor: box_encoding_1_predictor,\n class_predictor: class_predictor_1,\n };\n const box_predictor_2 = {\n box_encoding_predictor: box_encoding_2_predictor,\n class_predictor: class_predictor_2,\n };\n const box_predictor_3 = {\n box_encoding_predictor: box_encoding_3_predictor,\n class_predictor: class_predictor_3,\n };\n const box_predictor_4 = {\n box_encoding_predictor: box_encoding_4_predictor,\n class_predictor: class_predictor_4,\n };\n const box_predictor_5 = {\n box_encoding_predictor: box_encoding_5_predictor,\n class_predictor: class_predictor_5,\n };\n return {\n conv_0,\n conv_1,\n conv_2,\n conv_3,\n conv_4,\n conv_5,\n conv_6,\n conv_7,\n box_predictor_0,\n box_predictor_1,\n box_predictor_2,\n box_predictor_3,\n box_predictor_4,\n box_predictor_5,\n };\n }\n\n return {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n const {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n } = extractorsFactory(extractWeights, paramMappings);\n const mobilenetv1 = extractMobilenetV1Params();\n const prediction_layer = extractPredictionLayerParams();\n const extra_dim = tf.tensor3d(\n extractWeights(5118 * 4),\n [1, 5118, 4],\n );\n const output_layer = {\n extra_dim,\n };\n paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n params: {\n mobilenetv1,\n prediction_layer,\n output_layer,\n },\n paramMappings,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n return { filters, batch_norm_offset };\n }\n\n function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n const mappedPrefix = `mobilenetv1/conv_${idx}`;\n const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n return {\n depthwise_conv: {\n filters,\n batch_norm_scale,\n batch_norm_offset,\n batch_norm_mean,\n batch_norm_variance,\n },\n pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n };\n }\n\n function extractMobilenetV1Params(): MobileNetV1.Params {\n return {\n conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n conv_1: extractConvPairParams(1),\n conv_2: extractConvPairParams(2),\n conv_3: extractConvPairParams(3),\n conv_4: extractConvPairParams(4),\n conv_5: extractConvPairParams(5),\n conv_6: extractConvPairParams(6),\n conv_7: extractConvPairParams(7),\n conv_8: extractConvPairParams(8),\n conv_9: extractConvPairParams(9),\n conv_10: extractConvPairParams(10),\n conv_11: extractConvPairParams(11),\n conv_12: extractConvPairParams(12),\n conv_13: extractConvPairParams(13),\n };\n }\n\n function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n return { filters, bias };\n }\n\n function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n const box_encoding_predictor = extractConvParams(\n `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n );\n const class_predictor = extractConvParams(\n `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n `prediction_layer/box_predictor_${idx}/class_predictor`,\n );\n return { box_encoding_predictor, class_predictor };\n }\n\n function extractPredictionLayerParams(): PredictionLayerParams {\n return {\n conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n box_predictor_0: extractBoxPredictorParams(0),\n box_predictor_1: extractBoxPredictorParams(1),\n box_predictor_2: extractBoxPredictorParams(2),\n box_predictor_3: extractBoxPredictorParams(3),\n box_predictor_4: extractBoxPredictorParams(4),\n box_predictor_5: extractBoxPredictorParams(5),\n };\n }\n\n return {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n const {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n } = extractorsFactory(weightMap, paramMappings);\n const extra_dim = weightMap['Output/extra_dim'];\n paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n if (!isTensor3D(extra_dim)) {\n throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n }\n\n const params = {\n mobilenetv1: extractMobilenetV1Params(),\n prediction_layer: extractPredictionLayerParams(),\n output_layer: {\n extra_dim,\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]) {\n return tf.tidy(() => {\n let out = tf.conv2d(x, params.filters, strides, 'same');\n out = tf.add(out, params.batch_norm_offset);\n return tf.clipByValue(out, 0, 6);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(x: tf.Tensor4D, params: MobileNetV1.DepthwiseConvParams, strides: [number, number]) {\n return tf.tidy(() => {\n let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n out = tf.batchNorm(\n out,\n params.batch_norm_mean,\n params.batch_norm_variance,\n params.batch_norm_offset,\n params.batch_norm_scale,\n epsilon,\n );\n return tf.clipByValue(out, 0, 6);\n });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n return tf.tidy(() => {\n let conv11;\n let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n const convPairParams = [\n params.conv_1,\n params.conv_2,\n params.conv_3,\n params.conv_4,\n params.conv_5,\n params.conv_6,\n params.conv_7,\n params.conv_8,\n params.conv_9,\n params.conv_10,\n params.conv_11,\n params.conv_12,\n params.conv_13,\n ];\n\n convPairParams.forEach((param, i) => {\n const layerIdx = i + 1;\n const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n if (layerIdx === 11) conv11 = out;\n });\n\n if (conv11 === null) {\n throw new Error('mobileNetV1 - output of conv layer 11 is null');\n }\n\n return {\n out,\n conv11: conv11 as any,\n };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n const boxesData = boxes.arraySync();\n const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n if (areaI <= 0 || areaJ <= 0) return 0.0;\n const intersectionYmin = Math.max(yminI, yminJ);\n const intersectionXmin = Math.max(xminI, xminJ);\n const intersectionYmax = Math.min(ymaxI, ymaxJ);\n const intersectionXmax = Math.min(xmaxI, xmaxJ);\n const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) * Math.max(intersectionXmax - intersectionXmin, 0.0);\n return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n boxes: tf.Tensor2D,\n scores: number[],\n maxOutputSize: number,\n iouThreshold: number,\n scoreThreshold: number,\n): number[] {\n const numBoxes = boxes.shape[0];\n const outputSize = Math.min(maxOutputSize, numBoxes);\n\n const candidates = scores\n .map((score, boxIndex) => ({ score, boxIndex }))\n .filter((c) => c.score > scoreThreshold)\n .sort((c1, c2) => c2.score - c1.score);\n\n const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n const selected: number[] = [];\n\n candidates.forEach((c) => {\n if (selected.length >= outputSize) return;\n const originalScore = c.score;\n for (let j = selected.length - 1; j >= 0; --j) {\n const iou = IOU(boxes, c.boxIndex, selected[j]);\n if (iou === 0.0) continue;\n c.score *= suppressFunc(iou);\n if (c.score <= scoreThreshold) break;\n }\n if (originalScore === c.score) {\n selected.push(c.boxIndex);\n }\n });\n return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n const sizes = [\n tf.sub(vec[2], vec[0]),\n tf.sub(vec[3], vec[1]),\n ];\n const centers = [\n tf.add(vec[0], tf.div(sizes[0], 2)),\n tf.add(vec[1], tf.div(sizes[1], 2)),\n ];\n return { sizes, centers };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n const { sizes, centers } = getCenterCoordinatesAndSizesLayer(x0);\n\n const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], 5)), sizes[0]), 2);\n const add0_out = tf.add(tf.mul(tf.div(vec[0], 10), sizes[0]), centers[0]);\n const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], 5)), sizes[1]), 2);\n const add1_out = tf.add(tf.mul(tf.div(vec[1], 10), sizes[1]), centers[1]);\n\n return tf.transpose(\n tf.stack([\n tf.sub(add0_out, div0_out),\n tf.sub(add1_out, div1_out),\n tf.add(add0_out, div0_out),\n tf.add(add1_out, div1_out),\n ]),\n [1, 0],\n );\n}\n\nexport function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams) {\n return tf.tidy(() => {\n const batchSize = boxPredictions.shape[0];\n\n let boxes = decodeBoxesLayer(\n tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n );\n boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);\n\n const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n scores = tf.reshape(scores, [batchSize, scores.shape[1] as number]);\n\n const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n return { boxes: boxesByBatch, scores: scoresByBatch };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n x: tf.Tensor4D,\n params: BoxPredictionParams,\n) {\n return tf.tidy(() => {\n const batchSize = x.shape[0];\n const boxPredictionEncoding = tf.reshape(\n convLayer(x, params.box_encoding_predictor),\n [batchSize, -1, 1, 4],\n );\n const classPrediction = tf.reshape(\n convLayer(x, params.class_predictor),\n [batchSize, -1, 3],\n );\n return { boxPredictionEncoding, classPrediction };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n x: tf.Tensor4D,\n conv11: tf.Tensor4D,\n params: PredictionLayerParams,\n) {\n return tf.tidy(() => {\n const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n const boxPredictions = tf.concat([\n boxPrediction0.boxPredictionEncoding,\n boxPrediction1.boxPredictionEncoding,\n boxPrediction2.boxPredictionEncoding,\n boxPrediction3.boxPredictionEncoding,\n boxPrediction4.boxPredictionEncoding,\n boxPrediction5.boxPredictionEncoding,\n ], 1) as tf.Tensor4D;\n\n const classPredictions = tf.concat([\n boxPrediction0.classPrediction,\n boxPrediction1.classPrediction,\n boxPrediction2.classPrediction,\n boxPrediction3.classPrediction,\n boxPrediction4.classPrediction,\n boxPrediction5.classPrediction,\n ], 1) as tf.Tensor4D;\n\n return {\n boxPredictions,\n classPredictions,\n };\n });\n}\n", "export interface ISsdMobilenetv1Options {\n minConfidence?: number\n maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n protected _name = 'SsdMobilenetv1Options';\n\n private _minConfidence: number;\n\n private _maxResults: number;\n\n constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n this._minConfidence = minConfidence || 0.5;\n this._maxResults = maxResults || 100;\n\n if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n }\n\n if (typeof this._maxResults !== 'number') {\n throw new Error(`${this._name} - expected maxResults to be a number`);\n }\n }\n\n get minConfidence(): number { return this._minConfidence; }\n\n get maxResults(): number { return this._maxResults; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork {\n constructor() {\n super('SsdMobilenetv1');\n }\n\n public forwardInput(input: NetInput) {\n const { params } = this;\n if (!params) throw new Error('SsdMobilenetv1 - load model before inference');\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n const x = tf.sub(tf.div(batchTensor, 127.5), 1) as tf.Tensor4D; // input is normalized -1..1\n const features = mobileNetV1(x, params.mobilenetv1);\n const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n return outputLayer(boxPredictions, classPredictions, params.output_layer);\n });\n }\n\n public async forward(input: TNetInput) {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async locateFaces(input: TNetInput, options: ISsdMobilenetv1Options = {}): Promise {\n const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n const netInput = await toNetInput(input);\n const { boxes: _boxes, scores: _scores } = this.forwardInput(netInput);\n const boxes = _boxes[0];\n const scores = _scores[0];\n for (let i = 1; i < _boxes.length; i++) {\n _boxes[i].dispose();\n _scores[i].dispose();\n }\n const scoresData = Array.from(scores.dataSync());\n const iouThreshold = 0.5;\n const indices = nonMaxSuppression(boxes, scoresData as number[], maxResults, iouThreshold, minConfidence);\n const reshapedDims = netInput.getReshapedInputDimensions(0);\n const inputSize = netInput.inputSize as number;\n const padX = inputSize / reshapedDims.width;\n const padY = inputSize / reshapedDims.height;\n const boxesData = boxes.arraySync();\n const results = indices\n .map((idx) => {\n const [top, bottom] = [\n Math.max(0, boxesData[idx][0]),\n Math.min(1.0, boxesData[idx][2]),\n ].map((val) => val * padY);\n const [left, right] = [\n Math.max(0, boxesData[idx][1]),\n Math.min(1.0, boxesData[idx][3]),\n ].map((val) => val * padX);\n return new FaceDetection(\n scoresData[idx] as number,\n new Rect(left, top, right - left, bottom - top),\n { height: netInput.getInputHeight(0), width: netInput.getInputWidth(0) },\n );\n });\n boxes.dispose();\n scores.dispose();\n return results;\n }\n\n protected getDefaultModelName(): string {\n return 'ssd_mobilenetv1_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n const net = new SsdMobilenetv1();\n net.extractWeights(weights);\n return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n new Point(0.738768, 0.874946),\n new Point(2.42204, 2.65704),\n new Point(4.30971, 7.04493),\n new Point(10.246, 4.59428),\n new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n new Point(1.603231, 2.094468),\n new Point(6.041143, 7.080126),\n new Point(2.882459, 3.518061),\n new Point(4.266906, 5.178857),\n new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n withSeparableConvs: boolean\n iouThreshold: number\n anchors: Point[]\n classes: string[]\n meanRgb?: [number, number, number]\n withClassScores?: boolean,\n filterSizes?: number[]\n isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n if (!config) {\n throw new Error(`invalid config: ${config}`);\n }\n\n if (typeof config.withSeparableConvs !== 'boolean') {\n throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n }\n\n if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n }\n\n if (\n !Array.isArray(config.classes)\n || !config.classes.length\n || !config.classes.every((c: any) => typeof c === 'string')\n ) {\n throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n }\n\n if (\n !Array.isArray(config.anchors)\n || !config.anchors.length\n || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n ) {\n throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n }\n\n if (config.meanRgb && (\n !Array.isArray(config.meanRgb)\n || config.meanRgb.length !== 3\n || !config.meanRgb.every(isNumber)\n )) {\n throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n return tf.tidy(() => {\n const min = tf.mul(x, tf.scalar(0.10000000149011612));\n return tf.add(tf.relu(tf.sub(x, min)), min);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n out = tf.sub(out, params.bn.sub);\n out = tf.mul(out, params.bn.truediv);\n out = tf.add(out, params.conv.bias);\n return leaky(out);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n out = tf.add(out, params.bias);\n return leaky(out);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n const sub = tf.tensor1d(extractWeights(size));\n const truediv = tf.tensor1d(extractWeights(size));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/sub` },\n { paramPath: `${mappedPrefix}/truediv` },\n );\n return { sub, truediv };\n }\n\n function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n return { conv, bn };\n }\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n return {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n };\n}\n\nexport function extractParams(\n weights: Float32Array,\n config: TinyYolov2Config,\n boxEncodingSize: number,\n filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const paramMappings: ParamMapping[] = [];\n const {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n } = extractorsFactory(extractWeights, paramMappings);\n let params: TinyYolov2NetParams;\n\n if (config.withSeparableConvs) {\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n const conv0 = config.isFirstLayerConv2d\n ? extractConvParams(s0, s1, 3, 'conv0')\n : extractSeparableConvParams(s0, s1, 'conv0');\n const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n params = {\n conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n };\n } else {\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n params = {\n conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n };\n }\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractBatchNormParams(prefix: string): BatchNorm {\n const sub = extractWeightEntry(`${prefix}/sub`, 1);\n const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n return { sub, truediv };\n }\n\n function extractConvParams(prefix: string): ConvParams {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { filters, bias };\n }\n\n function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n const conv = extractConvParams(`${prefix}/conv`);\n const bn = extractBatchNormParams(`${prefix}/bn`);\n return { conv, bn };\n }\n\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n return {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n } = extractorsFactory(weightMap, paramMappings);\n\n let params: TinyYolov2NetParams;\n\n if (config.withSeparableConvs) {\n // eslint-disable-next-line no-mixed-operators\n const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n params = {\n conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n conv1: extractSeparableConvParams('conv1'),\n conv2: extractSeparableConvParams('conv2'),\n conv3: extractSeparableConvParams('conv3'),\n conv4: extractSeparableConvParams('conv4'),\n conv5: extractSeparableConvParams('conv5'),\n conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n conv8: extractConvParams('conv8'),\n };\n } else {\n params = {\n conv0: extractConvWithBatchNormParams('conv0'),\n conv1: extractConvWithBatchNormParams('conv1'),\n conv2: extractConvWithBatchNormParams('conv2'),\n conv3: extractConvWithBatchNormParams('conv3'),\n conv4: extractConvWithBatchNormParams('conv4'),\n conv5: extractConvWithBatchNormParams('conv5'),\n conv6: extractConvWithBatchNormParams('conv6'),\n conv7: extractConvWithBatchNormParams('conv7'),\n conv8: extractConvParams('conv8'),\n };\n }\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n inputSize?: number\n scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n protected _name = 'TinyYolov2Options';\n\n private _inputSize: number;\n\n private _scoreThreshold: number;\n\n constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n this._inputSize = inputSize || 416;\n this._scoreThreshold = scoreThreshold || 0.5;\n\n if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n }\n\n if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n }\n }\n\n get inputSize(): number { return this._inputSize; }\n\n get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork {\n public static DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024];\n\n private _config: TinyYolov2Config;\n\n constructor(config: TinyYolov2Config) {\n super('TinyYolov2');\n validateConfig(config);\n this._config = config;\n }\n\n public get config(): TinyYolov2Config {\n return this._config;\n }\n\n public get withClassScores(): boolean {\n return this.config.withClassScores || this.config.classes.length > 1;\n }\n\n public get boxEncodingSize(): number {\n return 5 + (this.withClassScores ? this.config.classes.length : 0);\n }\n\n public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n let out = convWithBatchNorm(x, params.conv0);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv1);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv2);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv3);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv4);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv5);\n out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n out = convWithBatchNorm(out, params.conv6);\n out = convWithBatchNorm(out, params.conv7);\n return convLayer(out, params.conv8, 'valid', false);\n }\n\n public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n let out = this.config.isFirstLayerConv2d\n ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv1);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv2);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv3);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv4);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv5);\n out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n return convLayer(out, params.conv8, 'valid', false);\n }\n\n public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('TinyYolov2 - load model before inference');\n }\n\n return tf.tidy(() => {\n let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n batchTensor = this.config.meanRgb\n ? normalize(batchTensor, this.config.meanRgb)\n : batchTensor;\n batchTensor = batchTensor.div(255) as tf.Tensor4D;\n return this.config.withSeparableConvs\n ? this.runMobilenet(batchTensor, params as MobilenetParams)\n : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n });\n }\n\n public async forward(input: TNetInput, inputSize: number): Promise {\n return this.forwardInput(await toNetInput(input), inputSize);\n }\n\n public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise {\n const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput, inputSize);\n const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n const inputDimensions = {\n width: netInput.getInputWidth(0),\n height: netInput.getInputHeight(0),\n };\n\n const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n out.dispose();\n out0.dispose();\n\n const boxes = results.map((res) => res.box);\n const scores = results.map((res) => res.score);\n const classScores = results.map((res) => res.classScore);\n const classNames = results.map((res) => this.config.classes[res.label]);\n\n const indices = nonMaxSuppression(\n boxes.map((box) => box.rescale(inputSize)),\n scores,\n this.config.iouThreshold,\n true,\n );\n\n const detections = indices.map((idx) => new ObjectDetection(\n scores[idx],\n classScores[idx],\n classNames[idx],\n boxes[idx],\n inputDimensions,\n ));\n return detections;\n }\n\n protected getDefaultModelName(): string {\n return '';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap, this.config);\n }\n\n protected extractParams(weights: Float32Array) {\n const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n const numFilters = filterSizes ? filterSizes.length : undefined;\n if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n }\n return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n }\n\n protected async extractBoxes(\n outputTensor: tf.Tensor4D,\n inputBlobDimensions: Dimensions,\n scoreThreshold?: number,\n ) {\n const { width, height } = inputBlobDimensions;\n const inputSize = Math.max(width, height);\n const correctionFactorX = inputSize / width;\n const correctionFactorY = inputSize / height;\n\n const numCells = outputTensor.shape[1];\n const numBoxes = this.config.anchors.length;\n\n const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n const classScores = this.withClassScores\n ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n : tf.scalar(0);\n return [boxes, scores, classScores];\n });\n\n const results = [] as any;\n const scoresData = await scoresTensor.array();\n const boxesData = await boxesTensor.array();\n for (let row = 0; row < numCells; row++) {\n for (let col = 0; col < numCells; col++) {\n for (let anchor = 0; anchor < numBoxes; anchor++) {\n const score = sigmoid(scoresData[row][col][anchor][0]);\n if (!scoreThreshold || score > scoreThreshold) {\n const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n const x = (ctX - (widthLocal / 2));\n const y = (ctY - (heightLocal / 2));\n const pos = { row, col, anchor };\n const { classScore, label } = this.withClassScores\n ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n : { classScore: 1, label: 0 };\n results.push({\n box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n score,\n classScore: score * classScore,\n label,\n ...pos,\n });\n }\n }\n }\n }\n\n boxesTensor.dispose();\n scoresTensor.dispose();\n classScoresTensor.dispose();\n return results;\n }\n\n private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n const { row, col, anchor } = pos;\n const classesData = await classesTensor.array();\n return Array(this.config.classes.length).fill(0)\n .map((_, i) => classesData[row][col][anchor][i])\n .map((classScore, label) => ({\n classScore,\n label,\n }))\n .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n BOX_ANCHORS,\n BOX_ANCHORS_SEPARABLE,\n DEFAULT_MODEL_NAME,\n DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n IOU_THRESHOLD,\n MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n constructor(withSeparableConvs = true) {\n const config = {\n withSeparableConvs,\n iouThreshold: IOU_THRESHOLD,\n classes: ['face'],\n ...(withSeparableConvs\n ? {\n anchors: BOX_ANCHORS_SEPARABLE,\n meanRgb: MEAN_RGB_SEPARABLE,\n }\n : {\n anchors: BOX_ANCHORS,\n withClassScores: true,\n }),\n };\n\n super(config);\n }\n\n public get withSeparableConvs(): boolean {\n return this.config.withSeparableConvs;\n }\n\n public get anchors(): Point[] {\n return this.config.anchors;\n }\n\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise {\n const objectDetections = await this.detect(input, forwardParams);\n return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n }\n\n protected override getDefaultModelName(): string {\n return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n }\n\n protected override extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n return super.extractParamsFromWeightMap(weightMap);\n }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\nexport * from './config';\nexport * from './types';\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs = true) {\n const net = new TinyYolov2(withSeparableConvs);\n net.extractWeights(weights);\n return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport type ITinyFaceDetectorOptions = ITinyYolov2Options\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n protected override _name = 'TinyFaceDetectorOptions';\n}\n", "export class ComposableTask {\n // eslint-disable-next-line no-unused-vars\n public async then(onfulfilled: (value: T) => T | PromiseLike): Promise {\n return onfulfilled(await this.run());\n }\n\n public async run(): Promise {\n throw new Error('ComposableTask - run is not implemented');\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults, TResult>(\n parentResults: TSource[],\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResults: (faces: Array) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment: (parentResult: WithFaceLandmarks) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n ? getRectForAlignment(parentResult)\n : parentResult.detection));\n const faces: Array = extractedFaces || (\n input instanceof tf.Tensor\n ? await extractFaceTensors(input, faceBoxes)\n : await extractFaces(input, faceBoxes)\n );\n const results = await computeResults(faces);\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n return results;\n}\n\nexport async function extractSingleFaceAndComputeResult, TResult>(\n parentResult: TSource,\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment?: (parentResultLocal: WithFaceLandmarks) => FaceDetection,\n) {\n return extractAllFacesAndComputeResults(\n [parentResult],\n input,\n async (faces) => computeResult(faces[0]),\n extractedFaces,\n getRectForAlignment,\n );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n new Point(1.603231, 2.094468),\n new Point(6.041143, 7.080126),\n new Point(2.882459, 3.518061),\n new Point(4.266906, 5.178857),\n new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n constructor() {\n const config = {\n withSeparableConvs: true,\n iouThreshold: IOU_THRESHOLD,\n classes: ['face'],\n anchors: BOX_ANCHORS,\n meanRgb: MEAN_RGB,\n isFirstLayerConv2d: true,\n filterSizes: [3, 16, 32, 64, 128, 256, 512],\n };\n\n super(config);\n }\n\n public get anchors(): Point[] {\n return this.config.anchors;\n }\n\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise {\n const objectDetections = await this.detect(input, forwardParams);\n return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n }\n\n protected override getDefaultModelName(): string {\n return 'tiny_face_detector_model';\n }\n\n protected override extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n return super.extractParamsFromWeightMap(weightMap);\n }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n ssdMobilenetv1: new SsdMobilenetv1(),\n tinyFaceDetector: new TinyFaceDetector(),\n tinyYolov2: new TinyYolov2(),\n faceLandmark68Net: new FaceLandmark68Net(),\n faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n faceRecognitionNet: new FaceRecognitionNet(),\n faceExpressionNet: new FaceExpressionNet(),\n ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllFaceExpressionsTask> extends PredictFaceExpressionsTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n\n const faceExpressionsByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(\n faces.map((face) => nets.faceExpressionNet.predictExpressions(face) as Promise),\n ),\n this.extractedFaces,\n );\n\n return parentResults.map(\n (parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]),\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsTask> extends PredictFaceExpressionsTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n\n const faceExpressions = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceExpressionNet.predictExpressions(face) as Promise,\n this.extractedFaces,\n );\n\n return extendWithFaceExpressions(parentResult, faceExpressions);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask>> extends PredictAllFaceExpressionsTask {\n override withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask>> extends PredictSingleFaceExpressionsTask {\n override withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllFaceExpressionsTask, PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllAgeAndGenderTask> extends PredictAgeAndGenderTaskBase>[], TSource[]> {\n public override async run(): Promise>[]> {\n const parentResults = await this.parentTask;\n const ageAndGenderByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise)),\n this.extractedFaces,\n );\n return parentResults.map((parentResult, i) => {\n const { age, gender, genderProbability } = ageAndGenderByFace[i];\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n });\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictSingleAgeAndGenderTask> extends PredictAgeAndGenderTaskBase> | undefined, TSource | undefined> {\n public override async run(): Promise> | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) return undefined;\n const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise,\n this.extractedFaces,\n );\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask>> extends PredictAllAgeAndGenderTask {\n override withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask>> extends PredictSingleAgeAndGenderTask {\n override withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n ) {\n super();\n }\n}\n\nexport class ComputeAllFaceDescriptorsTask>> extends ComputeFaceDescriptorsTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const descriptors = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise)),\n null,\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor));\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n}\n\nexport class ComputeSingleFaceDescriptorTask>> extends ComputeFaceDescriptorsTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) return undefined;\n const descriptor = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise,\n null,\n // eslint-disable-next-line no-shadow, @typescript-eslint/no-shadow\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n return extendWithFaceDescriptor(parentResult, descriptor);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected useTinyLandmarkNet: boolean,\n ) {\n super();\n }\n\n protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n return this.useTinyLandmarkNet\n ? nets.faceLandmark68TinyNet\n : nets.faceLandmark68Net;\n }\n}\n\nexport class DetectAllFaceLandmarksTask> extends DetectFaceLandmarksTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const detections = parentResults.map((res) => res.detection);\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, detections)\n : await extractFaces(this.input, detections);\n const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face))) as FaceLandmarks68[];\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n const result = parentResults\n .filter((_parentResult, i) => faceLandmarksByFace[i])\n .map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));\n return result;\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class DetectSingleFaceLandmarksTask> extends DetectFaceLandmarksTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n const { detection } = parentResult;\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, [detection])\n : await extractFaces(this.input, [detection]);\n const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n return extendWithFaceLandmarks(parentResult, landmarks);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase extends ComposableTask {\n // eslint-disable-next-line no-unused-vars\n constructor(protected input: TNetInput, protected options: FaceDetectionOptions = new SsdMobilenetv1Options()) {\n super();\n }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase {\n public override async run(): Promise {\n const { input, options } = this;\n let result;\n if (options instanceof TinyFaceDetectorOptions) result = nets.tinyFaceDetector.locateFaces(input, options);\n else if (options instanceof SsdMobilenetv1Options) result = nets.ssdMobilenetv1.locateFaces(input, options);\n else if (options instanceof TinyYolov2Options) result = nets.tinyYolov2.locateFaces(input, options);\n else throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options');\n return result;\n }\n\n private runAndExtendWithFaceDetections(): Promise[]> {\n return new Promise[]>((resolve, reject) => {\n this.run()\n .then((detections) => resolve(detections.map((detection) => extendWithFaceDetection({}, detection))))\n .catch((err) => reject(err));\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet = false) {\n return new DetectAllFaceLandmarksTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n useTinyLandmarkNet,\n );\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n );\n }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase {\n public override async run(): Promise {\n const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n let faceDetectionWithHighestScore = faceDetections[0];\n faceDetections.forEach((faceDetection) => {\n if (faceDetection.score > faceDetectionWithHighestScore.score) faceDetectionWithHighestScore = faceDetection;\n });\n return faceDetectionWithHighestScore;\n }\n\n private runAndExtendWithFaceDetection(): Promise | undefined> {\n // eslint-disable-next-line no-async-promise-executor\n return new Promise | undefined>(async (resolve) => {\n const detection = await this.run();\n resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet = false) {\n return new DetectSingleFaceLandmarksTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n useTinyLandmarkNet,\n );\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n );\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n );\n }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectSingleFaceTask {\n return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectAllFacesTask {\n return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\nexport async function allFacesSsdMobilenetv1(input: TNetInput, minConfidence?: number): Promise>>[]> {\n return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise>>[]> {\n return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n const desc1 = Array.from(arr1);\n const desc2 = Array.from(arr2);\n return Math.sqrt(\n desc1\n .map((val, i) => val - desc2[i])\n .reduce((res, diff) => res + (diff * diff), 0),\n );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n private _labeledDescriptors: LabeledFaceDescriptors[];\n private _distanceThreshold: number;\n\n constructor(inputs: LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>, distanceThreshold = 0.6) {\n this._distanceThreshold = distanceThreshold;\n const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n if (!inputArray.length) throw new Error('FaceRecognizer.constructor - expected atleast one input');\n let count = 1;\n const createUniqueLabel = () => `person ${count++}`;\n this._labeledDescriptors = inputArray.map((desc) => {\n if (desc instanceof LabeledFaceDescriptors) return desc;\n if (desc instanceof Float32Array) return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n if (desc.descriptor && desc.descriptor instanceof Float32Array) return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>');\n });\n }\n\n public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n public get distanceThreshold(): number { return this._distanceThreshold; }\n\n public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n return descriptors\n .map((d) => euclideanDistance(d, queryDescriptor))\n .reduce((d1, d2) => d1 + d2, 0) / (descriptors.length || 1);\n }\n\n public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n return this.labeledDescriptors\n .map(({ descriptors, label }) => new FaceMatch(label, this.computeMeanDistance(queryDescriptor, descriptors)))\n .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n }\n\n public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n const bestMatch = this.matchDescriptor(queryDescriptor);\n return (bestMatch.distance < this._distanceThreshold) ? bestMatch : new FaceMatch('unknown', bestMatch.distance);\n }\n\n public toJSON(): any {\n return {\n distanceThreshold: this._distanceThreshold,\n labeledDescriptors: this._labeledDescriptors.map((ld) => ld.toJSON()),\n };\n }\n\n public static fromJSON(json: any): FaceMatcher {\n const labeledDescriptors = json.labeledDescriptors.map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n const net = new TinyFaceDetector();\n net.extractWeights(weights);\n return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults(results: T, dimensions: IDimensions): T {\n const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n if (width <= 0 || height <= 0) {\n throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n }\n\n if (Array.isArray(results)) {\n // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n return (results as Array).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n }\n\n if (isWithFaceLandmarks(results)) {\n const resizedDetection = results.detection.forSize(width, height);\n const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n }\n\n if (isWithFaceDetection(results)) {\n return extendWithFaceDetection(results, results.detection.forSize(width, height));\n }\n\n if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n return (results as any).forSize(width, height);\n }\n\n return results;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nexport const version = pkg.version as string;\n\n// set webgl defaults\n// if (browser) tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n"], + "mappings": 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this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*$b(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof ps||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let 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e=="object"}function db(e){return e.kernelName!=null}var lk=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Kp=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new lk}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let 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o,l=db(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(db(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=Rh(h,this.backendName);A(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let b=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,b);let x=b.map(w=>w.rank!=null?w:this.makeTensorFromTensorInfo(w));if(a){let w=this.getTensorsForGradient(h,m,x);n=this.saveTensorsForBackwardMode(w)}return x}}else{let{forwardFunc:h}=e,m=f=>{!a||(n=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,g),g}}let{inputs:u,attrs:p}=e,d=db(e)?null:e.backwardsFunc,c;return 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this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*_h(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof ps||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let 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t={x:_(e,"x","tanh","float32")};return O.runKernel(yo,t)}var oi=L({tanh_:lM});function uM(e,t,n,a,r,s){let i=_(e,"forgetBias","basicLSTMCell"),o=_(t,"lstmKernel","basicLSTMCell"),l=_(n,"lstmBias","basicLSTMCell"),u=_(a,"data","basicLSTMCell"),p=_(r,"c","basicLSTMCell"),d=_(s,"h","basicLSTMCell"),c=Ze([u,d],1),h=Re(c,o),m=X(h,l),f=m.shape[0],g=m.shape[1]/4,y=[f,g],b=Ve(m,[0,0],y),x=Ve(m,[0,g],y),w=Ve(m,[0,g*2],y),I=Ve(m,[0,g*3],y),T=X(z(ha(b),oi(x)),z(p,ha(X(i,w)))),C=z(oi(T),ha(I));return[T,C]}var NS=L({basicLSTMCell_:uM});function pM(e,t,n){let a=_(e,"x","batchToSpaceND"),r=t.reduce((o,l)=>o*l);A(a.rank>=1+t.length,()=>`input rank is ${a.rank} but should be > than blockShape.length ${t.length}`),A(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),A(a.shape[0]%r===0,()=>`input tensor batch is ${a.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let s={x:a},i={blockShape:t,crops:n};return O.runKernel(Ul,s,i)}var Rc=L({batchToSpaceND_:pM});function cM(e){let t;return e.rank===0||e.rank===1?t=W(e,[1,1,1,e.size]):e.rank===2?t=W(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=W(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function dM(e,t,n,a,r,s){s==null&&(s=.001);let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),u;r!=null&&(u=_(r,"scale","batchNorm"));let p;a!=null&&(p=_(a,"offset","batchNorm")),A(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),A(p==null||o.rank===p.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),A(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let d={x:cM(i),scale:u,offset:p,mean:o,variance:l},c={varianceEpsilon:s},h=O.runKernel(Mi,d,c);return W(h,i.shape)}var Ts=L({batchNorm_:dM});function hM(e,t,n,a,r,s){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),u;r!=null&&(u=_(r,"scale","batchNorm"));let p;return a!=null&&(p=_(a,"offset","batchNorm")),A(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),A(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),A(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),p!=null&&A(p.rank===2||p.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${p.rank}.`),Ts(i,o,l,p,u,s)}var jx=L({batchNorm2d_:hM});function mM(e,t,n,a,r,s){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),u;r!=null&&(u=_(r,"scale","batchNorm"));let p;return a!=null&&(p=_(a,"offset","batchNorm")),A(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),A(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),A(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),p!=null&&A(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${p.rank}.`),Ts(i,o,l,p,u,s)}var Kx=L({batchNorm3d_:mM});function fM(e,t,n,a,r,s){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),u;r!=null&&(u=_(r,"scale","batchNorm"));let p;return a!=null&&(p=_(a,"offset","batchNorm")),A(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),A(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),A(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),p!=null&&A(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${p.rank}.`),Ts(i,o,l,p,u,s)}var Xx=L({batchNorm4d_:fM});function gM(e,t,n){let a=_(e,"x","bincount"),r=_(t,"weights","bincount");A(a.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${a.dtype}`),A(n>=0,()=>`size must be non-negative, but got ${n}.`),A(r.size===a.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${a.shape}, weights shape: ${r.shape}.`);let s={x:a,weights:r},i={size:n};return O.runKernel(dm,s,i)}var Yx=L({bincount_:gM});function yM(e,t){let n=_(e,"s0","broadcastArgs","int32"),a=_(t,"s1","broadcastArgs","int32");if(n.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${n.rank}`);if(a.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${a.rank}`);let r={s0:n,s1:a};return O.runKernel(hm,r)}var CS=L({broadcastArgs_:yM});function bM(e,t){let n=_(e,"broadcastTo","x"),a=n.shape;if(aa(t),t.lengthn.rank){let l=n.shape.slice();for(;l.length=0;l--)if(r[l]===t[l])s[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${a}] cannot be broadcast to [${t}].`);if(s.map((l,u)=>l>1?u:-1).filter(l=>l>=0).length===0)return or(n);let i={x:n},o={reps:s};return O.runKernel(ks,i,o)}var Zs=L({broadcastTo_:bM});function xM(e){let t={x:_(e,"x","ceil","float32")};return O.runKernel(Ii,t)}var Zx=L({ceil_:xM});function bn(e,t,n){aa(e);let a={shape:e,value:t,dtype:n};return O.runKernel(fc,{},a)}function vM(e,t,n){let a=_(e,"x","clipByValue");if(A(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`),t===n)return bn(a.shape,t,a.dtype);let r={x:a},s={clipValueMin:t,clipValueMax:n};return O.runKernel(ws,r,s)}var tn=L({clipByValue_:vM});function wM(e){return Ze(e,0)}var Jx=L({concat1d_:wM});function kM(e,t){return Ze(e,t)}var Qx=L({concat2d_:kM});function IM(e,t){return Ze(e,t)}var ev=L({concat3d_:IM});function SM(e,t){return Ze(e,t)}var tv=L({concat4d_:SM});function TM(e,t,n,a,r="NHWC",s=[1,1],i){let o=_(e,"x","conv2d","float32"),l=_(t,"filter","conv2d","float32"),u=o,p=!1;o.rank===3&&(p=!0,u=W(o,[1,o.shape[0],o.shape[1],o.shape[2]])),A(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),A(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),Nn("conv2d",a,i);let d=r==="NHWC"?u.shape[3]:u.shape[1];A(d===l.shape[2],()=>`Error in conv2d: depth of input (${d}) must match input depth for filter ${l.shape[2]}.`),A(hr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),A(ii(s),()=>"Error in conv2D: Dilated rates should be larger than 0."),A(ii(n),()=>"Error in conv2D: Strides should be larger than 0.");let c={x:u,filter:l},h={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i},m=O.runKernel(Si,c,h);return p?W(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Rt=L({conv2d_:TM});function NM(e,t,n,a,r="NWC",s=1,i){let o=_(e,"x","conv1d"),l=_(t,"filter","conv1d"),u=o,p=!1;o.rank===2&&(p=!0,u=W(o,[1,o.shape[0],o.shape[1]])),A(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),A(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),Nn("conv1d",a,i),A(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),A(hr(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),A(ii(s),()=>"Error in conv1D: Dilated rates should be larger than 0."),A(ii(n),()=>"Error in conv1D: Stride should be larger than 0."),A(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let d=W(l,[1,l.shape[0],l.shape[1],l.shape[2]]),c=W(u,[u.shape[0],1,u.shape[1],u.shape[2]]),h=Rt(c,d,[1,n],a,"NHWC",[1,s],i);return p?W(h,[h.shape[2],h.shape[3]]):W(h,[h.shape[0],h.shape[2],h.shape[3]])}var jm=L({conv1d_:NM});function CM(e,t,n,a,r,s="NHWC",i){A(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,u=!1;t.rank===3&&(u=!0,l=W(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),A(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),A(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),A(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let p=s==="NHWC"?o[3]:o[1],d=s==="NHWC"?l.shape[3]:l.shape[1];A(p===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${p}) must match input depth for filter ${n.shape[2]}.`),A(d===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${d}) must match output depth for filter ${n.shape[3]}.`),Nn("conv2dDerInput",r,i);let c={dy:l,filter:n},h={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,inputShape:o},m=O.runKernel(Ti,c,h);return u?W(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var nv=L({conv2DBackpropInput_:CM});function _M(e,t,n,a,r,s){let i=_(e,"x","conv2dTranspose"),o=_(t,"filter","conv2dTranspose");return nv(n,i,o,a,r,"NHWC",s)}var Km=L({conv2dTranspose_:_M});function EM(e,t,n,a,r="NDHWC",s=[1,1,1]){let i=_(e,"x","conv3d"),o=_(t,"filter","conv3d"),l=i,u=!1;i.rank===4&&(u=!0,l=W(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),A(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),A(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),A(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),A(hr(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. 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Got strides ${n} and dilations '${s}'`);let g=Fc(h.shape,c.shape,n,s,a,i),y;o!=null&&(y=_(o,"bias","fused conv2d"),[y]=Et(y,d),r==="NHWC"?lt(g.outShape,y.shape):(A(y.shape.length<=1,()=>`Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${y.shape.length}.`),A(y.shape.length===0||y.shape[0]===g.outChannels||y.shape[0]===1,()=>`Error in fused conv2d: bias shape (${y.shape}) is not compatible with the number of output channels (${g.outChannels})`)));let b;if(u!=null){let T=u.shape;if(A(T.length<=1||T.length===3,()=>`Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${T.length}.`),T.length===1)A(T[0]===1||T[0]===g.outChannels,()=>`Error in fused conv2d: PReLU activation weights (${T}) is not compatible with the number of output channels (${g.outChannels}).`);else if(T.length===3)try{lt(T,g.outShape)}catch(C){let E=`Error in fused conv2d: PReLU activation weights (${T}) is not compatible with the output shape of the conv2d (${g.outShape}).`;throw Error(E)}b=_(u,"prelu weights","fused conv2d")}let x=(T,C)=>{A(r==="NHWC",()=>`Error in gradient of fused conv2D: got dataFormat of ${r} but only NHWC is currently supported.`);let[E,$,R,F]=C,S=bf(T,R,l);A(cs(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let M=nv($.shape,S,E,n,a),B=Vv($,S,E.shape,n,a),U=[M,B];if(F!=null){let G=xf(F,S);U.push(G)}return U},w={x:h,filter:c,bias:y,preluActivationWeights:b},I={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:p};return o==null?cr((T,C,E)=>{let $=O.runKernel(ti,w,I);return E([C,T,$]),m&&($=W($,[$.shape[1],$.shape[2],$.shape[3]])),{value:$,gradFunc:x}})(h,c):cr((T,C,E,$)=>{let R=O.runKernel(ti,w,I);return $([C,T,R,E]),m&&(R=W(R,[R.shape[1],R.shape[2],R.shape[3]])),{value:R,gradFunc:x}})(h,c,y)}var C3=L({fusedConv2d_:N3});function _3(e,t,n,a,r,s=[1,1],i){let o=e;e.rank===3&&(o=W(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=W(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={x:o,dy:l},p={strides:a,pad:r,dimRoundingMode:i,dilations:s,filterShape:n};return O.runKernel(xm,u,p)}var xT=L({depthwiseConv2dNativeBackpropFilter_:_3});function E3(e,t,n,a,r,s=[1,1],i){let o=t,l=!1;t.rank===3&&(l=!0,o=W(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={dy:o,filter:n},p={strides:a,pad:r,dimRoundingMode:i,dilations:s,inputShape:e},d=O.runKernel(vm,u,p);return l?W(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var vT=L({depthwiseConv2dNativeBackpropInput_:E3});function A3({x:e,filter:t,strides:n,pad:a,dataFormat:r="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:p}){if(wf(O.state.gradientDepth,l)===!1){let I=Ns(e,t,n,a,r,s,i);return o!=null&&(I=X(I,o)),vf(I,l,u,p)}let d=_(e,"x","depthwiseConv2d","float32"),c=_(t,"filter","depthwiseConv2d","float32"),h=d,m=!1;d.rank===3&&(m=!0,h=W(d,[1,d.shape[0],d.shape[1],d.shape[2]])),A(h.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${h.rank}.`),A(c.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${c.rank}.`),A(h.shape[3]===c.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${h.shape[3]}) must match the inChannels dimension in filter ${c.shape[2]}.`),s==null&&(s=[1,1]),A(hr(n,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. 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Got dilations '${s}'`);let[C,E,$,R]=T,F=bf(I,$,l),S=vT(E.shape,F,C,n,a,s,i),M=xT(E,F,C.shape,n,a,s,i);if(R!=null){let B=xf(g,F);return[S,M,B]}return[S,M]},x={x:h,filter:c,bias:g,preluActivationWeights:y},w={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:p};return o==null?cr((I,T,C)=>{let E=O.runKernel(ni,x,w);return C([T,I,E]),m&&(E=W(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:b}})(h,c):cr((I,T,C,E)=>{let $=O.runKernel(ni,x,w);return E([T,I,$,C]),m&&($=W($,[$.shape[1],$.shape[2],$.shape[3]])),{value:$,gradFunc:b}})(h,c,g)}var $3=L({fusedDepthwiseConv2d_:A3});function F3({a:e,b:t,transposeA:n=!1,transposeB:a=!1,bias:r,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:o=.2}){if(wf(O.state.gradientDepth,s)===!1){let R=Re(e,t,n,a);return r!=null&&(R=X(R,r)),vf(R,s,i,o)}let l=_(e,"a","fused matMul"),u=_(t,"b","fused matMul");[l,u]=Et(l,u);let p=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=a?u.shape[u.rank-1]:u.shape[u.rank-2],c=n?l.shape[l.rank-1]:l.shape[l.rank-2],h=a?u.shape[u.rank-2]:u.shape[u.rank-1],m=l.shape.slice(0,-2),f=u.shape.slice(0,-2),g=mt(m),y=mt(f);A(p===d,()=>`Error in fused matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${l.shape} and ${u.shape} and transposeA=${n} and transposeB=${a} must match.`);let b=lt(l.shape.slice(0,-2),u.shape.slice(0,-2)).concat([c,h]),x=n?W(l,[g,p,c]):W(l,[g,c,p]),w=a?W(u,[y,h,d]):W(u,[y,d,h]),I;r!=null&&(I=_(r,"bias","fused matMul"),[I]=Et(I,l),lt(b,I.shape));let T;i!=null&&(T=_(i,"prelu weights","fused matMul"));let C=(R,F)=>{let[S,M,B,U]=F,G=bf(W(R,B.shape),B,s),j,K;if(!n&&!a?(j=Re(G,M,!1,!0),K=Re(S,G,!0,!1)):!n&&a?(j=Re(G,M,!1,!1),K=Re(G,S,!0,!1)):n&&!a?(j=Re(M,G,!1,!0),K=Re(S,G,!1,!1)):(j=Re(M,G,!0,!0),K=Re(G,S,!0,!0)),r!=null){let Z=xf(U,G);return[j,K,Z]}else return[j,K]},E={a:x,b:w,bias:I,preluActivationWeights:T},$={transposeA:n,transposeB:a,activation:s,leakyreluAlpha:o};return r==null?cr((R,F,S)=>{let M=O.runKernel(ei,E,$);return S([R,F,M]),{value:W(M,b),gradFunc:C}})(x,w):cr((R,F,S,M)=>{let B=O.runKernel(ei,E,$);return M([R,F,B,S]),{value:W(B,b),gradFunc:C}})(x,w,I)}var R3=L({fusedMatMul_:F3});function D3(e){return yf(e,.54,.46)}var M3=L({hammingWindow_:D3});function P3(e){return yf(e,.5,.5)}var wT=L({hannWindow_:P3});function O3(e,t,n,a=!1,r=0){let s=0,i=[];for(;s+t<=e.size;)i.push(Ve(e,s,t)),s+=n;if(a)for(;s`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),A(o.rank===2&&o.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${u},4] but had shape ${o.shape}.`),A(l.rank===1&&l.shape[0]===u,()=>`Error in cropAndResize: boxInd must be have size [${u}] but had shape ${o.shape}.`),A(a.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${a.length}.`),A(a[0]>=1&&a[1]>=1,()=>`cropSize must be atleast [1,1], but was ${a}`),A(r==="bilinear"||r==="nearest",()=>`method must be bilinear or nearest, but was ${r}`);let p={image:i,boxes:o,boxInd:l},d={method:r,extrapolationValue:s,cropSize:a};return O.runKernel(ql,p,d)}var B3=L({cropAndResize_:W3});function V3(e){let t=_(e,"image","flipLeftRight","float32");A(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let n={image:t};return O.runKernel(Jl,n,{})}var U3=L({flipLeftRight_:V3});function G3(e){let t=_(e,"image","grayscaleToRGB"),n=t.rank-1,a=t.shape[n];A(t.rank>=2,()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`),A(a===1,()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${a}.`);let r=new Array(t.rank);return r.fill(1,0,n),r[n]=3,Ln(t,r)}var H3=L({grayscaleToRGB_:G3});function q3(e,t,n=0,a=.5){let r=_(e,"image","rotateWithOffset","float32");A(r.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);let s={image:r},i={radians:t,fillValue:n,center:a};return O.runKernel(Du,s,i)}var j3=L({rotateWithOffset_:q3});function Vu(e,t,n,a,r,s){a==null&&(a=.5),r==null&&(r=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),A(0<=a&&a<=1,()=>`iouThreshold must be in [0, 1], but was '${a}'`),A(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),A(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),A(t.rank===1,()=>"scores must be a 1D tensor"),A(t.shape[0]===i,()=>`scores has incompatible shape with boxes. 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O.runKernel($u,l,u)}var bL=L({transform_:yL});function xL(e,t,n){A(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),A(n%1===0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let a=_(e,"a","bandPart");A(a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${a.rank}.`);let r=a.shape,[s,i]=a.shape.slice(-2);if(!(t<=s))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`);if(!(n<=i))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);t<0&&(t=s),n<0&&(n=i);let o=W(pi(0,s,1,"int32"),[-1,1]),l=pi(0,i,1,"int32"),u=pe(o,l),p=Ra(Cs(u,xe(+t,"int32")),Dr(u,xe(-n,"int32"))),d=It([s,i],a.dtype);return W(Ft(ut(W(a,[-1,s,i])).map(c=>gn(p,c,d))),r)}var vL=L({bandPart_:xL});function wL(e){let t;if(Array.isArray(e)){t=!1,A(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let r=e[0].shape[0];for(let s=1;s`Gram-Schmidt: Non-unique lengths 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r=_(e,"labels","hingeLoss"),s=_(t,"predictions","hingeLoss"),i=null;n!=null&&(i=_(n,"weights","hingeLoss")),Tn(r.shape,s.shape,"Error in hingeLoss: ");let o=xe(1);r=pe(z(xe(2),r),o);let l=Ke(pe(o,z(r,s)));return Mr(l,i,a)}var $L=L({hingeLoss_:AL});function FL(e,t,n,a=1,r=wn.SUM_BY_NONZERO_WEIGHTS){let s=_(e,"labels","huberLoss"),i=_(t,"predictions","huberLoss"),o=null;n!=null&&(o=_(n,"weights","huberLoss")),Tn(s.shape,i.shape,"Error in huberLoss: ");let l=xe(a),u=Lt(pe(i,s)),p=Wu(u,l),d=pe(u,p),c=X(z(xe(.5),ot(p)),z(l,d));return Mr(c,o,r)}var RL=L({huberLoss_:FL});function DL(e,t,n,a=1e-7,r=wn.SUM_BY_NONZERO_WEIGHTS){let s=_(e,"labels","logLoss"),i=_(t,"predictions","logLoss"),o=null;n!=null&&(o=_(n,"weights","logLoss")),Tn(s.shape,i.shape,"Error in logLoss: ");let l=xe(1),u=xe(a),p=vt(z(s,ea(X(i,u)))),d=z(pe(l,s),ea(X(pe(l,i),u))),c=pe(p,d);return Mr(c,o,r)}var ML=L({logLoss_:DL});function PL(e,t,n,a=wn.SUM_BY_NONZERO_WEIGHTS){let r=_(e,"labels","meanSquaredError"),s=_(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=_(n,"weights","meanSquaredError")),Tn(r.shape,s.shape,"Error in meanSquaredError: ");let o=mf(r,s);return Mr(o,i,a)}var OL=L({meanSquaredError_:PL});function LL(e,t){let n=_(e,"labels","sigmoidCrossEntropyWithLogits"),a=_(t,"logits","sigmoidCrossEntropyWithLogits");Tn(n.shape,a.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Ke(a),s=z(a,n),i=Oc(yn(vt(Lt(a))));return X(pe(r,s),i)}function zL(e,t,n,a=0,r=wn.SUM_BY_NONZERO_WEIGHTS){let s=_(e,"multiClassLabels","sigmoidCrossEntropy"),i=_(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=_(n,"weights","sigmoidCrossEntropy")),Tn(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),a>0){let u=xe(a),p=xe(1),d=xe(.5);s=X(z(s,pe(p,u)),z(d,u))}let l=LL(s,i);return Mr(l,o,r)}var WL=L({sigmoidCrossEntropy_:zL});function BL(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet 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Labels / logits was rank ${t.rank} and dim was ${n}`);return cr((a,r,s)=>{let i=tf(r,[n],!0),o=pe(ie(r,"float32"),i);s([a,o]);let l=vt(z(o,a));return{value:fe(l,[n]),gradFunc:(u,p)=>{let[d,c]=p,h=li(u.shape,[n]);return[z(W(u,h),pe(ie(d,"float32"),yn(c))),z(W(u,h),pe(yn(c),ie(d,"float32")))]}}})(e,t)}function VL(e,t,n,a=0,r=wn.SUM_BY_NONZERO_WEIGHTS){let s=_(e,"onehotLabels","softmaxCrossEntropy"),i=_(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=_(n,"weights","softmaxCrossEntropy")),Tn(s.shape,i.shape,"Error in softmaxCrossEntropy: "),a>0){let u=xe(a),p=xe(1),d=xe(s.shape[1]);s=X(z(s,pe(p,u)),he(u,d))}let l=BL(s,i);return Mr(l,o,r)}var UL=L({softmaxCrossEntropy_:VL});function GL(e,t,n,a){let r=_(e,"indices","sparseFillEmptyRows","int32"),s=_(t,"values","sparseFillEmptyRows"),i=_(n,"denseShape","sparseFillEmptyRows","int32"),o=_(a,"defaultValue","sparseFillEmptyRows",s.dtype);if(r.rank!==2)throw new Error(`Indices should be Tensor2D but received shape + 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Nf.className="Adamax";Ts(Nf);var Kc=class extends Pr{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=O.registeredVariables[t];P(()=>{let s=Y(z(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(xe(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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OS(e,t)}dispose(){this.iterations_!=null&&_e(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:xe(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Pr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Hv=class extends Pr{constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=O.backend.epsilon())}static get className(){return"Adadelta"}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=O.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:P(()=>qe(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:P(()=>qe(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;P(()=>{let l=X(z(i,this.rho),z(ot(s),1-this.rho)),u=z(he(cn(X(o,this.epsilon)),cn(X(i,this.epsilon))),s),p=X(z(o,this.rho),z(ot(u),1-this.rho));i.assign(l),o.assign(p);let d=X(z(u,-this.learningRate),a);a.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(_e(this.accumulatedGrads.map(e=>e.variable)),_e(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}},qv=class extends Pr{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}static get className(){return"Adagrad"}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=O.registeredVariables[t];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:P(()=>bn(a.shape,this.initialAccumulatorValue).variable(!1))});let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;P(()=>{let i=X(s,ot(r));s.assign(i);let o=X(z(he(r,cn(X(i,O.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&_e(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}},jv=class extends Pr{constructor(e,t,n,a=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],P(()=>{this.accBeta1=xe(t).variable(),this.accBeta2=xe(n).variable()}),a==null&&(this.epsilon=O.backend.epsilon())}static get className(){return"Adam"}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);P(()=>{let n=pe(1,this.accBeta1),a=pe(1,this.accBeta2);t.forEach((r,s)=>{let i=O.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:P(()=>qe(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:P(()=>qe(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedSecondMoment[s].variable,d=X(z(u,this.beta1),z(l,1-this.beta1)),c=X(z(p,this.beta2),z(ot(l),1-this.beta2)),h=he(d,n),m=he(c,a);u.assign(d),p.assign(c);let f=X(z(he(h,X(cn(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(z(this.accBeta1,this.beta1)),this.accBeta2.assign(z(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&_e(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&_e(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),P(()=>{this.accBeta1.assign(Rr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Rr(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}},Kv=class extends Pr{constructor(e,t,n,a=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],P(()=>{this.iteration=xe(0).variable(),this.accBeta1=xe(t).variable()}),a==null&&(this.epsilon=O.backend.epsilon())}static get className(){return"Adamax"}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);P(()=>{let n=pe(1,this.accBeta1),a=he(-this.learningRate,X(z(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=O.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:qe(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:qe(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,p=this.accumulatedWeightedInfNorm[s].variable,d=X(z(u,this.beta1),z(l,1-this.beta1)),c=z(p,this.beta2),h=Lt(l),m=mr(c,h);u.assign(d),p.assign(m);let f=X(z(he(a,n),he(d,X(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(X(this.iteration,1)),this.accBeta1.assign(z(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&_e(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&_e(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}},kf=class extends Pr{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}static get className(){return"SGD"}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=O.registeredVariables[t];P(()=>{let s=X(z(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Gt(xe(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}},Xv=class extends kf{constructor(e,t,n=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=xe(this.momentum)}static get className(){return"Momentum"}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=O.registeredVariables[t];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${t}/momentum`,variable:P(()=>qe(a).variable(!1))});let r=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];s!=null&&P(()=>{let i,o=X(z(this.m,r),s);this.useNesterov?i=X(z(this.c,X(s,z(o,this.m))),a):i=X(z(this.c,o),a),r.assign(o),a.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&_e(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}},Yv=class extends Pr{constructor(e,t=.9,n=0,a=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,a==null&&(this.epsilon=O.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}static get className(){return"RMSProp"}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=O.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:P(()=>qe(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:P(()=>qe(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:P(()=>qe(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;P(()=>{let l=X(z(i,this.decay),z(ot(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[n].variable,p=X(z(u,this.decay),z(s,1-this.decay)),d=he(z(s,this.learningRate),cn(pe(l,X(ot(p),this.epsilon)))),c=X(z(o,this.momentum),d);i.assign(l),u.assign(p),o.assign(c);let h=pe(a,c);a.assign(h)}else{let u=X(z(i,this.decay),z(ot(s),1-this.decay)),p=X(z(o,this.momentum),he(z(s,this.learningRate),cn(X(u,this.epsilon))));i.assign(u),o.assign(p);let d=pe(a,p);a.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&_e(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&_e(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&_e(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}},rz=[Hv,qv,jv,Kv,Xv,Yv,kf];function sz(){for(let e of rz)DT(e)}var Ht={};Ae(Ht,{browserFiles:()=>dz,browserHTTPRequest:()=>yz,concatenateArrayBuffers:()=>Rx,copyModel:()=>cD,decodeWeights:()=>pS,encodeWeights:()=>WR,fromMemory:()=>xz,fromMemorySync:()=>zT,getLoadHandlers:()=>ZR,getModelArtifactsForJSON:()=>Dx,getModelArtifactsForJSONSync:()=>dS,getModelArtifactsInfoForJSON:()=>$c,getSaveHandlers:()=>YR,getWeightSpecs:()=>hS,http:()=>Jv,isHTTPScheme:()=>Wb,listModels:()=>uD,loadWeights:()=>hz,moveModel:()=>dD,registerLoadRouter:()=>XR,registerSaveRouter:()=>KR,removeModel:()=>pD,weightsLoaderFactory:()=>PT,withSaveHandler:()=>vz,withSaveHandlerSync:()=>wz});var iz="model",oz=".json",lz=".weights.bin";function mk(e){return new Promise(t=>setTimeout(t)).then(e)}var kl=class{constructor(e){if(!H().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a 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this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},a=!1){let r,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");r=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof El))throw new Me(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=Ga(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new V("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new V("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};El.className="Sequential";ne.registerClass(El);function RU(e){return new $r(e)}function MU(e){return new El(e)}function VN(e){return wN(e)}function PU(e,t){Ea.registerCallbackConstructor(e,t)}var Un=class extends ne.Serializable{getConfig(){return{}}},UN=class extends Un{apply(e,t=1){return iV(e,t)}};UN.className="elu";ne.registerClass(UN);var GN=class extends Un{apply(e){return pf(e)}};GN.className="selu";ne.registerClass(GN);var HN=class extends Un{apply(e){return Xe(e)}};HN.className="relu";ne.registerClass(HN);var jN=class extends Un{apply(e){return P(()=>Uu(6,Xe(e)))}};jN.className="relu6";ne.registerClass(jN);var qN=class extends Un{apply(e){return e}};qN.className="linear";ne.registerClass(qN);var KN=class extends Un{apply(e){return ha(e)}};KN.className="sigmoid";ne.registerClass(KN);var XN=class extends Un{apply(e){return lV(e)}};XN.className="hardSigmoid";ne.registerClass(XN);var YN=class extends Un{apply(e){return vo(e)}};YN.className="softplus";ne.registerClass(YN);var ZN=class extends Un{apply(e){return oV(e)}};ZN.className="softsign";ne.registerClass(ZN);var JN=class extends Un{apply(e){return li(e)}};JN.className="tanh";ne.registerClass(JN);var Sw=class extends Un{apply(e,t=-1){return Xa(e,t)}};Sw.className="softmax";ne.registerClass(Sw);var QN=class extends Un{apply(e,t=-1){return tf(e,t)}};QN.className="logSoftmax";ne.registerClass(QN);var e2=class extends Un{apply(e,t=1){return P(()=>z(ha(z(e,t)),e))}};e2.className="swish";ne.registerClass(e2);var t2=class extends Un{apply(e){return P(()=>z(e,li(vo(e))))}};t2.className="mish";ne.registerClass(t2);function hs(e){return e.getClassName()}function Nb(e,t={}){return Xc(e,ne.SerializationMap.getMap().classNameMap,t,"activation")}function ms(e){if(e==null){let t={};return t.className="linear",t.config={},Nb(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Nb(t)}else return e instanceof Un?e:Nb(e)}function Tw(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var n2=class extends ne.Serializable{},ed=class extends n2{constructor(e){super(),Tw(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return P(()=>{let t=It([1]);return this.hasL1&&(t=Y(t,fe(z(this.l1,Lt(e))))),this.hasL2&&(t=Y(t,fe(z(this.l2,Zc(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};ed.className="L1L2";ne.registerClass(ed);function OU(e){return Tw(e),new ed({l1:e!=null?e.l1:null,l2:0})}function LU(e){return Tw(e),new ed({l2:e!=null?e.l2:null,l1:0})}var qk={l1l2:"L1L2"};function ct(e){return aw(e)}function Kk(e,t={}){return Xc(e,ne.SerializationMap.getMap().classNameMap,t,"regularizer")}function Tt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in qk?qk[e]:e,config:{}};return Kk(t)}else return e instanceof n2?e:Kk(e)}var Nw=class extends Ve{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ne(e);let n=Xe(e);return this.maxValue!=null&&(n=tn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Nw.className="ReLU";ne.registerClass(Nw);var Cw=class extends Ve{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Ne(e);return zc(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Cw.className="LeakyReLU";ne.registerClass(Cw);var Ew=class extends Ve{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=St(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Tt(e.alphaRegularizer),this.alphaConstraint=Yt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Je(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a(Rt(t),t==="channelsFirst"?_e(e,[0,2,3,1]):e))}function a2(e,t){return P(()=>(Rt(t),t==="channelsFirst"?_e(e,[0,2,3,4,1]):e))}function zU(e,t,n,a=1,r="valid",s,i=1){return P(()=>{if(s==null&&(s=qa()),Rt(s),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=_e(e,[0,2,1])),r==="causal")throw new Me("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Km(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ya(o,n)),o})}function Xk(e,t,n,a=[1,1],r="valid",s,i,o=null){return P(()=>{if(s==null&&(s=qa()),Rt(s),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=Fw(e,s);if(r==="causal")throw new Me("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Tl.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=_e(l,[0,3,1,2])),l})}function WU(e,t,n,a=[1,1,1],r="valid",s,i){return P(()=>{if(s==null&&(s=qa()),Rt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=a2(e,s);if(r==="causal")throw new Me("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=bv(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ya(o,n)),s==="channelsFirst"&&(o=_e(o,[0,4,1,2,3])),o})}var Dw=class extends Ve{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Dw.verifyArgs(t),this.rank=e,en(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Me(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=gl(t.kernelSize,e,"kernelSize"),this.strides=gl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,va(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Rt(this.dataFormat),this.activation=ms(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=St(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(t.biasConstraint),this.biasRegularizer=Tt(t.biasRegularizer),this.activityRegularizer=Tt(t.activityRegularizer),this.dilationRate=gl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new V(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(rr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!rw(e.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:hs(this.activation),useBias:this.useBias,biasInitializer:Ct(this.biasInitializer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},td=class extends Dw{constructor(e,t){super(e,t),this.kernel=null,td.verifyArgs(t),this.filters=t.filters,en(this.filters,"filters"),this.kernelInitializer=St(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(t.kernelConstraint),this.kernelRegularizer=Tt(t.kernelRegularizer)}build(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return P(()=>{e=Ne(e);let n,a=this.bias==null?null:this.bias.read(),r=cN(this.activation.getClassName());if(r!=null&&this.rank===2)n=Xk(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=zU(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Xk(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=WU(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Me("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=Je(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r 0 but got ${JSON.stringify(e.filters)}`)}},nd=class extends td{constructor(e){super(2,e),nd.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!rw(e.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};nd.className="Conv2D";ne.registerClass(nd);var ad=class extends td{constructor(e){super(3,e),ad.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};ad.className="Conv3D";ne.registerClass(ad);var Rw=class extends nd{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Ne(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],u=this.kernelSize[0],p=this.kernelSize[1],d=this.strides[0],c=this.strides[1],h=sr(o,d,u,this.padding),m=sr(l,c,p,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=_e(n,[0,2,3,1]));let g=Xm(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=_e(g,[0,3,1,2])),this.bias!=null&&(g=Ya(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=Je(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=sr(t[a],o,s,this.padding),t[r]=sr(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Rw.className="Conv2DTranspose";ne.registerClass(Rw);var Mw=class extends ad{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Ne(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],p=a[i],d=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],y=sr(l,m,d,this.padding),b=sr(u,f,c,this.padding),x=sr(p,g,h,this.padding),w=[r,y,b,x,this.filters];this.dataFormat!=="channelsLast"&&(n=_e(n,[0,2,3,4,1]));let I=xv(n,this.kernel.read(),w,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(I=_e(I,[0,4,1,2,3])),this.bias!==null&&(I=Ya(I,this.bias.read(),this.dataFormat)),this.activation!==null&&(I=this.activation.apply(I)),I})}computeOutputShape(e){e=Je(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],p=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[a]=sr(t[a],u,i,this.padding),t[r]=sr(t[r],p,o,this.padding),t[s]=sr(t[s],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Mw.className="Conv3DTranspose";ne.registerClass(Mw);var r2=class extends td{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Tt(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Tt(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Je(e),e.length{e=Ne(e);let n;if(this.rank===1)throw new Me("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=_e(e,[0,2,3,1])),n=_s(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ya(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=_e(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Ct(this.depthwiseInitializer),e.pointwiseInitializer=Ct(this.pointwiseInitializer),e.depthwiseRegularizer=ct(this.depthwiseRegularizer),e.pointwiseRegularizer=ct(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};r2.className="SeparableConv";var Pw=class extends r2{constructor(e){super(2,e)}};Pw.className="SeparableConv2D";ne.registerClass(Pw);var Bf=class extends td{constructor(e){super(1,e),Bf.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!rw(e.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Bf.className="Conv1D";ne.registerClass(Bf);var Ow=class extends Ve{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return P(()=>{if(e=Ne(e),this.dataFormat==="channelsLast"){let n=ph(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return ph(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=ph(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return ph(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ow.className="Cropping2D";ne.registerClass(Ow);var Lw=class extends Ve{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Q4(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return P(()=>{let n=Ne(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=_e(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?$a.resizeNearestNeighbor(n,[r,s]):$a.resizeBilinear(n,[r,s]);return _e(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?$a.resizeNearestNeighbor(n,[r,s]):$a.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};Lw.className="UpSampling2D";ne.registerClass(Lw);function BU(e,t,n=[1,1],a="valid",r,s){return P(()=>{r==null&&(r=qa()),Rt(r);let i=Fw(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Cs(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=_e(i,[0,3,1,2])),i})}var zw=class extends Dw{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Tt(e.depthwiseRegularizer)}build(e){if(e=Je(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return P(()=>{e=Ne(e);let n=BU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ya(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Ha(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ha(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Ct(this.depthwiseInitializer),e.depthwiseRegularizer=ct(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};zw.className="DepthwiseConv2D";ne.registerClass(zw);function s2(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function i2(e,t,n,a=!1,r,s,i=!1,o=!1){return P(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(ja(2,l));if(t=_e(t,u),s!=null)throw new Me("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=le(le(r,"bool"),"float32"),r.rank===l-1&&(r=Qt(r,-1)),r=_e(r,u)),a&&(t=ya(t,0),r!=null&&(r=ya(r,0)));let p=[],d,c=n,h=t.shape[0],m=pt(t),f;r!=null&&(f=pt(r));for(let y=0;ye(b,c));if(r==null)d=x[0],c=x[1];else{let w=P(()=>{let I=f[y],T=pe(ta(I),I),C=Y(z(x[0],I),z(c[0],T)),_=c.map(($,R)=>Y(z(x[1][R],I),z($,T)));return{output:C,newStates:_}});d=w.output,c=w.newStates}o&&p.push(d)}let g;return o&&(g=$t(p,1)),[d,g,c]})}var gr=class extends Ve{constructor(e){super(e);let t;if(e.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Gf({cells:e.cell}):t=e.cell,t.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ja(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Zb(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return P(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;ns.shape[s.shape.length-1]),r))throw new V(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=r.map(s=>new zt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){P(()=>{if(!this.stateful)throw new Tr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>It([n,a])):this.states_=[It([n,this.cell.stateSize])];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>It([n,a])):this.states_[0]=It([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let a=0;aHt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=s2(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new zt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Va){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let p=super.apply(o,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return P(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Ne(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new V(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=i2((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],p=o[2];this.stateful&&this.resetStates(p,a);let d=this.returnSequences?u:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return P(()=>{let t=It(e.shape);return t=fe(t,[1,2]),t=Yc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Xb(t,[1,n]):t):this.cell.stateSize>1?[Xb(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===gr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),e),t)}static fromConfig(e,t,n={}){let a=t.cell,r=Ga(a,n);return new e(Object.assign(t,{cell:r}))}};gr.className="RNN";ne.registerClass(gr);var rd=class extends Ve{},Vf=class extends rd{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,en(this.units,"units"),this.activation=ms(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Nl([1,ds([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Nl([1,ds([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return P(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0ta(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0ta(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=ur(z(e,s),this.kernel.read()):r=ur(e,this.kernel.read()),this.bias!=null&&(r=Ya(r,this.bias.read())),i!=null&&(n=z(n,i));let o=Y(r,ur(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:hs(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),recurrentInitializer:Ct(this.recurrentInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),recurrentRegularizer:ct(this.recurrentRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};Vf.className="SimpleRNNCell";ne.registerClass(Vf);var Ww=class extends gr{constructor(e){e.cell=new Vf(e),super(e)}call(e,t){return P(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};Ww.className="SimpleRNN";ne.registerClass(Ww);var Uf=class extends rd{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,en(this.units,"units"),this.activation=ms(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ms(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Nl([1,ds([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Nl([1,ds([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return P(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0ta(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0ta(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Bw.className="GRU";ne.registerClass(Bw);var sd=class extends rd{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,en(this.units,"units"),this.activation=ms(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ms(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Nl([1,ds([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Nl([1,ds([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Je(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends Pa{apply(i,o){let l=r.apply([s]),u=new $f().apply([s]),p=r.apply([s*2]);return Dk(Dk(l,u),p)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return P(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0ta(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0ta(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Vw.className="LSTM";ne.registerClass(Vw);var Gf=class extends rd{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return P(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{Qs(`RNNCell_${a}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign(Object.assign({},e),n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ga(r,n));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Jb(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;ss!=null?s(t(),n):bN(t(),n),o=()=>Jc(i,t,a);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var VU=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return P(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=It(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){P(()=>{if(!this.stateful)throw new Tr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>It(r)):this.states_=[It(r)];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>It(r)):this.states_[0]=It(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). 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Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Pa{apply(p,d){let c=l.apply([u]),h=Jn([u]),m=l.apply([u*2]);return sw([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return P(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0ta(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0ta(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[x,w,I,T]=zn(this.kernel.read(),i,b),[C,_,$,R]=this.useBias?zn(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,w,_,this.padding),d=this.inputConv(d,I,$,this.padding),c=this.inputConv(c,T,R,this.padding);let[F,S,M,B]=zn(this.recurrentKernel.read(),i,b);m=this.recurrentConv(m,F),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),y=this.recurrentConv(y,B);let U=this.recurrentActivation.apply(Y(u,m)),G=this.recurrentActivation.apply(Y(p,f)),q=Y(z(G,s),z(U,this.activation.apply(Y(d,g)))),K=z(this.recurrentActivation.apply(Y(c,y)),this.activation.apply(q));return[K,K,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=VU(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=Ft(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ya(r,n,this.dataFormat):r}recurrentConv(e,t){return Ft(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Hf.className="ConvLSTM2DCell";ne.registerClass(Hf);var Uw=class extends o2{constructor(e){let t=new Hf(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};Uw.className="ConvLSTM2D";ne.registerClass(Uw);var jf=class extends Ve{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a{this.invokeCallHook(e,t);let n=Ne(e);if(0bN(n,this.rate,r,this.seed),()=>n,a)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};jf.className="Dropout";ne.registerClass(jf);var Gw=class extends jf{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Gw.className="SpatialDropout1D";ne.registerClass(Gw);var Hw=class extends Ve{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,en(this.units,"units"),this.activation=ms(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Tt(e.kernelRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.activityRegularizer=Tt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Je(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=Je(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ne(e),a=cN(this.activation.getClassName()),r;return a!=null?r=ur(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=ur(n,this.kernel.read()),this.bias!=null&&(r=Ya(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:hs(this.activation),useBias:this.useBias,kernelInitializer:Ct(this.kernelInitializer),biasInitializer:Ct(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Hw.className="Dense";ne.registerClass(Hw);var jw=class extends Ve{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Je(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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t=Ha(t,this.poolSize[0],this.padding,this.strides[0]),n=Ha(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return P(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ne(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},m0=class extends p2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),va(a),qf(e,t,n,a,r,"max")}};m0.className="MaxPooling2D";ne.registerClass(m0);var f0=class extends p2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),va(a),qf(e,t,n,a,r,"avg")}};f0.className="AveragePooling2D";ne.registerClass(f0);var c2=class extends Ve{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new V(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];en(this.poolSize,"poolSize"),en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),va(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return 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e6=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}],V2={};Ae(V2,{json:()=>t6});var 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Ue{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Fw.verifyArgs(t),this.rank=e,en(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Oe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=hl(t.kernelSize,e,"kernelSize"),this.strides=hl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,va(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Mt(this.dataFormat),this.activation=ms(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=St(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(t.biasConstraint),this.biasRegularizer=Tt(t.biasRegularizer),this.activityRegularizer=Tt(t.activityRegularizer),this.dilationRate=hl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new V(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(rr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!aw(e.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:hs(this.activation),useBias:this.useBias,biasInitializer:_t(this.biasInitializer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Jc=class extends Fw{constructor(e,t){super(e,t),this.kernel=null,Jc.verifyArgs(t),this.filters=t.filters,en(this.filters,"filters"),this.kernelInitializer=St(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(t.kernelConstraint),this.kernelRegularizer=Tt(t.kernelRegularizer)}build(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return P(()=>{e=Ce(e);let n,a=this.bias==null?null:this.bias.read(),r=oN(this.activation.getClassName());if(r!=null&&this.rank===2)n=Uk(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=zU(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Uk(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=WU(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Oe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=Je(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r 0 but got ${JSON.stringify(e.filters)}`)}},Qc=class extends Jc{constructor(e){super(2,e),Qc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!aw(e.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Qc.className="Conv2D";ne.registerClass(Qc);var ed=class extends Jc{constructor(e){super(3,e),ed.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};ed.className="Conv3D";ne.registerClass(ed);var Rw=class extends Qc{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Ce(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],u=this.kernelSize[0],p=this.kernelSize[1],d=this.strides[0],c=this.strides[1],h=sr(o,d,u,this.padding),m=sr(l,c,p,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Ee(n,[0,2,3,1]));let g=Km(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ee(g,[0,3,1,2])),this.bias!=null&&(g=Ya(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=Je(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=sr(t[a],o,s,this.padding),t[r]=sr(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Rw.className="Conv2DTranspose";ne.registerClass(Rw);var Dw=class extends ed{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Ce(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],p=a[i],d=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],y=sr(l,m,d,this.padding),b=sr(u,f,c,this.padding),x=sr(p,g,h,this.padding),w=[r,y,b,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Ee(n,[0,2,3,4,1]));let I=rv(n,this.kernel.read(),w,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(I=Ee(I,[0,4,1,2,3])),this.bias!==null&&(I=Ya(I,this.bias.read(),this.dataFormat)),this.activation!==null&&(I=this.activation.apply(I)),I})}computeOutputShape(e){e=Je(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],p=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[a]=sr(t[a],u,i,this.padding),t[r]=sr(t[r],p,o,this.padding),t[s]=sr(t[s],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Dw.className="Conv3DTranspose";ne.registerClass(Dw);var e2=class extends Jc{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Tt(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Tt(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Je(e),e.length{e=Ce(e);let n;if(this.rank===1)throw new Oe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ee(e,[0,2,3,1])),n=_s(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ya(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ee(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.pointwiseInitializer=_t(this.pointwiseInitializer),e.depthwiseRegularizer=ct(this.depthwiseRegularizer),e.pointwiseRegularizer=ct(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};e2.className="SeparableConv";var Mw=class extends e2{constructor(e){super(2,e)}};Mw.className="SeparableConv2D";ne.registerClass(Mw);var Mf=class extends Jc{constructor(e){super(1,e),Mf.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!aw(e.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Mf.className="Conv1D";ne.registerClass(Mf);var Pw=class extends Ue{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return P(()=>{if(e=Ce(e),this.dataFormat==="channelsLast"){let n=uh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return uh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=uh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return uh(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Pw.className="Cropping2D";ne.registerClass(Pw);var Ow=class extends Ue{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Q4(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return P(()=>{let n=Ce(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Ee(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?$a.resizeNearestNeighbor(n,[r,s]):$a.resizeBilinear(n,[r,s]);return Ee(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?$a.resizeNearestNeighbor(n,[r,s]):$a.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};Ow.className="UpSampling2D";ne.registerClass(Ow);function BU(e,t,n=[1,1],a="valid",r,s){return P(()=>{r==null&&(r=ja()),Mt(r);let i=$w(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Ns(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Ee(i,[0,3,1,2])),i})}var Lw=class extends Fw{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Tt(e.depthwiseRegularizer)}build(e){if(e=Je(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return P(()=>{e=Ce(e);let n=BU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ya(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Ha(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ha(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.depthwiseRegularizer=ct(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};Lw.className="DepthwiseConv2D";ne.registerClass(Lw);function t2(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function n2(e,t,n,a=!1,r,s,i=!1,o=!1){return P(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(qa(2,l));if(t=Ee(t,u),s!=null)throw new Oe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=ie(ie(r,"bool"),"float32"),r.rank===l-1&&(r=Qt(r,-1)),r=Ee(r,u)),a&&(t=ya(t,0),r!=null&&(r=ya(r,0)));let p=[],d,c=n,h=t.shape[0],m=ut(t),f;r!=null&&(f=ut(r));for(let y=0;ye(b,c));if(r==null)d=x[0],c=x[1];else{let w=P(()=>{let I=f[y],T=pe(ta(I),I),C=X(z(x[0],I),z(c[0],T)),E=c.map(($,R)=>X(z(x[1][R],I),z($,T)));return{output:C,newStates:E}});d=w.output,c=w.newStates}o&&p.push(d)}let g;return o&&(g=Ft(p,1)),[d,g,c]})}var gr=class extends Ue{constructor(e){super(e);let t;if(e.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Lf({cells:e.cell}):t=e.cell,t.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return qa(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Hb(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return P(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;ns.shape[s.shape.length-1]),r))throw new V(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=r.map(s=>new zt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){P(()=>{if(!this.stateful)throw new Tr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>It([n,a])):this.states_=[It([n,this.cell.stateSize])];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>It([n,a])):this.states_[0]=It([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let a=0;aGt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=t2(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new zt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Va){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let p=super.apply(o,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return P(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Ce(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new V(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=n2((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],p=o[2];this.stateful&&this.resetStates(p,a);let d=this.returnSequences?u:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return P(()=>{let t=It(e.shape);return t=fe(t,[1,2]),t=jc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Ub(t,[1,n]):t):this.cell.stateSize>1?[Ub(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===gr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),e),t)}static fromConfig(e,t,n={}){let a=t.cell,r=Ga(a,n);return new e(Object.assign(t,{cell:r}))}};gr.className="RNN";ne.registerClass(gr);var td=class extends Ue{},Pf=class extends td{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,en(this.units,"units"),this.activation=ms(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Il([1,ds([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Il([1,ds([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return P(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0ta(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0ta(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=ur(z(e,s),this.kernel.read()):r=ur(e,this.kernel.read()),this.bias!=null&&(r=Ya(r,this.bias.read())),i!=null&&(n=z(n,i));let o=X(r,ur(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:hs(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),recurrentRegularizer:ct(this.recurrentRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};Pf.className="SimpleRNNCell";ne.registerClass(Pf);var zw=class extends gr{constructor(e){e.cell=new Pf(e),super(e)}call(e,t){return P(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};zw.className="SimpleRNN";ne.registerClass(zw);var Of=class extends td{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,en(this.units,"units"),this.activation=ms(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ms(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Il([1,ds([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Il([1,ds([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return P(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0ta(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0ta(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Ww.className="GRU";ne.registerClass(Ww);var nd=class extends td{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,en(this.units,"units"),this.activation=ms(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ms(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Il([1,ds([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Il([1,ds([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Je(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends Pa{apply(i,o){let l=r.apply([s]),u=new Tf().apply([s]),p=r.apply([s*2]);return Ck(Ck(l,u),p)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return P(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0ta(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0ta(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Bw.className="LSTM";ne.registerClass(Bw);var Lf=class extends td{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return P(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{Js(`RNNCell_${a}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign(Object.assign({},e),n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ga(r,n));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return qb(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;ss!=null?s(t(),n):mN(t(),n),o=()=>Xc(i,t,a);return!r||r<=1?Gt(o().clone()):Array(r).fill(void 0).map(o).map(l=>Gt(l.clone()))}var VU=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r{if(this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return P(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=It(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){P(()=>{if(!this.stateful)throw new Tr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>It(r)):this.states_=[It(r)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>It(r)):this.states_[0]=It(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let s=0;sGt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],p=Ha(l,a[0],r,s[0],i[0]),d=Ha(u,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,p,d]:[p,d,n]]}};a2.className="ConvRNN2D";var zf=class extends nd{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign(Object.assign({},e),{units:t})),this.filters=t,en(this.filters,"filters"),this.kernelSize=hl(n,2,"kernelSize"),this.kernelSize.forEach(o=>en(o,"kernelSize")),this.strides=hl(a||1,2,"strides"),this.strides.forEach(o=>en(o,"strides")),this.padding=r||"valid",va(this.padding),this.dataFormat=s||"channelsLast",Mt(this.dataFormat),this.dilationRate=hl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>en(o,"dilationRate"))}build(e){var t;e=Je(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Pa{apply(p,d){let c=l.apply([u]),h=Jn([u]),m=l.apply([u*2]);return rw([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return P(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0ta(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0ta(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[x,w,I,T]=zn(this.kernel.read(),i,b),[C,E,$,R]=this.useBias?zn(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,w,E,this.padding),d=this.inputConv(d,I,$,this.padding),c=this.inputConv(c,T,R,this.padding);let[F,S,M,B]=zn(this.recurrentKernel.read(),i,b);m=this.recurrentConv(m,F),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),y=this.recurrentConv(y,B);let U=this.recurrentActivation.apply(X(u,m)),G=this.recurrentActivation.apply(X(p,f)),j=X(z(G,s),z(U,this.activation.apply(X(d,g)))),K=z(this.recurrentActivation.apply(X(c,y)),this.activation.apply(j));return[K,K,j]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=VU(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=Rt(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ya(r,n,this.dataFormat):r}recurrentConv(e,t){return Rt(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};zf.className="ConvLSTM2DCell";ne.registerClass(zf);var Vw=class extends a2{constructor(e){let t=new zf(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};Vw.className="ConvLSTM2D";ne.registerClass(Vw);var Wf=class extends Ue{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a{this.invokeCallHook(e,t);let n=Ce(e);if(0mN(n,this.rate,r,this.seed),()=>n,a)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Wf.className="Dropout";ne.registerClass(Wf);var Uw=class extends Wf{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Uw.className="SpatialDropout1D";ne.registerClass(Uw);var Gw=class extends Ue{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,en(this.units,"units"),this.activation=ms(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Tt(e.kernelRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.activityRegularizer=Tt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Je(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=Je(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ce(e),a=oN(this.activation.getClassName()),r;return a!=null?r=ur(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=ur(n,this.kernel.read()),this.bias!=null&&(r=Ya(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:hs(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Gw.className="Dense";ne.registerClass(Gw);var Hw=class extends Ue{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Je(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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Array.isArray(this.axes)?a=this.axes.map((r,s)=>_p(r,e[s].shape.length)):a=[_p(this.axes,t.shape.length),_p(this.axes,n.shape.length)],this.normalize&&(t=Uh(t,a[0]),n=Uh(n,a[1])),UU(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[_p(this.axes,e.length),_p(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Oe("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};r0.className="Dot";ne.registerClass(r0);var s0=class extends Ue{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ce(e);return Xc(()=>X(Sf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};s0.className="GaussianNoise";ne.registerClass(s0);var i0=class extends Ue{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Ce(e);return this.rate>0&&this.rate<1?Xc(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return z(n,Sf(n.shape,1,a))},()=>n,t.training||!1):n})}};i0.className="GaussianDropout";ne.registerClass(i0);var o0=class extends Ue{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ce(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return P(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Xc(()=>{let a=Ce(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=Dr(Bu(n),this.rate);o=lr(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,p=X(z(a,o),z(X(o,-1),i));return X(z(p,l),u)},()=>Ce(e),t.training||!1)}return e})}};o0.className="AlphaDropout";ne.registerClass(o0);function nc(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=jx(e,t,n,a,r,s);else if(e.rank===3)i=Kx(e,t,n,a,r,s);else if(e.rank===4)i=Xx(e,t,n,a,r,s);else throw new Oe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function GU(e,t,n,a,r=.001){return P(()=>{let s=zc(e,a),i=s.mean,o=s.variance;return[nc(e,i,o,n,t,r),i,o]})}function HU(e,t,n,a,r=.001){return P(()=>{let s=zc(e,a),i=s.mean,o=s.variance,l=[];for(let h of 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t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new V(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new zt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return P(()=>{let n=t.training==null?!1:t.training,a=Ce(e),r=a.shape,s=r.length,i=qa(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=ci(1,s);l[o]=r[o];let u=i.slice();u.sort();let p=!v.arraysEqual(u,qa(0,s).slice(0,s-1)),d=()=>{if(p){let 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e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_t(this.betaInitializer),gammaInitializer:_t(this.gammaInitializer),movingMeanInitializer:_t(this.movingMeanInitializer),movingVarianceInitializer:_t(this.movingVarianceInitializer),betaRegularizer:ct(this.betaRegularizer),gammaRegularizer:ct(this.gammaRegularizer),betaConstraint:Xt(this.betaConstraint),gammaConstraint:Xt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};l0.className="BatchNormalization";ne.registerClass(l0);var u0=class extends Ue{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=St(e.betaInitializer||"zeros"),this.gammaInitializer=St(e.gammaInitializer||"ones"),this.betaRegularizer=Tt(e.betaRegularizer),this.gammaRegularizer=Tt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Je(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==is(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Ce(e),a=n.shape,r=a.length;return P(()=>{let{mean:s,variance:i}=zc(n,this.axis,!0),o=ci(1,r);for(let h of this.axis)o[h]=a[h];let l=h=>h!=null&&h.shape.length!==r?W(h,o):h,u=this.scale?l(this.gamma.read()):null,p=this.center?l(this.beta.read()):null,d=[],c=[];for(let h=0;h{if(e.rank!==4)throw new V(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new V("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ja()),n!=="channelsLast"&&n!=="channelsFirst")throw new V(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],xa(e,a)})}var p0=class extends Ue{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ja():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new V(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new V(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new V(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=Je(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return P(()=>jU(Ce(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};p0.className="ZeroPadding2D";ne.registerClass(p0);function Bf(e,t,n,a,r,s){return P(()=>{Mt(r),uN(s),va(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=ja()),s==null&&(s="max"),e=$w(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Dt(e,t,n,o):i=ba(e,t,n,o),r==="channelsFirst"&&(i=Ee(i,[0,3,1,2])),i})}function r2(e,t,n,a,r,s){return P(()=>{Mt(r),uN(s),va(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=ja()),s==null&&(s="max"),e=QN(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=vv(e,t,n,o):i=qx(e,t,n,o),r==="channelsFirst"&&(i=Ee(i,[0,4,1,2,3])),i})}var s2=class extends Ue{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(en(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new V(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,va(this.padding),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){e=Je(e);let t=Ha(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return P(()=>{this.invokeCallHook(e,t),e=jc(Ce(e),2);let n=this.poolingFunction(Ce(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Es(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},c0=class extends s2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),va(a),Bf(e,t,n,a,r,"max")}};c0.className="MaxPooling1D";ne.registerClass(c0);var d0=class extends s2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),va(a),Bf(e,t,n,a,r,"avg")}};d0.className="AveragePooling1D";ne.registerClass(d0);var i2=class extends Ue{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new V(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];en(this.poolSize,"poolSize"),en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),va(this.padding),this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ha(t,this.poolSize[0],this.padding,this.strides[0]),n=Ha(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return P(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ce(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},h0=class extends i2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),va(a),Bf(e,t,n,a,r,"max")}};h0.className="MaxPooling2D";ne.registerClass(h0);var m0=class extends i2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),va(a),Bf(e,t,n,a,r,"avg")}};m0.className="AveragePooling2D";ne.registerClass(m0);var o2=class extends Ue{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new V(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];en(this.poolSize,"poolSize"),en(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),va(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ha(t,this.poolSize[0],this.padding,this.strides[0]),n=Ha(n,this.poolSize[1],this.padding,this.strides[1]),a=Ha(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return P(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ce(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},f0=class extends o2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),va(a),r2(e,t,n,a,r,"max")}};f0.className="MaxPooling3D";ne.registerClass(f0);var g0=class extends o2{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),va(a),r2(e,t,n,a,r,"avg")}};g0.className="AveragePooling3D";ne.registerClass(g0);var l2=class extends Ue{constructor(e){super(e),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Oe}},y0=class extends l2{constructor(e){super(e||{})}call(e,t){return P(()=>{let n=Ce(e);return Ct(n,1)})}};y0.className="GlobalAveragePooling1D";ne.registerClass(y0);var b0=class extends l2{constructor(e){super(e||{})}call(e,t){return P(()=>{let n=Ce(e);return ma(n,1)})}};b0.className="GlobalMaxPooling1D";ne.registerClass(b0);var u2=class extends Ue{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Oe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},x0=class extends u2{call(e,t){return P(()=>{let n=Ce(e);return this.dataFormat==="channelsLast"?Ct(n,[1,2]):Ct(n,[2,3])})}};x0.className="GlobalAveragePooling2D";ne.registerClass(x0);var v0=class extends u2{call(e,t){return P(()=>{let n=Ce(e);return 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e(s)}},w0=class extends p2{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=Je(e),e.length<3)throw new V(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=Je(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return P(()=>(e=Ce(e),n2((n,a)=>[Ce(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};w0.className="TimeDistributed";ne.registerClass(w0);function KU(e){ko(J4,"BidirectionalMergeMode",e)}var XU="concat",k0=class extends p2{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Ga(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Ga(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?XU:e.mergeMode,KU(this.mergeMode),e.weights)throw new Oe("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,a,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,a=[n]):this.mergeMode==null?a=[n,n.slice()]:a=[n],this.returnState?this.mergeMode==null?a.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):On(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=t2(e,n,a,this.numConstants);if(e=r.inputs,n=r.initialState,a=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new V("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let u=n.map(p=>new 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i;return this.mergeMode==="concat"?i=rw([a,r]):this.mergeMode==="sum"?i=X(a,r):this.mergeMode==="ave"?i=z(.5,X(a,r)):this.mergeMode==="mul"?i=z(a,r):this.mergeMode==null&&(i=[a,r]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Js(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Js(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let a=this.forwardLayer.states.map(r=>null);return Array.isArray(n)?n.concat(a).concat(a):[n].concat(a).concat(a)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=Ga(t.layer);if(delete t.layer,t.numConstants!=null)throw new Oe("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let a=t;return a.layer=n,new e(a)}};k0.className="Bidirectional";ne.registerClass(k0);var I0=class extends Ue{constructor(e){super(e),this.scale=e.scale,e.offset?this.offset=e.offset:this.offset=0}getConfig(){let e={scale:this.scale,offset:this.offset},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return P(()=>(e=Ce(e),e.dtype!=="float32"&&(e=lr(e,"float32")),X(z(e,this.scale),this.offset)))}};I0.className="Rescaling";ne.registerClass(I0);var{resizeBilinear:YU,cropAndResize:ZU}=$a,S0=class extends Ue{constructor(e){super(e),this.height=e.height,this.width=e.width}centerCrop(e,t,n,a,r,s,i,o){return P(()=>{let l,u=!1,p=t/s,d=n/i,c=(a+t)/s,h=(r+n)/i,m=[p,d,c,h],f=[];e.rank===3?(u=!0,l=Ft([e])):l=e;for(let x=0;x{let r=YU(e,[t,n]);return lr(r,a)})}call(e,t){return P(()=>{let n=Ce(e),a=n.dtype,r=n.shape,s=r[r.length-3],i=r[r.length-2],o=0;s!==this.height&&(o=Math.floor((s-this.height)/2));let l=0;return i!==this.width&&(l=Math.floor((i-this.width)/2),l===0&&(l=1)),o>=0&&l>=0?this.centerCrop(n,o,l,this.height,this.width,s,i,a):this.upsize(e,this.height,this.width,a)})}getConfig(){let e={height:this.height,width:this.width},t=super.getConfig();return Object.assign(e,t),e}computeOutputShape(e){e=Je(e);let t=e.length-3,n=e.length-2;return 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r=k("axis",e,t,n);return[a.squeeze(k("x",e,t,n),r)]}case"Reshape":return[a.reshape(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[a.mirrorPad(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[a.pad(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),s=k("paddings",e,t,n);return[a.spaceToBatchND(k("x",e,t,n),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),s=k("crops",e,t,n);return[a.batchToSpaceND(k("x",e,t,n),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),s=k("dataFormat",e,t,n).toUpperCase();return[a.depthToSpace(k("x",e,t,n),r,s)]}case"BroadcastTo":return[a.broadcastTo(k("x",e,t,n),k("shape",e,t,n))];case"BroadcastArgs":return[a.broadcastArgs(k("s0",e,t,n),k("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function aI(e,t,n,a,r=P){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>s6(i,o,l));case"basic_math":return r(()=>i6(i,o,l));case"control":return d6(i,o,l);case"convolution":return r(()=>h6(i,o,l));case"creation":return r(()=>m6(i,o,l));case"dynamic":return f6(i,o,l);case"evaluation":return r(()=>g6(i,o,l));case"image":return r(()=>v6(i,o,l));case"graph":return r(()=>y6(i,o,l));case"logical":return r(()=>w6(i,o,l));case"matrices":return r(()=>k6(i,o,l));case"normalization":return r(()=>I6(i,o,l));case"ragged":return r(()=>S6(i,o,l));case"reduction":return r(()=>T6(i,o,l));case"slice_join":return r(()=>N6(i,o,l));case"sparse":return r(()=>C6(i,o,l));case"spectral":return r(()=>E6(i,o,l));case"string":return r(()=>_6(i,o,l));case"transformation":return r(()=>A6(i,o,l));case"hash_table":return x6(i,o,l,a);case"custom":let u=S2(i.op);if(u&&u.customExecutor)return u.customExecutor(new r6(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return v.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var rI=class{constructor(e={},t={},n={},a={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function sI(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(c=>Yn(c)[0]),p=[];a!=null&&(p=a.map(c=>Yn(c.name)[0]));let d=[...t];for(;d.length>0;){let c=d.pop();if((j2(c)||M6(c)||P6(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&u.indexOf(c.name)===-1&&p.indexOf(c.name)===-1){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function $6(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(p=>Yn(p)[0]).map(p=>e.nodes[p]),o=e.initNodes;i.forEach(p=>{a.has(p.name)&&s.push(p)}),e.weights.forEach(p=>{a.has(p.name)&&s.push(p)}),o!=null&&o.forEach(p=>{a.has(p.name)&&s.push(p)});let l=new Set,u=[];for(;s.length>0;){let p=s.pop();l.add(p.name),t[p.name]||u.push(p),p.children.forEach(d=>{!l.has(d.name)&&a.has(d.name)&&d.inputs.every(c=>l.has(c.name))&&s.push(d)})}return u}var F6=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],D6=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],R6=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function j2(e){return F6.indexOf(e.op)>=0}function M6(e){return D6.indexOf(e.op)>=0}function P6(e){return R6.indexOf(e.op)>=0}var mx=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new mx(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=sI(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(a.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return $6(this.graph,this.weightMap,n)}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return Ht(t),t}cloneTensorList(e){return e?e.map(t=>this.cloneAndKeepTensor(t)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,n])=>[t,this.cloneTensorList(n)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(p=>this.graph.nodes[Yn(p)[0]]),r=t.map(p=>Yn(p)[0]),s=r.map(p=>this.graph.nodes[p]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));try{this.keepIntermediateTensors=H().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(p){this.keepIntermediateTensors=!1,console.warn(p.message)}let l={},u={};return P(()=>{let p=new rI(this.weightMap,l,u,this.functionExecutorMap),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(m=>{let[f,g]=Yn(m),y=[];y[g]=e[m],d[f]=y,this.keepIntermediateTensors&&(this.clonedTensorsMap[f]=this.cloneTensorList(y))});let c=this.getFrozenTensorIds(d),h={};for(let m=0;mkn(m,d,p))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(a=>a.id)));return new Set(t)}checkTensorForDisposal(e,t,n,a,r,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=OH(o.name,n,a);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let p=i[u.id];p===1?(u.dispose(),delete i[u.id]):p!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.clonedTensorsMap||(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,n=!1,a={},r={}){this.disposeIntermediateTensors(),n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=H().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let s=new rI(this.weightMap,a,r,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,s,t,n),o=t.map(d=>kn(d,i,s)),l=o.map(d=>d.id),u=Object.keys(e).map(d=>e[d].id),p=new Set([...l,...u,...this.weightIds]);return Object.values(i).forEach(d=>{d.forEach(c=>{c&&!c.isDisposed&&!p.has(c.id)&&c.dispose()})}),this.parent==null&&s.dispose(p),o}async executeFunctionAsync(e,t,n){let a=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(a,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,a){let r=Object.keys(e),s=r.map(b=>this.graph.nodes[Yn(b)[0]]),i=n.map(b=>Yn(b)[0]),o=i.map(b=>this.graph.nodes[b]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:p,syncInputs:d}=sI(e,o,this.weightMap,this._initNodes),c=[...s,...this.graph.weights,...this._initNodes||[]].map(b=>({node:b,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(b=>{let[x,w]=Yn(b),I=[];I[w]=e[b],h[x]=I});let m={},f=this.getFrozenTensorIds(h),g={};for(;c.length>0;){let b=this.processStack(s,c,t,h,g,f,i,m,l);await Promise.all(b)}p==null&&!a&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(b=>!j2(b)&&!kn(b.name,h,t)).map(b=>b.name);if(y.length>0){let b="";throw p!=null&&(b=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${b}`)}return h}processStack(e,t,n,a,r,s,i,o,l){let u=[];for(;t.length>0;){let p=t.pop();n.currentContext=p.contexts;let d="";if(p.node.op==="Enter"&&k("isConstant",p.node,a,n)&&([d]=Cr(p.node.name,n)),a[p.node.name]==null){let c=aI(p.node,a,n,this._resourceManager);d||([d]=Cr(p.node.name,n));let h=n.currentContext;v.isPromise(c)?u.push(c.then(m=>(a[d]=m,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(m)),n.currentContext=h,this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l),m))):(a[d]=c,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(c)),this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l))}else this.processChildNodes(p.node,t,n,a,r,l)}return u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Cr(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!kn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!kn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Yn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){var t,n;let a={};for(let r in e){let s=(n=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||n===void 0?void 0:n[r];s!=null?a[s.name]=e[r]:a[r]=e[r]}return a}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Yn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var n,a;let r=(a=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||a===void 0?void 0:a[t];return r!=null?r.name:t},{})}checkOutputs(e){e.forEach(t=>{let[n]=Yn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},O6=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},L6="?tfjs-format=file",z6="model.json",D0=class{constructor(e,t={},n=Gt){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,t==null&&(this.loadOptions={}),this.resourceManager=new O6}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(n=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let a=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new mx(Qk.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Qk.Instance.transformGraph(e.modelInitializer);this.initializer=new mx(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof Te?[e]:e,n={};return t.forEach((a,r)=>n[this.structuredOutputKeys[r]]=a),n}return e}predict(e,t){let n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){let n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var t;if(!(e instanceof Te)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[a++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,n=Object.keys(t);for(let a=0;a1?n:n[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Ee(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function W6(e,t={},n=Gt){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof e=="string"&&(e=V6(e));let a=new D0(e,t,n);return await a.load(),a}function B6(e){if(e==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. 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Z2{constructor(){super(R0.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let a=0;at===!0)}rowMajorBatch(e,t=!0){return new sj(this,e,t)}columnMajorBatch(e,t=!0,n=X2){return this.rowMajorBatch(e,t).map(a=>q6(a,n))}concatenate(e,t){return new Q2(J2([this,e]),t)}take(e){return e<0||e==null?this:new rj(this,e)}skip(e){return e<0||e==null?this:new aj(this,e)}prefetch(e){return new eC(this,e)}shuffle(e,t){return new cj(this,e,t)}serial(){return new nj(this)}},ej=class extends nn{constructor(e){super(),this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:Y6(e),done:!1}}},tj=class extends nn{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return 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nn{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},ij=class extends nn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ee(e.value)}}},oj=class extends nn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ua.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ua.getTensorsInContainer(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},lj=class extends nn{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},iI=class extends nn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ua.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Ua.getTensorsInContainer(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},P0=class extends nn{constructor(){super(),this.outputQueue=new R0,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},uj=class extends P0{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Ua.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ua.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return!0}},Q2=class extends nn{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries 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Mismatched at element ${this.count}.`);case rs.SHORTEST:return{value:null,done:!0};case rs.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},eC=class extends nn{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new Z2(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},cj=class extends eC{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=H6.alea(n||v.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Ku=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is - ${e}`);let a;return this.size===1/0||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Xn(async()=>(await n.iterator()).columnMajorBatch(e,t,mj),a)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Xn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Xn(async()=>(await t.iterator()).filter(a=>P(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Xn(async()=>(await t.iterator()).map(n=>P(()=>e(n))),this.size)}mapAsync(e){let t=this;return Xn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Xn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Xn(async()=>{let a=M0(async()=>({value:await t.iterator(),done:!1}));return J6(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=G6.alea(t||v.now().toString());return Xn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Xn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Ku.MAX_BUFFER_SIZE=1e4;function Xn(e,t=null){return new class extends Ku{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function dj(e){return Xn(async()=>J2(e),e.length)}function hj(e){if(!Al(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await Y2(e,a=>{if(a instanceof Ku)return{value:a.iterator(),recurse:!1};if(Al(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return Q6(n,rs.SHORTEST)},t)}function mj(e){if(e===null)return null;let t=e[0];return K6(t)?{value:fj(e),recurse:!1}:{value:null,recurse:!0}}function fj(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Te?$t(e):mn(e)}var tC=class extends Ku{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` -`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},hh='"',Rp=Symbol("out"),oI=Symbol("field"),mh=Symbol("quote"),Eb=Symbol("quoteafterquote"),lI=Symbol("quoteinquote"),nC=class extends Ku{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new tC(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(v.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!H().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new aC(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&a({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),a({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),mn(n,t)}},rC=class extends nn{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ke([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=Fa([s,r,o,i],[1,4])}else this.cropBox=Fa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!H().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new rC(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=xo.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return P(()=>{let t=Qt(le(e,"float32"),0),n;n=$a.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return W(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},sC=class{},iC=class extends nn{split(e){return new gj(this,e)}},gj=class extends iC{constructor(e,t){super(),this.upstream=e,this.impl=new yj(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},yj=class extends P0{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},bj=class extends nn{decodeUTF8(){return new xj(this)}},xj=class extends iC{constructor(e){super(),this.upstream=e,this.impl=new vj(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},vj=class extends P0{constructor(e){if(super(),this.upstream=e,H().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=HI();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return H().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},oC=class extends bj{constructor(e,t={}){super(),this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(H().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let r=this.file.slice(this.offset,n);a.readAsArrayBuffer(r)}this.offset=n}),done:!1}}};async function wj(e,t={},n){let a,r;typeof e=="string"?a=e:(a=e.url,r=kj(e));let s=await(n||v.fetch)(a,r);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new oC(i,t)}else throw new Error(s.statusText)}var kj=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function lC(e){return typeof e=="string"&&e.slice(0,7)==="file://"}var uC=class extends sC{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(lC(this.input)&&H().get("IS_NODE")){let e=$x();this.input=e.readFileSync(this.input.slice(7))}return new oC(this.input,this.options)}},pC=class extends sC{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return lC(this.url)?new uC(this.url,this.fileOptions).iterator():wj(this.url,this.fileOptions)}};function Ij(e,t={}){return new nC(new pC(e),t)}function Sj(e){let t=M0(e);return Xn(async()=>t)}function Tj(e){return Xn(async()=>{let t=await e();return M0(()=>t.next())})}async function Nj(e,t){return rC.create(e,t)}async function Cj(e){return aC.create(e)}var Ej="4.1.0";function ge(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var _j=fr.whereImpl,O0=class extends cc{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new lm(this,_a())}nextDataId(){return O0.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,H().get("IS_NODE")&&N.warn(` + ${a}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=ox(s,n),o=a===0?0:e.size/a,l=P(()=>{let p=[];e=W(e,[1,a,o]);for(let d=0;d{switch(e.op){case"If":case"StatelessIf":{let a=k("thenBranch",e,t,n),r=k("elseBranch",e,t,n),s=k("cond",e,t,n),i=k("args",e,t,n);return(await s.data())[0]?n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let a=k("body",e,t,n),r=k("cond",e,t,n),s=k("args",e,t,n),i=await n.functionMap[r].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(p=>p.id),l=await i[0].data();i.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&p.dispose()});let u=s;for(;l[0];){let p=u;u=await n.functionMap[a].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);let d=u.map(h=>h.id);p.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&d.indexOf(h.id)===-1&&h.dispose()});let c=await n.functionMap[r].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);l=await c[0].data(),c.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&d.indexOf(h.id)===-1&&h.dispose()})}return 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implemented`)}},w6=(e,t,n,a=an)=>{switch(e.op){case"Equal":return[a.equal(k("a",e,t,n),k("b",e,t,n))];case"NotEqual":return[a.notEqual(k("a",e,t,n),k("b",e,t,n))];case"Greater":return[a.greater(k("a",e,t,n),k("b",e,t,n))];case"GreaterEqual":return[a.greaterEqual(k("a",e,t,n),k("b",e,t,n))];case"Less":return[a.less(k("a",e,t,n),k("b",e,t,n))];case"LessEqual":return[a.lessEqual(k("a",e,t,n),k("b",e,t,n))];case"LogicalAnd":return[a.logicalAnd(k("a",e,t,n),k("b",e,t,n))];case"LogicalNot":return[a.logicalNot(k("a",e,t,n))];case"LogicalOr":return[a.logicalOr(k("a",e,t,n),k("b",e,t,n))];case"Select":case"SelectV2":return[a.where(k("condition",e,t,n),k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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implemented`)}},I6=(e,t,n,a=an)=>{switch(e.op){case"EuclideanNorm":return[a.euclideanNorm(k("x",e,t,n),k("axis",e,t,n),k("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"FusedBatchNormV3":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"LRN":return[a.localResponseNormalization(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[a.softmax(k("x",e,t,n))];case"LogSoftmax":return[a.logSoftmax(k("x",e,t,n))];case"SparseToDense":return[a.sparseToDense(k("sparseIndices",e,t,n),k("outputShape",e,t,n),k("sparseValues",e,t,n),k("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},S6=(e,t,n,a=an)=>{switch(e.op){case"RaggedGather":{let{outputNestedSplits:r,outputDenseValues:s}=a.raggedGather(k("paramsNestedSplits",e,t,n),k("paramsDenseValues",e,t,n),k("indices",e,t,n),k("outputRaggedRank",e,t,n));return r.concat(s)}case"RaggedRange":{let{rtNestedSplits:r,rtDenseValues:s}=a.raggedRange(k("starts",e,t,n),k("limits",e,t,n),k("splits",e,t,n));return[r,s]}case"RaggedTensorToTensor":return[a.raggedTensorToTensor(k("shape",e,t,n),k("values",e,t,n),k("defaultValue",e,t,n),k("rowPartitionTensors",e,t,n),k("rowPartitionTypes",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},T6=(e,t,n,a=an)=>{switch(e.op){case"Max":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.max(k("x",e,t,n),o,l)]}case"Mean":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.mean(k("x",e,t,n),o,l)]}case"Min":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.min(k("x",e,t,n),o,l)]}case"Sum":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.sum(k("x",e,t,n),o,l)]}case"All":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.all(k("x",e,t,n),o,l)]}case"Any":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.any(k("x",e,t,n),o,l)]}case"ArgMax":{let o=k("axis",e,t,n);return[a.argMax(k("x",e,t,n),o)]}case"ArgMin":{let o=k("axis",e,t,n);return[a.argMin(k("x",e,t,n),o)]}case"Prod":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.prod(k("x",e,t,n),o,l)]}case"Cumprod":{let o=k("axis",e,t,n),l=k("exclusive",e,t,n),u=k("reverse",e,t,n);return[a.cumprod(k("x",e,t,n),o,l,u)]}case"Cumsum":{let o=k("axis",e,t,n),l=k("exclusive",e,t,n),u=k("reverse",e,t,n);return[a.cumsum(k("x",e,t,n),o,l,u)]}case"Bincount":let r=k("x",e,t,n),s=k("weights",e,t,n),i=k("size",e,t,n);return[a.bincount(r,s,i)];case"DenseBincount":{let o=k("x",e,t,n),l=k("weights",e,t,n),u=k("size",e,t,n),p=k("binaryOutput",e,t,n);return[a.denseBincount(o,l,u,p)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},N6=(e,t,n,a=an)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,n),s=k("axis",e,t,n),i=k("tensors",e,t,n);return i=i.slice(0,r),[a.concat(i,s)]}case"Gather":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gather(r,a.cast(s,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),s=k("batchDims",e,t,n),i=k("x",e,t,n),o=k("indices",e,t,n);return[a.gather(i,a.cast(o,"int32"),r,s)]}case"Reverse":{let r=k("dims",e,t,n),s=[];for(let o=0;o{let r=k("axis",e,t,n),s=k("tensors",e,t,n),i=s[0].shape,o=a.squeeze(s[0]).shape,l=s.map(u=>{let p=v.arraysEqual(u.shape,i);if(!p&&!v.arraysEqual(a.squeeze(u).shape,o))throw new Error("the input tensors shape does not match");return p?u:a.reshape(u,i)});return[a.stack(l,r)]});case"Unpack":{let r=k("axis",e,t,n),s=k("tensor",e,t,n);return a.unstack(s,r)}case"Tile":{let r=k("reps",e,t,n);return[a.tile(k("x",e,t,n),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,n),s=k("numOrSizeSplits",e,t,n),i=k("x",e,t,n);return a.split(i,s,r)}case"ScatterNd":{let r=k("indices",e,t,n),s=k("values",e,t,n),i=k("shape",e,t,n);return[a.scatterND(r,s,i)]}case"GatherNd":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gatherND(r,s)]}case"SparseToDense":{let r=k("sparseIndices",e,t,n),s=k("outputShape",e,t,n),i=k("sparseValues",e,t,n),o=k("defaultValue",e,t,n);return[a.sparseToDense(r,i,s,i.dtype===o.dtype?o:a.cast(o,i.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},C6=(e,t,n,a=an)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=a.sparse.sparseFillEmptyRows(k("indices",e,t,n),k("values",e,t,n),k("denseShape",e,t,n),k("defaultValue",e,t,n));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=a.sparse.sparseReshape(k("inputIndices",e,t,n),k("inputShape",e,t,n),k("newShape",e,t,n));return[r,s]}case"SparseSegmentMean":return[a.sparse.sparseSegmentMean(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];case"SparseSegmentSum":return[a.sparse.sparseSegmentSum(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},_6=(e,t,n,a=an)=>{switch(e.op){case"FFT":return[a.fft(k("x",e,t,n))];case"IFFT":return[a.ifft(k("x",e,t,n))];case"RFFT":return[a.rfft(k("x",e,t,n))];case"IRFFT":return[a.irfft(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},E6=(e,t,n,a=an)=>{switch(e.op){case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=a.string.stringNGrams(k("data",e,t,n),k("dataSplits",e,t,n),k("separator",e,t,n),k("nGramWidths",e,t,n),k("leftPad",e,t,n),k("rightPad",e,t,n),k("padWidth",e,t,n),k("preserveShortSequences",e,t,n));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=a.string.stringSplit(k("input",e,t,n),k("delimiter",e,t,n),k("skipEmpty",e,t,n));return[r,s,i]}case"StringToHashBucketFast":return[a.string.stringToHashBucketFast(k("input",e,t,n),k("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},A6=(e,t,n,a=an)=>{switch(e.op){case"Cast":return[a.cast(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let r=k("axis",e,t,n);return[a.expandDims(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[a.squeeze(k("x",e,t,n),r)]}case"Reshape":return[a.reshape(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[a.mirrorPad(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[a.pad(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),s=k("paddings",e,t,n);return[a.spaceToBatchND(k("x",e,t,n),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),s=k("crops",e,t,n);return[a.batchToSpaceND(k("x",e,t,n),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),s=k("dataFormat",e,t,n).toUpperCase();return[a.depthToSpace(k("x",e,t,n),r,s)]}case"BroadcastTo":return[a.broadcastTo(k("x",e,t,n),k("shape",e,t,n))];case"BroadcastArgs":return[a.broadcastArgs(k("s0",e,t,n),k("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Zk(e,t,n,a,r=P){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>s6(i,o,l));case"basic_math":return r(()=>i6(i,o,l));case"control":return d6(i,o,l);case"convolution":return r(()=>h6(i,o,l));case"creation":return r(()=>m6(i,o,l));case"dynamic":return f6(i,o,l);case"evaluation":return r(()=>g6(i,o,l));case"image":return r(()=>v6(i,o,l));case"graph":return r(()=>y6(i,o,l));case"logical":return r(()=>w6(i,o,l));case"matrices":return r(()=>k6(i,o,l));case"normalization":return r(()=>I6(i,o,l));case"ragged":return r(()=>S6(i,o,l));case"reduction":return r(()=>T6(i,o,l));case"slice_join":return r(()=>N6(i,o,l));case"sparse":return r(()=>C6(i,o,l));case"spectral":return r(()=>_6(i,o,l));case"string":return r(()=>E6(i,o,l));case"transformation":return r(()=>A6(i,o,l));case"hash_table":return x6(i,o,l,a);case"custom":let u=v2(i.op);if(u&&u.customExecutor)return u.customExecutor(new r6(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return v.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Jk=class{constructor(e={},t={},n={},a={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Qk(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(c=>Yn(c)[0]),p=[];a!=null&&(p=a.map(c=>Yn(c.name)[0]));let d=[...t];for(;d.length>0;){let c=d.pop();if((V2(c)||M6(c)||P6(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&u.indexOf(c.name)===-1&&p.indexOf(c.name)===-1){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function $6(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(p=>Yn(p)[0]).map(p=>e.nodes[p]),o=e.initNodes;i.forEach(p=>{a.has(p.name)&&s.push(p)}),e.weights.forEach(p=>{a.has(p.name)&&s.push(p)}),o!=null&&o.forEach(p=>{a.has(p.name)&&s.push(p)});let l=new Set,u=[];for(;s.length>0;){let p=s.pop();l.add(p.name),t[p.name]||u.push(p),p.children.forEach(d=>{!l.has(d.name)&&a.has(d.name)&&d.inputs.every(c=>l.has(c.name))&&s.push(d)})}return u}var F6=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],R6=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],D6=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function V2(e){return F6.indexOf(e.op)>=0}function M6(e){return R6.indexOf(e.op)>=0}function P6(e){return D6.indexOf(e.op)>=0}var lx=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new lx(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=Qk(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(a.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return $6(this.graph,this.weightMap,n)}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return Gt(t),t}cloneTensorList(e){return e?e.map(t=>this.cloneAndKeepTensor(t)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,n])=>[t,this.cloneTensorList(n)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(p=>this.graph.nodes[Yn(p)[0]]),r=t.map(p=>Yn(p)[0]),s=r.map(p=>this.graph.nodes[p]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));try{this.keepIntermediateTensors=H().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(p){this.keepIntermediateTensors=!1,console.warn(p.message)}let l={},u={};return P(()=>{let p=new Jk(this.weightMap,l,u,this.functionExecutorMap),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(m=>{let[f,g]=Yn(m),y=[];y[g]=e[m],d[f]=y,this.keepIntermediateTensors&&(this.clonedTensorsMap[f]=this.cloneTensorList(y))});let c=this.getFrozenTensorIds(d),h={};for(let m=0;mkn(m,d,p))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(a=>a.id)));return new Set(t)}checkTensorForDisposal(e,t,n,a,r,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=OH(o.name,n,a);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let p=i[u.id];p===1?(u.dispose(),delete i[u.id]):p!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.clonedTensorsMap||(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,n=!1,a={},r={}){this.disposeIntermediateTensors(),n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=H().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}let s=new Jk(this.weightMap,a,r,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,s,t,n),o=t.map(d=>kn(d,i,s)),l=o.map(d=>d.id),u=Object.keys(e).map(d=>e[d].id),p=new Set([...l,...u,...this.weightIds]);return Object.values(i).forEach(d=>{d.forEach(c=>{c&&!c.isDisposed&&!p.has(c.id)&&c.dispose()})}),this.parent==null&&s.dispose(p),o}async executeFunctionAsync(e,t,n){let a=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(a,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,a){let r=Object.keys(e),s=r.map(b=>this.graph.nodes[Yn(b)[0]]),i=n.map(b=>Yn(b)[0]),o=i.map(b=>this.graph.nodes[b]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:p,syncInputs:d}=Qk(e,o,this.weightMap,this._initNodes),c=[...s,...this.graph.weights,...this._initNodes||[]].map(b=>({node:b,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(b=>{let[x,w]=Yn(b),I=[];I[w]=e[b],h[x]=I});let m={},f=this.getFrozenTensorIds(h),g={};for(;c.length>0;){let b=this.processStack(s,c,t,h,g,f,i,m,l);await Promise.all(b)}p==null&&!a&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(b=>!V2(b)&&!kn(b.name,h,t)).map(b=>b.name);if(y.length>0){let b="";throw p!=null&&(b=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${b}`)}return h}processStack(e,t,n,a,r,s,i,o,l){let u=[];for(;t.length>0;){let p=t.pop();n.currentContext=p.contexts;let d="";if(p.node.op==="Enter"&&k("isConstant",p.node,a,n)&&([d]=Cr(p.node.name,n)),a[p.node.name]==null){let c=Zk(p.node,a,n,this._resourceManager);d||([d]=Cr(p.node.name,n));let h=n.currentContext;v.isPromise(c)?u.push(c.then(m=>(a[d]=m,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(m)),n.currentContext=h,this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l),m))):(a[d]=c,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(c)),this.checkTensorForDisposal(d,p.node,a,n,s,i,o),this.processChildNodes(p.node,t,n,a,r,l))}else this.processChildNodes(p.node,t,n,a,r,l)}return u}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Cr(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!kn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!kn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Yn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){var t,n;let a={};for(let r in e){let s=(n=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||n===void 0?void 0:n[r];s!=null?a[s.name]=e[r]:a[r]=e[r]}return a}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Yn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var n,a;let r=(a=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||a===void 0?void 0:a[t];return r!=null?r.name:t},{})}checkOutputs(e){e.forEach(t=>{let[n]=Yn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},O6=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},L6="?tfjs-format=file",z6="model.json",F0=class{constructor(e,t={},n=Ht){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,t==null&&(this.loadOptions={}),this.resourceManager=new O6}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(n=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let a=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new lx(jk.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=jk.Instance.transformGraph(e.modelInitializer);this.initializer=new lx(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof Ne?[e]:e,n={};return t.forEach((a,r)=>n[this.structuredOutputKeys[r]]=a),n}return e}predict(e,t){let n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){let n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var t;if(!(e instanceof Ne)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[a++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,n=Object.keys(t);for(let a=0;a1?n:n[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await 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U6="4.2.0",U2={};Ae(U2,{CSVDataset:()=>J2,Dataset:()=>Hu,FileDataSource:()=>sC,TextLineDataset:()=>Z2,URLDataSource:()=>iC,array:()=>dq,csv:()=>Iq,func:()=>Sq,generator:()=>Tq,microphone:()=>Cq,version_data:()=>_q,webcam:()=>Nq,zip:()=>hq});var G6=bs(sm()),H6=bs(sm());function q6(e,t){return Xh(e,t)}function Xh(e,t,n=new Map,a=new Set){if(e==null)return null;if(typeof Blob=="function"&&e instanceof Blob)return e.slice();if(a.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse)if(Cl(e)){let s=Array.isArray(e)?[]:{};a.add(e);for(let i in e){let o=e[i],l=Xh(o,t,n,a);s[i]=l}return a.delete(e),e.__proto__&&(s.__proto__=e.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function j6(e,t=H2){return G2(e,t)}function G2(e,t,n=new Set){let a=e[0];if(n.has(a))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(Cl(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(u=>u[i]),l=G2(o,t,n);s[i]=l}return n.delete(a),s}else throw new Error(`Can't recurse into non-iterable type: ${a}`);else return r.value}function H2(e){return e===null?null:Cl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function q2(e,t){let n=new Map;Xh(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(v.isPromise(r)){let s=await r;n.set(a,s)}}return Xh(e,t,n)}function Cl(e){let t=!1;if(H().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=LI();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ne)&&!(e instanceof Promise)&&!t)}function K6(e){return e==null||X6(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ne||v.isTypedArray(e)}function X6(e){return e===null||typeof e!="object"&&typeof e!="function"}function Y6(e){return q6(e,Z6)}function Z6(e){return e instanceof Ne?{value:e.clone(),recurse:!1}:Cl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var j2=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},R0=class extends j2{constructor(){super(R0.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let a=0;at===!0)}rowMajorBatch(e,t=!0){return new sq(this,e,t)}columnMajorBatch(e,t=!0,n=H2){return this.rowMajorBatch(e,t).map(a=>j6(a,n))}concatenate(e,t){return new X2(K2([this,e]),t)}take(e){return e<0||e==null?this:new rq(this,e)}skip(e){return e<0||e==null?this:new aq(this,e)}prefetch(e){return new Y2(this,e)}shuffle(e,t){return new cq(this,e,t)}serial(){return new nq(this)}},eq=class extends nn{constructor(e){super(),this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:Y6(e),done:!1}}},tq=class extends nn{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},nq=class extends nn{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},aq=class extends nn{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},sq=class extends nn{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},iq=class extends nn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;_e(e.value)}}},oq=class extends nn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ua.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ua.getTensorsInContainer(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},lq=class extends nn{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},eI=class extends nn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ua.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Ua.getTensorsInContainer(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},M0=class extends nn{constructor(){super(),this.outputQueue=new R0,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},uq=class extends M0{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Ua.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ua.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Ua.isTensorInList(r,a)||r.dispose();return!0}},X2=class extends nn{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},as;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(as||(as={}));var pq=class extends nn{constructor(e,t=as.FAIL){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function a(s){return s instanceof nn?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await q2(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case as.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case as.SHORTEST:return{value:null,done:!0};case as.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Y2=class extends nn{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new j2(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},cq=class extends Y2{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=H6.alea(n||v.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Hu=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is + ${e}`);let a;return this.size===1/0||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Xn(async()=>(await n.iterator()).columnMajorBatch(e,t,mq),a)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Xn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Xn(async()=>(await t.iterator()).filter(a=>P(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Xn(async()=>(await t.iterator()).map(n=>P(()=>e(n))),this.size)}mapAsync(e){let t=this;return Xn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Xn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Xn(async()=>{let a=D0(async()=>({value:await t.iterator(),done:!1}));return J6(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=G6.alea(t||v.now().toString());return Xn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Xn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Hu.MAX_BUFFER_SIZE=1e4;function Xn(e,t=null){return new class extends Hu{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function dq(e){return Xn(async()=>K2(e),e.length)}function hq(e){if(!Cl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await q2(e,a=>{if(a instanceof Hu)return{value:a.iterator(),recurse:!1};if(Cl(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return Q6(n,as.SHORTEST)},t)}function mq(e){if(e===null)return null;let t=e[0];return K6(t)?{value:fq(e),recurse:!1}:{value:null,recurse:!0}}function fq(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ne?Ft(e):fn(e)}var Z2=class extends Hu{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` +`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},dh='"',Ap=Symbol("out"),tI=Symbol("field"),hh=Symbol("quote"),Ib=Symbol("quoteafterquote"),nI=Symbol("quoteinquote"),J2=class extends Hu{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new Z2(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(v.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!H().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new Q2(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&a({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),a({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),fn(n,t)}},eC=class extends nn{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=je([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=Fa([s,r,o,i],[1,4])}else this.cropBox=Fa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!H().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new eC(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=wo.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return P(()=>{let t=Qt(ie(e,"float32"),0),n;n=$a.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return W(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},tC=class{},nC=class extends nn{split(e){return new gq(this,e)}},gq=class extends nC{constructor(e,t){super(),this.upstream=e,this.impl=new yq(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},yq=class extends M0{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},bq=class extends nn{decodeUTF8(){return new xq(this)}},xq=class extends nC{constructor(e){super(),this.upstream=e,this.impl=new vq(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},vq=class extends M0{constructor(e){if(super(),this.upstream=e,H().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=LI();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return H().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},aC=class extends bq{constructor(e,t={}){super(),this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(H().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let r=this.file.slice(this.offset,n);a.readAsArrayBuffer(r)}this.offset=n}),done:!1}}};async function wq(e,t={},n){let a,r;typeof e=="string"?a=e:(a=e.url,r=kq(e));let s=await(n||v.fetch)(a,r);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new aC(i,t)}else throw new Error(s.statusText)}var kq=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function rC(e){return typeof e=="string"&&e.slice(0,7)==="file://"}var sC=class extends tC{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(rC(this.input)&&H().get("IS_NODE")){let e=Tx();this.input=e.readFileSync(this.input.slice(7))}return new aC(this.input,this.options)}},iC=class extends tC{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return rC(this.url)?new sC(this.url,this.fileOptions).iterator():wq(this.url,this.fileOptions)}};function Iq(e,t={}){return new J2(new iC(e),t)}function Sq(e){let t=D0(e);return Xn(async()=>t)}function Tq(e){return Xn(async()=>{let t=await e();return D0(()=>t.next())})}async function Nq(e,t){return eC.create(e,t)}async function Cq(e){return Q2.create(e)}var _q="4.2.0";function ge(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var Eq=fr.whereImpl,P0=class extends uc{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new im(this,Ea())}nextDataId(){return P0.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,H().get("IS_NODE")&&N.warn(` ============================ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. -============================`));let a={id:this.nextDataId()};return this.data.set(a,{values:e,dtype:n,refCount:1}),a}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(s=>v.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return{dataId:a,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,a,r){this.data.set(e,{values:t,dtype:a,refCount:r})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let a=this.readSync(n.real.dataId),r=this.readSync(n.imag.dataId);return N.mergeRealAndImagArrays(a,r)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(a=>v.decodeString(a));return Oe(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Oe(e.shape,e.dtype,t)}makeOutput(e,t,n){return _a().makeTensorFromTensorInfo(this.makeTensorInfo(t,n,e),this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){ge([e],"where");let t=this.readSync(e.dataId);return _j(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};O0.nextDataId=0;var cC={};Ae(cC,{addImpl:()=>mC,bincountImpl:()=>z0,bincountReduceImpl:()=>fC,castImpl:()=>hC,ceilImpl:()=>gC,concatImpl:()=>W0,equalImpl:()=>yC,expImpl:()=>xC,expm1Impl:()=>wC,floorImpl:()=>kC,gatherNdImpl:()=>IC,gatherV2Impl:()=>SC,greaterEqualImpl:()=>NC,greaterImpl:()=>TC,lessEqualImpl:()=>EC,lessImpl:()=>CC,linSpaceImpl:()=>_C,logImpl:()=>AC,maxImpl:()=>$C,maximumImpl:()=>FC,minimumImpl:()=>DC,multiplyImpl:()=>B0,negImpl:()=>RC,notEqualImpl:()=>MC,prodImpl:()=>PC,raggedGatherImpl:()=>OC,raggedRangeImpl:()=>LC,raggedTensorToTensorImpl:()=>zC,rangeImpl:()=>U0,rsqrtImpl:()=>WC,scatterImpl:()=>dl,sigmoidImpl:()=>Iq,simpleAbsImpl:()=>dC,sliceImpl:()=>Qh,sparseFillEmptyRowsImpl:()=>VC,sparseReshapeImpl:()=>UC,sparseSegmentReductionImpl:()=>G0,sqrtImpl:()=>Nq,squaredDifferenceImpl:()=>GC,stridedSliceImpl:()=>HC,stringNGramsImpl:()=>H0,stringSplitImpl:()=>j0,stringToHashBucketFastImpl:()=>q0,subImpl:()=>jC,tileImpl:()=>qC,topKImpl:()=>XC,transposeImpl:()=>V0,uniqueImpl:()=>YC});function 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I5(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ge([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=p.strideHeight,c=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,y=p.effectiveFilterHeight,b=p.effectiveFilterWidth,x=b-1-p.padInfo.left,w=y-1-p.padInfo.top,I=Oe(i.shape,"float32"),T=1/(h*m),C=n.data.get(r.dataId).values,_=Oe(r.shape,"float32",C);for(let $=0;$=p.outHeight||Math.floor(q)!==q))for(let K=0;K=p.outWidth||Math.floor(Z)!==Z)continue;let J=_.get($,q,Z,R);U+=J}}I.set(U*T,$,F,S,R)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var S5={kernelName:cm,backendName:"cpu",kernelFunc:I5};function T5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient 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o=s.reduce((y,b)=>y*b),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=ft({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Vn({inputs:{x:h},backend:n,attrs:{perm:u}}),f=ft({inputs:{x:m},backend:n,attrs:{shape:p}}),g=mi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var E5={kernelName:jl,backendName:"cpu",kernelFunc:C5};function _5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=z0(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var A5={kernelName:hm,backendName:"cpu",kernelFunc:_5};function $5(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var F5={kernelName:mm,backendName:"cpu",kernelFunc:$5},D5=rt(ws,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(v.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;uf.shape);N.assertParamsConsistent(i,s);let o=N.computeOutShape(t.map(f=>f.shape),s);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>v.sizeFromShape(f.shape)>0);if(l.length===1)return dr({inputs:{x:l[0]},backend:n});if(l[0].dtype==="complex64"){let f=l.map(w=>hi({inputs:{input:w},backend:n})),g=l.map(w=>Fl({inputs:{input:w},backend:n})),y=Dl({inputs:f,backend:n,attrs:{axis:s}}),b=Dl({inputs:g,backend:n,attrs:{axis:s}}),x=Zn({inputs:{real:y,imag:b},backend:n});return f.forEach(w=>n.disposeIntermediateTensorInfo(w)),g.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(b),x}let u=l.map(f=>{let g=[-1,v.sizeFromShape(f.shape.slice(s))];return ft({inputs:{x:f},backend:n,attrs:{shape:g}})}),p=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));o=N.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=W0(p,o,t[0].dtype,d),h=N.computeOutShape(l.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var L5={kernelName:ql,backendName:"cpu",kernelFunc:Dl};function sE(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a;ge([r,s],"conv2d");let d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,y=c.padInfo.left,b=c.padInfo.top,x=c.dataFormat==="channelsLast",w=new qt(c.outShape,r.dtype),I=v.computeStrides(r.shape),T=v.computeStrides(s.shape),C=I[0],_=x?I[1]:I[2],$=x?I[2]:1,R=x?1:I[1],F=w.strides[0],S=x?w.strides[1]:w.strides[2],M=x?w.strides[2]:1,B=x?1:w.strides[1],U=n.data.get(r.dataId).values,G=n.data.get(s.dataId).values,q=w.values;for(let K=0;K=c.inHeight)continue;let ye=ie*T[0],ue=Z+oe*_;for(let be=0;be=c.inWidth)continue;let nt=ye+Le*T[1],it=ue+Ue*$,tt=nt;for(let at=0;at=u.inDepth)continue;let K=G*$[0],Z=F+q*_[1];for(let J=0;J=u.inHeight)continue;let oe=K+te*$[1],ye=Z+ie*_[2];for(let ue=0;ue=u.inWidth)continue;let Ue=oe+Se*$[2],nt=ye+Le*u.inChannels,it=Ue;for(let tt=0;ttMath.cos(e)),Z5={kernelName:Ci,backendName:"cpu",kernelFunc:Y5},J5=rt(Ei,e=>Math.cosh(e)),Q5={kernelName:Ei,backendName:"cpu",kernelFunc:J5};function e8(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,y=Oe([m,f,g,h],"float32"),b=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,w=n.data.get(r.dataId).values,I=v.computeStrides(r.shape),T=v.computeStrides(y.shape);for(let C=0;C=p)continue;let B=f>1?(F-$)*(d-1)/(f-1):0,U=g>1?(S-R)*(c-1)/(g-1):0;for(let G=0;G1?$*(d-1)+G*B:.5*($+F)*(d-1);if(q<0||q>d-1){for(let K=0;K1?R*(c-1)+ee*U:.5*(R+S)*(c-1);if(ae<0||ae>c-1){for(let ye=0;ye1?R*(c-1)+K*U:.5*(R+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;aey+m-b-1:(y,b)=>y+b;for(let y=0;yy+m-b-1:(y,b)=>y+b;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],d=l*s,c=u*s,h=p/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*d*c*h),g=0;for(let y=0;y`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=N.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:b}=h,x=b.left,w=b.top,I=h.outChannels/h.inChannels,T=new qt(h.outShape,r.dtype),C=n.data.get(r.dataId).values,_=n.data.get(s.dataId).values,$=T.values;for(let R=0;R=h.inHeight)continue;let K=G*d[0],Z=F+q*p[1];for(let J=0;J=h.inWidth)continue;let oe=K+te*d[1],ye=Z+ie*h.inChannels,ue=ee,be=oe;for(let ke=0;ke{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:b,padInfo:x,strideHeight:w,strideWidth:I,filterHeight:T,filterWidth:C,dilationHeight:_,dilationWidth:$,outShape:R}=N.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),F=v.sizeFromShape(R),S=R.length,M=v.getArrayFromDType(a.dtype,F);for(let B=0;B=0&&te=0&&oeJ&&(J=be)}}}let ee=v.locToIndex([B,U,q,Z],S,v.computeStrides(R));M[ee]=J}}}return{dataId:l.write(v.toTypedArray(M,a.dtype),R,a.dtype),shape:R,dtype:a.dtype}}},b8={kernelName:Fh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=v.toNestedArray(a.shape,u.data.get(a.dataId).values),d=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:_,outShape:$}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===$.length,()=>`Error in ${Fh}, dy must have the same rank as output ${$.length}, but got ${s.rank}`);let R=v.toNestedArray($,u.data.get(s.dataId).values),F=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&ae=0&&ieK&&(K=oe,Z=ee,J=te)}}}F[Z][J][q]+=R[S][M][U][q]}}}return{dataId:u.write(v.toTypedArray(F,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},x8={kernelName:$h,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=v.toNestedArray(a.shape,u.data.get(a.dataId).values),d=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:_,outShape:$}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===$.length,()=>`Error in ${$h}, dy must have the same rank as output ${$.length}, but got ${s.rank}`);let R=v.toNestedArray($,u.data.get(s.dataId).values),F=v.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S=0&&ae=0&&ieK&&(K=oe,Z=ae,J=ie)}}}F[S][Z][J][q]+=R[S][M][U][q]}}}return{dataId:u.write(v.toTypedArray(F,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function id(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ge(r,"sum");let o;r.dtype==="bool"?o=gs({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=dr({inputs:{x:r},backend:n});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),p=N.getAxesPermutation(u,l),d=u,c=o;p!=null&&(c=Vn({inputs:{x:o},backend:n,attrs:{perm:p}}),d=N.getInnerMostAxes(d.length,l)),N.assertAxesAreInnerMostDims("sum",d,c.shape.length);let[h,m]=N.computeOutAndReduceShapes(c.shape,d),f=N.upcastType(c.dtype,"int32"),g=Jh(n,h,f),y=v.sizeFromShape(m),b=n.data.get(g.dataId).values,x=n.data.get(c.dataId).values;for(let w=0;w=0&&(c=id({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var k8={kernelName:Im,backendName:"cpu",kernelFunc:w8};function I8(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;ge([a,r],"eluGrad");let s=new Float32Array(v.sizeFromShape(r.shape)),i=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values;for(let l=0;l=1?s[l]=o[l]:s[l]=o[l]*(u+1)}return n.makeTensorInfo(r.shape,"float32",s)}var S8={kernelName:Sm,backendName:"cpu",kernelFunc:I8},T8=N.ERF_P,N8=N.ERF_A1,C8=N.ERF_A2,E8=N.ERF_A3,_8=N.ERF_A4,A8=N.ERF_A5,$8=rt(Zl,e=>{let t=Math.sign(e),n=Math.abs(e),a=1/(1+T8*n);return t*(1-((((A8*a+_8)*a+E8)*a+C8)*a+N8)*a*Math.exp(-n*n))}),F8={kernelName:Zl,backendName:"cpu",kernelFunc:$8};function tm(e){let{inputs:t,backend:n,attrs:a}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),ft({inputs:{x:r},backend:n,attrs:{shape:o}})}var D8={kernelName:Ql,backendName:"cpu",kernelFunc:tm},R8=Vt((e,t)=>e/t),Y0=rn($i,R8),gx={kernelName:$i,backendName:"cpu",kernelFunc:Y0};function oE(e,t,n){let a=e.shape,r=a[0],s=a[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=v.sizeFromShape(u),d=v.getTypedArrayFromDType("float32",p),c=v.getTypedArrayFromDType("float32",p);for(let g=0;g{let{image:a}=e,r=n,s=v.getTypedArrayFromDType(a.dtype,v.sizeFromShape(a.shape)),[i,o,l,u]=a.shape,p=r.data.get(a.dataId).values;for(let d=0;d=0&&bMath.floor(e/t)),G8=rn(Mi,U8,null,"int32"),H8={kernelName:Mi,backendName:"cpu",kernelFunc:G8};function j8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=sE({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c}});if(i){let g=f;if(p==="NCHW"&&i.shape.length===1&&i.shape[0]!==1){let y=ft({inputs:{x:i},backend:n,attrs:{shape:[i.shape[0],1,1]}});f=$l({inputs:{a:f,b:y},backend:n}),n.disposeIntermediateTensorInfo(y)}else f=$l({inputs:{a:f,b:i},backend:n});n.disposeIntermediateTensorInfo(g)}if(h){let g=f;if(p==="NCHW"&&h==="prelu"&&o.shape.length===1&&o.shape[0]!==1){let y=ft({inputs:{x:o},backend:n,attrs:{shape:[o.shape[0],1,1]}});f=em(n,f,h,y,m),n.disposeIntermediateTensorInfo(y)}else f=em(n,f,h,o,m);n.disposeIntermediateTensorInfo(g)}return f}var q8={kernelName:ai,backendName:"cpu",kernelFunc:j8};function K8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=iE({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c}});if(i){let g=f;f=$l({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=em(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var X8={kernelName:ri,backendName:"cpu",kernelFunc:K8};function Y8(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=v.sizeFromShape(a.shape),i=r.shape,o=i[i.length-1],[l,u,p,d]=N.prepareAndValidate(a,r);if(u===0)return n.makeTensorInfo(l,a.dtype,[]);let c=n.data.get(r.dataId).values,h=n.bufferSync(a),m=IC(c,h,a.dtype,u,o,p,d,a.shape,s);return n.makeTensorInfo(l,a.dtype,m.values)}var Z8={kernelName:au,backendName:"cpu",kernelFunc:Y8};function J8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a;ge([r,s],"gatherV2");let l=v.parseAxisParam(i,r.shape)[0],u=n.data.get(s.dataId).values,p=r.shape[l];for(let w=0;w=0,()=>`GatherV2: the index value ${I} is not in [0, ${p-1}]`)}let d=o;o==null&&(d=0);let c=v.sizeFromShape(s.shape),h=N.segment_util.collectGatherOpShapeInfo(r,s,l,d),m=ft({inputs:{x:r},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),f=ft({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,c/h.batchSize]}}),g=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],y=n.bufferSync(f),b=n.bufferSync(m),x=SC(b,y,g);return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),n.makeTensorInfo(h.outputShape,x.dtype,x.values)}var Q8={kernelName:nu,backendName:"cpu",kernelFunc:J8};function eK(e){let{inputs:t,backend:n}=e,{input:a}=t,r=v.sizeFromShape(a.shape),s=a.shape[a.shape.length-1],i=r/s,o=ft({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),l=oE(o,!0,n),u=ft({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var tK={kernelName:Nm,backendName:"cpu",kernelFunc:eK},nK=rt(su,e=>Number.isFinite(e)?1:0,"bool"),aK={kernelName:su,backendName:"cpu",kernelFunc:nK},rK=rt(iu,e=>Math.abs(e)===1/0?1:0,"bool"),sK={kernelName:iu,backendName:"cpu",kernelFunc:rK},iK=rt(zi,e=>Number.isNaN(e)?1:0,"bool"),oK={kernelName:zi,backendName:"cpu",kernelFunc:iK};function lK(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=_C(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var uK={kernelName:Em,backendName:"cpu",kernelFunc:lK},pK=rt(uu,e=>Math.log1p(e)),cK={kernelName:uu,backendName:"cpu",kernelFunc:pK},dK=Vt((e,t)=>e&&t),hK=rn(pu,dK,null,"bool"),mK={kernelName:pu,backendName:"cpu",kernelFunc:hK},fK=rt(cu,e=>e?0:1,"bool"),gK={kernelName:cu,backendName:"cpu",kernelFunc:fK},yK=Vt((e,t)=>e||t),bK=rn(du,yK,null,"bool"),xK={kernelName:du,backendName:"cpu",kernelFunc:bK};function vK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;ge(r,"LRN");let 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a5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;ge(r,"argMin");let i=v.parseAxisParam(s,r.shape),o=N.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Vn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],N.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[p,d]=N.computeOutAndReduceShapes(l.shape,i),c=v.sizeFromShape(p),h=v.makeZerosTypedArray(c,"int32"),m=v.sizeFromShape(d),f=n.data.get(l.dataId).values;for(let g=0;gn.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(p,"int32",h)}var r5={kernelName:pc,backendName:"cpu",kernelFunc:a5},s5=rt(Ll,e=>Math.asin(e)),i5={kernelName:Ll,backendName:"cpu",kernelFunc:s5},o5=rt(zl,e=>Math.asinh(e)),l5={kernelName:zl,backendName:"cpu",kernelFunc:o5},u5=rt(Wl,e=>Math.atan(e)),p5={kernelName:Wl,backendName:"cpu",kernelFunc:u5},c5=Vt((e,t)=>Math.atan2(e,t)),d5=rn(Vl,c5),h5={kernelName:Vl,backendName:"cpu",kernelFunc:d5},m5=rt(Bl,e=>Math.atanh(e)),f5={kernelName:Bl,backendName:"cpu",kernelFunc:m5};function K0(e,t,n,a,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,p=r.effectiveFilterHeight,d=r.effectiveFilterWidth,c=r.padInfo.top,h=r.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=ze(r.outShape,n),g=f.values,y=r.outShape[1]*r.outShape[2]*r.outShape[3],b=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let w=0;wj?j=se:s==="avg"&&(K+=se,Z++)}if(isNaN(j))break}let J=S+M*x+C;g[J]=s==="avg"?K/Z:j}}}return f}function QC(e,t,n,a,r=!1,s=!1){let i=ze(a.outShape,"int32"),o=a.strideHeight,l=a.strideWidth,u=a.dilationHeight,p=a.dilationWidth,d=a.effectiveFilterHeight,c=a.effectiveFilterWidth,h=a.padInfo.top,m=a.padInfo.left,f=ze(t,n,e);for(let g=0;gR&&(R=G,r?F=s?((g*a.inHeight+S)*a.inWidth+B)*a.inChannels+y:(S*a.inWidth+B)*a.inChannels+y:F=M*c+U)}}i.set(F,g,b,T,y)}}return i}function e_(e,t,n,a,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,p=r.dilationHeight,d=r.dilationWidth,c=r.effectiveFilterDepth,h=r.effectiveFilterHeight,m=r.effectiveFilterWidth,f=r.padInfo.front,g=r.padInfo.top,y=r.padInfo.left,b=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=ze(r.outShape,n),w=x.values,I=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],T=r.outShape[2]*r.outShape[3]*r.outShape[4],C=r.outShape[3]*r.outShape[4],E=r.outShape[4];for(let $=0;$ye?ye=dt:s==="avg"&&(ke+=dt,Se++),isNaN(ye))break}if(isNaN(ye))break}if(isNaN(ye))break}let We=ue+S;w[We]=s==="avg"?ke/Math.max(Se,1):ye}}}}return 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p=N.computePool3DInfo(s.shape,i,o,1,l,u),d=p.strideDepth,c=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,y=p.dilationDepth,b=p.dilationHeight,x=p.dilationWidth,w=p.effectiveFilterDepth,I=p.effectiveFilterHeight,T=p.effectiveFilterWidth,C=w-1-p.padInfo.front,E=T-1-p.padInfo.left,$=I-1-p.padInfo.top,R=ze(s.shape,"float32"),F=1/(m*f*g),S=n.bufferSync(r);for(let M=0;M=p.outDepth||Math.floor(te)!==te))for(let re=0;re=p.outHeight||Math.floor(se)!==se))for(let be=0;be=p.outWidth||Math.floor(ue)!==ue)continue;let ye=S.get(M,te,se,ue,B);ee+=ye}}}R.set(ee*F,M,U,G,j,B)}return n.makeTensorInfo(R.shape,R.dtype,R.values)}var k5={kernelName:cm,backendName:"cpu",kernelFunc:w5};function I5(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ge([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=p.strideHeight,c=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,y=p.effectiveFilterHeight,b=p.effectiveFilterWidth,x=b-1-p.padInfo.left,w=y-1-p.padInfo.top,I=ze(i.shape,"float32"),T=1/(h*m),C=n.data.get(r.dataId).values,E=ze(r.shape,"float32",C);for(let $=0;$=p.outHeight||Math.floor(j)!==j))for(let K=0;K=p.outWidth||Math.floor(Z)!==Z)continue;let J=E.get($,j,Z,R);U+=J}}I.set(U*T,$,F,S,R)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var S5={kernelName:pm,backendName:"cpu",kernelFunc:I5};function T5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient 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o=s.reduce((y,b)=>y*b),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=ft({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Vn({inputs:{x:h},backend:n,attrs:{perm:u}}),f=ft({inputs:{x:m},backend:n,attrs:{shape:p}}),g=hi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var _5={kernelName:Ul,backendName:"cpu",kernelFunc:C5};function E5(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=L0(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var A5={kernelName:dm,backendName:"cpu",kernelFunc:E5};function $5(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var F5={kernelName:hm,backendName:"cpu",kernelFunc:$5},R5=rt(ws,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(v.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;uf.shape);N.assertParamsConsistent(i,s);let o=N.computeOutShape(t.map(f=>f.shape),s);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>v.sizeFromShape(f.shape)>0);if(l.length===1)return dr({inputs:{x:l[0]},backend:n});if(l[0].dtype==="complex64"){let f=l.map(w=>di({inputs:{input:w},backend:n})),g=l.map(w=>El({inputs:{input:w},backend:n})),y=Al({inputs:f,backend:n,attrs:{axis:s}}),b=Al({inputs:g,backend:n,attrs:{axis:s}}),x=Zn({inputs:{real:y,imag:b},backend:n});return f.forEach(w=>n.disposeIntermediateTensorInfo(w)),g.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(b),x}let u=l.map(f=>{let g=[-1,v.sizeFromShape(f.shape.slice(s))];return ft({inputs:{x:f},backend:n,attrs:{shape:g}})}),p=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));o=N.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=z0(p,o,t[0].dtype,d),h=N.computeOutShape(l.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var L5={kernelName:Gl,backendName:"cpu",kernelFunc:Al};function t_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a;ge([r,s],"conv2d");let d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,y=c.padInfo.left,b=c.padInfo.top,x=c.dataFormat==="channelsLast",w=new jt(c.outShape,r.dtype),I=v.computeStrides(r.shape),T=v.computeStrides(s.shape),C=I[0],E=x?I[1]:I[2],$=x?I[2]:1,R=x?1:I[1],F=w.strides[0],S=x?w.strides[1]:w.strides[2],M=x?w.strides[2]:1,B=x?1:w.strides[1],U=n.data.get(r.dataId).values,G=n.data.get(s.dataId).values,j=w.values;for(let K=0;K=c.inHeight)continue;let be=re*T[0],ue=Z+se*E;for(let ye=0;ye=c.inWidth)continue;let pt=be+We*T[1],at=ue+Ge*$,tt=pt;for(let nt=0;nt=u.inDepth)continue;let K=G*$[0],Z=F+j*E[1];for(let J=0;J=u.inHeight)continue;let se=K+te*$[1],be=Z+re*E[2];for(let ue=0;ue=u.inWidth)continue;let Ge=se+Se*$[2],pt=be+We*u.inChannels,at=Ge;for(let tt=0;ttMath.cos(e)),Z5={kernelName:Ni,backendName:"cpu",kernelFunc:Y5},J5=rt(Ci,e=>Math.cosh(e)),Q5={kernelName:Ci,backendName:"cpu",kernelFunc:J5};function eK(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,y=ze([m,f,g,h],"float32"),b=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,w=n.data.get(r.dataId).values,I=v.computeStrides(r.shape),T=v.computeStrides(y.shape);for(let C=0;C=p)continue;let B=f>1?(F-$)*(d-1)/(f-1):0,U=g>1?(S-R)*(c-1)/(g-1):0;for(let G=0;G1?$*(d-1)+G*B:.5*($+F)*(d-1);if(j<0||j>d-1){for(let K=0;K1?R*(c-1)+ee*U:.5*(R+S)*(c-1);if(ae<0||ae>c-1){for(let be=0;be1?R*(c-1)+K*U:.5*(R+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;aey+m-b-1:(y,b)=>y+b;for(let y=0;yy+m-b-1:(y,b)=>y+b;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],d=l*s,c=u*s,h=p/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*d*c*h),g=0;for(let y=0;y`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=N.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:b}=h,x=b.left,w=b.top,I=h.outChannels/h.inChannels,T=new jt(h.outShape,r.dtype),C=n.data.get(r.dataId).values,E=n.data.get(s.dataId).values,$=T.values;for(let R=0;R=h.inHeight)continue;let K=G*d[0],Z=F+j*p[1];for(let J=0;J=h.inWidth)continue;let se=K+te*d[1],be=Z+re*h.inChannels,ue=ee,ye=se;for(let ke=0;ke{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:b,padInfo:x,strideHeight:w,strideWidth:I,filterHeight:T,filterWidth:C,dilationHeight:E,dilationWidth:$,outShape:R}=N.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),F=v.sizeFromShape(R),S=R.length,M=v.getArrayFromDType(a.dtype,F);for(let B=0;B=0&&te=0&&seJ&&(J=ye)}}}let ee=v.locToIndex([B,U,j,Z],S,v.computeStrides(R));M[ee]=J}}}return{dataId:l.write(v.toTypedArray(M,a.dtype),R,a.dtype),shape:R,dtype:a.dtype}}},bK={kernelName:$h,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=v.toNestedArray(a.shape,u.data.get(a.dataId).values),d=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:$}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===$.length,()=>`Error in ${$h}, dy must have the same rank as output ${$.length}, but got ${s.rank}`);let R=v.toNestedArray($,u.data.get(s.dataId).values),F=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&ae=0&&reK&&(K=se,Z=ee,J=te)}}}F[Z][J][j]+=R[S][M][U][j]}}}return{dataId:u.write(v.toTypedArray(F,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},xK={kernelName:Ah,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=v.toNestedArray(a.shape,u.data.get(a.dataId).values),d=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:w,filterHeight:I,filterWidth:T,dilationHeight:C,dilationWidth:E,outShape:$}=N.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===$.length,()=>`Error in ${Ah}, dy must have the same rank as output ${$.length}, but got ${s.rank}`);let R=v.toNestedArray($,u.data.get(s.dataId).values),F=v.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S=0&&ae=0&&reK&&(K=se,Z=ae,J=re)}}}F[S][Z][J][j]+=R[S][M][U][j]}}}return{dataId:u.write(v.toTypedArray(F,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function ad(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ge(r,"sum");let o;r.dtype==="bool"?o=gs({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=dr({inputs:{x:r},backend:n});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),p=N.getAxesPermutation(u,l),d=u,c=o;p!=null&&(c=Vn({inputs:{x:o},backend:n,attrs:{perm:p}}),d=N.getInnerMostAxes(d.length,l)),N.assertAxesAreInnerMostDims("sum",d,c.shape.length);let[h,m]=N.computeOutAndReduceShapes(c.shape,d),f=N.upcastType(c.dtype,"int32"),g=Yh(n,h,f),y=v.sizeFromShape(m),b=n.data.get(g.dataId).values,x=n.data.get(c.dataId).values;for(let w=0;w=0&&(c=ad({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var kK={kernelName:km,backendName:"cpu",kernelFunc:wK};function IK(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;ge([a,r],"eluGrad");let s=new Float32Array(v.sizeFromShape(r.shape)),i=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values;for(let l=0;l=1?s[l]=o[l]:s[l]=o[l]*(u+1)}return n.makeTensorInfo(r.shape,"float32",s)}var SK={kernelName:Im,backendName:"cpu",kernelFunc:IK},TK=N.ERF_P,NK=N.ERF_A1,CK=N.ERF_A2,_K=N.ERF_A3,EK=N.ERF_A4,AK=N.ERF_A5,$K=rt(Kl,e=>{let t=Math.sign(e),n=Math.abs(e),a=1/(1+TK*n);return t*(1-((((AK*a+EK)*a+_K)*a+CK)*a+NK)*a*Math.exp(-n*n))}),FK={kernelName:Kl,backendName:"cpu",kernelFunc:$K};function Qh(e){let{inputs:t,backend:n,attrs:a}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),ft({inputs:{x:r},backend:n,attrs:{shape:o}})}var RK={kernelName:Yl,backendName:"cpu",kernelFunc:Qh},DK=Vt((e,t)=>e/t),X0=rn(Ai,DK),px={kernelName:Ai,backendName:"cpu",kernelFunc:X0};function a_(e,t,n){let a=e.shape,r=a[0],s=a[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=v.sizeFromShape(u),d=v.getTypedArrayFromDType("float32",p),c=v.getTypedArrayFromDType("float32",p);for(let g=0;g{let{image:a}=e,r=n,s=v.getTypedArrayFromDType(a.dtype,v.sizeFromShape(a.shape)),[i,o,l,u]=a.shape,p=r.data.get(a.dataId).values;for(let d=0;d=0&&bMath.floor(e/t)),GK=rn(Di,UK,null,"int32"),HK={kernelName:Di,backendName:"cpu",kernelFunc:GK};function qK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=t_({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c}});if(i){let g=f;if(p==="NCHW"&&i.shape.length===1&&i.shape[0]!==1){let y=ft({inputs:{x:i},backend:n,attrs:{shape:[i.shape[0],1,1]}});f=_l({inputs:{a:f,b:y},backend:n}),n.disposeIntermediateTensorInfo(y)}else f=_l({inputs:{a:f,b:i},backend:n});n.disposeIntermediateTensorInfo(g)}if(h){let g=f;if(p==="NCHW"&&h==="prelu"&&o.shape.length===1&&o.shape[0]!==1){let y=ft({inputs:{x:o},backend:n,attrs:{shape:[o.shape[0],1,1]}});f=Jh(n,f,h,y,m),n.disposeIntermediateTensorInfo(y)}else f=Jh(n,f,h,o,m);n.disposeIntermediateTensorInfo(g)}return f}var jK={kernelName:ti,backendName:"cpu",kernelFunc:qK};function KK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=n_({inputs:{x:r,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:p,dilations:d,dimRoundingMode:c}});if(i){let g=f;f=_l({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=Jh(n,f,h,o,m),n.disposeIntermediateTensorInfo(g)}return f}var XK={kernelName:ni,backendName:"cpu",kernelFunc:KK};function YK(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=v.sizeFromShape(a.shape),i=r.shape,o=i[i.length-1],[l,u,p,d]=N.prepareAndValidate(a,r);if(u===0)return n.makeTensorInfo(l,a.dtype,[]);let c=n.data.get(r.dataId).values,h=n.bufferSync(a),m=xC(c,h,a.dtype,u,o,p,d,a.shape,s);return n.makeTensorInfo(l,a.dtype,m.values)}var ZK={kernelName:eu,backendName:"cpu",kernelFunc:YK};function JK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a;ge([r,s],"gatherV2");let l=v.parseAxisParam(i,r.shape)[0],u=n.data.get(s.dataId).values,p=r.shape[l];for(let w=0;w=0,()=>`GatherV2: the index value ${I} is not in [0, ${p-1}]`)}let d=o;o==null&&(d=0);let c=v.sizeFromShape(s.shape),h=N.segment_util.collectGatherOpShapeInfo(r,s,l,d),m=ft({inputs:{x:r},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),f=ft({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,c/h.batchSize]}}),g=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],y=n.bufferSync(f),b=n.bufferSync(m),x=vC(b,y,g);return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),n.makeTensorInfo(h.outputShape,x.dtype,x.values)}var QK={kernelName:Ql,backendName:"cpu",kernelFunc:JK};function e8(e){let{inputs:t,backend:n}=e,{input:a}=t,r=v.sizeFromShape(a.shape),s=a.shape[a.shape.length-1],i=r/s,o=ft({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),l=a_(o,!0,n),u=ft({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var t8={kernelName:Tm,backendName:"cpu",kernelFunc:e8},n8=rt(nu,e=>Number.isFinite(e)?1:0,"bool"),a8={kernelName:nu,backendName:"cpu",kernelFunc:n8},r8=rt(au,e=>Math.abs(e)===1/0?1:0,"bool"),s8={kernelName:au,backendName:"cpu",kernelFunc:r8},i8=rt(Li,e=>Number.isNaN(e)?1:0,"bool"),o8={kernelName:Li,backendName:"cpu",kernelFunc:i8};function l8(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=TC(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var u8={kernelName:Cm,backendName:"cpu",kernelFunc:l8},p8=rt(iu,e=>Math.log1p(e)),c8={kernelName:iu,backendName:"cpu",kernelFunc:p8},d8=Vt((e,t)=>e&&t),h8=rn(ou,d8,null,"bool"),m8={kernelName:ou,backendName:"cpu",kernelFunc:h8},f8=rt(lu,e=>e?0:1,"bool"),g8={kernelName:lu,backendName:"cpu",kernelFunc:f8},y8=Vt((e,t)=>e||t),b8=rn(uu,y8,null,"bool"),x8={kernelName:uu,backendName:"cpu",kernelFunc:b8};function v8(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;ge(r,"LRN");let u=r.shape[3],p=u-1,d=n.data.get(r.dataId).values,c=v.sizeFromShape(r.shape),h=new Float32Array(c);function m(f){let g=f%u,y=f-g+Math.max(0,g-s),b=f-g+Math.min(g+s,p),x=0;for(;y<=b;y++){let w=d[y];x+=w*w}return x}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. 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WZ(e,t);case 3:return VZ(e,t);case 4:return GZ(e,t);case 5:return HZ(e);case 6:return jZ(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function BE(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return MZ(e);case 1:return OZ(e,t);case 2:return zZ(e,t);case 3:return BZ(e,t);default:return UZ(e,t)}}function mZ(e,t,n=!1,a){let r="";n?r+=BE(e,a):r+=Ju(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=qZ(e,t):r+=KZ(e,t)),r}function fZ(e,t,n){switch(e.length){case 0:return VE();case 1:return TZ(e,t,n);case 2:return DZ(e,t,n);case 3:return CZ(e,t,n);default:return _Z(e,t,n)}}function gZ(e,t,n){switch(e.length){case 0:return VE();case 1:return NZ(e,t,n);case 2:return RZ(e,t,n);case 3:return EZ(e,t,n);case 4:return AZ(e,t,n);case 5:return $Z(e,t);case 6:return FZ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function yZ(e){return` +`),i=t.texShape,o=_n(),l=yZ(o),u,p,d=vZ(o);return t.isPacked?(u=fZ(t.logicalShape,i,n.enableShapeUniforms),p=xZ(o)):(u=gZ(t.logicalShape,i,n.enableShapeUniforms),p=bZ(o)),n.packedInputs&&(d+=SZ),[d,l,p,r,u,s,n.userCode].join(` +`)}function Xu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return PZ(e,t);case 1:return LZ(e,t);case 2:return WZ(e,t);case 3:return VZ(e,t);case 4:return GZ(e,t);case 5:return HZ(e);case 6:return qZ(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function O_(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return MZ(e);case 1:return OZ(e,t);case 2:return zZ(e,t);case 3:return BZ(e,t);default:return UZ(e,t)}}function mZ(e,t,n=!1,a){let r="";n?r+=O_(e,a):r+=Xu(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=jZ(e,t):r+=KZ(e,t)),r}function fZ(e,t,n){switch(e.length){case 0:return L_();case 1:return TZ(e,t,n);case 2:return RZ(e,t,n);case 3:return CZ(e,t,n);default:return EZ(e,t,n)}}function gZ(e,t,n){switch(e.length){case 0:return L_();case 1:return NZ(e,t,n);case 2:return DZ(e,t,n);case 3:return _Z(e,t,n);case 4:return AZ(e,t,n);case 5:return $Z(e,t);case 6:return FZ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function yZ(e){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${e.texture2D}(textureSampler, uv).r; } @@ -268,7 +268,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function VE(){return` +`;function L_(){return` int getOutputCoords() { return 0; } @@ -360,15 +360,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec3(b, r, c); } - `}function EZ(e,t,n){if(n)return` + `}function _Z(e,t,n){if(n)return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Xf(["r","c","d"],e)} + ${Uf(["r","c","d"],e)} return ivec3(r, c, d); } -`;let a=No(["r","c","d"],e);return` +`;let a=To(["r","c","d"],e);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -376,7 +376,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${a} return ivec3(r, c, d); } - `}function _Z(e,t,n){if(n)return` + `}function EZ(e,t,n){if(n)return` ivec4 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * @@ -422,10 +422,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; - ${Xf(["r","c","d","d2"],e)} + ${Uf(["r","c","d","d2"],e)} return ivec4(r, c, d, d2); } - `;let a=No(["r","c","d","d2"],e);return` + `;let a=To(["r","c","d","d2"],e);return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -433,7 +433,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${a} return ivec4(r, c, d, d2); } - `}function $Z(e,t){let n=No(["r","c","d","d2","d3"],e);return` + `}function $Z(e,t){let n=To(["r","c","d","d2","d3"],e);return` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -445,7 +445,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec5 outShape = ivec5(r, c, d, d2, d3); return outShape; } - `}function FZ(e,t){let n=No(["r","c","d","d2","d3","d4"],e);return` + `}function FZ(e,t){let n=To(["r","c","d","d2","d3","d4"],e);return` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -456,7 +456,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec6 result = ivec6(r, c, d, d2, d3, d4); return result; } - `}function DZ(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?` + `}function RZ(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?` ivec2 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); @@ -489,7 +489,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ivec2(r, c); } - `}function RZ(e,t,n){return v.arraysEqual(e,t)?n?` + `}function DZ(e,t,n){return v.arraysEqual(e,t)?n?` ivec2 getOutputCoords() { return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); } @@ -543,7 +543,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int c = index - r * ${e[1]}; return ivec2(r, c); } - `}function Co(e){return`offset${e}`}function MZ(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=En();return` + `}function No(e){return`offset${e}`}function MZ(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=_n();return` vec4 ${n}() { return ${a.texture2D}(${t}, halfCR); } @@ -551,7 +551,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float ${a}() { return sampleTexture(${n}, halfCR); } - `;let i=Co(n);if(t)return` + `;let i=No(n);if(t)return` float ${a}() { vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i}); return sampleTexture(${n}, uv); @@ -561,7 +561,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${o}, ${l}, ${i}); return sampleTexture(${n}, uv); } - `}function OZ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=En();if(t)return` + `}function OZ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=_n();if(t)return` vec4 ${a}(int index) { ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0)); vec2 uv = packedUVfrom1D( @@ -576,13 +576,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } `}function LZ(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return` float ${a}(int index) { - ${Qu(e)} + ${Yu(e)} } `;let r=e.shapeInfo.texShape,s=r[0],i=r[1];if(i===1&&s===1)return` float ${a}(int index) { return sampleTexture(${n}, halfCR); } - `;let o=Co(n);return i===1?t?` + `;let o=No(n);return i===1?t?` float ${a}(int index) { vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0])); return sampleTexture(${n}, uv); @@ -612,7 +612,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${s}, ${i}, index + ${o}); return sampleTexture(${n}, uv); } - `}function zZ(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=En();if(s!=null&&v.arraysEqual(n,s))return t?` + `}function zZ(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=_n();if(s!=null&&v.arraysEqual(n,s))return t?` vec4 ${r}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]); @@ -646,17 +646,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${c}.0); return sampleTexture(${a}, uv); } - `}let{newShape:i,keptDims:o}=v.squeezeShape(n),l=i;if(l.length=1?p="coords = 0;":p=o.map(g=>`coords.${d[g+u]} = 0;`).join(` + `}function jZ(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=P_(e.shapeInfo.logicalShape,t.logicalShape),l=gt(i),u=i-s,p,d=["x","y","z","w","u","v"];s===0?p="":i<2&&o.length>=1?p="coords = 0;":p=o.map(g=>`coords.${d[g+u]} = 0;`).join(` `);let c="";i<2&&s>0?c="coords":c=e.shapeInfo.logicalShape.map((g,y)=>`coords.${d[y+u]}`).join(", ");let h="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,f=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=` return vec4(outputValue.xy, outputValue.xy); `;else if(m&&!f)i===1?h=` @@ -977,16 +977,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float ${r}() { return sampleTexture(${n}, resultUV); } - `;let u=gt(l),p=WE(e.shapeInfo.logicalShape,t.logicalShape),d=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&p.length>=1?c="coords = 0;":c=p.map(f=>`coords.${h[f+d]} = 0;`).join(` + `;let u=gt(l),p=P_(e.shapeInfo.logicalShape,t.logicalShape),d=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&p.length>=1?c="coords = 0;":c=p.map(f=>`coords.${h[f+d]} = 0;`).join(` `);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+d]}`).join(", "),` float ${r}() { ${u} coords = getOutputCoords(); ${c} return get${a}(${m}); } - `}function gt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function n1(e,t,n){let{newShape:a,keptDims:r}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):a,l=!e&&s>1&&!v.arraysEqual(t,n)&&a.lengthe[n]).join(", ")}function XZ(e,t,n,a){let r=n.map((p,d)=>{let c={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(c.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[d],shapeInfo:c}}),s=r.map(p=>p.shapeInfo),i={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},o=hZ(r,i,t),l=bE(e.gl,o),u=e.createProgram(l);return H().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},UE(e,t,u))}function UE(e,t,n){let a={},r={},s={},i=[],o,l,u,p=null,d=null;d=e.getUniformLocation(n,"NAN",!1),H().getNumber("WEBGL_VERSION")===1&&(p=e.getUniformLocation(n,"INFINITY",!1));let c=!1;for(let h=0;h{i[m]=e.getUniformLocation(n,h.name,c)}),{uniformLocations:a,customUniformLocations:i,infLoc:p,nanLoc:d,inShapesLocations:r,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function fI(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,a)=>{let r=n.logicalShape,s=t[a],i=s.shape;if(!v.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. 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Shape ${o} and ${l} must match`)})}function YZ(e,t,n,a,r){t.program.enableShapeUniforms||(fI(t.inShapeInfos,n),fI([t.outShapeInfo],[a]));let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),H().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,u)=>{let p=t.program.variableNames[u],d=t.uniformLocations[p],c=t.uniformLocations[`offset${p}`],h=t.inShapesLocations[`${p}Shape`],m=t.inTexShapesLocations[`${p}TexShape`];if(h){let{uniformShape:f}=n1(t.program.packedInputs,l.shape,l.texData.texShape);switch(f.length){case 1:e.gl.uniform1iv(h,new Int32Array(f));break;case 2:e.gl.uniform2iv(h,new Int32Array(f));break;case 3:e.gl.uniform3iv(h,new Int32Array(f));break;case 4:e.gl.uniform4iv(h,new Int32Array(f));break;default:break}}if(m&&e.gl.uniform2i(m,l.texData.texShape[0],l.texData.texShape[1]),d!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(d,l.uniformValues[0]);else{let f=l.uniformValues;f instanceof Float32Array||(f=new Float32Array(f)),e.gl.uniform1fv(d,f)}return}l.texData.slice!=null&&c!=null&&e.gl.uniform1i(c,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,d,u)}});let o=t.outShapeLocation;if(o)switch(a.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(a.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(a.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(a.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(a.shape));break;default:break}if(t.outShapeStridesLocation){let l=v.computeStrides(a.shape);switch(a.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,a.texData.texShape[0],a.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,u)=>{let p=t.customUniformLocations[u],d=r[u];if(l.type==="float")e.gl.uniform1fv(p,d);else if(l.type==="vec2")e.gl.uniform2fv(p,d);else if(l.type==="vec3")e.gl.uniform3fv(p,d);else if(l.type==="vec4")e.gl.uniform4fv(p,d);else if(l.type==="int")e.gl.uniform1iv(p,d);else if(l.type==="ivec2")e.gl.uniform2iv(p,d);else if(l.type==="ivec3")e.gl.uniform3iv(p,d);else if(l.type==="ivec4")e.gl.uniform4iv(p,d);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function ZZ(e,t,n){let a="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:p,keptDims:d}=n1(e.packedInputs,i.shape,l),c="",h="",m="";if(p.length===1&&e.packedInputs){let I=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];c=`${I[0]>1}_${I[1]>1}`}else if(p.length===2&&!e.packedInputs)h=`${p[0]>1}_${p[1]>1}`;else if(p.length>2&&!e.packedInputs){let I=v.computeStrides(p);m=`${I[0]===l[1]}_${I[I.length-1]===l[1]}`}let f=i.shape.length,g=p.length===2&&v.arraysEqual(i.shape,l),y=v.sizeFromShape(i.shape)===1,b=N.getBroadcastDims(i.shape,n.shape),x=!e.packedInputs&&f===n.shape.length&&v.arraysEqual(l,n.texData.texShape),w=e.packedInputs||p.length>2?"":`${l[0]>1}_${l[1]>1}`;a+=`${f}_${x}_${u?d:""}_${p.length}_${y}_${b}_${g}_${c}_${h}_${m}_${w}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r+`${H().getNumber("WEBGL_VERSION")}`,s}function _n(e){return H().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var JZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=sc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=En();this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length),this.userCode=` + `}function gt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function t1(e,t,n){let{newShape:a,keptDims:r}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):a,l=!e&&s>1&&!v.arraysEqual(t,n)&&a.lengthe[n]).join(", ")}function XZ(e,t,n,a){let r=n.map((p,d)=>{let c={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(c.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[d],shapeInfo:c}}),s=r.map(p=>p.shapeInfo),i={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},o=hZ(r,i,t),l=m_(e.gl,o),u=e.createProgram(l);return H().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i},z_(e,t,u))}function z_(e,t,n){let a={},r={},s={},i=[],o,l,u,p=null,d=null;d=e.getUniformLocation(n,"NAN",!1),H().getNumber("WEBGL_VERSION")===1&&(p=e.getUniformLocation(n,"INFINITY",!1));let c=!1;for(let h=0;h{i[m]=e.getUniformLocation(n,h.name,c)}),{uniformLocations:a,customUniformLocations:i,infLoc:p,nanLoc:d,inShapesLocations:r,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function uI(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,a)=>{let r=n.logicalShape,s=t[a],i=s.shape;if(!v.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. 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Int32Array(f));break;default:break}}if(m&&e.gl.uniform2i(m,l.texData.texShape[0],l.texData.texShape[1]),d!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(d,l.uniformValues[0]);else{let f=l.uniformValues;f instanceof Float32Array||(f=new Float32Array(f)),e.gl.uniform1fv(d,f)}return}l.texData.slice!=null&&c!=null&&e.gl.uniform1i(c,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,d,u)}});let o=t.outShapeLocation;if(o)switch(a.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(a.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(a.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(a.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(a.shape));break;default:break}if(t.outShapeStridesLocation){let l=v.computeStrides(a.shape);switch(a.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,a.texData.texShape[0],a.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,u)=>{let p=t.customUniformLocations[u],d=r[u];if(l.type==="float")e.gl.uniform1fv(p,d);else if(l.type==="vec2")e.gl.uniform2fv(p,d);else if(l.type==="vec3")e.gl.uniform3fv(p,d);else if(l.type==="vec4")e.gl.uniform4fv(p,d);else if(l.type==="int")e.gl.uniform1iv(p,d);else if(l.type==="ivec2")e.gl.uniform2iv(p,d);else if(l.type==="ivec3")e.gl.uniform3iv(p,d);else if(l.type==="ivec4")e.gl.uniform4iv(p,d);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function ZZ(e,t,n){let a="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let 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`}},QZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=ac.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=_n();this.outputShape=e,this.enableShapeUniforms=En(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { - ${this.enableShapeUniforms?Xf(["r","c","d"],e):No(["r","c","d"],e)} + ${this.enableShapeUniforms?Uf(["r","c","d"],e):To(["r","c","d"],e)} return ivec3(r, c, d); } @@ -1024,26 +1024,26 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${t.output} = result; } - `}},e7=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ca.DOWNLOAD;let t=En();this.outputShape=e,this.userCode=` - ${zE} + `}},e7=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ca.DOWNLOAD;let t=_n();this.outputShape=e,this.userCode=` + ${M_} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } - 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floats");this.vertexBuffer=jE(this.gl),this.indexBuffer=qE(this.gl),this.framebuffer=TE(this.gl),this.textureConfig=J0(this.gl,this.textureHalfFloatExtension)}get debug(){return H().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;ce(e,()=>e.finish()),ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ce(e,()=>e.deleteFramebuffer(this.framebuffer)),ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ce(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),KE(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),XE(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),YE(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),t_(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,a){this.throwIfDisposed(),e_(this.gl,e,t,n,a,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),JE(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),ZE(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(xx(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>r_(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return s_(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return a_(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=n_(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),a}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(H().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=a.clientWaitSync(r,0,0);return s===a.ALREADY_SIGNALED||s===a.CONDITION_SATISFIED},t=r}else H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>i_(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=HE(t));let n=xE(t);ce(t,()=>t.attachShader(n,this.vertexShader)),ce(t,()=>t.attachShader(n,e)),vE(t,n);let a;return a=Object.assign(n,{vao:this.createVertexArray()}),this.bindVertexArray(a.vao),ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),console.assert(QE(t,a,this.vertexBuffer),"gpgpu_util.bindVertexProgramAttributeStreams not fully successful."),this.debug&&kh(t,a),this.setProgram(a),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&(this.bindVertexArray(this.program.vao),this.debug&&kh(this.gl,this.program)),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?CE(this.gl,e,t):EE(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),_E(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=Yu(t,n);this.setOutputMatrixTextureDriver(e,a,r)}setOutputMatrixWriteRegion(e,t,n,a){this.setOutputMatrixWriteRegionDriver(n,e,a,t)}setOutputPackedMatrixWriteRegion(e,t,n,a){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&kh(this.gl,this.program),Bp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}ce(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ce(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Wp(this.gl,H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(a.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),a=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),a&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=r7(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let n;"setTimeoutCustom"in H().platform&&(n=H().platform.setTimeoutCustom.bind(H().platform)),v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Ih(this.gl,e,this.framebuffer),this.debug&&Bp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Ih(this.gl,this.outputTexture,this.framebuffer),this.debug&&Bp(this.gl)):xx(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;Ih(a,e,this.framebuffer),this.debug&&Bp(a),this.outputTexture=e,ce(a,()=>a.viewport(0,0,t,n)),ce(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,n,a))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function r7(e){let t=0;for(;t`${e}.${n}`)}function In(e,t){return t===1?[e]:p_(e,t)}function K7(e,t){if(e===1)return"rc";let n="";for(let a=0;ae.bindTexture(o,i)),ce(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ce(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ce(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),ce(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),H().getNumber("WEBGL_VERSION")===1?ce(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)):ce(e,()=>e.texStorage2D(o,1,a,t,n)),ce(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[n,t]}}function n1(e){return e.internalFormatFloat}function G_(e,t,n,a){let[r,s]=rd(t,n);return sd(e,r,s,n1(a),a.textureFormatFloat,e.FLOAT)}function a1(e){return e.internalFormatHalfFloat}function H_(e,t,n,a){let[r,s]=rd(t,n);return sd(e,r,s,a1(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function r1(e){return e.downloadTextureFormat}function q_(e,t,n,a){let[r,s]=rd(t,n);return sd(e,r,s,r1(a),e.RGBA,e.UNSIGNED_BYTE)}function s1(e){return e.internalFormatPackedFloat}function j_(e,t,n,a){let[r,s]=ju(t,n);return sd(e,r,s,s1(a),e.RGBA,e.FLOAT)}function i1(e){return e.internalFormatPackedHalfFloat}function K_(e,t,n,a){let[r,s]=ju(t,n);return sd(e,r,s,i1(a),e.RGBA,a.textureTypeHalfFloat)}function X_(e,t,n){return ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),dx(e,t,"clipSpacePos",n,3,20,0)&&dx(e,t,"uv",n,2,20,12)}function Y_(e,t,n,a,r,s){ce(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*a*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),H().getNumber("WEBGL_VERSION")===2?ce(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,a,e.RGBA,o,i)):ce(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),ce(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Z_(e,t,n){ce(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?H().getNumber("WEBGL_VERSION")===2?ce(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data)):ce(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):H().getNumber("WEBGL_VERSION")===2?ce(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n)):ce(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),ce(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function J_(e,t,n,a){let r=e.createBuffer();ce(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return ce(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),ce(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),ce(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function Q_(e,t,n){let a=e,r=new Float32Array(n);return a.bindBuffer(a.PIXEL_PACK_BUFFER,t),a.getBufferSubData(a.PIXEL_PACK_BUFFER,0,r),a.bindBuffer(a.PIXEL_PACK_BUFFER,null),r}function eE(e,t,n,a){let[r,s]=rd(t,n),i=4,o=new Uint8Array(QY(t*n,i));return ce(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function tE(e,t,n,a,r,s,i,o){let l=e,u=new Float32Array(eZ(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function nE(e,t,n){let a=new Float32Array(t*n*4);return ce(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var Th=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=H().getNumber("WEBGL_VERSION");if(e!=null?(this.gl=e,p_(t,e)):this.gl=Ka(t),e=this.gl,H().getNumber("WEBGL_VERSION")===2){let r=e;this.createVertexArray=()=>ce(r,()=>r.createVertexArray()),this.bindVertexArray=s=>ce(r,()=>r.bindVertexArray(s)),this.deleteVertexArray=s=>ce(r,()=>r.deleteVertexArray(s)),this.getVertexArray=()=>ce(r,()=>r.getParameter(r.VERTEX_ARRAY_BINDING))}else if(e!=null){let r=e.getExtension("OES_vertex_array_object");if(r==null)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>ce(e,()=>r.createVertexArrayOES()),this.bindVertexArray=s=>ce(e,()=>r.bindVertexArrayOES(s)),this.deleteVertexArray=s=>ce(e,()=>r.deleteVertexArrayOES(s)),this.getVertexArray=()=>ce(e,()=>e.getParameter(r.VERTEX_ARRAY_BINDING_OES))}let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),H().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Pp(this.gl,r),da(this.gl,s))this.textureHalfFloatExtension=Pp(this.gl,s);else if(H().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),da(this.gl,a))this.colorBufferHalfFloatExtension=Pp(this.gl,a);else if(H().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",da(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(da(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=V_(this.gl),this.indexBuffer=U_(this.gl),this.framebuffer=w_(this.gl),this.textureConfig=Z0(this.gl,this.textureHalfFloatExtension)}get debug(){return H().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;ce(e,()=>e.finish()),ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ce(e,()=>e.deleteFramebuffer(this.framebuffer)),ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ce(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),G_(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),H_(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),q_(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Z_(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,a){this.throwIfDisposed(),Y_(this.gl,e,t,n,a,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),K_(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),j_(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(hx(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>eE(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return tE(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return Q_(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=J_(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),a}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(H().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=a.clientWaitSync(r,0,0);return s===a.ALREADY_SIGNALED||s===a.CONDITION_SATISFIED},t=r}else H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>nE(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=B_(t));let n=f_(t);ce(t,()=>t.attachShader(n,this.vertexShader)),ce(t,()=>t.attachShader(n,e)),g_(t,n);let a;return a=Object.assign(n,{vao:this.createVertexArray()}),this.bindVertexArray(a.vao),ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),console.assert(X_(t,a,this.vertexBuffer),"gpgpu_util.bindVertexProgramAttributeStreams not fully successful."),this.debug&&wh(t,a),this.setProgram(a),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&(this.bindVertexArray(this.program.vao),this.debug&&wh(this.gl,this.program)),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?I_(this.gl,e,t):S_(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),T_(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=ju(t,n);this.setOutputMatrixTextureDriver(e,a,r)}setOutputMatrixWriteRegion(e,t,n,a){this.setOutputMatrixWriteRegionDriver(n,e,a,t)}setOutputPackedMatrixWriteRegion(e,t,n,a){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&wh(this.gl,this.program),Op(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}ce(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ce(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Pp(this.gl,H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(a.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),a=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),a&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=r7(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let n;"setTimeoutCustom"in H().platform&&(n=H().platform.setTimeoutCustom.bind(H().platform)),v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),kh(this.gl,e,this.framebuffer),this.debug&&Op(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(kh(this.gl,this.outputTexture,this.framebuffer),this.debug&&Op(this.gl)):hx(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;kh(a,e,this.framebuffer),this.debug&&Op(a),this.outputTexture=e,ce(a,()=>a.viewport(0,0,t,n)),ce(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,n,a))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function r7(e){let t=0;for(;t`${e}.${n}`)}function In(e,t){return t===1?[e]:iE(e,t)}function K7(e,t){if(e===1)return"rc";let n="";for(let a=0;a= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}), cEdge ? 0. : getA(${t[1]}), rEdge ? 0. : getA(${t[2]}), - rEdge || cEdge ? 0. : getA(${t[3]})`}},c_=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length);let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2===1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=` + rEdge || cEdge ? 0. : getA(${t[3]})`}},oE=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=En(this.outputShape.length);let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2===1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=` ${r} ${a>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); @@ -1155,7 +1155,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${a>0?"}":""} `}this.userCode=` ${Y7(t,this.enableShapeUniforms)} - ${this.enableShapeUniforms?t1():e1(e)} + ${this.enableShapeUniforms?e1():Q0(e)} void main() { ivec3 rc = getOutputCoords(); @@ -1172,10 +1172,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } `}};function Y7(e,t){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${t?dZ(["r","c","d"],"inputShape"):No(["r","c","d"],e)} + ${t?dZ(["r","c","d"],"inputShape"):To(["r","c","d"],e)} return ivec3(r, c, d); } - `}var Z7=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=bI(t,n),r=xI(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=yI(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===un.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===un.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===un.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===un.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===un.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=bI(n,a),s=xI(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=yI(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=H().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function J7(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function yI(e,t,n,a,r){let s=Q7(t,a),i;if(r){let[l,u]=Yu(e[0],e[1]);i=l*u}else{let[l,u]=od(e[0],e[1]);i=l*u}let o=J7(n,s);return i*o}function Q7(e,t){switch(e){case un.PACKED_2X2_FLOAT32:return i1(t);case un.PACKED_2X2_FLOAT16:return o1(t);case un.UNPACKED_FLOAT32:return a1(t);case un.UNPACKED_FLOAT16:return r1(t);case un.PACKED_4X1_UNSIGNED_BYTE:return s1(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function eJ(e){return H().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?un.PACKED_2X2_FLOAT32:un.UNPACKED_FLOAT32:e?un.PACKED_2X2_FLOAT16:un.UNPACKED_FLOAT16}function bI(e,t){if(e===ca.UPLOAD)return un.PACKED_2X2_FLOAT32;if(e===ca.RENDER||e==null)return eJ(t);if(e===ca.DOWNLOAD||e===ca.PIXELS)return un.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function xI(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var ir=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length),this.userCode=` + `}var Z7=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=dI(t,n),r=hI(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=cI(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===pn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===pn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===pn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===pn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===pn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=dI(n,a),s=hI(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=cI(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=H().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function J7(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function cI(e,t,n,a,r){let s=Q7(t,a),i;if(r){let[l,u]=ju(e[0],e[1]);i=l*u}else{let[l,u]=rd(e[0],e[1]);i=l*u}let o=J7(n,s);return i*o}function Q7(e,t){switch(e){case pn.PACKED_2X2_FLOAT32:return s1(t);case pn.PACKED_2X2_FLOAT16:return i1(t);case pn.UNPACKED_FLOAT32:return n1(t);case pn.UNPACKED_FLOAT16:return a1(t);case pn.PACKED_4X1_UNSIGNED_BYTE:return r1(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function eJ(e){return H().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?pn.PACKED_2X2_FLOAT32:pn.UNPACKED_FLOAT32:e?pn.PACKED_2X2_FLOAT16:pn.UNPACKED_FLOAT16}function dI(e,t){if(e===ca.UPLOAD)return pn.PACKED_2X2_FLOAT32;if(e===ca.RENDER||e==null)return eJ(t);if(e===ca.DOWNLOAD||e===ca.PIXELS)return pn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function hI(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var ir=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=En(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } @@ -1186,11 +1186,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},Oa="if (isnan(x)) return x;",tJ="return x;",vI="return abs(x);",nJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",aJ=Oa+` + `}},Oa="if (isnan(x)) return x;",tJ="return x;",mI="return abs(x);",nJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",aJ=Oa+` return (x < 0.0) ? 0.0 : x; `,rJ=Oa+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,es="return x;",sJ="return 1.0 / (1.0 + exp(-1.0 * x));",iJ="return x;",oJ=` +`,Qr="return x;",sJ="return 1.0 / (1.0 + exp(-1.0 * x));",iJ="return x;",oJ=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -1219,7 +1219,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,pJ="return 1.0 / (1.0 + exp(-1.0 * x));",ss=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length),this.userCode=` +`,pJ="return 1.0 / (1.0 + exp(-1.0 * x));",rs=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=En(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } @@ -1230,17 +1230,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},cJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=_n(this.outputShape.length);let t=e.length,n=In("rc",t),a=gt(t),r=K7(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` + `}},cJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=En(this.outputShape.length);let t=e.length,n=In("rc",t),a=gt(t),r=K7(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=` void main() { ${a} rc = getOutputCoords(); vec4 packedInput = getA(${r}); setOutput(getChannel(packedInput, ${i})); } - `}},dJ=fr.whereImpl,hJ=1e-7,mJ=1e-4,_b={};function fJ(e){return e in _b||(_b[e]={}),_b[e]}var gJ=H().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),yJ=600;function bJ(){return H().global.screen==null?1024:H().global.screen.height*H().global.screen.width*window.devicePixelRatio*yJ/1024/1024}var Yf=class extends cc{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!H().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Nh)t=e;else{let n=Ka(H().getNumber("WEBGL_VERSION"),e);t=new Nh(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Ka(H().getNumber("WEBGL_VERSION"));t=new Nh(n),this.binaryCache=fJ(H().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Z7(this.gpgpu),this.numMBBeforeWarning=bJ(),this.texData=new lm(this,_a())}nextDataId(){return Yf.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,n,a,r,s){let i=this.makeTensorInfo(t,n),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[a,r]},o.texShape=[a,r];let l=Vp(t),u=new gI(l,!1,s),p=this.runWebGLProgram(u,[i],n,[[a,r]]);return p.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(e,t,n){if((H().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||H().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:ca.UPLOAD,refCount:1}),a}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,a,r){if(H().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:ca.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let d;o?d=new ss(i,es):d=new ir(i,es);let c=this.runWebGLProgram(d,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let p;if(a==="complex64"){let d=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);p=N.mergeRealAndImagArrays(d,c)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new ss(a,es):h=new ir(a,es);let m=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(H().getBool("DEBUG")&&!H().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&H().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&H().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...gh(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];p=N.mergeRealAndImagArrays(m,f)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(a);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;ce(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,p),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&_a().removeDataId(e,this),this.pendingDeletes--),d}readToGPU(e,t={}){let n=this.texData.get(e),{values:a,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let c;o?c=new ss(r,es):c=new ir(r,es);let h=this.runWebGLProgram(c,[{dataId:e,shape:r,dtype:i}],i),m=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),m}if(l==null)throw a!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),p=_a().makeTensorFromTensorInfo(u),d=this.texData.get(u.dataId);return Object.assign({tensorRef:p},d.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(a=>v.decodeString(a));return Oe(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Oe(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:a,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(a,n),this.textureManager.releaseTexture(t,a,r,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=gJ){return H().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)0&&v.isString(n[0])){let r=n.map(s=>v.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){return _a().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new cJ(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new X7(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[fi(e.shape),...gi(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[fi(t),...gi(t)],s=new c_(r,n),i=!0,o=[n],l=this.runWebGLProgram(s,[a],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:a,shape:r,dtype:s}=n;if(t!=null){let d=v.sizeFromShape(r),c=t[0]*t[1]*4;v.assert(d<=c,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Vp(r),o;a?o=new QZ(i):o=new JZ(i);let l=!0,u=[t!=null?t:gh(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,n,a,r=!1,s){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===sc.DENSE){let g=s!=null?s:gh(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=H().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!ic(y.shape,g.shape)){let b=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),b.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},d=ZZ(e,u,p),c=this.getAndSaveBinary(d,()=>XZ(this.gpgpu,e,u,p)),h=this.activeTimers!=null,m;h&&(m=this.startTimer()),H().get("ENGINE_COMPILE_ONLY")||YZ(this.gpgpu,c,u,p,a),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));let f=H().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let g=v.now();g-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!H().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,a,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,a,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(H().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=P(()=>{if(!H().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=H().getBool("DEBUG");H().set("DEBUG",!1);let t=this.abs(xe(1e-8)).dataSync()[0];if(H().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?hJ:mJ}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let p=t.texShape;if(p==null&&(p=FE(n,o),t.texShape=p),r!=null){let d=Vp(n),c,h=p[1],m=p[0],f=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!f)&&([h,m]=Yu(p[0],p[1])),o?c=new a7(d,f):c=new gI(d,f);let g=f?[m,h]:p,y=this.makeTensorInfo(g,a),b=this.texData.get(y.dataId);f?b.usage=ca.PIXELS:b.usage=ca.UPLOAD,b.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,m,r);let x=[[m,h]],w=!0,I=this.runWebGLProgram(c,[y],a,x,w),T=this.texData.get(I.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,H().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let d=this.acquireTexture(p,i,a,o);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return t!=null&&(n.values=xJ(t,a)),n.values}acquireTexture(e,t,n,a){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,a)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(a=>{try{this.checkCompletion_(t),a(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await tw(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Q0(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:a,nanLoc:r,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=UE(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=a,e.nanLoc=r,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}createTensorFromTexture(e,t,n){let{texture:a,height:r,width:s,channels:i}=e,o=_a().backend;if(!o.gpgpu.gl.isTexture(a))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=o.writeTexture(a,t,n,r,s,i);return _a().makeTensorFromDataId(l,t,n,o)}};Yf.nextDataId=0;function xJ(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let a=0;anew Yf,2);var wJ={forceHalfFloat:d_},u1=` + `}},dJ=fr.whereImpl,hJ=1e-7,mJ=1e-4,Sb={};function fJ(e){return e in Sb||(Sb[e]={}),Sb[e]}var gJ=H().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),yJ=600;function bJ(){return H().global.screen==null?1024:H().global.screen.height*H().global.screen.width*window.devicePixelRatio*yJ/1024/1024}var Gf=class extends uc{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!H().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Th)t=e;else{let n=Ka(H().getNumber("WEBGL_VERSION"),e);t=new Th(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Ka(H().getNumber("WEBGL_VERSION"));t=new Th(n),this.binaryCache=fJ(H().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Z7(this.gpgpu),this.numMBBeforeWarning=bJ(),this.texData=new im(this,Ea())}nextDataId(){return Gf.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,n,a,r,s){let i=this.makeTensorInfo(t,n),o=this.texData.get(i.dataId);o.isPacked=!1,o.texture={texture:e,texShape:[a,r]},o.texShape=[a,r];let l=Lp(t),u=new pI(l,!1,s),p=this.runWebGLProgram(u,[i],n,[[a,r]]);return p.shape=t,o.texture=null,this.disposeIntermediateTensorInfo(i),p.dataId}write(e,t,n){if((H().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||H().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:ca.UPLOAD,refCount:1}),a}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,a,r){if(H().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:ca.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let d;o?d=new rs(i,Qr):d=new ir(i,Qr);let c=this.runWebGLProgram(d,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let p;if(a==="complex64"){let d=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);p=N.mergeRealAndImagArrays(d,c)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new rs(a,Qr):h=new ir(a,Qr);let m=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(H().getBool("DEBUG")&&!H().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&H().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&H().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...fh(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];p=N.mergeRealAndImagArrays(m,f)}else if(l==null)p=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(a);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;ce(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,p),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ea().removeDataId(e,this),this.pendingDeletes--),d}readToGPU(e,t={}){let n=this.texData.get(e),{values:a,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let c;o?c=new rs(r,Qr):c=new ir(r,Qr);let h=this.runWebGLProgram(c,[{dataId:e,shape:r,dtype:i}],i),m=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),m}if(l==null)throw a!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),p=Ea().makeTensorFromTensorInfo(u),d=this.texData.get(u.dataId);return Object.assign({tensorRef:p},d.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(a=>v.decodeString(a));return ze(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return ze(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:a,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(a,n),this.textureManager.releaseTexture(t,a,r,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=gJ){return H().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)0&&v.isString(n[0])){let r=n.map(s=>v.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){return Ea().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new cJ(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new X7(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[mi(e.shape),...fi(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[mi(t),...fi(t)],s=new oE(r,n),i=!0,o=[n],l=this.runWebGLProgram(s,[a],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:a,shape:r,dtype:s}=n;if(t!=null){let d=v.sizeFromShape(r),c=t[0]*t[1]*4;v.assert(d<=c,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Lp(r),o;a?o=new QZ(i):o=new JZ(i);let l=!0,u=[t!=null?t:fh(i)],p=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:p.dataId}}runWebGLProgram(e,t,n,a,r=!1,s){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===ac.DENSE){let g=s!=null?s:fh(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=H().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!rc(y.shape,g.shape)){let b=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),b.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let p={shape:i.shape,texData:o,isUniform:!1},d=ZZ(e,u,p),c=this.getAndSaveBinary(d,()=>XZ(this.gpgpu,e,u,p)),h=this.activeTimers!=null,m;h&&(m=this.startTimer()),H().get("ENGINE_COMPILE_ONLY")||YZ(this.gpgpu,c,u,p,a),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));let f=H().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let g=v.now();g-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!H().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,a,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,a,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(H().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=P(()=>{if(!H().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=H().getBool("DEBUG");H().set("DEBUG",!1);let t=this.abs(xe(1e-8)).dataSync()[0];if(H().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?hJ:mJ}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let p=t.texShape;if(p==null&&(p=__(n,o),t.texShape=p),r!=null){let d=Lp(n),c,h=p[1],m=p[0],f=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!f)&&([h,m]=ju(p[0],p[1])),o?c=new a7(d,f):c=new pI(d,f);let g=f?[m,h]:p,y=this.makeTensorInfo(g,a),b=this.texData.get(y.dataId);f?b.usage=ca.PIXELS:b.usage=ca.UPLOAD,b.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,m,r);let x=[[m,h]],w=!0,I=this.runWebGLProgram(c,[y],a,x,w),T=this.texData.get(I.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,H().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let d=this.acquireTexture(p,i,a,o);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return t!=null&&(n.values=xJ(t,a)),n.values}acquireTexture(e,t,n,a){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,a)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(a=>{try{this.checkCompletion_(t),a(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await ew(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(J0(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:a,nanLoc:r,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=z_(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=a,e.nanLoc=r,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}createTensorFromGPUData(e,t,n){e.channels=e.channels||"RGBA";let{texture:a,height:r,width:s,channels:i}=e,o=Ea().backend;if(!o.gpgpu.gl.isTexture(a))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=o.writeTexture(a,t,n,r,s,i);return Ea().makeTensorFromDataId(l,t,n,o)}};Gf.nextDataId=0;function xJ(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let a=0;anew Gf,2);var wJ={forceHalfFloat:lE},l1=` if (isnan(a)) return a; if (isnan(b)) return b; -`,Rl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=_n(this.outputShape.length),this.userCode=` +`,$l=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=En(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${e} } @@ -1250,12 +1250,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}},ud=` + `}},id=` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; -`,pd=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=_n(r);let s="";if(a)if(r===0||v.sizeFromShape(this.outputShape)===1)s=` +`,od=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=En(r);let s="";if(a)if(r===0||v.sizeFromShape(this.outputShape)===1)s=` result.y = 0.; result.z = 0.; result.w = 0.; @@ -1299,13 +1299,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function na(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var kJ={kernelName:Li,backendName:"webgl",kernelFunc:na};function Fs(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=na({inputs:{x:a},backend:n}),l=na({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var IJ={kernelName:fm,backendName:"webgl",kernelFunc:Fs},h_="return (a < 0.) ? b * a : a;",m_=` + `}};function na(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var kJ={kernelName:Oi,backendName:"webgl",kernelFunc:na};function $s(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=na({inputs:{x:a},backend:n}),l=na({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var IJ={kernelName:mm,backendName:"webgl",kernelFunc:$s},uE="return (a < 0.) ? b * a : a;",pE=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function SJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new pd(m_,r.shape,i.shape):new Rl(h_,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var TJ={kernelName:Wi,backendName:"webgl",kernelFunc:SJ},f_="return (a < 0.) ? b * a : a;",g_=` +`;function SJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new od(pE,r.shape,i.shape):new $l(uE,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var TJ={kernelName:zi,backendName:"webgl",kernelFunc:SJ},cE="return (a < 0.) ? b * a : a;",dE=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function NJ(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new pd(g_,a.shape,r.shape):new Rl(f_,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var CJ={kernelName:Qi,backendName:"webgl",kernelFunc:NJ},np="if (isnan(x)) return x;";function Ye({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let d=o.texData.get(i.dataId),c=n(d.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=H().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new ss(i.shape,t):p=new ir(i.shape,e),o.runWebGLProgram(p,[i],l)}}function cn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(a&&l.dtype==="complex64"){let 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Rl(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function oc(e,t=!1){if(e==="linear")return t?iJ:tJ;if(e==="relu")return t?lJ:aJ;if(e==="elu")return t?oJ:nJ;if(e==="relu6")return t?uJ:rJ;if(e==="prelu")return t?g_:f_;if(e==="leakyrelu")return t?m_:h_;if(e==="sigmoid")return t?pJ:sJ;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var y_=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=_n(this.outputShape.length);let u=a?e[1]:e[2],p=Math.ceil(u/2),d=a?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) { +`;function NJ(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new od(dE,a.shape,r.shape):new $l(cE,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var CJ={kernelName:Ji,backendName:"webgl",kernelFunc:NJ},Qu="if (isnan(x)) return x;";function Ye({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let d=o.texData.get(i.dataId),c=n(d.values,l);return o.makeTensorInfo(i.shape,l,c)}let u=H().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,p;return u?p=new rs(i.shape,t):p=new ir(i.shape,e),o.runWebGLProgram(p,[i],l)}}function dn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,p=o;if(a&&l.dtype==="complex64"){let m=p.texData.get(l.dataId),f=p.texData.get(u.dataId),[g,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[w,I]=x,T={dataId:w.dataId,dtype:w.dtype,shape:l.shape},C={dataId:I.dataId,dtype:I.dtype,shape:u.shape},E=new $l(e,l.shape,u.shape);return p.runWebGLProgram(E,[T,C],fa(w.dtype,I.dtype))}),b=$s({inputs:{real:g,imag:y},backend:p});return p.disposeIntermediateTensorInfo(g),p.disposeIntermediateTensorInfo(y),b}let d=s||fa(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||p.shouldExecuteOnCPU([l,u]))&&r!=null){let m=p.texData.get(l.dataId).values,f=p.texData.get(u.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(m):m,y=l.dtype==="string"?N.fromUint8ToStringArray(f):f,[b,x]=r(l.shape,u.shape,g,y,d),w=p.makeTensorInfo(x,d),I=p.texData.get(w.dataId);return I.values=b,w}let c=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new od(t,l.shape,u.shape,n):h=new $l(e,l.shape,u.shape),p.runWebGLProgram(h,[l,u],d)}}function sc(e,t=!1){if(e==="linear")return t?iJ:tJ;if(e==="relu")return t?lJ:aJ;if(e==="elu")return t?oJ:nJ;if(e==="relu6")return t?uJ:rJ;if(e==="prelu")return t?dE:cE;if(e==="leakyrelu")return t?pE:uE;if(e==="sigmoid")return t?pJ:sJ;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var hE=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=En(this.outputShape.length);let u=a?e[1]:e[2],p=Math.ceil(u/2),d=a?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",g="";i&&(o?f=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:l?f=`vec4 activation(vec4 a) { @@ -1313,16 +1313,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${i} }`:f=`vec4 activation(vec4 x) { ${i} - }`,g="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let b="rc.x",x="rc.x";e[0]`The new shape (${l}) has ${u} 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r=FJ(e.shape),s=e;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],a=new Array(t);for(let r=0;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=gt(this.rank),r=p_("rc",this.rank),s=new Array(this.rank);for(let u=0;u6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],a=new Array(t);for(let r=0;r6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=gt(this.rank),r=iE("rc",this.rank),s=new Array(this.rank);for(let u=0;u`Error in matMul: inner shapes (${d}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let w=n?[y,d,h]:[y,h,d],I=a?[b,m,c]:[b,c,m],T=he({inputs:{x:e},backend:r,attrs:{shape:w}}),C=he({inputs:{x:t},backend:r,attrs:{shape:I}}),_=[T,C],$=Math.max(y,b),R=n?T.shape[1]:T.shape[2],F=s!=null,S=i!=null,M=l==="leakyrelu",B=l!=null?oc(l,!0):null,U=F||S||M||B!=null,G;if((h===1||m===1)&&R>b_&&U===!1){let K=T,Z=C;n&&(K=Sn({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),_.push(K)),a&&(Z=Sn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),_.push(Z));let J=m!==1,ee=m===1,ae=K;J&&(ae=he({inputs:{x:K},backend:r,attrs:{shape:[$,R,1]}}),_.push(ae));let te=m===1?2:1,ie=Z;ee&&(ie=he({inputs:{x:Z},backend:r,attrs:{shape:[$,1,R]}}),_.push(ie));let oe=p1({inputs:{a:ae,b:ie},backend:r});G=Jf({inputs:{x:oe},backend:r,attrs:{axis:te,keepDims:!0}}),_.push(oe)}else{let K=fa(e.dtype,t.dtype),Z=new y_(w,I,[$,h,m],n,a,F,B,S,M),J=[T,C];if(s!=null&&J.push(s),S&&J.push(i),M){let ee=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));J.push(ee),_.push(ee)}G=r.runWebGLProgram(Z,J,K)}let q=he({inputs:{x:G},backend:r,attrs:{shape:x}});_.push(G);for(let K of _)r.disposeIntermediateTensorInfo(K);return q}function zJ(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a;return am({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var WJ={kernelName:ni,backendName:"webgl",kernelFunc:zJ},TI="return abs(x);";function BJ(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=l_(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return H().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ss(a.shape,TI):r=new ir(a.shape,TI),n.runWebGLProgram(r,[a],a.dtype)}var VJ={kernelName:Pl,backendName:"webgl",kernelFunc:BJ},UJ=Oa+` + `}};function Hf(e,t,n){let a=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new MJ(e.shape,t):new RJ(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function PJ(e,t,n,a){let r=t,s=e.shape.length,i=v.parseAxisParam(r,e.shape),o=i,l=N.getAxesPermutation(o,s),u=l!=null,p=e;u&&(p=Hf(e,l,a),o=N.getInnerMostAxes(o.length,s)),N.assertAxesAreInnerMostDims("sum",o,s);let[d,c]=N.computeOutAndReduceShapes(p.shape,o),h=d;n&&(h=N.expandShapeToKeepDim(d,i));let m=v.sizeFromShape(c),f=v.sizeFromShape(e.shape)/m,g=de({inputs:{x:p},attrs:{shape:[f,m]},backend:a}),y=Um(e.dtype),b=Co(g,y,"sum",a),x=de({inputs:{x:b},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(b),u&&a.disposeIntermediateTensorInfo(p),x}function qf(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return PJ(r,s,i,n)}var OJ={kernelName:co,backendName:"webgl",kernelFunc:qf};function Sn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let p=0;p`Error in matMul: inner shapes (${d}) and (${c}) of Tensors with shapes ${e.shape} and 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ee=r.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));J.push(ee),E.push(ee)}G=r.runWebGLProgram(Z,J,K)}let j=de({inputs:{x:G},backend:r,attrs:{shape:x}});E.push(G);for(let K of E)r.disposeIntermediateTensorInfo(K);return j}function zJ(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a;return tm({a:r,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:d,activation:p})}var WJ={kernelName:ei,backendName:"webgl",kernelFunc:zJ},xI="return abs(x);";function BJ(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=rE(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return H().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new rs(a.shape,xI):r=new ir(a.shape,xI),n.runWebGLProgram(r,[a],a.dtype)}var VJ={kernelName:Rl,backendName:"webgl",kernelFunc:BJ},UJ=Oa+` if (abs(x) > 1.) { return 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b;",XJ=dn({opSnippet:vI,packedOpSnippet:vI,supportsComplex:!0,cpuKernelImpl:s7}),YJ={kernelName:vs,backendName:"webgl",kernelFunc:XJ},ZJ=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} @@ -1547,7 +1547,7 @@ return log(x + sqrt(x * x - 1.0));`,qJ=Ye({opSnippet:jJ}),KJ={kernelName:Ll,back vec4 result = ${a}; setOutput(result); } - `}};function Ch(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return na({inputs:{x:a[0]},backend:n});if(a.length>H().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=Ch({inputs:a.slice(0,o),backend:n}),u=Ch({inputs:a.slice(o),backend:n});return Ch({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>fa(o,l)),s=a.map(o=>o.shape),i=H().getBool("WEBGL_PACK")?new JJ(a[0].shape,s):new 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n9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("any",u,o);let[c,h]=N.computeOutAndReduceShapes(d.shape,u),m=v.sizeFromShape(h),f=he({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=Eo(f,f.dtype,"any",n),y;if(i){let b=N.expandShapeToKeepDim(c,l);y=he({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=he({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),y}var a9={kernelName:Wl,backendName:"webgl",kernelFunc:n9},r9=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` + `}};function Nh(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return na({inputs:{x:a[0]},backend:n});if(a.length>H().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=Nh({inputs:a.slice(0,o),backend:n}),u=Nh({inputs:a.slice(o),backend:n});return Nh({inputs:[l,u],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>fa(o,l)),s=a.map(o=>o.shape),i=H().getBool("WEBGL_PACK")?new JJ(a[0].shape,s):new ZJ(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var QJ={kernelName:bi,backendName:"webgl",kernelFunc:Nh};function e9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("all",u,o);let[c,h]=N.computeOutAndReduceShapes(d.shape,u),m=v.sizeFromShape(h),f=de({inputs:{x:d},backend:n,attrs:{shape:[-1,m]}}),g=Co(f,f.dtype,"all",n),y;if(i){let 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n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),p!=null&&n.disposeIntermediateTensorInfo(d),y}var a9={kernelName:Ol,backendName:"webgl",kernelFunc:n9},r9=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -1629,25 +1629,25 @@ return log(x + sqrt(x * x - 1.0));`,qJ=Ye({opSnippet:jJ}),KJ={kernelName:Ll,back } setOutput(bestIndex); } - `}};function x_(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=N.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new r9(o,n,a==null),u=[t];a!=null&&u.push(a);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let d=x_(e,t,n,p);return e.disposeIntermediateTensorInfo(p),d}function v_(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=N.computeOptimalWindowSize(s),o=new s9(r,i,n,a==null),l=a==null?[t]:[t,a],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=v_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}return u}function w_(e,t,n,a){let r=[n];if(N.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!H().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=N.computeOutAndReduceShapes(l.shape,r),d=v.sizeFromShape(p),c=he({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});s.push(c);let h=x_(e,c,a);s.push(h);let m=he({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return v_(e,t,a)}function i9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=v.parseAxisParam(s,r.shape),o=N.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=w_(n,l,i[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var o9={kernelName:vi,backendName:"webgl",kernelFunc:i9};function l9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=v.parseAxisParam(s,r.shape),o=N.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=w_(n,l,i[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var u9={kernelName:dc,backendName:"webgl",kernelFunc:l9},p9=Oa+` + `}};function fE(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=N.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new r9(o,n,a==null),u=[t];a!=null&&u.push(a);let p=e.runWebGLProgram(l,u,"int32");if(p.shape[1]===1)return p;let d=fE(e,t,n,p);return e.disposeIntermediateTensorInfo(p),d}function gE(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=N.computeOptimalWindowSize(s),o=new s9(r,i,n,a==null),l=a==null?[t]:[t,a],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let p=gE(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}return u}function yE(e,t,n,a){let r=[n];if(N.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!H().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,p]=N.computeOutAndReduceShapes(l.shape,r),d=v.sizeFromShape(p),c=de({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});s.push(c);let h=fE(e,c,a);s.push(h);let m=de({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(f=>e.disposeIntermediateTensorInfo(f)),m}return gE(e,t,a)}function i9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=v.parseAxisParam(s,r.shape),o=N.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let p=yE(n,l,i[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var o9={kernelName:xi,backendName:"webgl",kernelFunc:i9};function l9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=v.parseAxisParam(s,r.shape),o=N.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Sn({inputs:{x:r},backend:n,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let p=yE(n,l,i[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),p}var u9={kernelName:pc,backendName:"webgl",kernelFunc:l9},p9=Oa+` if (abs(x) > 1.) { return NAN; } return asin(x); -`,c9=Ye({opSnippet:p9}),d9={kernelName:Bl,backendName:"webgl",kernelFunc:c9},h9=Oa+"return log(x + sqrt(x * x + 1.0));",m9=Ye({opSnippet:h9}),f9={kernelName:Vl,backendName:"webgl",kernelFunc:m9},g9=Oa+` +`,c9=Ye({opSnippet:p9}),d9={kernelName:Ll,backendName:"webgl",kernelFunc:c9},h9=Oa+"return log(x + sqrt(x * x + 1.0));",m9=Ye({opSnippet:h9}),f9={kernelName:zl,backendName:"webgl",kernelFunc:m9},g9=Oa+` return atan(x); -`,y9=Ye({opSnippet:g9}),b9={kernelName:Ul,backendName:"webgl",kernelFunc:y9},x9=u1+` +`,y9=Ye({opSnippet:g9}),b9={kernelName:Wl,backendName:"webgl",kernelFunc:y9},x9=l1+` return atan(a, b); `,v9=` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - `+ud+` + `+id+` return result; -`,w9=cn({opSnippet:x9,packedOpSnippet:v9}),k9={kernelName:Hl,backendName:"webgl",kernelFunc:w9},I9=Oa+` +`,w9=dn({opSnippet:x9,packedOpSnippet:v9}),k9={kernelName:Vl,backendName:"webgl",kernelFunc:w9},I9=Oa+` if ((x < -1.0) || (x > 1.0)) return NAN; -return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelName:Gl,backendName:"webgl",kernelFunc:S9},lc=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,c=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let C=">=";this.userCode=` +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelName:Bl,backendName:"webgl",kernelFunc:S9},ic=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,c=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let C=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${c}, ${h}); @@ -1698,7 +1698,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam } setOutput(float(minMaxPosition)); } - `;return}let b="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let w=Math.floor(s/4)*4,I=s%4,T=` + `;return}let b="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / max(count, 1.0)");let w=Math.floor(s/4)*4,I=s%4,T=` if (${m}) { avgValue += dot(values, ones); } else { @@ -1788,7 +1788,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam } setOutput(${x}); } - `}},c1=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,p=e.dilationHeight,d=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let b=t==="avg",x="0.0";if(b||(x="-1.0 / 1e-20"),n){let $=">=";this.userCode=` + `}},p1=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,p=e.dilationHeight,d=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let b=t==="avg",x="0.0";if(b||(x="-1.0 / 1e-20"),n){let $=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${f}, ${g}, ${y}); @@ -1850,7 +1850,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam } setOutput(float(minMaxPosition)); } - `;return}let w="max",I=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(I="avgValue / count");let T=Math.floor(s/4)*4,C=s%4,_=` + `;return}let w="max",I=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(I="avgValue / max(count, 1.0)");let T=Math.floor(s/4)*4,C=s%4,E=` if (${b}) { avgValue += dot(values, ones); } else { @@ -1915,7 +1915,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam getValue(batch, xD, xR, xC + 3 * ${d}, ch) ); - ${_} + ${E} } int xC = xCCorner + ${T}; @@ -1927,7 +1927,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam initializationValue ); - ${_} + ${E} } else if (${C===2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), @@ -1936,7 +1936,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam initializationValue ); - ${_} + ${E} } else if (${C===3}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), @@ -1945,13 +1945,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam initializationValue ); - ${_} + ${E} } } - setOutput(${I}); } + setOutput(${I}); } - `}};function N9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Zu(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;v.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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Got strides ${i} and dilations '${u}'`);let p=N.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return na({inputs:{x:r},backend:n});let d=new ic(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var C9={kernelName:vi,backendName:"webgl",kernelFunc:N9};function _9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a,p=[1,1,1],d=N.computePool3DInfo(r.shape,s,i,p,o,l,u),c=new p1(d,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var E9={kernelName:cc,backendName:"webgl",kernelFunc:_9},A9=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,p=l-1-e.padInfo.left,d=1/(t*n);this.userCode=` const ivec2 pads = ivec2(${u}, ${p}); const float avgMultiplier = float(${d}); @@ -2049,7 +2049,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam } setOutput(dotProd); } - `}};function F9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=N.computePool3DInfo(i.shape,o,l,d,u,p),h=new $9(c);return n.runWebGLProgram(h,[r],i.dtype)}var D9={kernelName:dm,backendName:"webgl",kernelFunc:F9};function R9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;Zu([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=new A9(p);return n.runWebGLProgram(d,[r],i.dtype)}var M9={kernelName:cm,backendName:"webgl",kernelFunc:R9};function P9(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return am({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var O9={kernelName:ki,backendName:"webgl",kernelFunc:P9},L9=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` + `}};function F9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=N.computePool3DInfo(i.shape,o,l,d,u,p),h=new $9(c);return n.runWebGLProgram(h,[r],i.dtype)}var R9={kernelName:cm,backendName:"webgl",kernelFunc:F9};function D9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;Ku([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=N.computePool2DInfo(i.shape,o,l,1,u),d=new A9(p);return n.runWebGLProgram(d,[r],i.dtype)}var M9={kernelName:pm,backendName:"webgl",kernelFunc:D9};function P9(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return tm({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var O9={kernelName:wi,backendName:"webgl",kernelFunc:P9},L9=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -2072,7 +2072,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam setOutput((x - mean) * inv + offset); } - `}},W9=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=H().getBool("WEBGL_PACK_NORMALIZATION")?new z9(a.shape,r.shape,s.shape,p,d,l):new L9(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},B9={kernelName:Pi,backendName:"webgl",kernelFunc:W9},V9=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=gt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=U9(this.rank),a,r=e.map((s,i)=>`sourceLoc.${kx[i]} = start[${i}] + coords.${kx[i]};`);a=` + `}},W9=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[a,r,s],p=null;i!=null&&(p=i.shape,u.push(i));let d=null;o!=null&&(d=o.shape,u.push(o));let c=H().getBool("WEBGL_PACK_NORMALIZATION")?new z9(a.shape,r.shape,s.shape,p,d,l):new L9(a.shape,r.shape,s.shape,p,d,l);return t.runWebGLProgram(c,u,u[0].dtype)},B9={kernelName:Mi,backendName:"webgl",kernelFunc:W9},V9=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=gt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=U9(this.rank),a,r=e.map((s,i)=>`sourceLoc.${gx[i]} = start[${i}] + coords.${gx[i]};`);a=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${r.join(` @@ -2082,7 +2082,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam ${a} setOutput(getSource(${n})); } - `}},kx=["x","y","z","w","u","v"];function U9(e){if(e===1)return"sourceLoc";if(e<=6)return kx.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var G9=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=gt(this.rank),n=In("coords",this.rank),a=In("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=` + `}},gx=["x","y","z","w","u","v"];function U9(e){if(e===1)return"sourceLoc";if(e<=6)return gx.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var G9=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=gt(this.rank),n=In("coords",this.rank),a=In("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=` result.x = ${s}; if (++${n[this.rank-1]} < ${e[this.rank-1]}) { ++${a[this.rank-1]}; @@ -2111,7 +2111,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam ${o} setOutput(result); } - `}};function H9(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=Kt.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function ap(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=Kt.parseSliceParams(r,s,i);if(Kt.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),c=P7(d.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),p=Kt.isSliceContinous(r.shape,o,l);if(u||!p){let d=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new G9(l):new V9(l),c=[o];return n.runWebGLProgram(d,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),H9(r,o,l,n)}var j9={kernelName:Tu,backendName:"webgl",kernelFunc:ap},q9=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((b,x)=>b*x),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=[],m=he({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Sn({inputs:{x:m},backend:n,attrs:{perm:u}}),g=he({inputs:{x:f},backend:n,attrs:{shape:p}}),y=ap({inputs:{x:g},backend:n,attrs:{begin:d,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y},K9={kernelName:jl,backendName:"webgl",kernelFunc:q9};function X9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),u=o_(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var Y9={kernelName:hm,backendName:"webgl",kernelFunc:X9};function Z9(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.readSync(a.dataId),i=n.readSync(r.dataId),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var J9={kernelName:mm,backendName:"webgl",kernelFunc:Z9},Q9="return float(a != b);",k_=cn({opSnippet:Q9,cpuKernelImpl:C7,dtype:"bool"}),eQ={kernelName:fu,backendName:"webgl",kernelFunc:k_};function cd(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return na({inputs:{x:r.complexTensorInfos.real},backend:n})}var tQ={kernelName:Om,backendName:"webgl",kernelFunc:cd},nQ="return float(int(x));";function aQ(e,t){let n=new ir(e.shape,nQ),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function Ix(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return na({inputs:{x:r},backend:n});let i=It(r.shape),o=Ix({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Fs({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=cd({inputs:{input:r},backend:n}),o=Ix({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=na({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(n.shouldExecuteOnCPU([r])){let i=n.texData.get(r.dataId).values,[o,l,u]=o7(i,r.shape,r.dtype,s);return n.makeTensorInfo(o,l,u)}if(s==="int32")return aQ(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=k_({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var rQ={kernelName:Ii,backendName:"webgl",kernelFunc:Ix},CI="return ceil(x);",sQ=Ye({opSnippet:CI,packedOpSnippet:CI,cpuKernelImpl:l7}),iQ={kernelName:Si,backendName:"webgl",kernelFunc:sQ},oQ=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` + `}};function H9(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=Kt.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function ep(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=Kt.parseSliceParams(r,s,i);if(Kt.assertParamsValid(r,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),c=P7(d.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:u}=n.texData.get(r.dataId),p=Kt.isSliceContinous(r.shape,o,l);if(u||!p){let d=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new G9(l):new V9(l),c=[o];return n.runWebGLProgram(d,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),H9(r,o,l,n)}var q9={kernelName:ku,backendName:"webgl",kernelFunc:ep},j9=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((b,x)=>b*x),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=[],m=de({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Sn({inputs:{x:m},backend:n,attrs:{perm:u}}),g=de({inputs:{x:f},backend:n,attrs:{shape:p}}),y=ep({inputs:{x:g},backend:n,attrs:{begin:d,size:c}});return h.push(m),h.push(f),h.push(g),h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y},K9={kernelName:Ul,backendName:"webgl",kernelFunc:j9};function X9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),u=aE(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var Y9={kernelName:dm,backendName:"webgl",kernelFunc:X9};function Z9(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.readSync(a.dataId),i=n.readSync(r.dataId),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var J9={kernelName:hm,backendName:"webgl",kernelFunc:Z9},Q9="return float(a != b);",bE=dn({opSnippet:Q9,cpuKernelImpl:C7,dtype:"bool"}),eQ={kernelName:du,backendName:"webgl",kernelFunc:bE};function ld(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return na({inputs:{x:r.complexTensorInfos.real},backend:n})}var tQ={kernelName:Pm,backendName:"webgl",kernelFunc:ld},nQ="return float(int(x));";function aQ(e,t){let n=new ir(e.shape,nQ),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function yx(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return na({inputs:{x:r},backend:n});let i=It(r.shape),o=yx({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=$s({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=ld({inputs:{input:r},backend:n}),o=yx({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=na({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(n.shouldExecuteOnCPU([r])){let i=n.texData.get(r.dataId).values,[o,l,u]=o7(i,r.shape,r.dtype,s);return n.makeTensorInfo(o,l,u)}if(s==="int32")return aQ(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=bE({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var rQ={kernelName:ki,backendName:"webgl",kernelFunc:yx},wI="return ceil(x);",sQ=Ye({opSnippet:wI,packedOpSnippet:wI,cpuKernelImpl:l7}),iQ={kernelName:Ii,backendName:"webgl",kernelFunc:sQ},oQ=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { float value = getAAtOutCoords(); @@ -2146,7 +2146,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function EI(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function dQ(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new cQ(a.shape),i=[EI(a,r.complexTensorInfos.real),EI(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var hQ={kernelName:mc,backendName:"webgl",kernelFunc:dQ},mQ=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s= ${o[m-1]}) { return getChannel( - getT${m}(${bh(i,l,f)}), - vec2(${bh(u,l,f)})); + getT${m}(${yh(i,l,f)}), + vec2(${yh(u,l,f)})); }`}let c=o.length,h=o[o.length-1];d+=` return getChannel( - getT${c}(${bh(i,l,h)}), - vec2(${bh(u,l,h)}));`,this.userCode=` + getT${c}(${yh(i,l,h)}), + vec2(${yh(u,l,h)}));`,this.userCode=` float getValue(${i.map(m=>"int "+m)}) { ${d} } @@ -2192,7 +2192,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam } setOutput(result); } - `}};function bh(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - 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K=nm(r.shape,m);K!=null&&(r=de({inputs:{x:r},backend:a,attrs:{shape:K}}),w.push(r))}let I=de({inputs:{x:t},backend:a,attrs:{shape:[1,f,v.sizeFromShape(t.shape)/f]}});w.push(I);let T=new vQ(y,n),C=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],E=a.runWebGLProgram(T,[e],"float32",C),$=de({inputs:{x:E},backend:a,attrs:{shape:y}});w.push(E),w.push($);let R=r!=null,F=s!=null,S=o==="leakyrelu",M=o?sc(o,!0):null,B=new hE(m?$.shape:I.shape,m?I.shape:$.shape,m?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],b,x,R,M,F,S),U=m?[$,I]:[I,$];if(r&&U.push(r),F&&U.push(s),S){let K=a.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));U.push(K),w.push(K)}let G=a.runWebGLProgram(B,U,"float32"),j=de({inputs:{x:G},backend:a,attrs:{shape:n.outShape}});w.push(G);for(let K of w)a.disposeIntermediateTensorInfo(K);return j}function wQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a,d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=kE({x:r,filter:s,convInfo:c,backend:n});else if(c.strideWidth<=2&&d==="channelsLast"&&H().getBool("WEBGL_EXP_CONV")){let f=new wE(c),g=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];h=n.runWebGLProgram(f,[r,s],"float32",g)}else if(H().getBool("WEBGL_CONV_IM2COL"))h=IE({x:r,filter:s,convInfo:c,backend:n});else{let f=new vE(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=de({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var kQ={kernelName:Si,backendName:"webgl",kernelFunc:wQ},IQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2851,12 +2851,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam } setOutput(dotProd); } - `}};function CQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a,d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),h=new IQ(c);return n.runWebGLProgram(h,[r,s],"float32")}var EQ={kernelName:gm,backendName:"webgl",kernelFunc:CQ};function _Q(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a,d=N.convertConv2DDataFormat(u),c=N.computeConv2DInfo(i,s.shape,o,1,l,p,!1,d),h=new SQ(c);return n.runWebGLProgram(h,[r,s],"float32")}var AQ={kernelName:Ni,backendName:"webgl",kernelFunc:_Q};function $Q(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=N.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new xQ(u);return n.runWebGLProgram(p,[r,s],"float32")}var FQ={kernelName:fc,backendName:"webgl",kernelFunc:$Q};function DQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=N.computeConv3DInfo(r.shape,l,i,1,o),p=new TQ(u);return n.runWebGLProgram(p,[r,s],"float32")}var RQ={kernelName:ym,backendName:"webgl",kernelFunc:DQ};function MQ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=N.computeConv3DInfo(l,s.shape,o,1,i),p=new NQ(u);return n.runWebGLProgram(p,[r,s],"float32")}var PQ={kernelName:bm,backendName:"webgl",kernelFunc:MQ},OQ=np+` + `}};function CQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:p}=a,d=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(r.shape,p,i,1,o,u,!1,d),h=new IQ(c);return n.runWebGLProgram(h,[r,s],"float32")}var _Q={kernelName:fm,backendName:"webgl",kernelFunc:CQ};function EQ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:p}=a,d=N.convertConv2DDataFormat(u),c=N.computeConv2DInfo(i,s.shape,o,1,l,p,!1,d),h=new SQ(c);return n.runWebGLProgram(h,[r,s],"float32")}var AQ={kernelName:Ti,backendName:"webgl",kernelFunc:EQ};function $Q(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=N.computeConv3DInfo(r.shape,s.shape,i,l,o),p=new xQ(u);return n.runWebGLProgram(p,[r,s],"float32")}var FQ={kernelName:hc,backendName:"webgl",kernelFunc:$Q};function RQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,u=N.computeConv3DInfo(r.shape,l,i,1,o),p=new TQ(u);return n.runWebGLProgram(p,[r,s],"float32")}var DQ={kernelName:gm,backendName:"webgl",kernelFunc:RQ};function MQ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,u=N.computeConv3DInfo(l,s.shape,o,1,i),p=new NQ(u);return n.runWebGLProgram(p,[r,s],"float32")}var PQ={kernelName:ym,backendName:"webgl",kernelFunc:MQ},OQ=Qu+` return cos(x); -`,LQ=Ye({opSnippet:OQ}),zQ={kernelName:Ci,backendName:"webgl",kernelFunc:LQ},WQ=` +`,LQ=Ye({opSnippet:OQ}),zQ={kernelName:Ni,backendName:"webgl",kernelFunc:LQ},WQ=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,BQ=Ye({opSnippet:WQ}),VQ={kernelName:Ei,backendName:"webgl",kernelFunc:BQ},UQ=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[b,x,w]=d>1?[`${(o-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` +`,BQ=Ye({opSnippet:WQ}),VQ={kernelName:Ci,backendName:"webgl",kernelFunc:BQ},UQ=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[p,d]=n;this.outputShape=[u,p,d,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[b,x,w]=d>1?[`${(o-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` const float height_ratio = float(${f}); const float width_ratio = float(${b}); void main() { @@ -2917,20 +2917,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam setOutput(newValue); } } - `}},GQ=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,p=new UQ(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},HQ={kernelName:Xl,backendName:"webgl",kernelFunc:GQ},uc;(function(e){e.Prod="*",e.Sum="+"})(uc||(uc={}));var _I=class{constructor(e,t,n,a){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===uc.Prod?"1.0":"0.0",i=n?s:`getX(${AI(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=a?`end != ${o-1}`:"end != 0",u=a?"end + 1":"end - 1"):(l=a?`end + pow2 < ${o}`:"end >= pow2",u=a?"end + pow2":"end - pow2"),this.userCode=` + `}},GQ=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,p=new UQ(r.shape,s.shape,o,l,u);return n.runWebGLProgram(p,[r,s,i],"float32")},HQ={kernelName:ql,backendName:"webgl",kernelFunc:GQ},oc;(function(e){e.Prod="*",e.Sum="+"})(oc||(oc={}));var II=class{constructor(e,t,n,a){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===oc.Prod?"1.0":"0.0",i=n?s:`getX(${SI(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=a?`end != ${o-1}`:"end != 0",u=a?"end + 1":"end - 1"):(l=a?`end + pow2 < ${o}`:"end >= pow2",u=a?"end + pow2":"end - pow2"),this.userCode=` void main() { ${gt(r)} coords = getOutputCoords(); - int end = ${$I(r,"coords",this.op)}; + int end = ${TI(r,"coords",this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${l}) { int idx = ${u}; - ${$I(r,"coords",this.op)} = idx; - val ${this.op}= getX(${AI(r,"coords",this.op)}); + ${TI(r,"coords",this.op)} = idx; + val ${this.op}= getX(${SI(r,"coords",this.op)}); } setOutput(val); } - `}};function AI(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function $I(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function E_(e,t,n,a,r,s){let i=t.shape.length,o=N.getAxesPermutation([a],i),l=t;o!=null&&(l=Sn({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=N.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${a}`);let p=l.shape[u],d=na({inputs:{x:l},backend:n});for(let c=0;c<=Math.ceil(Math.log2(p))-1;c++){let h=new _I(e,l.shape,!1,s),m=[[c]],f=d;d=n.runWebGLProgram(h,[d],d.dtype,m),n.disposeIntermediateTensorInfo(f)}if(r){let c=new _I(e,l.shape,r,s),h=d;d=n.runWebGLProgram(c,[d],d.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let c=N.getUndoAxesPermutation(o),h=Sn({inputs:{x:d},backend:n,attrs:{perm:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(l),h}return d}function jQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return E_(uc.Prod,r,n,s,i,o)}var qQ={kernelName:Kl,backendName:"webgl",kernelFunc:jQ};function KQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return E_(uc.Sum,r,n,s,i,o)}var XQ={kernelName:_i,backendName:"webgl",kernelFunc:KQ};function YQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=o_(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=i7(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var ZQ={kernelName:xm,backendName:"webgl",kernelFunc:YQ},JQ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` + `}};function SI(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function TI(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function SE(e,t,n,a,r,s){let i=t.shape.length,o=N.getAxesPermutation([a],i),l=t;o!=null&&(l=Sn({inputs:{x:t},backend:n,attrs:{perm:o}}));let u=N.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${a}`);let p=l.shape[u],d=na({inputs:{x:l},backend:n});for(let c=0;c<=Math.ceil(Math.log2(p))-1;c++){let h=new II(e,l.shape,!1,s),m=[[c]],f=d;d=n.runWebGLProgram(h,[d],d.dtype,m),n.disposeIntermediateTensorInfo(f)}if(r){let c=new II(e,l.shape,r,s),h=d;d=n.runWebGLProgram(c,[d],d.dtype),n.disposeIntermediateTensorInfo(h)}if(o!=null){let c=N.getUndoAxesPermutation(o),h=Sn({inputs:{x:d},backend:n,attrs:{perm:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(l),h}return d}function qQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return SE(oc.Prod,r,n,s,i,o)}var jQ={kernelName:Hl,backendName:"webgl",kernelFunc:qQ};function KQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;return SE(oc.Sum,r,n,s,i,o)}var XQ={kernelName:_i,backendName:"webgl",kernelFunc:KQ};function YQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=aE(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,p)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(s),p=i7(l,u,i,o);return n.makeTensorInfo(p.shape,s.dtype,p.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var ZQ={kernelName:bm,backendName:"webgl",kernelFunc:YQ},JQ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -2949,7 +2949,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam float result = ${this.getInputSamplingString()}; setOutput(result); } - `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function QQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=new JQ(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var eee={kernelName:Yl,backendName:"webgl",kernelFunc:QQ},__=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=_n(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(a?l=`float activation(float a) { + `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function QQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=new JQ(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var eee={kernelName:jl,backendName:"webgl",kernelFunc:QQ},TE=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=En(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";n&&(a?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?l=`float activation(float a) { @@ -3002,7 +3002,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam ${u} setOutput(result); } - `}},A_=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=_n(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,d=p,c=` + `}},NE=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=En(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,p=e.filterWidth,d=p,c=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=N.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),c;H().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?c=new A_(d):c=new __(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var nee={kernelName:Ai,backendName:"webgl",kernelFunc:tee},aee=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` + `}};function tee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=a,p=l;p==null&&(p=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=N.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),c;H().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels===1?c=new NE(d):c=new TE(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var nee={kernelName:Ei,backendName:"webgl",kernelFunc:tee},aee=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -3271,13 +3271,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam } setOutput(dotProd); } - `}};function see(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a,d=N.computeConv2DInfo(r.shape,p,i,o,l,u,!0),c=new aee(d);return n.runWebGLProgram(c,[r,s],"float32")}var iee={kernelName:vm,backendName:"webgl",kernelFunc:see};function oee(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a,d=N.computeConv2DInfo(p,s.shape,i,o,l,u,!0),c=new ree(d);return n.runWebGLProgram(c,[r,s],"float32")}var lee={kernelName:wm,backendName:"webgl",kernelFunc:oee},uee=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` + `}};function see(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:p}=a,d=N.computeConv2DInfo(r.shape,p,i,o,l,u,!0),c=new aee(d);return n.runWebGLProgram(c,[r,s],"float32")}var iee={kernelName:xm,backendName:"webgl",kernelFunc:see};function oee(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:p}=a,d=N.computeConv2DInfo(p,s.shape,i,o,l,u,!0),c=new ree(d);return n.runWebGLProgram(c,[r,s],"float32")}var lee={kernelName:vm,backendName:"webgl",kernelFunc:oee},uee=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } - `}};function pee(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=v.sizeFromShape(a.shape),i=he({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new uee(s),l=n.runWebGLProgram(o,[i],i.dtype),u=he({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var cee={kernelName:km,backendName:"webgl",kernelFunc:pee},dee=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:d}=a;this.userCode=` + `}};function pee(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=v.sizeFromShape(a.shape),i=de({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new uee(s),l=n.runWebGLProgram(o,[i],i.dtype),u=de({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var cee={kernelName:wm,backendName:"webgl",kernelFunc:pee},dee=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:p,left:d}=a;this.userCode=` const ivec2 strides = ivec2(${r}, ${s}); const ivec2 pads = ivec2(${p}, ${d}); const float neg_infinity = -3.4e38; @@ -3315,7 +3315,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam float result = curVal; setOutput(result); } - `}};function hee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=N.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,d=new dee(u);p=n.runWebGLProgram(d,[r,s],"float32");let c=he({inputs:{x:p},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(p),c}var mee={kernelName:gc,backendName:"webgl",kernelFunc:hee};function fee(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(r,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=N.getEinsumComputePath(o,l),d=p.length,c=null,h=i.length,m=[];for(let f=0;f=0&&(c=Jf({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var gee={kernelName:Im,backendName:"webgl",kernelFunc:fee},yee="return (x >= 0.0) ? x : (exp(x) - 1.0);",bee=` + `}};function hee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,u=N.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),p,d=new dee(u);p=n.runWebGLProgram(d,[r,s],"float32");let c=de({inputs:{x:p},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(p),c}var mee={kernelName:mc,backendName:"webgl",kernelFunc:hee};function fee(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(r,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:p}=N.getEinsumComputePath(o,l),d=p.length,c=null,h=i.length,m=[];for(let f=0;f=0&&(c=qf({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var gee={kernelName:km,backendName:"webgl",kernelFunc:fee},yee="return (x >= 0.0) ? x : (exp(x) - 1.0);",bee=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3324,12 +3324,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,xee=Ye({opSnippet:yee,packedOpSnippet:bee}),vee={kernelName:Fi,backendName:"webgl",kernelFunc:xee},wee="return (b >= 1.0) ? a : a * (b + 1.0);",kee=` +`,xee=Ye({opSnippet:yee,packedOpSnippet:bee}),vee={kernelName:$i,backendName:"webgl",kernelFunc:xee},wee="return (b >= 1.0) ? a : a * (b + 1.0);",kee=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,Iee=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new pd(kee,a.shape,r.shape):new Rl(wee,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},See={kernelName:Sm,backendName:"webgl",kernelFunc:Iee},Tee=` +`,Iee=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new od(kee,a.shape,r.shape):new $l(wee,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},See={kernelName:Im,backendName:"webgl",kernelFunc:Iee},Tee=` return vec4(equal(a, b)); -`,Nee="return float(a == b);",Cee=cn({opSnippet:Nee,packedOpSnippet:Tee,dtype:"bool",cpuKernelImpl:p7}),Eee={kernelName:Jl,backendName:"webgl",kernelFunc:Cee},_ee=` +`,Nee="return float(a == b);",Cee=dn({opSnippet:Nee,packedOpSnippet:Tee,dtype:"bool",cpuKernelImpl:p7}),_ee={kernelName:Xl,backendName:"webgl",kernelFunc:Cee},Eee=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -3344,9 +3344,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); -`,Aee=Ye({opSnippet:_ee}),$ee={kernelName:Zl,backendName:"webgl",kernelFunc:Aee},Fee=np+` +`,Aee=Ye({opSnippet:Eee}),$ee={kernelName:Kl,backendName:"webgl",kernelFunc:Aee},Fee=Qu+` return exp(x); -`,Dee=` +`,Ree=` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; @@ -3355,7 +3355,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam result.a = isNaN.a ? x.a : result.a; return result; -`,$_=Ye({opSnippet:Fee,packedOpSnippet:Dee,cpuKernelImpl:c7,dtype:"float32"}),Ree={kernelName:Di,backendName:"webgl",kernelFunc:$_};function Sx(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),he({inputs:{x:s},backend:a,attrs:{shape:o}})}var Mee={kernelName:Ql,backendName:"webgl",kernelFunc:Sx},FI="return exp(x) - 1.0;",Pee=Ye({opSnippet:FI,packedOpSnippet:FI,cpuKernelImpl:d7}),Oee={kernelName:eu,backendName:"webgl",kernelFunc:Pee},DI=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` +`,CE=Ye({opSnippet:Fee,packedOpSnippet:Ree,cpuKernelImpl:c7,dtype:"float32"}),Dee={kernelName:Fi,backendName:"webgl",kernelFunc:CE};function bx(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),de({inputs:{x:s},backend:a,attrs:{shape:o}})}var Mee={kernelName:Yl,backendName:"webgl",kernelFunc:bx},NI="return exp(x) - 1.0;",Pee=Ye({opSnippet:NI,packedOpSnippet:NI,cpuKernelImpl:d7}),Oee={kernelName:Zl,backendName:"webgl",kernelFunc:Pee},CI=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=` const float exponentMultiplier = ${r}; float unaryOpComplex(float real, float expR, float imag, float expI) { @@ -3388,12 +3388,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function F_(e,t,n){let a=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=he({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new DI("real",l,t),p=new DI("imag",l,t),d=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=Fs({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=he({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function Lee(e){let{inputs:t,backend:n}=e,{input:a}=t;return F_(a,!1,n)}var zee={kernelName:Tm,backendName:"webgl",kernelFunc:Lee},Wee=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` + `}};function _E(e,t,n){let a=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=de({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new CI("real",l,t),p=new CI("imag",l,t),d=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(p,d,"float32"),m=$s({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=de({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function Lee(e){let{inputs:t,backend:n}=e,{input:a}=t;return _E(a,!1,n)}var zee={kernelName:Sm,backendName:"webgl",kernelFunc:Lee},Wee=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } - `}};function dd(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new Wee(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var Bee={kernelName:yc,backendName:"webgl",kernelFunc:dd},Vee=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` + `}};function ud(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new Wee(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var Bee={kernelName:fc,backendName:"webgl",kernelFunc:ud},Vee=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; @@ -3407,7 +3407,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam } setOutput(outputValue); } - `}},Uee={kernelName:tu,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new Vee(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},RI="return floor(x);",Gee=Ye({opSnippet:RI,packedOpSnippet:RI,cpuKernelImpl:h7}),Hee={kernelName:Ri,backendName:"webgl",kernelFunc:Gee},jee=` + `}},Uee={kernelName:Jl,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new Vee(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},_I="return floor(x);",Gee=Ye({opSnippet:_I,packedOpSnippet:_I,cpuKernelImpl:h7}),Hee={kernelName:Ri,backendName:"webgl",kernelFunc:Gee},qee=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -3417,7 +3417,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam } else { return NAN; } -`,qee=` +`,jee=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); @@ -3438,7 +3438,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,Kee=cn({opSnippet:jee,packedOpSnippet:qee,dtype:"int32"}),Xee={kernelName:Mi,backendName:"webgl",kernelFunc:Kee},Yee=class{constructor(e){this.variableNames=["A"];let t=En(),[n,a]=e;this.outputShape=e,this.userCode=` +`,Kee=dn({opSnippet:qee,packedOpSnippet:jee,dtype:"int32"}),Xee={kernelName:Di,backendName:"webgl",kernelFunc:Kee},Yee=class{constructor(e){this.variableNames=["A"];let t=_n(),[n,a]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3460,7 +3460,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam setOutput(floor(value * 255.0 + 0.5)); } - `}},Zee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=En(),[n,a]=e;this.outputShape=e,this.userCode=` + `}},Zee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=_n(),[n,a]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); 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`,Wte=` vec4 result = log(x); @@ -3534,18 +3534,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b); result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; -`,Bte=Ye({opSnippet:zte,packedOpSnippet:Wte,cpuKernelImpl:w7}),Vte={kernelName:Bi,backendName:"webgl",kernelFunc:Bte},Ute=np+` +`,Bte=Ye({opSnippet:zte,packedOpSnippet:Wte,cpuKernelImpl:w7}),Vte={kernelName:Wi,backendName:"webgl",kernelFunc:Bte},Ute=Qu+` return log(1.0 + x); -`,Gte=Ye({opSnippet:Ute}),Hte={kernelName:uu,backendName:"webgl",kernelFunc:Gte},jte="return float(a >= 1.0 && b >= 1.0);",qte=` +`,Gte=Ye({opSnippet:Ute}),Hte={kernelName:iu,backendName:"webgl",kernelFunc:Gte},qte="return float(a >= 1.0 && b >= 1.0);",jte=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); -`,Kte=cn({opSnippet:jte,packedOpSnippet:qte,dtype:"bool"}),Xte={kernelName:pu,backendName:"webgl",kernelFunc:Kte},Yte="return float(!(x >= 1.0));",Zte=Ye({opSnippet:Yte}),Jte={kernelName:cu,backendName:"webgl",kernelFunc:Zte},Qte="return float(a >= 1.0 || b >= 1.0);",ene=` +`,Kte=dn({opSnippet:qte,packedOpSnippet:jte,dtype:"bool"}),Xte={kernelName:ou,backendName:"webgl",kernelFunc:Kte},Yte="return float(!(x >= 1.0));",Zte=Ye({opSnippet:Yte}),Jte={kernelName:lu,backendName:"webgl",kernelFunc:Zte},Qte="return float(a >= 1.0 || b >= 1.0);",ene=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); -`,tne=cn({opSnippet:Qte,packedOpSnippet:ene,dtype:"bool"}),nne={kernelName:du,backendName:"webgl",kernelFunc:tne},ane=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` +`,tne=dn({opSnippet:Qte,packedOpSnippet:ene,dtype:"bool"}),nne={kernelName:uu,backendName:"webgl",kernelFunc:tne},ane=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3626,7 +3626,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam vec4 result = xAtOutputCoords * ${o}; setOutput(result); } - `}},sne=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=H().getBool("WEBGL_PACK_NORMALIZATION")?new rne(r.shape,s,i,o,l):new ane(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},ine={kernelName:bc,backendName:"webgl",kernelFunc:sne},one=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=` + `}},sne=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,u=H().getBool("WEBGL_PACK_NORMALIZATION")?new rne(r.shape,s,i,o,l):new ane(r.shape,s,i,o,l);return n.runWebGLProgram(u,[r],r.dtype)},ine={kernelName:gc,backendName:"webgl",kernelFunc:sne},one=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3664,7 +3664,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam else if (k >= depthBegin && k < depthEnd){ float dyi = -2.0 * float(${a}) * float(${r}) - * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d) + * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { dyi += pow(norm, -1.0 * ${r}); @@ -3681,16 +3681,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam } setOutput(result); } - `}},lne=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a,d=new one(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},une={kernelName:_m,backendName:"webgl",kernelFunc:lne};function pne(e,t,n,a){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=he({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Eo(i,e.dtype,"max",a),l=he({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function R_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=p!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(d){if(c){let b=n.texData.get(h.dataId).values,x=new Array(o);for(let T=0;T{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a,d=new one(r.shape,o,l,u,p);return n.runWebGLProgram(d,[r,s,i],r.dtype)},une={kernelName:_m,backendName:"webgl",kernelFunc:lne};function pne(e,t,n,a){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=de({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Co(i,e.dtype,"max",a),l=de({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function AE(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,p=N.getAxesPermutation(u,o),d=p!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(d){if(c){let b=n.texData.get(h.dataId).values,x=new Array(o);for(let T=0;T`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=N.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return na({inputs:{x:r},backend:n});let d=new lc(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var yne={kernelName:Gi,backendName:"webgl",kernelFunc:gne};function bne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,p=[1,1,1],d=N.computePool3DInfo(r.shape,s,i,p,o,u,l),c=new c1(d,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var xne={kernelName:xc,backendName:"webgl",kernelFunc:bne},vne=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=` +`,mne=dn({opSnippet:dne,packedOpSnippet:hne,cpuKernelImpl:I7}),fne={kernelName:Vi,backendName:"webgl",kernelFunc:mne};function gne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Ku(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;v.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=N.computePool2DInfo(r.shape,s,i,u,o,l);if(p.filterWidth===1&&p.filterHeight===1&&v.arraysEqual(p.inShape,p.outShape))return na({inputs:{x:r},backend:n});let d=new ic(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var yne={kernelName:Ui,backendName:"webgl",kernelFunc:gne};function bne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=a,p=[1,1,1],d=N.computePool3DInfo(r.shape,s,i,p,o,u,l),c=new p1(d,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var xne={kernelName:yc,backendName:"webgl",kernelFunc:bne},vne=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { @@ -3800,16 +3800,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam } setOutput(dotProd); } - `}};function kne(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:p}=a,d=[1,1,1],c=N.computePool3DInfo(i.shape,o,l,d,u,p),h=new c1(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new wne(c),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var Ine={kernelName:$m,backendName:"webgl",kernelFunc:kne};function Sne(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;Zu([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=N.computePool2DInfo(o.shape,l,u,1,p,d),h=!0,m=new lc(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new vne(c),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var Tne={kernelName:Am,backendName:"webgl",kernelFunc:Sne};function Nne(e,t,n,a){let r=new lc(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new lc(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var Cne={kernelName:Fm,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];v.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let p=N.computePool2DInfo(a.shape,r,s,u,i),[d,c]=Nne(a,o,p,l);return[d,c]}};function Ene(e,t,n,a){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=he({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Eo(i,"float32","mean",a),l=he({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var _ne={kernelName:Hi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,p=N.getAxesPermutation(u,o),d=p!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(d){if(c){let x=i.texData.get(m.dataId).values,w=new Array(o);for(let C=0;C{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let u=[1,1];v.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. 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int end = ${i}; @@ -3894,13 +3894,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam ${c} setOutput(result); } - `}},Lne=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new One(a.shape,r,s):new Pne(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},zne={kernelName:Ki,backendName:"webgl",kernelFunc:Lne},Wne=`if (b == 0.0) return NAN; + `}},Lne=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new One(a.shape,r,s):new Pne(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},zne={kernelName:ji,backendName:"webgl",kernelFunc:Lne},Wne=`if (b == 0.0) return NAN; return mod(a, b);`,Bne=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); - `+ud+` + `+id+` return result; -`,Vne=cn({opSnippet:Wne,packedOpSnippet:Bne}),Une={kernelName:hu,backendName:"webgl",kernelFunc:Vne},Gne=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` +`,Vne=dn({opSnippet:Wne,packedOpSnippet:Bne}),Une={kernelName:pu,backendName:"webgl",kernelFunc:Vne},Gne=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -3924,7 +3924,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,S9=Ye({opSnippet:I9}),T9={kernelNam if (a == b) { return 1.0; }; -return a / b;`,jne=` +return a / b;`,qne=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; @@ -3942,7 +3942,7 @@ return a / b;`,jne=` } return result; -`,M_=cn({opSnippet:Hne,packedOpSnippet:jne,checkOutOfBounds:!0}),qne={kernelName:$i,backendName:"webgl",kernelFunc:M_},MI="return a - b;",P_=cn({opSnippet:MI,packedOpSnippet:MI,supportsComplex:!0,cpuKernelImpl:G7}),Kne={kernelName:go,backendName:"webgl",kernelFunc:P_};function O_(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=v.parseAxisParam([s],r.shape),o=R_({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=he({inputs:{x:o},backend:n,attrs:{shape:l}}),p=P_({inputs:{a:r,b:u},backend:n}),d=$_({inputs:{x:p},backend:n}),c=Jf({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=he({inputs:{x:c},backend:n,attrs:{shape:l}}),m=M_({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var Xne={kernelName:mo,backendName:"webgl",kernelFunc:O_};function Yne(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:O_({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new Gne(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var Zne={kernelName:Dm,backendName:"webgl",kernelFunc:Yne},Jne=Oa+` +`,$E=dn({opSnippet:Hne,packedOpSnippet:qne,checkOutOfBounds:!0}),jne={kernelName:Ai,backendName:"webgl",kernelFunc:$E},EI="return a - b;",FE=dn({opSnippet:EI,packedOpSnippet:EI,supportsComplex:!0,cpuKernelImpl:G7}),Kne={kernelName:fo,backendName:"webgl",kernelFunc:FE};function RE(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=v.parseAxisParam([s],r.shape),o=AE({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=de({inputs:{x:o},backend:n,attrs:{shape:l}}),p=FE({inputs:{a:r,b:u},backend:n}),d=CE({inputs:{x:p},backend:n}),c=qf({inputs:{x:d},backend:n,attrs:{axis:i,keepDims:!1}}),h=de({inputs:{x:c},backend:n,attrs:{shape:l}}),m=$E({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var Xne={kernelName:ho,backendName:"webgl",kernelFunc:RE};function Yne(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:RE({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],p=l.shape[1],d=new Gne(u,p,s),c=[[i]],h=n.runWebGLProgram(d,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var Zne={kernelName:Fm,backendName:"webgl",kernelFunc:Yne},Jne=Oa+` return -x; `,Qne=` vec4 result = -x; @@ -3954,14 +3954,14 @@ return a / b;`,jne=` result.a = isNaN.a ? x.a : result.a; return result; -`;function eae(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=N7(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return H().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ss(a.shape,Qne):r=new ir(a.shape,Jne),n.runWebGLProgram(r,[a],a.dtype)}var tae={kernelName:mu,backendName:"webgl",kernelFunc:eae},nae=fr.nonMaxSuppressionV3Impl;function aae(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d}=nae(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var rae={kernelName:gu,backendName:"webgl",kernelFunc:aae},sae=fr.nonMaxSuppressionV4Impl;function iae(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. 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Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=lae(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var pae={kernelName:bu,backendName:"webgl",kernelFunc:uae},cae=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` +`;function eae(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=N7(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return H().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new rs(a.shape,Qne):r=new ir(a.shape,Jne),n.runWebGLProgram(r,[a],a.dtype)}var tae={kernelName:cu,backendName:"webgl",kernelFunc:eae},nae=fr.nonMaxSuppressionV3Impl;function aae(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d}=nae(u,p,i,o,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var rae={kernelName:hu,backendName:"webgl",kernelFunc:aae},sae=fr.nonMaxSuppressionV4Impl;function iae(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=sae(p,d,i,o,l,u);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var oae={kernelName:mu,backendName:"webgl",kernelFunc:iae},lae=fr.nonMaxSuppressionV5Impl;function uae(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=a,p=n.readSync(r.dataId),d=n.readSync(s.dataId),c=i,h=o,m=l,f=u,{selectedIndices:g,selectedScores:y}=lae(p,d,c,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var pae={kernelName:fu,backendName:"webgl",kernelFunc:uae},cae=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${a}), float(${n}), float(index == coords.y))); } - `}},dae=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=v.sizeFromShape(r.shape),p=new cae(u,i,o,l),d=he({inputs:{x:r},backend:n,attrs:{shape:[u]}}),c=n.runWebGLProgram(p,[d],s);n.disposeIntermediateTensorInfo(d);let h=[...r.shape,i],m=he({inputs:{x:c},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(c),m},hae={kernelName:Yi,backendName:"webgl",kernelFunc:dae};function sm(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=cd({inputs:{input:a},backend:n}),s=sm({inputs:{x:r},backend:n}),i=Qf({inputs:{input:a},backend:n}),o=sm({inputs:{x:i},backend:n}),l=Fs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return dd({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var mae={kernelName:Pu,backendName:"webgl",kernelFunc:sm};function L_(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=cd({inputs:{input:a},backend:n}),s=L_({inputs:{x:r},backend:n}),i=Qf({inputs:{input:a},backend:n}),o=sm({inputs:{x:i},backend:n}),l=Fs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return dd({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var fae={kernelName:xu,backendName:"webgl",kernelFunc:L_};function gae(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Sx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=Sx({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=I_({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var yae={kernelName:vu,backendName:"webgl",kernelFunc:gae},bae=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=gt(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=` + `}},dae=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=v.sizeFromShape(r.shape),p=new cae(u,i,o,l),d=de({inputs:{x:r},backend:n,attrs:{shape:[u]}}),c=n.runWebGLProgram(p,[d],s);n.disposeIntermediateTensorInfo(d);let h=[...r.shape,i],m=de({inputs:{x:c},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(c),m},hae={kernelName:Xi,backendName:"webgl",kernelFunc:dae};function am(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=ld({inputs:{input:a},backend:n}),s=am({inputs:{x:r},backend:n}),i=jf({inputs:{input:a},backend:n}),o=am({inputs:{x:i},backend:n}),l=$s({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return ud({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var mae={kernelName:Ru,backendName:"webgl",kernelFunc:am};function DE(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=ld({inputs:{input:a},backend:n}),s=DE({inputs:{x:r},backend:n}),i=jf({inputs:{input:a},backend:n}),o=am({inputs:{x:i},backend:n}),l=$s({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return ud({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var fae={kernelName:gu,backendName:"webgl",kernelFunc:DE};function gae(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return bx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=bx({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=xE({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var yae={kernelName:yu,backendName:"webgl",kernelFunc:gae},bae=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let a=e.length,r=gt(a),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=` int start = ${s}; int end = ${i}; @@ -4010,7 +4010,7 @@ return a / b;`,jne=` ${h} setOutput(result); } - `}},z_=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return dd({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xae(r.shape,s,i):new bae(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},vae={kernelName:Zi,backendName:"webgl",kernelFunc:z_},wae=` + `}},ME=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(v.sizeFromShape(r.shape)===0){let u=s.map((p,d)=>p[0]+r.shape[d]+p[1]);return ud({backend:n,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xae(r.shape,s,i):new bae(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},vae={kernelName:Yi,backendName:"webgl",kernelFunc:ME},wae=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -4035,9 +4035,9 @@ return a / b;`,jne=` bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); - `+ud+` + `+id+` return result; -`,Iae=cn({opSnippet:wae,packedOpSnippet:kae}),Sae={kernelName:Ji,backendName:"webgl",kernelFunc:Iae};function Tae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=v.parseAxisParam(s,r.shape),p=u,d=N.getAxesPermutation(p,o),c=r;d!=null&&(c=Sn({inputs:{x:r},backend:n,attrs:{perm:d}}),p=N.getInnerMostAxes(p.length,o),l.push(c)),N.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:y}=E7(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=N.computeOutAndReduceShapes(c.shape,p),g=v.sizeFromShape(f),y=he({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),b=Gm(r.dtype),x=Eo(y,b,"prod",n);h=he({inputs:{x},backend:n,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(h);let m=N.expandShapeToKeepDim(h.shape,u);h=he({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var Nae={kernelName:eo,backendName:"webgl",kernelFunc:Tae};function Cae(e){let{inputs:t,backend:n,attrs:a}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=a,l=r.map(y=>n.readSync(y.dataId)),u=r.map(y=>y.shape),p=n.readSync(s.dataId),d=n.readSync(i.dataId),[c,h,m]=_7(l,u,p,s.shape,s.dtype,d,i.shape,o),f=c.map(y=>n.makeTensorInfo([y.length],"int32",y)),g=n.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var Eae={kernelName:Rm,backendName:"webgl",kernelFunc:Cae};function _ae(e){let{inputs:t,backend:n}=e,{starts:a,limits:r,deltas:s}=t,i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=A7(i,a.shape,a.dtype,o,r.shape,l,s.shape),d=n.makeTensorInfo([u.length],"int32",u),c=n.makeTensorInfo([p.length],a.dtype,p);return[d,c]}var Aae={kernelName:Mm,backendName:"webgl",kernelFunc:_ae};function $ae(e){let{inputs:t,backend:n,attrs:a}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=n.readSync(i.dataId),c=o.map(g=>n.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=$7(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var Fae={kernelName:Pm,backendName:"webgl",kernelFunc:$ae},W_=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=F7(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},Dae={kernelName:vc,backendName:"webgl",kernelFunc:W_},Rae="return 1.0 / x;",Mae=Ye({opSnippet:Rae}),Pae={kernelName:to,backendName:"webgl",kernelFunc:Mae},Oae=Oa+` +`,Iae=dn({opSnippet:wae,packedOpSnippet:kae}),Sae={kernelName:Zi,backendName:"webgl",kernelFunc:Iae};function Tae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],u=v.parseAxisParam(s,r.shape),p=u,d=N.getAxesPermutation(p,o),c=r;d!=null&&(c=Sn({inputs:{x:r},backend:n,attrs:{perm:d}}),p=N.getInnerMostAxes(p.length,o),l.push(c)),N.assertAxesAreInnerMostDims("prod",p,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:g,outDtype:y}=_7(c.shape,c.dtype,m,p);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=N.computeOutAndReduceShapes(c.shape,p),g=v.sizeFromShape(f),y=de({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),b=Um(r.dtype),x=Co(y,b,"prod",n);h=de({inputs:{x},backend:n,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(h);let m=N.expandShapeToKeepDim(h.shape,u);h=de({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var Nae={kernelName:Qi,backendName:"webgl",kernelFunc:Tae};function Cae(e){let{inputs:t,backend:n,attrs:a}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=a,l=r.map(y=>n.readSync(y.dataId)),u=r.map(y=>y.shape),p=n.readSync(s.dataId),d=n.readSync(i.dataId),[c,h,m]=E7(l,u,p,s.shape,s.dtype,d,i.shape,o),f=c.map(y=>n.makeTensorInfo([y.length],"int32",y)),g=n.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var _ae={kernelName:Rm,backendName:"webgl",kernelFunc:Cae};function Eae(e){let{inputs:t,backend:n}=e,{starts:a,limits:r,deltas:s}=t,i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=A7(i,a.shape,a.dtype,o,r.shape,l,s.shape),d=n.makeTensorInfo([u.length],"int32",u),c=n.makeTensorInfo([p.length],a.dtype,p);return[d,c]}var Aae={kernelName:Dm,backendName:"webgl",kernelFunc:Eae};function $ae(e){let{inputs:t,backend:n,attrs:a}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=n.readSync(i.dataId),c=o.map(g=>n.readSync(g.dataId)),h=o.map(g=>g.shape),[m,f]=$7(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var Fae={kernelName:Mm,backendName:"webgl",kernelFunc:$ae},PE=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=F7(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},Rae={kernelName:bc,backendName:"webgl",kernelFunc:PE},Dae="return 1.0 / x;",Mae=Ye({opSnippet:Dae}),Pae={kernelName:eo,backendName:"webgl",kernelFunc:Mae},Oae=Oa+` return (x < 0.0) ? 0.0 : x; `,Lae=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); @@ -4049,7 +4049,7 @@ return a / b;`,jne=` result.a = isNaN.a ? x.a : result.a; return result; -`,zae=Ye({opSnippet:Oae,packedOpSnippet:Lae}),Wae={kernelName:no,backendName:"webgl",kernelFunc:zae},Bae=Oa+` +`,zae=Ye({opSnippet:Oae,packedOpSnippet:Lae}),Wae={kernelName:to,backendName:"webgl",kernelFunc:zae},Bae=Oa+` return (x < 0.0) ? 0.0 : min(6.0, x); `,Vae=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); @@ -4061,7 +4061,7 @@ return a / b;`,jne=` result.a = isNaN.a ? x.a : result.a; return result; -`,Uae=Ye({opSnippet:Bae,packedOpSnippet:Vae}),Gae={kernelName:so,backendName:"webgl",kernelFunc:Uae},Hae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,Uae=Ye({opSnippet:Bae,packedOpSnippet:Vae}),Gae={kernelName:ro,backendName:"webgl",kernelFunc:Uae},Hae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4094,7 +4094,7 @@ return a / b;`,jne=` setOutput(newValue); } - `}},jae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}},qae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/p[0]}, ${u[1]/p[1]}, @@ -4171,7 +4171,7 @@ return a / b;`,jne=` setOutput(newValue); } - `}};function qae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=H().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new jae(r.shape,l,u,s,i):new Hae(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var Kae={kernelName:ro,backendName:"webgl",kernelFunc:qae},Xae=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` + `}};function jae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=H().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new qae(r.shape,l,u,s,i):new Hae(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],"float32")}var Kae={kernelName:ao,backendName:"webgl",kernelFunc:jae},Xae=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4252,7 +4252,7 @@ return a / b;`,jne=` setOutput(accumulator); } - `}};function Yae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Xae(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Zae={kernelName:zm,backendName:"webgl",kernelFunc:Yae},Jae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function Yae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Xae(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Zae={kernelName:Lm,backendName:"webgl",kernelFunc:Yae},Jae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[a&&t>1?i-1:i,a&&n>1?o-1:o],p=[a&&t>1?t-1:t,a&&n>1?n-1:n],d=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/p[0]}, ${u[1]/p[1]}); @@ -4315,7 +4315,7 @@ return a / b;`,jne=` setOutput(newValue); } - `}};function ere(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=H().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Qae(r.shape,l,u,s,i):new Jae(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var tre={kernelName:ao,backendName:"webgl",kernelFunc:ere},nre=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` + `}};function ere(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,p=H().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Qae(r.shape,l,u,s,i):new Jae(r.shape,l,u,s,i);return n.runWebGLProgram(p,[r],r.dtype)}var tre={kernelName:no,backendName:"webgl",kernelFunc:ere},nre=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],p=o[1]/l[1],d=1/u,c=1/p,h=Math.ceil(d)*2+2,m=Math.ceil(c)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4385,7 +4385,7 @@ return a / b;`,jne=` setOutput(accumulator); } - `}};function are(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new nre(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var rre={kernelName:Lm,backendName:"webgl",kernelFunc:are},sre=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` + `}};function are(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new nre(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var rre={kernelName:Om,backendName:"webgl",kernelFunc:are},sre=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); @@ -4423,7 +4423,7 @@ return a / b;`,jne=` } setOutput(result); } - `;function o(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function p(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((y,b)=>c(b,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function ore(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return na({inputs:{x:r},backend:n});let l=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ire(r.shape,o):new sre(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var lre={kernelName:io,backendName:"webgl",kernelFunc:ore},ure=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` + `;function o(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function p(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((y,b)=>c(b,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function ore(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return na({inputs:{x:r},backend:n});let l=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ire(r.shape,o):new sre(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var lre={kernelName:so,backendName:"webgl",kernelFunc:ore},ure=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` vec3 fill = vec3(${t.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { @@ -4442,7 +4442,7 @@ return a / b;`,jne=` } setOutput(outputValue); } - `}},pre={kernelName:Ou,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new ure(a.shape,s),[u,p]=N.getImageCenter(i,a.shape[1],a.shape[2]),d=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[a],a.dtype,d)}},cre=` + `}},pre={kernelName:Du,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new ure(a.shape,s),[u,p]=N.getImageCenter(i,a.shape[1],a.shape[2]),d=[[u,p,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[a],a.dtype,d)}},cre=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); @@ -4457,7 +4457,7 @@ return a / b;`,jne=` return base + 1.0; } } -`,dre=Ye({opSnippet:cre}),hre={kernelName:oo,backendName:"webgl",kernelFunc:dre},mre="return inversesqrt(x);",fre=Ye({opSnippet:mre,cpuKernelImpl:D7}),gre={kernelName:lo,backendName:"webgl",kernelFunc:fre},B_=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=gt(r.length),l=gt(s.length),u="";n===1?u="i":n===2&&(u="i, j");let p=`getIndices(${u})`,d="";a===1?d="i":a===2&&(d="i, coords[1]");let c=`getUpdates(${d})`,h=t>1?"strides[j]":"strides";this.userCode=` +`,dre=Ye({opSnippet:cre}),hre={kernelName:io,backendName:"webgl",kernelFunc:dre},mre="return inversesqrt(x);",fre=Ye({opSnippet:mre,cpuKernelImpl:R7}),gre={kernelName:oo,backendName:"webgl",kernelFunc:fre},OE=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=gt(r.length),l=gt(s.length),u="";n===1?u="i":n===2&&(u="i, j");let p=`getIndices(${u})`,d="";a===1?d="i":a===2&&(d="i, coords[1]");let c=`getUpdates(${d})`,h=t>1?"strides[j]":"strides";this.userCode=` ${o} strides = ${o}(${r}); void main() { @@ -4477,7 +4477,7 @@ return a / b;`,jne=` } setOutput(mix(getDefaultValue(), sum, float(found))); } - `}};function yre(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=N.calculateShapes(s,r,i),c=[d/u,u];if(d===0)return n.makeTensorInfo(i,r.dtype);let h=he({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=he({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new B_(l,o,h.shape.length,m.shape.length,p,c),y=n.runWebGLProgram(g,[m,h,f],m.dtype),b=he({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),b}var bre={kernelName:ku,backendName:"webgl",kernelFunc:yre},xre=class{constructor(e,t,n,a){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) 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i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); -`,Nre=Ye({opSnippet:Tre}),Cre={kernelName:Su,backendName:"webgl",kernelFunc:Nre},Ere=np+` +`,Nre=Ye({opSnippet:Tre}),Cre={kernelName:wu,backendName:"webgl",kernelFunc:Nre},_re=Qu+` return 1.0 / (1.0 + exp(-1.0 * x)); -`,_re=` +`,Ere=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); @@ -4530,15 +4530,15 @@ return a / b;`,jne=` result.a = isNaN.a ? x.a : result.a; return result; -`,Are=Ye({opSnippet:Ere,packedOpSnippet:_re,cpuKernelImpl:M7}),$re={kernelName:po,backendName:"webgl",kernelFunc:Are},Fre=` +`,Are=Ye({opSnippet:_re,packedOpSnippet:Ere,cpuKernelImpl:M7}),$re={kernelName:uo,backendName:"webgl",kernelFunc:Are},Fre=` if (isnan(x)) { return 0.0; } return sign(x); -`,Dre=Ye({opSnippet:Fre}),Rre={kernelName:Cu,backendName:"webgl",kernelFunc:Dre},Mre=np+` +`,Rre=Ye({opSnippet:Fre}),Dre={kernelName:Su,backendName:"webgl",kernelFunc:Rre},Mre=Qu+` return sin(x); -`,Pre=Ye({opSnippet:Mre}),Ore={kernelName:uo,backendName:"webgl",kernelFunc:Pre},Lre=` +`,Pre=Ye({opSnippet:Mre}),Ore={kernelName:lo,backendName:"webgl",kernelFunc:Pre},Lre=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; -`,zre=Ye({opSnippet:Lre}),Wre={kernelName:Nu,backendName:"webgl",kernelFunc:zre},Bre=` +`,zre=Ye({opSnippet:Lre}),Wre={kernelName:Iu,backendName:"webgl",kernelFunc:zre},Bre=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; @@ -4558,15 +4558,15 @@ return a / b;`,jne=` result = log(exp_x + 1.0); } return result; -`,Vre=Ye({opSnippet:Bre}),Ure={kernelName:Eu,backendName:"webgl",kernelFunc:Vre},Gre=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,b)=>y*b),l=[[0,0]];l.push(...i);for(let 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Error(`Values must be a vector, saw: ${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw: - ${i.shape}`);let o=n.readSync(a.dataId),l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=n.readSync(i.dataId)[0],[d,c,h,m,f]=O7(o,a.shape,a.dtype,l,r.dtype,u,p);return[n.makeTensorInfo(c,a.dtype,d),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var qre={kernelName:wc,backendName:"webgl",kernelFunc:jre};function Kre(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),l=Array.from(n.readSync(s.dataId)),[u,p,d]=L7(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(p,a.dtype,u),n.makeTensorInfo([d.length],s.dtype,new Int32Array(d))]}var Xre={kernelName:$u,backendName:"webgl",kernelFunc:Kre};function Yre(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape + ${i.shape}`);let o=n.readSync(a.dataId),l=n.readSync(r.dataId),u=n.readSync(s.dataId),p=n.readSync(i.dataId)[0],[d,c,h,m,f]=O7(o,a.shape,a.dtype,l,r.dtype,u,p);return[n.makeTensorInfo(c,a.dtype,d),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var jre={kernelName:xc,backendName:"webgl",kernelFunc:qre};function Kre(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),l=Array.from(n.readSync(s.dataId)),[u,p,d]=L7(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(p,a.dtype,u),n.makeTensorInfo([d.length],s.dtype,new Int32Array(d))]}var Xre={kernelName:_u,backendName:"webgl",kernelFunc:Kre};function Yre(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be 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nse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=v.parseAxisParam(i,r.shape)[0],l=N.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),d=r.shape.slice();return l.map(c=>{let h=[...d];h[o]=c;let m=ap({inputs:{x:r},backend:n,attrs:{begin:p,size:h}});return p[o]+=c,m})}var ase={kernelName:Au,backendName:"webgl",kernelFunc:nse},PI="return sqrt(x);",rse=Ye({opSnippet:PI,packedOpSnippet:PI,cpuKernelImpl:z7}),sse={kernelName:co,backendName:"webgl",kernelFunc:rse},ise="return x * x;",ose=Ye({opSnippet:ise}),lse={kernelName:Sc,backendName:"webgl",kernelFunc:ose},OI="return (a - b) * (a - b);",use=cn({opSnippet:OI,packedOpSnippet:OI}),pse={kernelName:fo,backendName:"webgl",kernelFunc:use};function cse({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Oa+` + ${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[u,p]=sE(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(p,a.dtype,u)}var Qre={kernelName:wc,backendName:"webgl",kernelFunc:Jre};function ese(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:u,sliceSize:p,strides:d,outputSize:c}=N.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let y=n.bufferSync(r),b=n.bufferSync(s),x=v.decodeString(n.readSync(i.dataId)[0]),w=D7(y,b,o,c,p,u,l,d,x,h);return n.makeTensorInfo(o,w.dtype,w.values)}let m=new OE(u,l,r.shape.length,s.shape.length,d,[c,1],h),f=n.runWebGLProgram(m,[s,r,i],s.dtype),g=de({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),g}var tse={kernelName:Wm,backendName:"webgl",kernelFunc:ese};function nse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=v.parseAxisParam(i,r.shape)[0],l=N.prepareSplitSize(r,s,o),u=r.shape.length,p=new Array(u).fill(0),d=r.shape.slice();return l.map(c=>{let h=[...d];h[o]=c;let m=ep({inputs:{x:r},backend:n,attrs:{begin:p,size:h}});return p[o]+=c,m})}var ase={kernelName:Cu,backendName:"webgl",kernelFunc:nse},AI="return sqrt(x);",rse=Ye({opSnippet:AI,packedOpSnippet:AI,cpuKernelImpl:z7}),sse={kernelName:po,backendName:"webgl",kernelFunc:rse},ise="return x * x;",ose=Ye({opSnippet:ise}),lse={kernelName:kc,backendName:"webgl",kernelFunc:ose},$I="return (a - b) * (a - b);",use=dn({opSnippet:$I,packedOpSnippet:$I}),pse={kernelName:mo,backendName:"webgl",kernelFunc:use};function cse({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Oa+` return x > 0.0 ? 1.0 : float(${t.alpha}); `,s=new ir(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var dse={kernelName:Is,backendName:"webgl",kernelFunc:cse},hse=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=gt(n.length),s=gt(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` ${r} begin = ${r}(${e}); @@ -4576,15 +4576,15 @@ return a / b;`,jne=` ${s} coords = getOutputCoords(); setOutput(getX(${i})); } - `}};function mse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:d,shrinkAxisMask:c}=a,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:b,end:x,strides:w}=Kt.sliceInfo(r.shape,s,i,o,l,u,p,d,c),I;if(f)I=he({inputs:{x:r},backend:n,attrs:{shape:m}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=Kt.computeOutShape(b,x,w),_=ap({inputs:{x:r},backend:n,attrs:{begin:b,size:C}});I=he({inputs:{x:_},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(_)}else if(n.shouldExecuteOnCPU([r])){let C=n.readSync(r.dataId),_=Oe(r.shape,r.dtype,C),$=W7(h,_,w,b);I=n.makeTensorInfo(m,r.dtype,$.values)}else{let C=new hse(b,w,h);I=n.runWebGLProgram(C,[r],r.dtype)}let T=he({inputs:{x:I},backend:n,attrs:{shape:m}});return n.disposeIntermediateTensorInfo(I),T}var fse={kernelName:Fu,backendName:"webgl",kernelFunc:mse};function gse(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=a,{data:p,dataSplits:d}=t,c=n.readSync(p.dataId),h=n.readSync(d.dataId),[m,f]=B7(c,h,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(d.shape,"int32",f)]}var yse={kernelName:Tc,backendName:"webgl",kernelFunc:gse};function bse(e){let{inputs:t,backend:n,attrs:a}=e,{skipEmpty:r}=a,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(s.dataId),l=n.readSync(i.dataId)[0],[u,p,d]=V7(o,l,r),c=p.length;return[n.makeTensorInfo([c,2],"int32",u),n.makeTensorInfo([c],"string",p),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var xse={kernelName:Nc,backendName:"webgl",kernelFunc:bse};function vse(e){let{inputs:t,backend:n,attrs:a}=e,{numBuckets:r}=a,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(s.dataId),o=U7(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var wse={kernelName:Cc,backendName:"webgl",kernelFunc:vse},kse="return tan(x);",Ise=Ye({opSnippet:kse}),Sse={kernelName:yo,backendName:"webgl",kernelFunc:Ise},Tse=` + `}};function mse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:d,shrinkAxisMask:c}=a,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:b,end:x,strides:w}=Kt.sliceInfo(r.shape,s,i,o,l,u,p,d,c),I;if(f)I=de({inputs:{x:r},backend:n,attrs:{shape:m}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=Kt.computeOutShape(b,x,w),E=ep({inputs:{x:r},backend:n,attrs:{begin:b,size:C}});I=de({inputs:{x:E},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let C=n.readSync(r.dataId),E=ze(r.shape,r.dtype,C),$=W7(h,E,w,b);I=n.makeTensorInfo(m,r.dtype,$.values)}else{let C=new hse(b,w,h);I=n.runWebGLProgram(C,[r],r.dtype)}let T=de({inputs:{x:I},backend:n,attrs:{shape:m}});return n.disposeIntermediateTensorInfo(I),T}var fse={kernelName:Eu,backendName:"webgl",kernelFunc:mse};function gse(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=a,{data:p,dataSplits:d}=t,c=n.readSync(p.dataId),h=n.readSync(d.dataId),[m,f]=B7(c,h,r,s,i,o,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(d.shape,"int32",f)]}var yse={kernelName:Ic,backendName:"webgl",kernelFunc:gse};function bse(e){let{inputs:t,backend:n,attrs:a}=e,{skipEmpty:r}=a,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(s.dataId),l=n.readSync(i.dataId)[0],[u,p,d]=V7(o,l,r),c=p.length;return[n.makeTensorInfo([c,2],"int32",u),n.makeTensorInfo([c],"string",p),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var xse={kernelName:Sc,backendName:"webgl",kernelFunc:bse};function vse(e){let{inputs:t,backend:n,attrs:a}=e,{numBuckets:r}=a,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(s.dataId),o=U7(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var wse={kernelName:Tc,backendName:"webgl",kernelFunc:vse},kse="return tan(x);",Ise=Ye({opSnippet:kse}),Sse={kernelName:go,backendName:"webgl",kernelFunc:Ise},Tse=` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); -`,Nse=Ye({opSnippet:Tse}),Cse={kernelName:bo,backendName:"webgl",kernelFunc:Nse},Ese=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(d=>v.decodeString(d)):o,u=Oe(r.shape,r.dtype,l),p=H7(u,s);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Ese(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var Ase={kernelName:ks,backendName:"webgl",kernelFunc:V_},$se=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` + `}};function Ese(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(d=>v.decodeString(d)):o,u=ze(r.shape,r.dtype,l),p=H7(u,s);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new _se(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var Ase={kernelName:ks,backendName:"webgl",kernelFunc:LE},$se=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -4658,7 +4658,7 @@ return a / b;`,jne=` setOutput(x0 >= x1 ? float(i0) : float(i1)); } - `}};function Vs(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function LI(e){let t=1;for(;tl){let $=n.readSync(r.dataId),[R,F]=j7($,u,r.dtype,s,i);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(s===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(p===1)return[r,dd({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),c=d!==null&&d.isPacked,h=c?n.unpackTensor(r):r,m=v.sizeFromShape(u)/p,f=he({inputs:{x:h},attrs:{shape:[m,p]},backend:n});c&&Vs(n,h);let g=LI(s),y=LI(p),b=null,x=()=>b===null?[f,f]:[f,b],w=($,R,F)=>{let S=x(),M=new $se(F),B=[[p],[b===null?1:0],[Number.NEGATIVE_INFINITY],[$],[R]],U=b;b=n.runWebGLProgram(M,S,"int32",B),Vs(n,U)};for(let $=1;$=1;F/=2)w(R,F,[m,y])}for(let $=y;$>g;$/=2){let R=x(),F=new Fse([m,$/2]),S=[[p],[b===null?1:0],[g]],M=b;b=n.runWebGLProgram(F,R,"int32",S),Vs(n,M);let B=g/2,U=B*2;for(let G=B;G>=1;G/=2)w(U,G,b.shape)}let I=b;b=ap({inputs:{x:b},backend:n,attrs:{begin:0,size:[m,s]}}),Vs(n,I);let T=D_({inputs:{x:f,indices:b},backend:n,attrs:{axis:1,batchDims:1}});Vs(n,f);let C=u.slice(0,-1);C.push(s),I=b,b=he({inputs:{x:b},attrs:{shape:C},backend:n}),Vs(n,I);let _=T;return T=he({inputs:{x:T},attrs:{shape:C},backend:n}),Vs(n,_),[T,b]}var Rse={kernelName:Du,backendName:"webgl",kernelFunc:Dse},Mse=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=` + `}};function Vs(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function FI(e){let t=1;for(;tl){let $=n.readSync(r.dataId),[R,F]=q7($,u,r.dtype,s,i);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(s===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(p===1)return[r,ud({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),c=d!==null&&d.isPacked,h=c?n.unpackTensor(r):r,m=v.sizeFromShape(u)/p,f=de({inputs:{x:h},attrs:{shape:[m,p]},backend:n});c&&Vs(n,h);let g=FI(s),y=FI(p),b=null,x=()=>b===null?[f,f]:[f,b],w=($,R,F)=>{let S=x(),M=new 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len) { float inCoord = outCoord; if(${o} == 2) { @@ -4770,7 +4770,7 @@ return a / b;`,jne=` } setOutput(outputValue); } - `}};function Pse(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[p,d,c,h]=r.shape,[m,f]=u!=null?u:[d,c],g=[p,m,f,h],y=new Mse(d,c,i,o,l,g);return n.runWebGLProgram(y,[r,s],"float32")}var Ose={kernelName:Ru,backendName:"webgl",kernelFunc:Pse};function Lse(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;Zu(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=q7(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var zse={kernelName:Vm,backendName:"webgl",kernelFunc:Lse};function Wse(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),p=0;for(let f=0;fn.disposeIntermediateTensorInfo(f)),m}var Bse={kernelName:Mu,backendName:"webgl",kernelFunc:Wse},Vse=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,p=n%4,d=` + `}};function Pse(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=a,[p,d,c,h]=r.shape,[m,f]=u!=null?u:[d,c],g=[p,m,f,h],y=new Mse(d,c,i,o,l,g);return n.runWebGLProgram(y,[r,s],"float32")}var Ose={kernelName:$u,backendName:"webgl",kernelFunc:Pse};function Lse(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;Ku(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=j7(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([u.length],"int32",u)]}var zse={kernelName:Bm,backendName:"webgl",kernelFunc:Lse};function Wse(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],u=new Array(o-1),p=0;for(let f=0;fn.disposeIntermediateTensorInfo(f)),m}var Bse={kernelName:Fu,backendName:"webgl",kernelFunc:Wse},Vse=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,p=n%4,d=` sumValue += dot(values, segFilter); `,c="";r%n>0&&(c=` if (inIdx < 0 || inIdx >= ${r}) { @@ -4876,6 +4876,6 @@ return a / b;`,jne=` } setOutput(${l}); } - `}};function Use(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,p=N.getAxesPermutation([u],o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),l.push(d),u=N.getInnerMostAxes(1,o)[0]);let c=N.segment_util.computeOutShape(d.shape,u,i),h=v.sizeFromShape([d.shape[u]]),m=he({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=Gm(r.dtype),g=(w,I,T,C,_)=>{let $=w.shape[0],R=w.shape[1],F=N.segment_util.segOpComputeOptimalWindowSize(R,_),S={windowSize:F,inSize:R,batchSize:$,numSegments:_},M=new Vse(S,I),B=n.compileAndRun(M,[w,T],C);if(l.push(B),B.shape[1]===_)return B;let U=W_({backend:n,attrs:{start:0,stop:_,step:1,dtype:"float32"}}),G=V_({inputs:{x:U},backend:n,attrs:{reps:[R/F]}});return l.push(U),l.push(G),g(B,I,G,C,_)},y=g(m,"unsortedSegmentSum",s,f,i),b=he({inputs:{x:y},backend:n,attrs:{shape:c}}),x=b;if(p!=null){l.push(b);let w=N.getUndoAxesPermutation(p);x=Sn({inputs:{x},backend:n,attrs:{perm:w}})}return l.forEach(w=>n.disposeIntermediateTensorInfo(w)),x}var Gse={kernelName:Ec,backendName:"webgl",kernelFunc:Use},Hse=[WJ,VJ,HJ,KJ,YJ,QJ,t9,a9,o9,u9,d9,f9,b9,k9,T9,C9,_9,D9,M9,O9,B9,K9,Y9,J9,rQ,iQ,pQ,IJ,hQ,bQ,kQ,EQ,AQ,FQ,RQ,PQ,zQ,VQ,HQ,qQ,XQ,ZQ,eee,nee,iee,lee,cee,mee,gee,vee,See,Eee,$ee,Ree,Mee,Oee,zee,Bee,Uee,Hee,Xee,Jee,tte,ate,ite,ute,hte,yte,kJ,xte,gQ,kte,Tte,Ete,TJ,Fte,Pte,Lte,Vte,Hte,Xte,Jte,nne,ine,une,cne,fne,yne,xne,Ine,Tne,Cne,_ne,$ne,Mne,zne,Une,Zne,EJ,tae,rae,oae,pae,eQ,hae,fae,yae,vae,Sae,CJ,Nae,Eae,Aae,Fae,Dae,tQ,qne,Pae,Wae,Gae,AJ,Kae,Zae,tre,rre,lre,pre,hre,gre,bre,wre,Sre,Cre,$re,Rre,Ore,Wre,j9,Xne,Ure,Hre,qre,Xre,Zre,Qre,tse,ase,sse,lse,pse,dse,fse,yse,xse,wse,Kne,OJ,Sse,Cse,Ase,Rse,Ose,LJ,zse,Bse,Gse,mae];for(let e of Hse)_c(e);var _t;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(_t||(_t={}));var pc;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(pc||(pc={}));var U_;function jse(e){U_=e.wasm.cwrap(ni,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function qse(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let _=n.dataIdMap.get(i.dataId);if(_.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${_.shape.length}.`);m=_.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=pc[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the 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eg(e){let{inputs:{x:t},backend:n}=e;if(t.dtype==="string")return mn(n.readSync(t.dataId),t.shape,t.dtype);let a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var tie={kernelName:Li,backendName:"wasm",kernelFunc:eg},H_;function nie(e){H_=e.wasm.cwrap(Ar,null,["number","array","number","number","number","array","number"])}function ys(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=rie(t.x.shape,a.perm),i=!0;for(let m=0;m=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var sie={kernelName:Ar,backendName:"wasm",kernelFunc:ys,setupFunc:nie};function Ds(e,t,n){let a=e.shape,r=e.shape.length,s=v.parseAxisParam(t,a),i=s,o=N.getAxesPermutation(i,r),l=null,u=!1;if(o!=null){let p=new Array(r);for(let c=0;c`new shape: ${i}, old shape: ${a.shape}. 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c=n.makeOutput(p.shape,p.dtype),h=p.shape[d],m=n.dataIdMap.get(p.dataId).id,f=n.dataIdMap.get(c.dataId).id;nA(m,i?1:0,o?1:0,h,f,_t[r.dtype]);let g=c;if(u!==null){let y=N.getUndoAxesPermutation(u);g=ys({inputs:{x:c},attrs:{perm:y},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var qie={kernelName:Kl,backendName:"wasm",setupFunc:Hie,kernelFunc:jie},aA;function Kie(e){aA=e.wasm.cwrap(_i,null,["number","number","number","number","number","number"])}function Xie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=N.getAxesPermutation([s],l),p=r;u!==null&&(p=ys({inputs:{x:r},attrs:{perm:u},backend:n}));let d=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumsum",[d],l);let c=n.makeOutput(p.shape,p.dtype),h=p.shape[d],m=n.dataIdMap.get(p.dataId).id,f=n.dataIdMap.get(c.dataId).id;aA(m,i?1:0,o?1:0,h,f,_t[r.dtype]);let g=c;if(u!==null){let y=N.getUndoAxesPermutation(u);g=ys({inputs:{x:c},attrs:{perm:y},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var Yie={kernelName:_i,backendName:"wasm",setupFunc:Kie,kernelFunc:Xie},rA;function Zie(e){rA=e.wasm.cwrap(Yl,null,["number","number","number","array","number","array","array","number","number"])}function Jie(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer),w=t.dataIdMap.get(f.dataId).id;return rA(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,x,m.length,w),f}var Qie={kernelName:Yl,backendName:"wasm",setupFunc:Zie,kernelFunc:Jie},sA;function eoe(e){sA=e.wasm.cwrap(Ai,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function toe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:d}=n,c=u==null?[1,1]:u,h=N.computeConv2DInfo(r.shape,s.shape,l,c,p,d,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,x=h.padInfo.left,w=h.dilationHeight,I=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,_=h.inChannels,$=h.outChannels,R=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let F=a.makeOutput(h.outShape,"float32"),S=a.dataIdMap.get(F.dataId).id;return sA(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,x,R,w,I,T,C,_,$,S),F}var noe={kernelName:Ai,backendName:"wasm",setupFunc:eoe,kernelFunc:toe},aoe=Ut(Fi),roe=!1,soe=dn(Jl,roe,"bool"),ioe=Ut(Di,"float32");function Nx(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Wn({inputs:{x:r},backend:a,attrs:{shape:o}})}var ooe={kernelName:Ql,backendName:"wasm",kernelFunc:Nx};function iA(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var loe={kernelName:yc,backendName:"wasm",kernelFunc:iA},oA;function uoe(e){oA=e.wasm.cwrap(tu,null,["number","number","number","number","number","number"])}function poe(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,p]=a.shape;return oA(s,o,l,u,p,i),r}var coe={kernelName:tu,backendName:"wasm",kernelFunc:poe,setupFunc:uoe},doe=Ut(Ri),hoe=!1,moe=dn(Mi,hoe),lA;function foe(e){lA=e.wasm.cwrap(Pi,null,["number","number","number","number","number","number","number"])}function goe(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return lA(p,d,c,h,m,r,g),f}var yoe={kernelName:Pi,backendName:"wasm",setupFunc:foe,kernelFunc:goe},uA;function boe(e){uA=e.wasm.cwrap(ai,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function xoe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=pc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);w=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,_=f.padInfo.right,$=f.padInfo.bottom,R=f.padInfo.left,F=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,G=f.padInfo.type==="SAME"?1:0,q=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return uA(y,q,K,Z,b,I,T,w,C,_,$,R,G,F,S,M,B,U,x,g,ae,m||0,ee),J}var voe={kernelName:ai,backendName:"wasm",setupFunc:boe,kernelFunc:xoe},pA;function woe(e){pA=e.wasm.cwrap(ri,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function koe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!0),g=pc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);w=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,_=f.padInfo.right,$=f.padInfo.bottom,R=f.padInfo.left,F=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,G=f.padInfo.type==="SAME"?1:0,q=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. 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Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=N.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,y=p.dilationHeight,b=p.dilationWidth,x=p.strideHeight,w=p.strideWidth,I=p.inChannels,T=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let C=a.makeOutput(p.outShape,"float32"),_=a.dataIdMap.get(C.dataId).id;return fA(s,r.shape[0],r.shape[1],r.shape[2],d,c,h,m,f,g,y,b,x,w,I,T,_),C}var ale={kernelName:Gi,backendName:"wasm",setupFunc:tle,kernelFunc:nle},gA;function rle(e){gA=e.wasm.cwrap(Hi,null,["number, number, number"])}function sle(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Ds(i,r,t),m=d;if(h){let w=t.dataIdMap.get(p.dataId).id;w!==o&&(u=p,l=w,m=N.getInnerMostAxes(m.length,u.shape.length))}N.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=N.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=rp({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let x=t.makeOutput(f,"float32");if(v.sizeFromShape(u.shape)!==0){let w=t.dataIdMap.get(x.dataId).id;gA(l,y,w)}if(h&&t.disposeData(p.dataId),s){let w=N.expandShapeToKeepDim(x.shape,c);x.shape=w}return u.dtype!=="float32"&&t.disposeData(b.dataId),x}var ile={kernelName:Hi,backendName:"wasm",setupFunc:rle,kernelFunc:sle},yA;function ole(e){yA=e.wasm.cwrap(ji,null,["number","number","number","number"])}function lle(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Ds(i,r,t);if(h){let x=t.dataIdMap.get(p.dataId).id;x!==o&&(u=p,l=x)}let m=u.shape.length;N.assertAxesAreInnerMostDims("min",d,m);let[f,g]=N.computeOutAndReduceShapes(u.shape,d),y=v.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;yA(l,_t[i.dtype],y,x)}if(h&&t.disposeData(p.dataId),s){let x=N.expandShapeToKeepDim(b.shape,c);b.shape=x}return b}var ule={kernelName:ji,backendName:"wasm",setupFunc:ole,kernelFunc:lle},ple=!1,cle=dn(qi,ple),Cx;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(Cx||(Cx={}));var bA;function dle(e){bA=e.wasm.cwrap(Ki,null,["number","array","number","number","array","array","number","number"])}function hle(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,mode:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=a.map(m=>m[0]),d=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(d).buffer);return bA(i,u,t.shape.length,_t[t.dtype],c,h,Cx[r],l),o}var mle={kernelName:Ki,backendName:"wasm",kernelFunc:hle,setupFunc:dle},fle=!0,gle=dn(Xi,fle),yle=Ut(mu);function d1(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var xA;function ble(e){xA=e.wasm.cwrap(gu,"number",["number","number","number","number","number"])}function xle(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=xA(u,p,s,r,i),{pSelectedIndices:c,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=d1(t,d);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",c)}var vle={kernelName:gu,backendName:"wasm",setupFunc:ble,kernelFunc:xle},vA;function wle(e){vA=e.wasm.cwrap(yu,"number",["number","number","number","number","number","bool"])}function kle(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=vA(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=d1(t,c);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var Ile={kernelName:yu,backendName:"wasm",setupFunc:wle,kernelFunc:kle},wA;function Sle(e){wA=e.wasm.cwrap(bu,"number",["number","number","number","number","number","number"])}function Tle(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=wA(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=d1(t,c);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var Nle={kernelName:bu,backendName:"wasm",setupFunc:Sle,kernelFunc:Tle},Cle=!1,Ele=dn(fu,Cle,"bool"),kA;function _le(e){kA=e.wasm.cwrap(Yi,null,["number","number","number","number","number"])}function Ale(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=n.makeOutput([...r.shape,i],s),p=n.dataIdMap.get(u.dataId).id,d=n.dataIdMap.get(r.dataId).id;return kA(d,i,o,l,p),u}var $le={kernelName:Yi,backendName:"wasm",setupFunc:_le,kernelFunc:Ale};function Fle(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var Dle={kernelName:xu,backendName:"wasm",kernelFunc:Fle};function Rle(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Nx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching 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lue(e){AA=e.wasm.cwrap(Ou,null,["number","number","number","number","number","number","number","number","array","number","number"])}function uue(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,p=n.dataIdMap.get(l.dataId).id,[d,c,h,m]=r.shape,[f,g]=N.getImageCenter(o,c,h),y=i===0,b=255,x=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],w=new Uint8Array(new Int32Array(x).buffer);return AA(u,d,c,h,m,s,f,g,w,x.length,p),l}var pue={kernelName:Ou,backendName:"wasm",kernelFunc:uue,setupFunc:lue},cue=Ut(oo),due=Ut(lo),$A;function hue(e){$A=e.wasm.cwrap(ku,null,["number","number","number","number","number","number","array","number","number"])}function mue(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(v.sizeFromShape(i)===0)return 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Gse={kernelName:Nc,backendName:"webgl",kernelFunc:Use},Hse=[WJ,VJ,HJ,KJ,YJ,QJ,t9,a9,o9,u9,d9,f9,b9,k9,T9,C9,E9,R9,M9,O9,B9,K9,Y9,J9,rQ,iQ,pQ,IJ,hQ,bQ,kQ,_Q,AQ,FQ,DQ,PQ,zQ,VQ,HQ,jQ,XQ,ZQ,eee,nee,iee,lee,cee,mee,gee,vee,See,_ee,$ee,Dee,Mee,Oee,zee,Bee,Uee,Hee,Xee,Jee,tte,ate,ite,ute,hte,yte,kJ,xte,gQ,kte,Tte,_te,TJ,Fte,Pte,Lte,Vte,Hte,Xte,Jte,nne,ine,une,cne,fne,yne,xne,Ine,Tne,Cne,Ene,$ne,Mne,zne,Une,Zne,_J,tae,rae,oae,pae,eQ,hae,fae,yae,vae,Sae,CJ,Nae,_ae,Aae,Fae,Rae,tQ,jne,Pae,Wae,Gae,AJ,Kae,Zae,tre,rre,lre,pre,hre,gre,bre,wre,Sre,Cre,$re,Dre,Ore,Wre,q9,Xne,Ure,Hre,jre,Xre,Zre,Qre,tse,ase,sse,lse,pse,dse,fse,yse,xse,wse,Kne,OJ,Sse,Cse,Ase,Dse,Ose,LJ,zse,Bse,Gse,mae];for(let e of Hse)Cc(e);var At;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(At||(At={}));var lc;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(lc||(lc={}));var zE;function qse(e){zE=e.wasm.cwrap(ei,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function jse(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);m=E.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=lc[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the 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s(i){let{backend:o,inputs:l}=i,{a:u,b:p}=l,d=o.dataIdMap.get(u.dataId).id,c=o.dataIdMap.get(p.dataId).id,h=n!=null?n:u.dtype,m=N.assertAndGetBroadcastShape(u.shape,p.shape),f=o.makeOutput(m,h);if(v.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(p.shape).buffer),b=o.dataIdMap.get(f.dataId).id;return a(d,g,u.shape.length,c,y,p.shape.length,At[u.dtype],b),f}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var Yse=!0,Zse=hn(vs,Yse),WE;function Jse(e){WE=e.wasm.cwrap(bi,null,["array","number","number","number"])}function Qse(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return WE(s,r.length,At[a.dtype],i),a}var eie={kernelName:bi,backendName:"wasm",setupFunc:Jse,kernelFunc:Qse};function Kf(e){let{inputs:{x:t},backend:n}=e;if(t.dtype==="string")return fn(n.readSync(t.dataId),t.shape,t.dtype);let a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var tie={kernelName:Oi,backendName:"wasm",kernelFunc:Kf},BE;function nie(e){BE=e.wasm.cwrap(Ar,null,["number","array","number","number","number","array","number"])}function ys(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=rie(t.x.shape,a.perm),i=!0;for(let m=0;m=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var sie={kernelName:Ar,backendName:"wasm",kernelFunc:ys,setupFunc:nie};function Fs(e,t,n){let a=e.shape,r=e.shape.length,s=v.parseAxisParam(t,a),i=s,o=N.getAxesPermutation(i,r),l=null,u=!1;if(o!=null){let p=new Array(r);for(let c=0;c`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var bie={kernelName:bu,backendName:"wasm",kernelFunc:Wn},qE;function xie(e){qE=e.wasm.cwrap(wi,null,["number","array","number","number","array","number","number","number","number"])}function vie(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=s.shape.length,p=i?r.shape[l-2]:r.shape[l-1],d=o?s.shape[u-1]:s.shape[u-2],c=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[u-2]:s.shape[u-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=v.sizeFromShape(m),y=v.sizeFromShape(f),b=Mu.assertAndGetBroadcastShape(r.shape.slice(0,-2),s.shape.slice(0,-2)).concat([c,h]);v.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and 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t.dtype==="string"?d.stringBytes=l.slice(m,m+v.sizeFromShape(i)):r.typedArrayFromHeap(u).set(l.subarray(m,m+v.sizeFromShape(i))),u}if(t.dtype==="string"){let m=Zh(l,s,i,t.shape,t.dtype);return d.stringBytes=m,u}let c=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)kie(l,p[0],c,s,i);else if(h===3)Iie(l,p[0],p[1],c,s,i);else if(h===4)Sie(l,p[0],p[1],p[2],c,s,i);else{let m=Zh(l,s,i,t.shape,t.dtype);c.set(m)}return u}function kie(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let u=i;uy*b),l=N.getReshaped(r.shape,s,o),u=N.getPermuted(l.length,s.length),p=N.getReshapedPermuted(r.shape,s,o),d=N.getSliceBeginCoords(i,s.length),c=N.getSliceSize(p,i,s.length),h=Wn({inputs:{x:r},backend:n,attrs:{shape:l}}),m=ys({inputs:{x:h},backend:n,attrs:{perm:u}}),f=Wn({inputs:{x:m},backend:n,attrs:{shape:p}}),g=gi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeData(h.dataId),n.disposeData(m.dataId),n.disposeData(h.dataId),g}var Cie={kernelName:Ul,backendName:"wasm",kernelFunc:Nie};function tp(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var _ie={kernelName:ki,backendName:"wasm",kernelFunc:tp},Eie=Ut(Ii),jE;function Aie(e){jE=e.wasm.cwrap(ws,null,["number","number","number","number"])}function $ie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return jE(o,s,i,u),l}var Fie={kernelName:ws,backendName:"wasm",setupFunc:Aie,kernelFunc:$ie};function KE(e){let{inputs:t,backend:n}=e,a=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=t.map(h=>h.shape);N.assertParamsConsistent(r,a);let s=N.computeOutShape(t.map(h=>h.shape),a),i=t.filter(h=>v.sizeFromShape(h.shape)>0);if(i.length===1)return Kf({inputs:{x:i[0]},backend:n});let o=n.makeOutput(s,t[0].dtype);if(v.sizeFromShape(s)===0)return o;if(i[0].dtype==="string"){let h=i.map(x=>{let w=[-1,v.sizeFromShape(x.shape.slice(a))];return Wn({inputs:{x},backend:n,attrs:{shape:w}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));s=N.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,g=z0(m,s,t[0].dtype,f),y=N.computeOutShape(i.map(x=>x.shape),a);o.shape=y;let b=n.dataIdMap.get(o.dataId);return b.stringBytes=N.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),o}let l=v.sizeFromShape(i[0].shape.slice(0,a)),u=0,p=i.map(h=>{let m=v.sizeFromShape(h.shape.slice(a));return u+=m,m}),d=i.map(h=>n.typedArrayFromHeap(h)),c=n.typedArrayFromHeap(o);for(let h=0;h`cumprod does not support ${r.dtype} tensors in the WASM backend`);let u=N.getAxesPermutation([s],l),p=r;u!==null&&(p=ys({inputs:{x:r},attrs:{perm:u},backend:n}));let d=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumprod",[d],l);let c=n.makeOutput(p.shape,p.dtype),h=p.shape[d],m=n.dataIdMap.get(p.dataId).id,f=n.dataIdMap.get(c.dataId).id;JE(m,i?1:0,o?1:0,h,f,At[r.dtype]);let g=c;if(u!==null){let y=N.getUndoAxesPermutation(u);g=ys({inputs:{x:c},attrs:{perm:y},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var jie={kernelName:Hl,backendName:"wasm",setupFunc:Hie,kernelFunc:qie},QE;function Kie(e){QE=e.wasm.cwrap(_i,null,["number","number","number","number","number","number"])}function Xie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=N.getAxesPermutation([s],l),p=r;u!==null&&(p=ys({inputs:{x:r},attrs:{perm:u},backend:n}));let d=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumsum",[d],l);let c=n.makeOutput(p.shape,p.dtype),h=p.shape[d],m=n.dataIdMap.get(p.dataId).id,f=n.dataIdMap.get(c.dataId).id;QE(m,i?1:0,o?1:0,h,f,At[r.dtype]);let g=c;if(u!==null){let y=N.getUndoAxesPermutation(u);g=ys({inputs:{x:c},attrs:{perm:y},backend:n}),n.disposeData(p.dataId),n.disposeData(c.dataId)}return g}var Yie={kernelName:_i,backendName:"wasm",setupFunc:Kie,kernelFunc:Xie},eA;function Zie(e){eA=e.wasm.cwrap(jl,null,["number","number","number","array","number","array","array","number","number"])}function Jie(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],p=i==="NHWC"?r.shape[3]:r.shape[1],d=l*s,c=u*s,h=p/(s*s),m=i==="NHWC"?[o,d,c,h]:[o,h,d,c],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer),w=t.dataIdMap.get(f.dataId).id;return eA(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,x,m.length,w),f}var Qie={kernelName:jl,backendName:"wasm",setupFunc:Zie,kernelFunc:Jie},tA;function eoe(e){tA=e.wasm.cwrap(Ei,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function toe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:p,dimRoundingMode:d}=n,c=u==null?[1,1]:u,h=N.computeConv2DInfo(r.shape,s.shape,l,c,p,d,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,x=h.padInfo.left,w=h.dilationHeight,I=h.dilationWidth,T=h.strideHeight,C=h.strideWidth,E=h.inChannels,$=h.outChannels,R=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let F=a.makeOutput(h.outShape,"float32"),S=a.dataIdMap.get(F.dataId).id;return tA(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,x,R,w,I,T,C,E,$,S),F}var noe={kernelName:Ei,backendName:"wasm",setupFunc:eoe,kernelFunc:toe},aoe=Ut($i),roe=!1,soe=hn(Xl,roe,"bool"),ioe=Ut(Fi,"float32");function vx(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Wn({inputs:{x:r},backend:a,attrs:{shape:o}})}var ooe={kernelName:Yl,backendName:"wasm",kernelFunc:vx};function nA(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var loe={kernelName:fc,backendName:"wasm",kernelFunc:nA},aA;function uoe(e){aA=e.wasm.cwrap(Jl,null,["number","number","number","number","number","number"])}function poe(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,p]=a.shape;return aA(s,o,l,u,p,i),r}var coe={kernelName:Jl,backendName:"wasm",kernelFunc:poe,setupFunc:uoe},doe=Ut(Ri),hoe=!1,moe=hn(Di,hoe),rA;function foe(e){rA=e.wasm.cwrap(Mi,null,["number","number","number","number","number","number","number"])}function goe(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return rA(p,d,c,h,m,r,g),f}var yoe={kernelName:Mi,backendName:"wasm",setupFunc:foe,kernelFunc:goe},sA;function boe(e){sA=e.wasm.cwrap(ti,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function xoe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=lc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);w=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,$=f.padInfo.bottom,R=f.padInfo.left,F=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,G=f.padInfo.type==="SAME"?1:0,j=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return sA(y,j,K,Z,b,I,T,w,C,E,$,R,G,F,S,M,B,U,x,g,ae,m||0,ee),J}var voe={kernelName:ti,backendName:"wasm",setupFunc:boe,kernelFunc:xoe},iA;function woe(e){iA=e.wasm.cwrap(ni,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function koe(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=N.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!0),g=lc[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);w=te.id}let I=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,E=f.padInfo.right,$=f.padInfo.bottom,R=f.padInfo.left,F=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,G=f.padInfo.type==="SAME"?1:0,j=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return iA(y,j,K,Z,b,I,T,w,C,E,$,R,G,F,S,M,B,U,x,g,ae,m||0,ee),J}var Ioe={kernelName:ni,backendName:"wasm",setupFunc:woe,kernelFunc:koe},oA;function Soe(e){oA=e.wasm.cwrap(eu,null,["number","number","number","number","number","number","array","number"])}function Toe(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Qv.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let p=r.shape,d=p[p.length-1],c=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return oA(c,At[a.dtype],h,i,d,o,m,f),u}var Noe={kernelName:eu,backendName:"wasm",setupFunc:Soe,kernelFunc:Toe},lA;function Coe(e){lA=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function _oe(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=v.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let C=0;C=0,()=>`GatherV2: the index value ${E} is not in [0, ${p-1}]`)}let d=N.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=Wn({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),m=Wn({inputs:{x:s},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),f=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(f,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let y=c.shape.length-1,b=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(m.dataId).id,w=t.dataIdMap.get(g.dataId).id,I=new Uint8Array(new Int32Array(v.computeStrides(c.shape)).buffer),T=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer);return lA(b,At[r.dtype],I,y,x,d.batchSize,T,w),t.disposeData(c.dataId),t.disposeData(m.dataId),g.shape=d.outputShape,g}var Eoe={kernelName:Ql,backendName:"wasm",setupFunc:Coe,kernelFunc:_oe},Aoe=!1,$oe=hn(tu,Aoe,"bool"),Foe=!1,Roe=hn(Pi,Foe,"bool"),Doe=Ut(Li,"bool"),uA;function Moe(e){uA=e.wasm.cwrap(zi,null,["number","number","number","number"])}function Poe(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;uA(r,At[t.dtype],n,i)}return s}var Ooe={kernelName:zi,backendName:"wasm",setupFunc:Moe,kernelFunc:Poe},Loe=!1,zoe=hn(ru,Loe,"bool"),Woe=!1,Boe=hn(su,Woe,"bool"),Voe=Ut(Wi),Uoe=!1,Goe=hn(ou,Uoe,"bool"),Hoe=Ut(lu),qoe=!1,joe=hn(uu,qoe,"bool"),Koe=!1,Xoe=hn(QI,Koe,"bool"),pA;function Yoe(e){pA=e.wasm.cwrap(Bi,null,["number","number","number","number"])}function Zoe(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:c}=Fs(i,r,t);if(c){let b=t.dataIdMap.get(u.dataId).id;l=u,o=b}let h=l.shape.length;N.assertAxesAreInnerMostDims("max",p,h);let[m,f]=N.computeOutAndReduceShapes(l.shape,p),g=v.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(v.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;pA(o,At[i.dtype],g,b)}if(c&&t.disposeData(u.dataId),s){let b=N.expandShapeToKeepDim(y.shape,d);y.shape=b}return y}var Joe={kernelName:Bi,backendName:"wasm",setupFunc:Yoe,kernelFunc:Zoe},Qoe=!1,ele=hn(Vi,Qoe),cA;function tle(e){cA=e.wasm.cwrap(Ui,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function nle(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=N.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,y=p.dilationHeight,b=p.dilationWidth,x=p.strideHeight,w=p.strideWidth,I=p.inChannels,T=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. 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ule={kernelName:Hi,backendName:"wasm",setupFunc:ole,kernelFunc:lle},ple=!1,cle=hn(qi,ple),wx;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(wx||(wx={}));var mA;function dle(e){mA=e.wasm.cwrap(ji,null,["number","array","number","number","array","array","number","number"])}function hle(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,mode:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),p=a.map(m=>m[0]),d=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(p).buffer),h=new Uint8Array(new Int32Array(d).buffer);return mA(i,u,t.shape.length,At[t.dtype],c,h,wx[r],l),o}var mle={kernelName:ji,backendName:"wasm",kernelFunc:hle,setupFunc:dle},fle=!0,gle=hn(Ki,fle),yle=Ut(cu);function c1(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return 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kle(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=gA(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=c1(t,c);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var Ile={kernelName:mu,backendName:"wasm",setupFunc:wle,kernelFunc:kle},yA;function Sle(e){yA=e.wasm.cwrap(fu,"number",["number","number","number","number","number","number"])}function Tle(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:u}=n,p=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,c=yA(p,d,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=c1(t,c);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var Nle={kernelName:fu,backendName:"wasm",setupFunc:Sle,kernelFunc:Tle},Cle=!1,_le=hn(du,Cle,"bool"),bA;function Ele(e){bA=e.wasm.cwrap(Xi,null,["number","number","number","number","number"])}function Ale(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=a,u=n.makeOutput([...r.shape,i],s),p=n.dataIdMap.get(u.dataId).id,d=n.dataIdMap.get(r.dataId).id;return bA(d,i,o,l,p),u}var $le={kernelName:Xi,backendName:"wasm",setupFunc:Ele,kernelFunc:Ale};function Fle(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var Rle={kernelName:gu,backendName:"wasm",kernelFunc:Fle};function Dle(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return vx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(p=>{v.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===p.dtype,()=>"All tensors passed to stack must have matching 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Wle(e){wA=e.wasm.cwrap(Ji,null,["number","number","number"])}function Ble(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=s,l=a,u=l;l.dtype!=="float32"&&(u=tp({backend:n,inputs:{x:a},attrs:{dtype:"float32"}}),o=n.dataIdMap.get(u.dataId).id);let p=n.makeOutput(a.shape,"float32"),d=n.dataIdMap.get(p.dataId).id;return wA(o,i,d),l.dtype!=="float32"&&n.disposeData(u.dataId),p}var Vle={kernelName:Ji,backendName:"wasm",setupFunc:Wle,kernelFunc:Ble},kA;function Ule(e){kA=e.wasm.cwrap(Qi,null,["number","number","number","number"])}function Gle(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Fs(i,r,t),m=d;if(h){let x=t.dataIdMap.get(p.dataId).id;x!==o&&(u=p,l=x,m=N.getInnerMostAxes(m.length,u.shape.length))}N.assertAxesAreInnerMostDims("prod",m,u.shape.length);let[f,g]=N.computeOutAndReduceShapes(u.shape,m),y=v.sizeFromShape(g),b=t.makeOutput(f,u.dtype);if(v.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;kA(l,y,At[b.dtype],x)}if(h&&t.disposeData(p.dataId),s){let x=N.expandShapeToKeepDim(b.shape,c);b.shape=x}return b}var Hle={kernelName:Qi,backendName:"wasm",setupFunc:Ule,kernelFunc:Gle},qle=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=V0(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},jle={kernelName:bc,backendName:"wasm",kernelFunc:qle},Kle=!0,Xle=hn(Ai,Kle),Yle=Ut(eo),Zle=Ut(to),Jle=Ut(ro),IA;function Qle(e){IA=e.wasm.cwrap(ao,null,["number","number","number","number","number","number","number","number","number","number"])}function eue(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,[p,d,c,h]=r.shape,m=[p,l,u,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=tp({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(v.sizeFromShape(r.shape)===0)return b;let x=t.dataIdMap.get(b.dataId).id;return IA(y,p,d,c,h,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),b}var tue={kernelName:ao,backendName:"wasm",setupFunc:Qle,kernelFunc:eue},SA;function nue(e){SA=e.wasm.cwrap(no,null,["number","number","number","number","number","number","number","number","number","number"])}function aue(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,u]=o,[p,d,c,h]=r.shape,m=[p,l,u,h],f=t.makeOutput(m,"float32");if(v.sizeFromShape(r.shape)===0)return f;let g=t.dataIdMap.get(r.dataId),y;g.dtype!=="float32"&&(y=tp({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),g=t.dataIdMap.get(y.dataId));let b=g.id,x=t.dataIdMap.get(f.dataId).id;return SA(b,p,d,c,h,l,u,s?1:0,i?1:0,x),y!=null&&t.disposeData(y.dataId),f}var rue={kernelName:no,backendName:"wasm",setupFunc:nue,kernelFunc:aue},TA;function sue(e){TA=e.wasm.cwrap(so,null,["number","array","number","array","number","number"])}function iue(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=v.parseAxisParam(s,r.shape);if(r.shape.length===0)return Kf({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(o.dataId).id,p=new Uint8Array(new Int32Array(i).buffer),d=new Uint8Array(new Int32Array(r.shape).buffer);TA(l,p,i.length,d,r.shape.length,u);let c=Wn({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),c}var oue={kernelName:so,backendName:"wasm",kernelFunc:iue,setupFunc:sue},NA;function lue(e){NA=e.wasm.cwrap(Du,null,["number","number","number","number","number","number","number","number","array","number","number"])}function uue(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,p=n.dataIdMap.get(l.dataId).id,[d,c,h,m]=r.shape,[f,g]=N.getImageCenter(o,c,h),y=i===0,b=255,x=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],w=new Uint8Array(new Int32Array(x).buffer);return NA(u,d,c,h,m,s,f,g,w,x.length,p),l}var pue={kernelName:Du,backendName:"wasm",kernelFunc:uue,setupFunc:lue},cue=Ut(io),due=Ut(oo),CA;function hue(e){CA=e.wasm.cwrap(xu,null,["number","number","number","number","number","number","array","number","number"])}function mue(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(v.sizeFromShape(i)===0)return 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vue(e){let{backend:t,inputs:{x:n}}=e,a=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(r.dataId).id;return v.sizeFromShape(r.shape)===0||EA(a,s),r}var wue={kernelName:"Sigmoid",backendName:"wasm",setupFunc:xue,kernelFunc:vue},kue=Ut(lo),AA;function Iue(e){AA=e.wasm.cwrap(ho,null,["number","number","number","number"])}function Sue(e){let{backend:t,inputs:{logits:n},attrs:{dim:a}}=e,r=t.dataIdMap.get(n.dataId).id,s=t.makeOutput(n.shape,n.dtype),i=t.dataIdMap.get(s.dataId).id,o=n.shape[a],l=v.sizeFromShape(n.shape)/o;return v.sizeFromShape(s.shape)===0||AA(r,i,o,l),s}var Tue={kernelName:ho,backendName:"wasm",setupFunc:Iue,kernelFunc:Sue};function Nue(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a,o=v.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let g=1+s.length;g0?l+1:0;if(u<0)throw new Error(N.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let p=r.shape.slice();p[0]=u;let 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Lue(e){let{inputs:t,attrs:n,backend:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=N.prepareSplitSize(r,s,o),u=new Array(r.shape.length).fill(0),p=r.shape.slice();return l.map(d=>{let c=[...p];c[o]=d;let h=yi({inputs:{x:r},attrs:{begin:u,size:c},backend:a});return u[o]+=d,h})}var zue={kernelName:Au,backendName:"wasm",kernelFunc:Lue},Wue=Ut(co),Bue=Ut(Sc),Vue=!0,Uue=dn(fo,Vue),WA;function Gue(e){WA=e.wasm.cwrap(Is,null,["number","number","number","number"])}function Hue(e){let{backend:t,inputs:n,attrs:a}=e,{alpha:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return WA(i,r,_t[s.dtype],l),o}var jue={kernelName:Is,backendName:"wasm",setupFunc:Gue,kernelFunc:Hue},BA;function que(e){BA=e.wasm.cwrap(Fu,null,["number","array","number","array","array","array","array","array","number","number"])}function Kue(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:p,newAxisMask:d,shrinkAxisMask:c}=a,{finalShapeSparse:h,finalShape:m,isIdentity:f,sliceDim0:g,isSimpleSlice:y,begin:b,end:x,strides:w}=Kt.sliceInfo(r.shape,s,i,o,l,u,p,d,c),I;if(f)I=Wn({inputs:{x:r},backend:t,attrs:{shape:m}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let T=Kt.computeOutShape(b,x,w),C=yi({inputs:{x:r},backend:t,attrs:{begin:b,size:T}});I=Wn({inputs:{x:C},backend:t,attrs:{shape:m}}),t.disposeData(C.dataId)}else{let T=t.makeOutput(h,"float32"),C=t.dataIdMap.get(r.dataId).id,_=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),$=new Uint8Array(new Int32Array(b).buffer),R=new Uint8Array(new Int32Array(x).buffer),F=new Uint8Array(new Int32Array(w).buffer),S=new Uint8Array(new Int32Array(h).buffer),M=new Uint8Array(new 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vr=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if(zr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(wa(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof Qe.getEnv().Canvas?a:yd(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return 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a=le(t.toBatchTensor(112,!0),"float32"),s=Ja(a,[122.782,117.001,104.298]).div(255),i=bd(s,n.dense0,!0);return i=bd(i,n.dense1),i=bd(i,n.dense2),i=bd(i,n.dense3),i=ba(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await bt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return YA(t)}extractParams(t){return XA(t)}};function vd(e,t){return P(()=>Y(Fe(e,t.weights),t.bias))}function ZA(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=$n(e),o=lg(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function JA(e){let t=[],n=sa(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return An(e,t),{params:r,paramMappings:t}}function dg(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var fp=class extends on{constructor(n,a){super(n);this._faceFeatureExtractor=a}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(n){let{params:a}=this;if(!a)throw new Error(`${this._name} - load model before inference`);return P(()=>{let r=n instanceof vr?this.faceFeatureExtractor.forwardInput(n):n;return vd(r.as2D(r.shape[0],-1),a.fc)})}dispose(n=!0){this.faceFeatureExtractor.dispose(n),super.dispose(n)}loadClassifierParams(n){let{params:a,paramMappings:r}=this.extractClassifierParams(n);this._params=a,this._paramMappings=r}extractClassifierParams(n){return ZA(n,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(n){let{featureExtractorMap:a,classifierMap:r}=dg(n);return this.faceFeatureExtractor.loadFromWeightMap(a),JA(r)}extractParams(n){let a=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),s=r*a+r,i=n.slice(0,n.length-s),o=n.slice(n.length-s);return this.faceFeatureExtractor.extractWeights(i),this.extractClassifierParams(o)}};var P1=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Ur=class{constructor(t){this.neutral=0;this.happy=0;this.sad=0;this.angry=0;this.fearful=0;this.disgusted=0;this.surprised=0;if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);P1.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return P1.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var wd=class extends fp{constructor(t=new mp){super("FaceExpressionNet",t)}forwardInput(t){return P(()=>Xa(this.runNet(t)))}async forward(t){return this.forwardInput(await bt(t))}async predictExpressions(t){let n=await bt(t),a=await this.forwardInput(n),r=await Promise.all(pt(a).map(async i=>{let o=i.dataSync();return i.dispose(),o}));a.dispose();let s=r.map(i=>new Ur(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function O1(e){return e.expressions instanceof Ur}function hg(e,t){return{...e,...{expressions:t}}}function Qpe(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof Ur?s:O1(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(d=>d.probability>n),u=xr(s)?s.detection.box.bottomLeft:a||new De(0,0);new Br(l.map(d=>`${d.expression} (${Ao(d.probability)})`),u).draw(e)})}function Wo(e){return xr(e)&&e.landmarks instanceof ra&&e.unshiftedLandmarks instanceof ra&&e.alignedRect instanceof yt}function ece(e){let t=l=>l*180/Math.PI,n=(l,u)=>Math.sqrt((l._x-u._x)**2+(l._y-u._y)**2),a={roll:void 0,pitch:void 0,yaw:void 0},r=(l,u,p)=>{let d=Math.floor(l._x-u._x),c=Math.floor(u._x-p._x);return d-c},s=(l,u)=>{let p=Math.hypot(u._x-l._x,u._y-l._y),d=u._y-l._y,c=Math.asin(d/p),h=t(c),m=Math.floor(90-h),f=u._x-l._x<0?-1:1;return m*f},i=(l,u,p)=>{let 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n=Gn(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof Ro&&(n.strokeStyle=i,n.lineWidth=s,Lr(n,this.faceLandmarks.getJawOutline()),Lr(n,this.faceLandmarks.getLeftEyeBrow()),Lr(n,this.faceLandmarks.getRightEyeBrow()),Lr(n,this.faceLandmarks.getNose()),Lr(n,this.faceLandmarks.getLeftEye(),!0),Lr(n,this.faceLandmarks.getRightEye(),!0),Lr(n,this.faceLandmarks.getMouth(),!0)),r){n.strokeStyle=l,n.fillStyle=l;let u=p=>{n.beginPath(),n.arc(p.x,p.y,o,0,2*Math.PI),n.fill()};this.faceLandmarks.positions.forEach(u)}}};function tce(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof ra?a:Wo(a)?a.landmarks:void 0;if(!r)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new fg(r).draw(e)})}var QA="1.7.7";function rce(e,t){let n=cp(e,t),a=dp(e,t);function r(i,o,l){let u=a(i,o,`${l}/separable_conv0`),p=a(o,o,`${l}/separable_conv1`),d=n(i,o,1,`${l}/expansion_conv`);return{separable_conv0:u,separable_conv1:p,expansion_conv:d}}function s(i,o){let l=a(i,i,`${o}/separable_conv0`),u=a(i,i,`${o}/separable_conv1`),p=a(i,i,`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:p}}return{extractConvParams:n,extractSeparableConvParams:a,extractReductionBlockParams:r,extractMainBlockParams:s}}function e$(e,t){let n=[],{extractWeights:a,getRemainingWeights:r}=$n(e),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:o,extractMainBlockParams:l}=rce(a,n),u=s(3,32,3,"entry_flow/conv_in"),p=o(32,64,"entry_flow/reduction_block_0"),d=o(64,128,"entry_flow/reduction_block_1"),c={conv_in:u,reduction_block_0:p,reduction_block_1:d},h={};yr(t,0,1).forEach(y=>{h[`main_block_${y}`]=l(128,`middle_flow/main_block_${y}`)});let m=o(128,256,"exit_flow/reduction_block"),f=i(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{entry_flow:c,middle_flow:h,exit_flow:g}}}function sce(e,t){let n=sa(e,t),a=pg(n),r=hp(n);function s(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),p=a(`${o}/expansion_conv`);return{separable_conv0:l,separable_conv1:u,expansion_conv:p}}function i(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),p=r(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:p}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function t$(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=sce(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),u=s("entry_flow/reduction_block_1"),p={conv_in:o,reduction_block_0:l,reduction_block_1:u},d={};yr(t,0,1).forEach(f=>{d[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let c=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:c,separable_conv:h};return An(e,n),{params:{entry_flow:p,middle_flow:d,exit_flow:m},paramMappings:n}}function n$(e,t,n){return Y(Ft(e,t.filters,n,"same"),t.bias)}function z1(e,t,n=!0){let a=n?Xe(e):e;return a=Hn(a,t.separable_conv0,[1,1]),a=Hn(Xe(a),t.separable_conv1,[1,1]),a=Dt(a,[3,3],[2,2],"same"),a=Y(a,n$(e,t.expansion_conv,[2,2])),a}function ice(e,t){let n=Hn(Xe(e),t.separable_conv0,[1,1]);return n=Hn(Xe(n),t.separable_conv1,[1,1]),n=Hn(Xe(n),t.separable_conv2,[1,1]),n=Y(n,e),n}var gg=class extends on{constructor(n){super("TinyXception");this._numMainBlocks=n}forwardInput(n){let{params:a}=this;if(!a)throw new Error("TinyXception - load model before inference");return P(()=>{let r=le(n.toBatchTensor(112,!0),"float32"),i=Ja(r,[122.782,117.001,104.298]).div(255),o=Xe(n$(i,a.entry_flow.conv_in,[2,2]));return o=z1(o,a.entry_flow.reduction_block_0,!1),o=z1(o,a.entry_flow.reduction_block_1),yr(this._numMainBlocks,0,1).forEach(l=>{o=ice(o,a.middle_flow[`main_block_${l}`])}),o=z1(o,a.exit_flow.reduction_block),o=Xe(Hn(o,a.exit_flow.separable_conv,[1,1])),o})}async forward(n){return this.forwardInput(await bt(n))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(n){return t$(n,this._numMainBlocks)}extractParams(n){return e$(n,this._numMainBlocks)}};function a$(e){let t=[],{extractWeights:n,getRemainingWeights:a}=$n(e),r=lg(n,t),s=r(512,1,"fc/age"),i=r(512,2,"fc/gender");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function r$(e){let t=[],n=sa(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return An(e,t),{params:r,paramMappings:t}}var yg=(n=>(n.FEMALE="female",n.MALE="male",n))(yg||{});var kd=class extends on{constructor(n=new gg(2)){super("AgeGenderNet");this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(n){let{params:a}=this;if(!a)throw new Error(`${this._name} - load model before inference`);return P(()=>{let r=n instanceof vr?this.faceFeatureExtractor.forwardInput(n):n,s=ba(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),i=vd(s,a.fc.age).as1D(),o=vd(s,a.fc.gender);return{age:i,gender:o}})}forwardInput(n){return P(()=>{let{age:a,gender:r}=this.runNet(n);return{age:a,gender:Xa(r)}})}async forward(n){return this.forwardInput(await bt(n))}async predictAgeAndGender(n){let a=await bt(n),r=await this.forwardInput(a),s=pt(r.age),i=pt(r.gender),o=s.map((u,p)=>({ageTensor:u,genderTensor:i[p]})),l=await Promise.all(o.map(async({ageTensor:u,genderTensor:p})=>{let d=u.dataSync()[0],c=p.dataSync()[0],h=c>.5,m=h?"male":"female",f=h?c:1-c;return u.dispose(),p.dispose(),{age:d,gender:m,genderProbability:f}}));return r.age.dispose(),r.gender.dispose(),a.isBatchInput?l:l[0]}getDefaultModelName(){return"age_gender_model"}dispose(n=!0){this.faceFeatureExtractor.dispose(n),super.dispose(n)}loadClassifierParams(n){let{params:a,paramMappings:r}=this.extractClassifierParams(n);this._params=a,this._paramMappings=r}extractClassifierParams(n){return a$(n)}extractParamsFromWeightMap(n){let{featureExtractorMap:a,classifierMap:r}=dg(n);return this.faceFeatureExtractor.loadFromWeightMap(a),r$(r)}extractParams(n){let r=n.slice(0,n.length-1539),s=n.slice(n.length-1539);return this.faceFeatureExtractor.extractWeights(r),this.extractClassifierParams(s)}};var yp=class extends fp{postProcess(t,n,a){let 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l.dispose(),u.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return d$(t)}extractParams(t){return c$(t)}};function y$(e){let t=new Rs;return t.extractWeights(e),t}function vce(e){return y$(e)}var U1=class extends Rs{};var b$=.4,x$=[new De(.738768,.874946),new De(2.42204,2.65704),new De(4.30971,7.04493),new De(10.246,4.59428),new De(12.6868,11.8741)],v$=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],w$=[117.001,114.697,97.404],k$="tiny_yolov2_model",I$="tiny_yolov2_separable_conv_model";var Ig=e=>typeof e=="number";function G1(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!Ig(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>Ig(t.x)&&Ig(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(Ig)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function bp(e){return P(()=>{let t=z(e,xe(.10000000149011612));return Y(Xe(pe(e,t)),t)})}function Gr(e,t){return P(()=>{let n=xa(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ft(n,t.conv.filters,[1,1],"valid"),n=pe(n,t.bn.sub),n=z(n,t.bn.truediv),n=Y(n,t.conv.bias),bp(n)})}function Hr(e,t){return P(()=>{let n=xa(e,[[0,0],[1,1],[1,1],[0,0]]);return n=_s(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=Y(n,t.bias),bp(n)})}function wce(e,t){let n=cp(e,t);function a(i,o){let l=Ke(e(i)),u=Ke(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),p=a(o,`${l}/bn`);return{conv:u,bn:p}}let s=dp(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function S$(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=$n(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=wce(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,y,b,x]=a,w=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),I=u(c,h,"conv1"),T=u(h,m,"conv2"),C=u(m,f,"conv3"),_=u(f,g,"conv4"),$=u(g,y,"conv5"),R=b?u(y,b,"conv6"):void 0,F=x?u(b,x,"conv7"):void 0,S=o(x||b||y,5*n,1,"conv8");p={conv0:w,conv1:I,conv2:T,conv3:C,conv4:_,conv5:$,conv6:R,conv7:F,conv8:S}}else{let[d,c,h,m,f,g,y,b,x]=a,w=l(d,c,"conv0"),I=l(c,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),_=l(f,g,"conv4"),$=l(g,y,"conv5"),R=l(y,b,"conv6"),F=l(b,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:w,conv1:I,conv2:T,conv3:C,conv4:_,conv5:$,conv6:R,conv7:F,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function kce(e,t){let n=sa(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=hp(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function T$(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=kce(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return An(e,n),{params:i,paramMappings:n}}var er=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var H1=class extends on{constructor(n){super("TinyYolov2");G1(n),this._config=n}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(n,a){let r=Gr(n,a.conv0);return r=Dt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv1),r=Dt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv2),r=Dt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv3),r=Dt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv4),r=Dt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv5),r=Dt(r,[2,2],[1,1],"same"),r=Gr(r,a.conv6),r=Gr(r,a.conv7),zo(r,a.conv8,"valid",!1)}runMobilenet(n,a){let r=this.config.isFirstLayerConv2d?bp(zo(n,a.conv0,"valid",!1)):Hr(n,a.conv0);return r=Dt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv1),r=Dt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv2),r=Dt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv3),r=Dt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv4),r=Dt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv5),r=Dt(r,[2,2],[1,1],"same"),r=a.conv6?Hr(r,a.conv6):r,r=a.conv7?Hr(r,a.conv7):r,zo(r,a.conv8,"valid",!1)}forwardInput(n,a){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return P(()=>{let s=le(n.toBatchTensor(a,!1),"float32");return s=this.config.meanRgb?Ja(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(n,a){return this.forwardInput(await bt(n),a)}async detect(n,a={}){let{inputSize:r,scoreThreshold:s}=new er(a),i=await bt(n),o=await this.forwardInput(i,r),l=P(()=>pt(o)[0].expandDims()),u={width:i.getInputWidth(0),height:i.getInputHeight(0)},p=await this.extractBoxes(l,i.getReshapedInputDimensions(0),s);o.dispose(),l.dispose();let d=p.map(y=>y.box),c=p.map(y=>y.score),h=p.map(y=>y.classScore),m=p.map(y=>this.config.classes[y.label]);return v1(d.map(y=>y.rescale(r)),c,this.config.iouThreshold,!0).map(y=>new Wr(c[y],h[y],m[y],d[y],u))}getDefaultModelName(){return""}extractParamsFromWeightMap(n){return T$(n,this.config)}extractParams(n){let a=this.config.filterSizes||H1.DEFAULT_FILTER_SIZES,r=a?a.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return S$(n,this.config,this.boxEncodingSize,a)}async extractBoxes(n,a,r){let{width:s,height:i}=a,o=Math.max(s,i),l=o/s,u=o/i,p=n.shape[1],d=this.config.anchors.length,[c,h,m]=P(()=>{let b=n.reshape([p,p,d,this.boxEncodingSize]),x=b.slice([0,0,0,0],[p,p,d,4]),w=b.slice([0,0,0,4],[p,p,d,1]),I=this.withClassScores?Xa(b.slice([0,0,0,5],[p,p,d,this.config.classes.length]),3):xe(0);return[x,w,I]}),f=[],g=await h.array(),y=await c.array();for(let b=0;br){let T=(x+hd(y[b][x][w][0]))/p*l,C=(b+hd(y[b][x][w][1]))/p*u,_=Math.exp(y[b][x][w][2])*this.config.anchors[w].x/p*l,$=Math.exp(y[b][x][w][3])*this.config.anchors[w].y/p*u,R=T-_/2,F=C-$/2,S={row:b,col:x,anchor:w},{classScore:M,label:B}=this.withClassScores?await this.extractPredictedClass(m,S):{classScore:1,label:0};f.push({box:new Fo(R,F,R+_,F+$),score:I,classScore:I*M,label:B,...S})}}return c.dispose(),h.dispose(),m.dispose(),f}async extractPredictedClass(n,a){let{row:r,col:s,anchor:i}=a,o=await n.array();return Array(this.config.classes.length).fill(0).map((l,u)=>o[r][s][i][u]).map((l,u)=>({classScore:l,label:u})).reduce((l,u)=>l.classScore>u.classScore?l:u)}},Go=H1;Go.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Ho=class extends Go{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:b$,classes:["face"],...t?{anchors:v$,meanRgb:w$}:{anchors:x$,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?I$:k$}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Ice(e,t=!0){let n=new Ho(t);return n.extractWeights(e),n}var Td=class extends er{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Ia=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function jo(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>Wo(l)?r(l):l.detection),i=a||(t instanceof Te?await pp(t,s):await up(t,s)),o=await n(i);return i.forEach(l=>l instanceof Te&&l.dispose()),o}async function xp(e,t,n,a,r){return jo([e],t,async s=>n(s[0]),a,r)}var N$=.4,C$=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],E$=[117.001,114.697,97.404];var qo=class extends Go{constructor(){let t={withSeparableConvs:!0,iouThreshold:N$,classes:["face"],anchors:C$,meanRgb:E$,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var et={ssdMobilenetv1:new Rs,tinyFaceDetector:new qo,tinyYolov2:new Ho,faceLandmark68Net:new Bo,faceLandmark68TinyNet:new Id,faceRecognitionNet:new Vo,faceExpressionNet:new wd,ageGenderNet:new kd},_$=(e,t)=>et.ssdMobilenetv1.locateFaces(e,t),Sce=(e,t)=>et.tinyFaceDetector.locateFaces(e,t),Tce=(e,t)=>et.tinyYolov2.locateFaces(e,t),A$=e=>et.faceLandmark68Net.detectLandmarks(e),Nce=e=>et.faceLandmark68TinyNet.detectLandmarks(e),Cce=e=>et.faceRecognitionNet.computeFaceDescriptor(e),Ece=e=>et.faceExpressionNet.predictExpressions(e),_ce=e=>et.ageGenderNet.predictAgeAndGender(e),$$=e=>et.ssdMobilenetv1.load(e),Ace=e=>et.tinyFaceDetector.load(e),$ce=e=>et.tinyYolov2.load(e),Fce=e=>et.faceLandmark68Net.load(e),Dce=e=>et.faceLandmark68TinyNet.load(e),Rce=e=>et.faceRecognitionNet.load(e),Mce=e=>et.faceExpressionNet.load(e),Pce=e=>et.ageGenderNet.load(e),Oce=$$,Lce=_$,zce=A$;var Sg=class extends Ia{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Ko=class extends Sg{async run(){let t=await this.parentTask,n=await jo(t,this.input,async a=>Promise.all(a.map(r=>et.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>hg(a,n[r]))}withAgeAndGender(){return new Yo(this,this.input)}},Xo=class extends Sg{async run(){let t=await this.parentTask;if(!t)return;let n=await xp(t,this.input,a=>et.faceExpressionNet.predictExpressions(a),this.extractedFaces);return hg(t,n)}withAgeAndGender(){return new Zo(this,this.input)}},Ms=class extends Ko{withAgeAndGender(){return new Os(this,this.input)}withFaceDescriptors(){return new jr(this,this.input)}},Ps=class extends Xo{withAgeAndGender(){return new Ls(this,this.input)}withFaceDescriptor(){return new qr(this,this.input)}};var Tg=class extends Ia{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Yo=class extends Tg{async run(){let t=await this.parentTask,n=await jo(t,this.input,async a=>Promise.all(a.map(r=>et.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return wg(kg(a,i,o),s)})}withFaceExpressions(){return new Ko(this,this.input)}},Zo=class extends Tg{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await xp(t,this.input,s=>et.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return wg(kg(t,a,r),n)}withFaceExpressions(){return new Xo(this,this.input)}},Os=class extends Yo{withFaceExpressions(){return new Ms(this,this.input)}withFaceDescriptors(){return new jr(this,this.input)}},Ls=class extends Zo{withFaceExpressions(){return new Ps(this,this.input)}withFaceDescriptor(){return new qr(this,this.input)}};var Nd=class extends Ia{constructor(n,a){super();this.parentTask=n;this.input=a}},jr=class extends Nd{async run(){let t=await this.parentTask;return(await jo(t,this.input,a=>Promise.all(a.map(r=>et.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>vg(t[r],a))}withFaceExpressions(){return new Ms(this,this.input)}withAgeAndGender(){return new Os(this,this.input)}},qr=class extends Nd{async run(){let t=await this.parentTask;if(!t)return;let n=await xp(t,this.input,a=>et.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return vg(t,n)}withFaceExpressions(){return new Ps(this,this.input)}withAgeAndGender(){return new Ls(this,this.input)}};var Cd=class extends Ia{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?et.faceLandmark68TinyNet:et.faceLandmark68Net}},Ed=class extends Cd{async run(){let t=await this.parentTask,n=t.map(i=>i.detection),a=this.input instanceof Te?await pp(this.input,n):await up(this.input,n),r=await Promise.all(a.map(i=>this.landmarkNet.detectLandmarks(i)));return a.forEach(i=>i instanceof Te&&i.dispose()),t.filter((i,o)=>r[o]).map((i,o)=>gp(i,r[o]))}withFaceExpressions(){return new Ms(this,this.input)}withAgeAndGender(){return new Os(this,this.input)}withFaceDescriptors(){return new jr(this,this.input)}},_d=class extends Cd{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Te?await pp(this.input,[n]):await up(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Te&&s.dispose()),gp(t,r)}withFaceExpressions(){return new Ps(this,this.input)}withAgeAndGender(){return new Ls(this,this.input)}withFaceDescriptor(){return new qr(this,this.input)}};var Ad=class extends Ia{constructor(n,a=new ka){super();this.input=n;this.options=a}},vp=class extends Ad{async run(){let{input:t,options:n}=this,a;if(n instanceof Td)a=et.tinyFaceDetector.locateFaces(t,n);else if(n instanceof ka)a=et.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof er)a=et.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(a=>t(a.map(r=>Mo({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new Ed(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Ko(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Yo(this.runAndExtendWithFaceDetections(),this.input)}},$d=class extends Ad{async run(){let t=await new vp(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?Mo({},n):void 0)})}withFaceLandmarks(t=!1){return new _d(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Xo(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Zo(this.runAndExtendWithFaceDetection(),this.input)}};function Wce(e,t=new ka){return new $d(e,t)}function Ng(e,t=new ka){return new vp(e,t)}async function F$(e,t){return Ng(e,new 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on{constructor(n,a){super(n);this._faceFeatureExtractor=a}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(n){let{params:a}=this;if(!a)throw new Error(`${this._name} - load model before inference`);return P(()=>{let r=n instanceof vr?this.faceFeatureExtractor.forwardInput(n):n;return yd(r.as2D(r.shape[0],-1),a.fc)})}dispose(n=!0){this.faceFeatureExtractor.dispose(n),super.dispose(n)}loadClassifierParams(n){let{params:a,paramMappings:r}=this.extractClassifierParams(n);this._params=a,this._paramMappings=r}extractClassifierParams(n){return jA(n,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(n){let{featureExtractorMap:a,classifierMap:r}=ig(n);return this.faceFeatureExtractor.loadFromWeightMap(a),KA(r)}extractParams(n){let a=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),s=r*a+r,i=n.slice(0,n.length-s),o=n.slice(n.length-s);return this.faceFeatureExtractor.extractWeights(i),this.extractClassifierParams(o)}};var M1=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Ur=class{constructor(t){this.neutral=0;this.happy=0;this.sad=0;this.angry=0;this.fearful=0;this.disgusted=0;this.surprised=0;if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);M1.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return M1.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var bd=class extends dp{constructor(t=new cp){super("FaceExpressionNet",t)}forwardInput(t){return P(()=>Xa(this.runNet(t)))}async forward(t){return this.forwardInput(await bt(t))}async predictExpressions(t){let n=await bt(t),a=await this.forwardInput(n),r=await Promise.all(ut(a).map(async i=>{let o=i.dataSync();return i.dispose(),o}));a.dispose();let s=r.map(i=>new Ur(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 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d=n(l,p),c={_x:(l._x+p._x)/2,_y:(l._y+p._y)/2},h=n(u,c),m=Math.atan(h/d),f=Math.floor(t(m)),g=c._y-u._y<0?-1:1;return f*g};if(!e||!e._positions||e._positions.length!==68)return a;let o=e._positions;return a.roll=s(o[27],o[66]),a.pitch=i(o[14],o[30],o[2]),a.yaw=r(o[14],o[33],o[2]),a}function hp(e,t){let{box:n}=e.detection,a=t.shiftBy(n.x,n.y),r=a.align(),{imageDims:s}=e.detection,i=new yt(e.detection.score,r.rescale(s.reverse()),s),o=ece(t);return{...e,...{landmarks:a,unshiftedLandmarks:t,alignedRect:i,angle:o}}}var lg=class{constructor(t={}){let{drawLines:n=!0,drawPoints:a=!0,lineWidth:r,lineColor:s,pointSize:i,pointColor:o}=t;this.drawLines=n,this.drawPoints=a,this.lineWidth=r||1,this.pointSize=i||2,this.lineColor=s||"rgba(0, 255, 255, 1)",this.pointColor=o||"rgba(255, 0, 255, 1)"}},ug=class{constructor(t,n={}){this.faceLandmarks=t,this.options=new lg(n)}draw(t){let n=Gn(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof Ro&&(n.strokeStyle=i,n.lineWidth=s,Lr(n,this.faceLandmarks.getJawOutline()),Lr(n,this.faceLandmarks.getLeftEyeBrow()),Lr(n,this.faceLandmarks.getRightEyeBrow()),Lr(n,this.faceLandmarks.getNose()),Lr(n,this.faceLandmarks.getLeftEye(),!0),Lr(n,this.faceLandmarks.getRightEye(),!0),Lr(n,this.faceLandmarks.getMouth(),!0)),r){n.strokeStyle=l,n.fillStyle=l;let u=p=>{n.beginPath(),n.arc(p.x,p.y,o,0,2*Math.PI),n.fill()};this.faceLandmarks.positions.forEach(u)}}};function tce(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof ra?a:zo(a)?a.landmarks:void 0;if(!r)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof");new ug(r).draw(e)})}var XA="1.7.8";function rce(e,t){let n=lp(e,t),a=up(e,t);function r(i,o,l){let 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${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function e$(e){let t=[],n=sa(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return An(e,t),{params:r,paramMappings:t}}var cg=(n=>(n.FEMALE="female",n.MALE="male",n))(cg||{});var xd=class extends on{constructor(n=new pg(2)){super("AgeGenderNet");this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(n){let{params:a}=this;if(!a)throw new Error(`${this._name} - load model before inference`);return P(()=>{let r=n instanceof vr?this.faceFeatureExtractor.forwardInput(n):n,s=ba(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),i=yd(s,a.fc.age).as1D(),o=yd(s,a.fc.gender);return{age:i,gender:o}})}forwardInput(n){return P(()=>{let{age:a,gender:r}=this.runNet(n);return{age:a,gender:Xa(r)}})}async forward(n){return this.forwardInput(await bt(n))}async predictAgeAndGender(n){let a=await bt(n),r=await 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i=tg(i,n.dense1),i=tg(i,n.dense2),i=ba(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await bt(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeightMap(t){return t$(t)}extractParams(t){return n$(t)}};var vd=class extends mp{constructor(t=new dg){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var z1=class extends Wo{};function a$(e,t){return X(z(e,t.weights),t.biases)}function W1(e,t,n,a,r="same"){let{filters:s,bias:i}=t.conv,o=Rt(e,s,n,r);return o=X(o,i),o=a$(o,t.scale),a?Ke(o):o}function r$(e,t){return W1(e,t,[1,1],!0)}function B1(e,t){return W1(e,t,[1,1],!1)}function hg(e,t){return W1(e,t,[2,2],!0,"valid")}function oce(e,t){function n(o,l,u){let p=e(o),d=p.length/(l*u*u);if(m1(d))throw new Error(`depth has to be an integer: ${d}, weights.length: ${p.length}, numFilters: ${l}, filterSize: ${u}`);return 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mg(e,t){return{...e,...{descriptor:t}}}function pce(e){return typeof e.age=="number"}function fg(e,t){return{...e,...{age:t}}}function cce(e){return(e.gender==="male"||e.gender==="female")&&np(e.genderProbability)}function gg(e,t,n){return{...e,...{gender:t,genderProbability:n}}}function dce(e,t){function n(l,u){let p=Ma(e(9*l),[3,3,l,1]),d=je(e(l)),c=je(e(l)),h=je(e(l)),m=je(e(l));return t.push({paramPath:`${u}/filters`},{paramPath:`${u}/batch_norm_scale`},{paramPath:`${u}/batch_norm_offset`},{paramPath:`${u}/batch_norm_mean`},{paramPath:`${u}/batch_norm_variance`}),{filters:p,batch_norm_scale:d,batch_norm_offset:c,batch_norm_mean:h,batch_norm_variance:m}}function a(l,u,p,d,c){let h=Ma(e(l*u*p*p),[p,p,l,u]),m=je(e(u));return t.push({paramPath:`${d}/filters`},{paramPath:`${d}/${c?"batch_norm_offset":"bias"}`}),{filters:h,bias:m}}function r(l,u,p,d){let{filters:c,bias:h}=a(l,u,p,d,!0);return{filters:c,batch_norm_offset:h}}function s(l,u,p){let d=n(l,`${p}/depthwise_conv`),c=r(l,u,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:c}}function i(){let l=r(3,32,3,"mobilenetv1/conv_0"),u=s(32,64,"mobilenetv1/conv_1"),p=s(64,128,"mobilenetv1/conv_2"),d=s(128,128,"mobilenetv1/conv_3"),c=s(128,256,"mobilenetv1/conv_4"),h=s(256,256,"mobilenetv1/conv_5"),m=s(256,512,"mobilenetv1/conv_6"),f=s(512,512,"mobilenetv1/conv_7"),g=s(512,512,"mobilenetv1/conv_8"),y=s(512,512,"mobilenetv1/conv_9"),b=s(512,512,"mobilenetv1/conv_10"),x=s(512,512,"mobilenetv1/conv_11"),w=s(512,1024,"mobilenetv1/conv_12"),I=s(1024,1024,"mobilenetv1/conv_13");return{conv_0:l,conv_1:u,conv_2:p,conv_3:d,conv_4:c,conv_5:h,conv_6:m,conv_7:f,conv_8:g,conv_9:y,conv_10:b,conv_11:x,conv_12:w,conv_13:I}}function o(){let 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this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Rs=class extends on{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return P(()=>{let a=ie(t.toBatchTensor(512,!1),"float32"),r=pe(he(a,127.5),1),s=u$(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=d$(s.out,s.conv11,n.prediction_layer);return c$(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await bt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new ka(n),s=await bt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],u=o[0];for(let x=1;x{let[w,I]=[Math.max(0,y[x][0]),Math.min(1,y[x][2])].map(E=>E*g),[T,C]=[Math.max(0,y[x][1]),Math.min(1,y[x][3])].map(E=>E*f);return new yt(p[x],new Fo(T,w,C-T,I-w),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),u.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return l$(t)}extractParams(t){return o$(t)}};function h$(e){let t=new Rs;return t.extractWeights(e),t}function vce(e){return h$(e)}var V1=class extends Rs{};var m$=.4,f$=[new De(.738768,.874946),new De(2.42204,2.65704),new De(4.30971,7.04493),new De(10.246,4.59428),new De(12.6868,11.8741)],g$=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],y$=[117.001,114.697,97.404],b$="tiny_yolov2_model",x$="tiny_yolov2_separable_conv_model";var yg=e=>typeof e=="number";function U1(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!yg(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>yg(t.x)&&yg(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(yg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function fp(e){return P(()=>{let t=z(e,xe(.10000000149011612));return X(Ke(pe(e,t)),t)})}function Gr(e,t){return P(()=>{let n=xa(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Rt(n,t.conv.filters,[1,1],"valid"),n=pe(n,t.bn.sub),n=z(n,t.bn.truediv),n=X(n,t.conv.bias),fp(n)})}function Hr(e,t){return P(()=>{let n=xa(e,[[0,0],[1,1],[1,1],[0,0]]);return n=_s(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=X(n,t.bias),fp(n)})}function wce(e,t){let n=lp(e,t);function a(i,o){let l=je(e(i)),u=je(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),p=a(o,`${l}/bn`);return{conv:u,bn:p}}let s=up(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function v$(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=$n(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=wce(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,y,b,x]=a,w=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),I=u(c,h,"conv1"),T=u(h,m,"conv2"),C=u(m,f,"conv3"),E=u(f,g,"conv4"),$=u(g,y,"conv5"),R=b?u(y,b,"conv6"):void 0,F=x?u(b,x,"conv7"):void 0,S=o(x||b||y,5*n,1,"conv8");p={conv0:w,conv1:I,conv2:T,conv3:C,conv4:E,conv5:$,conv6:R,conv7:F,conv8:S}}else{let[d,c,h,m,f,g,y,b,x]=a,w=l(d,c,"conv0"),I=l(c,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),E=l(f,g,"conv4"),$=l(g,y,"conv5"),R=l(y,b,"conv6"),F=l(b,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:w,conv1:I,conv2:T,conv3:C,conv4:E,conv5:$,conv6:R,conv7:F,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function kce(e,t){let n=sa(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=pp(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function w$(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=kce(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return An(e,n),{params:i,paramMappings:n}}var er=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var G1=class extends on{constructor(n){super("TinyYolov2");U1(n),this._config=n}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(n,a){let r=Gr(n,a.conv0);return r=Dt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv1),r=Dt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv2),r=Dt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv3),r=Dt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv4),r=Dt(r,[2,2],[2,2],"same"),r=Gr(r,a.conv5),r=Dt(r,[2,2],[1,1],"same"),r=Gr(r,a.conv6),r=Gr(r,a.conv7),Lo(r,a.conv8,"valid",!1)}runMobilenet(n,a){let r=this.config.isFirstLayerConv2d?fp(Lo(n,a.conv0,"valid",!1)):Hr(n,a.conv0);return r=Dt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv1),r=Dt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv2),r=Dt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv3),r=Dt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv4),r=Dt(r,[2,2],[2,2],"same"),r=Hr(r,a.conv5),r=Dt(r,[2,2],[1,1],"same"),r=a.conv6?Hr(r,a.conv6):r,r=a.conv7?Hr(r,a.conv7):r,Lo(r,a.conv8,"valid",!1)}forwardInput(n,a){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return P(()=>{let s=ie(n.toBatchTensor(a,!1),"float32");return s=this.config.meanRgb?Ja(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(n,a){return this.forwardInput(await bt(n),a)}async detect(n,a={}){let{inputSize:r,scoreThreshold:s}=new er(a),i=await bt(n),o=await this.forwardInput(i,r),l=P(()=>ut(o)[0].expandDims()),u={width:i.getInputWidth(0),height:i.getInputHeight(0)},p=await this.extractBoxes(l,i.getReshapedInputDimensions(0),s);o.dispose(),l.dispose();let d=p.map(y=>y.box),c=p.map(y=>y.score),h=p.map(y=>y.classScore),m=p.map(y=>this.config.classes[y.label]);return x1(d.map(y=>y.rescale(r)),c,this.config.iouThreshold,!0).map(y=>new Wr(c[y],h[y],m[y],d[y],u))}getDefaultModelName(){return""}extractParamsFromWeightMap(n){return w$(n,this.config)}extractParams(n){let a=this.config.filterSizes||G1.DEFAULT_FILTER_SIZES,r=a?a.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return v$(n,this.config,this.boxEncodingSize,a)}async extractBoxes(n,a,r){let{width:s,height:i}=a,o=Math.max(s,i),l=o/s,u=o/i,p=n.shape[1],d=this.config.anchors.length,[c,h,m]=P(()=>{let b=n.reshape([p,p,d,this.boxEncodingSize]),x=b.slice([0,0,0,0],[p,p,d,4]),w=b.slice([0,0,0,4],[p,p,d,1]),I=this.withClassScores?Xa(b.slice([0,0,0,5],[p,p,d,this.config.classes.length]),3):xe(0);return[x,w,I]}),f=[],g=await h.array(),y=await c.array();for(let b=0;br){let T=(x+pd(y[b][x][w][0]))/p*l,C=(b+pd(y[b][x][w][1]))/p*u,E=Math.exp(y[b][x][w][2])*this.config.anchors[w].x/p*l,$=Math.exp(y[b][x][w][3])*this.config.anchors[w].y/p*u,R=T-E/2,F=C-$/2,S={row:b,col:x,anchor:w},{classScore:M,label:B}=this.withClassScores?await this.extractPredictedClass(m,S):{classScore:1,label:0};f.push({box:new $o(R,F,R+E,F+$),score:I,classScore:I*M,label:B,...S})}}return c.dispose(),h.dispose(),m.dispose(),f}async extractPredictedClass(n,a){let{row:r,col:s,anchor:i}=a,o=await n.array();return Array(this.config.classes.length).fill(0).map((l,u)=>o[r][s][i][u]).map((l,u)=>({classScore:l,label:u})).reduce((l,u)=>l.classScore>u.classScore?l:u)}},Uo=G1;Uo.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Go=class extends Uo{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:m$,classes:["face"],...t?{anchors:g$,meanRgb:y$}:{anchors:f$,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return 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De(9.041765,10.66308)],S$=[117.001,114.697,97.404];var qo=class extends Uo{constructor(){let t={withSeparableConvs:!0,iouThreshold:k$,classes:["face"],anchors:I$,meanRgb:S$,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var et={ssdMobilenetv1:new Rs,tinyFaceDetector:new qo,tinyYolov2:new Go,faceLandmark68Net:new Wo,faceLandmark68TinyNet:new vd,faceRecognitionNet:new Bo,faceExpressionNet:new bd,ageGenderNet:new xd},T$=(e,t)=>et.ssdMobilenetv1.locateFaces(e,t),Sce=(e,t)=>et.tinyFaceDetector.locateFaces(e,t),Tce=(e,t)=>et.tinyYolov2.locateFaces(e,t),N$=e=>et.faceLandmark68Net.detectLandmarks(e),Nce=e=>et.faceLandmark68TinyNet.detectLandmarks(e),Cce=e=>et.faceRecognitionNet.computeFaceDescriptor(e),_ce=e=>et.faceExpressionNet.predictExpressions(e),Ece=e=>et.ageGenderNet.predictAgeAndGender(e),C$=e=>et.ssdMobilenetv1.load(e),Ace=e=>et.tinyFaceDetector.load(e),$ce=e=>et.tinyYolov2.load(e),Fce=e=>et.faceLandmark68Net.load(e),Rce=e=>et.faceLandmark68TinyNet.load(e),Dce=e=>et.faceRecognitionNet.load(e),Mce=e=>et.faceExpressionNet.load(e),Pce=e=>et.ageGenderNet.load(e),Oce=C$,Lce=T$,zce=N$;var bg=class extends Ia{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},jo=class extends bg{async run(){let t=await this.parentTask,n=await Ho(t,this.input,async a=>Promise.all(a.map(r=>et.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>og(a,n[r]))}withAgeAndGender(){return new Xo(this,this.input)}},Ko=class extends bg{async run(){let t=await this.parentTask;if(!t)return;let n=await gp(t,this.input,a=>et.faceExpressionNet.predictExpressions(a),this.extractedFaces);return og(t,n)}withAgeAndGender(){return new Yo(this,this.input)}},Ds=class extends jo{withAgeAndGender(){return new Ps(this,this.input)}withFaceDescriptors(){return new qr(this,this.input)}},Ms=class extends Ko{withAgeAndGender(){return new Os(this,this.input)}withFaceDescriptor(){return new jr(this,this.input)}};var xg=class extends Ia{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Xo=class extends xg{async run(){let t=await this.parentTask,n=await Ho(t,this.input,async a=>Promise.all(a.map(r=>et.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return fg(gg(a,i,o),s)})}withFaceExpressions(){return new jo(this,this.input)}},Yo=class extends xg{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await gp(t,this.input,s=>et.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return fg(gg(t,a,r),n)}withFaceExpressions(){return new Ko(this,this.input)}},Ps=class extends Xo{withFaceExpressions(){return new Ds(this,this.input)}withFaceDescriptors(){return new qr(this,this.input)}},Os=class extends Yo{withFaceExpressions(){return new Ms(this,this.input)}withFaceDescriptor(){return new jr(this,this.input)}};var Id=class extends Ia{constructor(n,a){super();this.parentTask=n;this.input=a}},qr=class extends Id{async run(){let t=await this.parentTask;return(await Ho(t,this.input,a=>Promise.all(a.map(r=>et.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>mg(t[r],a))}withFaceExpressions(){return new Ds(this,this.input)}withAgeAndGender(){return new Ps(this,this.input)}},jr=class extends Id{async run(){let t=await this.parentTask;if(!t)return;let n=await gp(t,this.input,a=>et.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return mg(t,n)}withFaceExpressions(){return new Ms(this,this.input)}withAgeAndGender(){return new Os(this,this.input)}};var Sd=class extends Ia{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?et.faceLandmark68TinyNet:et.faceLandmark68Net}},Td=class extends Sd{async run(){let t=await this.parentTask,n=t.map(i=>i.detection),a=this.input instanceof Ne?await op(this.input,n):await ip(this.input,n),r=await Promise.all(a.map(i=>this.landmarkNet.detectLandmarks(i)));return a.forEach(i=>i instanceof Ne&&i.dispose()),t.filter((i,o)=>r[o]).map((i,o)=>hp(i,r[o]))}withFaceExpressions(){return new Ds(this,this.input)}withAgeAndGender(){return new Ps(this,this.input)}withFaceDescriptors(){return new qr(this,this.input)}},Nd=class extends Sd{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ne?await op(this.input,[n]):await ip(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ne&&s.dispose()),hp(t,r)}withFaceExpressions(){return new Ms(this,this.input)}withAgeAndGender(){return new Os(this,this.input)}withFaceDescriptor(){return new jr(this,this.input)}};var Cd=class extends Ia{constructor(n,a=new ka){super();this.input=n;this.options=a}},yp=class extends Cd{async run(){let{input:t,options:n}=this,a;if(n instanceof kd)a=et.tinyFaceDetector.locateFaces(t,n);else if(n instanceof ka)a=et.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof er)a=et.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(a=>t(a.map(r=>Do({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new Td(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new jo(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Xo(this.runAndExtendWithFaceDetections(),this.input)}},_d=class extends Cd{async run(){let t=await new yp(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?Do({},n):void 0)})}withFaceLandmarks(t=!1){return new Nd(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Ko(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Yo(this.runAndExtendWithFaceDetection(),this.input)}};function Wce(e,t=new ka){return new _d(e,t)}function vg(e,t=new ka){return new yp(e,t)}async function _$(e,t){return vg(e,new ka(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Bce(e,t={}){return vg(e,new er(t)).withFaceLandmarks().withFaceDescriptors()}var Vce=_$;function H1(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s*s,0))}var Ed=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof br)return i;if(i instanceof Float32Array)return new br(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new br(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>H1(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new ap(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>br.fromJSON(a));return new Ed(n,t.distanceThreshold)}};function Uce(e){let t=new qo;return t.extractWeights(e),t}function E$(e,t){let{width:n,height:a}=new xn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>E$(r,{width:n,height:a}));if(zo(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return hp(Do(e,r),s)}return xr(e)?Do(e,e.detection.forSize(n,a)):e instanceof ra||e instanceof yt?e.forSize(n,a):e}var Gce=XA;return mF(Hce);})(); diff --git 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D({x:t,y:e,width:r,height:n})}toSquare(){let{x:t,y:e,width:r,height:n}=this,a=Math.abs(r-n);return re&&(i=-u+e+r,u=e),f>t&&(c=-f+t+n,f=t),m<1&&(c=2-m,m=1),p<1&&(c=2-p,p=1),{dy:s,edy:c,dx:a,edx:i,y:p,ey:f,x:m,ex:u,w:r,h:n}}calibrate(t){return new D({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var Vt=class extends D{constructor(t,e,r,n,a=!1){super({left:t,top:e,right:r,bottom:n},a)}};var bt=class{constructor(t,e,r,n,a){this._imageDims=new k(a.width,a.height),this._score=t,this._classScore=e,this._className=r,this._box=new D(n).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new D(this._box).rescale(this.imageDims.reverse())}forSize(t,e){return new bt(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:e})}};var M=class extends bt{constructor(t,e,r){super(t,t,"",e,r)}forSize(t,e){let{score:r,relativeBox:n,imageDims:a}=super.forSize(t,e);return new M(r,n,a)}};function zr(o,t,e=!0){let r=Math.max(0,Math.min(o.right,t.right)-Math.max(o.left,t.left)),n=Math.max(0,Math.min(o.bottom,t.bottom)-Math.max(o.top,t.top)),a=r*n;return e?a/(o.area+t.area-a):a/Math.min(o.area,t.area)}function Vr(o){let t=o.map(i=>i.x),e=o.map(i=>i.y),r=t.reduce((i,c)=>ccii({score:s,boxIndex:i})).sort((s,i)=>s.score-i.score).map(s=>s.boxIndex),a=[];for(;n.length>0;){let s=n.pop();a.push(s);let i=n,c=[];for(let m=0;mc[p]<=e)}return a}var ct=v(x());function rt(o,t){return ct.tidy(()=>{let[e,r,n]=t,a=ct.fill([...o.shape.slice(0,3),1],e,"float32"),s=ct.fill([...o.shape.slice(0,3),1],r,"float32"),i=ct.fill([...o.shape.slice(0,3),1],n,"float32"),c=ct.concat([a,s,i],3);return ct.sub(o,c)})}var Ct=v(x());function Gr(o,t=!1){return Ct.tidy(()=>{let[e,r]=o.shape.slice(1);if(e===r)return o;let n=Math.abs(e-r),a=Math.round(n*(t?.5:1)),s=e>r?2:1,i=f=>{let l=o.shape.slice();return l[s]=f,Ct.fill(l,0,"float32")},c=i(a),m=n-c.shape[s],u=[t&&m?i(m):null,o,c].filter(f=>!!f).map(f=>Ct.cast(f,"float32"));return Ct.concat(u,s)})}function Ln(o){let t=o.slice();for(let e=t.length-1;e>0;e--){let r=Math.floor(Math.random()*(e+1)),n=t[e];t[e]=t[r],t[r]=n}return t}function Ne(o){return 1/(1+Math.exp(-o))}function An(o){return Math.log(o/(1-o))}var Yt=class extends D{constructor(t,e,r,n,a=!1){super({x:t,y:e,width:r,height:n},a)}};var Wn=.5,kn=.43,Bn=.45,z=class{constructor(t,e,r=new g(0,0)){let{width:n,height:a}=e;this._imgDims=new k(n,a),this._shift=r,this._positions=t.map(s=>s.mul(new g(n,a)).add(r))}get shift(){return new g(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new g(this.imageWidth,this.imageHeight)))}forSize(t,e){return new this.constructor(this.relativePositions,{width:t,height:e})}shiftBy(t,e){return new this.constructor(this.relativePositions,this._imgDims,new g(t,e))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,e={}){if(t){let a=t instanceof M?t.box.floor():new D(t);return this.shiftBy(a.x,a.y).align(null,e)}let{useDlibAlignment:r,minBoxPadding:n}={useDlibAlignment:!1,minBoxPadding:.2,...e};return r?this.alignDlib():this.alignMinBbox(n)}alignDlib(){let t=this.getRefPointsForAlignment(),[e,r,n]=t,a=u=>n.sub(u).magnitude(),s=(a(e)+a(r))/2,i=Math.floor(s/Bn),c=zt(t),m=Math.floor(Math.max(0,c.x-Wn*i)),p=Math.floor(Math.max(0,c.y-kn*i));return new Yt(m,p,Math.min(i,this.imageWidth+m),Math.min(i,this.imageHeight+p))}alignMinBbox(t){let e=Vr(this.positions);return e.pad(e.width*t,e.height*t)}getRefPointsForAlignment(){throw new 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Ge=v(x());var Ye=v(x());function te(o,t){return Ye.tidy(()=>{let e=o.shape[0],r=Ye.reshape(qt(o,t.box_encoding_predictor),[e,-1,1,4]),n=Ye.reshape(qt(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:n}})}function Jo(o,t,e){return Ge.tidy(()=>{let r=Z(o,e.conv_0,[1,1]),n=Z(r,e.conv_1,[2,2]),a=Z(n,e.conv_2,[1,1]),s=Z(a,e.conv_3,[2,2]),i=Z(s,e.conv_4,[1,1]),c=Z(i,e.conv_5,[2,2]),m=Z(c,e.conv_6,[1,1]),p=Z(m,e.conv_7,[2,2]),u=te(t,e.box_predictor_0),f=te(o,e.box_predictor_1),l=te(n,e.box_predictor_2),b=te(s,e.box_predictor_3),y=te(c,e.box_predictor_4),F=te(p,e.box_predictor_5),h=Ge.concat([u.boxPredictionEncoding,f.boxPredictionEncoding,l.boxPredictionEncoding,b.boxPredictionEncoding,y.boxPredictionEncoding,F.boxPredictionEncoding],1),T=Ge.concat([u.classPrediction,f.classPrediction,l.classPrediction,b.classPrediction,y.classPrediction,F.classPrediction],1);return{boxPredictions:h,classPredictions:T}})}var X=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var St=class extends A{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return Lt.tidy(()=>{let r=Lt.cast(t.toBatchTensor(512,!1),"float32"),n=Lt.sub(Lt.div(r,127.5),1),a=jo(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=Jo(a.out,a.conv11,e.prediction_layer);return Xo(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await C(t))}async 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N=v(x());var Cr=o=>typeof o=="number";function ho(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!Cr(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>Cr(t.x)&&Cr(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(Cr)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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on(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=R(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=la(n,s),p;if(t.withSeparableConvs){let[u,f,l,b,y,F,h,T,_]=r,E=t.isFirstLayerConv2d?i(u,f,3,"conv0"):m(u,f,"conv0"),W=m(f,l,"conv1"),tt=m(l,b,"conv2"),lt=m(b,y,"conv3"),q=m(y,F,"conv4"),Dt=m(F,h,"conv5"),Et=T?m(h,T,"conv6"):void 0,Mt=_?m(T,_,"conv7"):void 0,$t=i(_||T||h,5*e,1,"conv8");p={conv0:E,conv1:W,conv2:tt,conv3:lt,conv4:q,conv5:Dt,conv6:Et,conv7:Mt,conv8:$t}}else{let[u,f,l,b,y,F,h,T,_]=r,E=c(u,f,"conv0"),W=c(f,l,"conv1"),tt=c(l,b,"conv2"),lt=c(b,y,"conv3"),q=c(y,F,"conv4"),Dt=c(F,h,"conv5"),Et=c(h,T,"conv6"),Mt=c(T,_,"conv7"),$t=i(_,5*e,1,"conv8");p={conv0:E,conv1:W,conv2:tt,conv3:lt,conv4:q,conv5:Dt,conv6:Et,conv7:Mt,conv8:$t}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function da(o,t){let e=Y(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=xe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=da(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return B(o,e),{params:s,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var go=class extends A{constructor(e){super("TinyYolov2");ho(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,r){let n=Tt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=Tt(n,r.conv6),n=Tt(n,r.conv7),qt(n,r.conv8,"valid",!1)}runMobilenet(e,r){let n=this.config.isFirstLayerConv2d?Me(qt(e,r.conv0,"valid",!1)):wt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=r.conv6?wt(n,r.conv6):n,n=r.conv7?wt(n,r.conv7):n,qt(n,r.conv8,"valid",!1)}forwardInput(e,r){let{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let a=N.cast(e.toBatchTensor(r,!1),"float32");return a=this.config.meanRgb?rt(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,n):this.runTinyYolov2(a,n)})}async forward(e,r){return this.forwardInput(await C(e),r)}async detect(e,r={}){let{inputSize:n,scoreThreshold:a}=new st(r),s=await C(e),i=await this.forwardInput(s,n),c=N.tidy(()=>N.unstack(i)[0].expandDims()),m={width:s.getInputWidth(0),height:s.getInputHeight(0)},p=await this.extractBoxes(c,s.getReshapedInputDimensions(0),a);i.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),b=p.map(h=>this.config.classes[h.label]);return Yr(u.map(h=>h.rescale(n)),f,this.config.iouThreshold,!0).map(h=>new bt(f[h],l[h],b[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return nn(e,this.config)}extractParams(e){let r=this.config.filterSizes||go.DEFAULT_FILTER_SIZES,n=r?r.length:void 0;if(n!==7&&n!==8&&n!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${n} filterSizes in config`);return on(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,n){let{width:a,height:s}=r,i=Math.max(a,s),c=i/a,m=i/s,p=e.shape[1],u=this.config.anchors.length,[f,l,b]=N.tidy(()=>{let T=e.reshape([p,p,u,this.boxEncodingSize]),_=T.slice([0,0,0,0],[p,p,u,4]),E=T.slice([0,0,0,4],[p,p,u,1]),W=this.withClassScores?N.softmax(T.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):N.scalar(0);return[_,E,W]}),y=[],F=await l.array(),h=await f.array();for(let T=0;Tn){let tt=(_+Ne(h[T][_][E][0]))/p*c,lt=(T+Ne(h[T][_][E][1]))/p*m,q=Math.exp(h[T][_][E][2])*this.config.anchors[E].x/p*c,Dt=Math.exp(h[T][_][E][3])*this.config.anchors[E].y/p*m,Et=tt-q/2,Mt=lt-Dt/2,$t={row:T,col:_,anchor:E},{classScore:yo,label:_o}=this.withClassScores?await this.extractPredictedClass(b,$t):{classScore:1,label:0};y.push({box:new Vt(Et,Mt,Et+q,Mt+Dt),score:W,classScore:W*yo,label:_o,...$t})}}return f.dispose(),l.dispose(),b.dispose(),y}async extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=go;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ha(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var xo=v(x());async function oe(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Zt(c)?n(c):c.detection),s=r||(t instanceof xo.Tensor?await de(t,a):await le(t,a)),i=await e(s);return s.forEach(c=>c instanceof xo.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return oe([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],cn=[117.001,114.697,97.404];var ne=class extends ee{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new 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He},mn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ba=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ga=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),xa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),va=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ya=o=>P.faceExpressionNet.predictExpressions(o),_a=o=>P.ageGenderNet.predictAgeAndGender(o),un=o=>P.ssdMobilenetv1.load(o),Ta=o=>P.tinyFaceDetector.load(o),wa=o=>P.tinyYolov2.load(o),Pa=o=>P.faceLandmark68Net.load(o),Fa=o=>P.faceLandmark68TinyNet.load(o),Da=o=>P.faceRecognitionNet.load(o),Ea=o=>P.faceExpressionNet.load(o),Ma=o=>P.ageGenderNet.load(o),Ca=un,Ia=mn,Na=pn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Ir{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>xr(r,e[n]))}withAgeAndGender(){return new 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ct.sub(o,c)})}var Ct=v(x());function Gr(o,t=!1){return Ct.tidy(()=>{let[e,r]=o.shape.slice(1);if(e===r)return o;let n=Math.abs(e-r),a=Math.round(n*(t?.5:1)),s=e>r?2:1,i=f=>{let l=o.shape.slice();return l[s]=f,Ct.fill(l,0,"float32")},c=i(a),m=n-c.shape[s],u=[t&&m?i(m):null,o,c].filter(f=>!!f).map(f=>Ct.cast(f,"float32"));return Ct.concat(u,s)})}function Ln(o){let t=o.slice();for(let e=t.length-1;e>0;e--){let r=Math.floor(Math.random()*(e+1)),n=t[e];t[e]=t[r],t[r]=n}return t}function Ne(o){return 1/(1+Math.exp(-o))}function An(o){return Math.log(o/(1-o))}var Yt=class extends D{constructor(t,e,r,n,a=!1){super({x:t,y:e,width:r,height:n},a)}};var Wn=.5,kn=.43,Bn=.45,z=class{constructor(t,e,r=new g(0,0)){let{width:n,height:a}=e;this._imgDims=new k(n,a),this._shift=r,this._positions=t.map(s=>s.mul(new g(n,a)).add(r))}get shift(){return new g(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return 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a=Xt(o),s=t/Math.max(a.height,a.width),i=s*a.width,c=s*a.height,m=Jt({width:t,height:t}),p=o instanceof n?o:We(o),u=Math.abs(i-c)/2,f=e&&i0&&p.height>0&&O(m).drawImage(p,f,l,i,c),m}var ut=class{constructor(t,e=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=e,this._batchSize=t.length,t.forEach((r,n)=>{if(ht(r)){this._imageTensors[n]=r,this._inputDimensions[n]=r.shape;return}if(U(r)){let s=r.shape[0];if(s!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${s} passed, but not supported in input array`);this._imageTensors[n]=r,this._inputDimensions[n]=r.shape.slice(1);return}let a=r instanceof w.getEnv().Canvas?r:We(r);this._canvases[n]=a,this._inputDimensions[n]=[a.height,a.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return it(this.batchSize,0,1).map((t,e)=>this.getReshapedInputDimensions(e))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let e=this.getInputWidth(t),r=this.getInputHeight(t);return Or({width:e,height:r},this.inputSize)}toBatchTensor(t,e=!0){return this._inputSize=t,V.tidy(()=>{let r=it(this.batchSize,0,1).map(a=>{let s=this.getInput(a);if(s instanceof V.Tensor){let 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M?i.forSize(r,e).box:i).map(i=>i.clipAtImageBorders(r,e)).filter(i=>i.width>0&&i.height>0).map(({x:i,y:c,width:m,height:p})=>cr.slice3d(o.as3D(e,r,n),[c,i,0],[p,m,n]))})}async function xt(o,t){let{fetch:e}=w.getEnv(),r=await e(o,t);if(!(r.status<400))throw new Error(`failed to fetch: (${r.status}) ${r.statusText}, from url: ${r.url}`);return r}async function Hn(o){let t=await xt(o),e=await t.blob();if(!e.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${e.type}, for url: ${t.url}`);return eo(e)}async function no(o){return(await xt(o)).json()}async function zn(o){return new Float32Array(await(await xt(o)).arrayBuffer())}function Fo(o){return new Promise((t,e)=>{o instanceof Blob||e(new Error("bufferToVideo - expected buf to be of type: Blob"));let r=w.getEnv().createVideoElement();r.oncanplay=()=>t(r),r.onerror=e,r.playsInline=!0,r.muted=!0,r.src=URL.createObjectURL(o),r.play()})}async function Vn(o){let t=await xt(o),e=await t.blob();if(!e.type.startsWith("video/"))throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${e.type}, for url: ${t.url}`);return Fo(e)}var Do=v(x());function mr(o,t){let e=`${t}-weights_manifest.json`;if(!o)return{modelBaseUri:"",manifestUri:e};if(o==="/")return{modelBaseUri:"/",manifestUri:`/${e}`};let r=o.startsWith("http://")?"http://":o.startsWith("https://")?"https://":"";o=o.replace(r,"");let n=o.split("/").filter(i=>i),a=o.endsWith(".json")?n[n.length-1]:e,s=r+(o.endsWith(".json")?n.slice(0,n.length-1):n).join("/");return s=o.startsWith("/")?`/${s}`:s,{modelBaseUri:s,manifestUri:s==="/"?`/${a}`:`${s}/${a}`}}async function ao(o,t){let{manifestUri:e,modelBaseUri:r}=mr(o,t),n=await no(e);return Do.io.loadWeights(n,r)}function Yn(o,t,e=!1){let{width:r,height:n}=e?Xt(t):t;return o.width=r,o.height=n,{width:r,height:n}}var ye=v(x());var vt=v(x());var A=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get 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He=class extends A{constructor(e=new _r(2)){super("AgeGenderNet");this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){let{params:r}=this;if(!r)throw new Error(`${this._name} - load model before inference`);return ft.tidy(()=>{let n=e instanceof ut?this.faceFeatureExtractor.forwardInput(e):e,a=ft.avgPool(n,[7,7],[2,2],"valid").as2D(n.shape[0],-1),s=$e(a,r.fc.age).as1D(),i=$e(a,r.fc.gender);return{age:s,gender:i}})}forwardInput(e){return ft.tidy(()=>{let{age:r,gender:n}=this.runNet(e);return{age:r,gender:ft.softmax(n)}})}async forward(e){return this.forwardInput(await C(e))}async predictAgeAndGender(e){let r=await C(e),n=await this.forwardInput(r),a=ft.unstack(n.age),s=ft.unstack(n.gender),i=a.map((m,p)=>({ageTensor:m,genderTensor:s[p]})),c=await Promise.all(i.map(async({ageTensor:m,genderTensor:p})=>{let u=m.dataSync()[0],f=p.dataSync()[0],l=f>.5,b=l?"male":"female",y=l?f:1-f;return 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X=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var St=class extends A{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return Lt.tidy(()=>{let r=Lt.cast(t.toBatchTensor(512,!1),"float32"),n=Lt.sub(Lt.div(r,127.5),1),a=jo(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=Jo(a.out,a.conv11,e.prediction_layer);return Xo(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await C(t))}async 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N=v(x());var Cr=o=>typeof o=="number";function ho(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!Cr(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>Cr(t.x)&&Cr(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(Cr)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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on(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=R(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=la(n,s),p;if(t.withSeparableConvs){let[u,f,l,b,y,F,h,T,_]=r,E=t.isFirstLayerConv2d?i(u,f,3,"conv0"):m(u,f,"conv0"),W=m(f,l,"conv1"),tt=m(l,b,"conv2"),lt=m(b,y,"conv3"),q=m(y,F,"conv4"),Dt=m(F,h,"conv5"),Et=T?m(h,T,"conv6"):void 0,Mt=_?m(T,_,"conv7"):void 0,$t=i(_||T||h,5*e,1,"conv8");p={conv0:E,conv1:W,conv2:tt,conv3:lt,conv4:q,conv5:Dt,conv6:Et,conv7:Mt,conv8:$t}}else{let[u,f,l,b,y,F,h,T,_]=r,E=c(u,f,"conv0"),W=c(f,l,"conv1"),tt=c(l,b,"conv2"),lt=c(b,y,"conv3"),q=c(y,F,"conv4"),Dt=c(F,h,"conv5"),Et=c(h,T,"conv6"),Mt=c(T,_,"conv7"),$t=i(_,5*e,1,"conv8");p={conv0:E,conv1:W,conv2:tt,conv3:lt,conv4:q,conv5:Dt,conv6:Et,conv7:Mt,conv8:$t}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function da(o,t){let e=Y(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=xe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=da(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return B(o,e),{params:s,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var go=class extends A{constructor(e){super("TinyYolov2");ho(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,r){let n=Tt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=Tt(n,r.conv6),n=Tt(n,r.conv7),qt(n,r.conv8,"valid",!1)}runMobilenet(e,r){let n=this.config.isFirstLayerConv2d?Me(qt(e,r.conv0,"valid",!1)):wt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=r.conv6?wt(n,r.conv6):n,n=r.conv7?wt(n,r.conv7):n,qt(n,r.conv8,"valid",!1)}forwardInput(e,r){let{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let a=N.cast(e.toBatchTensor(r,!1),"float32");return a=this.config.meanRgb?rt(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,n):this.runTinyYolov2(a,n)})}async forward(e,r){return this.forwardInput(await C(e),r)}async detect(e,r={}){let{inputSize:n,scoreThreshold:a}=new st(r),s=await C(e),i=await this.forwardInput(s,n),c=N.tidy(()=>N.unstack(i)[0].expandDims()),m={width:s.getInputWidth(0),height:s.getInputHeight(0)},p=await this.extractBoxes(c,s.getReshapedInputDimensions(0),a);i.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),b=p.map(h=>this.config.classes[h.label]);return Yr(u.map(h=>h.rescale(n)),f,this.config.iouThreshold,!0).map(h=>new bt(f[h],l[h],b[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return nn(e,this.config)}extractParams(e){let r=this.config.filterSizes||go.DEFAULT_FILTER_SIZES,n=r?r.length:void 0;if(n!==7&&n!==8&&n!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${n} filterSizes in config`);return on(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,n){let{width:a,height:s}=r,i=Math.max(a,s),c=i/a,m=i/s,p=e.shape[1],u=this.config.anchors.length,[f,l,b]=N.tidy(()=>{let T=e.reshape([p,p,u,this.boxEncodingSize]),_=T.slice([0,0,0,0],[p,p,u,4]),E=T.slice([0,0,0,4],[p,p,u,1]),W=this.withClassScores?N.softmax(T.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):N.scalar(0);return[_,E,W]}),y=[],F=await l.array(),h=await f.array();for(let T=0;Tn){let tt=(_+Ne(h[T][_][E][0]))/p*c,lt=(T+Ne(h[T][_][E][1]))/p*m,q=Math.exp(h[T][_][E][2])*this.config.anchors[E].x/p*c,Dt=Math.exp(h[T][_][E][3])*this.config.anchors[E].y/p*m,Et=tt-q/2,Mt=lt-Dt/2,$t={row:T,col:_,anchor:E},{classScore:yo,label:_o}=this.withClassScores?await this.extractPredictedClass(b,$t):{classScore:1,label:0};y.push({box:new Vt(Et,Mt,Et+q,Mt+Dt),score:W,classScore:W*yo,label:_o,...$t})}}return f.dispose(),l.dispose(),b.dispose(),y}async extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=go;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ha(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var xo=v(x());async function oe(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Zt(c)?n(c):c.detection),s=r||(t instanceof xo.Tensor?await de(t,a):await le(t,a)),i=await e(s);return s.forEach(c=>c instanceof xo.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return oe([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],cn=[117.001,114.697,97.404];var ne=class extends ee{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new St,tinyFaceDetector:new ne,tinyYolov2:new re,faceLandmark68Net:new Kt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Qt,faceExpressionNet:new Oe,ageGenderNet:new He},mn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ba=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ga=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),xa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),va=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ya=o=>P.faceExpressionNet.predictExpressions(o),_a=o=>P.ageGenderNet.predictAgeAndGender(o),un=o=>P.ssdMobilenetv1.load(o),Ta=o=>P.tinyFaceDetector.load(o),wa=o=>P.tinyYolov2.load(o),Pa=o=>P.faceLandmark68Net.load(o),Fa=o=>P.faceLandmark68TinyNet.load(o),Da=o=>P.faceRecognitionNet.load(o),Ea=o=>P.faceExpressionNet.load(o),Ma=o=>P.ageGenderNet.load(o),Ca=un,Ia=mn,Na=pn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Ir{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>xr(r,e[n]))}withAgeAndGender(){return new ie(this,this.input)}},se=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return xr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Nr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return Er(Mr(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Er(Mr(t,r,n),e)}withFaceExpressions(){return new se(this,this.input)}},Bt=class extends ie{withFaceExpressions(){return new Wt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Rt=class extends ce{withFaceExpressions(){return new kt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},Pt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Dr(t[n],r))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}},Ft=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await 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Promise((t,e)=>{this.run().then(r=>t(r.map(n=>jt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ie(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?jt({},e):void 0)})}withFaceLandmarks(t=!1){return new Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new se(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ce(this.runAndExtendWithFaceDetection(),this.input)}};function Sa(o,t=new X){return new Qe(o,t)}function Sr(o,t=new X){return new Ie(o,t)}async function fn(o,t){return Sr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Sr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Aa=fn;function vo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,a)=>n-r[a]).reduce((n,a)=>n+a*a,0))}var tr=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,a=()=>`person ${n++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof mt)return s;if(s instanceof Float32Array)return new mt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new mt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>vo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new pe(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>mt.fromJSON(r));return new tr(e,t.distanceThreshold)}};function Wa(o){let t=new ne;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new k(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(Zt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof z||o instanceof M?o.forSize(e,r):o}var Ba=So;0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,fetchVideo,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version}); 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this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return it(this.batchSize,0,1).map((t,e)=>this.getReshapedInputDimensions(e))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let e=this.getInputWidth(t),r=this.getInputHeight(t);return Or({width:e,height:r},this.inputSize)}toBatchTensor(t,e=!0){return this._inputSize=t,V.tidy(()=>{let r=it(this.batchSize,0,1).map(a=>{let s=this.getInput(a);if(s instanceof V.Tensor){let 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i=mo(i,r.entry_flow.reduction_block_0,!1),i=mo(i,r.entry_flow.reduction_block_1),it(this._numMainBlocks,0,1).forEach(c=>{i=Kn(i,r.middle_flow[`main_block_${c}`])}),i=mo(i,r.exit_flow.reduction_block),i=S.relu(H(i,r.exit_flow.separable_conv,[1,1])),i})}async forward(e){return this.forwardInput(await C(e))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(e){return Wo(e,this._numMainBlocks)}extractParams(e){return Ao(e,this._numMainBlocks)}};function Bo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=R(o),n=hr(e,t),a=n(512,1,"fc/age"),s=n(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:s}}}}function Ro(o){let t=[],e=Y(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:{age:r("fc/age"),gender:r("fc/gender")}};return B(o,t),{params:n,paramMappings:t}}var Pr=(e=>(e.FEMALE="female",e.MALE="male",e))(Pr||{});var He=class extends A{constructor(e=new wr(2)){super("AgeGenderNet");this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){let{params:r}=this;if(!r)throw new Error(`${this._name} - load model before inference`);return ft.tidy(()=>{let n=e instanceof ut?this.faceFeatureExtractor.forwardInput(e):e,a=ft.avgPool(n,[7,7],[2,2],"valid").as2D(n.shape[0],-1),s=$e(a,r.fc.age).as1D(),i=$e(a,r.fc.gender);return{age:s,gender:i}})}forwardInput(e){return ft.tidy(()=>{let{age:r,gender:n}=this.runNet(e);return{age:r,gender:ft.softmax(n)}})}async forward(e){return this.forwardInput(await C(e))}async predictAgeAndGender(e){let r=await C(e),n=await this.forwardInput(r),a=ft.unstack(n.age),s=ft.unstack(n.gender),i=a.map((m,p)=>({ageTensor:m,genderTensor:s[p]})),c=await Promise.all(i.map(async({ageTensor:m,genderTensor:p})=>{let u=m.dataSync()[0],f=p.dataSync()[0],l=f>.5,b=l?"male":"female",y=l?f:1-f;return 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X=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var St=class extends A{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return Lt.tidy(()=>{let r=Lt.cast(t.toBatchTensor(512,!1),"float32"),n=Lt.sub(Lt.div(r,127.5),1),a=Uo(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=qo(a.out,a.conv11,e.prediction_layer);return Jo(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await C(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:n}=new X(e),a=await C(t),{boxes:s,scores:i}=this.forwardInput(a),c=s[0],m=i[0];for(let _=1;_{let[E,W]=[Math.max(0,h[_][0]),Math.min(1,h[_][2])].map(q=>q*F),[tt,lt]=[Math.max(0,h[_][1]),Math.min(1,h[_][3])].map(q=>q*y);return new M(p[_],new Yt(tt,E,lt-tt,W-E),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),m.dispose(),T}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return jo(t)}extractParams(t){return Go(t)}};function Zo(o){let t=new St;return t.extractWeights(o),t}function fa(o){return Zo(o)}var lo=class extends St{};var Ko=.4,Qo=[new g(.738768,.874946),new g(2.42204,2.65704),new g(4.30971,7.04493),new g(10.246,4.59428),new g(12.6868,11.8741)],tn=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],en=[117.001,114.697,97.404],rn="tiny_yolov2_model",on="tiny_yolov2_separable_conv_model";var N=v(x());var Nr=o=>typeof o=="number";function ho(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!Nr(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>Nr(t.x)&&Nr(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(Nr)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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nn(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=R(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=la(n,s),p;if(t.withSeparableConvs){let[u,f,l,b,y,F,h,T,_]=r,E=t.isFirstLayerConv2d?i(u,f,3,"conv0"):m(u,f,"conv0"),W=m(f,l,"conv1"),tt=m(l,b,"conv2"),lt=m(b,y,"conv3"),q=m(y,F,"conv4"),Dt=m(F,h,"conv5"),Et=T?m(h,T,"conv6"):void 0,Mt=_?m(T,_,"conv7"):void 0,$t=i(_||T||h,5*e,1,"conv8");p={conv0:E,conv1:W,conv2:tt,conv3:lt,conv4:q,conv5:Dt,conv6:Et,conv7:Mt,conv8:$t}}else{let[u,f,l,b,y,F,h,T,_]=r,E=c(u,f,"conv0"),W=c(f,l,"conv1"),tt=c(l,b,"conv2"),lt=c(b,y,"conv3"),q=c(y,F,"conv4"),Dt=c(F,h,"conv5"),Et=c(h,T,"conv6"),Mt=c(T,_,"conv7"),$t=i(_,5*e,1,"conv8");p={conv0:E,conv1:W,conv2:tt,conv3:lt,conv4:q,conv5:Dt,conv6:Et,conv7:Mt,conv8:$t}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function da(o,t){let e=Y(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=xe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function an(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=da(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return B(o,e),{params:s,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var go=class extends A{constructor(e){super("TinyYolov2");ho(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,r){let n=Tt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=Tt(n,r.conv6),n=Tt(n,r.conv7),qt(n,r.conv8,"valid",!1)}runMobilenet(e,r){let n=this.config.isFirstLayerConv2d?Me(qt(e,r.conv0,"valid",!1)):wt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=r.conv6?wt(n,r.conv6):n,n=r.conv7?wt(n,r.conv7):n,qt(n,r.conv8,"valid",!1)}forwardInput(e,r){let{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let a=N.cast(e.toBatchTensor(r,!1),"float32");return a=this.config.meanRgb?rt(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,n):this.runTinyYolov2(a,n)})}async forward(e,r){return this.forwardInput(await C(e),r)}async detect(e,r={}){let{inputSize:n,scoreThreshold:a}=new st(r),s=await C(e),i=await this.forwardInput(s,n),c=N.tidy(()=>N.unstack(i)[0].expandDims()),m={width:s.getInputWidth(0),height:s.getInputHeight(0)},p=await this.extractBoxes(c,s.getReshapedInputDimensions(0),a);i.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),b=p.map(h=>this.config.classes[h.label]);return Yr(u.map(h=>h.rescale(n)),f,this.config.iouThreshold,!0).map(h=>new bt(f[h],l[h],b[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return an(e,this.config)}extractParams(e){let r=this.config.filterSizes||go.DEFAULT_FILTER_SIZES,n=r?r.length:void 0;if(n!==7&&n!==8&&n!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${n} filterSizes in config`);return nn(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,n){let{width:a,height:s}=r,i=Math.max(a,s),c=i/a,m=i/s,p=e.shape[1],u=this.config.anchors.length,[f,l,b]=N.tidy(()=>{let T=e.reshape([p,p,u,this.boxEncodingSize]),_=T.slice([0,0,0,0],[p,p,u,4]),E=T.slice([0,0,0,4],[p,p,u,1]),W=this.withClassScores?N.softmax(T.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):N.scalar(0);return[_,E,W]}),y=[],F=await l.array(),h=await f.array();for(let T=0;Tn){let tt=(_+Ne(h[T][_][E][0]))/p*c,lt=(T+Ne(h[T][_][E][1]))/p*m,q=Math.exp(h[T][_][E][2])*this.config.anchors[E].x/p*c,Dt=Math.exp(h[T][_][E][3])*this.config.anchors[E].y/p*m,Et=tt-q/2,Mt=lt-Dt/2,$t={row:T,col:_,anchor:E},{classScore:yo,label:_o}=this.withClassScores?await this.extractPredictedClass(b,$t):{classScore:1,label:0};y.push({box:new Vt(Et,Mt,Et+q,Mt+Dt),score:W,classScore:W*yo,label:_o,...$t})}}return f.dispose(),l.dispose(),b.dispose(),y}async extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=go;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Ko,classes:["face"],...t?{anchors:tn,meanRgb:en}:{anchors:Qo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?on:rn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ha(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var xo=v(x());async function oe(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Zt(c)?n(c):c.detection),s=r||(t instanceof xo.Tensor?await de(t,a):await le(t,a)),i=await e(s);return s.forEach(c=>c instanceof xo.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return oe([o],t,async a=>e(a[0]),r,n)}var sn=.4,cn=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],mn=[117.001,114.697,97.404];var ne=class extends ee{constructor(){let t={withSeparableConvs:!0,iouThreshold:sn,classes:["face"],anchors:cn,meanRgb:mn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new St,tinyFaceDetector:new ne,tinyYolov2:new re,faceLandmark68Net:new Kt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Qt,faceExpressionNet:new Oe,ageGenderNet:new He},pn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ba=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ga=(o,t)=>P.tinyYolov2.locateFaces(o,t),un=o=>P.faceLandmark68Net.detectLandmarks(o),xa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),va=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ya=o=>P.faceExpressionNet.predictExpressions(o),_a=o=>P.ageGenderNet.predictAgeAndGender(o),fn=o=>P.ssdMobilenetv1.load(o),Ta=o=>P.tinyFaceDetector.load(o),wa=o=>P.tinyYolov2.load(o),Pa=o=>P.faceLandmark68Net.load(o),Fa=o=>P.faceLandmark68TinyNet.load(o),Da=o=>P.faceRecognitionNet.load(o),Ea=o=>P.faceExpressionNet.load(o),Ma=o=>P.ageGenderNet.load(o),Ca=fn,Ia=pn,Na=un;var Sr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Sr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>yr(r,e[n]))}withAgeAndGender(){return new ie(this,this.input)}},se=class extends Sr{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return yr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Lr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Lr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return Cr(Ir(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Lr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Cr(Ir(t,r,n),e)}withFaceExpressions(){return new se(this,this.input)}},Bt=class extends ie{withFaceExpressions(){return new Wt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Rt=class extends ce{withFaceExpressions(){return new kt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},Pt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Mr(t[n],r))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}},Ft=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Mr(t,e)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof Xe.Tensor?await de(this.input,e):await le(this.input,e),n=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),t.filter((s,i)=>n[i]).map((s,i)=>Pe(s,n[i]))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Ze=class extends Je{async run(){let t=await 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f=a("exit_flow/reduction_block"),l=n("exit_flow/separable_conv"),b={reduction_block:f,separable_conv:l};return B(o,e),{params:{entry_flow:p,middle_flow:u,exit_flow:b},paramMappings:e}}function ko(o,t,e){return S.add(S.conv2d(o,t.filters,e,"same"),t.bias)}function mo(o,t,e=!0){let r=e?S.relu(o):o;return r=H(r,t.separable_conv0,[1,1]),r=H(S.relu(r),t.separable_conv1,[1,1]),r=S.maxPool(r,[3,3],[2,2],"same"),r=S.add(r,ko(o,t.expansion_conv,[2,2])),r}function Kn(o,t){let e=H(S.relu(o),t.separable_conv0,[1,1]);return e=H(S.relu(e),t.separable_conv1,[1,1]),e=H(S.relu(e),t.separable_conv2,[1,1]),e=S.add(e,o),e}var wr=class extends A{constructor(e){super("TinyXception");this._numMainBlocks=e}forwardInput(e){let{params:r}=this;if(!r)throw new Error("TinyXception - load model before inference");return S.tidy(()=>{let n=S.cast(e.toBatchTensor(112,!0),"float32"),s=rt(n,[122.782,117.001,104.298]).div(255),i=S.relu(ko(s,r.entry_flow.conv_in,[2,2]));return i=mo(i,r.entry_flow.reduction_block_0,!1),i=mo(i,r.entry_flow.reduction_block_1),it(this._numMainBlocks,0,1).forEach(c=>{i=Kn(i,r.middle_flow[`main_block_${c}`])}),i=mo(i,r.exit_flow.reduction_block),i=S.relu(H(i,r.exit_flow.separable_conv,[1,1])),i})}async forward(e){return this.forwardInput(await C(e))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(e){return Wo(e,this._numMainBlocks)}extractParams(e){return Ao(e,this._numMainBlocks)}};function Bo(o){let t=[],{extractWeights:e,getRemainingWeights:r}=R(o),n=hr(e,t),a=n(512,1,"fc/age"),s=n(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:s}}}}function Ro(o){let t=[],e=Y(o,t);function r(a){let s=e(`${a}/weights`,2),i=e(`${a}/bias`,1);return{weights:s,bias:i}}let n={fc:{age:r("fc/age"),gender:r("fc/gender")}};return B(o,t),{params:n,paramMappings:t}}var Pr=(e=>(e.FEMALE="female",e.MALE="male",e))(Pr||{});var He=class extends A{constructor(e=new wr(2)){super("AgeGenderNet");this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){let{params:r}=this;if(!r)throw new Error(`${this._name} - load model before inference`);return ft.tidy(()=>{let n=e instanceof ut?this.faceFeatureExtractor.forwardInput(e):e,a=ft.avgPool(n,[7,7],[2,2],"valid").as2D(n.shape[0],-1),s=$e(a,r.fc.age).as1D(),i=$e(a,r.fc.gender);return{age:s,gender:i}})}forwardInput(e){return ft.tidy(()=>{let{age:r,gender:n}=this.runNet(e);return{age:r,gender:ft.softmax(n)}})}async forward(e){return this.forwardInput(await C(e))}async predictAgeAndGender(e){let r=await C(e),n=await this.forwardInput(r),a=ft.unstack(n.age),s=ft.unstack(n.gender),i=a.map((m,p)=>({ageTensor:m,genderTensor:s[p]})),c=await Promise.all(i.map(async({ageTensor:m,genderTensor:p})=>{let u=m.dataSync()[0],f=p.dataSync()[0],l=f>.5,b=l?"male":"female",y=l?f:1-f;return 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X=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var St=class extends A{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return Lt.tidy(()=>{let r=Lt.cast(t.toBatchTensor(512,!1),"float32"),n=Lt.sub(Lt.div(r,127.5),1),a=Uo(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=qo(a.out,a.conv11,e.prediction_layer);return Jo(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await C(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:n}=new X(e),a=await C(t),{boxes:s,scores:i}=this.forwardInput(a),c=s[0],m=i[0];for(let _=1;_{let[E,W]=[Math.max(0,h[_][0]),Math.min(1,h[_][2])].map(q=>q*F),[tt,lt]=[Math.max(0,h[_][1]),Math.min(1,h[_][3])].map(q=>q*y);return new M(p[_],new Yt(tt,E,lt-tt,W-E),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),m.dispose(),T}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return jo(t)}extractParams(t){return Go(t)}};function Zo(o){let t=new St;return t.extractWeights(o),t}function fa(o){return Zo(o)}var lo=class extends St{};var Ko=.4,Qo=[new g(.738768,.874946),new g(2.42204,2.65704),new g(4.30971,7.04493),new g(10.246,4.59428),new g(12.6868,11.8741)],tn=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],en=[117.001,114.697,97.404],rn="tiny_yolov2_model",on="tiny_yolov2_separable_conv_model";var N=v(x());var Nr=o=>typeof o=="number";function ho(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!Nr(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>Nr(t.x)&&Nr(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(Nr)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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nn(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=R(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=la(n,s),p;if(t.withSeparableConvs){let[u,f,l,b,y,F,h,T,_]=r,E=t.isFirstLayerConv2d?i(u,f,3,"conv0"):m(u,f,"conv0"),W=m(f,l,"conv1"),tt=m(l,b,"conv2"),lt=m(b,y,"conv3"),q=m(y,F,"conv4"),Dt=m(F,h,"conv5"),Et=T?m(h,T,"conv6"):void 0,Mt=_?m(T,_,"conv7"):void 0,$t=i(_||T||h,5*e,1,"conv8");p={conv0:E,conv1:W,conv2:tt,conv3:lt,conv4:q,conv5:Dt,conv6:Et,conv7:Mt,conv8:$t}}else{let[u,f,l,b,y,F,h,T,_]=r,E=c(u,f,"conv0"),W=c(f,l,"conv1"),tt=c(l,b,"conv2"),lt=c(b,y,"conv3"),q=c(y,F,"conv4"),Dt=c(F,h,"conv5"),Et=c(h,T,"conv6"),Mt=c(T,_,"conv7"),$t=i(_,5*e,1,"conv8");p={conv0:E,conv1:W,conv2:tt,conv3:lt,conv4:q,conv5:Dt,conv6:Et,conv7:Mt,conv8:$t}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function da(o,t){let e=Y(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=xe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function an(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=da(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return B(o,e),{params:s,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var go=class extends A{constructor(e){super("TinyYolov2");ho(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,r){let n=Tt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=Tt(n,r.conv6),n=Tt(n,r.conv7),qt(n,r.conv8,"valid",!1)}runMobilenet(e,r){let n=this.config.isFirstLayerConv2d?Me(qt(e,r.conv0,"valid",!1)):wt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=r.conv6?wt(n,r.conv6):n,n=r.conv7?wt(n,r.conv7):n,qt(n,r.conv8,"valid",!1)}forwardInput(e,r){let{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let a=N.cast(e.toBatchTensor(r,!1),"float32");return a=this.config.meanRgb?rt(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,n):this.runTinyYolov2(a,n)})}async forward(e,r){return this.forwardInput(await C(e),r)}async detect(e,r={}){let{inputSize:n,scoreThreshold:a}=new st(r),s=await C(e),i=await this.forwardInput(s,n),c=N.tidy(()=>N.unstack(i)[0].expandDims()),m={width:s.getInputWidth(0),height:s.getInputHeight(0)},p=await this.extractBoxes(c,s.getReshapedInputDimensions(0),a);i.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),b=p.map(h=>this.config.classes[h.label]);return Yr(u.map(h=>h.rescale(n)),f,this.config.iouThreshold,!0).map(h=>new bt(f[h],l[h],b[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return an(e,this.config)}extractParams(e){let r=this.config.filterSizes||go.DEFAULT_FILTER_SIZES,n=r?r.length:void 0;if(n!==7&&n!==8&&n!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${n} filterSizes in config`);return nn(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,n){let{width:a,height:s}=r,i=Math.max(a,s),c=i/a,m=i/s,p=e.shape[1],u=this.config.anchors.length,[f,l,b]=N.tidy(()=>{let T=e.reshape([p,p,u,this.boxEncodingSize]),_=T.slice([0,0,0,0],[p,p,u,4]),E=T.slice([0,0,0,4],[p,p,u,1]),W=this.withClassScores?N.softmax(T.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):N.scalar(0);return[_,E,W]}),y=[],F=await l.array(),h=await f.array();for(let T=0;Tn){let tt=(_+Ne(h[T][_][E][0]))/p*c,lt=(T+Ne(h[T][_][E][1]))/p*m,q=Math.exp(h[T][_][E][2])*this.config.anchors[E].x/p*c,Dt=Math.exp(h[T][_][E][3])*this.config.anchors[E].y/p*m,Et=tt-q/2,Mt=lt-Dt/2,$t={row:T,col:_,anchor:E},{classScore:yo,label:_o}=this.withClassScores?await this.extractPredictedClass(b,$t):{classScore:1,label:0};y.push({box:new Vt(Et,Mt,Et+q,Mt+Dt),score:W,classScore:W*yo,label:_o,...$t})}}return f.dispose(),l.dispose(),b.dispose(),y}async extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=go;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Ko,classes:["face"],...t?{anchors:tn,meanRgb:en}:{anchors:Qo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?on:rn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ha(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var xo=v(x());async function oe(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Zt(c)?n(c):c.detection),s=r||(t instanceof xo.Tensor?await de(t,a):await le(t,a)),i=await e(s);return s.forEach(c=>c instanceof xo.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return oe([o],t,async a=>e(a[0]),r,n)}var sn=.4,cn=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],mn=[117.001,114.697,97.404];var ne=class extends ee{constructor(){let t={withSeparableConvs:!0,iouThreshold:sn,classes:["face"],anchors:cn,meanRgb:mn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new St,tinyFaceDetector:new ne,tinyYolov2:new re,faceLandmark68Net:new Kt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Qt,faceExpressionNet:new Oe,ageGenderNet:new He},pn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ba=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ga=(o,t)=>P.tinyYolov2.locateFaces(o,t),un=o=>P.faceLandmark68Net.detectLandmarks(o),xa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),va=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ya=o=>P.faceExpressionNet.predictExpressions(o),_a=o=>P.ageGenderNet.predictAgeAndGender(o),fn=o=>P.ssdMobilenetv1.load(o),Ta=o=>P.tinyFaceDetector.load(o),wa=o=>P.tinyYolov2.load(o),Pa=o=>P.faceLandmark68Net.load(o),Fa=o=>P.faceLandmark68TinyNet.load(o),Da=o=>P.faceRecognitionNet.load(o),Ea=o=>P.faceExpressionNet.load(o),Ma=o=>P.ageGenderNet.load(o),Ca=fn,Ia=pn,Na=un;var Sr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Sr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>yr(r,e[n]))}withAgeAndGender(){return new ie(this,this.input)}},se=class extends Sr{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return yr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Lr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Lr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return Cr(Ir(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Lr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Cr(Ir(t,r,n),e)}withFaceExpressions(){return new se(this,this.input)}},Bt=class extends ie{withFaceExpressions(){return new Wt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Rt=class extends ce{withFaceExpressions(){return new kt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},Pt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Mr(t[n],r))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}},Ft=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Mr(t,e)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof Xe.Tensor?await de(this.input,e):await le(this.input,e),n=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),t.filter((s,i)=>n[i]).map((s,i)=>Pe(s,n[i]))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await de(this.input,[e]):await le(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),Pe(t,n)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=P.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=P.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=P.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>jt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ie(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?jt({},e):void 0)})}withFaceLandmarks(t=!1){return new Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new se(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ce(this.runAndExtendWithFaceDetection(),this.input)}};function Sa(o,t=new X){return new Qe(o,t)}function Ar(o,t=new X){return new Ie(o,t)}async function ln(o,t){return Ar(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Ar(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Aa=ln;function vo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,a)=>n-r[a]).reduce((n,a)=>n+a*a,0))}var tr=class{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,a=()=>`person ${n++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof mt)return s;if(s instanceof Float32Array)return new mt(a(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new mt(a(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>vo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new pe(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>mt.fromJSON(r));return new tr(e,t.distanceThreshold)}};function Wa(o){let t=new ne;return t.extractWeights(o),t}function dn(o,t){let{width:e,height:r}=new k(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>dn(n,{width:e,height:r}));if(Zt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof z||o instanceof M?o.forSize(e,r):o}var Ba=Lo;0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,fetchVideo,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version}); 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He=class extends A{constructor(e=new _r(2)){super("AgeGenderNet");this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){let{params:r}=this;if(!r)throw new Error(`${this._name} - load model before inference`);return ft.tidy(()=>{let n=e instanceof ut?this.faceFeatureExtractor.forwardInput(e):e,a=ft.avgPool(n,[7,7],[2,2],"valid").as2D(n.shape[0],-1),s=$e(a,r.fc.age).as1D(),i=$e(a,r.fc.gender);return{age:s,gender:i}})}forwardInput(e){return ft.tidy(()=>{let{age:r,gender:n}=this.runNet(e);return{age:r,gender:ft.softmax(n)}})}async forward(e){return this.forwardInput(await C(e))}async predictAgeAndGender(e){let r=await C(e),n=await this.forwardInput(r),a=ft.unstack(n.age),s=ft.unstack(n.gender),i=a.map((m,p)=>({ageTensor:m,genderTensor:s[p]})),c=await Promise.all(i.map(async({ageTensor:m,genderTensor:p})=>{let u=m.dataSync()[0],f=p.dataSync()[0],l=f>.5,b=l?"male":"female",y=l?f:1-f;return 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X=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var St=class extends A{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return Lt.tidy(()=>{let r=Lt.cast(t.toBatchTensor(512,!1),"float32"),n=Lt.sub(Lt.div(r,127.5),1),a=jo(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=Jo(a.out,a.conv11,e.prediction_layer);return Xo(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await C(t))}async 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N=v(x());var Cr=o=>typeof o=="number";function ho(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!Cr(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>Cr(t.x)&&Cr(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(Cr)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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on(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=R(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=la(n,s),p;if(t.withSeparableConvs){let[u,f,l,b,y,F,h,T,_]=r,E=t.isFirstLayerConv2d?i(u,f,3,"conv0"):m(u,f,"conv0"),W=m(f,l,"conv1"),tt=m(l,b,"conv2"),lt=m(b,y,"conv3"),q=m(y,F,"conv4"),Dt=m(F,h,"conv5"),Et=T?m(h,T,"conv6"):void 0,Mt=_?m(T,_,"conv7"):void 0,$t=i(_||T||h,5*e,1,"conv8");p={conv0:E,conv1:W,conv2:tt,conv3:lt,conv4:q,conv5:Dt,conv6:Et,conv7:Mt,conv8:$t}}else{let[u,f,l,b,y,F,h,T,_]=r,E=c(u,f,"conv0"),W=c(f,l,"conv1"),tt=c(l,b,"conv2"),lt=c(b,y,"conv3"),q=c(y,F,"conv4"),Dt=c(F,h,"conv5"),Et=c(h,T,"conv6"),Mt=c(T,_,"conv7"),$t=i(_,5*e,1,"conv8");p={conv0:E,conv1:W,conv2:tt,conv3:lt,conv4:q,conv5:Dt,conv6:Et,conv7:Mt,conv8:$t}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function da(o,t){let e=Y(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=xe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=da(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return B(o,e),{params:s,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var go=class extends A{constructor(e){super("TinyYolov2");ho(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,r){let n=Tt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=Tt(n,r.conv6),n=Tt(n,r.conv7),qt(n,r.conv8,"valid",!1)}runMobilenet(e,r){let n=this.config.isFirstLayerConv2d?Me(qt(e,r.conv0,"valid",!1)):wt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=r.conv6?wt(n,r.conv6):n,n=r.conv7?wt(n,r.conv7):n,qt(n,r.conv8,"valid",!1)}forwardInput(e,r){let{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let a=N.cast(e.toBatchTensor(r,!1),"float32");return a=this.config.meanRgb?rt(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,n):this.runTinyYolov2(a,n)})}async forward(e,r){return this.forwardInput(await C(e),r)}async detect(e,r={}){let{inputSize:n,scoreThreshold:a}=new st(r),s=await C(e),i=await this.forwardInput(s,n),c=N.tidy(()=>N.unstack(i)[0].expandDims()),m={width:s.getInputWidth(0),height:s.getInputHeight(0)},p=await this.extractBoxes(c,s.getReshapedInputDimensions(0),a);i.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),b=p.map(h=>this.config.classes[h.label]);return Yr(u.map(h=>h.rescale(n)),f,this.config.iouThreshold,!0).map(h=>new bt(f[h],l[h],b[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return nn(e,this.config)}extractParams(e){let r=this.config.filterSizes||go.DEFAULT_FILTER_SIZES,n=r?r.length:void 0;if(n!==7&&n!==8&&n!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${n} filterSizes in config`);return on(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,n){let{width:a,height:s}=r,i=Math.max(a,s),c=i/a,m=i/s,p=e.shape[1],u=this.config.anchors.length,[f,l,b]=N.tidy(()=>{let T=e.reshape([p,p,u,this.boxEncodingSize]),_=T.slice([0,0,0,0],[p,p,u,4]),E=T.slice([0,0,0,4],[p,p,u,1]),W=this.withClassScores?N.softmax(T.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):N.scalar(0);return[_,E,W]}),y=[],F=await l.array(),h=await f.array();for(let T=0;Tn){let tt=(_+Ne(h[T][_][E][0]))/p*c,lt=(T+Ne(h[T][_][E][1]))/p*m,q=Math.exp(h[T][_][E][2])*this.config.anchors[E].x/p*c,Dt=Math.exp(h[T][_][E][3])*this.config.anchors[E].y/p*m,Et=tt-q/2,Mt=lt-Dt/2,$t={row:T,col:_,anchor:E},{classScore:yo,label:_o}=this.withClassScores?await this.extractPredictedClass(b,$t):{classScore:1,label:0};y.push({box:new Vt(Et,Mt,Et+q,Mt+Dt),score:W,classScore:W*yo,label:_o,...$t})}}return f.dispose(),l.dispose(),b.dispose(),y}async extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=go;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ha(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var xo=v(x());async function oe(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Zt(c)?n(c):c.detection),s=r||(t instanceof xo.Tensor?await de(t,a):await le(t,a)),i=await e(s);return s.forEach(c=>c instanceof xo.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return oe([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],cn=[117.001,114.697,97.404];var ne=class extends ee{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new St,tinyFaceDetector:new ne,tinyYolov2:new re,faceLandmark68Net:new Kt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Qt,faceExpressionNet:new Oe,ageGenderNet:new He},mn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ba=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ga=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),xa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),va=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ya=o=>P.faceExpressionNet.predictExpressions(o),_a=o=>P.ageGenderNet.predictAgeAndGender(o),un=o=>P.ssdMobilenetv1.load(o),Ta=o=>P.tinyFaceDetector.load(o),wa=o=>P.tinyYolov2.load(o),Pa=o=>P.faceLandmark68Net.load(o),Fa=o=>P.faceLandmark68TinyNet.load(o),Da=o=>P.faceRecognitionNet.load(o),Ea=o=>P.faceExpressionNet.load(o),Ma=o=>P.ageGenderNet.load(o),Ca=un,Ia=mn,Na=pn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Ir{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>xr(r,e[n]))}withAgeAndGender(){return new ie(this,this.input)}},se=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return xr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Nr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return Er(Mr(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Er(Mr(t,r,n),e)}withFaceExpressions(){return new se(this,this.input)}},Bt=class extends ie{withFaceExpressions(){return new Wt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Rt=class extends ce{withFaceExpressions(){return new kt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},Pt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Dr(t[n],r))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}},Ft=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Dr(t,e)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof Xe.Tensor?await de(this.input,e):await le(this.input,e),n=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),t.filter((s,i)=>n[i]).map((s,i)=>Pe(s,n[i]))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Ze=class extends Je{async run(){let t=await 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He=class extends A{constructor(e=new _r(2)){super("AgeGenderNet");this._faceFeatureExtractor=e}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(e){let{params:r}=this;if(!r)throw new Error(`${this._name} - load model before inference`);return ft.tidy(()=>{let n=e instanceof ut?this.faceFeatureExtractor.forwardInput(e):e,a=ft.avgPool(n,[7,7],[2,2],"valid").as2D(n.shape[0],-1),s=$e(a,r.fc.age).as1D(),i=$e(a,r.fc.gender);return{age:s,gender:i}})}forwardInput(e){return ft.tidy(()=>{let{age:r,gender:n}=this.runNet(e);return{age:r,gender:ft.softmax(n)}})}async forward(e){return this.forwardInput(await C(e))}async predictAgeAndGender(e){let r=await C(e),n=await this.forwardInput(r),a=ft.unstack(n.age),s=ft.unstack(n.gender),i=a.map((m,p)=>({ageTensor:m,genderTensor:s[p]})),c=await Promise.all(i.map(async({ageTensor:m,genderTensor:p})=>{let u=m.dataSync()[0],f=p.dataSync()[0],l=f>.5,b=l?"male":"female",y=l?f:1-f;return 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X=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var St=class extends A{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return Lt.tidy(()=>{let r=Lt.cast(t.toBatchTensor(512,!1),"float32"),n=Lt.sub(Lt.div(r,127.5),1),a=jo(n,e.mobilenetv1),{boxPredictions:s,classPredictions:i}=Jo(a.out,a.conv11,e.prediction_layer);return Xo(s,i,e.output_layer)})}async forward(t){return this.forwardInput(await C(t))}async 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N=v(x());var Cr=o=>typeof o=="number";function ho(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!Cr(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>Cr(t.x)&&Cr(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(Cr)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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on(o,t,e,r){let{extractWeights:n,getRemainingWeights:a}=R(o),s=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=la(n,s),p;if(t.withSeparableConvs){let[u,f,l,b,y,F,h,T,_]=r,E=t.isFirstLayerConv2d?i(u,f,3,"conv0"):m(u,f,"conv0"),W=m(f,l,"conv1"),tt=m(l,b,"conv2"),lt=m(b,y,"conv3"),q=m(y,F,"conv4"),Dt=m(F,h,"conv5"),Et=T?m(h,T,"conv6"):void 0,Mt=_?m(T,_,"conv7"):void 0,$t=i(_||T||h,5*e,1,"conv8");p={conv0:E,conv1:W,conv2:tt,conv3:lt,conv4:q,conv5:Dt,conv6:Et,conv7:Mt,conv8:$t}}else{let[u,f,l,b,y,F,h,T,_]=r,E=c(u,f,"conv0"),W=c(f,l,"conv1"),tt=c(l,b,"conv2"),lt=c(b,y,"conv3"),q=c(y,F,"conv4"),Dt=c(F,h,"conv5"),Et=c(h,T,"conv6"),Mt=c(T,_,"conv7"),$t=i(_,5*e,1,"conv8");p={conv0:E,conv1:W,conv2:tt,conv3:lt,conv4:q,conv5:Dt,conv6:Et,conv7:Mt,conv8:$t}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:p,paramMappings:s}}function da(o,t){let e=Y(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function a(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let s=xe(e);return{extractConvParams:n,extractConvWithBatchNormParams:a,extractSeparableConvParams:s}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:a}=da(o,e),s;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else s={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return B(o,e),{params:s,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var go=class extends A{constructor(e){super("TinyYolov2");ho(e),this._config=e}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(e,r){let n=Tt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=Tt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=Tt(n,r.conv6),n=Tt(n,r.conv7),qt(n,r.conv8,"valid",!1)}runMobilenet(e,r){let n=this.config.isFirstLayerConv2d?Me(qt(e,r.conv0,"valid",!1)):wt(e,r.conv0);return n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv1),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv2),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv3),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv4),n=N.maxPool(n,[2,2],[2,2],"same"),n=wt(n,r.conv5),n=N.maxPool(n,[2,2],[1,1],"same"),n=r.conv6?wt(n,r.conv6):n,n=r.conv7?wt(n,r.conv7):n,qt(n,r.conv8,"valid",!1)}forwardInput(e,r){let{params:n}=this;if(!n)throw new Error("TinyYolov2 - load model before inference");return N.tidy(()=>{let a=N.cast(e.toBatchTensor(r,!1),"float32");return a=this.config.meanRgb?rt(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,n):this.runTinyYolov2(a,n)})}async forward(e,r){return this.forwardInput(await C(e),r)}async detect(e,r={}){let{inputSize:n,scoreThreshold:a}=new st(r),s=await C(e),i=await this.forwardInput(s,n),c=N.tidy(()=>N.unstack(i)[0].expandDims()),m={width:s.getInputWidth(0),height:s.getInputHeight(0)},p=await this.extractBoxes(c,s.getReshapedInputDimensions(0),a);i.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),b=p.map(h=>this.config.classes[h.label]);return Yr(u.map(h=>h.rescale(n)),f,this.config.iouThreshold,!0).map(h=>new bt(f[h],l[h],b[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(e){return nn(e,this.config)}extractParams(e){let r=this.config.filterSizes||go.DEFAULT_FILTER_SIZES,n=r?r.length:void 0;if(n!==7&&n!==8&&n!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${n} filterSizes in config`);return on(e,this.config,this.boxEncodingSize,r)}async extractBoxes(e,r,n){let{width:a,height:s}=r,i=Math.max(a,s),c=i/a,m=i/s,p=e.shape[1],u=this.config.anchors.length,[f,l,b]=N.tidy(()=>{let T=e.reshape([p,p,u,this.boxEncodingSize]),_=T.slice([0,0,0,0],[p,p,u,4]),E=T.slice([0,0,0,4],[p,p,u,1]),W=this.withClassScores?N.softmax(T.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):N.scalar(0);return[_,E,W]}),y=[],F=await l.array(),h=await f.array();for(let T=0;Tn){let tt=(_+Ne(h[T][_][E][0]))/p*c,lt=(T+Ne(h[T][_][E][1]))/p*m,q=Math.exp(h[T][_][E][2])*this.config.anchors[E].x/p*c,Dt=Math.exp(h[T][_][E][3])*this.config.anchors[E].y/p*m,Et=tt-q/2,Mt=lt-Dt/2,$t={row:T,col:_,anchor:E},{classScore:yo,label:_o}=this.withClassScores?await this.extractPredictedClass(b,$t):{classScore:1,label:0};y.push({box:new Vt(Et,Mt,Et+q,Mt+Dt),score:W,classScore:W*yo,label:_o,...$t})}}return f.dispose(),l.dispose(),b.dispose(),y}async extractPredictedClass(e,r){let{row:n,col:a,anchor:s}=r,i=await e.array();return Array(this.config.classes.length).fill(0).map((c,m)=>i[n][a][s][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}},ee=go;ee.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var re=class extends ee{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function ha(o,t=!0){let e=new re(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var xo=v(x());async function oe(o,t,e,r,n=({alignedRect:a})=>a){let a=o.map(c=>Zt(c)?n(c):c.detection),s=r||(t instanceof xo.Tensor?await de(t,a):await le(t,a)),i=await e(s);return s.forEach(c=>c instanceof xo.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return oe([o],t,async a=>e(a[0]),r,n)}var an=.4,sn=[new g(1.603231,2.094468),new g(6.041143,7.080126),new g(2.882459,3.518061),new g(4.266906,5.178857),new g(9.041765,10.66308)],cn=[117.001,114.697,97.404];var ne=class extends ee{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new St,tinyFaceDetector:new ne,tinyYolov2:new re,faceLandmark68Net:new Kt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Qt,faceExpressionNet:new Oe,ageGenderNet:new He},mn=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),ba=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),ga=(o,t)=>P.tinyYolov2.locateFaces(o,t),pn=o=>P.faceLandmark68Net.detectLandmarks(o),xa=o=>P.faceLandmark68TinyNet.detectLandmarks(o),va=o=>P.faceRecognitionNet.computeFaceDescriptor(o),ya=o=>P.faceExpressionNet.predictExpressions(o),_a=o=>P.ageGenderNet.predictAgeAndGender(o),un=o=>P.ssdMobilenetv1.load(o),Ta=o=>P.tinyFaceDetector.load(o),wa=o=>P.tinyYolov2.load(o),Pa=o=>P.faceLandmark68Net.load(o),Fa=o=>P.faceLandmark68TinyNet.load(o),Da=o=>P.faceRecognitionNet.load(o),Ea=o=>P.faceExpressionNet.load(o),Ma=o=>P.ageGenderNet.load(o),Ca=un,Ia=mn,Na=pn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Ir{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>xr(r,e[n]))}withAgeAndGender(){return new ie(this,this.input)}},se=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return xr(t,e)}withAgeAndGender(){return new ce(this,this.input)}},Wt=class extends ae{withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},kt=class extends se{withAgeAndGender(){return new Rt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ie=class extends Nr{async run(){let t=await this.parentTask,e=await oe(t,this.input,async r=>Promise.all(r.map(n=>P.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:a,gender:s,genderProbability:i}=e[n];return Er(Mr(r,s,i),a)})}withFaceExpressions(){return new ae(this,this.input)}},ce=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,a=>P.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Er(Mr(t,r,n),e)}withFaceExpressions(){return new se(this,this.input)}},Bt=class extends ie{withFaceExpressions(){return new Wt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Rt=class extends ce{withFaceExpressions(){return new kt(this,this.input)}withFaceDescriptor(){return new Ft(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},Pt=class extends Ue{async run(){let t=await this.parentTask;return(await oe(t,this.input,r=>Promise.all(r.map(n=>P.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Dr(t[n],r))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}},Ft=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Dr(t,e)}withFaceExpressions(){return new kt(this,this.input)}withAgeAndGender(){return new Rt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof Xe.Tensor?await de(this.input,e):await le(this.input,e),n=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),t.filter((s,i)=>n[i]).map((s,i)=>Pe(s,n[i]))}withFaceExpressions(){return new Wt(this,this.input)}withAgeAndGender(){return new Bt(this,this.input)}withFaceDescriptors(){return new Pt(this,this.input)}},Ze=class extends Je{async run(){let t=await 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this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>vo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new pe(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>mt.fromJSON(r));return new tr(e,t.distanceThreshold)}};function Wa(o){let t=new ne;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new k(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(Zt(o)){let n=o.detection.forSize(e,r),a=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(jt(o,n),a)}return pt(o)?jt(o,o.detection.forSize(e,r)):o instanceof z||o instanceof M?o.forSize(e,r):o}var Ba=So;0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,fetchVideo,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version}); 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vS;(function(r){r.float32="float32",r.int32="float32",r.bool="float32",r.complex64="complex64"})(vS||(vS={}));var SS;(function(r){r.float32="complex64",r.int32="complex64",r.bool="complex64",r.complex64="complex64"})(SS||(SS={}));var I4={float32:vS,int32:CS,bool:IS,complex64:SS};function ar(r,t){if(r==="string"||t==="string"){if(r==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${r} with ${t}`)}return I4[r][t]}function Hu(r){return ar(r,"int32")}function Ut(r,t){if(r.dtype===t.dtype)return[r,t];let e=ar(r.dtype,t.dtype);return[r.cast(e),t.cast(e)]}function NS(r,t){_(r.dtype===t.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${t.dtype}) input must match`)}function v4(r,t){return t.some(e=>e.id===r.id)}function oh(r){let t=[];return V1(r,t,new Set),t}function V1(r,t,e){if(r==null)return;if(r instanceof Ft){t.push(r);return}if(!S4(r))return;let n=r;for(let o in n){let s=n[o];e.has(s)||(e.add(s),V1(s,t,e))}}function S4(r){return Array.isArray(r)||typeof r=="object"}function TS(r){return r.kernelName!=null}var Hg=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(t=>t.name)))}}}dispose(){for(let t in this.registeredVariables)this.registeredVariables[t].dispose()}},Xl=class{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Hg}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let t=this.getSortedBackends();for(let 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u,l=TS(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(TS(t)){let{kernelName:d,inputs:h,attrs:g}=t;this.backendName==null&&this.backend;let x=Qd(d,this.backendName);_(x!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),a=()=>{let b=this.backend.numDataIds();u=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let w=Array.isArray(u)?u:[u];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,b,w);let C=w.map(N=>N.rank!=null?N:this.makeTensorFromTensorInfo(N));if(o){let N=this.getTensorsForGradient(d,h,C);n=this.saveTensorsForBackwardMode(N)}return C}}else{let{forwardFunc:d}=t,h=g=>{!o||(n=g.map(x=>this.keep(this.clone(x))))};a=()=>{let g=this.backend.numDataIds();u=this.tidy(()=>d(this.backend,h));let x=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,g,x),x}}let{inputs:c,attrs:p}=t,m=TS(t)?null:t.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?e=a():(f=this.profiler.profileKernel(l,c,()=>a()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),e=f.outputs)}),o&&this.addTapeNode(l,c,e,m,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-i,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:e.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(u)?e:e[0]}saveTensorsForBackwardMode(t){return t.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(t,e,n){let o=mS(t);if(o!=null){let s=o.inputsToSave||[],i=o.outputsToSave||[],a;o.saveAllInputs?(_(Array.isArray(e),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(e).map(l=>e[l])):a=s.map(l=>e[l]);let 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this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(t,e){this.state.numTensors++,t.dtype==="string"&&this.state.numStringTensors++;let n=0;t.dtype!=="complex64"&&t.dtype!=="string"&&(n=t.size*zg(t.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(t.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:n})),t instanceof Ya||this.track(t)}incRef(t,e){this.trackTensor(t,e),this.backend.incRef(t.dataId)}removeDataId(t,e){this.state.tensorInfo.has(t)&&this.state.tensorInfo.get(t).backend===e&&(this.state.tensorInfo.delete(t),this.state.numDataBuffers--)}disposeTensor(t){if(!this.state.tensorInfo.has(t.dataId))return;let e=this.state.tensorInfo.get(t.dataId);if(this.state.numTensors--,t.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=e.bytes),t.dtype!=="complex64"&&t.dtype!=="string"){let n=t.size*zg(t.dtype);this.state.numBytes-=n}e.backend.disposeData(t.dataId)&&this.removeDataId(t.dataId,e.backend)}disposeVariables(){for(let t in this.state.registeredVariables){let e=this.state.registeredVariables[t];this.disposeVariable(e)}}disposeVariable(t){this.disposeTensor(t),this.state.registeredVariables[t.name]!=null&&delete this.state.registeredVariables[t.name]}memory(){let t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,t.reasons==null&&(t.reasons=[]),t.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),t}async profile(t){this.state.profiling=!0;let e=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(o=>o.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let o of this.state.activeProfile.kernels)o.kernelTimeMs=await o.kernelTimeMs,o.extraInfo=await o.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(t,e,n,o,s,i){let a={id:this.state.nextTapeNodeId++,kernelName:t,inputs:e,outputs:n,saved:s},u=mS(t);u!=null&&(o=u.gradFunc),o!=null&&(a.gradient=l=>(l=l.map((c,p)=>{if(c==null){let m=n[p],f=ap(m.size,m.dtype);return this.makeTensor(f,m.shape,m.dtype)}return c}),o(l.length>1?l:l[0],s,i))),this.state.activeTape.push(a)}keep(t){return t.kept=!0,t}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(t){let e={track:[],name:"unnamed scope",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e}endScope(t){let e=oh(t),n=new Set(e.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===o.id&&this.track(s)})}gradients(t,e,n,o=!1){if(_(e.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",t));_(s instanceof Ft,()=>"The result y returned by f() must be a tensor.");let i=R1(this.state.activeTape,e,s);if(!o&&i.length===0&&e.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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ME={};Wt(ME,{TEST_EPSILON_FLOAT16:()=>OE,createVideoElement:()=>RH,encodeStrings:()=>LE,expectArrayBuffersEqual:()=>DH,expectArraysClose:()=>EH,expectArraysEqual:()=>AH,expectNumbersClose:()=>PE,expectPromiseToFail:()=>_H,expectValuesInRange:()=>$H,play:()=>FH,testEpsilon:()=>ax});var kH=.001,OE=.1;function EH(r,t,e){return e==null&&(e=ax()),YS(r,t,(n,o)=>ZS(n,o,e))}function ax(){return k.backend.floatPrecision()===32?kH:OE}function YS(r,t,e){let n=!0;if((br(r)||br(t))&&(n=!1),br(r)&&br(t)&&(n=!0),n){let i=r.constructor.name,a=t.constructor.name;if(i!==a)throw new Error(`Arrays are of different type. 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u,l=T0(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(T0(t)){let{kernelName:d,inputs:h,attrs:g}=t;this.backendName==null&&this.backend;let x=Zd(d,this.backendName);E(x!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),a=()=>{let b=this.backend.numDataIds();u=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let w=Array.isArray(u)?u:[u];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,b,w);let C=w.map(N=>N.rank!=null?N:this.makeTensorFromTensorInfo(N));if(o){let N=this.getTensorsForGradient(d,h,C);n=this.saveTensorsForBackwardMode(N)}return C}}else{let{forwardFunc:d}=t,h=g=>{!o||(n=g.map(x=>this.keep(this.clone(x))))};a=()=>{let g=this.backend.numDataIds();u=this.tidy(()=>d(this.backend,h));let x=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,g,x),x}}let{inputs:c,attrs:p}=t,m=T0(t)?null:t.backwardsFunc,f;return 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c={x:u,filter:a},p={strides:e,pad:n,dilations:o},m=k.runKernel(Rl,c,p);return l?R(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Ix=T({dilation2d_:rq});var Wr={};Ut(Wr,{assertAndGetBroadcastShape:()=>Ot,getBroadcastDims:()=>gE,getReductionAxes:()=>xe});function gE(r,t){let e=r.length,n=[];for(let o=0;o1&&i===1&&n.unshift(s)}return n}function xe(r,t){let e=[];for(let n=0;n1)&&e.unshift(s)}return e}function Ot(r,t){let e=[],n=Math.max(r.length,t.length);for(let o=0;o`Error in dot: inputs must all be rank 1 or 2, but got ranks ${e.rank} and ${n.rank}.`);let o=e.rank===1?e.size:e.shape[1],s=n.rank===1?n.size:n.shape[0];if(E(o===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${o} and ${s}.`),e.rank===1&&n.rank===1){let i=R(e,[1,-1]),a=R(n,[-1,1]),u=Mt(i,a);return R(u,[])}else if(e.rank===1&&n.rank===2){let i=R(e,[1,-1]),a=R(n,[n.shape[0],n.shape[1]]),u=Mt(i,a);return R(u,[u.size])}else if(e.rank===2&&n.rank===1){let i=R(n,[-1,1]),a=Mt(e,i);return R(a,[a.size])}else{let i=R(n,[n.shape[0],n.shape[1]]);return Mt(e,i)}}var Sx=T({dot_:aq});function lq(r,...t){let e=t.map((o,s)=>v(o,`tensors${s}`,"einsum")),n={equation:r};return k.runKernel(wp,e,n)}var xE=T({einsum_:lq});function uq(r){let e={x:v(r,"x","elu","float32")};return k.runKernel(Qo,e)}var Pi=T({elu_:uq});function cq(r){let t=v(r,"x","erf");E(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=J(t,"float32"));let e={x:t};return k.runKernel(wa,e)}var Nx=T({erf_:cq});function U0(r,t){for(let e=0;er[s]);return[e,o]}function bo(r,t){let e=t.map(n=>1);return yE(r,e,t)}function pq(r,t,e){E(U0(t,e),()=>`${r} supports only inner-most axes for now. 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i=v(r,"forgetBias","basicLSTMCell"),a=v(t,"lstmKernel","basicLSTMCell"),u=v(e,"lstmBias","basicLSTMCell"),l=v(n,"data","basicLSTMCell"),c=v(o,"c","basicLSTMCell"),p=v(s,"h","basicLSTMCell"),m=oe([l,p],1),f=Lt(m,a),d=X(f,u),h=d.shape[0],g=d.shape[1]/4,x=[h,g],b=Rt(d,[0,0],x),w=Rt(d,[0,g],x),C=Rt(d,[0,g*2],x),N=Rt(d,[0,g*3],x),E=X(D(Jr(b),Fi(w)),D(c,Jr(X(i,C)))),A=D(Fi(E),Jr(N));return[E,A]}var WE=T({basicLSTMCell_:pq});function mq(r,t,e){let n=v(r,"x","batchToSpaceND"),o=t.reduce((a,u)=>a*u);_(n.rank>=1+t.length,()=>`input rank is ${n.rank} but should be > than blockShape.length ${t.length}`),_(e.length===t.length,()=>`crops.length is ${e.length} but should be equal to blockShape.length ${t.length}`),_(n.shape[0]%o===0,()=>`input tensor batch is ${n.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${o}`);let s={x:n},i={blockShape:t,crops:e};return k.runKernel(ci,s,i)}var tu=T({batchToSpaceND_:mq});function UE(r){let t;return r.rank===0||r.rank===1?t=R(r,[1,1,1,r.size]):r.rank===2?t=R(r,[1,1,r.shape[0],r.shape[1]]):r.rank===3?t=R(r,[1,r.shape[0],r.shape[1],r.shape[2]]):t=r,t}function fq(r,t,e,n,o,s){s==null&&(s=.001);let i=v(r,"x","batchNorm"),a=v(t,"mean","batchNorm"),u=v(e,"variance","batchNorm"),l;o!=null&&(l=v(o,"scale","batchNorm"));let c;n!=null&&(c=v(n,"offset","batchNorm")),_(a.rank===u.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_(c==null||a.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_(l==null||a.rank===l.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let m={x:UE(i),scale:l,offset:c,mean:a,variance:u},f={varianceEpsilon:s},d=k.runKernel(ss,m,f);return R(d,i.shape)}var Oi=T({batchNorm_:fq});function dq(r,t,e,n,o,s){let i=v(r,"x","batchNorm"),a=v(t,"mean","batchNorm"),u=v(e,"variance","batchNorm"),l;o!=null&&(l=v(o,"scale","batchNorm"));let c;return n!=null&&(c=v(n,"offset","batchNorm")),_(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),_(a.rank===2||a.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${a.rank}.`),_(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`),l!=null&&_(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&_(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),Oi(i,a,u,c,l,s)}var yx=T({batchNorm2d_:dq});function hq(r,t,e,n,o,s){let i=v(r,"x","batchNorm"),a=v(t,"mean","batchNorm"),u=v(e,"variance","batchNorm"),l;o!=null&&(l=v(o,"scale","batchNorm"));let c;return n!=null&&(c=v(n,"offset","batchNorm")),_(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),_(a.rank===3||a.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank 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but got rank ${l.rank}.`),c!=null&&_(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),Oi(i,a,u,c,l,s)}var wx=T({batchNorm4d_:gq});function xq(r,t,e){let n=v(r,"x","bincount"),o=v(t,"weights","bincount");_(n.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${n.dtype}`),_(e>=0,()=>`size must be non-negative, but got ${e}.`),_(o.size===n.size||o.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${n.shape}, weights shape: ${o.shape}.`);let s={x:n,weights:o},i={size:e};return k.runKernel(cp,s,i)}var Cx=T({bincount_:xq});function yq(r,t){let e=v(r,"s0","broadcastArgs","int32"),n=v(t,"s1","broadcastArgs","int32");if(e.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${e.rank}`);if(n.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${n.rank}`);let o={s0:e,s1:n};return k.runKernel(pp,o)}var HE=T({broadcastArgs_:yq});function bq(r,t){let e=v(r,"broadcastTo","x"),n=e.shape;if(Pe(t),t.lengthe.rank){let l=e.shape.slice();for(;l.length=0;l--)if(o[l]===t[l])s[l]=1;else if(e.shape[l]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${t}].`);if(s.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return an(e);let a={x:e},u={reps:s};return k.runKernel(to,a,u)}var Pi=T({broadcastTo_:bq});function wq(r){let e={x:v(r,"x","ceil","float32")};return k.runKernel(Ko,e)}var Ix=T({ceil_:wq});function bo(r,t,e){Pe(r);let n={shape:r,value:t,dtype:e};return k.runKernel(Ol,{},n)}function Cq(r,t,e){let n=v(r,"x","clipByValue");if(_(t<=e,()=>`Error in clip: min (${t}) must be less than or equal to max (${e}).`),t===e)return bo(n.shape,t,n.dtype);let o={x:n},s={clipValueMin:t,clipValueMax:e};return k.runKernel(po,o,s)}var vr=T({clipByValue_:Cq});function Iq(r){return oe(r,0)}var vx=T({concat1d_:Iq});function vq(r,t){return oe(r,t)}var Sx=T({concat2d_:vq});function Sq(r,t){return oe(r,t)}var Nx=T({concat3d_:Sq});function Nq(r,t){return oe(r,t)}var Tx=T({concat4d_:Nq});function Tq(r,t,e,n,o="NHWC",s=[1,1],i){let a=v(r,"x","conv2d","float32"),u=v(t,"filter","conv2d","float32"),l=a,c=!1;a.rank===3&&(c=!0,l=R(a,[1,a.shape[0],a.shape[1],a.shape[2]])),_(l.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${l.rank}.`),_(u.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`),Ie("conv2d",n,i);let p=o==="NHWC"?l.shape[3]:l.shape[1];_(p===u.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`),_(Dr(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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Got stride ${e} and dilation '${s}'`),_(o==="NWC",()=>`Error in conv1d: got dataFormat of ${o} but only NWC is currently supported.`);let p=R(u,[1,u.shape[0],u.shape[1],u.shape[2]]),m=R(l,[l.shape[0],1,l.shape[1],l.shape[2]]),g=Sn(m,p,[1,e],n,"NHWC",[1,s],i);return c?R(g,[g.shape[2],g.shape[3]]):R(g,[g.shape[0],g.shape[2],g.shape[3]])}var tm=T({conv1d_:kq});function Eq(r,t,e,n,o,s="NHWC",i){_(r.length===t.rank,()=>`Length of inShape (${r.length}) and rank of dy (${t.rank}) must match`);let a=r,u=t,l=!1;t.rank===3&&(l=!0,u=R(t,[1,t.shape[0],t.shape[1],t.shape[2]]),a=[1,r[0],r[1],r[2]]),_(a.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${a.length}.`),_(u.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${u.rank}`),_(e.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${e.rank}`);let c=s==="NHWC"?a[3]:a[1],p=s==="NHWC"?u.shape[3]:u.shape[1];_(c===e.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${e.shape[2]}.`),_(p===e.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${e.shape[3]}.`),Ie("conv2dDerInput",o,i);let m={dy:u,filter:e},f={strides:n,pad:o,dataFormat:s,dimRoundingMode:i,inputShape:a},d=k.runKernel(Xo,m,f);return l?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var em=T({conv2DBackpropInput_:Eq});function _q(r,t,e,n,o,s){let i=v(r,"x","conv2dTranspose"),a=v(t,"filter","conv2dTranspose");return em(e,i,a,n,o,"NHWC",s)}var rm=T({conv2dTranspose_:_q});function Aq(r,t,e,n,o="NDHWC",s=[1,1,1]){let i=v(r,"x","conv3d"),a=v(t,"filter","conv3d"),u=i,l=!1;i.rank===4&&(l=!0,u=R(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),_(u.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${u.rank}.`),_(a.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${a.rank}.`),_(u.shape[4]===a.shape[3],()=>`Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${a.shape[3]}.`),_(Dr(e,s),()=>`Error in conv3D: Either strides or dilations must be 1. 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i={inputIndices:n,inputShape:o,newShape:s},a=k.runKernel(Ua,i);return{outputIndices:a[0],outputShape:a[1]}}var K_=T({sparseReshape_:B6});function V6(r,t,e){let n=v(r,"data","sparseSegmentMean"),o=v(t,"indices","sparseSegmentMean","int32"),s=v(e,"segmentIds","sparseSegmentMean","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but received shape ${o.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape - ${s.shape}`);let i={data:n,indices:o,segmentIds:s};return k.runKernel(Bl,i)}var bA=T({sparseSegmentMean_:fX});function dX(r,t,e){let n=v(r,"data","sparseSegmentSum"),o=v(t,"indices","sparseSegmentSum","int32"),s=v(e,"segmentIds","sparseSegmentSum","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but received shape + ${s.shape}`);let 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Hr{constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=k.registeredVariables[n];this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:B(()=>bo(s.shape,this.initialAccumulatorValue).variable(!1))});let i=Array.isArray(t)?t[o].tensor:t[n];if(i==null)return;let a=this.accumulatedGrads[o].variable;B(()=>{let u=X(a,Mt(i));a.assign(u);let l=X(D(pt(i,ve(X(u,k.backend.epsilon()))),-this.learningRate),s);s.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Nt(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(t){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};hu.className="Adamax";vn(hu);var Ui=class extends Hr{constructor(t){super(),this.learningRate=t,this.setLearningRate(t)}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=Array.isArray(t)?t[o].tensor:t[n];if(s==null)return;let i=k.registeredVariables[n];B(()=>{let a=X(D(this.c,s),i);i.assign(a)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=Ae(mt(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error("SGD optimizer does 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getMap(){return Wi.instance==null&&(Wi.instance=new Wi),Wi.instance}static register(t){Wi.getMap().classNameMap[t.className]=[t,t.fromConfig]}};function aS(r){E(r.className!=null,()=>"Class being registered does not have the static className property defined."),E(typeof r.className=="string",()=>"className is required to be a string, but got type "+typeof r.className),E(r.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),Wi.register(r)}var Hr=class extends dh{minimize(t,e=!1,n){let{value:o,grads:s}=this.computeGradients(t,n);if(n!=null){let i=n.map(a=>({name:a.name,tensor:s[a.name]}));this.applyGradients(i)}else this.applyGradients(s);return Nt(s),e?o:(o.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(t,e){return Dx(t,e)}dispose(){this.iterations_!=null&&Nt(this.iterations_)}async 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s=k.registeredVariables[n],i=!1;this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accum_grad`,variable:B(()=>It(s).variable(i))}),this.accumulatedUpdates[o]==null&&(this.accumulatedUpdates[o]={originalName:`${n}/accum_var`,variable:B(()=>It(s).variable(i))});let a=Array.isArray(t)?t[o].tensor:t[n];if(a==null)return;let u=this.accumulatedGrads[o].variable,l=this.accumulatedUpdates[o].variable;B(()=>{let c=Y(D(u,this.rho),D(zt(a),1-this.rho)),p=D(ut(Se(Y(l,this.epsilon)),Se(Y(u,this.epsilon))),a),m=Y(D(l,this.rho),D(zt(p),1-this.rho));u.assign(c),l.assign(m);let f=Y(D(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Nt(this.accumulatedGrads.map(t=>t.variable)),Nt(this.accumulatedUpdates.map(t=>t.variable)))}async getWeights(){let t=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=t.length/2,n=!1;this.accumulatedGrads=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedUpdates=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}};var rc=class extends Hr{constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}static get className(){return"Adagrad"}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=k.registeredVariables[n];this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:B(()=>yo(s.shape,this.initialAccumulatorValue).variable(!1))});let i=Array.isArray(t)?t[o].tensor:t[n];if(i==null)return;let a=this.accumulatedGrads[o].variable;B(()=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(t){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};var rl=class extends Hr{constructor(t){super(),this.learningRate=t,this.setLearningRate(t)}static get className(){return"SGD"}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=Array.isArray(t)?t[o].tensor:t[n];if(s==null)return;let i=k.registeredVariables[n];B(()=>{let a=Y(D(this.c,s),i);i.assign(a)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=De(pt(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error("SGD optimizer 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B(()=>{if(s==null&&(s=dn()),Fe(s),r.shape.length!==3)throw new z(`The input of a conv1dWithBias operation should be 3, but is ${r.shape.length} instead.`);if(t.shape.length!==3)throw new z(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(e!=null&&e.shape.length!==1)throw new z(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(r=Ot(r,[0,2,1])),o==="causal")throw new vt("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let a=tm(r,t,n,o==="same"?"same":"valid","NWC",i);return e!=null&&(a=hn(a,e)),a})}function ID(r,t,e,n=[1,1],o="valid",s,i,a=null){return B(()=>{if(s==null&&(s=dn()),Fe(s),r.rank!==3&&r.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=$h(r,s);if(o==="causal")throw new vt("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=pu.conv2d({x:u,filter:t,strides:n,pad:o==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:e,activation:a}),s==="channelsFirst"&&(u=Ot(u,[0,3,1,2])),u})}function eY(r,t,e,n=[1,1,1],o="valid",s,i){return B(()=>{if(s==null&&(s=dn()),Fe(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=G0(r,s);if(o==="causal")throw new vt("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return a=kx(a,t,n,o==="same"?"same":"valid","NDHWC",i),e!=null&&(a=hn(a,e)),s==="channelsFirst"&&(a=Ot(a,[0,4,1,2,3])),a})}var Cc=class extends Et{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Cc.verifyArgs(e),this.rank=t,Qe(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new vt(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented 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if(this.dilationRate.length!==2)throw new z(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(io("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!Iy(t.kernelSize,"number",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ys(this.activation),useBias:this.useBias,biasInitializer:Te(this.biasInitializer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),biasConstraint:ze(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},Su=class extends Cc{constructor(t,e){super(t,e),this.kernel=null,Su.verifyArgs(e),this.filters=e.filters,Qe(this.filters,"filters"),this.kernelInitializer=de(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Be(e.kernelConstraint),this.kernelRegularizer=be(e.kernelRegularizer)}build(t){t=zt(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=St(t);let n,o=this.bias==null?null:this.bias.read(),s=vy(this.activation.getClassName());if(s!=null&&this.rank===2)n=ID(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=tY(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=ID(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=eY(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new vt("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=zt(t);let e=[],n=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},ul=class extends Su{constructor(t){super(2,t),ul.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Iy(t.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};ul.className="Conv2D";Q.registerClass(ul);var cl=class extends Su{constructor(t){super(3,t),cl.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};cl.className="Conv3D";Q.registerClass(cl);var tf=class extends ul{constructor(t){if(super(t),this.inputSpec=[new ye({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=zt(t),t.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new ye({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a;this.dataFormat==="channelsFirst"?(i=2,a=3):(i=1,a=2);let u=o[i],l=o[a],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=Js(u,m,c,this.padding),h=Js(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=Ot(n,[0,2,3,1]));let x=rm(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Ot(x,[0,3,1,2])),this.bias!=null&&(x=hn(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(t){t=zt(t);let e=t.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let i=this.kernelSize[0],a=this.kernelSize[1],u=this.strides[0],l=this.strides[1];return e[n]=this.filters,e[o]=Js(e[o],u,i,this.padding),e[s]=Js(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};tf.className="Conv2DTranspose";Q.registerClass(tf);var ef=class extends cl{constructor(t){if(super(t),this.inputSpec=[new ye({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=zt(t),t.length!==5)throw new z("Input should have rank 5; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new ye({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat==="channelsFirst"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=Js(l,h,m,this.padding),w=Js(c,g,f,this.padding),C=Js(p,x,d,this.padding),N=[s,b,w,C,this.filters];this.dataFormat!=="channelsLast"&&(n=Ot(n,[0,2,3,4,1]));let E=_x(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(E=Ot(E,[0,4,1,2,3])),this.bias!==null&&(E=hn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=zt(t);let e=t.slice(),n,o,s,i;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=Js(e[o],c,a,this.padding),e[s]=Js(e[s],p,u,this.padding),e[i]=Js(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};ef.className="Conv3DTranspose";Q.registerClass(ef);var db=class extends Su{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=de(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=be(e.depthwiseRegularizer),this.depthwiseConstraint=Be(e.depthwiseConstraint),this.pointwiseInitializer=de(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=be(e.pointwiseRegularizer),this.pointwiseConstraint=Be(e.pointwiseConstraint)}build(t){if(t=zt(t),t.length{t=St(t);let n;if(this.rank===1)throw new vt("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Ot(t,[0,2,3,1])),n=fm(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=hn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ot(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.pointwiseInitializer=Te(this.pointwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.pointwiseRegularizer=me(this.pointwiseRegularizer),t.depthwiseConstraint=ze(this.depthwiseConstraint),t.pointwiseConstraint=ze(this.pointwiseConstraint),t}};db.className="SeparableConv";var rf=class extends db{constructor(t){super(2,t)}};rf.className="SeparableConv2D";Q.registerClass(rf);var Nu=class extends Su{constructor(t){super(1,t),Nu.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Iy(t.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};Nu.className="Conv1D";Q.registerClass(Nu);var nf=class extends Et{constructor(t){super(t),typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat==="channelsLast"){let n=Ch(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ch(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ch(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ch(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};nf.className="Cropping2D";Q.registerClass(nf);var of=class extends Et{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Fe(this.dataFormat),this.interpolation=t.interpolation==null?"nearest":t.interpolation,$$(this.interpolation)}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=St(t),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Ot(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation==="nearest"?so.resizeNearestNeighbor(n,[s,i]):so.resizeBilinear(n,[s,i]);return Ot(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation==="nearest"?so.resizeNearestNeighbor(n,[s,i]):so.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};of.className="UpSampling2D";Q.registerClass(of);function rY(r,t,e=[1,1],n="valid",o,s){return B(()=>{o==null&&(o=dn()),Fe(o);let i=$h(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Li(i,t,e,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(i=Ot(i,[0,3,1,2])),i})}var sf=class extends Cc{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=de(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Be(t.depthwiseConstraint),this.depthwiseRegularizer=be(t.depthwiseRegularizer)}build(t){if(t=zt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{t=St(t);let n=rY(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=hn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=zt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=kn(e,this.kernelSize[0],this.padding,this.strides[0]),i=kn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.depthwiseConstraint=ze(this.depthwiseRegularizer),t}};sf.className="DepthwiseConv2D";Q.registerClass(sf);function W0(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function U0(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(fn(2,u));if(t=Ot(t,l),s!=null)throw new vt("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=J(J(o,"bool"),"float32"),o.rank===u-1&&(o=sr(o,-1)),o=Ot(o,l)),n&&(t=mr(t,0),o!=null&&(o=mr(o,0)));let c=[],p,m=e,f=t.shape[0],d=dr(t),h;o!=null&&(h=dr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let C=B(()=>{let N=h[x],E=ct(wr(N),N),A=X(D(w[0],N),D(m[0],E)),$=m.map((F,P)=>X(D(w[1][P],N),D(F,E)));return{output:A,newStates:$}});p=C.output,m=C.newStates}a&&c.push(p)}let g;return a&&(g=qe(c,1)),[p,g,m]})}var En=class extends Et{constructor(t){super(t);let e;if(t.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new Sc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new ye({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return fn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Dy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new ye({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new Tn("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>Ne([n,o])):this.states_=[Ne([n,this.cell.stateSize])];else if(t==null)Nt(this.states_),this.keptStates!=null&&(Nt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>Ne([n,o])):this.states_[0]=Ne([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Nt(this.states_);for(let o=0;oAe(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=W0(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new ye({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof tn){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=St(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let a={training:o},l=U0((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=Ne(t.shape);return e=ft(e,[1,2]),e=il(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?_y(e,[1,n]):e):this.cell.stateSize>1?[_y(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===En.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=yn(o,n);return new t(Object.assign(e,{cell:s}))}};En.className="RNN";Q.registerClass(En);var pl=class extends Et{},Ic=class extends pl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,Qe(this.units,"units"),this.activation=Zs(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=be(t.kernelRegularizer),this.recurrentRegularizer=be(t.recurrentRegularizer),this.biasRegularizer=be(t.biasRegularizer),this.kernelConstraint=Be(t.kernelConstraint),this.recurrentConstraint=Be(t.recurrentConstraint),this.biasConstraint=Be(t.biasConstraint),this.dropout=uc([1,js([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=uc([1,js([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=zt(t),this.kernel=this.addWeight("kernel",[t[t.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0wr(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0wr(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Eo(D(t,i),this.kernel.read()):s=Eo(t,this.kernel.read()),this.bias!=null&&(s=hn(s,this.bias.read())),a!=null&&(n=D(n,a));let u=X(s,Eo(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:Ys(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),recurrentInitializer:Te(this.recurrentInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:ze(this.kernelConstraint),recurrentConstraint:ze(this.recurrentConstraint),biasConstraint:ze(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};Ic.className="SimpleRNNCell";Q.registerClass(Ic);var af=class extends En{constructor(t){t.cell=new Ic(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Nt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Nt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};af.className="SimpleRNN";Q.registerClass(af);var vc=class extends pl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.resetAfter)throw new z("GRUCell does not support reset_after parameter set to true.");this.units=t.units,Qe(this.units,"units"),this.activation=Zs(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=Zs(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=be(t.kernelRegularizer),this.recurrentRegularizer=be(t.recurrentRegularizer),this.biasRegularizer=be(t.biasRegularizer),this.kernelConstraint=Be(t.kernelConstraint),this.recurrentConstraint=Be(t.recurrentConstraint),this.biasConstraint=Be(t.biasConstraint),this.dropout=uc([1,js([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=uc([1,js([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=zt(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0wr(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0{this.cell.dropoutMask!=null&&(Nt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Nt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};lf.className="GRU";Q.registerClass(lf);var ml=class extends pl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,Qe(this.units,"units"),this.activation=Zs(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=Zs(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=be(t.kernelRegularizer),this.recurrentRegularizer=be(t.recurrentRegularizer),this.biasRegularizer=be(t.biasRegularizer),this.kernelConstraint=Be(t.kernelConstraint),this.recurrentConstraint=Be(t.recurrentConstraint),this.biasConstraint=Be(t.biasConstraint),this.dropout=uc([1,js([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=uc([1,js([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=zt(t);let n=t[t.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends gn{apply(u,l){let c=s.apply([i]),p=new wu().apply([i]),m=s.apply([i*2]);return _0(_0(c,p),m)}},e.className="CustomInit",e)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0wr(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Nt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Nt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};uf.className="LSTM";Q.registerClass(uf);var Sc=class extends pl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{Ks(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign(Object.assign({},t),o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(yn(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return vh(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):$y(t(),e),a=()=>bu(i,t,n);return!o||o<=1?Ae(a().clone()):Array(o).fill(void 0).map(a).map(l=>Ae(l.clone()))}var nY=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o{if(this.cell.dropoutMask!=null&&(Nt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Nt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z("ConvRNN2D cell does not support constants");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=Ne(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new Tn("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ne(s)):this.states_=[Ne(s)];else if(t==null)Nt(this.states_),this.keptStates!=null&&(Nt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ne(s)):this.states_[0]=Ne(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Nt(this.states_);for(let a=0;aAe(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e==="channelsFirst",l=t[u?3:2],c=t[u?4:3],p=kn(l,o[0],s,i[0],a[0]),m=kn(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};hb.className="ConvRNN2D";var Nc=class extends ml{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,Qe(this.filters,"filters"),this.kernelSize=vu(n,2,"kernelSize"),this.kernelSize.forEach(u=>Qe(u,"kernelSize")),this.strides=vu(o||1,2,"strides"),this.strides.forEach(u=>Qe(u,"strides")),this.padding=s||"valid",mn(this.padding),this.dataFormat=i||"channelsLast",Fe(this.dataFormat),this.dilationRate=vu(a||1,2,"dilationRate"),this.dilationRate.forEach(u=>Qe(u,"dilationRate"))}build(t){var e;t=zt(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends gn{apply(m,f){let d=l.apply([c]),h=pr([c]),g=l.apply([c*2]);return Tm([d,h,g])}},e.className="CustomInit",e)}else u=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0wr(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(rt,ot,at)=>!ot||!ot[at]?rt:D(ot[at],rt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0wr(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[C,N,E,A]=fr(this.kernel.read(),a,w),[$,F,P,V]=this.useBias?fr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,C,$,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,E,P,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=fr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let j=this.recurrentActivation.apply(X(c,h)),Y=this.recurrentActivation.apply(X(p,g)),Z=X(D(Y,i),D(j,this.activation.apply(X(m,x)))),et=D(this.recurrentActivation.apply(X(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=nY(t,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=Sn(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?hn(s,n,this.dataFormat):s}recurrentConv(t,e){return Sn(t,e,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Nc.className="ConvLSTM2DCell";Q.registerClass(Nc);var cf=class extends hb{constructor(t){let e=new Nc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};cf.className="ConvLSTM2D";Q.registerClass(cf);var Tc=class extends Et{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);if(0$y(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Tc.className="Dropout";Q.registerClass(Tc);var pf=class extends Tc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};pf.className="SpatialDropout1D";Q.registerClass(pf);var mf=class extends Et{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,Qe(this.units,"units"),this.activation=Zs(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Be(t.kernelConstraint),this.biasConstraint=Be(t.biasConstraint),this.kernelRegularizer=be(t.kernelRegularizer),this.biasRegularizer=be(t.biasRegularizer),this.activityRegularizer=be(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=zt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[e,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:e}}],this.built=!0}computeOutputShape(t){t=zt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=vy(this.activation.getClassName()),s;return o!=null?s=Eo(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Eo(n,this.kernel.read()),this.bias!=null&&(s=hn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:Ys(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:ze(this.kernelConstraint),biasConstraint:ze(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};mf.className="Dense";Q.registerClass(mf);var ff=class extends Et{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=zt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). 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Array.isArray(this.axes)?o=this.axes.map((s,i)=>Dh(s,t[i].shape.length)):o=[Dh(this.axes,e.shape.length),Dh(this.axes,n.shape.length)],this.normalize&&(e=Sh(e,o[0]),n=Sh(n,o[1])),oY(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[Dh(this.axes,t.length),Dh(this.axes,e.length)],n}computeOutputShape(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new vt("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};Tf.className="Dot";Q.registerClass(Tf);var kf=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return bu(()=>X(km(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};kf.className="GaussianNoise";Q.registerClass(kf);var Ef=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.rate>0&&this.rate<1?bu(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return D(n,km(n.shape,1,s))},()=>n,e.training||!1):n})}};Ef.className="GaussianDropout";Q.registerClass(Ef);var _f=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||St(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(t);return bu(()=>{let s=St(t),i=1.6732632423543772,a=1.0507009873554805,u=-i*a,l=un(Gi(n),this.rate);l=Qr(l,"float32");let c=((1-this.rate)*(1+this.rate*u**2))**-.5,p=-c*u*this.rate,m=X(D(s,l),D(X(l,-1),u));return X(D(m,c),p)},()=>St(t),e.training||!1)}return t})}};_f.className="AlphaDropout";Q.registerClass(_f);function Rh(r,t,e,n,o,s=.001){let i;if(r.rank===2)i=yx(r,t,e,n,o,s);else if(r.rank===3)i=bx(r,t,e,n,o,s);else if(r.rank===4)i=wx(r,t,e,n,o,s);else throw new vt(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return i}function sY(r,t,e,n,o=.001){return B(()=>{let s=Qu(r,n),i=s.mean,a=s.variance;return[Rh(r,i,a,e,t,o),i,a]})}function iY(r,t,e,n,o=.001){return B(()=>{let s=Qu(r,n),i=s.mean,a=s.variance,u=[];for(let d of 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e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new ye({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=fn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=So(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,fn(0,i).slice(0,i-1)),m=()=>{if(p){let 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t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Te(this.betaInitializer),gammaInitializer:Te(this.gammaInitializer),movingMeanInitializer:Te(this.movingMeanInitializer),movingVarianceInitializer:Te(this.movingVarianceInitializer),betaRegularizer:me(this.betaRegularizer),gammaRegularizer:me(this.gammaRegularizer),betaConstraint:ze(this.betaConstraint),gammaConstraint:ze(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};Af.className="BatchNormalization";Q.registerClass(Af);var $f=class extends Et{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new 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n=St(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=Qu(n,this.axis,!0),l=So(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(e==null&&(e=dn()),e!=="channelsLast"&&e!=="channelsFirst")throw new z(`Unknown data format: ${e}. 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s==="max"?i=su(r,t,e,a):i=Ql(r,t,e,a),o==="channelsFirst"&&(i=Ot(i,[0,3,1,2])),i})}function vD(r,t,e,n,o,s){return B(()=>{Fe(o),N0(s),mn(n),e==null&&(e=[1,1,1]),n==null&&(n="valid"),o==null&&(o=dn()),s==null&&(s="max"),r=G0(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=qx(r,t,e,a):i=xx(r,t,e,a),o==="channelsFirst"&&(i=Ot(i,[0,4,1,2,3])),i})}var gb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(Qe(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]=="number")this.strides=t.strides;else throw new z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);Qe(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,mn(this.padding),this.inputSpec=[new ye({ndim:3})]}computeOutputShape(t){t=zt(t);let e=kn(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return B(()=>{this.invokeCallHook(t,e),t=il(St(t),2);let n=this.poolingFunction(St(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Bn(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Rf=class extends gb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Fe(s),mn(o),Cb(t,e,n,o,s,"max")}};Rf.className="MaxPooling1D";Q.registerClass(Rf);var Ff=class extends gb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Fe(s),mn(o),Cb(t,e,n,o,s,"avg")}};Ff.className="AveragePooling1D";Q.registerClass(Ff);var xb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];Qe(this.poolSize,"poolSize"),Qe(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Fe(this.dataFormat),mn(this.padding),this.inputSpec=[new ye({ndim:4})]}computeOutputShape(t){t=zt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2];return e=kn(e,this.poolSize[0],this.padding,this.strides[0]),n=kn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Of=class extends xb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Fe(s),mn(o),Cb(t,e,n,o,s,"max")}};Of.className="MaxPooling2D";Q.registerClass(Of);var Pf=class extends xb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Fe(s),mn(o),Cb(t,e,n,o,s,"avg")}};Pf.className="AveragePooling2D";Q.registerClass(Pf);var yb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];Qe(this.poolSize,"poolSize"),Qe(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Fe(this.dataFormat),mn(this.padding),this.inputSpec=[new ye({ndim:5})]}computeOutputShape(t){t=zt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[4]:t[3];return e=kn(e,this.poolSize[0],this.padding,this.strides[0]),n=kn(n,this.poolSize[1],this.padding,this.strides[1]),o=kn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Lf=class extends yb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Fe(s),mn(o),vD(t,e,n,o,s,"max")}};Lf.className="MaxPooling3D";Q.registerClass(Lf);var Mf=class extends yb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Fe(s),mn(o),vD(t,e,n,o,s,"avg")}};Mf.className="AveragePooling3D";Q.registerClass(Mf);var bb=class extends Et{constructor(t){super(t),this.inputSpec=[new ye({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new 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t(i)}},Wf=class extends Ib{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=zt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=zt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=St(t),U0((i,a)=>[St(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};Wf.className="TimeDistributed";Q.registerClass(Wf);function uY(r){Ki(_$,"BidirectionalMergeMode",r)}var cY="concat",Uf=class extends Ib{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=yn(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=yn(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?cY:t.mergeMode,uY(this.mergeMode),t.weights)throw new vt("weights support is not implemented for Bidirectional layer yet.");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let n,o,s;return this.returnState&&(s=e.slice(1)),n=e[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):Tr(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=W0(t,n,o,this.numConstants);if(t=s.inputs,n=s.initialState,o=s.constants,Array.isArray(t)&&(n=t.slice(1),t=t[0]),(n==null||n.length===0)&&o==null)return super.apply(t,e);let i=[],a=[];if(n!=null){let l=n.length;if(l%2>0)throw new z("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");e.initialState=n,i.push(...n);let c=n.map(p=>new 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a;return this.mergeMode==="concat"?a=Tm([o,s]):this.mergeMode==="sum"?a=X(o,s):this.mergeMode==="ave"?a=D(.5,X(o,s)):this.mergeMode==="mul"?a=D(o,s):this.mergeMode==null&&(a=[o,s]),this.returnState?this.mergeMode==null?a.concat(i):[a].concat(i):a})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){Ks(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),Ks(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[e,e]:n=e:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(i=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let n=yn(e.layer);if(delete e.layer,e.numConstants!=null)throw new vt("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let o=e;return o.layer=n,new t(o)}};Uf.className="Bidirectional";Q.registerClass(Uf);var Hf=class extends Et{constructor(t){super(t),this.scale=t.scale,t.offset?this.offset=t.offset:this.offset=0}getConfig(){let t={scale:this.scale,offset:this.offset},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>(t=St(t),t.dtype!=="float32"&&(t=Qr(t,"float32")),X(D(t,this.scale),this.offset)))}};Hf.className="Rescaling";Q.registerClass(Hf);var{resizeBilinear:pY,cropAndResize:mY}=so,qf=class extends Et{constructor(t){super(t),this.height=t.height,this.width=t.width}centerCrop(t,e,n,o,s,i,a,u){return B(()=>{let l,c=!1,p=e/i,m=n/a,f=(o+e)/i,d=(s+n)/a,h=[p,m,f,d],g=[];t.rank===3?(c=!0,l=qe([t])):l=t;for(let N=0;N{let s=pY(t,[e,n]);return Qr(s,o)})}call(t,e){return B(()=>{let n=St(t),o=n.dtype,s=n.shape,i=s[s.length-3],a=s[s.length-2],u=0;i!==this.height&&(u=Math.floor((i-this.height)/2));let l=0;return a!==this.width&&(l=Math.floor((a-this.width)/2),l===0&&(l=1)),u>=0&&l>=0?this.centerCrop(n,u,l,this.height,this.width,i,a,o):this.upsize(t,this.height,this.width,o)})}getConfig(){let t={height:this.height,width:this.width},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){t=zt(t);let e=t.length-3,n=t.length-2;return t[e]=this.height,t[n]=this.width,t}};qf.className="CenterCrop";Q.registerClass(qf);function SD(r,t,e,n){let o=St(r);if(o.dtype!=="int32"&&(o=Qr(o,"int32")),t==="int")return o;let s=o.shape;if(o.rank===0&&(o=sr(o,-1)),t==="oneHot"&&o.shape[o.shape.length-1]!==1&&(o=sr(o,-1)),o.rank>2)throw new z(`When outputMode is not int, maximum output rank is 2 Received outputMode ${t} and input shape ${s} which would result in output rank ${o.rank}.`);let i=["multiHot","oneHot"].includes(t),a=o,u;if(typeof n!="undefined"&&t==="count"?u=ph(a,n,e,i):u=ph(a,[],e,i),t!=="tfIdf")return u;if(n)return D(u,n);throw new z("When outputMode is 'tfIdf', weights must be provided.")}var Kf=class extends Et{constructor(t){super(t),this.numTokens=t.numTokens,t.outputMode?this.outputMode=t.outputMode:this.outputMode="multiHot"}getConfig(){let t={numTokens:this.numTokens,outputMode:this.outputMode},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){return 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Add some layers first.");this.model=new Vn({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(t,e,n=console.log){this.built||this.build(),super.summary(t,e,n)}setWeights(t){this.model==null&&this.build(),this.model.setWeights(t)}evaluate(t,e,n={}){if(!this.built)throw new Kr("The model needs to be compiled before being used.");return this.model.evaluate(t,e,n)}async evaluateDataset(t,e){if(!this.built)throw new Kr("The model needs to be compiled before being used.");return this.model.evaluateDataset(t,e)}predict(t,e={}){return this.model==null&&this.build(),this.model.predict(t,e)}predictOnBatch(t){return this.model==null&&this.build(),this.model.predictOnBatch(t)}compile(t){this.build(),this.model.compile(t),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(t){this.model.optimizer=t}async fit(t,e,n={}){if(!this.built)throw new Kr("The model needs to be compiled before being used.");return this.model.fit(t,e,n)}async fitDataset(t,e){if(!this.built)throw new Kr("The model needs to be compiled before being used.");return this.model.fitDataset(t,e)}async trainOnBatch(t,e){return this.model.trainOnBatch(t,e)}static fromConfig(t,e,n={},o=!1){let s,i={};if(e instanceof Array){if(e[0].className==null||e[0].className==="Merge")throw new z("Legacy serialization format not supported yet.");s=e}else y.assert(e.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),s=e.layers,delete e.layers,i=e;let a=new t(i);if(!(a instanceof Xi))throw new vt(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(let u of s){let c=yn(u,void 0,o);o&&c.setFastWeightInitDuringBuild(!0),a.add(c)}return a}set stopTraining(t){if(this.model==null)throw new z("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new z("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let t=[];for(let e of this.layers){let n={};n.className=e.getClassName(),n.config=e.getConfig(),t.push(n)}return{name:this.name,layers:t}}};Xi.className="Sequential";Q.registerClass(Xi);function Y8(r){return new Vn(r)}function Z8(r){return new Xi(r)}function BS(r){return Dy(r)}function J8(r,t){xn.registerCallbackConstructor(r,t)}var rn=class extends Q.Serializable{getConfig(){return{}}},Qy=class extends rn{apply(t,e=1){return O$(t,e)}};Qy.className="elu";Q.registerClass(Qy);var tb=class extends rn{apply(t){return mm(t)}};tb.className="selu";Q.registerClass(tb);var eb=class extends rn{apply(t){return Or(t)}};eb.className="relu";Q.registerClass(eb);var rb=class extends rn{apply(t){return B(()=>zi(6,Or(t)))}};rb.className="relu6";Q.registerClass(rb);var nb=class extends rn{apply(t){return t}};nb.className="linear";Q.registerClass(nb);var ob=class extends rn{apply(t){return Qr(t)}};ob.className="sigmoid";Q.registerClass(ob);var sb=class extends rn{apply(t){return L$(t)}};sb.className="hardSigmoid";Q.registerClass(sb);var ib=class extends rn{apply(t){return Vs(t)}};ib.className="softplus";Q.registerClass(ib);var ab=class extends rn{apply(t){return P$(t)}};ab.className="softsign";Q.registerClass(ab);var lb=class extends rn{apply(t){return Di(t)}};lb.className="tanh";Q.registerClass(lb);var Km=class extends rn{apply(t,e=-1){return au(t,e)}};Km.className="softmax";Q.registerClass(Km);var ub=class extends rn{apply(t,e=-1){return im(t,e)}};ub.className="logSoftmax";Q.registerClass(ub);var cb=class extends rn{apply(t,e=1){return B(()=>D(Qr(D(t,e)),t))}};cb.className="swish";Q.registerClass(cb);var pb=class extends rn{apply(t){return B(()=>D(t,Di(Vs(t))))}};pb.className="mish";Q.registerClass(pb);function js(r){return r.getClassName()}function VS(r,t={}){return Hi(r,Q.SerializationMap.getMap().classNameMap,t,"activation")}function Xs(r){if(r==null){let t={};return t.className="linear",t.config={},VS(t)}if(typeof r=="string"){let t={};return t.className=r,t.config={},VS(t)}else return r instanceof rn?r:VS(r)}function GS(r){if(r!=null&&typeof r!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var mb=class extends Q.Serializable{},hu=class extends mb{constructor(t){super(),GS(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=Te([1]);return this.hasL1&&(e=Y(e,mt(D(this.l1,Ee(t))))),this.hasL2&&(e=Y(e,mt(D(this.l2,cc(t))))),R(e,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}};hu.className="L1L2";Q.registerClass(hu);function yD(r){return GS(r),new hu({l1:r!=null?r.l1:null,l2:0})}function bD(r){return GS(r),new hu({l2:r!=null?r.l2:null,l1:0})}var gD={l1l2:"L1L2"};function me(r){return Sm(r)}function xD(r,t={}){return Hi(r,Q.SerializationMap.getMap().classNameMap,t,"regularizer")}function we(r){if(r==null)return null;if(typeof r=="string"){let e={className:r in gD?gD[r]:r,config:{}};return xD(e)}else return r instanceof mb?r:xD(r)}var jm=class extends kt{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=St(t);let n=Or(t);return this.maxValue!=null&&(n=vr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};jm.className="ReLU";Q.registerClass(jm);var Xm=class extends kt{constructor(t){super(t==null?{}:t),this.DEFAULT_ALPHA=.3,t==null&&(t={}),this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=St(t);return eu(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};Xm.className="LeakyReLU";Q.registerClass(Xm);var Ym=class extends kt{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA_INITIALIZER="zeros",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=de(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=we(t.alphaRegularizer),this.alphaConstraint=Ve(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes=="number")this.sharedAxes=[t.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Bt(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)e[o-1]=1;this.alpha=this.addWeight("alpha",e,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o(Oe(t),t==="channelsFirst"?Pt(r,[0,2,3,1]):r))}function WS(r,t){return B(()=>(Oe(t),t==="channelsFirst"?Pt(r,[0,2,3,4,1]):r))}function tY(r,t,e,n=1,o="valid",s,i=1){return B(()=>{if(s==null&&(s=dn()),Oe(s),r.shape.length!==3)throw new z(`The input of a conv1dWithBias operation should be 3, but is ${r.shape.length} instead.`);if(t.shape.length!==3)throw new z(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(e!=null&&e.shape.length!==1)throw new z(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(r=Pt(r,[0,2,1])),o==="causal")throw new vt("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let a=tm(r,t,n,o==="same"?"same":"valid","NWC",i);return e!=null&&(a=hn(a,e)),a})}function wD(r,t,e,n=[1,1],o="valid",s,i,a=null){return B(()=>{if(s==null&&(s=dn()),Oe(s),r.rank!==3&&r.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=Dh(r,s);if(o==="causal")throw new vt("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=cu.conv2d({x:u,filter:t,strides:n,pad:o==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:e,activation:a}),s==="channelsFirst"&&(u=Pt(u,[0,3,1,2])),u})}function eY(r,t,e,n=[1,1,1],o="valid",s,i){return B(()=>{if(s==null&&(s=dn()),Oe(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=WS(r,s);if(o==="causal")throw new vt("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return a=yx(a,t,n,o==="same"?"same":"valid","NDHWC",i),e!=null&&(a=hn(a,e)),s==="channelsFirst"&&(a=Pt(a,[0,4,1,2,3])),a})}var Cc=class extends kt{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Cc.verifyArgs(e),this.rank=t,Qe(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new vt(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=gu(e.kernelSize,t,"kernelSize"),this.strides=gu(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,mn(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Oe(this.dataFormat),this.activation=Xs(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=de(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ve(e.biasConstraint),this.biasRegularizer=we(e.biasRegularizer),this.activityRegularizer=we(e.activityRegularizer),this.dilationRate=gu(e.dilationRate==null?1:e.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new z(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(so("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!Cy(t.kernelSize,"number",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:js(this.activation),useBias:this.useBias,biasInitializer:ke(this.biasInitializer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),biasConstraint:Be(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},xu=class extends Cc{constructor(t,e){super(t,e),this.kernel=null,xu.verifyArgs(e),this.filters=e.filters,Qe(this.filters,"filters"),this.kernelInitializer=de(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ve(e.kernelConstraint),this.kernelRegularizer=we(e.kernelRegularizer)}build(t){t=Bt(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=St(t);let n,o=this.bias==null?null:this.bias.read(),s=Iy(this.activation.getClassName());if(s!=null&&this.rank===2)n=wD(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=tY(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=wD(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=eY(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new vt("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Bt(t);let e=[],n=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},ul=class extends xu{constructor(t){super(2,t),ul.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Cy(t.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};ul.className="Conv2D";Q.registerClass(ul);var cl=class extends xu{constructor(t){super(3,t),cl.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};cl.className="Conv3D";Q.registerClass(cl);var tf=class extends ul{constructor(t){if(super(t),this.inputSpec=[new be({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Bt(t),t.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new be({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a;this.dataFormat==="channelsFirst"?(i=2,a=3):(i=1,a=2);let u=o[i],l=o[a],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=Ys(u,m,c,this.padding),h=Ys(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=Pt(n,[0,2,3,1]));let x=rm(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Pt(x,[0,3,1,2])),this.bias!=null&&(x=hn(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(t){t=Bt(t);let e=t.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let i=this.kernelSize[0],a=this.kernelSize[1],u=this.strides[0],l=this.strides[1];return e[n]=this.filters,e[o]=Ys(e[o],u,i,this.padding),e[s]=Ys(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};tf.className="Conv2DTranspose";Q.registerClass(tf);var ef=class extends cl{constructor(t){if(super(t),this.inputSpec=[new be({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Bt(t),t.length!==5)throw new z("Input should have rank 5; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new be({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat==="channelsFirst"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=Ys(l,h,m,this.padding),w=Ys(c,g,f,this.padding),C=Ys(p,x,d,this.padding),N=[s,b,w,C,this.filters];this.dataFormat!=="channelsLast"&&(n=Pt(n,[0,2,3,4,1]));let _=wx(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(_=Pt(_,[0,4,1,2,3])),this.bias!==null&&(_=hn(_,this.bias.read(),this.dataFormat)),this.activation!==null&&(_=this.activation.apply(_)),_})}computeOutputShape(t){t=Bt(t);let e=t.slice(),n,o,s,i;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=Ys(e[o],c,a,this.padding),e[s]=Ys(e[s],p,u,this.padding),e[i]=Ys(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};ef.className="Conv3DTranspose";Q.registerClass(ef);var fb=class extends xu{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=de(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=we(e.depthwiseRegularizer),this.depthwiseConstraint=Ve(e.depthwiseConstraint),this.pointwiseInitializer=de(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=we(e.pointwiseRegularizer),this.pointwiseConstraint=Ve(e.pointwiseConstraint)}build(t){if(t=Bt(t),t.length{t=St(t);let n;if(this.rank===1)throw new vt("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Pt(t,[0,2,3,1])),n=fm(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=hn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Pt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=ke(this.depthwiseInitializer),t.pointwiseInitializer=ke(this.pointwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.pointwiseRegularizer=me(this.pointwiseRegularizer),t.depthwiseConstraint=Be(this.depthwiseConstraint),t.pointwiseConstraint=Be(this.pointwiseConstraint),t}};fb.className="SeparableConv";var rf=class extends fb{constructor(t){super(2,t)}};rf.className="SeparableConv2D";Q.registerClass(rf);var yu=class extends xu{constructor(t){super(1,t),yu.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Cy(t.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};yu.className="Conv1D";Q.registerClass(yu);var nf=class extends kt{constructor(t){super(t),typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat==="channelsLast"){let n=Ih(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ih(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ih(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ih(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};nf.className="Cropping2D";Q.registerClass(nf);var of=class extends kt{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),this.interpolation=t.interpolation==null?"nearest":t.interpolation,_$(this.interpolation)}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=St(t),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Pt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation==="nearest"?oo.resizeNearestNeighbor(n,[s,i]):oo.resizeBilinear(n,[s,i]);return Pt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation==="nearest"?oo.resizeNearestNeighbor(n,[s,i]):oo.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};of.className="UpSampling2D";Q.registerClass(of);function rY(r,t,e=[1,1],n="valid",o,s){return B(()=>{o==null&&(o=dn()),Oe(o);let i=Dh(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Oi(i,t,e,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(i=Pt(i,[0,3,1,2])),i})}var sf=class extends Cc{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=de(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ve(t.depthwiseConstraint),this.depthwiseRegularizer=we(t.depthwiseRegularizer)}build(t){if(t=Bt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{t=St(t);let n=rY(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=hn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Bt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=Tn(e,this.kernelSize[0],this.padding,this.strides[0]),i=Tn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=ke(this.depthwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.depthwiseConstraint=Be(this.depthwiseRegularizer),t}};sf.className="DepthwiseConv2D";Q.registerClass(sf);function US(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function HS(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(fn(2,u));if(t=Pt(t,l),s!=null)throw new vt("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=J(J(o,"bool"),"float32"),o.rank===u-1&&(o=ir(o,-1)),o=Pt(o,l)),n&&(t=dr(t,0),o!=null&&(o=dr(o,0)));let c=[],p,m=e,f=t.shape[0],d=gr(t),h;o!=null&&(h=gr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let C=B(()=>{let N=h[x],_=lt(wr(N),N),A=Y(D(w[0],N),D(m[0],_)),$=m.map((F,P)=>Y(D(w[1][P],N),D(F,_)));return{output:A,newStates:$}});p=C.output,m=C.newStates}a&&c.push(p)}let g;return a&&(g=qe(c,1)),[p,g,m]})}var kn=class extends kt{constructor(t){super(t);let e;if(t.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new Sc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new be({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return fn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){$y(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new be({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new Nn("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>Te([n,o])):this.states_=[Te([n,this.cell.stateSize])];else if(t==null)Nt(this.states_),this.keptStates!=null&&(Nt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>Te([n,o])):this.states_[0]=Te([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Nt(this.states_);for(let o=0;oDe(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=US(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new be({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof en){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=St(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let a={training:o},l=HS((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=Te(t.shape);return e=mt(e,[1,2]),e=il(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Ey(e,[1,n]):e):this.cell.stateSize>1?[Ey(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===kn.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=yn(o,n);return new t(Object.assign(e,{cell:s}))}};kn.className="RNN";Q.registerClass(kn);var pl=class extends kt{},Ic=class extends pl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,Qe(this.units,"units"),this.activation=Xs(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=we(t.kernelRegularizer),this.recurrentRegularizer=we(t.recurrentRegularizer),this.biasRegularizer=we(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=uc([1,qs([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=uc([1,qs([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Bt(t),this.kernel=this.addWeight("kernel",[t[t.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0wr(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0wr(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=ko(D(t,i),this.kernel.read()):s=ko(t,this.kernel.read()),this.bias!=null&&(s=hn(s,this.bias.read())),a!=null&&(n=D(n,a));let u=Y(s,ko(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:js(this.activation),useBias:this.useBias,kernelInitializer:ke(this.kernelInitializer),recurrentInitializer:ke(this.recurrentInitializer),biasInitializer:ke(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),recurrentConstraint:Be(this.recurrentConstraint),biasConstraint:Be(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};Ic.className="SimpleRNNCell";Q.registerClass(Ic);var af=class extends kn{constructor(t){t.cell=new Ic(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Nt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Nt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};af.className="SimpleRNN";Q.registerClass(af);var vc=class extends pl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.resetAfter)throw new z("GRUCell does not support reset_after parameter set to true.");this.units=t.units,Qe(this.units,"units"),this.activation=Xs(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=Xs(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=we(t.kernelRegularizer),this.recurrentRegularizer=we(t.recurrentRegularizer),this.biasRegularizer=we(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=uc([1,qs([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=uc([1,qs([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Bt(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0wr(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0{this.cell.dropoutMask!=null&&(Nt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Nt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};lf.className="GRU";Q.registerClass(lf);var ml=class extends pl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,Qe(this.units,"units"),this.activation=Xs(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=Xs(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=we(t.kernelRegularizer),this.recurrentRegularizer=we(t.recurrentRegularizer),this.biasRegularizer=we(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=uc([1,qs([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=uc([1,qs([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Bt(t);let n=t[t.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends gn{apply(u,l){let c=s.apply([i]),p=new fu().apply([i]),m=s.apply([i*2]);return AS(AS(c,p),m)}},e.className="CustomInit",e)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0wr(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Nt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Nt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};uf.className="LSTM";Q.registerClass(uf);var Sc=class extends pl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{Hs(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign(Object.assign({},t),o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(yn(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return Sh(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):Ay(t(),e),a=()=>mu(i,t,n);return!o||o<=1?De(a().clone()):Array(o).fill(void 0).map(a).map(l=>De(l.clone()))}var nY=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o{if(this.cell.dropoutMask!=null&&(Nt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Nt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z("ConvRNN2D cell does not support constants");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=Te(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new Nn("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Te(s)):this.states_=[Te(s)];else if(t==null)Nt(this.states_),this.keptStates!=null&&(Nt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Te(s)):this.states_[0]=Te(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Nt(this.states_);for(let a=0;aDe(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e==="channelsFirst",l=t[u?3:2],c=t[u?4:3],p=Tn(l,o[0],s,i[0],a[0]),m=Tn(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};db.className="ConvRNN2D";var Nc=class extends ml{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,Qe(this.filters,"filters"),this.kernelSize=gu(n,2,"kernelSize"),this.kernelSize.forEach(u=>Qe(u,"kernelSize")),this.strides=gu(o||1,2,"strides"),this.strides.forEach(u=>Qe(u,"strides")),this.padding=s||"valid",mn(this.padding),this.dataFormat=i||"channelsLast",Oe(this.dataFormat),this.dilationRate=gu(a||1,2,"dilationRate"),this.dilationRate.forEach(u=>Qe(u,"dilationRate"))}build(t){var e;t=Bt(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends gn{apply(m,f){let d=l.apply([c]),h=fr([c]),g=l.apply([c*2]);return Tm([d,h,g])}},e.className="CustomInit",e)}else u=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0wr(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(rt,ot,at)=>!ot||!ot[at]?rt:D(ot[at],rt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0wr(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[C,N,_,A]=hr(this.kernel.read(),a,w),[$,F,P,V]=this.useBias?hr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,C,$,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,_,P,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=hr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let K=this.recurrentActivation.apply(Y(c,h)),X=this.recurrentActivation.apply(Y(p,g)),Z=Y(D(X,i),D(K,this.activation.apply(Y(m,x)))),et=D(this.recurrentActivation.apply(Y(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=nY(t,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=vn(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?hn(s,n,this.dataFormat):s}recurrentConv(t,e){return vn(t,e,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Nc.className="ConvLSTM2DCell";Q.registerClass(Nc);var cf=class extends db{constructor(t){let e=new Nc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};cf.className="ConvLSTM2D";Q.registerClass(cf);var Tc=class extends kt{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);if(0Ay(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Tc.className="Dropout";Q.registerClass(Tc);var pf=class extends Tc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};pf.className="SpatialDropout1D";Q.registerClass(pf);var mf=class extends kt{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,Qe(this.units,"units"),this.activation=Xs(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ve(t.kernelConstraint),this.biasConstraint=Ve(t.biasConstraint),this.kernelRegularizer=we(t.kernelRegularizer),this.biasRegularizer=we(t.biasRegularizer),this.activityRegularizer=we(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Bt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[e,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:e}}],this.built=!0}computeOutputShape(t){t=Bt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=Iy(this.activation.getClassName()),s;return o!=null?s=ko(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=ko(n,this.kernel.read()),this.bias!=null&&(s=hn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:js(this.activation),useBias:this.useBias,kernelInitializer:ke(this.kernelInitializer),biasInitializer:ke(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),biasConstraint:Be(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};mf.className="Dense";Q.registerClass(mf);var ff=class extends kt{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Bt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). 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Got tensors with shapes: ${JSON.stringify(t)}.`);let n=t[0]==null?null:t[0].slice(1);for(let s=1;ss.length);t.indexOf(null)===-1&&No(o).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(t,e){return B(()=>{if(t=t,this.reshapeRequired){let n=[],o=t.map(s=>s.rank);if(o.indexOf(null)===-1){let s=qs(o);for(let i of t){let a=i.rank;for(let u=0;u1){let c=fn(1,l).concat([0]);n.push(Pt(u,c)),s=!0}else n.push(u)}let i=this.mergeFunction(n),a=i.rank;if(s){if(a==null){let u=i.shape,l=u.length,c=u[l-1],p=[c].concat(u.slice(0,u.length-1));i=R(Pt(R(i,[-1,c]),[1,0]),p)}else if(a>1){let u=[a-1].concat(fn(0,a-1));i=Pt(i,u)}}return i}}else return this.mergeFunction(t)})}computeOutputShape(t){t=t;let e;t[0]==null?e=null:e=t[0].slice(1);for(let o=1;o{if(e==null)return null;if(!Array.isArray(e))throw new z("`mask` should be an Array");if(!Array.isArray(t))throw new z("`inputs` should be an Array");if(e.length!==t.length)throw new z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${t.length} vs ${e.length})`);if(e.every(o=>o==null))return null;e=e.map(o=>o==null?o:ir(o,0));let n=e[0];for(let o=1;o{let e=t[0].clone();for(let n=1;n{let e=t[0].clone();for(let n=1;n{let e=t[0].clone();for(let n=1;n{let e=t[0];for(let n=1;n{let e=t[0];for(let n=1;n1)throw new z("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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${r.shape.length}`),y.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof e=="number"&&(e=[e,e]),r.dtype==="complex64"||t.dtype==="complex64")throw new vt("batchDot is not implemented for complex64-type Tensors yet.");let n=r.shape.length,o=t.shape.length;e==null&&(e=[n-1,o-2]);let s=e;return B(()=>{let i;if(n>o){i=n-o;let u=[];for(let l=0;ln){i=o-n;let u=[];for(let l=0;l0){let u;n>o?u=n+o-3:u=n-1;let l=[];for(let c=u;c"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0],n=t[1];if(e.length>3||n.length>3)throw new vt("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);if(e[o[0]]!==n[o[1]])throw new z(`Dimension incompatibility: ${e[o[0]]} !== ${n[o[1]]}`)}mergeFunction(t){if(t.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${t.length} input(s).`);let e=t[0],n=t[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,i)=>Rh(s,t[i].shape.length)):o=[Rh(this.axes,e.shape.length),Rh(this.axes,n.shape.length)],this.normalize&&(e=Nh(e,o[0]),n=Nh(n,o[1])),oY(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[Rh(this.axes,t.length),Rh(this.axes,e.length)],n}computeOutputShape(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new vt("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};Tf.className="Dot";Q.registerClass(Tf);var kf=class extends kt{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return mu(()=>Y(km(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};kf.className="GaussianNoise";Q.registerClass(kf);var Ef=class extends kt{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.rate>0&&this.rate<1?mu(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return D(n,km(n.shape,1,s))},()=>n,e.training||!1):n})}};Ef.className="GaussianDropout";Q.registerClass(Ef);var _f=class extends kt{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||St(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(t);return mu(()=>{let s=St(t),i=1.6732632423543772,a=1.0507009873554805,u=-i*a,l=un(Vi(n),this.rate);l=tn(l,"float32");let c=((1-this.rate)*(1+this.rate*u**2))**-.5,p=-c*u*this.rate,m=Y(D(s,l),D(Y(l,-1),u));return Y(D(m,c),p)},()=>St(t),e.training||!1)}return t})}};_f.className="AlphaDropout";Q.registerClass(_f);function Fh(r,t,e,n,o,s=.001){let i;if(r.rank===2)i=ux(r,t,e,n,o,s);else if(r.rank===3)i=cx(r,t,e,n,o,s);else if(r.rank===4)i=px(r,t,e,n,o,s);else throw new vt(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return i}function sY(r,t,e,n,o=.001){return B(()=>{let s=qu(r,n),i=s.mean,a=s.variance;return[Fh(r,i,a,e,t,o),i,a]})}function iY(r,t,e,n,o=.001){return B(()=>{let s=qu(r,n),i=s.mean,a=s.variance,u=[];for(let d of fn(0,r.rank))n.indexOf(d)!==-1?u.push(1):u.push(r.shape[d]);let l=R(i,u),c=R(a,u),p=t==null?null:R(t,u),m=e==null?null:R(e,u);return[Fh(r,l,c,m,p,o),i,a]})}function aY(r,t,e,n,o=.001){return y.arraysEqual(n.slice().sort(),fn(0,r.rank-1))?sY(r,t,e,n,o):iY(r,t,e,n,o)}var Af=class extends kt{constructor(t){t==null&&(t={}),super(t),this.supportsMasking=!0,this.axis=t.axis==null?-1:t.axis,this.momentum=t.momentum==null?.99:t.momentum,this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=de(t.betaInitializer||"zeros"),this.gammaInitializer=de(t.gammaInitializer||"ones"),this.movingMeanInitializer=de(t.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=de(t.movingVarianceInitializer||"ones"),this.betaConstraint=Ve(t.betaConstraint),this.gammaConstraint=Ve(t.gammaConstraint),this.betaRegularizer=we(t.betaRegularizer),this.gammaRegularizer=we(t.gammaRegularizer)}build(t){t=Bt(t);let e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new be({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=fn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=vo(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,fn(0,i).slice(0,i-1)),m=()=>{if(p){let b=R(this.movingMean.read(),l),w=R(this.movingVariance.read(),l),C=this.center?R(this.beta.read(),l):null,N=this.scale?R(this.gamma.read(),l):null;return Fh(o,b,w,C,N,this.epsilon)}else return Fh(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return m();let[f,d,h]=aY(o,this.gamma.read(),this.beta.read(),a,this.epsilon),g=(b,w,C)=>{B(()=>{let N=1-C,_=b.read(),A=D(lt(_,w),N);b.write(lt(_,A))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:ke(this.betaInitializer),gammaInitializer:ke(this.gammaInitializer),movingMeanInitializer:ke(this.movingMeanInitializer),movingVarianceInitializer:ke(this.movingVarianceInitializer),betaRegularizer:me(this.betaRegularizer),gammaRegularizer:me(this.gammaRegularizer),betaConstraint:Be(this.betaConstraint),gammaConstraint:Be(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};Af.className="BatchNormalization";Q.registerClass(Af);var $f=class extends kt{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=de(t.betaInitializer||"zeros"),this.gammaInitializer=de(t.gammaInitializer||"ones"),this.betaRegularizer=we(t.betaRegularizer),this.gammaRegularizer=we(t.gammaRegularizer),this.supportsMasking=!0}build(t){t=Bt(t);let e=t.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s=e)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==No(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let n=St(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=qu(n,this.axis,!0),l=vo(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(e==null&&(e=dn()),e!=="channelsLast"&&e!=="channelsFirst")throw new z(`Unknown data format: ${e}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return e==="channelsFirst"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],pn(r,n)})}var Df=class extends kt{constructor(t){if(t==null&&(t={}),super(t),this.dataFormat=t.dataFormat==null?dn():t.dataFormat,t.padding==null)this.padding=[[1,1],[1,1]];else if(typeof t.padding=="number")this.padding=[[t.padding,t.padding],[t.padding,t.padding]];else{if(t.padding=t.padding,t.padding.length!==2)throw new z(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${t.padding.length} array.`);let e,n;if(typeof t.padding[0]=="number")e=[t.padding[0],t.padding[0]],n=[t.padding[1],t.padding[1]];else{if(t.padding=t.padding,t.padding[0].length!==2)throw new z(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${t.padding[0].length} array.`);if(e=t.padding[0],t.padding[1].length!==2)throw new z(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${t.padding[1].length} array.`);n=t.padding[1]}this.padding=[e,n]}this.inputSpec=[new be({ndim:4})]}computeOutputShape(t){t=Bt(t);let e,n;return this.dataFormat==="channelsFirst"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?n=t[3]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],t[1],e,n]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?n=t[2]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],e,n,t[3]])}call(t,e){return B(()=>lY(St(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};Df.className="ZeroPadding2D";Q.registerClass(Df);function wb(r,t,e,n,o,s){return B(()=>{Oe(o),TS(s),mn(n),e==null&&(e=[1,1]),n==null&&(n="valid"),o==null&&(o=dn()),s==null&&(s="max"),r=Dh(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=ou(r,t,e,a):i=Zl(r,t,e,a),o==="channelsFirst"&&(i=Pt(i,[0,3,1,2])),i})}function CD(r,t,e,n,o,s){return B(()=>{Oe(o),TS(s),mn(n),e==null&&(e=[1,1,1]),n==null&&(n="valid"),o==null&&(o=dn()),s==null&&(s="max"),r=WS(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=Lx(r,t,e,a):i=lx(r,t,e,a),o==="channelsFirst"&&(i=Pt(i,[0,4,1,2,3])),i})}var hb=class extends kt{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(Qe(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]=="number")this.strides=t.strides;else throw new z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);Qe(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,mn(this.padding),this.inputSpec=[new be({ndim:3})]}computeOutputShape(t){t=Bt(t);let e=Tn(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return B(()=>{this.invokeCallHook(t,e),t=il(St(t),2);let n=this.poolingFunction(St(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return zn(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Rf=class extends hb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),mn(o),wb(t,e,n,o,s,"max")}};Rf.className="MaxPooling1D";Q.registerClass(Rf);var Ff=class extends hb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),mn(o),wb(t,e,n,o,s,"avg")}};Ff.className="AveragePooling1D";Q.registerClass(Ff);var gb=class extends kt{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];Qe(this.poolSize,"poolSize"),Qe(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),mn(this.padding),this.inputSpec=[new be({ndim:4})]}computeOutputShape(t){t=Bt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Of=class extends gb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),mn(o),wb(t,e,n,o,s,"max")}};Of.className="MaxPooling2D";Q.registerClass(Of);var Pf=class extends gb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),mn(o),wb(t,e,n,o,s,"avg")}};Pf.className="AveragePooling2D";Q.registerClass(Pf);var xb=class extends kt{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];Qe(this.poolSize,"poolSize"),Qe(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),mn(this.padding),this.inputSpec=[new be({ndim:5})]}computeOutputShape(t){t=Bt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[4]:t[3];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),o=Tn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Lf=class extends xb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),mn(o),CD(t,e,n,o,s,"max")}};Lf.className="MaxPooling3D";Q.registerClass(Lf);var Mf=class extends xb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),mn(o),CD(t,e,n,o,s,"avg")}};Mf.className="AveragePooling3D";Q.registerClass(Mf);var yb=class extends kt{constructor(t){super(t),this.inputSpec=[new be({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new vt}},zf=class extends yb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Ne(n,1)})}};zf.className="GlobalAveragePooling1D";Q.registerClass(zf);var Bf=class extends yb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Sr(n,1)})}};Bf.className="GlobalMaxPooling1D";Q.registerClass(Bf);var bb=class extends kt{constructor(t){super(t),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),this.inputSpec=[new be({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat==="channelsLast"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new vt}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Vf=class extends bb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat==="channelsLast"?Ne(n,[1,2]):Ne(n,[2,3])})}};Vf.className="GlobalAveragePooling2D";Q.registerClass(Vf);var Gf=class extends bb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat==="channelsLast"?Sr(n,[1,2]):Sr(n,[2,3])})}};Gf.className="GlobalMaxPooling2D";Q.registerClass(Gf);var Cb=class extends kt{constructor(t){super(t),this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,n={}){let o=e.layer,s=yn(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},Wf=class extends Cb{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=Bt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Bt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=St(t),HS((i,a)=>[St(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};Wf.className="TimeDistributed";Q.registerClass(Wf);function uY(r){qi(k$,"BidirectionalMergeMode",r)}var cY="concat",Uf=class extends Cb{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=yn(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=yn(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?cY:t.mergeMode,uY(this.mergeMode),t.weights)throw new vt("weights support is not implemented for Bidirectional layer yet.");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let n,o,s;return this.returnState&&(s=e.slice(1)),n=e[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):Tr(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=US(t,n,o,this.numConstants);if(t=s.inputs,n=s.initialState,o=s.constants,Array.isArray(t)&&(n=t.slice(1),t=t[0]),(n==null||n.length===0)&&o==null)return super.apply(t,e);let i=[],a=[];if(n!=null){let l=n.length;if(l%2>0)throw new z("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");e.initialState=n,i.push(...n);let c=n.map(p=>new be({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),a.push(...c)}if(o!=null)throw new vt("Support for constants in Bidirectional layers is not implemented yet.");let u=i[0]instanceof en;for(let l of i)if(l instanceof en!==u)throw new z("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(u){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e.initialState,o,s;if(n==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let u=n.slice(0,n.length/2),l=n.slice(n.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:u})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:l}))}let i;this.returnState&&(Array.isArray(o)&&(i=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=dr(s,1));let a;return this.mergeMode==="concat"?a=Tm([o,s]):this.mergeMode==="sum"?a=Y(o,s):this.mergeMode==="ave"?a=D(.5,Y(o,s)):this.mergeMode==="mul"?a=D(o,s):this.mergeMode==null&&(a=[o,s]),this.returnState?this.mergeMode==null?a.concat(i):[a].concat(i):a})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){Hs(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),Hs(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[e,e]:n=e:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(i=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let n=yn(e.layer);if(delete e.layer,e.numConstants!=null)throw new vt("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let o=e;return o.layer=n,new t(o)}};Uf.className="Bidirectional";Q.registerClass(Uf);var Hf=class extends kt{constructor(t){super(t),this.scale=t.scale,t.offset?this.offset=t.offset:this.offset=0}getConfig(){let t={scale:this.scale,offset:this.offset},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>(t=St(t),t.dtype!=="float32"&&(t=tn(t,"float32")),Y(D(t,this.scale),this.offset)))}};Hf.className="Rescaling";Q.registerClass(Hf);var{resizeBilinear:pY,cropAndResize:mY}=oo,qf=class extends kt{constructor(t){super(t),this.height=t.height,this.width=t.width}centerCrop(t,e,n,o,s,i,a,u){return B(()=>{let l,c=!1,p=e/i,m=n/a,f=(o+e)/i,d=(s+n)/a,h=[p,m,f,d],g=[];t.rank===3?(c=!0,l=qe([t])):l=t;for(let N=0;N{let s=pY(t,[e,n]);return tn(s,o)})}call(t,e){return B(()=>{let n=St(t),o=n.dtype,s=n.shape,i=s[s.length-3],a=s[s.length-2],u=0;i!==this.height&&(u=Math.floor((i-this.height)/2));let l=0;return a!==this.width&&(l=Math.floor((a-this.width)/2),l===0&&(l=1)),u>=0&&l>=0?this.centerCrop(n,u,l,this.height,this.width,i,a,o):this.upsize(t,this.height,this.width,o)})}getConfig(){let t={height:this.height,width:this.width},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){t=Bt(t);let e=t.length-3,n=t.length-2;return t[e]=this.height,t[n]=this.width,t}};qf.className="CenterCrop";Q.registerClass(qf);function ID(r,t,e,n){let o=St(r);if(o.dtype!=="int32"&&(o=tn(o,"int32")),t==="int")return o;let s=o.shape;if(o.rank===0&&(o=ir(o,-1)),t==="oneHot"&&o.shape[o.shape.length-1]!==1&&(o=ir(o,-1)),o.rank>2)throw new z(`When outputMode is not int, maximum output rank is 2 Received outputMode ${t} and input shape ${s} which would result in output rank ${o.rank}.`);let i=["multiHot","oneHot"].includes(t),a=o,u;if(typeof n!="undefined"&&t==="count"?u=ah(a,n,e,i):u=ah(a,[],e,i),t!=="tfIdf")return u;if(n)return D(u,n);throw new z("When outputMode is 'tfIdf', weights must be provided.")}var Kf=class extends kt{constructor(t){super(t),this.numTokens=t.numTokens,t.outputMode?this.outputMode=t.outputMode:this.outputMode="multiHot"}getConfig(){let t={numTokens:this.numTokens,outputMode:this.outputMode},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){return t=Bt(t),t==null?[this.numTokens]:this.outputMode==="oneHot"&&t[t.length-1]!==1?(t.push(this.numTokens),t):(t[t.length-1]=this.numTokens,t)}call(t,e){return B(()=>{t=St(t),t.dtype!=="int32"&&(t=tn(t,"int32"));let n;if(typeof e.countWeights!="undefined"){if(this.outputMode!=="count")throw new z(`countWeights is not used when outputMode !== count. + Received countWeights=${e.countWeights}`);n=St(e.countWeights)}let o=Sr(t),s=Ja(t),i=Fe(this.numTokens,o).bufferSync().get(0),a=un(s,0).bufferSync().get(0);if(!(i&&a))throw new z(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return ID(t,this.outputMode,this.numTokens,n)})}};Kf.className="CategoryEncoding";Q.registerClass(Kf);var dY=["bilinear","nearest"],vD=new Set(dY),jf=class extends kt{constructor(t){if(super(t),this.height=t.height,this.width=t.width,t.interpolation)if(vD.has(t.interpolation))this.interpolation=t.interpolation;else throw new z(`Invalid interpolation parameter: ${t.interpolation} 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written.`);n.tensor=e,De(e),n.written=!0,this.tensors[t]=n}writeMany(t,e){if(t.length!==e.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${t.length} is not the same as tensors size: ${e.length}.`);t.forEach((n,o)=>this.write(n,e[o]))}gather(t,e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${e}`);if(t)t=t.slice(0,this.size());else{t=[];for(let o=0;o=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(t,gr(e,0))}split(t,e){if(e.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${e.dtype}`);let n=0,o=t.map(u=>(n+=u,n));if(n!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is - ${n}, and tensor's shape is: ${e.shape}`);if(!this.dynamicSize&&t.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size 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");let o=Xf(this.elementShape,this.tensors,t);return B(()=>{let s=this.tensors.map(i=>R(i,o));return qe(s,0)})}popBack(t,e){if(e!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=Xf(this.elementShape,this.tensors,t),o=this.tensors.pop();return o.kept=!1,Wn(o.shape,t,"TensorList shape mismatch: "),R(o,n)}pushBack(t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(Wn(t.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Ae(t),this.tensors.push(t)}resize(t){if(t<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${t}`);if(this.maxNumElements!==-1&&t>this.maxNumElements)throw new Error(`TensorListResize input size ${t} is greater maxNumElement ${this.maxNumElements}.`);let e=new hl([],this.elementShape,this.elementDtype,this.maxNumElements);e.tensors.length=t;for(let n=0;nthis.tensors.length)throw new Error(`Trying to access element ${t} in a list with ${this.tensors.length} elements.`);if(this.tensors[t]==null)throw new Error(`element at index ${t} is null.`);Wn(this.tensors[t].shape,e,"TensorList shape mismatch: ");let o=Xf(this.elementShape,this.tensors,e);return R(this.tensors[t],o)}setItem(t,e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(t<0||this.maxNumElements!==-1&&t>=this.maxNumElements)throw new Error(`Trying to set element ${t} in a list with max ${this.maxNumElements} elements.`);Wn(this.elementShape,e.shape,"TensorList shape mismatch: "),Ae(e),this.tensors[t]!=null&&(this.tensors[t].kept=!1),this.tensors[t]=e}gather(t,e,n){if(e!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`);Wn(this.elementShape,n,"TensorList shape mismatch: "),t=t.slice(0,this.size());let o=Xf(this.elementShape,this.tensors,n);return t.length===0?nr([],[0].concat(o)):B(()=>{let s=t.map(i=>R(this.tensors[i],o));return qe(s,0)})}concat(t,e){if(!!t&&t!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${t}`);Wn(this.elementShape,e,"TensorList shape mismatch: ");let n=Xf(this.elementShape,this.tensors,e);return this.size()===0?nr([],[0].concat(n)):B(()=>{let o=this.tensors.map(s=>R(s,n));return oe(o,0)})}};function WD(r,t,e){let n=r.dtype;if(r.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${r.shape}`);if(r.dtype!==e)throw new Error(`Invalid data types; op elements ${r.dtype}, but list elements ${e}`);let o=r.shape.slice(1);Wn(o,t,"TensorList shape mismatch: ");let s=dr(r);return new hl(s,t,n)}function UD(r,t,e,n){return new hl([],r,t,n)}function HD(r,t,e,n){if(t.length!==r.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${r.shape[0]}`);let o=Math.max(...t);if(n!=null&&n!==-1&&o>=n)throw new Error(`Max index must be < array size (${o} vs. ${n})`);let s=new hl([],e,r.dtype,n),i=dr(r,0);return t.forEach((a,u)=>{s.setItem(a,i[u])}),s}function qD(r,t,e){let n=0,o=t.map(c=>(n+=c,n));if(n!==r.shape[0])throw new Error(`Expected sum of lengths to be equal to + ${n}, and tensor's shape is: ${e.shape}`);if(!this.dynamicSize&&t.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${t.length}), and the TensorArray is not marked as dynamically resizeable`);let s=n===0?0:e.size/n,i=[];B(()=>{e=R(e,[1,n,s]);for(let u=0;u{if(n!==s.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${s.dtype}`);Gn(e,s.shape,"TensorList shape mismatch: "),De(s)}),this.idTensor=pt(0),this.maxNumElements=o,De(this.idTensor)}get id(){return this.idTensor.id}copy(){return new hl([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(t){this.tensors.forEach(e=>{(t==null||!t.has(e.id))&&e.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(t,e,n=-1){if(e!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);Gn(t,this.elementShape,"TensorList shape mismatch: ");let o=Xf(this.elementShape,this.tensors,t);return B(()=>{let s=this.tensors.map(i=>R(i,o));return qe(s,0)})}popBack(t,e){if(e!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=Xf(this.elementShape,this.tensors,t),o=this.tensors.pop();return o.kept=!1,Gn(o.shape,t,"TensorList shape mismatch: "),R(o,n)}pushBack(t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(Gn(t.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");De(t),this.tensors.push(t)}resize(t){if(t<0)throw new Error(`TensorListResize expects size to be non-negative. 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${e}`);let o=r.shape.slice(1);Gn(o,t,"TensorList shape mismatch: ");let s=gr(r);return new hl(s,t,n)}function GD(r,t,e,n){return new hl([],r,t,n)}function WD(r,t,e,n){if(t.length!==r.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${r.shape[0]}`);let o=Math.max(...t);if(n!=null&&n!==-1&&o>=n)throw new Error(`Max index must be < array size (${o} vs. ${n})`);let s=new hl([],e,r.dtype,n),i=gr(r,0);return t.forEach((a,u)=>{s.setItem(a,i[u])}),s}function UD(r,t,e){let n=0,o=t.map(c=>(n+=c,n));if(n!==r.shape[0])throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is - ${n}, and tensor's shape is: ${r.shape}`);let s=r.shape.slice(1),i=zb(s,e),a=n===0?0:r.size/n,u=B(()=>{let c=[];r=R(r,[1,n,a]);for(let p=0;p{switch(r.op){case"If":case"StatelessIf":{let n=I("thenBranch",r,t,e),o=I("elseBranch",r,t,e),s=I("cond",r,t,e),i=I("args",r,t,e);return(await 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0,o]:[o,void 0]}case"Merge":{let n=r.inputNames.find(o=>Cr(o,t,e)!==void 0);if(n){let o=Cr(n,t,e);return[ti(o)]}return}case"Enter":{let n=I("frameName",r,t,e),o=I("tensor",r,t,e);return e.enterFrame(n),[ti(o)]}case"Exit":{let n=I("tensor",r,t,e);return e.exitFrame(),[ti(n)]}case"NextIteration":{let n=I("tensor",r,t,e);return e.nextIteration(),[ti(n)]}case"TensorArrayV3":{let n=I("size",r,t,e),o=I("dtype",r,t,e),s=I("elementShape",r,t,e),i=I("dynamicSize",r,t,e),a=I("clearAfterRead",r,t,e),u=I("identicalElementShapes",r,t,e),l=I("name",r,t,e),c=new Bb(l,o,n,s,u,i,a);return e.addTensorArray(c),[c.idTensor,mt(1)]}case"TensorArrayWriteV3":{let n=I("tensorArrayId",r,t,e),o=I("index",r,t,e),s=I("tensor",r,t,e),i=e.getTensorArray(n.id);return i.write(o,s),[i.idTensor]}case"TensorArrayReadV3":{let n=I("tensorArrayId",r,t,e),o=I("index",r,t,e);return[e.getTensorArray(n.id).read(o)]}case"TensorArrayGatherV3":{let n=I("tensorArrayId",r,t,e),o=I("indices",r,t,e),s=I("dtype",r,t,e);return[e.getTensorArray(n.id).gather(o,s)]}case"TensorArrayScatterV3":{let n=I("tensorArrayId",r,t,e),o=I("indices",r,t,e),s=I("tensor",r,t,e),i=e.getTensorArray(n.id);return i.scatter(o,s),[i.idTensor]}case"TensorArrayConcatV3":{let n=I("tensorArrayId",r,t,e),o=e.getTensorArray(n.id),s=I("dtype",r,t,e);return[o.concat(s)]}case"TensorArraySplitV3":{let n=I("tensorArrayId",r,t,e),o=I("tensor",r,t,e),s=I("lengths",r,t,e),i=e.getTensorArray(n.id);return i.split(s,o),[i.idTensor]}case"TensorArraySizeV3":{let n=I("tensorArrayId",r,t,e),o=e.getTensorArray(n.id);return[mt(o.size(),"int32")]}case"TensorArrayCloseV3":{let n=I("tensorArrayId",r,t,e),o=e.getTensorArray(n.id);return o.clearAndClose(),[o.idTensor]}case"TensorListSetItem":{let n=I("tensorListId",r,t,e),o=I("index",r,t,e),s=I("tensor",r,t,e),i=e.getTensorList(n.id);return i.setItem(o,s),[i.idTensor]}case"TensorListGetItem":{let n=I("tensorListId",r,t,e),o=I("index",r,t,e),s=I("elementShape",r,t,e),i=I("elementDType",r,t,e);return[e.getTensorList(n.id).getItem(o,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let n=I("indices",r,t,e),o=I("tensor",r,t,e),s=I("elementShape",r,t,e),i=I("numElements",r,t,e),a=HD(o,n,s,i);return e.addTensorList(a),[a.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let n=I("elementShape",r,t,e),o=I("elementDType",r,t,e),s;r.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,r,t,e),a=r.op==="TensorListReserve"?-1:i,u=UD(n,o,i,a);return e.addTensorList(u),[u.idTensor]}case"TensorListGather":{let n=I("tensorListId",r,t,e),o=I("indices",r,t,e),s=I("elementShape",r,t,e),i=I("elementDType",r,t,e);return[e.getTensorList(n.id).gather(o,i,s)]}case"TensorListStack":{let n=I("tensorListId",r,t,e),o=I("elementShape",r,t,e),s=I("elementDType",r,t,e),i=I("numElements",r,t,e);return[e.getTensorList(n.id).stack(o,s,i)]}case"TensorListFromTensor":{let n=I("tensor",r,t,e),o=I("elementShape",r,t,e),s=I("elementDType",r,t,e),i=WD(n,o,s);return e.addTensorList(i),[i.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let n=I("tensorListId",r,t,e),o=e.getTensorList(n.id),s=I("dtype",r,t,e),i=I("elementShape",r,t,e);return[o.concat(s,i)]}case"TensorListPushBack":{let n=I("tensorListId",r,t,e),o=I("tensor",r,t,e),s=e.getTensorList(n.id);return s.pushBack(o),[s.idTensor]}case"TensorListPopBack":{let n=I("tensorListId",r,t,e),o=I("elementShape",r,t,e),s=I("elementDType",r,t,e);return[e.getTensorList(n.id).popBack(o,s)]}case"TensorListSplit":{let n=I("tensor",r,t,e),o=I("elementShape",r,t,e),s=I("lengths",r,t,e),i=qD(n,s,o);return e.addTensorList(i),[i.idTensor]}case"TensorListLength":{let n=I("tensorListId",r,t,e),o=e.getTensorList(n.id);return[mt(o.size(),"int32")]}case"TensorListResize":{let n=I("tensorListId",r,t,e),o=I("size",r,t,e),i=e.getTensorList(n.id).resize(o);return e.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function jD(r,t,e){let[n,o]=I("fusedOps",r,t,e),s=n==="biasadd",i=!s,a=o==="prelu",u=n==="fusedbatchnorm",l=I("numArgs",r,t,e);if(s){if(a&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&s&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(u)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let c=I("strides",r,t,e),p=Fh(r,t,e),m=I("dataFormat",r,t,e).toUpperCase(),f=I("dilations",r,t,e),[d,h]=I("args",r,t,e);i&&(h=d,d=void 0);let g=I("leakyreluAlpha",r,t,e);return{stride:c,pad:p,dataFormat:m,dilations:f,biasArg:d,preluArg:h,activationFunc:o,leakyreluAlpha:g}}var XD=(r,t,e,n=se)=>{switch(r.op){case"Conv1D":{let 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o=I("strides",r,t,e),s=Fh(r,t,e),i=I("dataFormat",r,t,e).toUpperCase(),a=I("dilations",r,t,e);return[n.conv2d(I("x",r,t,e),I("filter",r,t,e),[o[1],o[2]],s,i,[a[1],a[2]])]}case"_FusedConv2D":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=jD(r,t,e);return[n.fused.conv2d({x:I("x",r,t,e),filter:I("filter",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case"FusedDepthwiseConv2dNative":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=jD(r,t,e);return[n.fused.depthwiseConv2d({x:I("x",r,t,e),filter:I("filter",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let 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nR=(r,t,e,n=se)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[n.matMul(I("a",r,t,e),I("b",r,t,e),I("transposeA",r,t,e),I("transposeB",r,t,e))];case"Einsum":return[n.einsum(I("equation",r,t,e),...I("tensors",r,t,e))];case"Transpose":return[n.transpose(I("x",r,t,e),I("perm",r,t,e))];case"_FusedMatMul":let[o,s]=I("fusedOps",r,t,e),i=o==="biasadd",a=s==="prelu",u=I("numArgs",r,t,e),l=I("leakyreluAlpha",r,t,e);if(i){if(a&&u!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&u!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,p]=I("args",r,t,e);return[n.fused.matMul({a:I("a",r,t,e),b:I("b",r,t,e),transposeA:I("transposeA",r,t,e),transposeB:I("transposeB",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var oR=(r,t,e,n=se)=>{switch(r.op){case"EuclideanNorm":return[n.euclideanNorm(I("x",r,t,e),I("axis",r,t,e),I("keepDims",r,t,e))];case"FusedBatchNorm":case"FusedBatchNormV2":return[n.batchNorm(I("x",r,t,e),I("mean",r,t,e),I("variance",r,t,e),I("offset",r,t,e),I("scale",r,t,e),I("epsilon",r,t,e))];case"FusedBatchNormV3":return[n.batchNorm(I("x",r,t,e),I("mean",r,t,e),I("variance",r,t,e),I("offset",r,t,e),I("scale",r,t,e),I("epsilon",r,t,e))];case"LRN":return[n.localResponseNormalization(I("x",r,t,e),I("radius",r,t,e),I("bias",r,t,e),I("alpha",r,t,e),I("beta",r,t,e))];case"Softmax":return[n.softmax(I("x",r,t,e))];case"LogSoftmax":return[n.logSoftmax(I("x",r,t,e))];case"SparseToDense":return[n.sparseToDense(I("sparseIndices",r,t,e),I("outputShape",r,t,e),I("sparseValues",r,t,e),I("defaultValue",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var 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cR=(r,t,e,n=se)=>{switch(r.op){case"StringNGrams":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(I("data",r,t,e),I("dataSplits",r,t,e),I("separator",r,t,e),I("nGramWidths",r,t,e),I("leftPad",r,t,e),I("rightPad",r,t,e),I("padWidth",r,t,e),I("preserveShortSequences",r,t,e));return[o,s]}case"StringSplit":{let{indices:o,values:s,shape:i}=n.string.stringSplit(I("input",r,t,e),I("delimiter",r,t,e),I("skipEmpty",r,t,e));return[o,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(I("input",r,t,e),I("numBuckets",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var pR=(r,t,e,n=se)=>{switch(r.op){case"Cast":return[n.cast(I("x",r,t,e),I("dtype",r,t,e))];case"ExpandDims":{let o=I("axis",r,t,e);return[n.expandDims(I("x",r,t,e),o)]}case"Squeeze":{let o=I("axis",r,t,e);return[n.squeeze(I("x",r,t,e),o)]}case"Reshape":return[n.reshape(I("x",r,t,e),I("shape",r,t,e))];case"MirrorPad":return[n.mirrorPad(I("x",r,t,e),I("padding",r,t,e),I("mode",r,t,e))];case"PadV2":case"Pad":return[n.pad(I("x",r,t,e),I("padding",r,t,e),I("constantValue",r,t,e))];case"SpaceToBatchND":{let o=I("blockShape",r,t,e),s=I("paddings",r,t,e);return[n.spaceToBatchND(I("x",r,t,e),o,s)]}case"BatchToSpaceND":{let o=I("blockShape",r,t,e),s=I("crops",r,t,e);return[n.batchToSpaceND(I("x",r,t,e),o,s)]}case"DepthToSpace":{let o=I("blockSize",r,t,e),s=I("dataFormat",r,t,e).toUpperCase();return[n.depthToSpace(I("x",r,t,e),o,s)]}case"BroadcastTo":return[n.broadcastTo(I("x",r,t,e),I("shape",r,t,e))];case"BroadcastArgs":return[n.broadcastArgs(I("s0",r,t,e),I("s1",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function gN(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case"arithmetic":return o(()=>BD(i,a,u));case"basic_math":return o(()=>VD(i,a,u));case"control":return KD(i,a,u);case"convolution":return o(()=>XD(i,a,u));case"creation":return o(()=>YD(i,a,u));case"dynamic":return ZD(i,a,u);case"evaluation":return o(()=>JD(i,a,u));case"image":return o(()=>eR(i,a,u));case"graph":return o(()=>QD(i,a,u));case"logical":return o(()=>rR(i,a,u));case"matrices":return o(()=>nR(i,a,u));case"normalization":return o(()=>oR(i,a,u));case"ragged":return o(()=>sR(i,a,u));case"reduction":return o(()=>iR(i,a,u));case"slice_join":return o(()=>aR(i,a,u));case"sparse":return o(()=>lR(i,a,u));case"spectral":return o(()=>uR(i,a,u));case"string":return o(()=>cR(i,a,u));case"transformation":return o(()=>pR(i,a,u));case"hash_table":return tR(i,a,u,n);case"custom":let l=Tb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Mb(i,a,u));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Ph=class{constructor(t={},e={},n={},o={}){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=n,this.functionMap=o,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;ee.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function xN(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=Object.keys(r).map(m=>bn(m)[0]),c=[];n!=null&&(c=n.map(m=>bn(m.name)[0]));let p=[...t];for(;p.length>0;){let m=p.pop();if((yN(m)||G7(m)||W7(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&l.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function mR(r,t,e){let{usedNodes:n,inputs:o}=e,s=[],i=Object.keys(o).map(c=>bn(c)[0]).map(c=>r.nodes[c]),a=r.initNodes;i.forEach(c=>{n.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{n.has(c.name)&&s.push(c)}),a!=null&&a.forEach(c=>{n.has(c.name)&&s.push(c)});let u=new Set,l=[];for(;s.length>0;){let c=s.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&n.has(p.name)&&p.inputs.every(m=>u.has(m.name))&&s.push(p)})}return l}var z7=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],B7=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],V7=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function yN(r){return z7.indexOf(r.op)>=0}function G7(r){return B7.indexOf(r.op)>=0}function W7(r){return V7.indexOf(r.op)>=0}var kc=class{constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new kc(t.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(t){let e=Object.keys(t).map(n=>t[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPERATOR)+"--"+o.join(this.SEPERATOR)}compile(t,e){let n=xN(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let a=e.map(l=>l.name),u=Object.keys(t);throw new Error(`Cannot compute the outputs [${a}] from the provided inputs [${u}]. Missing the following inputs: [${o}]`)}return mR(this.graph,this.weightMap,n)}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return Ae(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(p=>this.graph.nodes[bn(p)[0]]),s=e.map(p=>bn(p)[0]),i=s.map(p=>this.graph.nodes[p]);i.length===0&&(i=this._outputs);let a=this.getCompilationKey(o,i),u=this.compiledMap.get(a);u==null&&(u=this.compile(t,i),this.compiledMap.set(a,u));try{this.keepIntermediateTensors=M().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(p){this.keepIntermediateTensors=!1,console.warn(p.message)}let l={},c={};return B(()=>{let p=new Ph(this.weightMap,l,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(h=>{let[g,x]=bn(h),b=[];b[x]=t[h],m[g]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[g]=this.cloneTensorList(b))});let f=this.getFrozenTensorIds(m),d={};for(let h=0;hCr(h,m,p))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,n,o,s,i,a){e.category==="control"||i.indexOf(t)!==-1||(n[t].forEach(u=>{u!=null&&(a[u.id]=(a[u.id]||0)+e.children.length)}),e.inputs.forEach(u=>{if(u.category!=="control"){let l=PD(u.name,n,o);l!=null&&l.forEach(c=>{if(c&&!c.kept&&!s.has(c.id)){let p=a[c.id];p===1?(c.dispose(),delete a[c.id]):p!=null&&a[c.id]--}})}}))}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){!this.clonedTensorsMap||(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,n=!1,o={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=M().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let i=new Ph(this.weightMap,o,s,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let a=await this.executeWithControlFlow(t,i,e,n),u=e.map(m=>Cr(m,a,i)),l=u.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),p=new Set([...l,...c,...this.weightIds]);return Object.values(a).forEach(m=>{m.forEach(f=>{f&&!f.isDisposed&&!p.has(f.id)&&f.dispose()})}),this.parent==null&&i.dispose(p),u}async executeFunctionAsync(t,e,n){let o=t.reduce((s,i,a)=>(s[this.inputs[a].name]=i,s),{});return this._executeAsync(o,this.outputNodes,!0,e,n)}async executeWithControlFlow(t,e,n,o){let s=Object.keys(t),i=s.map(w=>this.graph.nodes[bn(w)[0]]),a=n.map(w=>bn(w)[0]),u=a.map(w=>this.graph.nodes[w]);u.length===0&&(u=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:p,syncInputs:m}=xN(t,u,this.weightMap,this._initNodes),f=[...i,...this.graph.weights,...this._initNodes||[]].map(w=>({node:w,contexts:e.currentContext})),d=Object.assign({},this.weightMap);Object.keys(t).forEach(w=>{let[C,N]=bn(w),E=[];E[N]=t[w],d[C]=E});let h={},g=this.getFrozenTensorIds(d),x={};for(;f.length>0;){let w=this.processStack(i,f,e,d,x,g,a,h,l);await Promise.all(w)}p==null&&!o&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let b=u.filter(w=>!yN(w)&&!Cr(w.name,d,e)).map(w=>w.name);if(b.length>0){let w="";throw p!=null&&(w=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${w}`)}return d}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m="";if(p.node.op==="Enter"&&I("isConstant",p.node,o,n)&&([m]=Qs(p.node.name,n)),o[p.node.name]==null){let f=gN(p.node,o,n,this._resourceManager);m||([m]=Qs(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(f)),this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=Qs(a.name,n);s[u]||!i.has(a.name)||(a.op==="Merge"?a.inputNames.some(l=>!!Cr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!Cr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=bn(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=bn(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var n,o;let s=(o=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||o===void 0?void 0:o[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[n]=bn(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var Gb=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var U7="?tfjs-format=file",H7="model.json",Lh=class{constructor(t,e={},n=$r){this.modelUrl=t,this.loadOptions=e,this.version="n/a",this.io=n,e==null&&(this.loadOptions={}),this.resourceManager=new Gb}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let t=this.handler.load();return y.isPromise(t)?t.then(e=>this.loadSync(e)):this.loadSync(t)}loadSync(t){this.artifacts=t;let e=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(n=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=n,this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let o=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new kc(Oh.Instance.transformGraph(e,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(o),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Oh.Instance.transformGraph(t.modelInitializer);this.initializer=new kc(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t=="string"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${t}'`);t=n[0]}if(t.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)}addStructuredOutputNames(t){if(this.structuredOutputKeys){let e=t instanceof Ft?[t]:t,n={};return e.forEach((o,s)=>n[this.structuredOutputKeys[s]]=o),n}return t}predict(t,e){let n=this.execute(t,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(t,e){let n=await this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(t){var e;if(!(t instanceof Ft)&&!Array.isArray(t)){let s=(e=this.signature)===null||e===void 0?void 0:e.inputs;if(s!=null)for(let i in s){let a=s[i];a.resourceId!=null&&(t[i]=this.resourceIdToCapturedInput[a.resourceId])}return t}t=Array.isArray(t)?t:[t];let n=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${t.length} input tensors provided.`);let o=0;return this.inputNodes.reduce((s,i)=>{var a,u,l;let 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tr{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},kN=class extends tr{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Nt(t.value)}}},EN=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=yo.getTensorsInContainer(t.value),n=this.transform(t.value),o=yo.getTensorsInContainer(n);for(let s of e)yo.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},_N=class extends tr{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},Hb=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=yo.getTensorsInContainer(t.value),n=await this.transform(t.value),o=yo.getTensorsInContainer(n);for(let s of e)yo.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},_c=class extends tr{constructor(){super(),this.outputQueue=new Ec,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},AN=class extends _c{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=yo.getTensorsInContainer(t.value),n=this.transform(t.value),o=yo.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)yo.isTensorInList(s,o)||s.dispose();return!0}},qb=class extends tr{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return"TODO: fill in upstream of chained summaries 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At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!M().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let e=new Qf(t);return await e.start(),e}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),nr(n,e)}};var td=class extends tr{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ke([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=Us([i,s,u,a],[1,4])}else this.cropBox=Us([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(!M().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new td(t,e);return await n.start(),n}async start(){this.webcamConfig.facingMode&&y.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=ox.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=sr(J(t,"float32"),0),n;n=so.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var ed=class{};var Bh=class extends tr{split(t){return new ON(this,t)}},ON=class extends Bh{constructor(t,e){super(),this.upstream=t,this.impl=new PN(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},PN=class extends _c{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var Yb=class extends tr{decodeUTF8(){return new LN(this)}},LN=class extends Bh{constructor(t){super(),this.upstream=t,this.impl=new MN(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},MN=class extends _c{constructor(t){if(super(),this.upstream=t,M().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=bN();this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let n;return M().get("IS_BROWSER")?n=this.decoder.decode(e,{stream:!0}):n=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(n),!0}};var rd=class extends Yb{constructor(t,e={}){super(),this.file=t,this.options=e,y.assert(t instanceof Uint8Array||(M().get("IS_BROWSER")?t instanceof File||t instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=e.offset||0,this.chunkSize=e.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=a=>{let u=s.result;if(u instanceof ArrayBuffer&&(u=new Uint8Array(u)),!(u instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));e(u)},s.onabort=a=>n(new Error("Aborted")),s.onerror=a=>n(new Error(a.type));let i=this.file.slice(this.offset,o);s.readAsArrayBuffer(i)}this.offset=o}),done:!1}}};async function kR(r,t={},e){let n,o;typeof r=="string"?n=r:(n=r.url,o=Q7(r));let s=await(e||y.fetch)(n,o);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new rd(i,t)}else throw new Error(s.statusText)}var Q7=r=>({method:r.method,headers:r.headers,body:r.body,mode:r.mode,credentials:r.credentials,cache:r.cache,redirect:r.redirect,referrer:r.referrer,integrity:r.integrity});function Zb(r){return typeof r=="string"&&r.slice(0,7)==="file://"}var nd=class extends ed{constructor(t,e={}){super(),this.input=t,this.options=e}async iterator(){if(Zb(this.input)&&M().get("IS_NODE")){let t=Jb();this.input=t.readFileSync(this.input.slice(7))}return new rd(this.input,this.options)}};var od=class extends ed{constructor(t,e={}){super(),this.url=t,this.fileOptions=e}async iterator(){return Zb(this.url)?new nd(this.url,this.fileOptions).iterator():kR(this.url,this.fileOptions)}};function ER(r,t={}){return new Jf(new od(r),t)}function _R(r){let t=Mh(r);return _n(async()=>t)}function AR(r){return _n(async()=>{let t=await r();return Mh(()=>t.next())})}async function $R(r,t){return td.create(r,t)}async function DR(r){return Qf.create(r)}var zN="4.1.0";function tt(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var tJ=qr.whereImpl,ku=class extends Bo{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new aa(this,Mn())}nextDataId(){return ku.nextDataId++}write(t,e,n){this.firstUse&&(this.firstUse=!1,M().get("IS_NODE")&&S.warn(` + ${n}, and tensor's shape is: ${r.shape}`);let s=r.shape.slice(1),i=Mb(s,e),a=n===0?0:r.size/n,u=B(()=>{let c=[];r=R(r,[1,n,a]);for(let 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n=I("tensorListId",r,t,e),o=I("index",r,t,e),s=I("tensor",r,t,e),i=e.getTensorList(n.id);return i.setItem(o,s),[i.idTensor]}case"TensorListGetItem":{let n=I("tensorListId",r,t,e),o=I("index",r,t,e),s=I("elementShape",r,t,e),i=I("elementDType",r,t,e);return[e.getTensorList(n.id).getItem(o,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let n=I("indices",r,t,e),o=I("tensor",r,t,e),s=I("elementShape",r,t,e),i=I("numElements",r,t,e),a=WD(o,n,s,i);return e.addTensorList(a),[a.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let n=I("elementShape",r,t,e),o=I("elementDType",r,t,e),s;r.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,r,t,e),a=r.op==="TensorListReserve"?-1:i,u=GD(n,o,i,a);return e.addTensorList(u),[u.idTensor]}case"TensorListGather":{let n=I("tensorListId",r,t,e),o=I("indices",r,t,e),s=I("elementShape",r,t,e),i=I("elementDType",r,t,e);return[e.getTensorList(n.id).gather(o,i,s)]}case"TensorListStack":{let n=I("tensorListId",r,t,e),o=I("elementShape",r,t,e),s=I("elementDType",r,t,e),i=I("numElements",r,t,e);return[e.getTensorList(n.id).stack(o,s,i)]}case"TensorListFromTensor":{let n=I("tensor",r,t,e),o=I("elementShape",r,t,e),s=I("elementDType",r,t,e),i=VD(n,o,s);return e.addTensorList(i),[i.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let n=I("tensorListId",r,t,e),o=e.getTensorList(n.id),s=I("dtype",r,t,e),i=I("elementShape",r,t,e);return[o.concat(s,i)]}case"TensorListPushBack":{let n=I("tensorListId",r,t,e),o=I("tensor",r,t,e),s=e.getTensorList(n.id);return s.pushBack(o),[s.idTensor]}case"TensorListPopBack":{let n=I("tensorListId",r,t,e),o=I("elementShape",r,t,e),s=I("elementDType",r,t,e);return[e.getTensorList(n.id).popBack(o,s)]}case"TensorListSplit":{let n=I("tensor",r,t,e),o=I("elementShape",r,t,e),s=I("lengths",r,t,e),i=UD(n,s,o);return e.addTensorList(i),[i.idTensor]}case"TensorListLength":{let n=I("tensorListId",r,t,e),o=e.getTensorList(n.id);return[pt(o.size(),"int32")]}case"TensorListResize":{let n=I("tensorListId",r,t,e),o=I("size",r,t,e),i=e.getTensorList(n.id).resize(o);return e.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function qD(r,t,e){let[n,o]=I("fusedOps",r,t,e),s=n==="biasadd",i=!s,a=o==="prelu",u=n==="fusedbatchnorm",l=I("numArgs",r,t,e);if(s){if(a&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&s&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(u)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let c=I("strides",r,t,e),p=Oh(r,t,e),m=I("dataFormat",r,t,e).toUpperCase(),f=I("dilations",r,t,e),[d,h]=I("args",r,t,e);i&&(h=d,d=void 0);let g=I("leakyreluAlpha",r,t,e);return{stride:c,pad:p,dataFormat:m,dilations:f,biasArg:d,preluArg:h,activationFunc:o,leakyreluAlpha:g}}var KD=(r,t,e,n=se)=>{switch(r.op){case"Conv1D":{let o=I("stride",r,t,e),s=I("pad",r,t,e),i=I("dataFormat",r,t,e).toUpperCase(),a=I("dilation",r,t,e);return[n.conv1d(I("x",r,t,e),I("filter",r,t,e),o,s,i,a)]}case"Conv2D":{let o=I("strides",r,t,e),s=Oh(r,t,e),i=I("dataFormat",r,t,e).toUpperCase(),a=I("dilations",r,t,e);return[n.conv2d(I("x",r,t,e),I("filter",r,t,e),[o[1],o[2]],s,i,[a[1],a[2]])]}case"_FusedConv2D":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=qD(r,t,e);return[n.fused.conv2d({x:I("x",r,t,e),filter:I("filter",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case"FusedDepthwiseConv2dNative":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=qD(r,t,e);return[n.fused.depthwiseConv2d({x:I("x",r,t,e),filter:I("filter",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let o=I("outputShape",r,t,e),s=I("strides",r,t,e),i=Oh(r,t,e);return[n.conv2dTranspose(I("x",r,t,e),I("filter",r,t,e),o,[s[1],s[2]],i)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let o=I("strides",r,t,e),s=Oh(r,t,e),i=I("dilations",r,t,e),a=I("dataFormat",r,t,e).toUpperCase();return[n.depthwiseConv2d(I("input",r,t,e),I("filter",r,t,e),[o[1],o[2]],s,a,[i[1],i[2]])]}case"Conv3D":{let o=I("strides",r,t,e),s=I("pad",r,t,e),i=I("dataFormat",r,t,e).toUpperCase(),a=I("dilations",r,t,e);return[n.conv3d(I("x",r,t,e),I("filter",r,t,e),[o[1],o[2],o[3]],s,i,[a[1],a[2],a[3]])]}case"AvgPool":{let o=I("strides",r,t,e),s=I("pad",r,t,e),i=I("kernelSize",r,t,e);return[n.avgPool(I("x",r,t,e),[i[1],i[2]],[o[1],o[2]],s)]}case"MaxPool":{let o=I("strides",r,t,e),s=I("pad",r,t,e),i=I("kernelSize",r,t,e);return[n.maxPool(I("x",r,t,e),[i[1],i[2]],[o[1],o[2]],s)]}case"MaxPoolWithArgmax":{let o=I("strides",r,t,e),s=I("pad",r,t,e),i=I("kernelSize",r,t,e),a=I("includeBatchInIndex",r,t,e),{result:u,indexes:l}=n.maxPoolWithArgmax(I("x",r,t,e),[i[1],i[2]],[o[1],o[2]],s,a);return[u,l]}case"AvgPool3D":{let o=I("strides",r,t,e),s=I("pad",r,t,e),i=I("kernelSize",r,t,e);return[n.avgPool3d(I("x",r,t,e),[i[1],i[2],i[3]],[o[1],o[2],o[3]],s)]}case"MaxPool3D":{let o=I("strides",r,t,e),s=I("pad",r,t,e),i=I("kernelSize",r,t,e);return[n.maxPool3d(I("x",r,t,e),[i[1],i[2],i[3]],[o[1],o[2],o[3]],s)]}case"Dilation2D":{let o=I("strides",r,t,e),s=I("pad",r,t,e),i=I("dilations",r,t,e),a=o[1],u=o[2],l=i[1],c=i[2];return[n.dilation2d(I("x",r,t,e),I("filter",r,t,e),[a,u],s,[l,c],"NHWC")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var jD=(r,t,e,n=se)=>{switch(r.op){case"Fill":{let o=I("shape",r,t,e),s=I("dtype",r,t,e),i=I("value",r,t,e);return[n.fill(o,i,s)]}case"LinSpace":{let o=I("start",r,t,e),s=I("stop",r,t,e),i=I("num",r,t,e);return[n.linspace(o,s,i)]}case"Multinomial":{let o=I("logits",r,t,e),s=I("numSamples",r,t,e),i=I("seed",r,t,e);return[n.multinomial(o,s,i)]}case"OneHot":{let o=I("indices",r,t,e),s=I("depth",r,t,e),i=I("onValue",r,t,e),a=I("offValue",r,t,e),u=I("dtype",r,t,e);return[n.oneHot(o,s,i,a,u)]}case"Ones":return[n.ones(I("shape",r,t,e),I("dtype",r,t,e))];case"OnesLike":return[n.onesLike(I("x",r,t,e))];case"RandomStandardNormal":return[n.randomStandardNormal(I("shape",r,t,e),I("dtype",r,t,e),I("seed",r,t,e))];case"RandomUniform":return[n.randomUniform(I("shape",r,t,e),I("minval",r,t,e),I("maxval",r,t,e),I("dtype",r,t,e))];case"Range":{let o=I("start",r,t,e),s=I("stop",r,t,e),i=I("step",r,t,e);return[n.range(o,s,i,I("dtype",r,t,e))]}case"TruncatedNormal":{let o=I("shape",r,t,e),s=I("mean",r,t,e),i=I("stdDev",r,t,e),a=I("seed",r,t,e);return[n.truncatedNormal(o,s,i,I("dtype",r,t,e),a)]}case"Zeros":return[n.zeros(I("shape",r,t,e),I("dtype",r,t,e))];case"ZerosLike":return[n.zerosLike(I("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function gN(r,t,e){let n=I("boxes",r,t,e),o=I("scores",r,t,e),s=I("maxOutputSize",r,t,e),i=I("iouThreshold",r,t,e),a=I("scoreThreshold",r,t,e),u=I("softNmsSigma",r,t,e);return{boxes:n,scores:o,maxOutputSize:s,iouThreshold:i,scoreThreshold:a,softNmsSigma:u}}var XD=async(r,t,e,n,o=se)=>{switch(r.op){case"NonMaxSuppressionV5":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l,softNmsSigma:c}=gN(r,t,e),p=await o.image.nonMaxSuppressionWithScoreAsync(s,i,a,u,l,c);return[p.selectedIndices,p.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l}=gN(r,t,e),c=I("padToMaxOutputSize",r,t,e),p=await o.image.nonMaxSuppressionPaddedAsync(s,i,a,u,l,c);return[p.selectedIndices,p.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l}=gN(r,t,e);return[await o.image.nonMaxSuppressionAsync(s,i,a,u,l)]}case"Where":{let s=o.cast(I("condition",r,t,e),"bool"),i=[await o.whereAsync(s)];return s.dispose(),i}case"ListDiff":return o.setdiff1dAsync(I("x",r,t,e),I("y",r,t,e));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var YD=(r,t,e,n=se)=>{switch(r.op){case"LowerBound":{let o=I("sortedSequence",r,t,e),s=I("values",r,t,e);return[n.lowerBound(o,s)]}case"TopKV2":{let o=I("x",r,t,e),s=I("k",r,t,e),i=I("sorted",r,t,e),a=n.topk(o,s,i);return[a.values,a.indices]}case"UpperBound":{let o=I("sortedSequence",r,t,e),s=I("values",r,t,e);return[n.upperBound(o,s)]}case"Unique":{let o=I("x",r,t,e),s=n.unique(o);return[s.values,s.indices]}case"UniqueV2":{let o=I("x",r,t,e),s=I("axis",r,t,e),i=n.unique(o,s);return[i.values,i.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ZD=(r,t,e,n=se)=>{switch(r.op){case"Const":return t[r.name];case"PlaceholderWithDefault":let o=I("default",r,t,e);return[Cr(r.name,t,e)||o];case"Placeholder":return[Cr(r.name,t,e)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=I("x",r,t,e);return[Js(c)]}case"IdentityN":return I("x",r,t,e).map(c=>Js(c));case"Snapshot":let s=I("x",r,t,e);return[Js(s)];case"Shape":return[n.tensor1d(I("x",r,t,e).shape,"int32")];case"ShapeN":return I("x",r,t,e).map(c=>n.tensor1d(c.shape));case"Size":return[n.scalar(I("x",r,t,e).size,"int32")];case"Rank":return[n.scalar(I("x",r,t,e).rank,"int32")];case"NoOp":return[n.scalar(1)];case"Print":let i=I("x",r,t,e),a=I("data",r,t,e),u=I("message",r,t,e),l=I("summarize",r,t,e);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(u);for(let c=0;ct.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return pt(this.size(),"int32")}async import(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return 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TypeError(`Node type ${r.op} is not implemented`)}};var QD=(r,t,e,n=se)=>{switch(r.op){case"ResizeBilinear":{let o=I("images",r,t,e),s=I("size",r,t,e),i=I("alignCorners",r,t,e),a=I("halfPixelCenters",r,t,e);return[n.image.resizeBilinear(o,[s[0],s[1]],i,a)]}case"ResizeNearestNeighbor":{let o=I("images",r,t,e),s=I("size",r,t,e),i=I("alignCorners",r,t,e),a=I("halfPixelCenters",r,t,e);return[n.image.resizeNearestNeighbor(o,[s[0],s[1]],i,a)]}case"CropAndResize":{let o=I("image",r,t,e),s=I("boxes",r,t,e),i=I("boxInd",r,t,e),a=I("cropSize",r,t,e),u=I("method",r,t,e),l=I("extrapolationValue",r,t,e);return[n.image.cropAndResize(o,s,i,a,u,l)]}case"ImageProjectiveTransformV3":{let o=I("images",r,t,e),s=I("transforms",r,t,e),i=I("outputShape",r,t,e),a=I("fillValue",r,t,e),u=I("interpolation",r,t,e),l=I("fillMode",r,t,e);return[n.image.transform(o,s,u.toLowerCase(),l.toLowerCase(),a,i)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var tR=(r,t,e,n=se)=>{switch(r.op){case"Equal":return[n.equal(I("a",r,t,e),I("b",r,t,e))];case"NotEqual":return[n.notEqual(I("a",r,t,e),I("b",r,t,e))];case"Greater":return[n.greater(I("a",r,t,e),I("b",r,t,e))];case"GreaterEqual":return[n.greaterEqual(I("a",r,t,e),I("b",r,t,e))];case"Less":return[n.less(I("a",r,t,e),I("b",r,t,e))];case"LessEqual":return[n.lessEqual(I("a",r,t,e),I("b",r,t,e))];case"LogicalAnd":return[n.logicalAnd(I("a",r,t,e),I("b",r,t,e))];case"LogicalNot":return[n.logicalNot(I("a",r,t,e))];case"LogicalOr":return[n.logicalOr(I("a",r,t,e),I("b",r,t,e))];case"Select":case"SelectV2":return[n.where(I("condition",r,t,e),I("a",r,t,e),I("b",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var eR=(r,t,e,n=se)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[n.matMul(I("a",r,t,e),I("b",r,t,e),I("transposeA",r,t,e),I("transposeB",r,t,e))];case"Einsum":return[n.einsum(I("equation",r,t,e),...I("tensors",r,t,e))];case"Transpose":return[n.transpose(I("x",r,t,e),I("perm",r,t,e))];case"_FusedMatMul":let[o,s]=I("fusedOps",r,t,e),i=o==="biasadd",a=s==="prelu",u=I("numArgs",r,t,e),l=I("leakyreluAlpha",r,t,e);if(i){if(a&&u!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&u!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,p]=I("args",r,t,e);return[n.fused.matMul({a:I("a",r,t,e),b:I("b",r,t,e),transposeA:I("transposeA",r,t,e),transposeB:I("transposeB",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var rR=(r,t,e,n=se)=>{switch(r.op){case"EuclideanNorm":return[n.euclideanNorm(I("x",r,t,e),I("axis",r,t,e),I("keepDims",r,t,e))];case"FusedBatchNorm":case"FusedBatchNormV2":return[n.batchNorm(I("x",r,t,e),I("mean",r,t,e),I("variance",r,t,e),I("offset",r,t,e),I("scale",r,t,e),I("epsilon",r,t,e))];case"FusedBatchNormV3":return[n.batchNorm(I("x",r,t,e),I("mean",r,t,e),I("variance",r,t,e),I("offset",r,t,e),I("scale",r,t,e),I("epsilon",r,t,e))];case"LRN":return[n.localResponseNormalization(I("x",r,t,e),I("radius",r,t,e),I("bias",r,t,e),I("alpha",r,t,e),I("beta",r,t,e))];case"Softmax":return[n.softmax(I("x",r,t,e))];case"LogSoftmax":return[n.logSoftmax(I("x",r,t,e))];case"SparseToDense":return[n.sparseToDense(I("sparseIndices",r,t,e),I("outputShape",r,t,e),I("sparseValues",r,t,e),I("defaultValue",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var nR=(r,t,e,n=se)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(I("paramsNestedSplits",r,t,e),I("paramsDenseValues",r,t,e),I("indices",r,t,e),I("outputRaggedRank",r,t,e));return o.concat(s)}case"RaggedRange":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(I("starts",r,t,e),I("limits",r,t,e),I("splits",r,t,e));return[o,s]}case"RaggedTensorToTensor":return[n.raggedTensorToTensor(I("shape",r,t,e),I("values",r,t,e),I("defaultValue",r,t,e),I("rowPartitionTensors",r,t,e),I("rowPartitionTypes",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var oR=(r,t,e,n=se)=>{switch(r.op){case"Max":{let a=I("axis",r,t,e),u=I("keepDims",r,t,e);return[n.max(I("x",r,t,e),a,u)]}case"Mean":{let a=I("axis",r,t,e),u=I("keepDims",r,t,e);return[n.mean(I("x",r,t,e),a,u)]}case"Min":{let a=I("axis",r,t,e),u=I("keepDims",r,t,e);return[n.min(I("x",r,t,e),a,u)]}case"Sum":{let a=I("axis",r,t,e),u=I("keepDims",r,t,e);return[n.sum(I("x",r,t,e),a,u)]}case"All":{let a=I("axis",r,t,e),u=I("keepDims",r,t,e);return[n.all(I("x",r,t,e),a,u)]}case"Any":{let a=I("axis",r,t,e),u=I("keepDims",r,t,e);return[n.any(I("x",r,t,e),a,u)]}case"ArgMax":{let a=I("axis",r,t,e);return[n.argMax(I("x",r,t,e),a)]}case"ArgMin":{let a=I("axis",r,t,e);return[n.argMin(I("x",r,t,e),a)]}case"Prod":{let a=I("axis",r,t,e),u=I("keepDims",r,t,e);return[n.prod(I("x",r,t,e),a,u)]}case"Cumprod":{let a=I("axis",r,t,e),u=I("exclusive",r,t,e),l=I("reverse",r,t,e);return[n.cumprod(I("x",r,t,e),a,u,l)]}case"Cumsum":{let a=I("axis",r,t,e),u=I("exclusive",r,t,e),l=I("reverse",r,t,e);return[n.cumsum(I("x",r,t,e),a,u,l)]}case"Bincount":let o=I("x",r,t,e),s=I("weights",r,t,e),i=I("size",r,t,e);return[n.bincount(o,s,i)];case"DenseBincount":{let a=I("x",r,t,e),u=I("weights",r,t,e),l=I("size",r,t,e),c=I("binaryOutput",r,t,e);return[n.denseBincount(a,u,l,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var sR=(r,t,e,n=se)=>{switch(r.op){case"ConcatV2":case"Concat":{let o=I("n",r,t,e),s=I("axis",r,t,e),i=I("tensors",r,t,e);return i=i.slice(0,o),[n.concat(i,s)]}case"Gather":{let o=I("x",r,t,e),s=I("indices",r,t,e);return[n.gather(o,n.cast(s,"int32"),0)]}case"GatherV2":{let o=I("axis",r,t,e),s=I("batchDims",r,t,e),i=I("x",r,t,e),a=I("indices",r,t,e);return[n.gather(i,n.cast(a,"int32"),o,s)]}case"Reverse":{let o=I("dims",r,t,e),s=[];for(let a=0;a{let o=I("axis",r,t,e),s=I("tensors",r,t,e),i=s[0].shape,a=n.squeeze(s[0]).shape,u=s.map(l=>{let c=y.arraysEqual(l.shape,i);if(!c&&!y.arraysEqual(n.squeeze(l).shape,a))throw new Error("the input tensors shape does not match");return c?l:n.reshape(l,i)});return[n.stack(u,o)]});case"Unpack":{let o=I("axis",r,t,e),s=I("tensor",r,t,e);return n.unstack(s,o)}case"Tile":{let o=I("reps",r,t,e);return[n.tile(I("x",r,t,e),o)]}case"Split":case"SplitV":{let o=I("axis",r,t,e),s=I("numOrSizeSplits",r,t,e),i=I("x",r,t,e);return n.split(i,s,o)}case"ScatterNd":{let o=I("indices",r,t,e),s=I("values",r,t,e),i=I("shape",r,t,e);return[n.scatterND(o,s,i)]}case"GatherNd":{let o=I("x",r,t,e),s=I("indices",r,t,e);return[n.gatherND(o,s)]}case"SparseToDense":{let o=I("sparseIndices",r,t,e),s=I("outputShape",r,t,e),i=I("sparseValues",r,t,e),a=I("defaultValue",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var iR=(r,t,e,n=se)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(I("indices",r,t,e),I("values",r,t,e),I("denseShape",r,t,e),I("defaultValue",r,t,e));return[o,s,i,a]}case"SparseReshape":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(I("inputIndices",r,t,e),I("inputShape",r,t,e),I("newShape",r,t,e));return[o,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(I("data",r,t,e),I("indices",r,t,e),I("segmentIds",r,t,e))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(I("data",r,t,e),I("indices",r,t,e),I("segmentIds",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var aR=(r,t,e,n=se)=>{switch(r.op){case"FFT":return[n.fft(I("x",r,t,e))];case"IFFT":return[n.ifft(I("x",r,t,e))];case"RFFT":return[n.rfft(I("x",r,t,e))];case"IRFFT":return[n.irfft(I("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var lR=(r,t,e,n=se)=>{switch(r.op){case"StringNGrams":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(I("data",r,t,e),I("dataSplits",r,t,e),I("separator",r,t,e),I("nGramWidths",r,t,e),I("leftPad",r,t,e),I("rightPad",r,t,e),I("padWidth",r,t,e),I("preserveShortSequences",r,t,e));return[o,s]}case"StringSplit":{let{indices:o,values:s,shape:i}=n.string.stringSplit(I("input",r,t,e),I("delimiter",r,t,e),I("skipEmpty",r,t,e));return[o,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(I("input",r,t,e),I("numBuckets",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var uR=(r,t,e,n=se)=>{switch(r.op){case"Cast":return[n.cast(I("x",r,t,e),I("dtype",r,t,e))];case"ExpandDims":{let o=I("axis",r,t,e);return[n.expandDims(I("x",r,t,e),o)]}case"Squeeze":{let o=I("axis",r,t,e);return[n.squeeze(I("x",r,t,e),o)]}case"Reshape":return[n.reshape(I("x",r,t,e),I("shape",r,t,e))];case"MirrorPad":return[n.mirrorPad(I("x",r,t,e),I("padding",r,t,e),I("mode",r,t,e))];case"PadV2":case"Pad":return[n.pad(I("x",r,t,e),I("padding",r,t,e),I("constantValue",r,t,e))];case"SpaceToBatchND":{let o=I("blockShape",r,t,e),s=I("paddings",r,t,e);return[n.spaceToBatchND(I("x",r,t,e),o,s)]}case"BatchToSpaceND":{let o=I("blockShape",r,t,e),s=I("crops",r,t,e);return[n.batchToSpaceND(I("x",r,t,e),o,s)]}case"DepthToSpace":{let o=I("blockSize",r,t,e),s=I("dataFormat",r,t,e).toUpperCase();return[n.depthToSpace(I("x",r,t,e),o,s)]}case"BroadcastTo":return[n.broadcastTo(I("x",r,t,e),I("shape",r,t,e))];case"BroadcastArgs":return[n.broadcastArgs(I("s0",r,t,e),I("s1",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function xN(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case"arithmetic":return o(()=>MD(i,a,u));case"basic_math":return o(()=>zD(i,a,u));case"control":return HD(i,a,u);case"convolution":return o(()=>KD(i,a,u));case"creation":return o(()=>jD(i,a,u));case"dynamic":return XD(i,a,u);case"evaluation":return o(()=>YD(i,a,u));case"image":return o(()=>QD(i,a,u));case"graph":return o(()=>ZD(i,a,u));case"logical":return o(()=>tR(i,a,u));case"matrices":return o(()=>eR(i,a,u));case"normalization":return o(()=>rR(i,a,u));case"ragged":return o(()=>nR(i,a,u));case"reduction":return o(()=>oR(i,a,u));case"slice_join":return o(()=>sR(i,a,u));case"sparse":return o(()=>iR(i,a,u));case"spectral":return o(()=>aR(i,a,u));case"string":return o(()=>lR(i,a,u));case"transformation":return o(()=>uR(i,a,u));case"hash_table":return JD(i,a,u,n);case"custom":let l=Nb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Lb(i,a,u));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. 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this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function yN(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=Object.keys(r).map(m=>bn(m)[0]),c=[];n!=null&&(c=n.map(m=>bn(m.name)[0]));let p=[...t];for(;p.length>0;){let m=p.pop();if((bN(m)||G7(m)||W7(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&l.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function cR(r,t,e){let{usedNodes:n,inputs:o}=e,s=[],i=Object.keys(o).map(c=>bn(c)[0]).map(c=>r.nodes[c]),a=r.initNodes;i.forEach(c=>{n.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{n.has(c.name)&&s.push(c)}),a!=null&&a.forEach(c=>{n.has(c.name)&&s.push(c)});let u=new Set,l=[];for(;s.length>0;){let c=s.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&n.has(p.name)&&p.inputs.every(m=>u.has(m.name))&&s.push(p)})}return l}var z7=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],B7=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],V7=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function bN(r){return z7.indexOf(r.op)>=0}function G7(r){return B7.indexOf(r.op)>=0}function W7(r){return V7.indexOf(r.op)>=0}var kc=class{constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new kc(t.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(t){let e=Object.keys(t).map(n=>t[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPERATOR)+"--"+o.join(this.SEPERATOR)}compile(t,e){let n=yN(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let a=e.map(l=>l.name),u=Object.keys(t);throw new Error(`Cannot compute the outputs [${a}] from the provided inputs [${u}]. Missing the following inputs: [${o}]`)}return cR(this.graph,this.weightMap,n)}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return De(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(p=>this.graph.nodes[bn(p)[0]]),s=e.map(p=>bn(p)[0]),i=s.map(p=>this.graph.nodes[p]);i.length===0&&(i=this._outputs);let a=this.getCompilationKey(o,i),u=this.compiledMap.get(a);u==null&&(u=this.compile(t,i),this.compiledMap.set(a,u));try{this.keepIntermediateTensors=M().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(p){this.keepIntermediateTensors=!1,console.warn(p.message)}let l={},c={};return B(()=>{let p=new Lh(this.weightMap,l,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(h=>{let[g,x]=bn(h),b=[];b[x]=t[h],m[g]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[g]=this.cloneTensorList(b))});let f=this.getFrozenTensorIds(m),d={};for(let h=0;hCr(h,m,p))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,n,o,s,i,a){e.category==="control"||i.indexOf(t)!==-1||(n[t].forEach(u=>{u!=null&&(a[u.id]=(a[u.id]||0)+e.children.length)}),e.inputs.forEach(u=>{if(u.category!=="control"){let l=FD(u.name,n,o);l!=null&&l.forEach(c=>{if(c&&!c.kept&&!s.has(c.id)){let p=a[c.id];p===1?(c.dispose(),delete a[c.id]):p!=null&&a[c.id]--}})}}))}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){!this.clonedTensorsMap||(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,n=!1,o={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=M().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let i=new Lh(this.weightMap,o,s,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let a=await this.executeWithControlFlow(t,i,e,n),u=e.map(m=>Cr(m,a,i)),l=u.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),p=new Set([...l,...c,...this.weightIds]);return Object.values(a).forEach(m=>{m.forEach(f=>{f&&!f.isDisposed&&!p.has(f.id)&&f.dispose()})}),this.parent==null&&i.dispose(p),u}async executeFunctionAsync(t,e,n){let o=t.reduce((s,i,a)=>(s[this.inputs[a].name]=i,s),{});return this._executeAsync(o,this.outputNodes,!0,e,n)}async executeWithControlFlow(t,e,n,o){let s=Object.keys(t),i=s.map(w=>this.graph.nodes[bn(w)[0]]),a=n.map(w=>bn(w)[0]),u=a.map(w=>this.graph.nodes[w]);u.length===0&&(u=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:p,syncInputs:m}=yN(t,u,this.weightMap,this._initNodes),f=[...i,...this.graph.weights,...this._initNodes||[]].map(w=>({node:w,contexts:e.currentContext})),d=Object.assign({},this.weightMap);Object.keys(t).forEach(w=>{let[C,N]=bn(w),_=[];_[N]=t[w],d[C]=_});let h={},g=this.getFrozenTensorIds(d),x={};for(;f.length>0;){let w=this.processStack(i,f,e,d,x,g,a,h,l);await Promise.all(w)}p==null&&!o&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let b=u.filter(w=>!bN(w)&&!Cr(w.name,d,e)).map(w=>w.name);if(b.length>0){let w="";throw p!=null&&(w=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${w}`)}return d}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m="";if(p.node.op==="Enter"&&I("isConstant",p.node,o,n)&&([m]=Zs(p.node.name,n)),o[p.node.name]==null){let f=xN(p.node,o,n,this._resourceManager);m||([m]=Zs(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(f)),this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=Zs(a.name,n);s[u]||!i.has(a.name)||(a.op==="Merge"?a.inputNames.some(l=>!!Cr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!Cr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=bn(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=bn(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var n,o;let s=(o=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||o===void 0?void 0:o[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[n]=bn(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var Vb=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in 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if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let t=this.handler.load();return y.isPromise(t)?t.then(e=>this.loadSync(e)):this.loadSync(t)}loadSync(t){this.artifacts=t;let e=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(n=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=n,this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let o=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new kc(Ph.Instance.transformGraph(e,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(o),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Ph.Instance.transformGraph(t.modelInitializer);this.initializer=new kc(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t=="string"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${t}'`);t=n[0]}if(t.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)}addStructuredOutputNames(t){if(this.structuredOutputKeys){let e=t instanceof Ft?[t]:t,n={};return e.forEach((o,s)=>n[this.structuredOutputKeys[s]]=o),n}return t}predict(t,e){let n=this.execute(t,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(t,e){let n=await this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(t){var e;if(!(t instanceof Ft)&&!Array.isArray(t)){let s=(e=this.signature)===null||e===void 0?void 0:e.inputs;if(s!=null)for(let i in s){let a=s[i];a.resourceId!=null&&(t[i]=this.resourceIdToCapturedInput[a.resourceId])}return t}t=Array.isArray(t)?t:[t];let n=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${t.length} input tensors provided.`);let o=0;return this.inputNodes.reduce((s,i)=>{var a,u,l;let 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$R={};Ut($R,{CSVDataset:()=>Jf,Dataset:()=>Qs,FileDataSource:()=>nd,TextLineDataset:()=>Zf,URLDataSource:()=>od,array:()=>CR,csv:()=>TR,func:()=>kR,generator:()=>ER,microphone:()=>AR,version_data:()=>BN,webcam:()=>_R,zip:()=>IR});var wR=El(ch());var xR=El(ch());function mR(r,t){return Gb(r,t)}function Gb(r,t,e=new Map,n=new Set){if(r==null)return null;if(typeof Blob=="function"&&r instanceof Blob)return r.slice();if(n.has(r))throw new Error("Circular references are not supported.");if(e.has(r))return e.get(r);let o=t(r);if(o.recurse&&o.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(o.recurse)if(bu(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let a=r[i],u=Gb(a,t,e,n);s[i]=u}return n.delete(r),r.__proto__&&(s.__proto__=r.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return e.set(r,o.value),o.value}function fR(r,t=CN){return dR(r,t)}function dR(r,t,e=new Set){let 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r=="object"&&r instanceof Ft||y.isTypedArray(r)}function X7(r){return r===null||typeof r!="object"&&typeof r!="function"}function gR(r){return mR(r,Y7)}function Y7(r){return r instanceof Ft?{value:r.clone(),recurse:!1}:bu(r)?{value:null,recurse:!0}:{value:r,recurse:!1}}var Yf=class{constructor(t){if(this.capacity=t,this.begin=0,this.end=0,t==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(t<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(t),this.doubledCapacity=2*t}wrap(t){for(;t<0;)t+=this.doubledCapacity;return t%this.doubledCapacity}get(t){if(t<0)throw new RangeError("Can't get item at a negative index.");return this.data[t%this.capacity]}set(t,e){if(t<0)throw new RangeError("Can't set item at a negative index.");this.data[t%this.capacity]=e}length(){let t=this.end-this.begin;return t<0&&(t=this.doubledCapacity+t),t}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,t),this.end=this.wrap(this.end+1)}pushAll(t){for(let e of t)this.push(e)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let t=this.get(this.end);return this.set(this.end,void 0),t}unshift(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,t)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),t}shuffleExcise(t){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.wrap(this.begin+t),n=this.get(e);return this.set(e,this.pop()),n}};var Ec=class extends Yf{constructor(){super(Ec.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,e=new Array(t),n=this.length();for(let o=0;oe===!0)}rowMajorBatch(t,e=!0){return new kN(this,t,e)}columnMajorBatch(t,e=!0,n=CN){return this.rowMajorBatch(t,e).map(s=>fR(s,n))}concatenate(t,e){return new Hb(FN([this,t]),e)}take(t){return t<0||t==null?this:new TN(this,t)}skip(t){return t<0||t==null?this:new NN(this,t)}prefetch(t){return new qb(this,t)}shuffle(t,e){return new RN(this,t,e)}serial(){return new SN(this)}},IN=class extends tr{constructor(t){super(),this.items=t,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let t=this.items[this.trav];return this.trav++,{value:gR(t),done:!1}}},vN=class extends tr{constructor(t){super(),this.nextFn=t}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(t){throw t.message=`Error thrown while iterating through a dataset: ${t.message}`,t}}},SN=class extends tr{constructor(t){super(),this.upstream=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},NN=class extends tr{constructor(t,e){super(),this.upstream=t,this.maxCount=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},kN=class extends tr{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},EN=class extends tr{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Nt(t.value)}}},_N=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=xo.getTensorsInContainer(t.value),n=this.transform(t.value),o=xo.getTensorsInContainer(n);for(let s of e)xo.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},AN=class extends tr{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},Ub=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=xo.getTensorsInContainer(t.value),n=await this.transform(t.value),o=xo.getTensorsInContainer(n);for(let s of e)xo.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},_c=class extends tr{constructor(){super(),this.outputQueue=new Ec,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},$N=class extends _c{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=xo.getTensorsInContainer(t.value),n=this.transform(t.value),o=xo.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)xo.isTensorInList(s,o)||s.dispose();return!0}},Hb=class extends tr{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(t){if(await t,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let e=await this.iterator.next();return e.done?(this.iterator=null,this.readFromChain(t)):e}},gl;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(gl||(gl={}));var DN=class extends tr{constructor(t,e=gl.FAIL){super(),this.iterators=t,this.mismatchMode=e,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(t){await t;let e=0,n=0;function o(i){return i instanceof tr?{value:i.next().then(u=>(e++,u.done&&n++,u.value)),recurse:!1}:{value:null,recurse:!0}}let s=await Wb(this.iterators,o);if(e===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case gl.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case gl.SHORTEST:return{value:null,done:!0};case gl.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},qb=class extends tr{constructor(t,e){super(),this.upstream=t,this.bufferSize=e,this.buffer=new Yf(e)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let t=this.upstream.next();this.buffer.push(t)}}next(){return this.refill(),this.buffer.shift()}},RN=class extends qb{constructor(t,e,n){super(t,e),this.upstream=t,this.windowSize=e,this.upstreamExhausted=!1,this.random=xR.alea(n||y.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(t){return Math.floor(this.random()*t)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let t=this.chooseIndex(),e=await this.buffer.shuffleExcise(t);if(e.done)this.upstreamExhausted=!0;else return this.refill(),e}return{value:null,done:!0}}};var Qs=class{constructor(){this.size=null}batch(t,e=!0){let n=this;y.assert(t>0,()=>`batchSize needs to be positive, but it is + ${t}`);let o;return this.size===1/0||this.size==null?o=this.size:e?o=Math.ceil(this.size/t):o=Math.floor(this.size/t),En(async()=>(await n.iterator()).columnMajorBatch(t,e,Z7),o)}concatenate(t){let e=this,n;return this.size===1/0||t.size===1/0?n=1/0:this.size!=null&&t.size!=null?n=this.size+t.size:n=null,En(async()=>(await e.iterator()).concatenate(await t.iterator()),n)}filter(t){let e=this,n;return this.size===1/0?n=1/0:n=null,En(async()=>(await e.iterator()).filter(o=>B(()=>t(o))),n)}async forEachAsync(t){return(await this.iterator()).forEachAsync(t)}map(t){let e=this;return En(async()=>(await e.iterator()).map(n=>B(()=>t(n))),this.size)}mapAsync(t){let e=this;return En(async()=>(await e.iterator()).mapAsync(t),this.size)}prefetch(t){if(t==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let e=this;return En(async()=>(await e.iterator()).prefetch(t),this.size)}repeat(t){let e=this,n;return this.size!=null&&t>0?n=this.size*t:t===0?n=0:this.size!=null&&(t===void 0||t<0)?n=1/0:n=null,En(async()=>{let o=zh(async()=>({value:await e.iterator(),done:!1}));return yR(o.take(t))},n)}skip(t){let e=this,n;return this.size!=null&&t>=0&&this.size>=t?n=this.size-t:this.size!=null&&(this.size(await e.iterator()).skip(t),n)}shuffle(t,e,n=!0){if(t==null||t<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let o=this,s=wR.alea(e||y.now().toString());return En(async()=>{let i=s.int32();return n&&(i+=s.int32()),(await o.iterator()).shuffle(t,i.toString())},this.size)}take(t){let e=this,n;return this.size!=null&&this.size>t?n=t:this.size!=null&&this.size<=t?n=this.size:n=null,En(async()=>(await e.iterator()).take(t),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Qs.MAX_BUFFER_SIZE=1e4;function En(r,t=null){return new class extends Qs{constructor(){super(...arguments),this.size=t}async iterator(){return r()}}}function CR(r){return En(async()=>FN(r),r.length)}function IR(r){if(!bu(r))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(r))for(let e=0;e{let e=await Wb(r,n=>{if(n instanceof Qs)return{value:n.iterator(),recurse:!1};if(bu(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return bR(e,gl.SHORTEST)},t)}function Z7(r){if(r===null)return null;let t=r[0];return hR(t)?{value:J7(r),recurse:!1}:{value:null,recurse:!0}}function J7(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof Ft?qe(r):or(r)}var Zf=class extends Qs{constructor(t){super(),this.input=t}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` +`).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o))}};var Kb='"',Bh=Symbol("out"),vR=Symbol("field"),jb=Symbol("quote"),ON=Symbol("quoteafterquote"),SR=Symbol("quoteinquote"),Jf=class extends Qs{constructor(t,e){super(),this.input=t,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new Zf(t),e||(e={}),this.hasHeader=e.hasHeader!==!1,this.fullColumnNames=e.columnNames,this.columnConfigs=e.columnConfigs,this.configuredColumnsOnly=e.configuredColumnsOnly,e.delimWhitespace?(y.assert(e.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=e.delimiter?e.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let t=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!t)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&t&&y.assert(t.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+t.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=t);let e=this.fullColumnNames.reduce((o,s)=>(o[s]=o[s]+1||1,o),{}),n=Object.keys(e).filter(o=>e[o]>1);if(y.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let o of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(o)===-1)throw new Error('The key "'+o+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let n=e.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let t=await this.base.iterator();return this.hasHeader&&(t=t.skip(1)),t.map(e=>this.makeDataElement(e))}makeDataElement(t){let e=this.parseRow(t),n={},o={};for(let s=0;s14||!Number.isInteger(e))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!M().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let e=new Qf(t);return await e.start(),e}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),or(n,e)}};var td=class extends tr{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ke([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=Ws([i,s,u,a],[1,4])}else this.cropBox=Ws([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(!M().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new td(t,e);return await n.start(),n}async start(){this.webcamConfig.facingMode&&y.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=gy.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=ir(J(t,"float32"),0),n;n=oo.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var ed=class{};var Vh=class extends tr{split(t){return new PN(this,t)}},PN=class extends Vh{constructor(t,e){super(),this.upstream=t,this.impl=new LN(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},LN=class extends _c{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var Xb=class extends tr{decodeUTF8(){return new MN(this)}},MN=class extends Vh{constructor(t){super(),this.upstream=t,this.impl=new zN(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},zN=class extends _c{constructor(t){if(super(),this.upstream=t,M().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=wN();this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let n;return M().get("IS_BROWSER")?n=this.decoder.decode(e,{stream:!0}):n=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(n),!0}};var rd=class extends Xb{constructor(t,e={}){super(),this.file=t,this.options=e,y.assert(t instanceof Uint8Array||(M().get("IS_BROWSER")?t instanceof File||t instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=e.offset||0,this.chunkSize=e.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=a=>{let u=s.result;if(u instanceof 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ed{constructor(t,e={}){super(),this.url=t,this.fileOptions=e}async iterator(){return Yb(this.url)?new nd(this.url,this.fileOptions).iterator():NR(this.url,this.fileOptions)}};function TR(r,t={}){return new Jf(new od(r),t)}function kR(r){let t=zh(r);return En(async()=>t)}function ER(r){return En(async()=>{let t=await r();return zh(()=>t.next())})}async function _R(r,t){return td.create(r,t)}async function AR(r){return Qf.create(r)}var BN="4.2.0";function tt(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var tJ=qr.whereImpl,wu=class extends zo{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new ia(this,Ln())}nextDataId(){return wu.nextDataId++}write(t,e,n){this.firstUse&&(this.firstUse=!1,M().get("IS_NODE")&&S.warn(` ============================ Hi, looks like you are running TensorFlow.js in Node.js. 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KJ(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;tt([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,C=x-1-c.padInfo.top,N=wt(i.shape,"float32"),E=1/(f*d),A=e.data.get(o.dataId).values,$=wt(o.shape,"float32",A);for(let F=0;F=c.outHeight||Math.floor(Y)!==Y))for(let Z=0;Z=c.outWidth||Math.floor(et)!==et)continue;let rt=$.get(F,Y,et,P);H+=rt}}N.set(H*E,F,V,G,P)}return e.makeTensorInfo(N.shape,N.dtype,N.values)}var BF={kernelName:lp,backendName:"cpu",kernelFunc:KJ};function jJ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,scale:s,offset:i,mean:a,variance:u}=t;y.assert(a.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient 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a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=Yt({inputs:{x:o},backend:e,attrs:{shape:u}}),d=Ve({inputs:{x:f},backend:e,attrs:{perm:l}}),h=Yt({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Oo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var GF={kernelName:ci,backendName:"cpu",kernelFunc:XJ};function YJ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=ad(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var WF={kernelName:cp,backendName:"cpu",kernelFunc:YJ};function ZJ(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var UF={kernelName:pp,backendName:"cpu",kernelFunc:ZJ};var JJ=_t(po,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;lh.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Xr({inputs:{x:u[0]},backend:e});if(u[0].dtype==="complex64"){let h=u.map(C=>$o({inputs:{input:C},backend:e})),g=u.map(C=>Ji({inputs:{input:C},backend:e})),x=Eu({inputs:h,backend:e,attrs:{axis:s}}),b=Eu({inputs:g,backend:e,attrs:{axis:s}}),w=Ir({inputs:{real:x,imag:b},backend:e});return h.forEach(C=>e.disposeIntermediateTensorInfo(C)),g.forEach(C=>e.disposeIntermediateTensorInfo(C)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),w}let l=u.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return Yt({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));a=S.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=Ac(c,a,t[0].dtype,p),f=S.computeOutShape(u.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var jF={kernelName:pi,backendName:"cpu",kernelFunc:Eu};function xT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;tt([o,s],"conv2d");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",C=new pe(m.outShape,o.dtype),N=y.computeStrides(o.shape),E=y.computeStrides(s.shape),A=N[0],$=w?N[1]:N[2],F=w?N[2]:1,P=w?1:N[1],V=C.strides[0],G=w?C.strides[1]:C.strides[2],W=w?C.strides[2]:1,q=w?1:C.strides[1],H=e.data.get(o.dataId).values,j=e.data.get(s.dataId).values,Y=C.values;for(let Z=0;Z=m.inHeight)continue;let gt=st*E[0],bt=et+dt*$;for(let kt=0;kt=m.inWidth)continue;let he=gt+Zt*E[1],jt=bt+ce*F,ke=he;for(let fe=0;fe=l.inDepth)continue;let Z=j*F[0],et=V+Y*$[1];for(let rt=0;rt=l.inHeight)continue;let dt=Z+nt*F[1],gt=et+st*$[2];for(let bt=0;bt=l.inWidth)continue;let ce=dt+Vt*F[2],he=gt+Zt*l.inChannels,jt=ce;for(let ke=0;keMath.cos(r)),eO={kernelName:Yo,backendName:"cpu",kernelFunc:sQ};var iQ=_t(Zo,r=>Math.cosh(r)),rO={kernelName:Zo,backendName:"cpu",kernelFunc:iQ};function aQ(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=wt([d,h,g,f],"float32"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,C=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),E=y.computeStrides(x.shape);for(let A=0;A=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-P)*(m-1)/(g-1):0;for(let j=0;j1?F*(p-1)+j*q:.5*(F+V)*(p-1);if(Y<0||Y>p-1){for(let Z=0;Z1?P*(m-1)+ot*H:.5*(P+G)*(m-1);if(at<0||at>m-1){for(let gt=0;gt1?P*(m-1)+Z*H:.5*(P+G)*(m-1);if(et<0||et>m-1){for(let at=0;atx+d-b-1:(x,b)=>x+b;for(let x=0;xx+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let a=o.shape[0],u=o.shape[1],l=o.shape[2],c=o.shape[3],p=u*s,m=l*s,f=c/(s*s),d=e.data.get(o.dataId).values,h=new Float32Array(a*p*m*f),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${m}'`);let f=S.computeConv2DInfo(o.shape,s.shape,i,m,a,l,!0),{filterHeight:d,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=f,w=b.left,C=b.top,N=f.outChannels/f.inChannels,E=new pe(f.outShape,o.dtype),A=e.data.get(o.dataId).values,$=e.data.get(s.dataId).values,F=E.values;for(let P=0;P=f.inHeight)continue;let Z=j*p[0],et=V+Y*c[1];for(let rt=0;rt=f.inWidth)continue;let dt=Z+nt*p[1],gt=et+st*f.inChannels,bt=ot,kt=dt;for(let At=0;At{let{x:n,filter:o}=r,{strides:s,pad:i,dilations:a}=e,u=t,l=u.data.get(n.dataId).values,c=n.shape.length,p=u.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:C,strideWidth:N,filterHeight:E,filterWidth:A,dilationHeight:$,dilationWidth:F,outShape:P}=S.computeDilation2DInfo(n.shape,o.shape,s,i,"NHWC",a),V=y.sizeFromShape(P),G=P.length,W=y.getArrayFromDType(n.dtype,V);for(let H=0;H=0&&st=0&>ot&&(ot=At)}}}let at=y.locToIndex([H,j,Z,rt],G,y.computeStrides(P));W[at]=ot}}}return{dataId:u.write(y.toTypedArray(W,n.dtype),P,n.dtype),shape:P,dtype:n.dtype}}};var fO={kernelName:Yd,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:C,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:$,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${Yd}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let W=0;W=0&&nt=0&&dtet&&(et=gt,rt=at,ot=st)}}}V[rt][ot][Z]+=P[W][q][j][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};var dO={kernelName:Xd,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:C,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:$,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${Xd}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let W=0;W=0&&nt=0&&dtet&&(et=gt,rt=nt,ot=dt)}}}V[W][rt][ot][Z]+=P[W][q][j][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function yl(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,"sum");let a;o.dtype==="bool"?a=Do({inputs:{x:o},backend:e,attrs:{dtype:"int32"}}):a=Xr({inputs:{x:o},backend:e});let u=a.shape.length,l=y.parseAxisParam(s,a.shape),c=S.getAxesPermutation(l,u),p=l,m=a;c!=null&&(m=Ve({inputs:{x:a},backend:e,attrs:{perm:c}}),p=S.getInnerMostAxes(p.length,u)),S.assertAxesAreInnerMostDims("sum",p,m.shape.length);let[f,d]=S.computeOutAndReduceShapes(m.shape,p),h=S.upcastType(m.dtype,"int32"),g=sd(e,f,h),x=y.sizeFromShape(d),b=e.data.get(g.dataId).values,w=e.data.get(m.dataId).values;for(let C=0;C=0&&(m=yl({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var gO={kernelName:wp,backendName:"cpu",kernelFunc:hQ};function gQ(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t;tt([n,o],"eluGrad");let s=new Float32Array(y.sizeFromShape(o.shape)),i=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values;for(let u=0;u=1?s[u]=a[u]:s[u]=a[u]*(l+1)}return e.makeTensorInfo(o.shape,"float32",s)}var xO={kernelName:Cp,backendName:"cpu",kernelFunc:gQ};var xQ=S.ERF_P,yQ=S.ERF_A1,bQ=S.ERF_A2,wQ=S.ERF_A3,CQ=S.ERF_A4,IQ=S.ERF_A5,vQ=_t(Ca,r=>{let t=Math.sign(r),e=Math.abs(r),n=1/(1+xQ*e);return t*(1-((((IQ*n+CQ)*n+wQ)*n+bQ)*n+yQ)*n*Math.exp(-e*e))}),yO={kernelName:Ca,backendName:"cpu",kernelFunc:vQ};function md(r){let{inputs:t,backend:e,attrs:n}=r,{input:o}=t,{dim:s}=n,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),Yt({inputs:{x:o},backend:e,attrs:{shape:a}})}var bO={kernelName:mi,backendName:"cpu",kernelFunc:md};var SQ=Qt((r,t)=>r/t),Uh=ie(ts,SQ),Hh={kernelName:ts,backendName:"cpu",kernelFunc:Uh};function gw(r,t,e){let n=r.shape,o=n[0],s=n[1],i=e.data.get(r.dataId),a=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[o,s],c=y.sizeFromShape(l),p=y.getTypedArrayFromDType("float32",c),m=y.getTypedArrayFromDType("float32",c);for(let g=0;g{let{image:n}=r,o=e,s=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(n.shape)),[i,a,u,l]=n.shape,c=o.data.get(n.dataId).values;for(let m=0;m=0&&wMath.floor(r/t)),$Q=ie(os,AQ,null,"int32"),vO={kernelName:os,backendName:"cpu",kernelFunc:$Q};function DQ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=xT({inputs:{x:o,filter:s},backend:e,attrs:{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m}});if(i){let g=h;if(c==="NCHW"&&i.shape.length===1&&i.shape[0]!==1){let x=Yt({inputs:{x:i},backend:e,attrs:{shape:[i.shape[0],1,1]}});h=Zi({inputs:{a:h,b:x},backend:e}),e.disposeIntermediateTensorInfo(x)}else h=Zi({inputs:{a:h,b:i},backend:e});e.disposeIntermediateTensorInfo(g)}if(f){let g=h;if(c==="NCHW"&&f==="prelu"&&a.shape.length===1&&a.shape[0]!==1){let x=Yt({inputs:{x:a},backend:e,attrs:{shape:[a.shape[0],1,1]}});h=Lc(e,h,f,x,d),e.disposeIntermediateTensorInfo(x)}else h=Lc(e,h,f,a,d);e.disposeIntermediateTensorInfo(g)}return h}var SO={kernelName:Ni,backendName:"cpu",kernelFunc:DQ};function RQ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=yT({inputs:{x:o,filter:s},backend:e,attrs:{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m}});if(i){let g=h;h=Zi({inputs:{a:h,b:i},backend:e}),e.disposeIntermediateTensorInfo(g)}if(f){let g=h;h=Lc(e,h,f,a,d),e.disposeIntermediateTensorInfo(g)}return h}var NO={kernelName:Ti,backendName:"cpu",kernelFunc:RQ};function FQ(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=y.sizeFromShape(n.shape),i=o.shape,a=i[i.length-1],[u,l,c,p]=S.prepareAndValidate(n,o);if(l===0)return e.makeTensorInfo(u,n.dtype,[]);let m=e.data.get(o.dataId).values,f=e.bufferSync(n),d=tw(m,f,n.dtype,l,a,c,p,n.shape,s);return e.makeTensorInfo(u,n.dtype,d.values)}var TO={kernelName:Na,backendName:"cpu",kernelFunc:FQ};function OQ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,indices:s}=t,{axis:i,batchDims:a}=n;tt([o,s],"gatherV2");let u=y.parseAxisParam(i,o.shape)[0],l=e.data.get(s.dataId).values,c=o.shape[u];for(let C=0;C=0,()=>`GatherV2: the index value ${N} is not in [0, ${c-1}]`)}let p=a;a==null&&(p=0);let m=y.sizeFromShape(s.shape),f=S.segment_util.collectGatherOpShapeInfo(o,s,u,p),d=Yt({inputs:{x:o},backend:e,attrs:{shape:[f.batchSize,f.outerSize,f.dimSize,f.sliceSize]}}),h=Yt({inputs:{x:s},backend:e,attrs:{shape:[f.batchSize,m/f.batchSize]}}),g=[f.batchSize,f.outerSize,m/f.batchSize,f.sliceSize],x=e.bufferSync(h),b=e.bufferSync(d),w=ew(b,x,g);return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.makeTensorInfo(f.outputShape,w.dtype,w.values)}var kO={kernelName:fi,backendName:"cpu",kernelFunc:OQ};function PQ(r){let{inputs:t,backend:e}=r,{input:n}=t,o=y.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=o/s,a=Yt({inputs:{x:n},backend:e,attrs:{shape:[i,s]}}),u=gw(a,!0,e),l=Yt({inputs:{x:u},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(u),l}var EO={kernelName:vp,backendName:"cpu",kernelFunc:PQ};var LQ=_t(ka,r=>Number.isFinite(r)?1:0,"bool"),_O={kernelName:ka,backendName:"cpu",kernelFunc:LQ};var 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KJ(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;tt([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,C=x-1-c.padInfo.top,N=bt(i.shape,"float32"),_=1/(f*d),A=e.data.get(o.dataId).values,$=bt(o.shape,"float32",A);for(let F=0;F=c.outHeight||Math.floor(X)!==X))for(let Z=0;Z=c.outWidth||Math.floor(et)!==et)continue;let rt=$.get(F,X,et,P);H+=rt}}N.set(H*_,F,V,G,P)}return e.makeTensorInfo(N.shape,N.dtype,N.values)}var MF={kernelName:lp,backendName:"cpu",kernelFunc:KJ};function jJ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,scale:s,offset:i,mean:a,variance:u}=t;y.assert(a.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient 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a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=Zt({inputs:{x:o},backend:e,attrs:{shape:u}}),d=Ge({inputs:{x:f},backend:e,attrs:{perm:l}}),h=Zt({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Fo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var BF={kernelName:ui,backendName:"cpu",kernelFunc:XJ};function YJ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=ad(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var VF={kernelName:cp,backendName:"cpu",kernelFunc:YJ};function ZJ(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var GF={kernelName:pp,backendName:"cpu",kernelFunc:ZJ};var JJ=Et(co,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;lh.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Xr({inputs:{x:u[0]},backend:e});if(u[0].dtype==="complex64"){let h=u.map(C=>Ao({inputs:{input:C},backend:e})),g=u.map(C=>Zi({inputs:{input:C},backend:e})),x=Cu({inputs:h,backend:e,attrs:{axis:s}}),b=Cu({inputs:g,backend:e,attrs:{axis:s}}),w=Ir({inputs:{real:x,imag:b},backend:e});return h.forEach(C=>e.disposeIntermediateTensorInfo(C)),g.forEach(C=>e.disposeIntermediateTensorInfo(C)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),w}let l=u.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return Zt({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));a=S.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=Ac(c,a,t[0].dtype,p),f=S.computeOutShape(u.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var qF={kernelName:ci,backendName:"cpu",kernelFunc:Cu};function yT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;tt([o,s],"conv2d");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",C=new pe(m.outShape,o.dtype),N=y.computeStrides(o.shape),_=y.computeStrides(s.shape),A=N[0],$=w?N[1]:N[2],F=w?N[2]:1,P=w?1:N[1],V=C.strides[0],G=w?C.strides[1]:C.strides[2],W=w?C.strides[2]:1,q=w?1:C.strides[1],H=e.data.get(o.dataId).values,K=e.data.get(s.dataId).values,X=C.values;for(let Z=0;Z=m.inHeight)continue;let gt=st*_[0],Ct=et+ft*$;for(let $t=0;$t=m.inWidth)continue;let he=gt+Wt*_[1],ee=Ct+ue*F,ge=he;for(let fe=0;fe=l.inDepth)continue;let Z=K*F[0],et=V+X*$[1];for(let rt=0;rt=l.inHeight)continue;let ft=Z+nt*F[1],gt=et+st*$[2];for(let Ct=0;Ct=l.inWidth)continue;let ue=ft+Gt*F[2],he=gt+Wt*l.inChannels,ee=ue;for(let ge=0;geMath.cos(r)),QF={kernelName:jo,backendName:"cpu",kernelFunc:sQ};var iQ=Et(Xo,r=>Math.cosh(r)),tO={kernelName:Xo,backendName:"cpu",kernelFunc:iQ};function aQ(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=bt([d,h,g,f],"float32"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,C=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),_=y.computeStrides(x.shape);for(let A=0;A=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-P)*(m-1)/(g-1):0;for(let K=0;K1?F*(p-1)+K*q:.5*(F+V)*(p-1);if(X<0||X>p-1){for(let Z=0;Z1?P*(m-1)+ot*H:.5*(P+G)*(m-1);if(at<0||at>m-1){for(let gt=0;gt1?P*(m-1)+Z*H:.5*(P+G)*(m-1);if(et<0||et>m-1){for(let at=0;atx+d-b-1:(x,b)=>x+b;for(let x=0;xx+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let a=o.shape[0],u=o.shape[1],l=o.shape[2],c=o.shape[3],p=u*s,m=l*s,f=c/(s*s),d=e.data.get(o.dataId).values,h=new Float32Array(a*p*m*f),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${m}'`);let f=S.computeConv2DInfo(o.shape,s.shape,i,m,a,l,!0),{filterHeight:d,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=f,w=b.left,C=b.top,N=f.outChannels/f.inChannels,_=new pe(f.outShape,o.dtype),A=e.data.get(o.dataId).values,$=e.data.get(s.dataId).values,F=_.values;for(let P=0;P=f.inHeight)continue;let Z=K*p[0],et=V+X*c[1];for(let rt=0;rt=f.inWidth)continue;let ft=Z+nt*p[1],gt=et+st*f.inChannels,Ct=ot,$t=ft;for(let _t=0;_t{let{x:n,filter:o}=r,{strides:s,pad:i,dilations:a}=e,u=t,l=u.data.get(n.dataId).values,c=n.shape.length,p=u.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:C,strideWidth:N,filterHeight:_,filterWidth:A,dilationHeight:$,dilationWidth:F,outShape:P}=S.computeDilation2DInfo(n.shape,o.shape,s,i,"NHWC",a),V=y.sizeFromShape(P),G=P.length,W=y.getArrayFromDType(n.dtype,V);for(let H=0;H=0&&st=0&>ot&&(ot=_t)}}}let at=y.locToIndex([H,K,Z,rt],G,y.computeStrides(P));W[at]=ot}}}return{dataId:u.write(y.toTypedArray(W,n.dtype),P,n.dtype),shape:P,dtype:n.dtype}}};var pO={kernelName:jd,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:C,filterHeight:N,filterWidth:_,dilationHeight:A,dilationWidth:$,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${jd}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let W=0;W=0&&nt=0&&ftet&&(et=gt,rt=at,ot=st)}}}V[rt][ot][Z]+=P[W][q][K][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};var mO={kernelName:Kd,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:C,filterHeight:N,filterWidth:_,dilationHeight:A,dilationWidth:$,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${Kd}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let W=0;W=0&&nt=0&&ftet&&(et=gt,rt=nt,ot=ft)}}}V[W][rt][ot][Z]+=P[W][q][K][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function yl(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,"sum");let a;o.dtype==="bool"?a=$o({inputs:{x:o},backend:e,attrs:{dtype:"int32"}}):a=Xr({inputs:{x:o},backend:e});let u=a.shape.length,l=y.parseAxisParam(s,a.shape),c=S.getAxesPermutation(l,u),p=l,m=a;c!=null&&(m=Ge({inputs:{x:a},backend:e,attrs:{perm:c}}),p=S.getInnerMostAxes(p.length,u)),S.assertAxesAreInnerMostDims("sum",p,m.shape.length);let[f,d]=S.computeOutAndReduceShapes(m.shape,p),h=S.upcastType(m.dtype,"int32"),g=sd(e,f,h),x=y.sizeFromShape(d),b=e.data.get(g.dataId).values,w=e.data.get(m.dataId).values;for(let C=0;C=0&&(m=yl({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var dO={kernelName:wp,backendName:"cpu",kernelFunc:hQ};function gQ(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t;tt([n,o],"eluGrad");let s=new Float32Array(y.sizeFromShape(o.shape)),i=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values;for(let u=0;u=1?s[u]=a[u]:s[u]=a[u]*(l+1)}return e.makeTensorInfo(o.shape,"float32",s)}var hO={kernelName:Cp,backendName:"cpu",kernelFunc:gQ};var xQ=S.ERF_P,yQ=S.ERF_A1,bQ=S.ERF_A2,wQ=S.ERF_A3,CQ=S.ERF_A4,IQ=S.ERF_A5,vQ=Et(wa,r=>{let t=Math.sign(r),e=Math.abs(r),n=1/(1+xQ*e);return t*(1-((((IQ*n+CQ)*n+wQ)*n+bQ)*n+yQ)*n*Math.exp(-e*e))}),gO={kernelName:wa,backendName:"cpu",kernelFunc:vQ};function md(r){let{inputs:t,backend:e,attrs:n}=r,{input:o}=t,{dim:s}=n,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),Zt({inputs:{x:o},backend:e,attrs:{shape:a}})}var xO={kernelName:pi,backendName:"cpu",kernelFunc:md};var SQ=Qt((r,t)=>r/t),Hh=ie(Jo,SQ),qh={kernelName:Jo,backendName:"cpu",kernelFunc:Hh};function hw(r,t,e){let n=r.shape,o=n[0],s=n[1],i=e.data.get(r.dataId),a=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[o,s],c=y.sizeFromShape(l),p=y.getTypedArrayFromDType("float32",c),m=y.getTypedArrayFromDType("float32",c);for(let g=0;g{let{image:n}=r,o=e,s=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(n.shape)),[i,a,u,l]=n.shape,c=o.data.get(n.dataId).values;for(let m=0;m=0&&wMath.floor(r/t)),$Q=ie(rs,AQ,null,"int32"),CO={kernelName:rs,backendName:"cpu",kernelFunc:$Q};function DQ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=yT({inputs:{x:o,filter:s},backend:e,attrs:{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m}});if(i){let g=h;if(c==="NCHW"&&i.shape.length===1&&i.shape[0]!==1){let x=Zt({inputs:{x:i},backend:e,attrs:{shape:[i.shape[0],1,1]}});h=Yi({inputs:{a:h,b:x},backend:e}),e.disposeIntermediateTensorInfo(x)}else h=Yi({inputs:{a:h,b:i},backend:e});e.disposeIntermediateTensorInfo(g)}if(f){let g=h;if(c==="NCHW"&&f==="prelu"&&a.shape.length===1&&a.shape[0]!==1){let x=Zt({inputs:{x:a},backend:e,attrs:{shape:[a.shape[0],1,1]}});h=Lc(e,h,f,x,d),e.disposeIntermediateTensorInfo(x)}else h=Lc(e,h,f,a,d);e.disposeIntermediateTensorInfo(g)}return h}var IO={kernelName:Si,backendName:"cpu",kernelFunc:DQ};function RQ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=bT({inputs:{x:o,filter:s},backend:e,attrs:{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m}});if(i){let g=h;h=Yi({inputs:{a:h,b:i},backend:e}),e.disposeIntermediateTensorInfo(g)}if(f){let g=h;h=Lc(e,h,f,a,d),e.disposeIntermediateTensorInfo(g)}return h}var vO={kernelName:Ni,backendName:"cpu",kernelFunc:RQ};function FQ(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=y.sizeFromShape(n.shape),i=o.shape,a=i[i.length-1],[u,l,c,p]=S.prepareAndValidate(n,o);if(l===0)return e.makeTensorInfo(u,n.dtype,[]);let m=e.data.get(o.dataId).values,f=e.bufferSync(n),d=Qb(m,f,n.dtype,l,a,c,p,n.shape,s);return e.makeTensorInfo(u,n.dtype,d.values)}var SO={kernelName:Sa,backendName:"cpu",kernelFunc:FQ};function OQ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,indices:s}=t,{axis:i,batchDims:a}=n;tt([o,s],"gatherV2");let u=y.parseAxisParam(i,o.shape)[0],l=e.data.get(s.dataId).values,c=o.shape[u];for(let C=0;C=0,()=>`GatherV2: the index value ${N} is not in [0, ${c-1}]`)}let p=a;a==null&&(p=0);let m=y.sizeFromShape(s.shape),f=S.segment_util.collectGatherOpShapeInfo(o,s,u,p),d=Zt({inputs:{x:o},backend:e,attrs:{shape:[f.batchSize,f.outerSize,f.dimSize,f.sliceSize]}}),h=Zt({inputs:{x:s},backend:e,attrs:{shape:[f.batchSize,m/f.batchSize]}}),g=[f.batchSize,f.outerSize,m/f.batchSize,f.sliceSize],x=e.bufferSync(h),b=e.bufferSync(d),w=tw(b,x,g);return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.makeTensorInfo(f.outputShape,w.dtype,w.values)}var NO={kernelName:mi,backendName:"cpu",kernelFunc:OQ};function PQ(r){let{inputs:t,backend:e}=r,{input:n}=t,o=y.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=o/s,a=Zt({inputs:{x:n},backend:e,attrs:{shape:[i,s]}}),u=hw(a,!0,e),l=Zt({inputs:{x:u},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(u),l}var TO={kernelName:vp,backendName:"cpu",kernelFunc:PQ};var LQ=Et(Ta,r=>Number.isFinite(r)?1:0,"bool"),kO={kernelName:Ta,backendName:"cpu",kernelFunc:LQ};var MQ=Et(ka,r=>Math.abs(r)===1/0?1:0,"bool"),EO={kernelName:ka,backendName:"cpu",kernelFunc:MQ};var zQ=Et(ss,r=>Number.isNaN(r)?1:0,"bool"),_O={kernelName:ss,backendName:"cpu",kernelFunc:zQ};function BQ(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=ew(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var AO={kernelName:Np,backendName:"cpu",kernelFunc:BQ};var VQ=Et(Aa,r=>Math.log1p(r)),$O={kernelName:Aa,backendName:"cpu",kernelFunc:VQ};var GQ=Qt((r,t)=>r&&t),WQ=ie($a,GQ,null,"bool"),DO={kernelName:$a,backendName:"cpu",kernelFunc:WQ};var UQ=Et(Da,r=>r?0:1,"bool"),RO={kernelName:Da,backendName:"cpu",kernelFunc:UQ};var HQ=Qt((r,t)=>r||t),qQ=ie(Ra,HQ,null,"bool"),FO={kernelName:Ra,backendName:"cpu",kernelFunc:qQ};function KQ(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;tt(o,"LRN");let l=o.shape[3],c=l-1,p=e.data.get(o.dataId).values,m=y.sizeFromShape(o.shape),f=new Float32Array(m);function d(h){let 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Tt=M();Tt.registerFlag("HAS_WEBGL",()=>Tt.getNumber("WEBGL_VERSION")>0);Tt.registerFlag("WEBGL_VERSION",()=>Sw(2)?2:Sw(1)?1:0);Tt.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Tt.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Tt.get("WEBGL_VERSION")===2);Tt.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Tt.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Tt.registerFlag("WEBGL_PACK",()=>Tt.getBool("HAS_WEBGL"));Tt.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Tt.getBool("WEBGL_PACK"));Tt.registerFlag("WEBGL_PACK_CLIP",()=>Tt.getBool("WEBGL_PACK"));Tt.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Tt.getBool("WEBGL_PACK"));Tt.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Tt.getBool("WEBGL_PACK"));Tt.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Tt.getBool("WEBGL_PACK"));Tt.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Tt.getBool("WEBGL_PACK"));Tt.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Tt.getBool("WEBGL_PACK"));Tt.registerFlag("WEBGL_PACK_REDUCE",()=>Tt.getBool("WEBGL_PACK"));Tt.registerFlag("WEBGL_LAZILY_UNPACK",()=>Tt.getBool("WEBGL_PACK"));Tt.registerFlag("WEBGL_CONV_IM2COL",()=>Tt.getBool("WEBGL_PACK"));Tt.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>VT(Tt.getNumber("WEBGL_VERSION")));Tt.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>GT(Tt.getNumber("WEBGL_VERSION")));Tt.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let 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To speed things up dram ivec4 round(vec4 value) { return ivec4(floor(value + vec4(0.5))); } - `),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function ni(r,t,e="index"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join("")}function Bc(r,t,e="index"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join("")}function Stt(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function oL(r,t,e="index"){let n=r.map((s,i)=>i),o=Stt(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join("")}function xd(r){let t=y.computeStrides(r).map(e=>e.toString());return` + `),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function ei(r,t,e="index"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join("")}function Bc(r,t,e="index"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join("")}function Stt(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function rL(r,t,e="index"){let n=r.map((s,i)=>i),o=Stt(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join("")}function xd(r){let t=y.computeStrides(r).map(e=>e.toString());return` int getFlatIndex(ivec3 coords) { return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z; } @@ -116,7 +116,7 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } -`}var Tw=` +`}var Nw=` const float FLOAT_MAX = 1.70141184e38; const float FLOAT_MIN = 1.17549435e-38; @@ -155,10 +155,10 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram return c / 255.0; } -`;var{getBroadcastDims:sL}=S;function iL(r,t,e){let n=[];if(r.forEach(f=>{let d=y.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:""};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=kw(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:""};`)});let o=n.join(` +`;var{getBroadcastDims:nL}=S;function oL(r,t,e){let n=[];if(r.forEach(f=>{let d=y.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:""};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=Tw(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:""};`)});let o=n.join(` `),s=r.map(f=>Ntt(f,t,e.packedInputs,e.enableShapeUniforms)).join(` -`),i=t.texShape,a=Ge(),u=Ett(a),l,c,p=$tt(a);return t.isPacked?(l=Ttt(t.logicalShape,i,e.enableShapeUniforms),c=Att(a)):(l=ktt(t.logicalShape,i,e.enableShapeUniforms),c=_tt(a)),e.packedInputs&&(p+=Ott),[p,u,c,o,l,s,e.userCode].join(` -`)}function wd(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return Ktt(r,t);case 1:return Xtt(r,t);case 2:return Ztt(r,t);case 3:return Qtt(r,t);case 4:return eet(r,t);case 5:return ret(r);case 6:return net(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function aL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return qtt(r);case 1:return jtt(r,t);case 2:return Ytt(r,t);case 3:return Jtt(r,t);default:return tet(r,t)}}function Ntt(r,t,e=!1,n){let o="";e?o+=aL(r,n):o+=wd(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=oet(r,t):o+=set(r,t)),o}function Ttt(r,t,e){switch(r.length){case 0:return lL();case 1:return Ptt(r,t,e);case 2:return Utt(r,t,e);case 3:return Mtt(r,t,e);default:return Btt(r,t,e)}}function ktt(r,t,e){switch(r.length){case 0:return lL();case 1:return Ltt(r,t,e);case 2:return Htt(r,t,e);case 3:return ztt(r,t,e);case 4:return Vtt(r,t,e);case 5:return Gtt(r,t);case 6:return Wtt(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function Ett(r){return` +`),i=t.texShape,a=We(),u=Ett(a),l,c,p=$tt(a);return t.isPacked?(l=Ttt(t.logicalShape,i,e.enableShapeUniforms),c=Att(a)):(l=ktt(t.logicalShape,i,e.enableShapeUniforms),c=_tt(a)),e.packedInputs&&(p+=Ott),[p,u,c,o,l,s,e.userCode].join(` +`)}function wd(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return Ktt(r,t);case 1:return Xtt(r,t);case 2:return Ztt(r,t);case 3:return Qtt(r,t);case 4:return eet(r,t);case 5:return ret(r);case 6:return net(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function sL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return qtt(r);case 1:return jtt(r,t);case 2:return Ytt(r,t);case 3:return Jtt(r,t);default:return tet(r,t)}}function Ntt(r,t,e=!1,n){let o="";e?o+=sL(r,n):o+=wd(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=oet(r,t):o+=set(r,t)),o}function Ttt(r,t,e){switch(r.length){case 0:return iL();case 1:return Ptt(r,t,e);case 2:return Utt(r,t,e);case 3:return Mtt(r,t,e);default:return Btt(r,t,e)}}function ktt(r,t,e){switch(r.length){case 0:return iL();case 1:return Ltt(r,t,e);case 2:return Htt(r,t,e);case 3:return ztt(r,t,e);case 4:return Vtt(r,t,e);case 5:return Gtt(r,t);case 6:return Wtt(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function Ett(r){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${r.texture2D}(textureSampler, uv).r; } @@ -268,7 +268,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } -`;function lL(){return` +`;function iL(){return` int getOutputCoords() { return 0; } @@ -368,7 +368,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${Bc(["r","c","d"],r)} return ivec3(r, c, d); } -`;let n=ni(["r","c","d"],r);return` +`;let n=ei(["r","c","d"],r);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -425,7 +425,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${Bc(["r","c","d","d2"],r)} return ivec4(r, c, d, d2); } - `;let n=ni(["r","c","d","d2"],r);return` + `;let n=ei(["r","c","d","d2"],r);return` ivec4 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -433,7 +433,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${n} return ivec4(r, c, d, d2); } - `}function Gtt(r,t){let e=ni(["r","c","d","d2","d3"],r);return` + `}function Gtt(r,t){let e=ei(["r","c","d","d2","d3"],r);return` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -445,7 +445,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ivec5 outShape = ivec5(r, c, d, d2, d3); return outShape; } - `}function Wtt(r,t){let e=ni(["r","c","d","d2","d3","d4"],r);return` + `}function Wtt(r,t){let e=ei(["r","c","d","d2","d3","d4"],r);return` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); @@ -543,7 +543,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, int c = index - r * ${r[1]}; return ivec2(r, c); } - `}function Vc(r){return`offset${r}`}function qtt(r){let t=r.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Ge();return` + `}function Vc(r){return`offset${r}`}function qtt(r){let t=r.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),n=We();return` vec4 ${e}() { return ${n.texture2D}(${t}, halfCR); } @@ -561,7 +561,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${a}, ${u}, ${i}); return sampleTexture(${e}, uv); } - `}function jtt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,s=Ge();if(t)return` + `}function jtt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,s=We();if(t)return` vec4 ${n}(int index) { ivec2 packedTexShape = ivec2(ceil(float(${e}TexShape[0]) / 2.0), ceil(float(${e}TexShape[1]) / 2.0)); vec2 uv = packedUVfrom1D( @@ -612,7 +612,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${s}, ${i}, index + ${a}); return sampleTexture(${e}, uv); } - `}function Ytt(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,i=s[0],a=s[1],u=Ge();if(s!=null&&y.arraysEqual(e,s))return t?` + `}function Ytt(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,i=s[0],a=s[1],u=We();if(s!=null&&y.arraysEqual(e,s))return t?` vec4 ${o}(int row, int col) { vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]); @@ -695,11 +695,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return sampleTexture(${n}, uv); } `}function Jtt(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(e[0]===1){let m=e.slice(1),f=[1,2],d=Id(r,m),h=["b","row","col"];return` - ${aL(d,t)} + ${sL(d,t)} vec4 ${o}(int b, int row, int col) { return ${o}(${vd(h,f)}); } - `}let a=Ge();if(t)return` + `}let a=We();if(t)return` vec4 ${o}(int b, int row, int col) { ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0)); int valuesPerRow = int(ceil(float(${n}Shape[2]) / 2.0)); @@ -772,7 +772,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, vec2 uv = uvFromFlat(${p}, ${m}, index); return sampleTexture(${n}, uv); } - `}function tet(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=Ge();if(t)return` + `}function tet(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=We();if(t)return` vec4 ${n}(int b2, int b, int row, int col) { int valuesPerRow = int(ceil(float(${e}Shape[3]) / 2.0)); int texelsInBatch = valuesPerRow * int(ceil(float(${e}Shape[2]) / 2.0)); @@ -959,7 +959,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, return ${t}[i]; } } - `}function oet(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o="get"+n+"AtOutCoords",s=r.shapeInfo.logicalShape.length,i=t.logicalShape.length,a=sL(r.shapeInfo.logicalShape,t.logicalShape),u=Bt(i),l=i-s,c,p=["x","y","z","w","u","v"];s===0?c="":i<2&&a.length>=1?c="coords = 0;":c=a.map(b=>`coords.${p[b+l]} = 0;`).join(` + `}function oet(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o="get"+n+"AtOutCoords",s=r.shapeInfo.logicalShape.length,i=t.logicalShape.length,a=nL(r.shapeInfo.logicalShape,t.logicalShape),u=Vt(i),l=i-s,c,p=["x","y","z","w","u","v"];s===0?c="":i<2&&a.length>=1?c="coords = 0;":c=a.map(b=>`coords.${p[b+l]} = 0;`).join(` `);let m="";i<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+l]}`).join(", ");let f="return outputValue;",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(t.logicalShape)===1;if(s===1&&!h&&!x)f=` return vec4(outputValue.xy, outputValue.xy); `;else if(h&&!x)i===1?f=` @@ -977,16 +977,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float ${o}() { return sampleTexture(${e}, resultUV); } - `;let l=Bt(u),c=sL(r.shapeInfo.logicalShape,t.logicalShape),p=u-a,m,f=["x","y","z","w","u","v"];a===0?m="":u<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(` + `;let l=Vt(u),c=nL(r.shapeInfo.logicalShape,t.logicalShape),p=u-a,m,f=["x","y","z","w","u","v"];a===0?m="":u<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(` `);let d="";return u<2&&a>0?d="coords":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(", "),` float ${o}() { ${l} coords = getOutputCoords(); ${m} return get${n}(${d}); } - `}function Bt(r){if(r<=1)return"int";if(r===2)return"ivec2";if(r===3)return"ivec3";if(r===4)return"ivec4";if(r===5)return"ivec5";if(r===6)return"ivec6";throw Error(`GPU for rank ${r} is not yet supported`)}function kw(r,t,e){let{newShape:n,keptDims:o}=y.squeezeShape(t),s=t.length,i=r&&s===3&&t[0]===1,a=i?t.slice(1):n,u=!r&&s>1&&!y.arraysEqual(t,e)&&n.lengthr[e]).join(", ")}function cL(r,t,e,n){let o=e.map((c,p)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:m}}),s=o.map(c=>c.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},a=iL(o,i,t),u=ET(r.gl,a),l=r.createProgram(u);return M().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i},qT(r,t,l))}function qT(r,t,e){let n={},o={},s={},i=[],a,u,l,c=null,p=null;p=r.getUniformLocation(e,"NAN",!1),M().getNumber("WEBGL_VERSION")===1&&(c=r.getUniformLocation(e,"INFINITY",!1));let m=!1;for(let f=0;f{i[d]=r.getUniformLocation(e,f.name,m)}),{uniformLocations:n,customUniformLocations:i,infLoc:c,nanLoc:p,inShapesLocations:o,inTexShapesLocations:s,outShapeLocation:a,outShapeStridesLocation:l,outTexShapeLocation:u}}function uL(r,t){if(r.length!==t.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${t.length} inputs`);r.forEach((e,n)=>{let o=e.logicalShape,s=t[n],i=s.shape;if(!y.arraysEqual(o,i))throw Error(`Binary was compiled with different shapes than the current args. 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$w=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Yr.DOWNLOAD;let e=Ge();this.outputShape=t,this.userCode=` - ${Tw} + `}};var Aw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Yr.DOWNLOAD;let e=We();this.outputShape=t,this.userCode=` + ${Nw} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${e.output} = encode_float(x); } - `}};var uet={R:0,G:1,B:2,A:3},Qh=class{constructor(t,e=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let o=Ge();this.outputShape=t,this.enableShapeUniforms=we(this.outputShape.length);let s="result";e&&(s="floor(result * 255. + 0.5)");let i="";for(let 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This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let t=this.gl;ht(t,()=>t.finish()),ht(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),ht(t,()=>t.deleteFramebuffer(this.framebuffer)),ht(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),ht(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),ht(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),YT(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),ZT(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),JT(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),nk(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),rk(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return 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successful."),this.debug&&Zh(e,o),this.setProgram(o),o}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ht(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&(this.bindVertexArray(this.program.vao),this.debug&&Zh(this.gl,this.program)),ht(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,n=!0){return this.throwIfDisposed(),n?PT(this.gl,t,e):LT(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ht(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return 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this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(t,e,n){this.throwIfDisposed();let o=this.gl;Jh(o,t,this.framebuffer),this.debug&&dd(o),this.outputTexture=t,ht(o,()=>o.viewport(0,0,e,n)),ht(o,()=>o.scissor(0,0,e,n))}setOutputMatrixWriteRegionDriver(t,e,n,o){this.throwIfDisposed(),ht(this.gl,()=>this.gl.scissor(t,e,n,o))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function cet(r){let t=0;for(;t`${r}.${e}`)}function er(r,t){return t===1?[r]:ck(r,t)}function rM(r,t){if(r===1)return"rc";let e="";for(let 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ht(r,()=>r.readPixels(0,0,o,s,n.downloadTextureFormat,r.UNSIGNED_BYTE,a)),new Float32Array(a.buffer)}function lk(r,t,e,n,o,s,i,a){let u=r,l=new Float32Array(ZP(s,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function uk(r,t,e){let n=new Float32Array(t*e*4);return ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,n)),n}var Gc=class{constructor(t){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let e=M().getNumber("WEBGL_VERSION");if(t!=null?(this.gl=t,ST(e,t)):this.gl=Wn(e),t=this.gl,M().getNumber("WEBGL_VERSION")===2){let s=t;this.createVertexArray=()=>ht(s,()=>s.createVertexArray()),this.bindVertexArray=i=>ht(s,()=>s.bindVertexArray(i)),this.deleteVertexArray=i=>ht(s,()=>s.deleteVertexArray(i)),this.getVertexArray=()=>ht(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(t!=null){let s=t.getExtension("OES_vertex_array_object");if(s==null)throw new Error("All WebGL1 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if(M().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Un(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Un(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=XT(this.gl),this.indexBuffer=YT(this.gl),this.framebuffer=PT(this.gl),this.textureConfig=Zh(this.gl,this.textureHalfFloatExtension)}get debug(){return M().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let t=this.gl;ht(t,()=>t.finish()),ht(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),ht(t,()=>t.deleteFramebuffer(this.framebuffer)),ht(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),ht(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),ht(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),ZT(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),JT(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),QT(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),ok(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),nk(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return 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successful."),this.debug&&Jh(e,o),this.setProgram(o),o}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ht(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&(this.bindVertexArray(this.program.vao),this.debug&&Jh(this.gl,this.program)),ht(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,n=!0){return this.throwIfDisposed(),n?LT(this.gl,t,e):MT(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ht(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),zT(this.gl,t,e,n)}setOutputMatrixTexture(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)}setOutputPackedMatrixTexture(t,e,n){this.throwIfDisposed();let[o,s]=Ji(e,n);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,n,o){this.setOutputMatrixWriteRegionDriver(n,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,n,o){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Jh(this.gl,this.program),dd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;if(this.debug){let e=this.getVertexArray();console.assert(e===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}ht(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ht(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=fd(this.gl,M().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(M().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(M().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(t,M().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(t,M().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(t,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(t,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=cet(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:n}=this.itemsToPoll[e];n()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;let n;"setTimeoutCustom"in M().platform&&(n=M().platform.setTimeoutCustom.bind(M().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(t){this.throwIfDisposed(),Qh(this.gl,t,this.framebuffer),this.debug&&dd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Qh(this.gl,this.outputTexture,this.framebuffer),this.debug&&dd(this.gl)):vw(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let n=e();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(t,e,n){this.throwIfDisposed();let o=this.gl;Qh(o,t,this.framebuffer),this.debug&&dd(o),this.outputTexture=t,ht(o,()=>o.viewport(0,0,e,n)),ht(o,()=>o.scissor(0,0,e,n))}setOutputMatrixWriteRegionDriver(t,e,n,o){this.throwIfDisposed(),ht(this.gl,()=>this.gl.scissor(t,e,n,o))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function cet(r){let t=0;for(;t`${r}.${e}`)}function er(r,t){return t===1?[r]:pk(r,t)}function tM(r,t){if(r===1)return"rc";let e="";for(let n=0;n= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}), cEdge ? 0. : getA(${e[1]}), rEdge ? 0. : getA(${e[2]}), - rEdge || cEdge ? 0. : getA(${e[3]})`}};var Sd=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=t,this.enableShapeUniforms=we(this.outputShape.length);let n="";for(let o=0;o<4;o++){let s="thisRC = rc;";o%2===1&&(s+="thisRC.z += 1;"),o>1&&(s+="thisRC.y += 1;"),n+=` + rEdge || cEdge ? 0. : getA(${e[3]})`}};var Sd=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=t,this.enableShapeUniforms=Ce(this.outputShape.length);let n="";for(let o=0;o<4;o++){let s="thisRC = rc;";o%2===1&&(s+="thisRC.z += 1;"),o>1&&(s+="thisRC.y += 1;"),n+=` ${s} ${o>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); @@ -1172,10 +1172,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } `}};function pet(r,t){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { - ${t?oL(["r","c","d"],"inputShape"):ni(["r","c","d"],r)} + ${t?rL(["r","c","d"],"inputShape"):ei(["r","c","d"],r)} return ivec3(r, c, d); } - `}var Gw=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(t,e,n){let o=oM(e,n),s=sM(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=nM(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].shift();return this.usedTextures[s].push(u),u}let a;return o===Lr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Lr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Lr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=oM(n,o),i=sM(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=nM(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=M().get("WEBGL_DELETE_TEXTURE_THRESHOLD");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l.indexOf(t);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function met(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function nM(r,t,e,n,o){let s=fet(t,n),i;if(o){let[u,l]=Qi(r[0],r[1]);i=u*l}else{let[u,l]=zc(r[0],r[1]);i=u*l}let a=met(e,s);return i*a}function fet(r,t){switch(r){case Lr.PACKED_2X2_FLOAT32:return Pw(t);case Lr.PACKED_2X2_FLOAT16:return Lw(t);case Lr.UNPACKED_FLOAT32:return Rw(t);case Lr.UNPACKED_FLOAT16:return Fw(t);case Lr.PACKED_4X1_UNSIGNED_BYTE:return Ow(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function det(r){return M().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?Lr.PACKED_2X2_FLOAT32:Lr.UNPACKED_FLOAT32:r?Lr.PACKED_2X2_FLOAT16:Lr.UNPACKED_FLOAT16}function oM(r,t){if(r===Yr.UPLOAD)return Lr.PACKED_2X2_FLOAT32;if(r===Yr.RENDER||r==null)return det(t);if(r===Yr.DOWNLOAD||r===Yr.PIXELS)return Lr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function sM(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var Mr=class{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.enableShapeUniforms=we(this.outputShape.length),this.userCode=` + `}var Vw=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(t,e,n){let o=rM(e,n),s=nM(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=eM(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].shift();return this.usedTextures[s].push(u),u}let a;return o===Lr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Lr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Lr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=rM(n,o),i=nM(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=eM(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=M().get("WEBGL_DELETE_TEXTURE_THRESHOLD");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l.indexOf(t);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function met(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function eM(r,t,e,n,o){let s=fet(t,n),i;if(o){let[u,l]=Ji(r[0],r[1]);i=u*l}else{let[u,l]=zc(r[0],r[1]);i=u*l}let a=met(e,s);return i*a}function fet(r,t){switch(r){case Lr.PACKED_2X2_FLOAT32:return Ow(t);case Lr.PACKED_2X2_FLOAT16:return Pw(t);case Lr.UNPACKED_FLOAT32:return Dw(t);case Lr.UNPACKED_FLOAT16:return Rw(t);case Lr.PACKED_4X1_UNSIGNED_BYTE:return Fw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function det(r){return M().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?Lr.PACKED_2X2_FLOAT32:Lr.UNPACKED_FLOAT32:r?Lr.PACKED_2X2_FLOAT16:Lr.UNPACKED_FLOAT16}function rM(r,t){if(r===Yr.UPLOAD)return Lr.PACKED_2X2_FLOAT32;if(r===Yr.RENDER||r==null)return det(t);if(r===Yr.DOWNLOAD||r===Yr.PIXELS)return Lr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function nM(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var Mr=class{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.enableShapeUniforms=Ce(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${e} } @@ -1186,11 +1186,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}},hr="if (isnan(x)) return x;",iM="return x;",pk="return abs(x);";var aM="return (x >= 0.0) ? x : (exp(x) - 1.0);",lM=hr+` + `}},xr="if (isnan(x)) return x;",oM="return x;",mk="return abs(x);";var sM="return (x >= 0.0) ? x : (exp(x) - 1.0);",iM=xr+` return (x < 0.0) ? 0.0 : x; -`,uM=hr+` +`,aM=xr+` return (x < 0.0) ? 0.0 : min(6.0, x); -`,ta="return x;",cM="return 1.0 / (1.0 + exp(-1.0 * x));";var mM="return x;",fM=` +`,Qi="return x;",lM="return 1.0 / (1.0 + exp(-1.0 * x));";var cM="return x;",pM=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -1199,7 +1199,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,dM=` +`,mM=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1209,7 +1209,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,hM=` +`,fM=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); @@ -1219,7 +1219,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, result.a = isNaN.a ? x.a : result.a; return result; -`,gM="return 1.0 / (1.0 + exp(-1.0 * x));",An=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=we(this.outputShape.length),this.userCode=` +`,dM="return 1.0 / (1.0 + exp(-1.0 * x));",_n=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=Ce(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${e} } @@ -1230,17 +1230,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(y); } - `}};var Ww=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=we(this.outputShape.length);let e=t.length,n=er("rc",e),o=Bt(e),s=rM(e,n),i=n.slice(-2),a=e<=1?"rc":`vec2(${i.join(",")})`;this.userCode=` + `}};var Gw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=Ce(this.outputShape.length);let e=t.length,n=er("rc",e),o=Vt(e),s=tM(e,n),i=n.slice(-2),a=e<=1?"rc":`vec2(${i.join(",")})`;this.userCode=` void main() { ${o} rc = getOutputCoords(); vec4 packedInput = getA(${s}); setOutput(getChannel(packedInput, ${a})); } - `}};var get=qr.whereImpl,xet=1e-7,yet=1e-4,Uw={};function bet(r){return r in Uw||(Uw[r]={}),Uw[r]}var wet=M().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Cet=600;function Iet(){return M().global.screen==null?1024:M().global.screen.height*M().global.screen.width*window.devicePixelRatio*Cet/1024/1024}var $u=class extends Bo{constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!M().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let e;if(t!=null){if(t instanceof Gc)e=t;else{let n=Un(M().getNumber("WEBGL_VERSION"),t);e=new Gc(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Un(M().getNumber("WEBGL_VERSION"));e=new Gc(n),this.binaryCache=bet(M().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Gw(this.gpgpu),this.numMBBeforeWarning=Iet(),this.texData=new aa(this,Mn())}nextDataId(){return $u.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=hd(e),c=new Qh(l,!1,i),p=this.runWebGLProgram(c,[a],n,[[o,s]]);return p.shape=e,u.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(t,e,n){if((M().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||M().getBool("DEBUG"))&&this.checkNumericalProblems(t),n==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Yr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(M().getBool("DEBUG")&&this.checkNumericalProblems(e),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Yr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new An(a,ta):m=new Mr(a,ta);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o==="string")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new An(o,ta):d=new Mr(o,ta);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(M().getBool("DEBUG")&&!M().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&M().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(i!=="complex64"&&M().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...Xh(o))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(i==="complex64"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Mn().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let f;u?f=new An(s,ta):f=new Mr(s,ta);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(t,e.customTexShape),p=Mn().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let n=e.map(o=>y.decodeString(o));return wt(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return wt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(M().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return M().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return M().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(M().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=wet){return M().getBool("WEBGL_CPU_FORWARD")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Mn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new Ww(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new Vw(t.shape),n=!0;return this.runWebGLProgram(e,[t],t.dtype,null,n)}packedReshape(t,e){let n=[wl(t.shape),...Cl(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[wl(e),...Cl(e)],i=new Sd(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let a=hd(s),u;o?u=new _w(a):u=new Ew(a);let l=!0,c=[e!=null?e:Xh(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===_u.DENSE){let x=i!=null?i:Xh(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=M().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Au(b.shape,x.shape)){let w=x,C=x.shape;x.shape=b.shape,x=this.packedReshape(x,C),l.push(x),b=this.texData.get(x.dataId),w.shape=C}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=mL(t,c,p),f=this.getAndSaveBinary(m,()=>cL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),M().get("ENGINE_COMPILE_ONLY")||pL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=M().get("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!M().getBool("WEBGL_LAZILY_UNPACK")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(M().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!M().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=M().getBool("DEBUG");M().set("DEBUG",!1);let e=this.abs(mt(1e-8)).dataSync()[0];if(M().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?xet:yet}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=zT(n,u),e.texShape=p),s!=null){let m=hd(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=Qi(p[0],p[1])),u?f=new Dw(m,g):f=new Qh(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Yr.PIXELS:w.usage=Yr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let C=[[h,d]],N=!0,E=this.runWebGLProgram(f,[b],o,C,N),A=this.texData.get(E.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,M().get("ENGINE_COMPILE_ONLY")?this.disposeData(E.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=vet(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await xh(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Iw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,t]of Object.entries(this.binaryCache)){let{uniformLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,inShapesLocations:i,inTexShapesLocations:a,outShapeLocation:u,outShapeStridesLocation:l,outTexShapeLocation:c}=qT(this.gpgpu,t.program,t.webGLProgram);t.uniformLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.inShapesLocations=i,t.inTexShapesLocations=a,t.outShapeLocation=u,t.outShapeStridesLocation=l,t.outTexShapeLocation=c}}createTensorFromTexture(t,e,n){let{texture:o,height:s,width:i,channels:a}=t,u=Mn().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=u.writeTexture(o,e,n,s,i,a);return Mn().makeTensorFromDataId(l,e,n,u)}};$u.nextDataId=0;function vet(r,t){if(t==="float32"||t==="complex64")return r;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;nnew $u,2);var b1e={forceHalfFloat:yM};var Nd=` + `}};var get=qr.whereImpl,xet=1e-7,yet=1e-4,Ww={};function bet(r){return r in Ww||(Ww[r]={}),Ww[r]}var wet=M().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Cet=600;function Iet(){return M().global.screen==null?1024:M().global.screen.height*M().global.screen.width*window.devicePixelRatio*Cet/1024/1024}var Su=class extends zo{constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!M().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let e;if(t!=null){if(t instanceof Gc)e=t;else{let n=Wn(M().getNumber("WEBGL_VERSION"),t);e=new Gc(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Wn(M().getNumber("WEBGL_VERSION"));e=new Gc(n),this.binaryCache=bet(M().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Vw(this.gpgpu),this.numMBBeforeWarning=Iet(),this.texData=new ia(this,Ln())}nextDataId(){return Su.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=hd(e),c=new tg(l,!1,i),p=this.runWebGLProgram(c,[a],n,[[o,s]]);return p.shape=e,u.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(t,e,n){if((M().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||M().getBool("DEBUG"))&&this.checkNumericalProblems(t),n==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Yr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(M().getBool("DEBUG")&&this.checkNumericalProblems(e),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Yr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new _n(a,Qi):m=new Mr(a,Qi);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o==="string")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new _n(o,Qi):d=new Mr(o,Qi);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(M().getBool("DEBUG")&&!M().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&M().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(i!=="complex64"&&M().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...Yh(o))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(i==="complex64"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Ln().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let f;u?f=new _n(s,Qi):f=new Mr(s,Qi);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(t,e.customTexShape),p=Ln().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let n=e.map(o=>y.decodeString(o));return bt(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return bt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(M().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return M().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return M().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(M().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=wet){return M().getBool("WEBGL_CPU_FORWARD")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Ln().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new Gw(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new Bw(t.shape),n=!0;return this.runWebGLProgram(e,[t],t.dtype,null,n)}packedReshape(t,e){let n=[wl(t.shape),...Cl(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[wl(e),...Cl(e)],i=new Sd(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let a=hd(s),u;o?u=new Ew(a):u=new kw(a);let l=!0,c=[e!=null?e:Yh(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===Iu.DENSE){let x=i!=null?i:Yh(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=M().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!vu(b.shape,x.shape)){let w=x,C=x.shape;x.shape=b.shape,x=this.packedReshape(x,C),l.push(x),b=this.texData.get(x.dataId),w.shape=C}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=cL(t,c,p),f=this.getAndSaveBinary(m,()=>lL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),M().get("ENGINE_COMPILE_ONLY")||uL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=M().get("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!M().getBool("WEBGL_LAZILY_UNPACK")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(M().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!M().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=M().getBool("DEBUG");M().set("DEBUG",!1);let e=this.abs(pt(1e-8)).dataSync()[0];if(M().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?xet:yet}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=BT(n,u),e.texShape=p),s!=null){let m=hd(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=Ji(p[0],p[1])),u?f=new $w(m,g):f=new tg(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Yr.PIXELS:w.usage=Yr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let C=[[h,d]],N=!0,_=this.runWebGLProgram(f,[b],o,C,N),A=this.texData.get(_.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,M().get("ENGINE_COMPILE_ONLY")?this.disposeData(_.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=vet(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await yh(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Cw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,t]of Object.entries(this.binaryCache)){let{uniformLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,inShapesLocations:i,inTexShapesLocations:a,outShapeLocation:u,outShapeStridesLocation:l,outTexShapeLocation:c}=KT(this.gpgpu,t.program,t.webGLProgram);t.uniformLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.inShapesLocations=i,t.inTexShapesLocations=a,t.outShapeLocation=u,t.outShapeStridesLocation=l,t.outTexShapeLocation=c}}createTensorFromGPUData(t,e,n){t.channels=t.channels||"RGBA";let{texture:o,height:s,width:i,channels:a}=t,u=Ln().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=u.writeTexture(o,e,n,s,i,a);return Ln().makeTensorFromDataId(l,e,n,u)}};Su.nextDataId=0;function vet(r,t){if(t==="float32"||t==="complex64")return r;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;nnew Su,2);var g1e={forceHalfFloat:gM};var Nd=` if (isnan(a)) return a; if (isnan(b)) return b; -`;var lo=class{constructor(t,e,n){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=we(this.outputShape.length),this.userCode=` +`;var ao=class{constructor(t,e,n){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=Ce(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${t} } @@ -1250,17 +1250,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } - `}};var ea=` + `}};var ta=` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; -`;var Po=class{constructor(t,e,n,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(e,n);let s=this.outputShape.length;this.enableShapeUniforms=we(s);let i="";if(o)if(s===0||y.sizeFromShape(this.outputShape)===1)i=` +`;var Oo=class{constructor(t,e,n,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(e,n);let s=this.outputShape.length;this.enableShapeUniforms=Ce(s);let i="";if(o)if(s===0||y.sizeFromShape(this.outputShape)===1)i=` result.y = 0.; result.z = 0.; result.w = 0.; `;else if(i=` - ${Bt(s)} coords = getOutputCoords(); + ${Vt(s)} coords = getOutputCoords(); `,s===1)this.enableShapeUniforms?i+=` result.y = (coords + 1) >= outShape ? 0. : result.y; result.z = 0.; @@ -1299,13 +1299,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, setOutput(result); } - `}};function rr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var bM={kernelName:mo,backendName:"webgl",kernelFunc:rr};function $n(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,"complex64"),i=e.texData.get(s.dataId),a=rr({inputs:{x:n},backend:e}),u=rr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var wM={kernelName:mp,backendName:"webgl",kernelFunc:$n};var mk="return (a < 0.) ? b * a : a;",fk=` + `}};function rr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var xM={kernelName:po,backendName:"webgl",kernelFunc:rr};function An(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,"complex64"),i=e.texData.get(s.dataId),a=rr({inputs:{x:n},backend:e}),u=rr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var yM={kernelName:mp,backendName:"webgl",kernelFunc:An};var fk="return (a < 0.) ? b * a : a;",dk=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function Net(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),a=M().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Po(fk,o.shape,i.shape):new lo(mk,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],"float32");return e.disposeIntermediateTensorInfo(i),u}var CM={kernelName:ls,backendName:"webgl",kernelFunc:Net};var dk="return (a < 0.) ? b * a : a;",hk=` +`;function Net(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),a=M().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Oo(dk,o.shape,i.shape):new ao(fk,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],"float32");return e.disposeIntermediateTensorInfo(i),u}var bM={kernelName:is,backendName:"webgl",kernelFunc:Net};var hk="return (a < 0.) ? b * a : a;",gk=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); -`;function Tet(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=M().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Po(hk,n.shape,o.shape):new lo(dk,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],"float32")}var IM={kernelName:Cs,backendName:"webgl",kernelFunc:Tet};var Lo="if (isnan(x)) return x;";function Ct({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=M().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new An(i.shape,t):c=new Mr(i.shape,r),a.runWebGLProgram(c,[i],u)}}function le({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype==="complex64"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[C,N]=w,E={dataId:C.dataId,dtype:C.dtype,shape:u.shape},A={dataId:N.dataId,dtype:N.dtype,shape:l.shape},$=new lo(r,u.shape,l.shape);return c.runWebGLProgram($,[E,A],ar(C.dtype,N.dtype))}),b=$n({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||ar(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype==="string"?S.fromUint8ToStringArray(d):d,x=u.dtype==="string"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,x,p),C=c.makeTensorInfo(w,p),N=c.texData.get(C.dataId);return N.values=b,C}let m=M().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,f;return m?f=new Po(t,u.shape,l.shape,e):f=new lo(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Il(r,t=!1){if(r==="linear")return t?mM:iM;if(r==="relu")return t?dM:lM;if(r==="elu")return t?fM:aM;if(r==="relu6")return t?hM:uM;if(r==="prelu")return t?hk:dk;if(r==="leakyrelu")return t?fk:mk;if(r==="sigmoid")return t?gM:cM;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Td=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=we(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";a&&(u?g=`vec4 activation(vec4 a) { +`;function Tet(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=M().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Oo(gk,n.shape,o.shape):new ao(hk,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],"float32")}var wM={kernelName:bs,backendName:"webgl",kernelFunc:Tet};var Po="if (isnan(x)) return x;";function wt({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=M().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new _n(i.shape,t):c=new Mr(i.shape,r),a.runWebGLProgram(c,[i],u)}}function ae({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype==="complex64"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[C,N]=w,_={dataId:C.dataId,dtype:C.dtype,shape:u.shape},A={dataId:N.dataId,dtype:N.dtype,shape:l.shape},$=new ao(r,u.shape,l.shape);return c.runWebGLProgram($,[_,A],lr(C.dtype,N.dtype))}),b=An({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||lr(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype==="string"?S.fromUint8ToStringArray(d):d,x=u.dtype==="string"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,x,p),C=c.makeTensorInfo(w,p),N=c.texData.get(C.dataId);return N.values=b,C}let m=M().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,f;return m?f=new Oo(t,u.shape,l.shape,e):f=new ao(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Il(r,t=!1){if(r==="linear")return t?cM:oM;if(r==="relu")return t?mM:iM;if(r==="elu")return t?pM:sM;if(r==="relu6")return t?fM:aM;if(r==="prelu")return t?gk:hk;if(r==="leakyrelu")return t?dk:fk;if(r==="sigmoid")return t?dM:lM;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Td=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Ce(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";a&&(u?g=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${a} }`:l?g=`vec4 activation(vec4 a) { @@ -1313,16 +1313,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, ${a} }`:g=`vec4 activation(vec4 x) { ${a} - }`,x="result = activation(result);");let b=i?"result += getBiasAtOutCoords();":"";i&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let w="rc.x",C="rc.x";t[0]`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!Au(o.shape,u)&&!(c.texture!==null&&Au(c.shape,u))?NM(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var TM={kernelName:xi,backendName:"webgl",kernelFunc:it};var ng=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l="sumValue += dot(values, ones);";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%n>0&&(c=` + `}};var CM="return a * b;";function ng(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=S.upcastType(n.dtype,o.dtype);if(n.dtype==="complex64"){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),l=new rg(xk.REAL,n.shape,o.shape),c=new rg(xk.IMAG,n.shape,o.shape),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:n.shape},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:o.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:o.shape}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=An({inputs:{real:m,imag:f},backend:e});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}if(e.shouldExecuteOnCPU([n,o])){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),[l,c]=$L(n.shape,o.shape,a.values,u.values,s),p=e.makeTensorInfo(c,s),m=e.texData.get(p.dataId);return m.values=l,p}let i;return M().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Oo(CM,n.shape,o.shape):i=new ao(CM,n.shape,o.shape),e.runWebGLProgram(i,[n,o],s)}var IM={kernelName:hs,backendName:"webgl",kernelFunc:ng};function vM(r,t,e){let n=[wl(r.shape),...Cl(r.shape)],o={dtype:r.dtype,shape:n,dataId:r.dataId},s=[wl(t),...Cl(t)],i=new Sd(s,n),a=!0,u=[n],l=e.runWebGLProgram(i,[o],r.dtype,u,a);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function it(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{shape:s}=n,i=e,a=y.sizeFromShape(o.shape),u=y.inferFromImplicitShape(s,a),l=y.sizeFromShape(u);y.assert(a===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!vu(o.shape,u)&&!(c.texture!==null&&vu(c.shape,u))?vM(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var SM={kernelName:gi,backendName:"webgl",kernelFunc:it};var og=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l="sumValue += dot(values, ones);";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%n>0&&(c=` if (inIdx < 0 || inIdx >= ${s}) { return 0.0; } @@ -1410,7 +1410,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(sumValue); } - `}};var Hw=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a="0.0",u="";e==="prod"?a="1.0":e==="min"?(a="1.0 / 1e-20",u="min"):e==="max"&&(a="-1.0 / 1e-20",u="max");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?l="sumValue":e==="prod"?l="prodValue":e==="all"?l="allValue":e==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,p=n%4,m=` + `}};var Uw=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a="0.0",u="";e==="prod"?a="1.0":e==="min"?(a="1.0 / 1e-20",u="min"):e==="max"&&(a="-1.0 / 1e-20",u="max");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?l="sumValue":e==="prod"?l="prodValue":e==="all"?l="allValue":e==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,p=n%4,m=` if (${e==="sum"}) { sumValue += dot(values, ones); } else if (${e==="prod"}) { @@ -1502,12 +1502,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC, } setOutput(${l}); } - `}};function Eet(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],n=S.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:n,outSize:Math.ceil(e/n)})}return t}function qn(r,t,e,n){let o=Eet(r.shape),s=r;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let o=0;o6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=Bt(this.rank),s=ck("rc",this.rank),i=new Array(this.rank);for(let c=0;c6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let o=0;o6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=Vt(this.rank),s=pk("rc",this.rank),i=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let N=e?[x,p,f]:[x,f,p],E=n?[b,d,m]:[b,m,d],A=it({inputs:{x:r},backend:o,attrs:{shape:N}}),$=it({inputs:{x:t},backend:o,attrs:{shape:E}}),F=[A,$],P=Math.max(x,b),V=e?A.shape[1]:A.shape[2],G=s!=null,W=i!=null,q=u==="leakyrelu",H=u!=null?Il(u,!0):null,j=G||W||q||H!=null,Y;if((f===1||d===1)&&V>xk&&j===!1){let et=A,rt=$;e&&(et=Oe({inputs:{x:A},backend:o,attrs:{perm:[0,2,1]}}),F.push(et)),n&&(rt=Oe({inputs:{x:$},backend:o,attrs:{perm:[0,2,1]}}),F.push(rt));let ot=d!==1,at=d===1,nt=et;ot&&(nt=it({inputs:{x:et},backend:o,attrs:{shape:[P,V,1]}}),F.push(nt));let st=d===1?2:1,dt=rt;at&&(dt=it({inputs:{x:rt},backend:o,attrs:{shape:[P,1,V]}}),F.push(dt));let gt=rg({inputs:{a:nt,b:dt},backend:o});Y=Uc({inputs:{x:gt},backend:o,attrs:{axis:st,keepDims:!0}}),F.push(gt)}else{let et=ar(r.dtype,t.dtype),rt=new Td(N,E,[P,f,d],e,n,G,H,W,q),ot=[A,$];if(s!=null&&ot.push(s),W&&ot.push(i),q){let at=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));ot.push(at),F.push(at)}Y=o.runWebGLProgram(rt,ot,et)}let Z=it({inputs:{x:Y},backend:o,attrs:{shape:C}});F.push(Y);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function Aet(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return Hc({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var AM={kernelName:Si,backendName:"webgl",kernelFunc:Aet};var $M="return abs(x);";function $et(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=e.texData.get(n.dataId),i=zw(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return M().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new An(n.shape,$M):o=new Mr(n.shape,$M),e.runWebGLProgram(o,[n],n.dtype)}var DM={kernelName:ui,backendName:"webgl",kernelFunc:$et};var Det=hr+` + `}};function Nu(r,t,e){let n=M().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new qw(r.shape,t):new Hw(r.shape,t);return e.runWebGLProgram(n,[r],r.dtype)}function NM(r,t,e,n){let o=t,s=r.shape.length,i=y.parseAxisParam(o,r.shape),a=i,u=S.getAxesPermutation(a,s),l=u!=null,c=r;l&&(c=Nu(r,u,n),a=S.getInnerMostAxes(a.length,s)),S.assertAxesAreInnerMostDims("sum",a,s);let[p,m]=S.computeOutAndReduceShapes(c.shape,a),f=p;e&&(f=S.expandShapeToKeepDim(p,i));let d=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/d,x=it({inputs:{x:c},attrs:{shape:[g,d]},backend:n}),b=Bu(r.dtype),w=Hn(x,b,"sum",n),C=it({inputs:{x:w},attrs:{shape:f},backend:n});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(w),l&&n.disposeIntermediateTensorInfo(c),C}function Uc(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;return NM(o,s,i,e)}var TM={kernelName:Ds,backendName:"webgl",kernelFunc:Uc};function Pe(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{perm:s}=n,i=e,a=o.shape.length,u=new Array(a);for(let c=0;c`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let N=e?[x,p,f]:[x,f,p],_=n?[b,d,m]:[b,m,d],A=it({inputs:{x:r},backend:o,attrs:{shape:N}}),$=it({inputs:{x:t},backend:o,attrs:{shape:_}}),F=[A,$],P=Math.max(x,b),V=e?A.shape[1]:A.shape[2],G=s!=null,W=i!=null,q=u==="leakyrelu",H=u!=null?Il(u,!0):null,K=G||W||q||H!=null,X;if((f===1||d===1)&&V>yk&&K===!1){let et=A,rt=$;e&&(et=Pe({inputs:{x:A},backend:o,attrs:{perm:[0,2,1]}}),F.push(et)),n&&(rt=Pe({inputs:{x:$},backend:o,attrs:{perm:[0,2,1]}}),F.push(rt));let ot=d!==1,at=d===1,nt=et;ot&&(nt=it({inputs:{x:et},backend:o,attrs:{shape:[P,V,1]}}),F.push(nt));let st=d===1?2:1,ft=rt;at&&(ft=it({inputs:{x:rt},backend:o,attrs:{shape:[P,1,V]}}),F.push(ft));let gt=ng({inputs:{a:nt,b:ft},backend:o});X=Uc({inputs:{x:gt},backend:o,attrs:{axis:st,keepDims:!0}}),F.push(gt)}else{let et=lr(r.dtype,t.dtype),rt=new Td(N,_,[P,f,d],e,n,G,H,W,q),ot=[A,$];if(s!=null&&ot.push(s),W&&ot.push(i),q){let at=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));ot.push(at),F.push(at)}X=o.runWebGLProgram(rt,ot,et)}let Z=it({inputs:{x:X},backend:o,attrs:{shape:C}});F.push(X);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function Aet(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return Hc({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var EM={kernelName:vi,backendName:"webgl",kernelFunc:Aet};var _M="return abs(x);";function $et(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=e.texData.get(n.dataId),i=Mw(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return M().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new _n(n.shape,_M):o=new Mr(n.shape,_M),e.runWebGLProgram(o,[n],n.dtype)}var AM={kernelName:li,backendName:"webgl",kernelFunc:$et};var Det=xr+` if (abs(x) > 1.) { return NAN; } return acos(x); -`,Ret=Ct({opSnippet:Det}),RM={kernelName:ua,backendName:"webgl",kernelFunc:Ret};var Fet=hr+` +`,Ret=wt({opSnippet:Det}),$M={kernelName:la,backendName:"webgl",kernelFunc:Ret};var Fet=xr+` if (x < 1.0) return NAN; -return log(x + sqrt(x * x - 1.0));`,Oet=Ct({opSnippet:Fet}),FM={kernelName:ca,backendName:"webgl",kernelFunc:Oet};var OM="return a + b;",Pet=le({opSnippet:OM,packedOpSnippet:OM,supportsComplex:!0,cpuKernelImpl:fL}),PM={kernelName:Qn,backendName:"webgl",kernelFunc:Pet};var jw=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` +return log(x + sqrt(x * x - 1.0));`,Oet=wt({opSnippet:Fet}),DM={kernelName:ua,backendName:"webgl",kernelFunc:Oet};var RM="return a + b;",Pet=ae({opSnippet:RM,packedOpSnippet:RM,supportsComplex:!0,cpuKernelImpl:pL}),FM={kernelName:Jn,backendName:"webgl",kernelFunc:Pet};var Kw=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} @@ -1539,7 +1539,7 @@ return log(x + sqrt(x * x - 1.0));`,Oet=Ct({opSnippet:Fet}),FM={kernelName:ca,ba float result = ${o}; setOutput(result); } - `}};var Xw=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` + `}};var jw=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` void main() { ${n.join(` `)} @@ -1547,7 +1547,7 @@ return log(x + sqrt(x * x - 1.0));`,Oet=Ct({opSnippet:Fet}),FM={kernelName:ca,ba vec4 result = ${o}; setOutput(result); } - `}};function Yw(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return rr({inputs:{x:n[0]},backend:e});if(n.length>M().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(n.length/2),l=Yw({inputs:n.slice(0,u),backend:e}),c=Yw({inputs:n.slice(u),backend:e});return Yw({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>ar(u,l)),s=n.map(u=>u.shape),a=M().getBool("WEBGL_PACK")?new Xw(n[0].shape,s):new jw(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var LM={kernelName:Wo,backendName:"webgl",kernelFunc:Yw};function Let(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Oe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("all",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=it({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=qn(h,h.dtype,"all",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=it({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=it({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var MM={kernelName:pa,backendName:"webgl",kernelFunc:Let};function Met(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Oe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("any",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=it({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=qn(h,h.dtype,"any",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=it({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=it({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var zM={kernelName:ma,backendName:"webgl",kernelFunc:Met};var Zw=class{constructor(t,e,n){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,i];let a=e==="max"?">":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` + `}};function Xw(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return rr({inputs:{x:n[0]},backend:e});if(n.length>M().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(n.length/2),l=Xw({inputs:n.slice(0,u),backend:e}),c=Xw({inputs:n.slice(u),backend:e});return Xw({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>lr(u,l)),s=n.map(u=>u.shape),a=M().getBool("WEBGL_PACK")?new jw(n[0].shape,s):new Kw(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var OM={kernelName:Vo,backendName:"webgl",kernelFunc:Xw};function Let(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("all",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=it({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Hn(h,h.dtype,"all",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=it({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=it({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var PM={kernelName:ca,backendName:"webgl",kernelFunc:Let};function Met(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("any",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=it({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Hn(h,h.dtype,"any",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=it({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=it({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var LM={kernelName:pa,backendName:"webgl",kernelFunc:Met};var Yw=class{constructor(t,e,n){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,i];let a=e==="max"?">":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -1567,7 +1567,7 @@ return log(x + sqrt(x * x - 1.0));`,Oet=Ct({opSnippet:Fet}),FM={kernelName:ca,ba } setOutput(float(bestIndex)); } - `}};var Jw=class{constructor(t,e,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push("bestIndicesA");let a=this.outputShape,u=a.length,l=Bt(u),c=er("coords",u),p,m;if(i===1){m=u+1;let $=Bt(m);p=` + `}};var Zw=class{constructor(t,e,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push("bestIndicesA");let a=this.outputShape,u=a.length,l=Vt(u),c=er("coords",u),p,m;if(i===1){m=u+1;let $=Vt(m);p=` ${$} sourceLocR = ${$}(${c.join()}, 0); ++${c[u-1]}; ${$} sourceLocG = ${$}(${c.join()}, 0); @@ -1587,7 +1587,7 @@ return log(x + sqrt(x * x - 1.0));`,Oet=Ct({opSnippet:Fet}),FM={kernelName:ca,ba inIdx = round(vec4(getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${x.join()}), getBestIndicesAChannel(${b.join()}), - getBestIndicesAChannel(${w.join()})));`,E=`vec4( + getBestIndicesAChannel(${w.join()})));`,_=`vec4( getAChannel(${g.join()}), hasNextCol ? getAChannel(${x.join()}) : 0., hasNextRow ? getAChannel(${b.join()}) : 0., @@ -1610,12 +1610,12 @@ return log(x + sqrt(x * x - 1.0));`,Oet=Ct({opSnippet:Fet}),FM={kernelName:ca,ba sourceLocB${d}, sourceLocA${d}) * ${e}; ivec4 inIdx = srcIdx; vec4 bestIndex = vec4(inIdx); - vec4 bestValue = ${E}; + vec4 bestValue = ${_}; for (int i = 0; i < ${e}; i++) { inIdx = srcIdx; ${N} - vec4 candidate = ${E}; + vec4 candidate = ${_}; bvec4 nan = isnan(candidate); bvec4 replace = bvec4( vec4(${C}(candidate, bestValue)) * (vec4(1.0) - vec4(nan))); @@ -1629,25 +1629,25 @@ return log(x + sqrt(x * x - 1.0));`,Oet=Ct({opSnippet:Fet}),FM={kernelName:ca,ba } setOutput(bestIndex); } - `}};function BM(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new Zw(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=BM(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function VM(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new Jw(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,"int32");if(l.shape.length===t.shape.length){let c=VM(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function Qw(r,t,e,n){let o=[e];if(S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!M().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,o),p=y.sizeFromShape(c),m=it({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=BM(r,m,n);s.push(f);let d=it({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return VM(r,t,n)}function zet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Oe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=Qw(e,u,i[0],"max");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var GM={kernelName:Uo,backendName:"webgl",kernelFunc:zet};function Bet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Oe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=Qw(e,u,i[0],"min");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var WM={kernelName:Al,backendName:"webgl",kernelFunc:Bet};var Vet=hr+` + `}};function MM(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new Yw(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=MM(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function zM(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new Zw(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,"int32");if(l.shape.length===t.shape.length){let c=zM(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function Jw(r,t,e,n){let o=[e];if(S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!M().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,o),p=y.sizeFromShape(c),m=it({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=MM(r,m,n);s.push(f);let d=it({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return zM(r,t,n)}function zet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=Jw(e,u,i[0],"max");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var BM={kernelName:Go,backendName:"webgl",kernelFunc:zet};function Bet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=Jw(e,u,i[0],"min");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var VM={kernelName:_l,backendName:"webgl",kernelFunc:Bet};var Vet=xr+` if (abs(x) > 1.) { return NAN; } return asin(x); -`,Get=Ct({opSnippet:Vet}),UM={kernelName:fa,backendName:"webgl",kernelFunc:Get};var Wet=hr+"return log(x + sqrt(x * x + 1.0));",Uet=Ct({opSnippet:Wet}),HM={kernelName:da,backendName:"webgl",kernelFunc:Uet};var Het=hr+` +`,Get=wt({opSnippet:Vet}),GM={kernelName:ma,backendName:"webgl",kernelFunc:Get};var Wet=xr+"return log(x + sqrt(x * x + 1.0));",Uet=wt({opSnippet:Wet}),WM={kernelName:fa,backendName:"webgl",kernelFunc:Uet};var Het=xr+` return atan(x); -`,qet=Ct({opSnippet:Het}),qM={kernelName:ha,backendName:"webgl",kernelFunc:qet};var Ket=Nd+` +`,qet=wt({opSnippet:Het}),UM={kernelName:da,backendName:"webgl",kernelFunc:qet};var Ket=Nd+` return atan(a, b); `,jet=` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); - `+ea+` + `+ta+` return result; -`,Xet=le({opSnippet:Ket,packedOpSnippet:jet}),KM={kernelName:xa,backendName:"webgl",kernelFunc:Xet};var Yet=hr+` +`,Xet=ae({opSnippet:Ket,packedOpSnippet:jet}),HM={kernelName:ga,backendName:"webgl",kernelFunc:Xet};var Yet=xr+` if ((x < -1.0) || (x > 1.0)) return NAN; -return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelName:ga,backendName:"webgl",kernelFunc:Zet};var oi=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e==="avg",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),n){let $=">=";this.userCode=` +return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=wt({opSnippet:Yet}),qM={kernelName:ha,backendName:"webgl",kernelFunc:Zet};var ri=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e==="avg",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),n){let $=">=";this.userCode=` const ivec2 strides = ivec2(${a}, ${u}); const ivec2 pads = ivec2(${f}, ${d}); @@ -1698,7 +1698,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } setOutput(float(minMaxPosition)); } - `;return}let w="max",C=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(C="avgValue / count");let N=Math.floor(i/4)*4,E=i%4,A=` + `;return}let w="max",C=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(C="avgValue / max(count, 1.0)");let N=Math.floor(i/4)*4,_=i%4,A=` if (${h}) { avgValue += dot(values, ones); } else { @@ -1757,7 +1757,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } int xC = xCCorner + ${N}; - if (${E===1}) { + if (${_===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, @@ -1766,7 +1766,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN ); ${A} - } else if (${E===2}) { + } else if (${_===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), @@ -1775,7 +1775,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN ); ${A} - } else if (${E===3}) { + } else if (${_===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), @@ -1788,7 +1788,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } setOutput(${C}); } - `}},Ru=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e==="avg",C="0.0";if(w||(C="-1.0 / 1e-20"),n){let P=">=";this.userCode=` + `}},Tu=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e==="avg",C="0.0";if(w||(C="-1.0 / 1e-20"),n){let P=">=";this.userCode=` const ivec3 strides = ivec3(${a}, ${u}, ${l}); const ivec3 pads = ivec3(${g}, ${x}, ${b}); @@ -1850,7 +1850,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } setOutput(float(minMaxPosition)); } - `;return}let N="max",E=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(E="avgValue / count");let A=Math.floor(i/4)*4,$=i%4,F=` + `;return}let N="max",_=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(_="avgValue / max(count, 1.0)");let A=Math.floor(i/4)*4,$=i%4,F=` if (${w}) { avgValue += dot(values, ones); } else { @@ -1948,10 +1948,10 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN ${F} } } - setOutput(${E}); } + setOutput(${_}); } - `}};function Jet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;ri(o,"avgPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new oi(c,"avg",!1);return e.runWebGLProgram(p,[o],"float32")}var XM={kernelName:Ho,backendName:"webgl",kernelFunc:Jet};function Qet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new Ru(p,"avg",!1);return e.runWebGLProgram(m,[o],"float32")}var YM={kernelName:$l,backendName:"webgl",kernelFunc:Qet};var tC=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=` + `}};function Jet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;ti(o,"avgPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new ri(c,"avg",!1);return e.runWebGLProgram(p,[o],"float32")}var KM={kernelName:Wo,backendName:"webgl",kernelFunc:Jet};function Qet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new Tu(p,"avg",!1);return e.runWebGLProgram(m,[o],"float32")}var jM={kernelName:Al,backendName:"webgl",kernelFunc:Qet};var Qw=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=` const ivec2 pads = ivec2(${c}, ${p}); const float avgMultiplier = float(${m}); @@ -1993,7 +1993,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } setOutput(dotProd); } - `}},eC=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=` + `}},tC=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=` const ivec3 pads = ivec3(${d}, ${h}, ${g}); const float avgMultiplier = float(${x}); @@ -2049,7 +2049,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } setOutput(dotProd); } - `}};function trt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new eC(m);return e.runWebGLProgram(f,[o],i.dtype)}var ZM={kernelName:up,backendName:"webgl",kernelFunc:trt};function ert(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;ri([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new tC(c);return e.runWebGLProgram(p,[o],i.dtype)}var JM={kernelName:lp,backendName:"webgl",kernelFunc:ert};function rrt(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return Hc({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var QM={kernelName:qo,backendName:"webgl",kernelFunc:rrt};var rC=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="0.0";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="1.0";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` + `}};function trt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new tC(m);return e.runWebGLProgram(f,[o],i.dtype)}var XM={kernelName:up,backendName:"webgl",kernelFunc:trt};function ert(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;ti([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new Qw(c);return e.runWebGLProgram(p,[o],i.dtype)}var YM={kernelName:lp,backendName:"webgl",kernelFunc:ert};function rrt(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return Hc({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var ZM={kernelName:Uo,backendName:"webgl",kernelFunc:rrt};var eC=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="0.0";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="1.0";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); @@ -2059,7 +2059,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN float inv = scale * inversesqrt(variance + float(${i})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } - `}};var nC=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="vec4(0.0)";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="vec4(1.0)";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` + `}};var rC=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="vec4(0.0)";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="vec4(1.0)";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=` void main() { vec4 offset = ${a}; vec4 scale = ${u}; @@ -2072,7 +2072,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN setOutput((x - mean) * inv + offset); } - `}};var nrt=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=M().getBool("WEBGL_PACK_NORMALIZATION")?new nC(n.shape,o.shape,s.shape,c,p,u):new rC(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},tz={kernelName:ss,backendName:"webgl",kernelFunc:nrt};var oC=class{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=Bt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=ort(this.rank),o,s=t.map((i,a)=>`sourceLoc.${yk[a]} = start[${a}] + coords.${yk[a]};`);o=` + `}};var nrt=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=M().getBool("WEBGL_PACK_NORMALIZATION")?new rC(n.shape,o.shape,s.shape,c,p,u):new eC(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},JM={kernelName:ns,backendName:"webgl",kernelFunc:nrt};var nC=class{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=Vt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=ort(this.rank),o,s=t.map((i,a)=>`sourceLoc.${bk[a]} = start[${a}] + coords.${bk[a]};`);o=` ${e} sourceLoc; ${e} coords = getOutputCoords(); ${s.join(` @@ -2082,7 +2082,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN ${o} setOutput(getSource(${n})); } - `}},yk=["x","y","z","w","u","v"];function ort(r){if(r===1)return"sourceLoc";if(r<=6)return yk.slice(0,r).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var sC=class{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let e=Bt(this.rank),n=er("coords",this.rank),o=er("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=` + `}},bk=["x","y","z","w","u","v"];function ort(r){if(r===1)return"sourceLoc";if(r<=6)return bk.slice(0,r).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var oC=class{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let e=Vt(this.rank),n=er("coords",this.rank),o=er("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=` result.x = ${i}; if (++${n[this.rank-1]} < ${t[this.rank-1]}) { ++${o[this.rank-1]}; @@ -2111,7 +2111,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN ${u} setOutput(result); } - `}};function srt(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=Me.computeFlatOffset(t,y.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function si(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=Me.parseSliceParams(o,s,i);if(Me.assertParamsValid(o,a,u),y.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype==="string"){let p=e.texData.get(o.dataId),m=UL(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=Me.isSliceContinous(o.shape,a,u);if(l||!c){let p=M().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new sC(u):new oC(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),srt(o,a,u,e)}var ez={kernelName:bi,backendName:"webgl",kernelFunc:si};var irt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;y.assert(o.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=it({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Oe({inputs:{x:d},backend:e,attrs:{perm:l}}),g=it({inputs:{x:h},backend:e,attrs:{shape:c}}),x=si({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return 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iz={kernelName:Fp,backendName:"webgl",kernelFunc:vl};var crt="return float(int(x));";function az(r,t){let e=new Mr(r.shape,crt),n=t.runWebGLProgram(e,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function wk(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return rr({inputs:{x:o},backend:e});let i=Ne(o.shape),a=wk({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),u=$n({inputs:{real:a,imag:i},backend:e});return i.dispose(),e.disposeIntermediateTensorInfo(a),u}if(o.dtype==="complex64"){let i=vl({inputs:{input:o},backend:e}),a=wk({inputs:{x:i},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(i),a}if(!y.hasEncodingLoss(o.dtype,s)){let i=rr({inputs:{x:o},backend:e});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(e.shouldExecuteOnCPU([o])){let i=e.texData.get(o.dataId).values,[a,u,l]=hL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s==="int32")return az(o,e);if(s==="bool"){let 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n.dataRefCount.set(i.slice.origDataId,u+1),s}function ni(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=ze.parseSliceParams(o,s,i);if(ze.assertParamsValid(o,a,u),y.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype==="string"){let p=e.texData.get(o.dataId),m=GL(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=ze.isSliceContinous(o.shape,a,u);if(l||!c){let p=M().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new oC(u):new nC(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),srt(o,a,u,e)}var QM={kernelName:yi,backendName:"webgl",kernelFunc:ni};var irt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;y.assert(o.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=it({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Pe({inputs:{x:d},backend:e,attrs:{perm:l}}),g=it({inputs:{x:h},backend:e,attrs:{shape:c}}),x=ni({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},tz={kernelName:ui,backendName:"webgl",kernelFunc:irt};function art(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=Lw(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var ez={kernelName:cp,backendName:"webgl",kernelFunc:art};function lrt(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return 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i=vl({inputs:{input:o},backend:e}),a=Ck({inputs:{x:i},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(i),a}if(!y.hasEncodingLoss(o.dtype,s)){let i=rr({inputs:{x:o},backend:e});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(e.shouldExecuteOnCPU([o])){let i=e.texData.get(o.dataId).values,[a,u,l]=fL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s==="int32")return sz(o,e);if(s==="bool"){let i=e.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),u=wk({inputs:{a:o,b:i},backend:e});return e.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var iz={kernelName:uo,backendName:"webgl",kernelFunc:Ck};var az="return ceil(x);",prt=wt({opSnippet:az,packedOpSnippet:az,cpuKernelImpl:dL}),lz={kernelName:Ho,backendName:"webgl",kernelFunc:prt};var sC=class{constructor(t){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=` void main() { float value = getAAtOutCoords(); @@ -2122,7 +2122,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN setOutput(clamp(value, minVal, maxVal)); } - `}};var aC=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=` + `}};var iC=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=` void main() { vec4 value = getAAtOutCoords(); @@ -2133,7 +2133,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } - `}};function mrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;M().getBool("WEBGL_PACK_CLIP")?a=new aC(o.shape):a=new iC(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var pz={kernelName:po,backendName:"webgl",kernelFunc:mrt};var lC=class{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=` + `}};function mrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;M().getBool("WEBGL_PACK_CLIP")?a=new iC(o.shape):a=new sC(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var uz={kernelName:co,backendName:"webgl",kernelFunc:mrt};var aC=class{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=` void main() { float re = abs(getRealAtOutCoords()); float im = abs(getImagAtOutCoords()); @@ -2146,7 +2146,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx)) ); } - `}};function mz(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function frt(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new 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2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } setOutput(result); } - `}};function cC(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function qc(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var dz={kernelName:Sp,backendName:"webgl",kernelFunc:qc};function kd(r,t,e){let n=r[0].dtype;if(n==="complex64"){let f=r.map(b=>vl({inputs:{input:b},backend:e})),d=r.map(b=>qc({inputs:{input:b},backend:e})),h=kd(f,t,e),g=kd(d,t,e),x=$n({inputs:{real:h,imag:g},backend:e});return f.forEach(b=>e.disposeIntermediateTensorInfo(b)),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let o=e.shouldExecuteOnCPU(r);if(n==="string"&&(o=!0),o){let f=r.map(C=>{let E=[-1,y.sizeFromShape(C.shape.slice(t))];return it({inputs:{x:C},backend:e,attrs:{shape:E}})}),d=f.map(C=>({vals:e.readSync(C.dataId),shape:C.shape})),h=S.computeOutShape(f.map(C=>C.shape),1),g=f[0].shape[0]===1,x=xL(d,h,n,g),b=S.computeOutShape(r.map(C=>C.shape),t),w=e.makeTensorInfo(b,n,x);return f.forEach(C=>e.disposeIntermediateTensorInfo(C)),w}let s=r.filter(f=>y.sizeFromShape(f.shape)>0),i=M().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let f=i?new Mr(r[0].shape,ta):new An(r[0].shape,ta);return e.runWebGLProgram(f,r,n)}let a=M().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>a){let f=[];for(let h=0;hd.shape),t);return e.runWebGLProgram(f,s,n)}let{tensors2D:u,outShape:l}=drt(s,t,e),c=new uC(u.map(f=>f.shape)),p=e.runWebGLProgram(c,u,n);u.forEach(f=>e.disposeIntermediateTensorInfo(f));let m=it({inputs:{x:p},attrs:{shape:l},backend:e});return e.disposeIntermediateTensorInfo(p),m}function drt(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>it({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function Ck(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(l=>l.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(l=>l.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(l=>y.sizeFromShape(l.shape)>0);return u.length===1?rr({inputs:{x:u[0]},backend:e}):kd(u,s,e)}var hz={kernelName:pi,backendName:"webgl",kernelFunc:Ck};var Ed=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,w=g?3:1,C="",N="";n&&(o?C=`float activation(float a) { + `}};function uC(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function qc(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var mz={kernelName:Sp,backendName:"webgl",kernelFunc:qc};function kd(r,t,e){let n=r[0].dtype;if(n==="complex64"){let f=r.map(b=>vl({inputs:{input:b},backend:e})),d=r.map(b=>qc({inputs:{input:b},backend:e})),h=kd(f,t,e),g=kd(d,t,e),x=An({inputs:{real:h,imag:g},backend:e});return f.forEach(b=>e.disposeIntermediateTensorInfo(b)),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let o=e.shouldExecuteOnCPU(r);if(n==="string"&&(o=!0),o){let f=r.map(C=>{let _=[-1,y.sizeFromShape(C.shape.slice(t))];return it({inputs:{x:C},backend:e,attrs:{shape:_}})}),d=f.map(C=>({vals:e.readSync(C.dataId),shape:C.shape})),h=S.computeOutShape(f.map(C=>C.shape),1),g=f[0].shape[0]===1,x=hL(d,h,n,g),b=S.computeOutShape(r.map(C=>C.shape),t),w=e.makeTensorInfo(b,n,x);return f.forEach(C=>e.disposeIntermediateTensorInfo(C)),w}let s=r.filter(f=>y.sizeFromShape(f.shape)>0),i=M().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let f=i?new Mr(r[0].shape,Qi):new _n(r[0].shape,Qi);return e.runWebGLProgram(f,r,n)}let a=M().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>a){let f=[];for(let h=0;hd.shape),t);return e.runWebGLProgram(f,s,n)}let{tensors2D:u,outShape:l}=drt(s,t,e),c=new lC(u.map(f=>f.shape)),p=e.runWebGLProgram(c,u,n);u.forEach(f=>e.disposeIntermediateTensorInfo(f));let m=it({inputs:{x:p},attrs:{shape:l},backend:e});return e.disposeIntermediateTensorInfo(p),m}function drt(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>it({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function Ik(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(l=>l.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(l=>l.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(l=>y.sizeFromShape(l.shape)>0);return u.length===1?rr({inputs:{x:u[0]},backend:e}):kd(u,s,e)}var fz={kernelName:ci,backendName:"webgl",kernelFunc:Ik};var Ed=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,w=g?3:1,C="",N="";n&&(o?C=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:s?C=`float activation(float a) { @@ -2202,7 +2202,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN float activation(float x) { ${n} } - `,N="result = activation(result);");let E=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=` + `,N="result = activation(result);");let _=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${C} const ivec2 strides = ivec2(${u}, ${l}); @@ -2322,11 +2322,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } float result = dotProd; - ${E} + ${_} ${N} setOutput(result); } - `}},mC=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=` + `}},pC=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${s}, ${i}, ${a}); const ivec3 pads = ivec3(${e}, ${n}, ${o}); @@ -2414,7 +2414,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } setOutput(dotProd); } - `}};var _d=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=we(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=` + `}};var _d=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=Ce(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function hC({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat==="channelsLast",d=!1,h=!1,g,x=[];if(s!=null){let C=dC(s.shape,f);C!=null&&(s=it({inputs:{x:s},backend:n,attrs:{shape:C}}),x.push(s))}if(o!=null){let C=dC(o.shape,f);C!=null&&(o=it({inputs:{x:o},backend:n,attrs:{shape:C}}),x.push(o))}if(!((p===1||m===1)&&c>xk)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&y.arraysEqual(l.shape.slice(-3),u.slice(-3))){let C=u[0]*u[1]*(u[2]+1),N={dataId:r.dataId,shape:[1,C,e.inChannels],dtype:r.dtype},E=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,y.assert(Au(l.shape,N.shape),()=>`packed reshape ${l.shape} to ${N.shape} isn't free`);let A=it({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(A);let $=Hc({a:N,b:A,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get($.dataId);y.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=E,F.shape=e.outShape,g=rr({inputs:{x:$},backend:n}),g.shape=e.outShape,x.push($)}else{let C=e.outHeight*e.outWidth,N=it({inputs:{x:r},backend:n,attrs:{shape:f?[e.batchSize,C,e.inChannels]:[e.batchSize,e.inChannels,C]}}),E=it({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),A=Hc({a:f?N:E,b:f?E:N,transposeA:!f,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=it({inputs:{x:A},backend:n,attrs:{shape:e.outShape}}),x.push(N),x.push(E),x.push(A)}for(let C of x)n.disposeIntermediateTensorInfo(C);return g}function gC({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f==="channelsLast",h=u*l*c,g=m*p,x=[e.batchSize,h,g],b=!0,w=!1,C=[];if(s!=null){let Z=dC(s.shape,d);Z!=null&&(s=it({inputs:{x:s},backend:n,attrs:{shape:Z}}),C.push(s))}if(o!=null){let Z=dC(o.shape,d);Z!=null&&(o=it({inputs:{x:o},backend:n,attrs:{shape:Z}}),C.push(o))}let N=it({inputs:{x:t},backend:n,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});C.push(N);let E=new fC(x,e),A=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],$=n.runWebGLProgram(E,[r],"float32",A),F=it({inputs:{x:$},backend:n,attrs:{shape:x}});C.push($),C.push(F);let P=o!=null,V=s!=null,G=a==="leakyrelu",W=a?Il(a,!0):null,q=new Td(d?F.shape:N.shape,d?N.shape:F.shape,d?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,w,P,W,V,G),H=d?[F,N]:[N,F];if(o&&H.push(o),V&&H.push(s),G){let Z=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));H.push(Z),C.push(Z)}let j=n.runWebGLProgram(q,H,"float32"),Y=it({inputs:{x:j},backend:n,attrs:{shape:e.outShape}});C.push(j);for(let Z of C)n.disposeIntermediateTensorInfo(Z);return Y}function hrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=hC({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p==="channelsLast"&&M().getBool("WEBGL_EXP_CONV")){let h=new _d(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];f=e.runWebGLProgram(h,[o,s],"float32",g)}else if(M().getBool("WEBGL_CONV_IM2COL"))f=gC({x:o,filter:s,convInfo:m,backend:e});else{let h=new Ed(m);f=e.runWebGLProgram(h,[o,s],"float32")}let d=it({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var gz={kernelName:jo,backendName:"webgl",kernelFunc:hrt};var xC=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.dataFormat==="channelsLast";this.userCode=` + `}};function fC(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function dC({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat==="channelsLast",d=!1,h=!1,g,x=[];if(s!=null){let C=fC(s.shape,f);C!=null&&(s=it({inputs:{x:s},backend:n,attrs:{shape:C}}),x.push(s))}if(o!=null){let C=fC(o.shape,f);C!=null&&(o=it({inputs:{x:o},backend:n,attrs:{shape:C}}),x.push(o))}if(!((p===1||m===1)&&c>yk)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&y.arraysEqual(l.shape.slice(-3),u.slice(-3))){let C=u[0]*u[1]*(u[2]+1),N={dataId:r.dataId,shape:[1,C,e.inChannels],dtype:r.dtype},_=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,y.assert(vu(l.shape,N.shape),()=>`packed reshape ${l.shape} to ${N.shape} isn't free`);let A=it({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(A);let $=Hc({a:N,b:A,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get($.dataId);y.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=_,F.shape=e.outShape,g=rr({inputs:{x:$},backend:n}),g.shape=e.outShape,x.push($)}else{let C=e.outHeight*e.outWidth,N=it({inputs:{x:r},backend:n,attrs:{shape:f?[e.batchSize,C,e.inChannels]:[e.batchSize,e.inChannels,C]}}),_=it({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),A=Hc({a:f?N:_,b:f?_:N,transposeA:!f,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=it({inputs:{x:A},backend:n,attrs:{shape:e.outShape}}),x.push(N),x.push(_),x.push(A)}for(let C of x)n.disposeIntermediateTensorInfo(C);return g}function hC({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f==="channelsLast",h=u*l*c,g=m*p,x=[e.batchSize,h,g],b=!0,w=!1,C=[];if(s!=null){let Z=fC(s.shape,d);Z!=null&&(s=it({inputs:{x:s},backend:n,attrs:{shape:Z}}),C.push(s))}if(o!=null){let Z=fC(o.shape,d);Z!=null&&(o=it({inputs:{x:o},backend:n,attrs:{shape:Z}}),C.push(o))}let N=it({inputs:{x:t},backend:n,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});C.push(N);let _=new mC(x,e),A=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],$=n.runWebGLProgram(_,[r],"float32",A),F=it({inputs:{x:$},backend:n,attrs:{shape:x}});C.push($),C.push(F);let P=o!=null,V=s!=null,G=a==="leakyrelu",W=a?Il(a,!0):null,q=new Td(d?F.shape:N.shape,d?N.shape:F.shape,d?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,w,P,W,V,G),H=d?[F,N]:[N,F];if(o&&H.push(o),V&&H.push(s),G){let Z=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));H.push(Z),C.push(Z)}let K=n.runWebGLProgram(q,H,"float32"),X=it({inputs:{x:K},backend:n,attrs:{shape:e.outShape}});C.push(K);for(let Z of C)n.disposeIntermediateTensorInfo(Z);return X}function hrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=dC({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p==="channelsLast"&&M().getBool("WEBGL_EXP_CONV")){let h=new _d(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];f=e.runWebGLProgram(h,[o,s],"float32",g)}else if(M().getBool("WEBGL_CONV_IM2COL"))f=hC({x:o,filter:s,convInfo:m,backend:e});else{let h=new Ed(m);f=e.runWebGLProgram(h,[o,s],"float32")}let d=it({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var dz={kernelName:qo,backendName:"webgl",kernelFunc:hrt};var gC=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -2699,7 +2699,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } setOutput(dotProd); } - `}},yC=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat==="channelsLast",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=` + `}},xC=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat==="channelsLast",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=` const ivec2 pads = ivec2(${a}, ${u}); void main() { @@ -2752,7 +2752,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } setOutput(dotProd); } - `}},bC=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode=` + `}},yC=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; @@ -2794,7 +2794,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } setOutput(dotProd); } - `}},wC=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=` + `}},bC=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${u}, ${l}, ${c}); void main() { @@ -2851,12 +2851,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } setOutput(dotProd); } - `}};function grt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new xC(m);return e.runWebGLProgram(f,[o,s],"float32")}var xz={kernelName:fp,backendName:"webgl",kernelFunc:grt};function xrt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p),f=new yC(m);return e.runWebGLProgram(f,[o,s],"float32")}var yz={kernelName:Xo,backendName:"webgl",kernelFunc:xrt};function yrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new mC(l);return e.runWebGLProgram(c,[o,s],"float32")}var bz={kernelName:Rl,backendName:"webgl",kernelFunc:yrt};function brt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new bC(l);return e.runWebGLProgram(c,[o,s],"float32")}var wz={kernelName:dp,backendName:"webgl",kernelFunc:brt};function wrt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new wC(l);return e.runWebGLProgram(c,[o,s],"float32")}var Cz={kernelName:hp,backendName:"webgl",kernelFunc:wrt};var Crt=Lo+` + `}};function grt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new gC(m);return e.runWebGLProgram(f,[o,s],"float32")}var hz={kernelName:fp,backendName:"webgl",kernelFunc:grt};function xrt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p),f=new xC(m);return e.runWebGLProgram(f,[o,s],"float32")}var gz={kernelName:Ko,backendName:"webgl",kernelFunc:xrt};function yrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new pC(l);return e.runWebGLProgram(c,[o,s],"float32")}var xz={kernelName:Dl,backendName:"webgl",kernelFunc:yrt};function brt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new yC(l);return e.runWebGLProgram(c,[o,s],"float32")}var yz={kernelName:dp,backendName:"webgl",kernelFunc:brt};function wrt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new bC(l);return e.runWebGLProgram(c,[o,s],"float32")}var bz={kernelName:hp,backendName:"webgl",kernelFunc:wrt};var Crt=Po+` return cos(x); -`,Irt=Ct({opSnippet:Crt}),Iz={kernelName:Yo,backendName:"webgl",kernelFunc:Irt};var vrt=` +`,Irt=wt({opSnippet:Crt}),wz={kernelName:jo,backendName:"webgl",kernelFunc:Irt};var vrt=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; -`,Srt=Ct({opSnippet:vrt}),vz={kernelName:Zo,backendName:"webgl",kernelFunc:Srt};var CC=class{constructor(t,e,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o==="bilinear"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,x,b]=p>1?[`${(a-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,C,N]=m>1?[`${(u-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=` +`,Srt=wt({opSnippet:vrt}),Cz={kernelName:Xo,backendName:"webgl",kernelFunc:Srt};var wC=class{constructor(t,e,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o==="bilinear"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,x,b]=p>1?[`${(a-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,C,N]=m>1?[`${(u-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=` const float height_ratio = float(${g}); const float width_ratio = float(${w}); void main() { @@ -2917,20 +2917,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN setOutput(newValue); } } - `}};var Nrt=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new CC(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],"float32")},Sz={kernelName:ba,backendName:"webgl",kernelFunc:Nrt};var Kc;(function(r){r.Prod="*",r.Sum="+"})(Kc||(Kc={}));var og=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,i=this.op===Kc.Prod?"1.0":"0.0",a=n?i:`getX(${Nz(s,"coords",this.op)})`,u=this.outputShape[this.outputShape.length-1],l="",c="";n?(l=o?`end != ${u-1}`:"end != 0",c=o?"end + 1":"end - 1"):(l=o?`end + pow2 < ${u}`:"end >= pow2",c=o?"end + pow2":"end - pow2"),this.userCode=` + `}};var Nrt=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new wC(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],"float32")},Iz={kernelName:ya,backendName:"webgl",kernelFunc:Nrt};var Kc;(function(r){r.Prod="*",r.Sum="+"})(Kc||(Kc={}));var sg=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,i=this.op===Kc.Prod?"1.0":"0.0",a=n?i:`getX(${vz(s,"coords",this.op)})`,u=this.outputShape[this.outputShape.length-1],l="",c="";n?(l=o?`end != ${u-1}`:"end != 0",c=o?"end + 1":"end - 1"):(l=o?`end + pow2 < ${u}`:"end >= pow2",c=o?"end + pow2":"end - pow2"),this.userCode=` void main() { - ${Bt(s)} coords = getOutputCoords(); - int end = ${Tz(s,"coords",this.op)}; + ${Vt(s)} coords = getOutputCoords(); + int end = ${Sz(s,"coords",this.op)}; float val = ${a}; int pow2 = int(pow(2.0, index)); if (${l}) { int idx = ${c}; - ${Tz(s,"coords",this.op)} = idx; - val ${this.op}= getX(${Nz(s,"coords",this.op)}); + ${Sz(s,"coords",this.op)} = idx; + val ${this.op}= getX(${vz(s,"coords",this.op)}); } setOutput(val); } - `}};function Nz(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function Tz(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function IC(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=Oe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=rr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new og(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new og(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Oe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function Trt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return IC(Kc.Prod,o,e,s,i,a)}var kz={kernelName:ya,backendName:"webgl",kernelFunc:Trt};function krt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return IC(Kc.Sum,o,e,s,i,a)}var Ez={kernelName:Jo,backendName:"webgl",kernelFunc:krt};function Ert(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=Mw(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=dL(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var _z={kernelName:gp,backendName:"webgl",kernelFunc:Ert};var vC=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=` + `}};function vz(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function Sz(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function CC(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=Pe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=rr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new sg(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new sg(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Pe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function Trt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return CC(Kc.Prod,o,e,s,i,a)}var Nz={kernelName:xa,backendName:"webgl",kernelFunc:Trt};function krt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return CC(Kc.Sum,o,e,s,i,a)}var Tz={kernelName:Yo,backendName:"webgl",kernelFunc:krt};function Ert(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=Lw(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=mL(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var kz={kernelName:gp,backendName:"webgl",kernelFunc:Ert};var IC=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -2949,7 +2949,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN float result = ${this.getInputSamplingString()}; setOutput(result); } - `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function _rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=new vC(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var Az={kernelName:wa,backendName:"webgl",kernelFunc:_rt};var Ad=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=we(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l="",c="";n&&(o?l=`float activation(float a) { + `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function _rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=new IC(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var Ez={kernelName:ba,backendName:"webgl",kernelFunc:_rt};var Ad=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=Ce(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l="",c="";n&&(o?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:s?l=`float activation(float a) { @@ -3002,7 +3002,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN ${c} setOutput(result); } - `}};var $d=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=we(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=` + `}};var $d=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=Ce(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;M().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new $d(p):m=new Ad(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],"float32",f)}var $z={kernelName:Qo,backendName:"webgl",kernelFunc:Art};var SC=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode=` + `}};function Art(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u,dimRoundingMode:l}=n,c=u;c==null&&(c=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;M().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new $d(p):m=new Ad(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],"float32",f)}var _z={kernelName:Zo,backendName:"webgl",kernelFunc:Art};var vC=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; @@ -3226,7 +3226,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } setOutput(dotProd); } - `}},NC=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=` + `}},SC=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=` const ivec2 pads = ivec2(${i}, ${a}); void main() { @@ -3271,13 +3271,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } setOutput(dotProd); } - `}};function $rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new SC(p);return e.runWebGLProgram(m,[o,s],"float32")}var Dz={kernelName:xp,backendName:"webgl",kernelFunc:$rt};function Drt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new NC(p);return e.runWebGLProgram(m,[o,s],"float32")}var Rz={kernelName:yp,backendName:"webgl",kernelFunc:Drt};var TC=class{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=` + `}};function $rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new vC(p);return e.runWebGLProgram(m,[o,s],"float32")}var Az={kernelName:xp,backendName:"webgl",kernelFunc:$rt};function Drt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new SC(p);return e.runWebGLProgram(m,[o,s],"float32")}var $z={kernelName:yp,backendName:"webgl",kernelFunc:Drt};var NC=class{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } - `}};function Rrt(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=it({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new TC(s),u=e.runWebGLProgram(a,[i],i.dtype),l=it({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var Fz={kernelName:bp,backendName:"webgl",kernelFunc:Rrt};var kC=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=` + `}};function Rrt(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=it({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new NC(s),u=e.runWebGLProgram(a,[i],i.dtype),l=it({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var Dz={kernelName:bp,backendName:"webgl",kernelFunc:Rrt};var TC=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=` const ivec2 strides = ivec2(${s}, ${i}); const ivec2 pads = ivec2(${p}, ${m}); const float neg_infinity = -3.4e38; @@ -3315,7 +3315,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN float result = curVal; setOutput(result); } - `}};function Frt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,"NHWC",u),c,p=new kC(l);c=e.runWebGLProgram(p,[o,s],"float32");let m=it({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var Oz={kernelName:Fl,backendName:"webgl",kernelFunc:Frt};function Ort(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h=0&&(m=Uc({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var Pz={kernelName:wp,backendName:"webgl",kernelFunc:Ort};var Prt="return (x >= 0.0) ? x : (exp(x) - 1.0);",Lrt=` + `}};function Frt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,"NHWC",u),c,p=new TC(l);c=e.runWebGLProgram(p,[o,s],"float32");let m=it({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var Rz={kernelName:Rl,backendName:"webgl",kernelFunc:Frt};function Ort(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h=0&&(m=Uc({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var Fz={kernelName:wp,backendName:"webgl",kernelFunc:Ort};var Prt="return (x >= 0.0) ? x : (exp(x) - 1.0);",Lrt=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); @@ -3324,12 +3324,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; -`,Mrt=Ct({opSnippet:Prt,packedOpSnippet:Lrt}),Lz={kernelName:es,backendName:"webgl",kernelFunc:Mrt};var zrt="return (b >= 1.0) ? a : a * (b + 1.0);",Brt=` +`,Mrt=wt({opSnippet:Prt,packedOpSnippet:Lrt}),Oz={kernelName:Qo,backendName:"webgl",kernelFunc:Mrt};var zrt="return (b >= 1.0) ? a : a * (b + 1.0);",Brt=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); -`,Vrt=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=M().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Po(Brt,n.shape,o.shape):new lo(zrt,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},Mz={kernelName:Cp,backendName:"webgl",kernelFunc:Vrt};var Grt=` +`,Vrt=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=M().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Oo(Brt,n.shape,o.shape):new ao(zrt,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},Pz={kernelName:Cp,backendName:"webgl",kernelFunc:Vrt};var Grt=` return vec4(equal(a, b)); -`,Wrt="return float(a == b);",Urt=le({opSnippet:Wrt,packedOpSnippet:Grt,dtype:"bool",cpuKernelImpl:yL}),zz={kernelName:Ia,backendName:"webgl",kernelFunc:Urt};var Hrt=` +`,Wrt="return float(a == b);",Urt=ae({opSnippet:Wrt,packedOpSnippet:Grt,dtype:"bool",cpuKernelImpl:gL}),Lz={kernelName:Ca,backendName:"webgl",kernelFunc:Urt};var Hrt=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. @@ -3344,7 +3344,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); -`,qrt=Ct({opSnippet:Hrt}),Bz={kernelName:Ca,backendName:"webgl",kernelFunc:qrt};var Krt=Lo+` +`,qrt=wt({opSnippet:Hrt}),Mz={kernelName:wa,backendName:"webgl",kernelFunc:qrt};var Krt=Po+` return exp(x); `,jrt=` vec4 result = exp(x); @@ -3355,7 +3355,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN result.a = isNaN.a ? x.a : result.a; return result; -`,Ik=Ct({opSnippet:Krt,packedOpSnippet:jrt,cpuKernelImpl:bL,dtype:"float32"}),Vz={kernelName:rs,backendName:"webgl",kernelFunc:Ik};function EC(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),it({inputs:{x:s},backend:n,attrs:{shape:a}})}var Gz={kernelName:mi,backendName:"webgl",kernelFunc:EC};var Wz="return exp(x) - 1.0;",Xrt=Ct({opSnippet:Wz,packedOpSnippet:Wz,cpuKernelImpl:wL}),Uz={kernelName:va,backendName:"webgl",kernelFunc:Xrt};var sg=class{constructor(t,e,n){this.variableNames=["real","imag"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:"1.0",a;if(t==="real")a="return real * expR - imag * expI;";else if(t==="imag")a="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=` +`,vk=wt({opSnippet:Krt,packedOpSnippet:jrt,cpuKernelImpl:xL,dtype:"float32"}),zz={kernelName:ts,backendName:"webgl",kernelFunc:vk};function kC(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),it({inputs:{x:s},backend:n,attrs:{shape:a}})}var Bz={kernelName:pi,backendName:"webgl",kernelFunc:kC};var Vz="return exp(x) - 1.0;",Xrt=wt({opSnippet:Vz,packedOpSnippet:Vz,cpuKernelImpl:yL}),Gz={kernelName:Ia,backendName:"webgl",kernelFunc:Xrt};var ig=class{constructor(t,e,n){this.variableNames=["real","imag"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:"1.0",a;if(t==="real")a="return real * expR - imag * expI;";else if(t==="imag")a="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=` const float exponentMultiplier = ${s}; float unaryOpComplex(float real, float expR, float imag, float expI) { @@ -3388,12 +3388,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } - `}};function _C(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=it({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new sg("real",u,t),c=new sg("imag",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=$n({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=it({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function Yrt(r){let{inputs:t,backend:e}=r,{input:n}=t;return _C(n,!1,e)}var Hz={kernelName:Ip,backendName:"webgl",kernelFunc:Yrt};var AC=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=t,this.userCode=` + `}};function EC(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=it({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new ig("real",u,t),c=new ig("imag",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=An({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=it({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function Yrt(r){let{inputs:t,backend:e}=r,{input:n}=t;return EC(n,!1,e)}var Wz={kernelName:Ip,backendName:"webgl",kernelFunc:Yrt};var _C=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=t,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } - `}};function Sl(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||y.inferDtype(o),s==="string"){let i=y.getArrayFromDType(s,y.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new AC(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var qz={kernelName:Ol,backendName:"webgl",kernelFunc:Sl};var $C=class{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=` + `}};function Sl(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||y.inferDtype(o),s==="string"){let i=y.getArrayFromDType(s,y.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new _C(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var Uz={kernelName:Fl,backendName:"webgl",kernelFunc:Sl};var AC=class{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; @@ -3407,7 +3407,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } setOutput(outputValue); } - `}};var Kz={kernelName:Sa,backendName:"webgl",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new $C(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var jz="return floor(x);",Zrt=Ct({opSnippet:jz,packedOpSnippet:jz,cpuKernelImpl:CL}),Xz={kernelName:ns,backendName:"webgl",kernelFunc:Zrt};var Jrt=` + `}};var Hz={kernelName:va,backendName:"webgl",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new AC(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var qz="return floor(x);",Zrt=wt({opSnippet:qz,packedOpSnippet:qz,cpuKernelImpl:bL}),Kz={kernelName:es,backendName:"webgl",kernelFunc:Zrt};var Jrt=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); @@ -3438,7 +3438,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); -`,tnt=le({opSnippet:Jrt,packedOpSnippet:Qrt,dtype:"int32"}),Yz={kernelName:os,backendName:"webgl",kernelFunc:tnt};var DC=class{constructor(t){this.variableNames=["A"];let e=Ge(),[n,o]=t;this.outputShape=t,this.userCode=` +`,tnt=ae({opSnippet:Jrt,packedOpSnippet:Qrt,dtype:"int32"}),jz={kernelName:rs,backendName:"webgl",kernelFunc:tnt};var $C=class{constructor(t){this.variableNames=["A"];let e=We(),[n,o]=t;this.outputShape=t,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3460,7 +3460,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN setOutput(floor(value * 255.0 + 0.5)); } - `}};var RC=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=Ge(),[n,o]=t;this.outputShape=t,this.userCode=` + `}};var DC=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=We(),[n,o]=t;this.outputShape=t,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; @@ -3494,7 +3494,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN ${e.output} = result; } - `}};var Zz={kernelName:Zd,backendName:"webgl",kernelFunc:ent},Dd,vk=M().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function ent(r){let{inputs:t,backend:e,attrs:n}=r,{pixels:o}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,a=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,[u,l]=i?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[l,u],p=[l,u,s];if(a||i){let h=M().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Dd==null||h!==vk)&&(vk=h,Dd=document.createElement("canvas").getContext("2d",{willReadFrequently:vk})),Dd.canvas.width=u,Dd.canvas.height=l,Dd.drawImage(o,0,0,u,l),o=Dd.canvas}let m=e.makeTensorInfo(c,"int32");e.texData.get(m.dataId).usage=Yr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),o);let f=M().getBool("WEBGL_PACK")?new RC(p):new DC(p),d=e.runWebGLProgram(f,[m],"int32");return e.disposeData(m.dataId),d}function rnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(o.shape,s.shape,u,p,l,m,!1,h),x,b=[],w=i!=null,C=a!=null,N=f==="leakyrelu",E=()=>{let $=[o,s],F=(P,V)=>{if(V==="NCHW"&&P.shape.length===1&&P.shape[0]!==1){let G=it({inputs:{x:P},backend:e,attrs:{shape:[P.shape[0],1,1]}});return b.push(G),G}return P};if(w&&$.push(F(i,c)),C&&$.push(F(a,c)),N){let P=e.makeTensorInfo([],"float32",y.createScalarValue(d,"float32"));$.push(P),b.push(P)}return $};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))x=hC({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else if(g.strideWidth<=2&&h==="channelsLast"&&M().getBool("WEBGL_EXP_CONV")){let $=f?Il(f,!0):null,F=new _d(g,w,$,C,N),P=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],V=E();x=e.runWebGLProgram(F,V,"float32",P)}else if(M().getBool("WEBGL_CONV_IM2COL"))x=gC({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else{let $=f?Il(f,!1):null,F=new Ed(g,w,$,C,N),P=E();x=e.runWebGLProgram(F,P,"float32")}let A=it({inputs:{x},backend:e,attrs:{shape:g.outShape}});return b.push(x),b.forEach($=>e.disposeIntermediateTensorInfo($)),A}var Jz={kernelName:Ni,backendName:"webgl",kernelFunc:rnt};function nnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let g=S.computeConv2DInfo(o.shape,s.shape,u,h,l,p,!0),x=M().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?Il(m,x):null,w=[o,s],C=i!=null,N=a!=null,E=m==="leakyrelu";if(C&&w.push(i),N&&w.push(a),E){let P=e.makeTensorInfo([],"float32",y.createScalarValue(f,"float32"));w.push(P),d.push(P)}let A;x?A=new $d(g,C,b,N,E):A=new Ad(g,C,b,N,E);let $=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=e.runWebGLProgram(A,w,"float32",$);return d.forEach(P=>e.disposeIntermediateTensorInfo(P)),F}var Qz={kernelName:Ti,backendName:"webgl",kernelFunc:nnt};var FC=class{constructor(t,e,n,o){this.sliceDim=t,this.strides=e,this.paramsShape=o,this.variableNames=["x","indices"],this.outputShape=n;let s=Bt(n.length),i=` + `}};var Xz={kernelName:Xd,backendName:"webgl",kernelFunc:ent},Dd,Sk=M().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function ent(r){let{inputs:t,backend:e,attrs:n}=r,{pixels:o}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,a=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,[u,l]=i?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[l,u],p=[l,u,s];if(a||i){let h=M().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Dd==null||h!==Sk)&&(Sk=h,Dd=document.createElement("canvas").getContext("2d",{willReadFrequently:Sk})),Dd.canvas.width=u,Dd.canvas.height=l,Dd.drawImage(o,0,0,u,l),o=Dd.canvas}let m=e.makeTensorInfo(c,"int32");e.texData.get(m.dataId).usage=Yr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),o);let f=M().getBool("WEBGL_PACK")?new DC(p):new $C(p),d=e.runWebGLProgram(f,[m],"int32");return e.disposeData(m.dataId),d}function rnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(o.shape,s.shape,u,p,l,m,!1,h),x,b=[],w=i!=null,C=a!=null,N=f==="leakyrelu",_=()=>{let $=[o,s],F=(P,V)=>{if(V==="NCHW"&&P.shape.length===1&&P.shape[0]!==1){let G=it({inputs:{x:P},backend:e,attrs:{shape:[P.shape[0],1,1]}});return b.push(G),G}return P};if(w&&$.push(F(i,c)),C&&$.push(F(a,c)),N){let P=e.makeTensorInfo([],"float32",y.createScalarValue(d,"float32"));$.push(P),b.push(P)}return $};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))x=dC({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else if(g.strideWidth<=2&&h==="channelsLast"&&M().getBool("WEBGL_EXP_CONV")){let $=f?Il(f,!0):null,F=new _d(g,w,$,C,N),P=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],V=_();x=e.runWebGLProgram(F,V,"float32",P)}else if(M().getBool("WEBGL_CONV_IM2COL"))x=hC({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else{let $=f?Il(f,!1):null,F=new Ed(g,w,$,C,N),P=_();x=e.runWebGLProgram(F,P,"float32")}let A=it({inputs:{x},backend:e,attrs:{shape:g.outShape}});return b.push(x),b.forEach($=>e.disposeIntermediateTensorInfo($)),A}var Yz={kernelName:Si,backendName:"webgl",kernelFunc:rnt};function nnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let g=S.computeConv2DInfo(o.shape,s.shape,u,h,l,p,!0),x=M().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?Il(m,x):null,w=[o,s],C=i!=null,N=a!=null,_=m==="leakyrelu";if(C&&w.push(i),N&&w.push(a),_){let P=e.makeTensorInfo([],"float32",y.createScalarValue(f,"float32"));w.push(P),d.push(P)}let A;x?A=new $d(g,C,b,N,_):A=new Ad(g,C,b,N,_);let $=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=e.runWebGLProgram(A,w,"float32",$);return d.forEach(P=>e.disposeIntermediateTensorInfo(P)),F}var Zz={kernelName:Ni,backendName:"webgl",kernelFunc:nnt};var RC=class{constructor(t,e,n,o){this.sliceDim=t,this.strides=e,this.paramsShape=o,this.variableNames=["x","indices"],this.outputShape=n;let s=Vt(n.length),i=` int index;`;for(let a=0;a= 0) && (index < ${t[2]}) ? 1.0 : 0.0; setOutput(inBounds * getA(${o})); } - `}};function snt(r,t){let e=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let o=0;o=0,()=>`GatherV2: the index value ${N} is not in [0, ${w-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),c=y.sizeFromShape(s.shape),p=[],m=it({inputs:{x:o},backend:e,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=it({inputs:{x:s},backend:e,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(m),p.push(f);let d=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(e.shouldExecuteOnCPU([o,s])||o.dtype==="string"){let b=e.bufferSync(f),w=e.bufferSync(m),C=vL(w,b,d);return p.forEach(N=>e.disposeIntermediateTensorInfo(N)),e.makeTensorInfo(l.outputShape,C.dtype,C.values)}let h=new OC(m.shape,d),g=e.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=it({inputs:{x:g},backend:e,attrs:{shape:l.outputShape}});return p.forEach(b=>e.disposeIntermediateTensorInfo(b)),x}var e3={kernelName:fi,backendName:"webgl",kernelFunc:Sk};var int="return float(a > b);",ant=` + `}};function snt(r,t){let e=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let o=0;o=0,()=>`GatherV2: the index value ${N} is not in [0, ${w-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),c=y.sizeFromShape(s.shape),p=[],m=it({inputs:{x:o},backend:e,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=it({inputs:{x:s},backend:e,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(m),p.push(f);let d=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(e.shouldExecuteOnCPU([o,s])||o.dtype==="string"){let b=e.bufferSync(f),w=e.bufferSync(m),C=CL(w,b,d);return p.forEach(N=>e.disposeIntermediateTensorInfo(N)),e.makeTensorInfo(l.outputShape,C.dtype,C.values)}let h=new FC(m.shape,d),g=e.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=it({inputs:{x:g},backend:e,attrs:{shape:l.outputShape}});return p.forEach(b=>e.disposeIntermediateTensorInfo(b)),x}var Qz={kernelName:mi,backendName:"webgl",kernelFunc:Nk};var int="return float(a > b);",ant=` return vec4(greaterThan(a, b)); -`,lnt=le({opSnippet:int,packedOpSnippet:ant,cpuKernelImpl:SL,dtype:"bool"}),r3={kernelName:Ta,backendName:"webgl",kernelFunc:lnt};var unt="return float(a >= b);",cnt=` +`,lnt=ae({opSnippet:int,packedOpSnippet:ant,cpuKernelImpl:IL,dtype:"bool"}),t3={kernelName:Na,backendName:"webgl",kernelFunc:lnt};var unt="return float(a >= b);",cnt=` return vec4(greaterThanEqual(a, b)); -`,pnt=le({opSnippet:unt,packedOpSnippet:cnt,dtype:"bool",cpuKernelImpl:NL}),n3={kernelName:is,backendName:"webgl",kernelFunc:pnt};function mnt(r){let{inputs:t,backend:e}=r,{input:n}=t;return _C(n,!0,e)}var o3={kernelName:vp,backendName:"webgl",kernelFunc:mnt};var fnt="return float(!isnan(x) && !isinf(x));",dnt=Ct({opSnippet:fnt,dtype:"bool"}),s3={kernelName:ka,backendName:"webgl",kernelFunc:dnt};var hnt="return float(isinf(x));",gnt=Ct({opSnippet:hnt,dtype:"bool"}),i3={kernelName:Ea,backendName:"webgl",kernelFunc:gnt};var xnt="return float(isnan(x));",ynt=Ct({opSnippet:xnt,dtype:"bool"}),a3={kernelName:as,backendName:"webgl",kernelFunc:ynt};var bnt="return float(a < b);",wnt=` +`,pnt=ae({opSnippet:unt,packedOpSnippet:cnt,dtype:"bool",cpuKernelImpl:vL}),e3={kernelName:os,backendName:"webgl",kernelFunc:pnt};function mnt(r){let{inputs:t,backend:e}=r,{input:n}=t;return EC(n,!0,e)}var r3={kernelName:vp,backendName:"webgl",kernelFunc:mnt};var fnt="return float(!isnan(x) && !isinf(x));",dnt=wt({opSnippet:fnt,dtype:"bool"}),n3={kernelName:Ta,backendName:"webgl",kernelFunc:dnt};var hnt="return float(isinf(x));",gnt=wt({opSnippet:hnt,dtype:"bool"}),o3={kernelName:ka,backendName:"webgl",kernelFunc:gnt};var xnt="return float(isnan(x));",ynt=wt({opSnippet:xnt,dtype:"bool"}),s3={kernelName:ss,backendName:"webgl",kernelFunc:ynt};var bnt="return float(a < b);",wnt=` return vec4(lessThan(a, b)); -`,Cnt=le({opSnippet:bnt,packedOpSnippet:wnt,cpuKernelImpl:TL,dtype:"bool"}),l3={kernelName:_a,backendName:"webgl",kernelFunc:Cnt};var Int="return float(a <= b);",vnt=` +`,Cnt=ae({opSnippet:bnt,packedOpSnippet:wnt,cpuKernelImpl:SL,dtype:"bool"}),i3={kernelName:Ea,backendName:"webgl",kernelFunc:Cnt};var Int="return float(a <= b);",vnt=` return vec4(lessThanEqual(a, b)); -`,Snt=le({opSnippet:Int,packedOpSnippet:vnt,cpuKernelImpl:kL,dtype:"bool"}),u3={kernelName:Aa,backendName:"webgl",kernelFunc:Snt};function Nnt(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=EL(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var c3={kernelName:Np,backendName:"webgl",kernelFunc:Nnt};var Tnt=Lo+` +`,Snt=ae({opSnippet:Int,packedOpSnippet:vnt,cpuKernelImpl:NL,dtype:"bool"}),a3={kernelName:_a,backendName:"webgl",kernelFunc:Snt};function Nnt(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=TL(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var l3={kernelName:Np,backendName:"webgl",kernelFunc:Nnt};var Tnt=Po+` return x < 0.0 ? 0./0. : log(x); `,knt=` vec4 result = log(x); @@ -3534,18 +3534,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b); result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; -`,Ent=Ct({opSnippet:Tnt,packedOpSnippet:knt,cpuKernelImpl:_L}),p3={kernelName:us,backendName:"webgl",kernelFunc:Ent};var _nt=Lo+` +`,Ent=wt({opSnippet:Tnt,packedOpSnippet:knt,cpuKernelImpl:kL}),u3={kernelName:as,backendName:"webgl",kernelFunc:Ent};var _nt=Po+` return log(1.0 + x); -`,Ant=Ct({opSnippet:_nt}),m3={kernelName:$a,backendName:"webgl",kernelFunc:Ant};var $nt="return float(a >= 1.0 && b >= 1.0);",Dnt=` +`,Ant=wt({opSnippet:_nt}),c3={kernelName:Aa,backendName:"webgl",kernelFunc:Ant};var $nt="return float(a >= 1.0 && b >= 1.0);",Dnt=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); -`,Rnt=le({opSnippet:$nt,packedOpSnippet:Dnt,dtype:"bool"}),f3={kernelName:Da,backendName:"webgl",kernelFunc:Rnt};var Fnt="return float(!(x >= 1.0));",Ont=Ct({opSnippet:Fnt}),d3={kernelName:Ra,backendName:"webgl",kernelFunc:Ont};var Pnt="return float(a >= 1.0 || b >= 1.0);",Lnt=` +`,Rnt=ae({opSnippet:$nt,packedOpSnippet:Dnt,dtype:"bool"}),p3={kernelName:$a,backendName:"webgl",kernelFunc:Rnt};var Fnt="return float(!(x >= 1.0));",Ont=wt({opSnippet:Fnt}),m3={kernelName:Da,backendName:"webgl",kernelFunc:Ont};var Pnt="return float(a >= 1.0 || b >= 1.0);",Lnt=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); -`,Mnt=le({opSnippet:Pnt,packedOpSnippet:Lnt,dtype:"bool"}),h3={kernelName:Fa,backendName:"webgl",kernelFunc:Mnt};var PC=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=` +`,Mnt=ae({opSnippet:Pnt,packedOpSnippet:Lnt,dtype:"bool"}),f3={kernelName:Ra,backendName:"webgl",kernelFunc:Mnt};var OC=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3564,7 +3564,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN float val = x * ${u}; setOutput(val); } - `}};var LC=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=` + `}};var PC=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; @@ -3626,7 +3626,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN vec4 result = xAtOutputCoords * ${u}; setOutput(result); } - `}};var znt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=M().getBool("WEBGL_PACK_NORMALIZATION")?new LC(o.shape,s,i,a,u):new PC(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},g3={kernelName:Pl,backendName:"webgl",kernelFunc:znt};var MC=class{constructor(t,e,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,this.userCode=` + `}};var znt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=M().getBool("WEBGL_PACK_NORMALIZATION")?new PC(o.shape,s,i,a,u):new OC(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},d3={kernelName:Ol,backendName:"webgl",kernelFunc:znt};var LC=class{constructor(t,e,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -3664,7 +3664,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN else if (k >= depthBegin && k < depthEnd){ float dyi = -2.0 * float(${o}) * float(${s}) - * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d) + * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { dyi += pow(norm, -1.0 * ${s}); @@ -3681,16 +3681,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } setOutput(result); } - `}};var Bnt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new MC(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},x3={kernelName:Tp,backendName:"webgl",kernelFunc:Bnt};function y3(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=it({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=qn(a,r.dtype,"max",n),l=it({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function Nk(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,C=new Array(a);for(let A=0;A{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new LC(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},h3={kernelName:Tp,backendName:"webgl",kernelFunc:Bnt};function g3(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=it({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Hn(a,r.dtype,"max",n),l=it({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function Tk(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,C=new Array(a);for(let A=0;A`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new oi(c,"max",!1);return e.runWebGLProgram(p,[o],o.dtype)}var C3={kernelName:ms,backendName:"webgl",kernelFunc:Unt};function Hnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new Ru(p,"max",!1);return e.runWebGLProgram(m,[o],o.dtype)}var I3={kernelName:Ll,backendName:"webgl",kernelFunc:Hnt};var zC=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=` +`,Wnt=ae({opSnippet:Vnt,packedOpSnippet:Gnt,cpuKernelImpl:_L}),y3={kernelName:us,backendName:"webgl",kernelFunc:Wnt};function Unt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;ti(o,"maxPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new ri(c,"max",!1);return e.runWebGLProgram(p,[o],o.dtype)}var b3={kernelName:cs,backendName:"webgl",kernelFunc:Unt};function Hnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new Tu(p,"max",!1);return e.runWebGLProgram(m,[o],o.dtype)}var w3={kernelName:Pl,backendName:"webgl",kernelFunc:Hnt};var MC=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=` const ivec2 pads = ivec2(${a}, ${u}); void main() { @@ -3736,7 +3736,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } setOutput(dotProd); } - `}},BC=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=` + `}},zC=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=` const ivec3 pads = ivec3(${p}, ${m}, ${f}); void main() { @@ -3800,16 +3800,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN } setOutput(dotProd); } - `}};function qnt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new Ru(m,"max",!0),d=e.runWebGLProgram(f,[i],i.dtype),h=new BC(m),g=e.runWebGLProgram(h,[o,d],i.dtype);return e.disposeIntermediateTensorInfo(d),g}var v3={kernelName:Ep,backendName:"webgl",kernelFunc:qnt};function Knt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;ri([s,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=!0,d=new oi(m,"max",f),h=e.runWebGLProgram(d,[a],a.dtype),g=new zC(m),x=e.runWebGLProgram(g,[o,h],a.dtype);return e.disposeIntermediateTensorInfo(h),x}var S3={kernelName:kp,backendName:"webgl",kernelFunc:Knt};function N3(r,t,e,n){let o=new oi(e,"max",!1),s=n.runWebGLProgram(o,[r],"float32");o=new oi(e,"max",!0,!0,t);let i=n.runWebGLProgram(o,[r],"float32");return[s,i]}var T3={kernelName:_p,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${l}'`);let c=S.computePool2DInfo(n.shape,o,s,l,i),[p,m]=N3(n,a,c,u);return[p,m]}};function k3(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=it({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=qn(a,"float32","mean",n),l=it({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}var E3={kernelName:fs,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{keepDims:o,axis:s}=t,i=e,a=n.shape.length,u=y.parseAxisParam(s,n.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=i.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let C=i.texData.get(d.dataId).values,N=new Array(a);for(let $=0;${let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${l}'`);let c=S.computePool2DInfo(n.shape,o,s,l,i),[p,m]=v3(n,a,c,u);return[p,m]}};function N3(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=it({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Hn(a,"float32","mean",n),l=it({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}var T3={kernelName:ps,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{keepDims:o,axis:s}=t,i=e,a=n.shape.length,u=y.parseAxisParam(s,n.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=i.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let C=i.texData.get(d.dataId).values,N=new Array(a);for(let $=0;$c[0]+t[p]+c[1]);let o=t.length,s=Bt(o),i=e.map(c=>c[0]).join(","),a=e.map((c,p)=>c[0]+t[p]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o),l=n==="reflect"?0:1;if(o===1){this.userCode=` +`,Znt=ae({opSnippet:Xnt,packedOpSnippet:Ynt,cpuKernelImpl:AL}),E3={kernelName:fs,backendName:"webgl",kernelFunc:Znt};var BC=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=e.map((c,p)=>c[0]+t[p]+c[1]);let o=t.length,s=Vt(o),i=e.map(c=>c[0]).join(","),a=e.map((c,p)=>c[0]+t[p]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o),l=n==="reflect"?0:1;if(o===1){this.userCode=` int start = ${i}; int end = ${a}; @@ -3838,7 +3838,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN ${s} coords = outC - start; setOutput(getX(${u})); } - `}};var GC=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((d,h)=>d[0]+t[h]+d[1]);let o=t.length,s=Bt(o),i=e.map(d=>d[0]).join(","),a=e.map((d,h)=>d[0]+t[h]).join(","),u=er("rc",o),l=er("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=n==="reflect"?0:1,f="";if(o===1){let d=` + `}};var VC=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((d,h)=>d[0]+t[h]+d[1]);let o=t.length,s=Vt(o),i=e.map(d=>d[0]).join(","),a=e.map((d,h)=>d[0]+t[h]).join(","),u=er("rc",o),l=er("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=n==="reflect"?0:1,f="";if(o===1){let d=` ${s} source = rc; if (source < start) { source = start * 2 - source - ${m}; @@ -3894,13 +3894,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Zet=Ct({opSnippet:Yet}),jM={kernelN ${f} setOutput(result); } - `}};var Jnt=({inputs:r,backend:t,attrs:e})=>{let{x:n}=r,{paddings:o,mode:s}=e,i=M().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new GC(n.shape,o,s):new VC(n.shape,o,s);return t.runWebGLProgram(i,[n],n.dtype)},$3={kernelName:gs,backendName:"webgl",kernelFunc:Jnt};var Qnt=`if (b == 0.0) return NAN; + `}};var Jnt=({inputs:r,backend:t,attrs:e})=>{let{x:n}=r,{paddings:o,mode:s}=e,i=M().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new VC(n.shape,o,s):new BC(n.shape,o,s);return t.runWebGLProgram(i,[n],n.dtype)},_3={kernelName:ds,backendName:"webgl",kernelFunc:Jnt};var Qnt=`if (b == 0.0) return NAN; return mod(a, b);`,tot=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); - `+ea+` + `+ta+` return result; -`,eot=le({opSnippet:Qnt,packedOpSnippet:tot}),D3={kernelName:Oa,backendName:"webgl",kernelFunc:eot};var WC=class{constructor(t,e,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[t,n],this.userCode=` +`,eot=ae({opSnippet:Qnt,packedOpSnippet:tot}),A3={kernelName:Fa,backendName:"webgl",kernelFunc:eot};var GC=class{constructor(t,e,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[t,n],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; @@ -3942,7 +3942,7 @@ return a / b;`,not=` } return result; -`,Tk=le({opSnippet:rot,packedOpSnippet:not,checkOutOfBounds:!0}),R3={kernelName:ts,backendName:"webgl",kernelFunc:Tk};var F3="return a - b;",kk=le({opSnippet:F3,packedOpSnippet:F3,supportsComplex:!0,cpuKernelImpl:JL}),O3={kernelName:Ls,backendName:"webgl",kernelFunc:kk};function Ek(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{dim:s}=n,i=y.parseAxisParam([s],o.shape),a=Nk({inputs:{x:o},backend:e,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(a.shape,i),l=it({inputs:{x:a},backend:e,attrs:{shape:u}}),c=kk({inputs:{a:o,b:l},backend:e}),p=Ik({inputs:{x:c},backend:e}),m=Uc({inputs:{x:p},backend:e,attrs:{axis:i,keepDims:!1}}),f=it({inputs:{x:m},backend:e,attrs:{shape:u}}),d=Tk({inputs:{a:p,b:f},backend:e});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}var P3={kernelName:Os,backendName:"webgl",kernelFunc:Ek};function oot(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n,u=a?o:Ek({inputs:{logits:o},backend:e,attrs:{dim:o.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new WC(l,c,s),m=[[i]],f=e.runWebGLProgram(p,[u],"int32",m);return a||e.disposeIntermediateTensorInfo(u),f}var L3={kernelName:Ap,backendName:"webgl",kernelFunc:oot};var sot=hr+` +`,kk=ae({opSnippet:rot,packedOpSnippet:not,checkOutOfBounds:!0}),$3={kernelName:Jo,backendName:"webgl",kernelFunc:kk};var D3="return a - b;",Ek=ae({opSnippet:D3,packedOpSnippet:D3,supportsComplex:!0,cpuKernelImpl:YL}),R3={kernelName:Os,backendName:"webgl",kernelFunc:Ek};function _k(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{dim:s}=n,i=y.parseAxisParam([s],o.shape),a=Tk({inputs:{x:o},backend:e,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(a.shape,i),l=it({inputs:{x:a},backend:e,attrs:{shape:u}}),c=Ek({inputs:{a:o,b:l},backend:e}),p=vk({inputs:{x:c},backend:e}),m=Uc({inputs:{x:p},backend:e,attrs:{axis:i,keepDims:!1}}),f=it({inputs:{x:m},backend:e,attrs:{shape:u}}),d=kk({inputs:{a:p,b:f},backend:e});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}var F3={kernelName:Rs,backendName:"webgl",kernelFunc:_k};function oot(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n,u=a?o:_k({inputs:{logits:o},backend:e,attrs:{dim:o.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new GC(l,c,s),m=[[i]],f=e.runWebGLProgram(p,[u],"int32",m);return a||e.disposeIntermediateTensorInfo(u),f}var O3={kernelName:Ap,backendName:"webgl",kernelFunc:oot};var sot=xr+` return -x; `,iot=` vec4 result = -x; @@ -3954,14 +3954,14 @@ return a / b;`,not=` result.a = isNaN.a ? x.a : result.a; return result; -`;function aot(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])){let s=e.texData.get(n.dataId),[i,a]=FL(s.values,n.shape,n.dtype);return e.makeTensorInfo(a,n.dtype,i)}let o;return M().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new An(n.shape,iot):o=new Mr(n.shape,sot),e.runWebGLProgram(o,[n],n.dtype)}var M3={kernelName:di,backendName:"webgl",kernelFunc:aot};var lot=qr.nonMaxSuppressionV3Impl;function uot(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),{selectedIndices:p}=lot(l,c,i,a,u);return e.makeTensorInfo([p.length],"int32",new Int32Array(p))}var z3={kernelName:La,backendName:"webgl",kernelFunc:uot};var cot=qr.nonMaxSuppressionV4Impl;function pot(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,padToMaxOutputSize:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=cot(c,p,i,a,u,l);return[e.makeTensorInfo([m.length],"int32",new Int32Array(m)),e.makeTensorInfo([],"int32",new Int32Array([f]))]}var B3={kernelName:Ma,backendName:"webgl",kernelFunc:pot};var mot=qr.nonMaxSuppressionV5Impl;function fot(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,softNmsSigma:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),m=i,f=a,d=u,h=l,{selectedIndices:g,selectedScores:x}=mot(c,p,m,f,d,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var V3={kernelName:za,backendName:"webgl",kernelFunc:fot};var UC=class{constructor(t,e,n,o){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=` +`;function aot(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])){let s=e.texData.get(n.dataId),[i,a]=DL(s.values,n.shape,n.dtype);return e.makeTensorInfo(a,n.dtype,i)}let o;return M().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new _n(n.shape,iot):o=new Mr(n.shape,sot),e.runWebGLProgram(o,[n],n.dtype)}var P3={kernelName:fi,backendName:"webgl",kernelFunc:aot};var lot=qr.nonMaxSuppressionV3Impl;function uot(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),{selectedIndices:p}=lot(l,c,i,a,u);return e.makeTensorInfo([p.length],"int32",new Int32Array(p))}var L3={kernelName:Pa,backendName:"webgl",kernelFunc:uot};var cot=qr.nonMaxSuppressionV4Impl;function pot(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,padToMaxOutputSize:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=cot(c,p,i,a,u,l);return[e.makeTensorInfo([m.length],"int32",new Int32Array(m)),e.makeTensorInfo([],"int32",new Int32Array([f]))]}var M3={kernelName:La,backendName:"webgl",kernelFunc:pot};var mot=qr.nonMaxSuppressionV5Impl;function fot(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,softNmsSigma:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),m=i,f=a,d=u,h=l,{selectedIndices:g,selectedScores:x}=mot(c,p,m,f,d,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var z3={kernelName:Ma,backendName:"webgl",kernelFunc:fot};var WC=class{constructor(t,e,n,o){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${o}), float(${n}), float(index == coords.y))); } - `}};var dot=r=>{let{inputs:t,backend:e,attrs:n}=r,{indices:o}=t,{dtype:s,depth:i,onValue:a,offValue:u}=n,l=y.sizeFromShape(o.shape),c=new UC(l,i,a,u),p=it({inputs:{x:o},backend:e,attrs:{shape:[l]}}),m=e.runWebGLProgram(c,[p],s);e.disposeIntermediateTensorInfo(p);let f=[...o.shape,i],d=it({inputs:{x:m},backend:e,attrs:{shape:f}});return e.disposeIntermediateTensorInfo(m),d},G3={kernelName:ys,backendName:"webgl",kernelFunc:dot};function ig(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="complex64"){let o=vl({inputs:{input:n},backend:e}),s=ig({inputs:{x:o},backend:e}),i=qc({inputs:{input:n},backend:e}),a=ig({inputs:{x:i},backend:e}),u=$n({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Sl({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:e})}var W3={kernelName:vi,backendName:"webgl",kernelFunc:ig};function U3(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=vl({inputs:{input:n},backend:e}),s=U3({inputs:{x:o},backend:e}),i=qc({inputs:{input:n},backend:e}),a=ig({inputs:{x:i},backend:e}),u=$n({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Sl({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:e})}var H3={kernelName:hi,backendName:"webgl",kernelFunc:U3};function hot(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n;if(t.length===1)return EC({inputs:{input:t[0]},backend:e,attrs:{dim:o}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=EC({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=Ck({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var q3={kernelName:gi,backendName:"webgl",kernelFunc:hot};var HC=class{constructor(t,e,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((l,c)=>l[0]+t[c]+l[1]);let o=t.length,s=Bt(o),i=e.map(l=>l[0]).join(","),a=e.map((l,c)=>l[0]+t[c]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=` + `}};var dot=r=>{let{inputs:t,backend:e,attrs:n}=r,{indices:o}=t,{dtype:s,depth:i,onValue:a,offValue:u}=n,l=y.sizeFromShape(o.shape),c=new WC(l,i,a,u),p=it({inputs:{x:o},backend:e,attrs:{shape:[l]}}),m=e.runWebGLProgram(c,[p],s);e.disposeIntermediateTensorInfo(p);let f=[...o.shape,i],d=it({inputs:{x:m},backend:e,attrs:{shape:f}});return e.disposeIntermediateTensorInfo(m),d},B3={kernelName:gs,backendName:"webgl",kernelFunc:dot};function ag(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="complex64"){let o=vl({inputs:{input:n},backend:e}),s=ag({inputs:{x:o},backend:e}),i=qc({inputs:{input:n},backend:e}),a=ag({inputs:{x:i},backend:e}),u=An({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Sl({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:e})}var V3={kernelName:Ii,backendName:"webgl",kernelFunc:ag};function G3(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=vl({inputs:{input:n},backend:e}),s=G3({inputs:{x:o},backend:e}),i=qc({inputs:{input:n},backend:e}),a=ag({inputs:{x:i},backend:e}),u=An({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Sl({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:e})}var W3={kernelName:di,backendName:"webgl",kernelFunc:G3};function hot(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n;if(t.length===1)return kC({inputs:{input:t[0]},backend:e,attrs:{dim:o}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=kC({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=Ik({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var U3={kernelName:hi,backendName:"webgl",kernelFunc:hot};var UC=class{constructor(t,e,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((l,c)=>l[0]+t[c]+l[1]);let o=t.length,s=Vt(o),i=e.map(l=>l[0]).join(","),a=e.map((l,c)=>l[0]+t[c]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=` int start = ${i}; int end = ${a}; @@ -3986,7 +3986,7 @@ return a / b;`,not=` setOutput(getX(${u})); } } - `}};var qC=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=Bt(o),i=e.map(h=>h[0]).join(","),a=e.map((h,g)=>h[0]+t[g]).join(","),u=er("rc",o),l=er("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1; + `}};var HC=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=Vt(o),i=e.map(h=>h[0]).join(","),a=e.map((h,g)=>h[0]+t[g]).join(","),u=er("rc",o),l=er("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1; if(${c}) { `,o===1?"":`} rc = outputLoc; @@ -4010,7 +4010,7 @@ return a / b;`,not=` ${d} setOutput(result); } - `}};var _k=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(y.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Sl({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=M().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new qC(o.shape,s,i):new HC(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},K3={kernelName:bs,backendName:"webgl",kernelFunc:_k};var got=` + `}};var Ak=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(y.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Sl({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=M().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new HC(o.shape,s,i):new UC(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},H3={kernelName:xs,backendName:"webgl",kernelFunc:Ak};var got=` if(a < 0.0 && floor(b) < b){ return NAN; } @@ -4035,9 +4035,9 @@ return a / b;`,not=` bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); - `+ea+` + `+ta+` return result; -`,yot=le({opSnippet:got,packedOpSnippet:xot}),j3={kernelName:ws,backendName:"webgl",kernelFunc:yot};function bot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=[],l=y.parseAxisParam(s,o.shape),c=l,p=S.getAxesPermutation(c,a),m=o;p!=null&&(m=Oe({inputs:{x:o},backend:e,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,a),u.push(m)),S.assertAxesAreInnerMostDims("prod",c,a);let f;if(e.shouldExecuteOnCPU([m])){let d=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=PL(m.shape,m.dtype,d,c);f=e.makeTensorInfo(g,x,h)}else{let[d,h]=S.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=it({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=Hu(o.dtype),w=qn(x,b,"prod",e);f=it({inputs:{x:w},backend:e,attrs:{shape:d}}),u.push(x),u.push(w)}if(i){u.push(f);let d=S.expandShapeToKeepDim(f.shape,l);f=it({inputs:{x:f},backend:e,attrs:{shape:d}})}return u.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var X3={kernelName:Is,backendName:"webgl",kernelFunc:bot};function wot(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=LL(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],"int32",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var Y3={kernelName:$p,backendName:"webgl",kernelFunc:wot};function Cot(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=ML(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],"int32",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var Z3={kernelName:Dp,backendName:"webgl",kernelFunc:Cot};function Iot(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),p=e.readSync(i.dataId),m=a.map(g=>e.readSync(g.dataId)),f=a.map(g=>g.shape),[d,h]=zL(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var J3={kernelName:Rp,backendName:"webgl",kernelFunc:Iot};var Ak=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=BL(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},Q3={kernelName:Ml,backendName:"webgl",kernelFunc:Ak};var vot="return 1.0 / x;",Sot=Ct({opSnippet:vot}),tB={kernelName:vs,backendName:"webgl",kernelFunc:Sot};var Not=hr+` +`,yot=ae({opSnippet:got,packedOpSnippet:xot}),q3={kernelName:ys,backendName:"webgl",kernelFunc:yot};function bot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=[],l=y.parseAxisParam(s,o.shape),c=l,p=S.getAxesPermutation(c,a),m=o;p!=null&&(m=Pe({inputs:{x:o},backend:e,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,a),u.push(m)),S.assertAxesAreInnerMostDims("prod",c,a);let f;if(e.shouldExecuteOnCPU([m])){let d=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=FL(m.shape,m.dtype,d,c);f=e.makeTensorInfo(g,x,h)}else{let[d,h]=S.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=it({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=Bu(o.dtype),w=Hn(x,b,"prod",e);f=it({inputs:{x:w},backend:e,attrs:{shape:d}}),u.push(x),u.push(w)}if(i){u.push(f);let d=S.expandShapeToKeepDim(f.shape,l);f=it({inputs:{x:f},backend:e,attrs:{shape:d}})}return u.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var K3={kernelName:ws,backendName:"webgl",kernelFunc:bot};function wot(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=OL(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],"int32",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var j3={kernelName:$p,backendName:"webgl",kernelFunc:wot};function Cot(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=PL(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],"int32",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var X3={kernelName:Dp,backendName:"webgl",kernelFunc:Cot};function Iot(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),p=e.readSync(i.dataId),m=a.map(g=>e.readSync(g.dataId)),f=a.map(g=>g.shape),[d,h]=LL(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var Y3={kernelName:Rp,backendName:"webgl",kernelFunc:Iot};var $k=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=ML(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},Z3={kernelName:Ll,backendName:"webgl",kernelFunc:$k};var vot="return 1.0 / x;",Sot=wt({opSnippet:vot}),J3={kernelName:Cs,backendName:"webgl",kernelFunc:Sot};var Not=xr+` return (x < 0.0) ? 0.0 : x; `,Tot=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); @@ -4049,7 +4049,7 @@ return a / b;`,not=` result.a = isNaN.a ? x.a : result.a; return result; -`,kot=Ct({opSnippet:Not,packedOpSnippet:Tot}),eB={kernelName:Ss,backendName:"webgl",kernelFunc:kot};var Eot=hr+` +`,kot=wt({opSnippet:Not,packedOpSnippet:Tot}),Q3={kernelName:Is,backendName:"webgl",kernelFunc:kot};var Eot=xr+` return (x < 0.0) ? 0.0 : min(6.0, x); `,_ot=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); @@ -4061,7 +4061,7 @@ return a / b;`,not=` result.a = isNaN.a ? x.a : result.a; return result; -`,Aot=Ct({opSnippet:Eot,packedOpSnippet:_ot}),rB={kernelName:ks,backendName:"webgl",kernelFunc:Aot};var KC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` +`,Aot=wt({opSnippet:Eot,packedOpSnippet:_ot}),tB={kernelName:Ns,backendName:"webgl",kernelFunc:Aot};var qC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/p[0]}, ${c[1]/p[1]}); @@ -4094,7 +4094,7 @@ return a / b;`,not=` setOutput(newValue); } - `}};var jC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};var KC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/p[0]}, ${c[1]/p[1]}, @@ -4171,7 +4171,7 @@ return a / b;`,not=` setOutput(newValue); } - `}};function $ot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=M().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new jC(o.shape,u,l,s,i):new KC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],"float32")}var nB={kernelName:Ts,backendName:"webgl",kernelFunc:$ot};var XC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=` + `}};function $ot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=M().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new KC(o.shape,u,l,s,i):new qC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],"float32")}var eB={kernelName:Ss,backendName:"webgl",kernelFunc:$ot};var jC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4252,7 +4252,7 @@ return a / b;`,not=` setOutput(accumulator); } - `}};function Dot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new XC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var oB={kernelName:Pp,backendName:"webgl",kernelFunc:Dot};var YC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};function Dot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new jC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var rB={kernelName:Pp,backendName:"webgl",kernelFunc:Dot};var XC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/p[0]}, ${c[1]/p[1]}); @@ -4274,7 +4274,7 @@ return a / b;`,not=` setOutput(newValue); } - `}};var ZC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":f="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` + `}};var YC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":f="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/p[0]}, ${c[1]/p[1]}, @@ -4315,7 +4315,7 @@ return a / b;`,not=` setOutput(newValue); } - `}};function Rot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=M().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new ZC(o.shape,u,l,s,i):new YC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var sB={kernelName:Ns,backendName:"webgl",kernelFunc:Rot};var JC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=` + `}};function Rot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=M().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new YC(o.shape,u,l,s,i):new XC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var nB={kernelName:vs,backendName:"webgl",kernelFunc:Rot};var ZC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; @@ -4385,17 +4385,17 @@ return a / b;`,not=` setOutput(accumulator); } - `}};function Fot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new JC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var iB={kernelName:Op,backendName:"webgl",kernelFunc:Fot};var QC=class{constructor(t,e){this.variableNames=["x"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=` + `}};function Fot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new ZC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var oB={kernelName:Op,backendName:"webgl",kernelFunc:Fot};var JC=class{constructor(t,e){this.variableNames=["x"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${t[0]} - coord - 1)); } - `;return}let o=a=>e.indexOf(a)!==-1&&t[a]!==1?`${t[a]} - coords[${a}] - 1`:`coords[${a}]`,s=t.map((a,u)=>o(u)).join(","),i=Bt(n);this.userCode=` + `;return}let o=a=>e.indexOf(a)!==-1&&t[a]!==1?`${t[a]} - coords[${a}] - 1`:`coords[${a}]`,s=t.map((a,u)=>o(u)).join(","),i=Vt(n);this.userCode=` void main() { ${i} coords = getOutputCoords(); setOutput(getX(${s})); } - `}};var tI=class{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=er("rc",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=Bt(n);n===1?this.userCode=` + `}};var QC=class{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=er("rc",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=Vt(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); @@ -4423,7 +4423,7 @@ return a / b;`,not=` } setOutput(result); } - `;function u(d){return m(d)}function l(d){return d[n-1]="("+d[n-1]+" + 1)",m(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",m(d)}function p(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} 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u=Bt(s.length),l=Bt(i.length),c="";n===1?c="i":n===2&&(c="i, j");let p=`getIndices(${c})`,m="";o===1?m="i":o===2&&(m="i, coords[1]");let f=`getUpdates(${m})`,d=e>1?"strides[j]":"strides";this.userCode=` +`,Lot=wt({opSnippet:Pot}),aB={kernelName:ks,backendName:"webgl",kernelFunc:Lot};var Mot="return inversesqrt(x);",zot=wt({opSnippet:Mot,cpuKernelImpl:zL}),lB={kernelName:Es,backendName:"webgl",kernelFunc:zot};var Rd=class{constructor(t,e,n,o,s,i,a=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=i;let u=Vt(s.length),l=Vt(i.length),c="";n===1?c="i":n===2&&(c="i, j");let p=`getIndices(${c})`,m="";o===1?m="i":o===2&&(m="i, coords[1]");let f=`getUpdates(${m})`,d=e>1?"strides[j]":"strides";this.userCode=` ${u} strides = ${u}(${s}); void main() { @@ -4477,7 +4477,7 @@ return a / b;`,not=` } setOutput(mix(getDefaultValue(), sum, float(found))); } - `}};function 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$V;function pit(r){$V=r.wasm.cwrap(Zo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function mit(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=e,m=l==null?[1,1]:l,f=S.computeConv2DInfo(o.shape,s.shape,u,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,x=f.padInfo.right,b=f.padInfo.bottom,w=f.padInfo.left,C=f.dilationHeight,N=f.dilationWidth,_=f.strideHeight,A=f.strideWidth,$=f.inChannels,F=f.outChannels,P=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. 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Pk;(function(r){r[r.reflect=0]="reflect",r[r.symmetric=1]="symmetric"})(Pk||(Pk={}));var wG;function qit(r){wG=r.wasm.cwrap(ds,null,["number","array","number","number","array","array","number","number"])}function Kit(r){let{inputs:{x:t},backend:e,attrs:{paddings:n,mode:o}}=r,s=n.map((d,h)=>d[0]+t.shape[h]+d[1]),i=e.dataIdMap.get(t.dataId).id,a=e.makeOutput(s,t.dtype),u=e.dataIdMap.get(a.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=n.map(d=>d[0]),p=n.map(d=>d[1]),m=new Uint8Array(new Int32Array(c).buffer),f=new Uint8Array(new Int32Array(p).buffer);return wG(i,l,t.shape.length,Kt[t.dtype],m,f,Pk[o],u),a}var CG={kernelName:ds,backendName:"wasm",kernelFunc:Kit,setupFunc:qit};var jit=!0,IG=le(hs,jit);var vG=te(fi);function Fd(r,t){let e=new Int32Array(r.wasm.HEAPU8.buffer,t,4),n=e[0],o=e[1],s=e[2],i=e[3];return r.wasm._free(t),{pSelectedIndices:n,selectedSize:o,pSelectedScores:s,pValidOutputs:i}}var SG;function 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disableDeprecationWarnings,Nt as dispose,Npt as disposeVariables,pt as div,Dx as divNoNan,Rx as dot,p0 as dropout,KE as einsum,Mi as elu,vpt as enableDebugMode,Ipt as enableProdMode,m0 as enclosingPowerOfTwo,Mn as engine,M as env,Rr as equal,Fx as erf,Ox as euclideanNorm,or as exp,sr as expandDims,Px as expm1,Ju as eye,uu as fft,bo as fill,$pt as findBackend,Dpt as findBackendFactory,zi as floor,Zp as floorDiv,yM as forceHalfFloat,pu as fused,Bi as gather,x6 as gatherND,sx as gather_util,_pt as getBackend,mS as getGradient,Qd as getKernel,Bg as getKernelsForBackend,mlt as getThreadsCount,uk as gpgpu_util,SK as grad,NK as grads,Re as greater,un as greaterEqual,nl as ifft,Jl as imag,so as image,w6 as inTopKAsync,Z$ as initializers,z0 as input,$r as io,ym as irfft,Lx as isFinite,Mx as isInf,zx as isNaN,Ae as keep,qr as kernel_impls,AD as layers,ru as leakyRelu,sm as less,zn as lessEqual,d0 as linalg,YE as linspace,q7 as loadGraphModel,K7 as loadGraphModelSync,xD as loadLayersModel,Bx as 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function QW(){let[r,t]=await Promise.all([M().getAsync("WASM_HAS_SIMD_SUPPORT"),M().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((e,n)=>{let o={};o.locateFile=(a,u)=>{if(a.endsWith(".worker.js")){let l=JW.wasmWorkerContents.replace(/\n/g,"\\n"),c=new Blob([l],{type:"application/javascript"});return URL.createObjectURL(c)}return a.endsWith(".wasm")?ZW(r,t,lg!=null?lg:u):u+a},qk&&(o.instantiateWasm=ilt(ZW(r,t,lg!=null?lg:"")));let s=!1;o.onAbort=()=>{if(s||cg)return;cg=!0,n({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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History,vp as IFFT,po as Identity,Sp as Imag,be as InputSpec,Ta as IsFinite,ka as IsInf,ss as IsNan,zo as KernelBackend,Ol as LRN,Tp as LRNGrad,vh as LayerVariable,Vn as LayersModel,is as LeakyRelu,Ea as Less,_a as LessEqual,Np as LinSpace,as as Log,Aa as Log1p,f1 as LogSoftmax,$a as LogicalAnd,Da as LogicalNot,Ra as LogicalOr,m1 as LogicalXor,Nlt as LowerBound,Su as MathBackendWebGL,ls as Max,cs as MaxPool,Pl as MaxPool3D,Ep as MaxPool3DGrad,kp as MaxPoolGrad,_p as MaxPoolWithArgmax,us as Maximum,ps as Mean,ms as Min,fs as Minimum,ds as MirrorPad,Fa as Mod,sc as MomentumOptimizer,Ap as Multinomial,hs as Multiply,fi as Neg,Pa as NonMaxSuppressionV3,La as NonMaxSuppressionV4,Ma as NonMaxSuppressionV5,Oa as NotEqual,A0 as OP_SCOPE_SUFFIX,gs as OneHot,di as OnesLike,Hr as Optimizer,xh as OptimizerConstructors,hi as Pack,xs as PadV2,Tlt as Pool,ys as Pow,bs as Prelu,ws as Prod,ic as RMSPropOptimizer,kn as RNN,$p as RaggedGather,Dp as RaggedRange,Rp as RaggedTensorToTensor,Ll as Range,w0 as Rank,Fp as Real,Jo as RealDiv,Cs as Reciprocal,Ze as Reduction,Is as Relu,Ns as Relu6,gi as Reshape,Ss as ResizeBilinear,Pp as ResizeBilinearGrad,vs as ResizeNearestNeighbor,Op as ResizeNearestNeighborGrad,Ts as Reverse,ja as RotateWithOffset,ks as Round,Es as Rsqrt,rl as SGDOptimizer,za as ScatterNd,Lp as SearchSorted,xi as Select,Ba as Selu,Xi as Sequential,As as Sigmoid,Ga as Sign,_s as Sin,Va as Sinh,yi as Slice,Rs as Softmax,Wa as Softplus,bi as SpaceToBatchND,Ml as SparseFillEmptyRows,Ua as SparseReshape,zl as SparseSegmentMean,Bl as SparseSegmentSum,Mp as SparseToDense,wi as SplitV,$s as Sqrt,Vl as Square,Fs as SquaredDifference,mo as Step,Ha as StridedSlice,Gl as StringNGrams,Wl as StringSplit,Ul as StringToHashBucketFast,Os as Sub,Ds as Sum,en as SymbolicTensor,Ps as Tan,Ls as Tanh,Ft as Tensor,pe as TensorBuffer,Qn as Tile,qa as TopK,Ka as Transform,to as Transpose,zp as Unique,Ci as Unpack,Hl as UnsortedSegmentSum,klt as UpperBound,Xa as Variable,Ii as ZerosLike,vi as _FusedMatMul,Ee as abs,tx as acos,ex as acosh,Y as add,uE as addN,Jp as all,Gu as any,Ai as argMax,rx as argMin,nx as asin,ox as asinh,sx as atan,ix as atan2,ax as atanh,Zl as avgPool,lx as avgPool3d,lE as backend,S as backend_util,mE as basicLSTMCell,Ri as batchNorm,ux as batchNorm2d,cx as batchNorm3d,px as batchNorm4d,Jl as batchToSpaceND,mx as bincount,Oj as booleanMaskAsync,dE as broadcastArgs,Fi as broadcastTo,Wr as broadcast_util,gy as browser,bt as buffer,jZ as callbacks,J as cast,fx as ceil,vr as clipByValue,an as clone,In as complex,oe as concat,dx as concat1d,hx as concat2d,gx as concat3d,xx as concat4d,j$ as constraints,tm as conv1d,vn as conv2d,rm as conv2dTranspose,yx as conv3d,wx as conv3dTranspose,Flt as copyRegisteredKernels,Ql as cos,nm as cosh,fh as cosineWindow,Uu as cumprod,om as cumsum,cn as customGrad,$R as data,ah as denseBincount,B0 as deprecationWarn,Cx as depthToSpace,Oi as depthwiseConv2d,JZ as deregisterOp,Xl as device_util,hE as diag,Ix as dilation2d,Bct as disableDeprecationWarnings,Nt as dispose,Vct as disposeVariables,ut as div,vx as divNoNan,Sx as dot,nS as dropout,xE as einsum,Pi as elu,zct as enableDebugMode,Mct as enableProdMode,oS as enclosingPowerOfTwo,Ln as engine,M as env,Dr as equal,Nx as erf,Tx as euclideanNorm,sr as exp,ir as expandDims,kx as expm1,Hu as eye,lu as fft,yo as fill,Kct as findBackend,jct as findBackendFactory,Li as floor,Zp as floorDiv,gM as forceHalfFloat,cu as fused,Mi as gather,Hj as gatherND,xy as gather_util,Hct as getBackend,m0 as getGradient,Zd as getKernel,Bg as getKernelsForBackend,mlt as getThreadsCount,ck as gpgpu_util,Bq as grad,Vq as grads,Fe as greater,un as greaterEqual,el as ifft,tu as imag,oo as image,jj as inTopKAsync,X$ as initializers,BS as input,Pr as io,ym as irfft,Ex as isFinite,_x as isInf,Ax as isNaN,De as keep,qr as kernel_impls,ED as layers,eu as leakyRelu,sm as less,Mn as lessEqual,iS as linalg,wE as linspace,q7 as loadGraphModel,K7 as loadGraphModelSync,hD as loadLayersModel,$x as localResponseNormalization,Nr as log,ru as log1p,Fx as logSigmoid,im as logSoftmax,am as logSumExp,Fr as logicalAnd,nu as logicalNot,lm as logicalOr,Ox as logicalXor,j6 as losses,CE as lowerBound,Mt as matMul,uA as math,Sr as max,ou as maxPool,Lx as maxPool3d,IE as maxPoolWithArgmax,Sn as maximum,Ne as mean,oh as memory,vE as meshgrid,_D as metrics,Ja as min,zi as minimum,Mx as mirrorPad,zx as mod,Y8 as model,AD as models,qu as moments,Mj as movingAverage,D as mul,SE as multiRNNCell,NE as multinomial,qt as neg,yh as nextFrame,Qa as norm,Gs as notEqual,Bi as oneHot,fr as ones,wr as onesLike,T as op,TE as outerProduct,pn as pad,kE as pad1d,EE as pad2d,_E as pad3d,AE as pad4d,Bx as pool,ln as pow,iu as prelu,Qg as print,Vx as prod,Gct as profile,$E as raggedGather,DE as raggedRange,RE as raggedTensorToTensor,FE as rand,e_ as randomGamma,Xu as randomNormal,r_ as randomStandardNormal,Vi as randomUniform,Gi as range,Uct as ready,tl as real,Kx as reciprocal,Yp as registerBackend,J8 as registerCallbackConstructor,h1 as registerGradient,Fu as registerKernel,ZZ as registerOp,$D as regularizers,Or as relu,um as relu6,qct as removeBackend,R as reshape,dr as reverse,n_ as reverse1d,o_ as reverse2d,s_ as reverse3d,i_ as reverse4d,uu as rfft,cm as round,pm as rsqrt,pt as scalar,Bj as scatterND,mh as scatter_util,uh as searchSorted,mm as selu,fm as separableConv2d,Z8 as sequential,Q as serialization,j4 as setBackend,Xct as setPlatform,plt as setThreadsCount,ult as setWasmPath,clt as setWasmPaths,ST as setWebGLContext,a_ as setdiff1dAsync,Qr as sigmoid,jx as sign,K6 as signal,dm as sin,hm as sinh,Rt as slice,gm as slice1d,ph as slice2d,xm as slice3d,Yu as slice4d,ze as slice_util,au as softmax,Vs as softplus,su as spaceToBatchND,X6 as sparse,Wj as sparseToDense,q6 as spectral,hr as split,Se as sqrt,zt as square,bm as squaredDifference,zn as squeeze,qe as stack,wo as step,Xx as stridedSlice,Y6 as string,lt as sub,mt as sum,Bu as sumOutType,Yx as tan,Di as tanh,or as tensor,Ke as tensor1d,Ws as tensor2d,Zx as tensor3d,l_ as tensor4d,u_ as tensor5d,c_ as tensor6d,xo as tensor_util,t_ as test_util,B as tidy,Rr as tile,Wct as time,Jx as topk,lc as train,Pt as transpose,wm as truncatedNormal,Qx as unique,Rlt as unregisterGradient,Dlt as unregisterKernel,Cm as unsortedSegmentSum,gr as unstack,lr as upcastType,p_ as upperBound,y as util,Gq as valueAndGrad,Wq as valueAndGrads,ty as variable,Dx as variableGrads,blt as version,pR as version_converter,CA as version_core,Hm as version_layers,flt as version_wasm,hM as version_webgl,g1e as webgl,gd as webgl_util,_e as where,ry as whereAsync,Te as zeros,It as zerosLike}; diff --git a/dist/tfjs.version.js b/dist/tfjs.version.js index a2a6e363..d750f8d2 100644 --- a/dist/tfjs.version.js +++ b/dist/tfjs.version.js @@ -4,4 +4,4 @@ author: ' */ -var e="4.1.0";var s="4.1.0";var t="4.1.0";var n="4.1.0";var i="4.1.0";var w={tfjs:e,"tfjs-core":e,"tfjs-converter":s,"tfjs-backend-cpu":t,"tfjs-backend-webgl":n,"tfjs-backend-wasm":i};export{w as version}; +var e="4.2.0";var s="4.2.0";var t="4.2.0";var n="4.2.0";var i="4.2.0";var w={tfjs:e,"tfjs-core":e,"tfjs-converter":s,"tfjs-backend-cpu":t,"tfjs-backend-webgl":n,"tfjs-backend-wasm":i};export{w as version}; diff --git a/typedoc/classes/AgeGenderNet.html b/typedoc/classes/AgeGenderNet.html index 793537ce..cc6581da 100644 --- a/typedoc/classes/AgeGenderNet.html +++ b/typedoc/classes/AgeGenderNet.html @@ -1,4 +1,4 @@ -AgeGenderNet | @vladmandic/face-api - v1.7.7
+AgeGenderNet | @vladmandic/face-api - v1.7.8
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    Class AgeGenderNet

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    Class BoundingBox

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    Class Box<BoxType>

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    Class ComposableTask<T>

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    Class ComputeAllFaceDescriptorsTask<TSource>

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    Class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn>

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    Class ComputeSingleFaceDescriptorTask<TSource>

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    Class DetectAllFaceLandmarksTask<TSource>

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    Class DetectAllFacesTask

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    Class DetectFaceLandmarksTaskBase<TReturn, TParentReturn>

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    Class DetectFacesTaskBase<TReturn>

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    Class DetectSingleFaceLandmarksTask<TSource>

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    Class DetectSingleFaceTask

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    Class Dimensions

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    Class FaceDetection

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    Class FaceDetectionNet

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    Class FaceExpressionNet

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    Class FaceExpressions

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    Class FaceLandmark68Net

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    Class FaceLandmark68TinyNet

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    Class FaceLandmarkNet

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    Class FaceLandmarks

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    Class FaceLandmarks5

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    Class FaceLandmarks68

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    Class FaceMatch

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    Class FaceMatcher

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    Class FaceRecognitionNet

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    Class LabeledBox

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    Class LabeledFaceDescriptors

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    Class NetInput

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    Class NeuralNetwork<TNetParams>Abstract

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    Class ObjectDetection

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    Class Point

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    Class PredictedBox

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    Class Rect

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    Class SsdMobilenetv1

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    Class SsdMobilenetv1Options

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    Class TinyFaceDetector

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    Class TinyFaceDetectorOptions

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    Class TinyYolov2

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    Class TinyYolov2Options

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    Class DrawBox

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    Class DrawBoxOptions

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    Class DrawFaceLandmarks

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    Class DrawFaceLandmarksOptions

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    Class DrawTextField

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    Class DrawTextFieldOptions

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    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Enumeration Gender

    @@ -6,13 +6,13 @@
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Enumeration AnchorPosition

    @@ -6,13 +6,13 @@
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function allFaces

    @@ -45,7 +45,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function allFacesSsdMobilenetv1

    @@ -45,7 +45,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function allFacesTinyYolov2

    @@ -45,7 +45,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function awaitMediaLoaded

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function bufferToImage

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function computeFaceDescriptor

    @@ -50,7 +50,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function createCanvas

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function createCanvasFromMedia

    @@ -45,7 +45,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function createFaceDetectionNet

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function createFaceRecognitionNet

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function createSsdMobilenetv1

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function createTinyFaceDetector

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function createTinyYolov2

    @@ -45,7 +45,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function detectAllFaces

    @@ -45,7 +45,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function detectFaceLandmarks

    @@ -47,7 +47,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function detectFaceLandmarksTiny

    @@ -49,7 +49,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function detectLandmarks

    @@ -47,7 +47,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function detectSingleFace

    @@ -45,7 +45,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function drawContour

    @@ -48,7 +48,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function drawDetections

    @@ -46,7 +46,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function drawFaceExpressions

    @@ -50,7 +50,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function drawFaceLandmarks

    @@ -46,7 +46,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function euclideanDistance

    @@ -45,7 +45,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function extendWithAge

    @@ -50,7 +50,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function extendWithFaceDescriptor

    @@ -50,7 +50,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function extendWithFaceDetection

    @@ -50,7 +50,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function extendWithFaceExpressions

    @@ -50,7 +50,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function extendWithFaceLandmarks

    @@ -52,7 +52,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function extendWithGender

    @@ -52,7 +52,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function extractFaceTensors

    @@ -56,7 +56,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
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    • @vladmandic/face-api - v1.7.8

    Function extractFaces

    @@ -53,7 +53,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function fetchImage

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function fetchJson

    @@ -48,7 +48,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function fetchNetWeights

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function fetchOrThrow

    @@ -45,7 +45,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function fetchVideo

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function getContext2dOrThrow

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
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    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function getMediaDimensions

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function imageTensorToCanvas

    @@ -45,7 +45,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function imageToSquare

    @@ -47,7 +47,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function inverseSigmoid

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function iou

    @@ -47,7 +47,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function isMediaElement

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function isMediaLoaded

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function isWithAge

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function isWithFaceDetection

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function isWithFaceExpressions

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function isWithFaceLandmarks

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function isWithGender

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function loadAgeGenderModel

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function loadFaceDetectionModel

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function loadFaceExpressionModel

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function loadFaceLandmarkModel

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function loadFaceLandmarkTinyModel

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function loadFaceRecognitionModel

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function loadSsdMobilenetv1Model

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function loadTinyFaceDetectorModel

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function loadTinyYolov2Model

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function loadWeightMap

    @@ -45,7 +45,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function locateFaces

    @@ -53,7 +53,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function matchDimensions

    @@ -52,7 +52,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function minBbox

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function nonMaxSuppression

    @@ -49,7 +49,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function normalize

    @@ -45,7 +45,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function padToSquare

    @@ -54,7 +54,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function predictAgeAndGender

    @@ -47,7 +47,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function recognizeFaceExpressions

    @@ -47,7 +47,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function resizeResults

    @@ -50,7 +50,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function resolveInput

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function shuffleArray

    @@ -43,7 +43,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Function sigmoid

    @@ -43,7 +43,7 @@

    Theme

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    Function ssdMobilenetv1

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    Function tinyFaceDetector

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    Function tinyYolov2

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    Function toNetInput

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    Function computeReshapedDimensions

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    Function getCenterPoint

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    Function isDimensions

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    Function isEven

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    Function isFloat

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    Function isTensor

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    Function isTensor1D

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    Function isTensor2D

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    Function isTensor3D

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    Function isTensor4D

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    Function isValidNumber

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    Function isValidProbablitiy

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    Function range

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    Function round

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    Function validateConfig

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    -

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    +

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    Interface IBoundingBox

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    Interface IDimensions

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    Interface IFaceDetecion

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    Interface IFaceLandmarks

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    Interface IFaceMatch

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    Interface IPoint

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    Interface IRect

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    Interface ISsdMobilenetv1Options

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    Interface ITinyYolov2Options

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    Interface IDrawBoxOptions

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    Interface IDrawFaceLandmarksOptions

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    Interface IDrawTextFieldOptions

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    Namespace draw

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    Namespace utils

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    Type alias AgeAndGenderPrediction

    AgeAndGenderPrediction: {
        age: number;
        gender: Gender;
        genderProbability: number;
    }
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    Type alias BatchNorm

    BatchNorm: {
        sub: tf.Tensor1D;
        truediv: tf.Tensor1D;
    }
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    Type alias ConvWithBatchNorm

    ConvWithBatchNorm: {
        bn: BatchNorm;
        conv: ConvParams;
    }
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    Type alias DefaultTinyYolov2NetParams

    DefaultTinyYolov2NetParams: {
        conv0: ConvWithBatchNorm;
        conv1: ConvWithBatchNorm;
        conv2: ConvWithBatchNorm;
        conv3: ConvWithBatchNorm;
        conv4: ConvWithBatchNorm;
        conv5: ConvWithBatchNorm;
        conv6: ConvWithBatchNorm;
        conv7: ConvWithBatchNorm;
        conv8: ConvParams;
    }
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    Type alias Environment

    Environment: FileSystem & {
        Canvas: typeof HTMLCanvasElement;
        CanvasRenderingContext2D: typeof CanvasRenderingContext2D;
        Image: typeof HTMLImageElement;
        ImageData: typeof ImageData;
        Video: typeof HTMLVideoElement;
        createCanvasElement: (() => HTMLCanvasElement);
        createImageElement: (() => HTMLImageElement);
        createVideoElement: (() => HTMLVideoElement);
        fetch: ((url: string, init?: RequestInit) => Promise<Response>);
    }
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    Type alias FaceDetectionFunction

    FaceDetectionFunction: ((input: TNetInput) => Promise<FaceDetection[]>)
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    Type alias FaceDetectionOptions

    FaceDetectionOptions: TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options
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    Type alias FileSystem

    FileSystem: {
        readFile: ((filePath: string) => Promise<any>);
    }
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    Type alias ITinyFaceDetectorOptions

    ITinyFaceDetectorOptions: ITinyYolov2Options
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    Type alias MobilenetParams

    MobilenetParams: {
        conv0: SeparableConvParams | ConvParams;
        conv1: SeparableConvParams;
        conv2: SeparableConvParams;
        conv3: SeparableConvParams;
        conv4: SeparableConvParams;
        conv5: SeparableConvParams;
        conv6?: SeparableConvParams;
        conv7?: SeparableConvParams;
        conv8: ConvParams;
    }
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    Type alias NetOutput

    NetOutput: {
        age: tf.Tensor1D;
        gender: tf.Tensor2D;
    }
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    Type alias NetParams

    NetParams: {
        fc: {
            age: FCParams;
            gender: FCParams;
        };
    }
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    Type alias TMediaElement

    TMediaElement: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement
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    Type alias TNetInput

    TNetInput: TNetInputArg | TNetInputArg[] | NetInput | tf.Tensor4D
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    Type alias TNetInputArg

    TNetInputArg: string | TResolvedNetInput
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    Type alias TResolvedNetInput

    TResolvedNetInput: TMediaElement | tf.Tensor3D | tf.Tensor4D
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    Type alias TinyYolov2Config

    TinyYolov2Config: {
        anchors: Point[];
        classes: string[];
        filterSizes?: number[];
        iouThreshold: number;
        isFirstLayerConv2d?: boolean;
        meanRgb?: [number, number, number];
        withClassScores?: boolean;
        withSeparableConvs: boolean;
    }
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    Type alias TinyYolov2NetParams

    TinyYolov2NetParams: DefaultTinyYolov2NetParams | MobilenetParams
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    Type alias WithAge<TSource>

    WithAge<TSource>: TSource & {
        age: number;
    }
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    Type alias WithFaceDescriptor<TSource>

    WithFaceDescriptor<TSource>: TSource & {
        descriptor: Float32Array;
    }
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    Type alias WithFaceDetection<TSource>

    WithFaceDetection<TSource>: TSource & {
        detection: FaceDetection;
    }
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    Type alias WithFaceExpressions<TSource>

    WithFaceExpressions<TSource>: TSource & {
        expressions: FaceExpressions;
    }
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    Type alias WithFaceLandmarks<TSource, TFaceLandmarks>

    WithFaceLandmarks<TSource, TFaceLandmarks>: TSource & {
        alignedRect: FaceDetection;
        angle: {
            pitch: number | undefined;
            roll: number | undefined;
            yaw: number | undefined;
        };
        landmarks: TFaceLandmarks;
        unshiftedLandmarks: TFaceLandmarks;
    }
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    Type alias WithGender<TSource>

    WithGender<TSource>: TSource & {
        gender: Gender;
        genderProbability: number;
    }
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    Type alias DrawFaceExpressionsInput

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    Type alias DrawFaceLandmarksInput

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    Type alias TDrawDetectionsInput

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    Variable FACE_EXPRESSION_LABELSConst

    FACE_EXPRESSION_LABELS: string[] = ...
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    Variable envConst

    env: {
        createBrowserEnv: (() => Environment);
        createFileSystem: ((fs?: any) => FileSystem);
        createNodejsEnv: (() => Environment);
        getEnv: (() => Environment);
        initialize: (() => null | void);
        isBrowser: (() => boolean);
        isNodejs: (() => boolean);
        monkeyPatch: ((env: Partial<Environment>) => void);
        setEnv: ((env: Environment) => void);
    } = ...
    @@ -124,7 +124,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Variable netsConst

    nets: {
        ageGenderNet: AgeGenderNet;
        faceExpressionNet: FaceExpressionNet;
        faceLandmark68Net: FaceLandmark68Net;
        faceLandmark68TinyNet: FaceLandmark68TinyNet;
        faceRecognitionNet: FaceRecognitionNet;
        ssdMobilenetv1: SsdMobilenetv1;
        tinyFaceDetector: TinyFaceDetector;
        tinyYolov2: TinyYolov2;
    } = ...
    @@ -53,7 +53,7 @@

    Theme

    @@ -6,13 +6,13 @@
    • Preparing search index...
      • -
    • The search index is not available
    @vladmandic/face-api - v1.7.7 +
  • The search index is not available
    • @vladmandic/face-api - v1.7.8

    Variable versionConst

    version: string = ...