Applies the rest of the init refactor except to modular files (#35238)

* [test_all] Applies the rest of the init refactor except to modular files

* Revert modular that doesn't work

* [test_all] TFGPT2Tokenizer

src/transformers/models/audio_spectrogram_transformer/__init__.py

@@ -19,7 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_audio_spectrogram_transformer import *
-    from .convert_audio_spectrogram_transformer_original_to_pytorch import *
     from .feature_extraction_audio_spectrogram_transformer import *
     from .modeling_audio_spectrogram_transformer import *
 else:

src/transformers/models/bark/__init__.py

@@ -19,8 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_bark import *
-    from .convert_suno_to_hf import *
-    from .generation_configuration_bark import *
     from .modeling_bark import *
     from .processing_bark import *
 else:

src/transformers/models/bart/__init__.py

@@ -19,7 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_bart import *
-    from .convert_bart_original_pytorch_checkpoint_to_pytorch import *
     from .modeling_bart import *
     from .modeling_flax_bart import *
     from .modeling_tf_bart import *

src/transformers/models/beit/__init__.py

@@ -19,7 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_beit import *
-    from .convert_beit_unilm_to_pytorch import *
     from .feature_extraction_beit import *
     from .image_processing_beit import *
     from .modeling_beit import *

src/transformers/models/bert/__init__.py

@@ -19,10 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_bert import *
-    from .convert_bert_original_tf2_checkpoint_to_pytorch import *
-    from .convert_bert_original_tf_checkpoint_to_pytorch import *
-    from .convert_bert_pytorch_checkpoint_to_original_tf import *
-    from .convert_bert_token_dropping_original_tf2_checkpoint_to_pytorch import *
     from .modeling_bert import *
     from .modeling_flax_bert import *
     from .modeling_tf_bert import *

src/transformers/models/big_bird/__init__.py

@@ -19,7 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_big_bird import *
-    from .convert_bigbird_original_tf_checkpoint_to_pytorch import *
     from .modeling_big_bird import *
     from .modeling_flax_big_bird import *
     from .tokenization_big_bird import *

src/transformers/models/bigbird_pegasus/__init__.py

@@ -19,7 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_bigbird_pegasus import *
-    from .convert_bigbird_pegasus_tf_to_pytorch import *
     from .modeling_bigbird_pegasus import *
 else:
     import sys

src/transformers/models/biogpt/__init__.py

@@ -19,7 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_biogpt import *
-    from .convert_biogpt_original_pytorch_checkpoint_to_pytorch import *
     from .modeling_biogpt import *
     from .tokenization_biogpt import *
 else:

src/transformers/models/bit/__init__.py

@@ -19,7 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_bit import *
-    from .convert_bit_to_pytorch import *
     from .image_processing_bit import *
     from .modeling_bit import *
 else:

src/transformers/models/blenderbot/__init__.py

@@ -19,7 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_blenderbot import *
-    from .convert_blenderbot_original_pytorch_checkpoint_to_pytorch import *
     from .modeling_blenderbot import *
     from .modeling_flax_blenderbot import *
     from .modeling_tf_blenderbot import *

src/transformers/models/blip/__init__.py

@@ -19,12 +19,9 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_blip import *
-    from .convert_blip_original_pytorch_to_hf import *
     from .image_processing_blip import *
     from .modeling_blip import *
-    from .modeling_blip_text import *
     from .modeling_tf_blip import *
-    from .modeling_tf_blip_text import *
     from .processing_blip import *
 else:
     import sys

src/transformers/models/blip_2/__init__.py

@@ -19,7 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_blip_2 import *
-    from .convert_blip_2_original_to_pytorch import *
     from .modeling_blip_2 import *
     from .processing_blip_2 import *
 else:

src/transformers/models/bloom/__init__.py

@@ -19,7 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_bloom import *
-    from .convert_bloom_original_checkpoint_to_pytorch import *
     from .modeling_bloom import *
     from .modeling_flax_bloom import *
     from .tokenization_bloom_fast import *

src/transformers/models/bros/__init__.py

@@ -19,7 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_bros import *
-    from .convert_bros_to_pytorch import *
     from .modeling_bros import *
     from .processing_bros import *
 else:

src/transformers/models/byt5/__init__.py

@@ -18,7 +18,6 @@ from ...utils.import_utils import define_import_structure
 
 
 if TYPE_CHECKING:
-    from .convert_byt5_original_tf_checkpoint_to_pytorch import *
     from .tokenization_byt5 import *
 else:
     import sys

src/transformers/models/canine/__init__.py

@@ -19,7 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_canine import *
-    from .convert_canine_original_tf_checkpoint_to_pytorch import *
     from .modeling_canine import *
     from .tokenization_canine import *
 else:

src/transformers/models/chameleon/__init__.py

@@ -19,7 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_chameleon import *
-    from .convert_chameleon_weights_to_hf import *
     from .image_processing_chameleon import *
     from .modeling_chameleon import *
     from .processing_chameleon import *

src/transformers/models/chinese_clip/__init__.py

@@ -19,7 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_chinese_clip import *
-    from .convert_chinese_clip_original_pytorch_to_hf import *
     from .feature_extraction_chinese_clip import *
     from .image_processing_chinese_clip import *
     from .modeling_chinese_clip import *

src/transformers/models/clap/__init__.py

@@ -19,7 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_clap import *
-    from .convert_clap_original_pytorch_to_hf import *
     from .feature_extraction_clap import *
     from .modeling_clap import *
     from .processing_clap import *

src/transformers/models/clip/__init__.py

@@ -19,7 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_clip import *
-    from .convert_clip_original_pytorch_to_hf import *
     from .feature_extraction_clip import *
     from .image_processing_clip import *
     from .modeling_clip import *

src/transformers/models/clipseg/__init__.py

@@ -19,7 +19,6 @@ from ...utils.import_utils import define_import_structure
 
 if TYPE_CHECKING:
     from .configuration_clipseg import *
-    from .convert_clipseg_original_pytorch_to_hf import *
     from .modeling_clipseg import *
     from .processing_clipseg import *
 else:

src/transformers/models/clvp/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
+# Copyright 2024 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,67 +13,18 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import (
+from ...utils import _LazyModule
-    OptionalDependencyNotAvailable,
+from ...utils.import_utils import define_import_structure
-    _LazyModule,
-    is_torch_available,
-)
-
-
-_import_structure = {
-    "configuration_clvp": [
-        "ClvpConfig",
-        "ClvpDecoderConfig",
-        "ClvpEncoderConfig",
-    ],
-    "feature_extraction_clvp": ["ClvpFeatureExtractor"],
-    "processing_clvp": ["ClvpProcessor"],
-    "tokenization_clvp": ["ClvpTokenizer"],
-}
-
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_clvp"] = [
-        "ClvpModelForConditionalGeneration",
-        "ClvpForCausalLM",
-        "ClvpModel",
-        "ClvpPreTrainedModel",
-        "ClvpEncoder",
-        "ClvpDecoder",
-    ]
 
 
 if TYPE_CHECKING:
-    from .configuration_clvp import (
+    from .configuration_clvp import *
-        ClvpConfig,
+    from .feature_extraction_clvp import *
-        ClvpDecoderConfig,
+    from .modeling_clvp import *
-        ClvpEncoderConfig,
+    from .processing_clvp import *
-    )
+    from .tokenization_clvp import *
-    from .feature_extraction_clvp import ClvpFeatureExtractor
-    from .processing_clvp import ClvpProcessor
-    from .tokenization_clvp import ClvpTokenizer
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_clvp import (
-            ClvpDecoder,
-            ClvpEncoder,
-            ClvpForCausalLM,
-            ClvpModel,
-            ClvpModelForConditionalGeneration,
-            ClvpPreTrainedModel,
-        )
-
 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/clvp/configuration_clvp.py

@@ -438,3 +438,6 @@ class ClvpConfig(PretrainedConfig):
             decoder_config=decoder_config.to_dict(),
             **kwargs,
         )
+
+
+__all__ = ["ClvpConfig", "ClvpDecoderConfig", "ClvpEncoderConfig"]

src/transformers/models/clvp/feature_extraction_clvp.py

@@ -236,3 +236,6 @@ class ClvpFeatureExtractor(SequenceFeatureExtractor):
             padded_inputs["input_features"] = input_features
 
         return padded_inputs.convert_to_tensors(return_tensors)
+
+
+__all__ = ["ClvpFeatureExtractor"]

src/transformers/models/clvp/modeling_clvp.py

@@ -2021,3 +2021,13 @@ class ClvpModelForConditionalGeneration(ClvpPreTrainedModel, GenerationMixin):
             text_encoder_hidden_states=text_outputs.hidden_states,
             speech_encoder_hidden_states=speech_outputs.hidden_states,
         )
+
+
+__all__ = [
+    "ClvpModelForConditionalGeneration",
+    "ClvpForCausalLM",
+    "ClvpModel",
+    "ClvpPreTrainedModel",
+    "ClvpEncoder",
+    "ClvpDecoder",
+]

src/transformers/models/clvp/processing_clvp.py

@@ -88,3 +88,6 @@ class ClvpProcessor(ProcessorMixin):
         the docstring of this method for more information.
         """
         return self.tokenizer.decode(*args, **kwargs)
+
+
+__all__ = ["ClvpProcessor"]

src/transformers/models/clvp/tokenization_clvp.py

@@ -362,3 +362,6 @@ class ClvpTokenizer(PreTrainedTokenizer):
                 index += 1
 
         return vocab_file, merge_file
+
+
+__all__ = ["ClvpTokenizer"]

src/transformers/models/code_llama/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2023 MetaAI and The HuggingFace Inc. team. All rights reserved.
+# Copyright 2024 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,45 +13,15 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
 
 
-_import_structure = {}
-
-try:
-    if not is_sentencepiece_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["tokenization_code_llama"] = ["CodeLlamaTokenizer"]
-
-try:
-    if not is_tokenizers_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["tokenization_code_llama_fast"] = ["CodeLlamaTokenizerFast"]
-
 if TYPE_CHECKING:
-    try:
+    from .tokenization_code_llama import *
-        if not is_sentencepiece_available():
+    from .tokenization_code_llama_fast import *
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .tokenization_code_llama import CodeLlamaTokenizer
-
-    try:
-        if not is_tokenizers_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .tokenization_code_llama_fast import CodeLlamaTokenizerFast
-
 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/code_llama/tokenization_code_llama.py

@@ -447,3 +447,6 @@ class CodeLlamaTokenizer(PreTrainedTokenizer):
         self.__dict__ = d
         self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
+
+
+__all__ = ["CodeLlamaTokenizer"]

src/transformers/models/code_llama/tokenization_code_llama_fast.py

@@ -376,3 +376,6 @@ class CodeLlamaTokenizerFast(PreTrainedTokenizerFast):
         if token_ids_1 is None:
             return self.bos_token_id + token_ids_0 + self.eos_token_id
         return self.bos_token_id + token_ids_0 + token_ids_1 + self.eos_token_id
+
+
+__all__ = ["CodeLlamaTokenizerFast"]

src/transformers/models/codegen/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2022 Salesforce authors, The EleutherAI, and HuggingFace Teams. All rights reserved.
+# Copyright 2024 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,59 +13,17 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
 
 
-_import_structure = {
-    "configuration_codegen": ["CodeGenConfig", "CodeGenOnnxConfig"],
-    "tokenization_codegen": ["CodeGenTokenizer"],
-}
-
-try:
-    if not is_tokenizers_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["tokenization_codegen_fast"] = ["CodeGenTokenizerFast"]
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_codegen"] = [
-        "CodeGenForCausalLM",
-        "CodeGenModel",
-        "CodeGenPreTrainedModel",
-    ]
-
 if TYPE_CHECKING:
-    from .configuration_codegen import CodeGenConfig, CodeGenOnnxConfig
+    from .configuration_codegen import *
-    from .tokenization_codegen import CodeGenTokenizer
+    from .modeling_codegen import *
-
+    from .tokenization_codegen import *
-    try:
+    from .tokenization_codegen_fast import *
-        if not is_tokenizers_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .tokenization_codegen_fast import CodeGenTokenizerFast
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_codegen import (
-            CodeGenForCausalLM,
-            CodeGenModel,
-            CodeGenPreTrainedModel,
-        )
-
 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/codegen/configuration_codegen.py

@@ -225,3 +225,6 @@ class CodeGenOnnxConfig(OnnxConfigWithPast):
     @property
     def default_onnx_opset(self) -> int:
         return 13
+
+
+__all__ = ["CodeGenConfig", "CodeGenOnnxConfig"]

src/transformers/models/codegen/modeling_codegen.py

@@ -809,3 +809,6 @@ class CodeGenForCausalLM(CodeGenPreTrainedModel, GenerationMixin):
             tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
             for layer_past in past_key_values
         )
+
+
+__all__ = ["CodeGenForCausalLM", "CodeGenModel", "CodeGenPreTrainedModel"]

src/transformers/models/codegen/tokenization_codegen.py

@@ -414,3 +414,6 @@ class CodeGenTokenizer(PreTrainedTokenizer):
             return completion[: min(terminals_pos)]
         else:
             return completion
+
+
+__all__ = ["CodeGenTokenizer"]

src/transformers/models/codegen/tokenization_codegen_fast.py

@@ -270,3 +270,6 @@ class CodeGenTokenizerFast(PreTrainedTokenizerFast):
             return completion[: min(terminals_pos)]
         else:
             return completion
+
+
+__all__ = ["CodeGenTokenizerFast"]

src/transformers/models/cohere/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2024 Cohere and The HuggingFace Inc. team. All rights reserved.
+# Copyright 2024 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,65 +13,16 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import (
+from ...utils import _LazyModule
-    OptionalDependencyNotAvailable,
+from ...utils.import_utils import define_import_structure
-    _LazyModule,
-    is_sentencepiece_available,
-    is_tokenizers_available,
-    is_torch_available,
-)
-
-
-_import_structure = {
-    "configuration_cohere": ["CohereConfig"],
-}
-
-
-try:
-    if not is_tokenizers_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["tokenization_cohere_fast"] = ["CohereTokenizerFast"]
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_cohere"] = [
-        "CohereForCausalLM",
-        "CohereModel",
-        "CoherePreTrainedModel",
-    ]
 
 
 if TYPE_CHECKING:
-    from .configuration_cohere import CohereConfig
+    from .configuration_cohere import *
-
+    from .modeling_cohere import *
-    try:
+    from .tokenization_cohere_fast import *
-        if not is_tokenizers_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .tokenization_cohere_fast import CohereTokenizerFast
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_cohere import (
-            CohereForCausalLM,
-            CohereModel,
-            CoherePreTrainedModel,
-        )
-
 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/cohere/configuration_cohere.py

@@ -198,3 +198,6 @@ class CohereConfig(PretrainedConfig):
             tie_word_embeddings=tie_word_embeddings,
             **kwargs,
         )
+
+
+__all__ = ["CohereConfig"]

src/transformers/models/cohere/modeling_cohere.py

@@ -1145,3 +1145,6 @@ class CohereForCausalLM(CoherePreTrainedModel, GenerationMixin):
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+
+__all__ = ["CohereForCausalLM", "CohereModel", "CoherePreTrainedModel"]

src/transformers/models/cohere/tokenization_cohere_fast.py

@@ -510,3 +510,6 @@ class CohereTokenizerFast(PreTrainedTokenizerFast):
             output = output + bos_token_id + token_ids_1 + eos_token_id
 
         return output
+
+
+__all__ = ["CohereTokenizerFast"]

src/transformers/models/conditional_detr/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
+# Copyright 2024 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,71 +11,19 @@
 # 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.
-
 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_conditional_detr": [
-        "ConditionalDetrConfig",
-        "ConditionalDetrOnnxConfig",
-    ]
-}
-
-try:
-    if not is_vision_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["feature_extraction_conditional_detr"] = ["ConditionalDetrFeatureExtractor"]
-    _import_structure["image_processing_conditional_detr"] = ["ConditionalDetrImageProcessor"]
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_conditional_detr"] = [
-        "ConditionalDetrForObjectDetection",
-        "ConditionalDetrForSegmentation",
-        "ConditionalDetrModel",
-        "ConditionalDetrPreTrainedModel",
-    ]
 
 
 if TYPE_CHECKING:
-    from .configuration_conditional_detr import (
+    from .configuration_conditional_detr import *
-        ConditionalDetrConfig,
+    from .feature_extraction_conditional_detr import *
-        ConditionalDetrOnnxConfig,
+    from .image_processing_conditional_detr import *
-    )
+    from .modeling_conditional_detr import *
-
-    try:
-        if not is_vision_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
-        from .image_processing_conditional_detr import ConditionalDetrImageProcessor
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_conditional_detr import (
-            ConditionalDetrForObjectDetection,
-            ConditionalDetrForSegmentation,
-            ConditionalDetrModel,
-            ConditionalDetrPreTrainedModel,
-        )
-
 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/conditional_detr/configuration_conditional_detr.py

@@ -273,3 +273,6 @@ class ConditionalDetrOnnxConfig(OnnxConfig):
     @property
     def default_onnx_opset(self) -> int:
         return 12
+
+
+__all__ = ["ConditionalDetrConfig", "ConditionalDetrOnnxConfig"]

src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py

@@ -41,3 +41,6 @@ class ConditionalDetrFeatureExtractor(ConditionalDetrImageProcessor):
             FutureWarning,
         )
         super().__init__(*args, **kwargs)
+
+
+__all__ = ["ConditionalDetrFeatureExtractor"]

src/transformers/models/conditional_detr/image_processing_conditional_detr.py

@@ -1851,3 +1851,6 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
 
             results.append({"segmentation": segmentation, "segments_info": segments})
         return results
+
+
+__all__ = ["ConditionalDetrImageProcessor"]

src/transformers/models/conditional_detr/modeling_conditional_detr.py

@@ -2105,3 +2105,11 @@ class ConditionalDetrMHAttentionMap(nn.Module):
         weights = nn.functional.softmax(weights.flatten(2), dim=-1).view(weights.size())
         weights = self.dropout(weights)
         return weights
+
+
+__all__ = [
+    "ConditionalDetrForObjectDetection",
+    "ConditionalDetrForSegmentation",
+    "ConditionalDetrModel",
+    "ConditionalDetrPreTrainedModel",
+]

src/transformers/models/convbert/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2020 The HuggingFace Team. All rights reserved.
+# Copyright 2024 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,114 +13,18 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import (
+from ...utils import _LazyModule
-    OptionalDependencyNotAvailable,
+from ...utils.import_utils import define_import_structure
-    _LazyModule,
-    is_tf_available,
-    is_tokenizers_available,
-    is_torch_available,
-)
-
-
-_import_structure = {
-    "configuration_convbert": ["ConvBertConfig", "ConvBertOnnxConfig"],
-    "tokenization_convbert": ["ConvBertTokenizer"],
-}
-
-try:
-    if not is_tokenizers_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["tokenization_convbert_fast"] = ["ConvBertTokenizerFast"]
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_convbert"] = [
-        "ConvBertForMaskedLM",
-        "ConvBertForMultipleChoice",
-        "ConvBertForQuestionAnswering",
-        "ConvBertForSequenceClassification",
-        "ConvBertForTokenClassification",
-        "ConvBertLayer",
-        "ConvBertModel",
-        "ConvBertPreTrainedModel",
-        "load_tf_weights_in_convbert",
-    ]
-
-
-try:
-    if not is_tf_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_tf_convbert"] = [
-        "TFConvBertForMaskedLM",
-        "TFConvBertForMultipleChoice",
-        "TFConvBertForQuestionAnswering",
-        "TFConvBertForSequenceClassification",
-        "TFConvBertForTokenClassification",
-        "TFConvBertLayer",
-        "TFConvBertModel",
-        "TFConvBertPreTrainedModel",
-    ]
 
 
 if TYPE_CHECKING:
-    from .configuration_convbert import ConvBertConfig, ConvBertOnnxConfig
+    from .configuration_convbert import *
-    from .tokenization_convbert import ConvBertTokenizer
+    from .modeling_convbert import *
-
+    from .modeling_tf_convbert import *
-    try:
+    from .tokenization_convbert import *
-        if not is_tokenizers_available():
+    from .tokenization_convbert_fast import *
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .tokenization_convbert_fast import ConvBertTokenizerFast
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_convbert import (
-            ConvBertForMaskedLM,
-            ConvBertForMultipleChoice,
-            ConvBertForQuestionAnswering,
-            ConvBertForSequenceClassification,
-            ConvBertForTokenClassification,
-            ConvBertLayer,
-            ConvBertModel,
-            ConvBertPreTrainedModel,
-            load_tf_weights_in_convbert,
-        )
-
-    try:
-        if not is_tf_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_tf_convbert import (
-            TFConvBertForMaskedLM,
-            TFConvBertForMultipleChoice,
-            TFConvBertForQuestionAnswering,
-            TFConvBertForSequenceClassification,
-            TFConvBertForTokenClassification,
-            TFConvBertLayer,
-            TFConvBertModel,
-            TFConvBertPreTrainedModel,
-        )
-
-
 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/convbert/configuration_convbert.py

@@ -155,3 +155,6 @@ class ConvBertOnnxConfig(OnnxConfig):
                 ("token_type_ids", dynamic_axis),
             ]
         )
+
+
+__all__ = ["ConvBertConfig", "ConvBertOnnxConfig"]

src/transformers/models/convbert/modeling_convbert.py

@@ -1331,3 +1331,16 @@ class ConvBertForQuestionAnswering(ConvBertPreTrainedModel):
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+
+__all__ = [
+    "ConvBertForMaskedLM",
+    "ConvBertForMultipleChoice",
+    "ConvBertForQuestionAnswering",
+    "ConvBertForSequenceClassification",
+    "ConvBertForTokenClassification",
+    "ConvBertLayer",
+    "ConvBertModel",
+    "ConvBertPreTrainedModel",
+    "load_tf_weights_in_convbert",
+]

src/transformers/models/convbert/modeling_tf_convbert.py

@@ -1462,3 +1462,15 @@ class TFConvBertForQuestionAnswering(TFConvBertPreTrainedModel, TFQuestionAnswer
         if getattr(self, "qa_outputs", None) is not None:
             with tf.name_scope(self.qa_outputs.name):
                 self.qa_outputs.build([None, None, self.config.hidden_size])
+
+
+__all__ = [
+    "TFConvBertForMaskedLM",
+    "TFConvBertForMultipleChoice",
+    "TFConvBertForQuestionAnswering",
+    "TFConvBertForSequenceClassification",
+    "TFConvBertForTokenClassification",
+    "TFConvBertLayer",
+    "TFConvBertModel",
+    "TFConvBertPreTrainedModel",
+]

src/transformers/models/convbert/tokenization_convbert.py

@@ -507,3 +507,6 @@ class WordpieceTokenizer:
             else:
                 output_tokens.extend(sub_tokens)
         return output_tokens
+
+
+__all__ = ["ConvBertTokenizer"]

src/transformers/models/convbert/tokenization_convbert_fast.py

@@ -171,3 +171,6 @@ class ConvBertTokenizerFast(PreTrainedTokenizerFast):
     def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
         files = self._tokenizer.model.save(save_directory, name=filename_prefix)
         return tuple(files)
+
+
+__all__ = ["ConvBertTokenizerFast"]

src/transformers/models/convnext/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
+# Copyright 2024 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,86 +13,18 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import (
+from ...utils import _LazyModule
-    OptionalDependencyNotAvailable,
+from ...utils.import_utils import define_import_structure
-    _LazyModule,
-    is_tf_available,
-    is_torch_available,
-    is_vision_available,
-)
 
 
-_import_structure = {"configuration_convnext": ["ConvNextConfig", "ConvNextOnnxConfig"]}
-
-try:
-    if not is_vision_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["feature_extraction_convnext"] = ["ConvNextFeatureExtractor"]
-    _import_structure["image_processing_convnext"] = ["ConvNextImageProcessor"]
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_convnext"] = [
-        "ConvNextForImageClassification",
-        "ConvNextModel",
-        "ConvNextPreTrainedModel",
-        "ConvNextBackbone",
-    ]
-
-try:
-    if not is_tf_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_tf_convnext"] = [
-        "TFConvNextForImageClassification",
-        "TFConvNextModel",
-        "TFConvNextPreTrainedModel",
-    ]
-
 if TYPE_CHECKING:
-    from .configuration_convnext import ConvNextConfig, ConvNextOnnxConfig
+    from .configuration_convnext import *
-
+    from .feature_extraction_convnext import *
-    try:
+    from .image_processing_convnext import *
-        if not is_vision_available():
+    from .modeling_convnext import *
-            raise OptionalDependencyNotAvailable()
+    from .modeling_tf_convnext import *
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .feature_extraction_convnext import ConvNextFeatureExtractor
-        from .image_processing_convnext import ConvNextImageProcessor
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_convnext import (
-            ConvNextBackbone,
-            ConvNextForImageClassification,
-            ConvNextModel,
-            ConvNextPreTrainedModel,
-        )
-
-    try:
-        if not is_tf_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
-
-
 else:
     import sys
 
-    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)

src/transformers/models/convnext/configuration_convnext.py

@@ -137,3 +137,6 @@ class ConvNextOnnxConfig(OnnxConfig):
     @property
     def atol_for_validation(self) -> float:
         return 1e-5
+
+
+__all__ = ["ConvNextConfig", "ConvNextOnnxConfig"]

src/transformers/models/convnext/feature_extraction_convnext.py

@@ -31,3 +31,6 @@ class ConvNextFeatureExtractor(ConvNextImageProcessor):
             FutureWarning,
         )
         super().__init__(*args, **kwargs)
+
+
+__all__ = ["ConvNextFeatureExtractor"]

src/transformers/models/convnext/image_processing_convnext.py

@@ -318,3 +318,6 @@ class ConvNextImageProcessor(BaseImageProcessor):
 
         data = {"pixel_values": images}
         return BatchFeature(data=data, tensor_type=return_tensors)
+
+
+__all__ = ["ConvNextImageProcessor"]

src/transformers/models/convnext/modeling_convnext.py

@@ -546,3 +546,6 @@ class ConvNextBackbone(ConvNextPreTrainedModel, BackboneMixin):
             hidden_states=hidden_states if output_hidden_states else None,
             attentions=None,
         )
+
+
+__all__ = ["ConvNextForImageClassification", "ConvNextModel", "ConvNextPreTrainedModel", "ConvNextBackbone"]

src/transformers/models/convnext/modeling_tf_convnext.py

@@ -664,3 +664,6 @@ class TFConvNextForImageClassification(TFConvNextPreTrainedModel, TFSequenceClas
             if hasattr(self.classifier, "name"):
                 with tf.name_scope(self.classifier.name):
                     self.classifier.build([None, None, self.config.hidden_sizes[-1]])
+
+
+__all__ = ["TFConvNextForImageClassification", "TFConvNextModel", "TFConvNextPreTrainedModel"]

src/transformers/models/convnextv2/__init__.py

@@ -1,8 +1,4 @@
-# flake8: noqa
+# Copyright 2024 The HuggingFace Team. All rights reserved.
-# There's no way to ignore "F401 '...' imported but unused" warnings in this
-# module, but to preserve other warnings. So, don't check this module at all.
-
-# Copyright 2023 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -17,73 +13,16 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-# rely on isort to merge the imports
+from ...utils import _LazyModule
-from ...utils import (
+from ...utils.import_utils import define_import_structure
-    OptionalDependencyNotAvailable,
-    _LazyModule,
-    is_torch_available,
-    is_tf_available,
-)
 
 
-_import_structure = {"configuration_convnextv2": ["ConvNextV2Config"]}
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_convnextv2"] = [
-        "ConvNextV2ForImageClassification",
-        "ConvNextV2Model",
-        "ConvNextV2PreTrainedModel",
-        "ConvNextV2Backbone",
-    ]
-
-try:
-    if not is_tf_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_tf_convnextv2"] = [
-        "TFConvNextV2ForImageClassification",
-        "TFConvNextV2Model",
-        "TFConvNextV2PreTrainedModel",
-    ]
-
 if TYPE_CHECKING:
-    from .configuration_convnextv2 import (
+    from .configuration_convnextv2 import *
-        ConvNextV2Config,
+    from .modeling_convnextv2 import *
-    )
+    from .modeling_tf_convnextv2 import *
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_convnextv2 import (
-            ConvNextV2Backbone,
-            ConvNextV2ForImageClassification,
-            ConvNextV2Model,
-            ConvNextV2PreTrainedModel,
-        )
-
-    try:
-        if not is_tf_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_tf_convnextv2 import (
-            TFConvNextV2ForImageClassification,
-            TFConvNextV2Model,
-            TFConvNextV2PreTrainedModel,
-        )
-
 else:
     import sys
 
-    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)

src/transformers/models/convnextv2/configuration_convnextv2.py

@@ -113,3 +113,6 @@ class ConvNextV2Config(BackboneConfigMixin, PretrainedConfig):
         self._out_features, self._out_indices = get_aligned_output_features_output_indices(
             out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
         )
+
+
+__all__ = ["ConvNextV2Config"]

src/transformers/models/convnextv2/modeling_convnextv2.py

@@ -569,3 +569,6 @@ class ConvNextV2Backbone(ConvNextV2PreTrainedModel, BackboneMixin):
             hidden_states=hidden_states if output_hidden_states else None,
             attentions=None,
         )
+
+
+__all__ = ["ConvNextV2ForImageClassification", "ConvNextV2Model", "ConvNextV2PreTrainedModel", "ConvNextV2Backbone"]

src/transformers/models/convnextv2/modeling_tf_convnextv2.py

@@ -678,3 +678,6 @@ class TFConvNextV2ForImageClassification(TFConvNextV2PreTrainedModel, TFSequence
         if getattr(self, "classifier", None) is not None:
             with tf.name_scope(self.classifier.name):
                 self.classifier.build([None, None, self.config.hidden_sizes[-1]])
+
+
+__all__ = ["TFConvNextV2ForImageClassification", "TFConvNextV2Model", "TFConvNextV2PreTrainedModel"]

src/transformers/models/cpm/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2020 The HuggingFace Team. All rights reserved.
+# Copyright 2024 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,49 +11,17 @@
 # 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.
-
 from typing import TYPE_CHECKING
 
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available
+from ...utils import _LazyModule
-
+from ...utils.import_utils import define_import_structure
-
-_import_structure = {}
-
-try:
-    if not is_sentencepiece_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["tokenization_cpm"] = ["CpmTokenizer"]
-
-try:
-    if not is_tokenizers_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["tokenization_cpm_fast"] = ["CpmTokenizerFast"]
 
 
 if TYPE_CHECKING:
-    try:
+    from .tokenization_cpm import *
-        if not is_sentencepiece_available():
+    from .tokenization_cpm_fast import *
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .tokenization_cpm import CpmTokenizer
-
-    try:
-        if not is_tokenizers_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .tokenization_cpm_fast import CpmTokenizerFast
-
 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/cpm/tokenization_cpm.py

@@ -343,3 +343,6 @@ class CpmTokenizer(PreTrainedTokenizer):
         text = super()._decode(*args, **kwargs)
         text = text.replace(" ", "").replace("\u2582", " ").replace("\u2583", "\n")
         return text
+
+
+__all__ = ["CpmTokenizer"]

src/transformers/models/cpm/tokenization_cpm_fast.py

@@ -236,3 +236,6 @@ class CpmTokenizerFast(PreTrainedTokenizerFast):
         text = super()._decode(*args, **kwargs)
         text = text.replace(" ", "").replace("\u2582", " ").replace("\u2583", "\n")
         return text
+
+
+__all__ = ["CpmTokenizerFast"]

src/transformers/models/cpmant/__init__.py

@@ -1,8 +1,4 @@
-# flake8: noqa
+# Copyright 2024 The HuggingFace Team. All rights reserved.
-# There's no way to ignore "F401 '...' imported but unused" warnings in this
-# module, but to preserve other warnings. So, don't check this module at all.
-
-# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -17,46 +13,16 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-# rely on isort to merge the imports
+from ...utils import _LazyModule
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
+from ...utils.import_utils import define_import_structure
-
-
-_import_structure = {
-    "configuration_cpmant": ["CpmAntConfig"],
-    "tokenization_cpmant": ["CpmAntTokenizer"],
-}
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_cpmant"] = [
-        "CpmAntForCausalLM",
-        "CpmAntModel",
-        "CpmAntPreTrainedModel",
-    ]
 
 
 if TYPE_CHECKING:
-    from .configuration_cpmant import CpmAntConfig
+    from .configuration_cpmant import *
-    from .tokenization_cpmant import CpmAntTokenizer
+    from .modeling_cpmant import *
-
+    from .tokenization_cpmant import *
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_cpmant import (
-            CpmAntForCausalLM,
-            CpmAntModel,
-            CpmAntPreTrainedModel,
-        )
-
-
 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/cpmant/configuration_cpmant.py

@@ -117,3 +117,6 @@ class CpmAntConfig(PretrainedConfig):
         self.use_cache = use_cache
         self.vocab_size = vocab_size
         self.init_std = init_std
+
+
+__all__ = ["CpmAntConfig"]

src/transformers/models/cpmant/modeling_cpmant.py

@@ -855,3 +855,6 @@ class CpmAntForCausalLM(CpmAntPreTrainedModel, GenerationMixin):
             key_value_layer[0] = key_value_layer[0][beam_idx]
             key_value_layer[1] = key_value_layer[1][beam_idx]
         return past_key_values
+
+
+__all__ = ["CpmAntForCausalLM", "CpmAntModel", "CpmAntPreTrainedModel"]

src/transformers/models/cpmant/tokenization_cpmant.py

@@ -265,3 +265,6 @@ class CpmAntTokenizer(PreTrainedTokenizer):
         if token_ids_1 is not None:
             return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
         return [1] + ([0] * len(token_ids_0))
+
+
+__all__ = ["CpmAntTokenizer"]

src/transformers/models/ctrl/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2020 The HuggingFace Team. All rights reserved.
+# Copyright 2024 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,75 +11,19 @@
 # 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.
-
 from typing import TYPE_CHECKING
 
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
+from ...utils import _LazyModule
-
+from ...utils.import_utils import define_import_structure
-
-_import_structure = {
-    "configuration_ctrl": ["CTRLConfig"],
-    "tokenization_ctrl": ["CTRLTokenizer"],
-}
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_ctrl"] = [
-        "CTRLForSequenceClassification",
-        "CTRLLMHeadModel",
-        "CTRLModel",
-        "CTRLPreTrainedModel",
-    ]
-
-try:
-    if not is_tf_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_tf_ctrl"] = [
-        "TFCTRLForSequenceClassification",
-        "TFCTRLLMHeadModel",
-        "TFCTRLModel",
-        "TFCTRLPreTrainedModel",
-    ]
 
 
 if TYPE_CHECKING:
-    from .configuration_ctrl import CTRLConfig
+    from .configuration_ctrl import *
-    from .tokenization_ctrl import CTRLTokenizer
+    from .modeling_ctrl import *
-
+    from .modeling_tf_ctrl import *
-    try:
+    from .tokenization_ctrl import *
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_ctrl import (
-            CTRLForSequenceClassification,
-            CTRLLMHeadModel,
-            CTRLModel,
-            CTRLPreTrainedModel,
-        )
-
-    try:
-        if not is_tf_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_tf_ctrl import (
-            TFCTRLForSequenceClassification,
-            TFCTRLLMHeadModel,
-            TFCTRLModel,
-            TFCTRLPreTrainedModel,
-        )
-
 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/ctrl/configuration_ctrl.py

@@ -111,3 +111,6 @@ class CTRLConfig(PretrainedConfig):
         self.use_cache = use_cache
 
         super().__init__(**kwargs)
+
+
+__all__ = ["CTRLConfig"]

src/transformers/models/ctrl/modeling_ctrl.py

@@ -839,3 +839,6 @@ class CTRLForSequenceClassification(CTRLPreTrainedModel):
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
         )
+
+
+__all__ = ["CTRLForSequenceClassification", "CTRLLMHeadModel", "CTRLModel", "CTRLPreTrainedModel"]

src/transformers/models/ctrl/modeling_tf_ctrl.py

@@ -926,3 +926,6 @@ class TFCTRLForSequenceClassification(TFCTRLPreTrainedModel, TFSequenceClassific
         if getattr(self, "transformer", None) is not None:
             with tf.name_scope(self.transformer.name):
                 self.transformer.build(None)
+
+
+__all__ = ["TFCTRLForSequenceClassification", "TFCTRLLMHeadModel", "TFCTRLModel", "TFCTRLPreTrainedModel"]

src/transformers/models/ctrl/tokenization_ctrl.py

@@ -246,3 +246,6 @@ class CTRLTokenizer(PreTrainedTokenizer):
     #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
     #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
     #     return ''.join(tokens_generated_so_far)
+
+
+__all__ = ["CTRLTokenizer"]

src/transformers/models/cvt/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
+# Copyright 2024 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,65 +13,16 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
 
 
-_import_structure = {"configuration_cvt": ["CvtConfig"]}
-
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_cvt"] = [
-        "CvtForImageClassification",
-        "CvtModel",
-        "CvtPreTrainedModel",
-    ]
-
-try:
-    if not is_tf_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_tf_cvt"] = [
-        "TFCvtForImageClassification",
-        "TFCvtModel",
-        "TFCvtPreTrainedModel",
-    ]
-
 if TYPE_CHECKING:
-    from .configuration_cvt import CvtConfig
+    from .configuration_cvt import *
-
+    from .modeling_cvt import *
-    try:
+    from .modeling_tf_cvt import *
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_cvt import (
-            CvtForImageClassification,
-            CvtModel,
-            CvtPreTrainedModel,
-        )
-
-    try:
-        if not is_tf_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_tf_cvt import (
-            TFCvtForImageClassification,
-            TFCvtModel,
-            TFCvtPreTrainedModel,
-        )
-
-
 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/cvt/configuration_cvt.py

@@ -141,3 +141,6 @@ class CvtConfig(PretrainedConfig):
         self.stride_q = stride_q
         self.initializer_range = initializer_range
         self.layer_norm_eps = layer_norm_eps
+
+
+__all__ = ["CvtConfig"]

src/transformers/models/cvt/modeling_cvt.py

@@ -720,3 +720,6 @@ class CvtForImageClassification(CvtPreTrainedModel):
             return ((loss,) + output) if loss is not None else output
 
         return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
+
+
+__all__ = ["CvtForImageClassification", "CvtModel", "CvtPreTrainedModel"]

src/transformers/models/cvt/modeling_tf_cvt.py

@@ -1091,3 +1091,6 @@ class TFCvtForImageClassification(TFCvtPreTrainedModel, TFSequenceClassification
             if hasattr(self.classifier, "name"):
                 with tf.name_scope(self.classifier.name):
                     self.classifier.build([None, None, self.config.embed_dim[-1]])
+
+
+__all__ = ["TFCvtForImageClassification", "TFCvtModel", "TFCvtPreTrainedModel"]

src/transformers/models/dac/__init__.py

@@ -1,5 +1,4 @@
-# coding=utf-8
+# Copyright 2024 The HuggingFace Team. All rights reserved.
-# Copyright 2024 Descript and The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -14,47 +13,16 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import (
+from ...utils import _LazyModule
-    OptionalDependencyNotAvailable,
+from ...utils.import_utils import define_import_structure
-    _LazyModule,
-    is_torch_available,
-)
 
 
-_import_structure = {
-    "configuration_dac": ["DacConfig"],
-    "feature_extraction_dac": ["DacFeatureExtractor"],
-}
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_dac"] = [
-        "DacModel",
-        "DacPreTrainedModel",
-    ]
-
 if TYPE_CHECKING:
-    from .configuration_dac import (
+    from .configuration_dac import *
-        DacConfig,
+    from .feature_extraction_dac import *
-    )
+    from .modeling_dac import *
-    from .feature_extraction_dac import DacFeatureExtractor
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_dac import (
-            DacModel,
-            DacPreTrainedModel,
-        )
-
 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/dac/configuration_dac.py

@@ -109,3 +109,6 @@ class DacConfig(PretrainedConfig):
     def frame_rate(self) -> int:
         hop_length = np.prod(self.upsampling_ratios)
         return math.ceil(self.sampling_rate / hop_length)
+
+
+__all__ = ["DacConfig"]

src/transformers/models/dac/feature_extraction_dac.py

@@ -168,3 +168,6 @@ class DacFeatureExtractor(SequenceFeatureExtractor):
             padded_inputs = padded_inputs.convert_to_tensors(return_tensors)
 
         return padded_inputs
+
+
+__all__ = ["DacFeatureExtractor"]

src/transformers/models/dac/modeling_dac.py

@@ -719,3 +719,6 @@ class DacModel(DacPreTrainedModel):
             return (loss, audio_values, quantized_representation, audio_codes, projected_latents)
 
         return DacOutput(loss, audio_values, quantized_representation, audio_codes, projected_latents)
+
+
+__all__ = ["DacModel", "DacPreTrainedModel"]

src/transformers/models/data2vec/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
+# Copyright 2024 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,115 +11,22 @@
 # 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.
-
 from typing import TYPE_CHECKING
 
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
 
 
-_import_structure = {
-    "configuration_data2vec_audio": ["Data2VecAudioConfig"],
-    "configuration_data2vec_text": [
-        "Data2VecTextConfig",
-        "Data2VecTextOnnxConfig",
-    ],
-    "configuration_data2vec_vision": [
-        "Data2VecVisionConfig",
-        "Data2VecVisionOnnxConfig",
-    ],
-}
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_data2vec_audio"] = [
-        "Data2VecAudioForAudioFrameClassification",
-        "Data2VecAudioForCTC",
-        "Data2VecAudioForSequenceClassification",
-        "Data2VecAudioForXVector",
-        "Data2VecAudioModel",
-        "Data2VecAudioPreTrainedModel",
-    ]
-    _import_structure["modeling_data2vec_text"] = [
-        "Data2VecTextForCausalLM",
-        "Data2VecTextForMaskedLM",
-        "Data2VecTextForMultipleChoice",
-        "Data2VecTextForQuestionAnswering",
-        "Data2VecTextForSequenceClassification",
-        "Data2VecTextForTokenClassification",
-        "Data2VecTextModel",
-        "Data2VecTextPreTrainedModel",
-    ]
-    _import_structure["modeling_data2vec_vision"] = [
-        "Data2VecVisionForImageClassification",
-        "Data2VecVisionForMaskedImageModeling",
-        "Data2VecVisionForSemanticSegmentation",
-        "Data2VecVisionModel",
-        "Data2VecVisionPreTrainedModel",
-    ]
-
-if is_tf_available():
-    _import_structure["modeling_tf_data2vec_vision"] = [
-        "TFData2VecVisionForImageClassification",
-        "TFData2VecVisionForSemanticSegmentation",
-        "TFData2VecVisionModel",
-        "TFData2VecVisionPreTrainedModel",
-    ]
-
 if TYPE_CHECKING:
-    from .configuration_data2vec_audio import Data2VecAudioConfig
+    from .configuration_data2vec_audio import *
-    from .configuration_data2vec_text import (
+    from .configuration_data2vec_text import *
-        Data2VecTextConfig,
+    from .configuration_data2vec_vision import *
-        Data2VecTextOnnxConfig,
+    from .modeling_data2vec_audio import *
-    )
+    from .modeling_data2vec_text import *
-    from .configuration_data2vec_vision import (
+    from .modeling_data2vec_vision import *
-        Data2VecVisionConfig,
+    from .modeling_tf_data2vec_vision import *
-        Data2VecVisionOnnxConfig,
-    )
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_data2vec_audio import (
-            Data2VecAudioForAudioFrameClassification,
-            Data2VecAudioForCTC,
-            Data2VecAudioForSequenceClassification,
-            Data2VecAudioForXVector,
-            Data2VecAudioModel,
-            Data2VecAudioPreTrainedModel,
-        )
-        from .modeling_data2vec_text import (
-            Data2VecTextForCausalLM,
-            Data2VecTextForMaskedLM,
-            Data2VecTextForMultipleChoice,
-            Data2VecTextForQuestionAnswering,
-            Data2VecTextForSequenceClassification,
-            Data2VecTextForTokenClassification,
-            Data2VecTextModel,
-            Data2VecTextPreTrainedModel,
-        )
-        from .modeling_data2vec_vision import (
-            Data2VecVisionForImageClassification,
-            Data2VecVisionForMaskedImageModeling,
-            Data2VecVisionForSemanticSegmentation,
-            Data2VecVisionModel,
-            Data2VecVisionPreTrainedModel,
-        )
-    if is_tf_available():
-        from .modeling_tf_data2vec_vision import (
-            TFData2VecVisionForImageClassification,
-            TFData2VecVisionForSemanticSegmentation,
-            TFData2VecVisionModel,
-            TFData2VecVisionPreTrainedModel,
-        )
-
 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/data2vec/configuration_data2vec_audio.py

@@ -283,3 +283,6 @@ class Data2VecAudioConfig(PretrainedConfig):
     @property
     def inputs_to_logits_ratio(self):
         return math.prod(self.conv_stride)
+
+
+__all__ = ["Data2VecAudioConfig"]

src/transformers/models/data2vec/configuration_data2vec_text.py

@@ -149,3 +149,6 @@ class Data2VecTextOnnxConfig(OnnxConfig):
                 ("attention_mask", dynamic_axis),
             ]
         )
+
+
+__all__ = ["Data2VecTextConfig", "Data2VecTextOnnxConfig"]

src/transformers/models/data2vec/configuration_data2vec_vision.py

@@ -189,3 +189,6 @@ class Data2VecVisionOnnxConfig(OnnxConfig):
     @property
     def atol_for_validation(self) -> float:
         return 1e-4
+
+
+__all__ = ["Data2VecVisionConfig", "Data2VecVisionOnnxConfig"]

src/transformers/models/data2vec/modeling_data2vec_audio.py

@@ -1763,3 +1763,13 @@ class Data2VecAudioForXVector(Data2VecAudioPreTrainedModel):
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+
+__all__ = [
+    "Data2VecAudioForAudioFrameClassification",
+    "Data2VecAudioForCTC",
+    "Data2VecAudioForSequenceClassification",
+    "Data2VecAudioForXVector",
+    "Data2VecAudioModel",
+    "Data2VecAudioPreTrainedModel",
+]

src/transformers/models/data2vec/modeling_data2vec_text.py

@@ -1539,3 +1539,15 @@ def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_l
     mask = input_ids.ne(padding_idx).int()
     incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
     return incremental_indices.long() + padding_idx
+
+
+__all__ = [
+    "Data2VecTextForCausalLM",
+    "Data2VecTextForMaskedLM",
+    "Data2VecTextForMultipleChoice",
+    "Data2VecTextForQuestionAnswering",
+    "Data2VecTextForSequenceClassification",
+    "Data2VecTextForTokenClassification",
+    "Data2VecTextModel",
+    "Data2VecTextPreTrainedModel",
+]

src/transformers/models/data2vec/modeling_data2vec_vision.py

@@ -1444,3 +1444,11 @@ class Data2VecVisionForSemanticSegmentation(Data2VecVisionPreTrainedModel):
             hidden_states=outputs.hidden_states if output_hidden_states else None,
             attentions=outputs.attentions,
         )
+
+
+__all__ = [
+    "Data2VecVisionForImageClassification",
+    "Data2VecVisionForSemanticSegmentation",
+    "Data2VecVisionModel",
+    "Data2VecVisionPreTrainedModel",
+]

src/transformers/models/data2vec/modeling_tf_data2vec_vision.py

@@ -1714,3 +1714,11 @@ class TFData2VecVisionForSemanticSegmentation(TFData2VecVisionPreTrainedModel):
         if getattr(self, "fpn2", None) is not None:
             with tf.name_scope(self.fpn2[0].name):
                 self.fpn2[0].build([None, None, None, self.config.hidden_size])
+
+
+__all__ = [
+    "TFData2VecVisionForImageClassification",
+    "TFData2VecVisionForSemanticSegmentation",
+    "TFData2VecVisionModel",
+    "TFData2VecVisionPreTrainedModel",
+]

src/transformers/models/dbrx/__init__.py

@@ -13,39 +13,15 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
+from ...utils import _LazyModule
-
+from ...utils.import_utils import define_import_structure
-
-_import_structure = {
-    "configuration_dbrx": ["DbrxConfig"],
-}
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_dbrx"] = [
-        "DbrxForCausalLM",
-        "DbrxModel",
-        "DbrxPreTrainedModel",
-    ]
 
 
 if TYPE_CHECKING:
-    from .configuration_dbrx import DbrxConfig
+    from .configuration_dbrx import *
-
+    from .modeling_dbrx import *
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_dbrx import DbrxForCausalLM, DbrxModel, DbrxPreTrainedModel
-
-
 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/dbrx/configuration_dbrx.py

@@ -227,3 +227,6 @@ class DbrxConfig(PretrainedConfig):
             raise ValueError("tie_word_embeddings is not supported for DBRX models.")
 
         super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+__all__ = ["DbrxConfig"]

src/transformers/models/dbrx/modeling_dbrx.py

@@ -1374,3 +1374,6 @@ class DbrxForCausalLM(DbrxPreTrainedModel, GenerationMixin):
             attentions=outputs.attentions,
             router_logits=outputs.router_logits,
         )
+
+
+__all__ = ["DbrxForCausalLM", "DbrxModel", "DbrxPreTrainedModel"]

src/transformers/models/deberta/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2020 The HuggingFace Team. All rights reserved.
+# Copyright 2024 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,106 +11,20 @@
 # 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.
-
 from typing import TYPE_CHECKING
 
-from ...utils import (
+from ...utils import _LazyModule
-    OptionalDependencyNotAvailable,
+from ...utils.import_utils import define_import_structure
-    _LazyModule,
-    is_tf_available,
-    is_tokenizers_available,
-    is_torch_available,
-)
-
-
-_import_structure = {
-    "configuration_deberta": ["DebertaConfig", "DebertaOnnxConfig"],
-    "tokenization_deberta": ["DebertaTokenizer"],
-}
-
-try:
-    if not is_tokenizers_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["tokenization_deberta_fast"] = ["DebertaTokenizerFast"]
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_deberta"] = [
-        "DebertaForMaskedLM",
-        "DebertaForQuestionAnswering",
-        "DebertaForSequenceClassification",
-        "DebertaForTokenClassification",
-        "DebertaModel",
-        "DebertaPreTrainedModel",
-    ]
-
-try:
-    if not is_tf_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_tf_deberta"] = [
-        "TFDebertaForMaskedLM",
-        "TFDebertaForQuestionAnswering",
-        "TFDebertaForSequenceClassification",
-        "TFDebertaForTokenClassification",
-        "TFDebertaModel",
-        "TFDebertaPreTrainedModel",
-    ]
 
 
 if TYPE_CHECKING:
-    from .configuration_deberta import DebertaConfig, DebertaOnnxConfig
+    from .configuration_deberta import *
-    from .tokenization_deberta import DebertaTokenizer
+    from .modeling_deberta import *
-
+    from .modeling_tf_deberta import *
-    try:
+    from .tokenization_deberta import *
-        if not is_tokenizers_available():
+    from .tokenization_deberta_fast import *
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .tokenization_deberta_fast import DebertaTokenizerFast
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_deberta import (
-            DebertaForMaskedLM,
-            DebertaForQuestionAnswering,
-            DebertaForSequenceClassification,
-            DebertaForTokenClassification,
-            DebertaModel,
-            DebertaPreTrainedModel,
-        )
-
-    try:
-        if not is_tf_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_tf_deberta import (
-            TFDebertaForMaskedLM,
-            TFDebertaForQuestionAnswering,
-            TFDebertaForSequenceClassification,
-            TFDebertaForTokenClassification,
-            TFDebertaModel,
-            TFDebertaPreTrainedModel,
-        )
-
-
 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/deberta/configuration_deberta.py

@@ -194,3 +194,6 @@ class DebertaOnnxConfig(OnnxConfig):
         if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
             del dummy_inputs["token_type_ids"]
         return dummy_inputs
+
+
+__all__ = ["DebertaConfig", "DebertaOnnxConfig"]

src/transformers/models/deberta/modeling_deberta.py

@@ -1332,3 +1332,13 @@ class DebertaForQuestionAnswering(DebertaPreTrainedModel):
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+
+__all__ = [
+    "DebertaForMaskedLM",
+    "DebertaForQuestionAnswering",
+    "DebertaForSequenceClassification",
+    "DebertaForTokenClassification",
+    "DebertaModel",
+    "DebertaPreTrainedModel",
+]

src/transformers/models/deberta/modeling_tf_deberta.py

@@ -1640,3 +1640,13 @@ class TFDebertaForQuestionAnswering(TFDebertaPreTrainedModel, TFQuestionAnswerin
         if getattr(self, "qa_outputs", None) is not None:
             with tf.name_scope(self.qa_outputs.name):
                 self.qa_outputs.build([None, None, self.config.hidden_size])
+
+
+__all__ = [
+    "TFDebertaForMaskedLM",
+    "TFDebertaForQuestionAnswering",
+    "TFDebertaForSequenceClassification",
+    "TFDebertaForTokenClassification",
+    "TFDebertaModel",
+    "TFDebertaPreTrainedModel",
+]

src/transformers/models/deberta/tokenization_deberta.py

@@ -391,3 +391,6 @@ class DebertaTokenizer(PreTrainedTokenizer):
         if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
             text = " " + text
         return (text, kwargs)
+
+
+__all__ = ["DebertaTokenizer"]

src/transformers/models/deberta/tokenization_deberta_fast.py

@@ -245,3 +245,6 @@ class DebertaTokenizerFast(PreTrainedTokenizerFast):
     def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
         files = self._tokenizer.model.save(save_directory, name=filename_prefix)
         return tuple(files)
+
+
+__all__ = ["DebertaTokenizerFast"]

src/transformers/models/deberta_v2/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2020 The HuggingFace Team. All rights reserved.
+# Copyright 2024 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,112 +11,20 @@
 # 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.
-
 from typing import TYPE_CHECKING
 
-from ...utils import (
+from ...utils import _LazyModule
-    OptionalDependencyNotAvailable,
+from ...utils.import_utils import define_import_structure
-    _LazyModule,
-    is_tf_available,
-    is_tokenizers_available,
-    is_torch_available,
-)
-
-
-_import_structure = {
-    "configuration_deberta_v2": ["DebertaV2Config", "DebertaV2OnnxConfig"],
-    "tokenization_deberta_v2": ["DebertaV2Tokenizer"],
-}
-
-try:
-    if not is_tokenizers_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["tokenization_deberta_v2_fast"] = ["DebertaV2TokenizerFast"]
-
-try:
-    if not is_tf_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_tf_deberta_v2"] = [
-        "TFDebertaV2ForMaskedLM",
-        "TFDebertaV2ForQuestionAnswering",
-        "TFDebertaV2ForMultipleChoice",
-        "TFDebertaV2ForSequenceClassification",
-        "TFDebertaV2ForTokenClassification",
-        "TFDebertaV2Model",
-        "TFDebertaV2PreTrainedModel",
-    ]
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_deberta_v2"] = [
-        "DebertaV2ForMaskedLM",
-        "DebertaV2ForMultipleChoice",
-        "DebertaV2ForQuestionAnswering",
-        "DebertaV2ForSequenceClassification",
-        "DebertaV2ForTokenClassification",
-        "DebertaV2Model",
-        "DebertaV2PreTrainedModel",
-    ]
 
 
 if TYPE_CHECKING:
-    from .configuration_deberta_v2 import (
+    from .configuration_deberta_v2 import *
-        DebertaV2Config,
+    from .modeling_deberta_v2 import *
-        DebertaV2OnnxConfig,
+    from .modeling_tf_deberta_v2 import *
-    )
+    from .tokenization_deberta_v2 import *
-    from .tokenization_deberta_v2 import DebertaV2Tokenizer
+    from .tokenization_deberta_v2_fast import *
-
-    try:
-        if not is_tokenizers_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .tokenization_deberta_v2_fast import DebertaV2TokenizerFast
-
-    try:
-        if not is_tf_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_tf_deberta_v2 import (
-            TFDebertaV2ForMaskedLM,
-            TFDebertaV2ForMultipleChoice,
-            TFDebertaV2ForQuestionAnswering,
-            TFDebertaV2ForSequenceClassification,
-            TFDebertaV2ForTokenClassification,
-            TFDebertaV2Model,
-            TFDebertaV2PreTrainedModel,
-        )
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_deberta_v2 import (
-            DebertaV2ForMaskedLM,
-            DebertaV2ForMultipleChoice,
-            DebertaV2ForQuestionAnswering,
-            DebertaV2ForSequenceClassification,
-            DebertaV2ForTokenClassification,
-            DebertaV2Model,
-            DebertaV2PreTrainedModel,
-        )
-
 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/deberta_v2/configuration_deberta_v2.py

@@ -193,3 +193,6 @@ class DebertaV2OnnxConfig(OnnxConfig):
         if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
             del dummy_inputs["token_type_ids"]
         return dummy_inputs
+
+
+__all__ = ["DebertaV2Config", "DebertaV2OnnxConfig"]

src/transformers/models/deberta_v2/modeling_deberta_v2.py

@@ -1506,3 +1506,14 @@ class DebertaV2ForMultipleChoice(DebertaV2PreTrainedModel):
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+
+__all__ = [
+    "DebertaV2ForMaskedLM",
+    "DebertaV2ForMultipleChoice",
+    "DebertaV2ForQuestionAnswering",
+    "DebertaV2ForSequenceClassification",
+    "DebertaV2ForTokenClassification",
+    "DebertaV2Model",
+    "DebertaV2PreTrainedModel",
+]