Cleanup: continue the init refactor (#35167)

Round 2

src/transformers/models/audio_spectrogram_transformer/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2021 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.
@@ -13,47 +13,17 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
-
-
-_import_structure = {
-    "configuration_audio_spectrogram_transformer": ["ASTConfig"],
-    "feature_extraction_audio_spectrogram_transformer": ["ASTFeatureExtractor"],
-}
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_audio_spectrogram_transformer"] = [
-        "ASTForAudioClassification",
-        "ASTModel",
-        "ASTPreTrainedModel",
-    ]
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
 
 
 if TYPE_CHECKING:
-    from .configuration_audio_spectrogram_transformer import (
-        ASTConfig,
-    )
-    from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_audio_spectrogram_transformer import (
-            ASTForAudioClassification,
-            ASTModel,
-            ASTPreTrainedModel,
-        )
-
-
+    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:
     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/audio_spectrogram_transformer/configuration_audio_spectrogram_transformer.py

@@ -126,3 +126,6 @@ def __init__(
     # generative parameters deprecation cycle, overwriting this function prevents this from happening.
     def _get_non_default_generation_parameters(self) -> Dict[str, Any]:
         return {}
+
+
+__all__ = ["ASTConfig"]

src/transformers/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.py

@@ -234,3 +234,6 @@ def __call__(
             padded_inputs = padded_inputs.convert_to_tensors(return_tensors)
 
         return padded_inputs
+
+
+__all__ = ["ASTFeatureExtractor"]

src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py

@@ -670,3 +670,6 @@ def forward(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+
+__all__ = ["ASTForAudioClassification", "ASTModel", "ASTPreTrainedModel"]

src/transformers/models/bark/__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.
@@ -13,63 +13,18 @@
 # 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_bark": [
-        "BarkCoarseConfig",
-        "BarkConfig",
-        "BarkFineConfig",
-        "BarkSemanticConfig",
-    ],
-    "processing_bark": ["BarkProcessor"],
-}
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_bark"] = [
-        "BarkFineModel",
-        "BarkSemanticModel",
-        "BarkCoarseModel",
-        "BarkModel",
-        "BarkPreTrainedModel",
-        "BarkCausalModel",
-    ]
-
 if TYPE_CHECKING:
-    from .configuration_bark import (
-        BarkCoarseConfig,
-        BarkConfig,
-        BarkFineConfig,
-        BarkSemanticConfig,
-    )
-    from .processing_bark import BarkProcessor
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_bark import (
-            BarkCausalModel,
-            BarkCoarseModel,
-            BarkFineModel,
-            BarkModel,
-            BarkPreTrainedModel,
-            BarkSemanticModel,
-        )
-
+    from .configuration_bark import *
+    from .convert_suno_to_hf import *
+    from .generation_configuration_bark import *
+    from .modeling_bark import *
+    from .processing_bark 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/bark/configuration_bark.py

@@ -298,3 +298,6 @@ def from_sub_model_configs(
             codec_config=codec_config.to_dict(),
             **kwargs,
         )
+
+
+__all__ = ["BarkCoarseConfig", "BarkConfig", "BarkFineConfig", "BarkSemanticConfig"]

src/transformers/models/bark/modeling_bark.py

@@ -1819,3 +1819,13 @@ def _check_and_enable_flash_attn_2(
         config.coarse_acoustics_config._attn_implementation = config._attn_implementation
         config.fine_acoustics_config._attn_implementation = config._attn_implementation
         return config
+
+
+__all__ = [
+    "BarkFineModel",
+    "BarkSemanticModel",
+    "BarkCoarseModel",
+    "BarkModel",
+    "BarkPreTrainedModel",
+    "BarkCausalModel",
+]

src/transformers/models/bark/processing_bark.py

@@ -285,3 +285,6 @@ def __call__(
             encoded_text["history_prompt"] = voice_preset
 
         return encoded_text
+
+
+__all__ = ["BarkProcessor"]

src/transformers/models/bart/__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.
@@ -13,134 +13,20 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import (
-    OptionalDependencyNotAvailable,
-    _LazyModule,
-    is_flax_available,
-    is_tf_available,
-    is_tokenizers_available,
-    is_torch_available,
-)
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
 
 
-_import_structure = {
-    "configuration_bart": ["BartConfig", "BartOnnxConfig"],
-    "tokenization_bart": ["BartTokenizer"],
-}
-
-try:
-    if not is_tokenizers_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["tokenization_bart_fast"] = ["BartTokenizerFast"]
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_bart"] = [
-        "BartForCausalLM",
-        "BartForConditionalGeneration",
-        "BartForQuestionAnswering",
-        "BartForSequenceClassification",
-        "BartModel",
-        "BartPreTrainedModel",
-        "BartPretrainedModel",
-        "PretrainedBartModel",
-    ]
-
-try:
-    if not is_tf_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_tf_bart"] = [
-        "TFBartForConditionalGeneration",
-        "TFBartForSequenceClassification",
-        "TFBartModel",
-        "TFBartPretrainedModel",
-    ]
-
-try:
-    if not is_flax_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_flax_bart"] = [
-        "FlaxBartDecoderPreTrainedModel",
-        "FlaxBartForCausalLM",
-        "FlaxBartForConditionalGeneration",
-        "FlaxBartForQuestionAnswering",
-        "FlaxBartForSequenceClassification",
-        "FlaxBartModel",
-        "FlaxBartPreTrainedModel",
-    ]
-
 if TYPE_CHECKING:
-    from .configuration_bart import BartConfig, BartOnnxConfig
-    from .tokenization_bart import BartTokenizer
-
-    try:
-        if not is_tokenizers_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .tokenization_bart_fast import BartTokenizerFast
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_bart import (
-            BartForCausalLM,
-            BartForConditionalGeneration,
-            BartForQuestionAnswering,
-            BartForSequenceClassification,
-            BartModel,
-            BartPreTrainedModel,
-            BartPretrainedModel,
-            PretrainedBartModel,
-        )
-
-    try:
-        if not is_tf_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_tf_bart import (
-            TFBartForConditionalGeneration,
-            TFBartForSequenceClassification,
-            TFBartModel,
-            TFBartPretrainedModel,
-        )
-
-    try:
-        if not is_flax_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_flax_bart import (
-            FlaxBartDecoderPreTrainedModel,
-            FlaxBartForCausalLM,
-            FlaxBartForConditionalGeneration,
-            FlaxBartForQuestionAnswering,
-            FlaxBartForSequenceClassification,
-            FlaxBartModel,
-            FlaxBartPreTrainedModel,
-        )
-
+    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 *
+    from .tokenization_bart import *
+    from .tokenization_bart_fast 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/bart/configuration_bart.py

@@ -400,3 +400,6 @@ def _flatten_past_key_values_(self, flattened_output, name, idx, t):
             flattened_output = super(OnnxSeq2SeqConfigWithPast, self)._flatten_past_key_values_(
                 flattened_output, name, idx, t
             )
+
+
+__all__ = ["BartConfig", "BartOnnxConfig"]

src/transformers/models/bart/modeling_bart.py

@@ -2158,3 +2158,15 @@ def _reorder_cache(past_key_values, beam_idx):
                 tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
             )
         return reordered_past
+
+
+__all__ = [
+    "BartForCausalLM",
+    "BartForConditionalGeneration",
+    "BartForQuestionAnswering",
+    "BartForSequenceClassification",
+    "BartModel",
+    "BartPreTrainedModel",
+    "BartPretrainedModel",
+    "PretrainedBartModel",
+]

src/transformers/models/bart/modeling_flax_bart.py

@@ -1993,3 +1993,14 @@ def update_inputs_for_generation(self, model_outputs, model_kwargs):
     FlaxCausalLMOutputWithCrossAttentions,
     _CONFIG_FOR_DOC,
 )
+
+
+__all__ = [
+    "FlaxBartDecoderPreTrainedModel",
+    "FlaxBartForCausalLM",
+    "FlaxBartForConditionalGeneration",
+    "FlaxBartForQuestionAnswering",
+    "FlaxBartForSequenceClassification",
+    "FlaxBartModel",
+    "FlaxBartPreTrainedModel",
+]

src/transformers/models/bart/modeling_tf_bart.py

@@ -1709,3 +1709,6 @@ def build(self, input_shape=None):
         if getattr(self, "classification_head", None) is not None:
             with tf.name_scope(self.classification_head.name):
                 self.classification_head.build(None)
+
+
+__all__ = ["TFBartForConditionalGeneration", "TFBartForSequenceClassification", "TFBartModel", "TFBartPretrainedModel"]

src/transformers/models/bart/tokenization_bart.py

@@ -388,3 +388,6 @@ def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
         if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
             text = " " + text
         return (text, kwargs)
+
+
+__all__ = ["BartTokenizer"]