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check_docstrings.py
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# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Utility that checks all docstrings of public objects have an argument section matching their signature.
Use from the root of the repo with:
```bash
python utils/check_docstrings.py
```
for a check that will error in case of inconsistencies (used by `make repo-consistency`).
To auto-fix issues run:
```bash
python utils/check_docstrings.py --fix_and_overwrite
```
which is used by `make fix-copies` (note that this fills what it cans, you might have to manually fill information
like argument descriptions).
"""
import argparse
import ast
import enum
import inspect
import operator as op
import re
from pathlib import Path
from typing import Any, Optional, Tuple, Union
from check_repo import ignore_undocumented
from transformers.utils import direct_transformers_import
PATH_TO_TRANSFORMERS = Path("src").resolve() / "transformers"
# This is to make sure the transformers module imported is the one in the repo.
transformers = direct_transformers_import(PATH_TO_TRANSFORMERS)
OPTIONAL_KEYWORD = "*optional*"
# Re pattern that catches args blocks in docstrings (with all variation around the name supported).
_re_args = re.compile(r"^\s*(Args?|Arguments?|Attributes?|Params?|Parameters?):\s*$")
# Re pattern that parses the start of an arg block: catches <name> (<description>) in those lines.
_re_parse_arg = re.compile(r"^(\s*)(\S+)\s+\((.+)\)(?:\:|$)")
# Re pattern that parses the end of a description of an arg (catches the default in *optional*, defaults to xxx).
_re_parse_description = re.compile(r"\*optional\*, defaults to (.*)$")
# This is a temporary list of objects to ignore while we progressively fix them. Do not add anything here, fix the
# docstrings instead. If formatting should be ignored for the docstring, you can put a comment # no-format on the
# line before the docstring.
OBJECTS_TO_IGNORE = [
# Deprecated
"InputExample",
"InputFeatures",
# Signature is *args/**kwargs
# "PretrainedConfig", #ignored but could be fixed
# "GenerationConfig", #ignored but could be fixed
"TFSequenceSummary",
"TFBertTokenizer",
"TFGPT2Tokenizer",
# Missing arguments in the docstring
"ASTFeatureExtractor",
"AlbertModel",
"AlbertTokenizerFast",
"AlignTextModel",
"AlignVisionConfig",
"AudioClassificationPipeline",
"AutoformerConfig",
"AutomaticSpeechRecognitionPipeline",
"AzureOpenAiAgent",
"BarkCoarseConfig",
"BarkConfig",
"BarkFineConfig",
"BarkSemanticConfig",
"BartConfig",
"BartTokenizerFast",
"BarthezTokenizerFast",
"BeitModel",
"BertConfig",
"BertJapaneseTokenizer",
"BertModel",
"BertTokenizerFast",
"BigBirdConfig",
"BigBirdForQuestionAnswering",
"BigBirdModel",
"BigBirdPegasusConfig",
"BigBirdTokenizerFast",
"BitImageProcessor",
"BlenderbotConfig",
"BlenderbotSmallConfig",
"BlenderbotSmallTokenizerFast",
"BlenderbotTokenizerFast",
"Blip2QFormerConfig",
"Blip2VisionConfig",
"BlipTextConfig",
"BlipVisionConfig",
"BloomConfig",
"BloomTokenizerFast",
"BridgeTowerTextConfig",
"BridgeTowerVisionConfig",
"BrosModel",
"CamembertConfig",
"CamembertModel",
"CamembertTokenizerFast",
"CanineModel",
"CanineTokenizer",
"ChineseCLIPTextModel",
"ClapTextConfig",
"ConditionalDetrConfig",
"ConditionalDetrImageProcessor",
"ConvBertConfig",
"ConvBertTokenizerFast",
"ConvNextConfig",
"ConvNextV2Config",
"ConversationalPipeline",
"CpmAntTokenizer",
"CvtConfig",
"CvtModel",
"DeiTImageProcessor",
"DPRReaderTokenizer",
"DPRReaderTokenizerFast",
"DPTModel",
"Data2VecAudioConfig",
"Data2VecTextConfig",
"Data2VecTextModel",
"Data2VecVisionModel",
"DataCollatorForLanguageModeling",
"DebertaConfig",
"DebertaV2Config",
"DebertaV2Tokenizer",
"DebertaV2TokenizerFast",
"DecisionTransformerConfig",
"DeformableDetrConfig",
"DeformableDetrImageProcessor",
"DeiTModel",
"DepthEstimationPipeline",
"DetaConfig",
"DetaImageProcessor",
"DetrConfig",
"DetrImageProcessor",
"DinatModel",
"DistilBertConfig",
"DistilBertTokenizerFast",
"DocumentQuestionAnsweringPipeline",
"DonutSwinModel",
"EarlyStoppingCallback",
"EfficientFormerConfig",
"EfficientFormerImageProcessor",
"EfficientNetConfig",
"ElectraConfig",
"ElectraTokenizerFast",
"EncoderDecoderModel",
"EncoderRepetitionPenaltyLogitsProcessor",
"ErnieConfig",
"ErnieMConfig",
"ErnieMModel",
"ErnieModel",
"ErnieMTokenizer",
"EsmConfig",
"EsmModel",
"FlaxAlbertForMaskedLM",
"FlaxAlbertForMultipleChoice",
"FlaxAlbertForPreTraining",
"FlaxAlbertForQuestionAnswering",
"FlaxAlbertForSequenceClassification",
"FlaxAlbertForTokenClassification",
"FlaxAlbertModel",
"FlaxBartForCausalLM",
"FlaxBartForConditionalGeneration",
"FlaxBartForQuestionAnswering",
"FlaxBartForSequenceClassification",
"FlaxBartModel",
"FlaxBeitForImageClassification",
"FlaxBeitForMaskedImageModeling",
"FlaxBeitModel",
"FlaxBertForCausalLM",
"FlaxBertForMaskedLM",
"FlaxBertForMultipleChoice",
"FlaxBertForNextSentencePrediction",
"FlaxBertForPreTraining",
"FlaxBertForQuestionAnswering",
"FlaxBertForSequenceClassification",
"FlaxBertForTokenClassification",
"FlaxBertModel",
"FlaxBigBirdForCausalLM",
"FlaxBigBirdForMaskedLM",
"FlaxBigBirdForMultipleChoice",
"FlaxBigBirdForPreTraining",
"FlaxBigBirdForQuestionAnswering",
"FlaxBigBirdForSequenceClassification",
"FlaxBigBirdForTokenClassification",
"FlaxBigBirdModel",
"FlaxBlenderbotForConditionalGeneration",
"FlaxBlenderbotModel",
"FlaxBlenderbotSmallForConditionalGeneration",
"FlaxBlenderbotSmallModel",
"FlaxBloomForCausalLM",
"FlaxBloomModel",
"FlaxCLIPModel",
"FlaxDistilBertForMaskedLM",
"FlaxDistilBertForMultipleChoice",
"FlaxDistilBertForQuestionAnswering",
"FlaxDistilBertForSequenceClassification",
"FlaxDistilBertForTokenClassification",
"FlaxDistilBertModel",
"FlaxElectraForCausalLM",
"FlaxElectraForMaskedLM",
"FlaxElectraForMultipleChoice",
"FlaxElectraForPreTraining",
"FlaxElectraForQuestionAnswering",
"FlaxElectraForSequenceClassification",
"FlaxElectraForTokenClassification",
"FlaxElectraModel",
"FlaxEncoderDecoderModel",
"FlaxGPT2LMHeadModel",
"FlaxGPT2Model",
"FlaxGPTJForCausalLM",
"FlaxGPTJModel",
"FlaxGPTNeoForCausalLM",
"FlaxGPTNeoModel",
"FlaxLlamaForCausalLM",
"FlaxLlamaModel",
"FlaxMBartForConditionalGeneration",
"FlaxMBartForQuestionAnswering",
"FlaxMBartForSequenceClassification",
"FlaxMBartModel",
"FlaxMarianMTModel",
"FlaxMarianModel",
"FlaxOPTForCausalLM",
"FlaxPegasusForConditionalGeneration",
"FlaxPegasusModel",
"FlaxRegNetForImageClassification",
"FlaxRegNetModel",
"FlaxResNetForImageClassification",
"FlaxResNetModel",
"FlaxRoFormerForMaskedLM",
"FlaxRoFormerForMultipleChoice",
"FlaxRoFormerForQuestionAnswering",
"FlaxRoFormerForSequenceClassification",
"FlaxRoFormerForTokenClassification",
"FlaxRoFormerModel",
"FlaxRobertaForCausalLM",
"FlaxRobertaForMaskedLM",
"FlaxRobertaForMultipleChoice",
"FlaxRobertaForQuestionAnswering",
"FlaxRobertaForSequenceClassification",
"FlaxRobertaForTokenClassification",
"FlaxRobertaModel",
"FlaxRobertaPreLayerNormForCausalLM",
"FlaxRobertaPreLayerNormForMaskedLM",
"FlaxRobertaPreLayerNormForMultipleChoice",
"FlaxRobertaPreLayerNormForQuestionAnswering",
"FlaxRobertaPreLayerNormForSequenceClassification",
"FlaxRobertaPreLayerNormForTokenClassification",
"FlaxRobertaPreLayerNormModel",
"FlaxSpeechEncoderDecoderModel",
"FlaxViTForImageClassification",
"FlaxViTModel",
"FlaxVisionEncoderDecoderModel",
"FlaxVisionTextDualEncoderModel",
"FlaxWav2Vec2ForCTC",
"FlaxWav2Vec2ForPreTraining",
"FlaxWav2Vec2Model",
"FlaxWhisperForAudioClassification",
"FlaxWhisperForConditionalGeneration",
"FlaxWhisperModel",
"FlaxWhisperTimeStampLogitsProcessor",
"FlaxXGLMForCausalLM",
"FlaxXGLMModel",
"FlaxXLMRobertaForCausalLM",
"FlaxXLMRobertaForMaskedLM",
"FlaxXLMRobertaForMultipleChoice",
"FlaxXLMRobertaForQuestionAnswering",
"FlaxXLMRobertaForSequenceClassification",
"FlaxXLMRobertaForTokenClassification",
"FlaxXLMRobertaModel",
"FNetConfig",
"FNetModel",
"FNetTokenizerFast",
"FSMTConfig",
"FeatureExtractionPipeline",
"FillMaskPipeline",
"FlaubertConfig",
"FlavaConfig",
"FlavaForPreTraining",
"FlavaImageModel",
"FlavaImageProcessor",
"FlavaMultimodalModel",
"FlavaTextConfig",
"FlavaTextModel",
"FocalNetModel",
"FunnelTokenizerFast",
"GPTBigCodeConfig",
"GPTJConfig",
"GPTNeoXConfig",
"GPTNeoXJapaneseConfig",
"GPTNeoXTokenizerFast",
"GPTSanJapaneseConfig",
"GitConfig",
"GitVisionConfig",
"GraphormerConfig",
"GroupViTTextConfig",
"GroupViTVisionConfig",
"HerbertTokenizerFast",
"HubertConfig",
"HubertForCTC",
"IBertConfig",
"IBertModel",
"IdeficsConfig",
"IdeficsProcessor",
"ImageClassificationPipeline",
"ImageGPTConfig",
"ImageSegmentationPipeline",
"ImageToImagePipeline",
"ImageToTextPipeline",
"InformerConfig",
"InstructBlipQFormerConfig",
"JukeboxPriorConfig",
"JukeboxTokenizer",
"LEDConfig",
"LEDTokenizerFast",
"LayoutLMForQuestionAnswering",
"LayoutLMTokenizerFast",
"LayoutLMv2Config",
"LayoutLMv2ForQuestionAnswering",
"LayoutLMv2TokenizerFast",
"LayoutLMv3Config",
"LayoutLMv3ImageProcessor",
"LayoutLMv3TokenizerFast",
"LayoutXLMTokenizerFast",
"LevitConfig",
"LiltConfig",
"LiltModel",
"LongT5Config",
"LongformerConfig",
"LongformerModel",
"LongformerTokenizerFast",
"LukeModel",
"LukeTokenizer",
"LxmertTokenizerFast",
"M2M100Config",
"M2M100Tokenizer",
"MarkupLMProcessor",
"MaskGenerationPipeline",
"MBart50TokenizerFast",
"MBartConfig",
"MCTCTFeatureExtractor",
"MPNetConfig",
"MPNetModel",
"MPNetTokenizerFast",
"MT5Config",
"MT5TokenizerFast",
"MarianConfig",
"MarianTokenizer",
"MarkupLMConfig",
"MarkupLMModel",
"MarkupLMTokenizer",
"MarkupLMTokenizerFast",
"Mask2FormerConfig",
"MaskFormerConfig",
"MaxTimeCriteria",
"MegaConfig",
"MegaModel",
"MegatronBertConfig",
"MegatronBertForPreTraining",
"MegatronBertModel",
"MobileBertConfig",
"MobileBertModel",
"MobileBertTokenizerFast",
"MobileNetV1ImageProcessor",
"MobileNetV1Model",
"MobileNetV2ImageProcessor",
"MobileNetV2Model",
"MobileViTModel",
"MobileViTV2Model",
"MLukeTokenizer",
"MraConfig",
"MusicgenDecoderConfig",
"MusicgenForConditionalGeneration",
"MvpConfig",
"MvpTokenizerFast",
"MT5Tokenizer",
"NatModel",
"NerPipeline",
"NezhaConfig",
"NezhaModel",
"NllbMoeConfig",
"NllbTokenizer",
"NllbTokenizerFast",
"NystromformerConfig",
"OPTConfig",
"ObjectDetectionPipeline",
"OneFormerProcessor",
"OpenAIGPTTokenizerFast",
"OpenLlamaConfig",
"PLBartConfig",
"PegasusConfig",
"PegasusTokenizer",
"PegasusTokenizerFast",
"PegasusXConfig",
"PerceiverImageProcessor",
"PerceiverModel",
"PerceiverTokenizer",
"PersimmonConfig",
"Pipeline",
"Pix2StructConfig",
"Pix2StructTextConfig",
"PLBartTokenizer",
"Pop2PianoConfig",
"PreTrainedTokenizer",
"PreTrainedTokenizerBase",
"PreTrainedTokenizerFast",
"PrefixConstrainedLogitsProcessor",
"ProphetNetConfig",
"QDQBertConfig",
"QDQBertModel",
"QuestionAnsweringPipeline",
"RagConfig",
"RagModel",
"RagRetriever",
"RagSequenceForGeneration",
"RagTokenForGeneration",
"RealmConfig",
"RealmForOpenQA",
"RealmScorer",
"RealmTokenizerFast",
"ReformerConfig",
"ReformerTokenizerFast",
"RegNetConfig",
"RemBertConfig",
"RemBertModel",
"RemBertTokenizer",
"RemBertTokenizerFast",
"RepetitionPenaltyLogitsProcessor",
"RetriBertConfig",
"RetriBertTokenizerFast",
"RoCBertConfig",
"RoCBertModel",
"RoCBertTokenizer",
"RoFormerConfig",
"RobertaConfig",
"RobertaModel",
"RobertaPreLayerNormConfig",
"RobertaPreLayerNormModel",
"RobertaTokenizerFast",
"SEWConfig",
"SEWDConfig",
"SEWDForCTC",
"SEWForCTC",
"SamConfig",
"SamPromptEncoderConfig",
"SeamlessM4TConfig", # use of unconventional markdown
"SeamlessM4Tv2Config", # use of unconventional markdown
"Seq2SeqTrainingArguments",
"SpecialTokensMixin",
"Speech2Text2Config",
"Speech2Text2Tokenizer",
"Speech2TextTokenizer",
"SpeechEncoderDecoderModel",
"SpeechT5Config",
"SpeechT5Model",
"SplinterConfig",
"SplinterTokenizerFast",
"SqueezeBertTokenizerFast",
"SummarizationPipeline",
"Swin2SRImageProcessor",
"Swinv2Model",
"SwitchTransformersConfig",
"T5Config",
"T5Tokenizer",
"T5TokenizerFast",
"TableQuestionAnsweringPipeline",
"TableTransformerConfig",
"TapasConfig",
"TapasModel",
"TapasTokenizer",
"Text2TextGenerationPipeline",
"TextClassificationPipeline",
"TextGenerationPipeline",
"TFAlbertForMaskedLM",
"TFAlbertForMultipleChoice",
"TFAlbertForPreTraining",
"TFAlbertForQuestionAnswering",
"TFAlbertForSequenceClassification",
"TFAlbertForTokenClassification",
"TFAlbertModel",
"TFBartForConditionalGeneration",
"TFBartForSequenceClassification",
"TFBartModel",
"TFBertForMaskedLM",
"TFBertForMultipleChoice",
"TFBertForNextSentencePrediction",
"TFBertForPreTraining",
"TFBertForQuestionAnswering",
"TFBertForSequenceClassification",
"TFBertForTokenClassification",
"TFBertModel",
"TFBlenderbotForConditionalGeneration",
"TFBlenderbotModel",
"TFBlenderbotSmallForConditionalGeneration",
"TFBlenderbotSmallModel",
"TFBlipForConditionalGeneration",
"TFBlipForImageTextRetrieval",
"TFBlipForQuestionAnswering",
"TFCLIPModel",
"TFCTRLForSequenceClassification",
"TFCTRLLMHeadModel",
"TFCTRLModel",
"TFCamembertForCausalLM",
"TFCamembertForMaskedLM",
"TFCamembertForMultipleChoice",
"TFCamembertForQuestionAnswering",
"TFCamembertForSequenceClassification",
"TFCamembertForTokenClassification",
"TFCamembertModel",
"TFConvBertForMaskedLM",
"TFConvBertForMultipleChoice",
"TFConvBertForQuestionAnswering",
"TFConvBertForSequenceClassification",
"TFConvBertForTokenClassification",
"TFConvBertModel",
"TFConvNextForImageClassification",
"TFConvNextModel",
"TFConvNextV2Model", # Parsing issue. Equivalent to PT ConvNextV2Model, see PR #25558
"TFConvNextV2ForImageClassification",
"TFCvtForImageClassification",
"TFCvtModel",
"TFDPRReader",
"TFData2VecVisionForImageClassification",
"TFData2VecVisionForSemanticSegmentation",
"TFData2VecVisionModel",
"TFDebertaForMaskedLM",
"TFDebertaForQuestionAnswering",
"TFDebertaForSequenceClassification",
"TFDebertaForTokenClassification",
"TFDebertaModel",
"TFDebertaV2ForMaskedLM",
"TFDebertaV2ForMultipleChoice",
"TFDebertaV2ForQuestionAnswering",
"TFDebertaV2ForSequenceClassification",
"TFDebertaV2ForTokenClassification",
"TFDebertaV2Model",
"TFDeiTForImageClassification",
"TFDeiTForImageClassificationWithTeacher",
"TFDeiTForMaskedImageModeling",
"TFDeiTModel",
"TFDistilBertForMaskedLM",
"TFDistilBertForMultipleChoice",
"TFDistilBertForQuestionAnswering",
"TFDistilBertForSequenceClassification",
"TFDistilBertForTokenClassification",
"TFDistilBertModel",
"TFEfficientFormerForImageClassification",
"TFEfficientFormerForImageClassificationWithTeacher",
"TFEfficientFormerModel",
"TFElectraForMaskedLM",
"TFElectraForMultipleChoice",
"TFElectraForPreTraining",
"TFElectraForQuestionAnswering",
"TFElectraForSequenceClassification",
"TFElectraForTokenClassification",
"TFElectraModel",
"TFEncoderDecoderModel",
"TFEsmForMaskedLM",
"TFEsmForSequenceClassification",
"TFEsmForTokenClassification",
"TFEsmModel",
"TFFlaubertForMultipleChoice",
"TFFlaubertForQuestionAnsweringSimple",
"TFFlaubertForSequenceClassification",
"TFFlaubertForTokenClassification",
"TFFlaubertModel",
"TFFlaubertWithLMHeadModel",
"TFFunnelBaseModel",
"TFFunnelForMaskedLM",
"TFFunnelForMultipleChoice",
"TFFunnelForPreTraining",
"TFFunnelForQuestionAnswering",
"TFFunnelForSequenceClassification",
"TFFunnelForTokenClassification",
"TFFunnelModel",
"TFGPT2DoubleHeadsModel",
"TFGPT2ForSequenceClassification",
"TFGPT2LMHeadModel",
"TFGPT2Model",
"TFGPTJForCausalLM",
"TFGPTJForQuestionAnswering",
"TFGPTJForSequenceClassification",
"TFGPTJModel",
"TFGroupViTModel",
"TFHubertForCTC",
"TFHubertModel",
"TFLEDForConditionalGeneration",
"TFLEDModel",
"TFLayoutLMForMaskedLM",
"TFLayoutLMForQuestionAnswering",
"TFLayoutLMForSequenceClassification",
"TFLayoutLMForTokenClassification",
"TFLayoutLMModel",
"TFLayoutLMv3ForQuestionAnswering",
"TFLayoutLMv3ForSequenceClassification",
"TFLayoutLMv3ForTokenClassification",
"TFLayoutLMv3Model",
"TFLongformerForMaskedLM",
"TFLongformerForMultipleChoice",
"TFLongformerForQuestionAnswering",
"TFLongformerForSequenceClassification",
"TFLongformerForTokenClassification",
"TFLongformerModel",
"TFLxmertForPreTraining",
"TFLxmertModel",
"TFMBartForConditionalGeneration",
"TFMBartModel",
"TFMPNetForMaskedLM",
"TFMPNetForMultipleChoice",
"TFMPNetForQuestionAnswering",
"TFMPNetForSequenceClassification",
"TFMPNetForTokenClassification",
"TFMPNetModel",
"TFMarianMTModel",
"TFMarianModel",
"TFMobileBertForMaskedLM",
"TFMobileBertForMultipleChoice",
"TFMobileBertForNextSentencePrediction",
"TFMobileBertForPreTraining",
"TFMobileBertForQuestionAnswering",
"TFMobileBertForSequenceClassification",
"TFMobileBertForTokenClassification",
"TFMobileBertModel",
"TFMobileViTForImageClassification",
"TFMobileViTForSemanticSegmentation",
"TFMobileViTModel",
"TFOPTForCausalLM",
"TFOPTModel",
"TFOpenAIGPTDoubleHeadsModel",
"TFOpenAIGPTForSequenceClassification",
"TFOpenAIGPTLMHeadModel",
"TFOpenAIGPTModel",
"TFPegasusForConditionalGeneration",
"TFPegasusModel",
"TFRagModel",
"TFRagSequenceForGeneration",
"TFRagTokenForGeneration",
"TFRemBertForCausalLM",
"TFRemBertForMaskedLM",
"TFRemBertForMultipleChoice",
"TFRemBertForQuestionAnswering",
"TFRemBertForSequenceClassification",
"TFRemBertForTokenClassification",
"TFRemBertModel",
"TFRepetitionPenaltyLogitsProcessor",
"TFResNetForImageClassification",
"TFResNetModel",
"TFRoFormerForCausalLM",
"TFRoFormerForMaskedLM",
"TFRoFormerForMultipleChoice",
"TFRoFormerForQuestionAnswering",
"TFRoFormerForSequenceClassification",
"TFRoFormerForTokenClassification",
"TFRoFormerModel",
"TFRobertaForMaskedLM",
"TFRobertaForMultipleChoice",
"TFRobertaForQuestionAnswering",
"TFRobertaForSequenceClassification",
"TFRobertaForTokenClassification",
"TFRobertaModel",
"TFRobertaPreLayerNormForMaskedLM",
"TFRobertaPreLayerNormForMultipleChoice",
"TFRobertaPreLayerNormForQuestionAnswering",
"TFRobertaPreLayerNormForSequenceClassification",
"TFRobertaPreLayerNormForTokenClassification",
"TFRobertaPreLayerNormModel",
"TFSamModel",
"TFSegformerForImageClassification",
"TFSegformerForSemanticSegmentation",
"TFSegformerModel",
"TFSpeech2TextForConditionalGeneration",
"TFSpeech2TextModel",
"TFSwinForImageClassification",
"TFSwinForMaskedImageModeling",
"TFSwinModel",
"TFT5EncoderModel",
"TFT5ForConditionalGeneration",
"TFT5Model",
"TFTapasForMaskedLM",
"TFTapasForQuestionAnswering",
"TFTapasForSequenceClassification",
"TFTapasModel",
"TFTransfoXLForSequenceClassification",
"TFTransfoXLLMHeadModel",
"TFTransfoXLModel",
"TFViTForImageClassification",
"TFViTMAEForPreTraining",
"TFViTMAEModel",
"TFViTModel",
"TFVisionEncoderDecoderModel",
"TFVisionTextDualEncoderModel",
"TFWav2Vec2ForCTC",
"TFWav2Vec2Model",
"TFWhisperForConditionalGeneration",
"TFWhisperModel",
"TFXGLMForCausalLM",
"TFXGLMModel",
"TFXLMForMultipleChoice",
"TFXLMForQuestionAnsweringSimple",
"TFXLMForSequenceClassification",
"TFXLMForTokenClassification",
"TFXLMModel",
"TFXLMRobertaForCausalLM",
"TFXLMRobertaForMaskedLM",
"TFXLMRobertaForMultipleChoice",
"TFXLMRobertaForQuestionAnswering",
"TFXLMRobertaForSequenceClassification",
"TFXLMRobertaForTokenClassification",
"TFXLMRobertaModel",
"TFXLMWithLMHeadModel",
"TFXLNetForMultipleChoice",
"TFXLNetForQuestionAnsweringSimple",
"TFXLNetForSequenceClassification",
"TFXLNetForTokenClassification",
"TFXLNetLMHeadModel",
"TFXLNetModel",
"TimeSeriesTransformerConfig",
"TokenClassificationPipeline",
"TrOCRConfig",
"TrainerState",
"TrainingArguments",
"TrajectoryTransformerConfig",
"TranslationPipeline",
"TvltImageProcessor",
"UMT5Config",
"UperNetConfig",
"UperNetForSemanticSegmentation",
"ViTHybridImageProcessor",
"ViTHybridModel",
"ViTMSNModel",
"ViTModel",
"VideoClassificationPipeline",
"ViltConfig",
"ViltForImagesAndTextClassification",
"ViltModel",
"VisionEncoderDecoderModel",
"VisionTextDualEncoderModel",
"VisualBertConfig",
"VisualBertModel",
"VisualQuestionAnsweringPipeline",
"VitMatteForImageMatting",
"VitsTokenizer",
"VivitModel",
"Wav2Vec2CTCTokenizer",
"Wav2Vec2Config",
"Wav2Vec2ConformerConfig",
"Wav2Vec2ConformerForCTC",
"Wav2Vec2FeatureExtractor",
"Wav2Vec2PhonemeCTCTokenizer",
"WavLMConfig",
"WavLMForCTC",
"WhisperConfig",
"WhisperFeatureExtractor",
"WhisperForAudioClassification",
"XCLIPTextConfig",
"XCLIPVisionConfig",
"XGLMConfig",
"XGLMModel",
"XGLMTokenizerFast",
"XLMConfig",
"XLMProphetNetConfig",
"XLMRobertaConfig",
"XLMRobertaModel",
"XLMRobertaTokenizerFast",
"XLMRobertaXLConfig",
"XLMRobertaXLModel",
"XLNetConfig",
"XLNetTokenizerFast",
"XmodConfig",
"XmodModel",
"YolosImageProcessor",
"YolosModel",
"YosoConfig",
"ZeroShotAudioClassificationPipeline",
"ZeroShotClassificationPipeline",
"ZeroShotImageClassificationPipeline",
"ZeroShotObjectDetectionPipeline",
]
# Supported math operations when interpreting the value of defaults.
MATH_OPERATORS = {
ast.Add: op.add,
ast.Sub: op.sub,
ast.Mult: op.mul,
ast.Div: op.truediv,
ast.Pow: op.pow,
ast.BitXor: op.xor,
ast.USub: op.neg,
}
def find_indent(line: str) -> int:
"""
Returns the number of spaces that start a line indent.
"""
search = re.search(r"^(\s*)(?:\S|$)", line)
if search is None:
return 0
return len(search.groups()[0])
def stringify_default(default: Any) -> str:
"""
Returns the string representation of a default value, as used in docstring: numbers are left as is, all other
objects are in backtiks.
Args:
default (`Any`): The default value to process
Returns:
`str`: The string representation of that default.
"""
if isinstance(default, bool):
# We need to test for bool first as a bool passes isinstance(xxx, (int, float))
return f"`{default}`"
elif isinstance(default, enum.Enum):
# We need to test for enum first as an enum with int values will pass isinstance(xxx, (int, float))
return f"`{str(default)}`"
elif isinstance(default, int):
return str(default)
elif isinstance(default, float):
result = str(default)
return str(round(default, 2)) if len(result) > 6 else result
elif isinstance(default, str):
return str(default) if default.isnumeric() else f'`"{default}"`'
elif isinstance(default, type):
return f"`{default.__name__}`"
else:
return f"`{default}`"
def eval_math_expression(expression: str) -> Optional[Union[float, int]]:
# Mainly taken from the excellent https://stackoverflow.com/a/9558001
"""
Evaluate (safely) a mathematial expression and returns its value.
Args:
expression (`str`): The expression to evaluate.
Returns:
`Optional[Union[float, int]]`: Returns `None` if the evaluation fails in any way and the value computed
otherwise.
Example:
```py
>>> eval_expr('2^6')
4
>>> eval_expr('2**6')
64
>>> eval_expr('1 + 2*3**(4^5) / (6 + -7)')
-5.0
```
"""
try:
return eval_node(ast.parse(expression, mode="eval").body)
except TypeError:
return
def eval_node(node):
if isinstance(node, ast.Num): # <number>
return node.n
elif isinstance(node, ast.BinOp): # <left> <operator> <right>
return MATH_OPERATORS[type(node.op)](eval_node(node.left), eval_node(node.right))
elif isinstance(node, ast.UnaryOp): # <operator> <operand> e.g., -1
return MATH_OPERATORS[type(node.op)](eval_node(node.operand))
else:
raise TypeError(node)
def replace_default_in_arg_description(description: str, default: Any) -> str:
"""
Catches the default value in the description of an argument inside a docstring and replaces it by the value passed.
Args:
description (`str`): The description of an argument in a docstring to process.
default (`Any`): The default value that whould be in the docstring of that argument.
Returns:
`str`: The description updated with the new default value.
"""
# Lots of docstrings have `optional` or **opational** instead of *optional* so we do this fix here.
description = description.replace("`optional`", OPTIONAL_KEYWORD)
description = description.replace("**optional**", OPTIONAL_KEYWORD)
if default is inspect._empty:
# No default, make sure the description doesn't have any either
idx = description.find(OPTIONAL_KEYWORD)
if idx != -1:
description = description[:idx].rstrip()
if description.endswith(","):
description = description[:-1].rstrip()
elif default is None:
# Default None are not written, we just set `*optional*`. If there is default that is not None specified in the
# description, we do not erase it (as sometimes we set the default to `None` because the default is a mutable
# object).
idx = description.find(OPTIONAL_KEYWORD)
if idx == -1:
description = f"{description}, {OPTIONAL_KEYWORD}"
elif re.search(r"defaults to `?None`?", description) is not None:
len_optional = len(OPTIONAL_KEYWORD)
description = description[: idx + len_optional]
else:
str_default = None
# For numbers we may have a default that is given by a math operation (1/255 is really popular). We don't
# want to replace those by their actual values.
if isinstance(default, (int, float)) and re.search("defaults to `?(.*?)(?:`|$)", description) is not None:
# Grab the default and evaluate it.
current_default = re.search("defaults to `?(.*?)(?:`|$)", description).groups()[0]
if default == eval_math_expression(current_default):
try:
# If it can be directly converted to the type of the default, it's a simple value
str_default = str(type(default)(current_default))
except Exception:
# Otherwise there is a math operator so we add a code block.
str_default = f"`{current_default}`"
if str_default is None:
str_default = stringify_default(default)
# Make sure default match
if OPTIONAL_KEYWORD not in description:
description = f"{description}, {OPTIONAL_KEYWORD}, defaults to {str_default}"
elif _re_parse_description.search(description) is None:
idx = description.find(OPTIONAL_KEYWORD)
len_optional = len(OPTIONAL_KEYWORD)
description = f"{description[:idx + len_optional]}, defaults to {str_default}"
else:
description = _re_parse_description.sub(rf"*optional*, defaults to {str_default}", description)
return description
def get_default_description(arg: inspect.Parameter) -> str:
"""
Builds a default description for a parameter that was not documented.
Args:
arg (`inspect.Parameter`): The argument in the signature to generate a description for.
Returns:
`str`: The description.
"""
if arg.annotation is inspect._empty:
arg_type = "<fill_type>"
elif hasattr(arg.annotation, "__name__"):
arg_type = arg.annotation.__name__
else:
arg_type = str(arg.annotation)
if arg.default is inspect._empty:
return f"`{arg_type}`"
elif arg.default is None:
return f"`{arg_type}`, {OPTIONAL_KEYWORD}"
else:
str_default = stringify_default(arg.default)
return f"`{arg_type}`, {OPTIONAL_KEYWORD}, defaults to {str_default}"
def find_source_file(obj: Any) -> Path:
"""
Finds the source file of an object.
Args:
obj (`Any`): The object whose source file we are looking for.
Returns:
`Path`: The source file.
"""
module = obj.__module__
obj_file = PATH_TO_TRANSFORMERS
for part in module.split(".")[1:]:
obj_file = obj_file / part
return obj_file.with_suffix(".py")
def match_docstring_with_signature(obj: Any) -> Optional[Tuple[str, str]]:
"""
Matches the docstring of an object with its signature.