Register -> Export. Export all in __all__. Sensible defaults accordin…

…g to filename.

src/transformers/models/albert/configuration_albert.py

@@ -20,10 +20,8 @@
 
 from ...configuration_utils import PretrainedConfig
 from ...onnx import OnnxConfig
-from ...utils.import_utils import register
 
 
-@register()
 class AlbertConfig(PretrainedConfig):
     r"""
     This is the configuration class to store the configuration of a [`AlbertModel`] or a [`TFAlbertModel`]. It is used
@@ -153,7 +151,6 @@ def __init__(
 
 
 # Copied from transformers.models.bert.configuration_bert.BertOnnxConfig with Roberta->Albert
-@register()
 class AlbertOnnxConfig(OnnxConfig):
     @property
     def inputs(self) -> Mapping[str, Mapping[int, str]]:

src/transformers/models/albert/modeling_albert.py

@@ -43,7 +43,6 @@
     logging,
     replace_return_docstrings,
 )
-from ...utils.import_utils import register
 from .configuration_albert import AlbertConfig
 
 
@@ -53,7 +52,6 @@
 _CONFIG_FOR_DOC = "AlbertConfig"
 
 
-@register()
 def load_tf_weights_in_albert(model, config, tf_checkpoint_path):
     """Load tf checkpoints in a pytorch model."""
     try:
@@ -489,7 +487,6 @@ def forward(
         )
 
 
-@register(backends=("torch",))
 class AlbertPreTrainedModel(PreTrainedModel):
     """
     An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
@@ -621,7 +618,6 @@ class AlbertForPreTrainingOutput(ModelOutput):
     "The bare ALBERT Model transformer outputting raw hidden-states without any specific head on top.",
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("torch",))
 class AlbertModel(AlbertPreTrainedModel):
     config_class = AlbertConfig
     base_model_prefix = "albert"
@@ -751,7 +747,6 @@ def forward(
     """,
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("torch",))
 class AlbertForPreTraining(AlbertPreTrainedModel):
     _tied_weights_keys = ["predictions.decoder.bias", "predictions.decoder.weight"]
 
@@ -904,7 +899,6 @@ def forward(self, pooled_output: torch.Tensor) -> torch.Tensor:
     "Albert Model with a `language modeling` head on top.",
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("torch",))
 class AlbertForMaskedLM(AlbertPreTrainedModel):
     _tied_weights_keys = ["predictions.decoder.bias", "predictions.decoder.weight"]
 
@@ -1020,7 +1014,6 @@ def forward(
     """,
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("torch",))
 class AlbertForSequenceClassification(AlbertPreTrainedModel):
     def __init__(self, config: AlbertConfig):
         super().__init__(config)
@@ -1122,7 +1115,6 @@ def forward(
     """,
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("torch",))
 class AlbertForTokenClassification(AlbertPreTrainedModel):
     def __init__(self, config: AlbertConfig):
         super().__init__(config)
@@ -1206,7 +1198,6 @@ def forward(
     """,
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("torch",))
 class AlbertForQuestionAnswering(AlbertPreTrainedModel):
     def __init__(self, config: AlbertConfig):
         super().__init__(config)
@@ -1310,7 +1301,6 @@ def forward(
     """,
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("torch",))
 class AlbertForMultipleChoice(AlbertPreTrainedModel):
     def __init__(self, config: AlbertConfig):
         super().__init__(config)

src/transformers/models/albert/modeling_flax_albert.py

@@ -42,7 +42,6 @@
     overwrite_call_docstring,
 )
 from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging
-from ...utils.import_utils import register
 from .configuration_albert import AlbertConfig
 
 
@@ -506,7 +505,6 @@ def __call__(self, pooled_output, deterministic=True):
         return logits
 
 
-@register(backends=("flax",))
 class FlaxAlbertPreTrainedModel(FlaxPreTrainedModel):
     """
     An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
@@ -676,7 +674,6 @@ def __call__(
     "The bare Albert Model transformer outputting raw hidden-states without any specific head on top.",
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("flax",))
 class FlaxAlbertModel(FlaxAlbertPreTrainedModel):
     module_class = FlaxAlbertModule
 
@@ -745,7 +742,6 @@ def __call__(
     """,
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("flax",))
 class FlaxAlbertForPreTraining(FlaxAlbertPreTrainedModel):
     module_class = FlaxAlbertForPreTrainingModule
 
@@ -829,7 +825,6 @@ def __call__(
 
 
 @add_start_docstrings("""Albert Model with a `language modeling` head on top.""", ALBERT_START_DOCSTRING)
-@register(backends=("flax",))
 class FlaxAlbertForMaskedLM(FlaxAlbertPreTrainedModel):
     module_class = FlaxAlbertForMaskedLMModule
 
@@ -900,7 +895,6 @@ def __call__(
     """,
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("flax",))
 class FlaxAlbertForSequenceClassification(FlaxAlbertPreTrainedModel):
     module_class = FlaxAlbertForSequenceClassificationModule
 
@@ -974,7 +968,6 @@ def __call__(
     """,
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("flax",))
 class FlaxAlbertForMultipleChoice(FlaxAlbertPreTrainedModel):
     module_class = FlaxAlbertForMultipleChoiceModule
 
@@ -1048,7 +1041,6 @@ def __call__(
     """,
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("flax",))
 class FlaxAlbertForTokenClassification(FlaxAlbertPreTrainedModel):
     module_class = FlaxAlbertForTokenClassificationModule
 
@@ -1117,7 +1109,6 @@ def __call__(
     """,
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("flax",))
 class FlaxAlbertForQuestionAnswering(FlaxAlbertPreTrainedModel):
     module_class = FlaxAlbertForQuestionAnsweringModule
 

src/transformers/models/albert/modeling_tf_albert.py

@@ -56,7 +56,6 @@
     logging,
     replace_return_docstrings,
 )
-from ...utils.import_utils import register
 from .configuration_albert import AlbertConfig
 
 
@@ -511,7 +510,6 @@ def build(self, input_shape=None):
                     layer.build(None)
 
 
-@register(backends=("tf",))
 class TFAlbertPreTrainedModel(TFPreTrainedModel):
     """
     An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
@@ -587,7 +585,6 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
 
 @keras_serializable
-@register(backends=("tf",))
 class TFAlbertMainLayer(keras.layers.Layer):
     config_class = AlbertConfig
 
@@ -861,7 +858,6 @@ class TFAlbertForPreTrainingOutput(ModelOutput):
     "The bare Albert Model transformer outputting raw hidden-states without any specific head on top.",
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("tf",))
 class TFAlbertModel(TFAlbertPreTrainedModel):
     def __init__(self, config: AlbertConfig, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -919,7 +915,6 @@ def build(self, input_shape=None):
     """,
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("tf",))
 class TFAlbertForPreTraining(TFAlbertPreTrainedModel, TFAlbertPreTrainingLoss):
     # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
     _keys_to_ignore_on_load_unexpected = [r"predictions.decoder.weight"]
@@ -1051,7 +1046,6 @@ def build(self, input_shape=None):
 
 
 @add_start_docstrings("""Albert Model with a `language modeling` head on top.""", ALBERT_START_DOCSTRING)
-@register(backends=("tf",))
 class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss):
     # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
     _keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions.decoder.weight"]
@@ -1165,7 +1159,6 @@ def build(self, input_shape=None):
     """,
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("tf",))
 class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, TFSequenceClassificationLoss):
     # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
     _keys_to_ignore_on_load_unexpected = [r"predictions"]
@@ -1260,7 +1253,6 @@ def build(self, input_shape=None):
     """,
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("tf",))
 class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificationLoss):
     # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
     _keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions"]
@@ -1356,7 +1348,6 @@ def build(self, input_shape=None):
     """,
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("tf",))
 class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringLoss):
     # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
     _keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions"]
@@ -1464,7 +1455,6 @@ def build(self, input_shape=None):
     """,
     ALBERT_START_DOCSTRING,
 )
-@register(backends=("tf",))
 class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
     # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
     _keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions"]

src/transformers/models/albert/tokenization_albert.py

@@ -23,7 +23,7 @@
 
 from ...tokenization_utils import AddedToken, PreTrainedTokenizer
 from ...utils import logging
-from ...utils.import_utils import register
+from ...utils.import_utils import export
 
 
 logger = logging.get_logger(__name__)
@@ -33,7 +33,7 @@
 SPIECE_UNDERLINE = "▁"
 
 
-@register(backends=("sentencepiece",))
+@export(backends=("sentencepiece",))
 class AlbertTokenizer(PreTrainedTokenizer):
     """
     Construct an ALBERT tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).

src/transformers/models/albert/tokenization_albert_fast.py

@@ -21,7 +21,6 @@
 from ...tokenization_utils import AddedToken
 from ...tokenization_utils_fast import PreTrainedTokenizerFast
 from ...utils import is_sentencepiece_available, logging
-from ...utils.import_utils import register
 
 
 if is_sentencepiece_available():
@@ -36,7 +35,6 @@
 SPIECE_UNDERLINE = "▁"
 
 
-@register(backends=("tokenizers",))
 class AlbertTokenizerFast(PreTrainedTokenizerFast):
     """
     Construct a "fast" ALBERT tokenizer (backed by HuggingFace's *tokenizers* library). Based on

src/transformers/models/align/configuration_align.py

@@ -17,8 +17,6 @@
 import os
 from typing import TYPE_CHECKING, List, Union
 
-from ...utils.import_utils import register
-
 
 if TYPE_CHECKING:
     pass
@@ -30,7 +28,6 @@
 logger = logging.get_logger(__name__)
 
 
-@register()
 class AlignTextConfig(PretrainedConfig):
     r"""
     This is the configuration class to store the configuration of a [`AlignTextModel`]. It is used to instantiate a
@@ -155,7 +152,6 @@ def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike],
         return cls.from_dict(config_dict, **kwargs)
 
 
-@register()
 class AlignVisionConfig(PretrainedConfig):
     r"""
     This is the configuration class to store the configuration of a [`AlignVisionModel`]. It is used to instantiate a
@@ -295,7 +291,6 @@ def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike],
         return cls.from_dict(config_dict, **kwargs)
 
 
-@register()
 class AlignConfig(PretrainedConfig):
     r"""
     [`AlignConfig`] is the configuration class to store the configuration of a [`AlignModel`]. It is used to

src/transformers/models/align/modeling_align.py

@@ -20,6 +20,7 @@
 
 import torch
 import torch.utils.checkpoint
+from IPython.terminal.pt_inputhooks import backends
 from torch import nn
 
 from ...activations import ACT2FN
@@ -38,7 +39,6 @@
     logging,
     replace_return_docstrings,
 )
-from ...utils.import_utils import register
 from .configuration_align import AlignConfig, AlignTextConfig, AlignVisionConfig
 
 
@@ -1166,7 +1166,6 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         return pooled_output
 
 
-@register(backends=("torch",))
 class AlignPreTrainedModel(PreTrainedModel):
     """
     An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
@@ -1200,7 +1199,6 @@ def _init_weights(self, module):
     """The text model from ALIGN without any head or projection on top.""",
     ALIGN_START_DOCSTRING,
 )
-@register(backends=("torch",))
 class AlignTextModel(AlignPreTrainedModel):
     config_class = AlignTextConfig
     _no_split_modules = ["AlignTextEmbeddings"]
@@ -1328,7 +1326,6 @@ def forward(
     """The vision model from ALIGN without any head or projection on top.""",
     ALIGN_START_DOCSTRING,
 )
-@register(backends=("torch",))
 class AlignVisionModel(AlignPreTrainedModel):
     config_class = AlignVisionConfig
     main_input_name = "pixel_values"
@@ -1415,7 +1412,6 @@ def forward(
 
 
 @add_start_docstrings(ALIGN_START_DOCSTRING)
-@register(backends=("torch",))
 class AlignModel(AlignPreTrainedModel):
     config_class = AlignConfig
 

src/transformers/models/align/processing_align.py

@@ -16,8 +16,6 @@
 Image/Text processor class for ALIGN
 """
 
-from ...utils.import_utils import register
-
 
 try:
     from typing import Unpack
@@ -42,7 +40,6 @@ class AlignProcessorKwargs(ProcessingKwargs, total=False):
     }
 
 
-@register()
 class AlignProcessor(ProcessorMixin):
     r"""
     Constructs an ALIGN processor which wraps [`EfficientNetImageProcessor`] and