Simplify init

src/transformers/models/vitpose/__init__.py

@@ -13,55 +13,16 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
 
 
-_import_structure = {"configuration_vitpose": ["VitPoseConfig"]}
-
-
-try:
-    if not is_vision_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["image_processing_vitpose"] = ["VitPoseImageProcessor"]
-
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_vitpose"] = [
-        "VitPosePreTrainedModel",
-        "VitPoseForPoseEstimation",
-    ]
-
 if TYPE_CHECKING:
-    from .configuration_vitpose import VitPoseConfig
-
-    try:
-        if not is_vision_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .image_processing_vitpose import VitPoseImageProcessor
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_vitpose import (
-            VitPoseForPoseEstimation,
-            VitPosePreTrainedModel,
-        )
-
+    from .configuration_vitpose import *
+    from .image_processing_vitpose import *
+    from .modeling_vitpose import *
 else:
     import sys
 
-    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)

src/transformers/models/vitpose/configuration_vitpose.py

@@ -119,3 +119,6 @@ def __init__(
         self.initializer_range = initializer_range
         self.scale_factor = scale_factor
         self.use_simple_decoder = use_simple_decoder
+
+
+__all__ = ["VitPoseConfig"]

src/transformers/models/vitpose/image_processing_vitpose.py

@@ -671,3 +671,6 @@ def post_process_pose_estimation(
             results.append(batch_results)
 
         return results
+
+
+__all__ = ["VitPoseImageProcessor"]

src/transformers/models/vitpose/modeling_vitpose.py

@@ -334,3 +334,6 @@ def forward(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+
+__all__ = ["VitPosePreTrainedModel", "VitPoseForPoseEstimation"]

src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py

@@ -91,9 +91,7 @@ def __init__(self, config: VitPoseBackboneConfig) -> None:
         num_patches = self.patch_embeddings.num_patches
         position_embeddings = torch.zeros(1, num_patches + 1, config.hidden_size)
         # Pre-compute the modified position embeddings
-        self.position_embeddings = nn.Parameter(
-            position_embeddings[:, 1:] + position_embeddings[:, :1]
-        )
+        self.position_embeddings = nn.Parameter(position_embeddings[:, 1:] + position_embeddings[:, :1])
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
 
     def forward(self, pixel_values: torch.Tensor) -> torch.Tensor: