Style
This commit is contained in:
parent
09830ffdd5
commit
e179280a11
2
Makefile
2
Makefile
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@ -53,7 +53,6 @@ quality:
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@python -c "from transformers import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1)
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ruff check $(check_dirs) setup.py conftest.py
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ruff format --check $(check_dirs) setup.py conftest.py
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python utils/custom_init_isort.py --check_only
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python utils/sort_auto_mappings.py --check_only
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python utils/check_doc_toc.py
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@ -61,7 +60,6 @@ quality:
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# Format source code automatically and check is there are any problems left that need manual fixing
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extra_style_checks:
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python utils/custom_init_isort.py
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python utils/sort_auto_mappings.py
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python utils/check_doc_toc.py --fix_and_overwrite
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@ -244,9 +244,7 @@ class DataTrainingArguments:
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)
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image_square_size: Optional[int] = field(
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default=600,
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metadata={
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"help": "Image longest size will be resized to this value, then image will be padded to square."
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},
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metadata={"help": "Image longest size will be resized to this value, then image will be padded to square."},
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)
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max_train_samples: Optional[int] = field(
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default=None,
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17
setup.py
17
setup.py
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@ -260,7 +260,15 @@ extras["ja"] = deps_list("fugashi", "ipadic", "unidic_lite", "unidic", "sudachip
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extras["sklearn"] = deps_list("scikit-learn")
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extras["tf"] = deps_list("tensorflow", "onnxconverter-common", "tf2onnx", "tensorflow-text", "keras-nlp")
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extras["tf-cpu"] = deps_list("keras", "tensorflow-cpu", "onnxconverter-common", "tf2onnx", "tensorflow-text", "keras-nlp", "tensorflow-probability")
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extras["tf-cpu"] = deps_list(
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"keras",
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"tensorflow-cpu",
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"onnxconverter-common",
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"tf2onnx",
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"tensorflow-text",
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"keras-nlp",
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"tensorflow-probability",
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)
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extras["torch"] = deps_list("torch", "accelerate")
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extras["accelerate"] = deps_list("accelerate")
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@ -380,12 +388,7 @@ extras["dev-tensorflow"] = (
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+ extras["tf-speech"]
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)
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extras["dev"] = (
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extras["all"]
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+ extras["testing"]
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+ extras["quality"]
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+ extras["ja"]
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+ extras["sklearn"]
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+ extras["modelcreation"]
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extras["all"] + extras["testing"] + extras["quality"] + extras["ja"] + extras["sklearn"] + extras["modelcreation"]
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)
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extras["torchhub"] = deps_list(
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@ -1168,7 +1168,9 @@ try:
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except OptionalDependencyNotAvailable:
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from .utils import dummy_torch_objects
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_import_structure["utils.dummy_torch_objects"] = [name for name in dir(dummy_torch_objects) if not name.startswith("_")]
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_import_structure["utils.dummy_torch_objects"] = [
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name for name in dir(dummy_torch_objects) if not name.startswith("_")
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]
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else:
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_import_structure["activations"] = []
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_import_structure["benchmark.benchmark"] = ["PyTorchBenchmark"]
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@ -168,7 +168,5 @@ class AlbertOnnxConfig(OnnxConfig):
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]
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)
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__all__ = [
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"AlbertConfig",
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"AlbertOnnxConfig"
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]
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__all__ = ["AlbertConfig", "AlbertOnnxConfig"]
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@ -1393,6 +1393,7 @@ class AlbertForMultipleChoice(AlbertPreTrainedModel):
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attentions=outputs.attentions,
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)
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__all__ = [
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"load_tf_weights_in_albert",
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"AlbertPreTrainedModel",
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@ -1402,5 +1403,5 @@ __all__ = [
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"AlbertForSequenceClassification",
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"AlbertForTokenClassification",
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"AlbertForQuestionAnswering",
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"AlbertForMultipleChoice"
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"AlbertForMultipleChoice",
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]
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@ -1137,5 +1137,5 @@ __all__ = [
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"FlaxAlbertForSequenceClassification",
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"FlaxAlbertForMultipleChoice",
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"FlaxAlbertForTokenClassification",
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"FlaxAlbertForQuestionAnswering"
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"FlaxAlbertForQuestionAnswering",
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]
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@ -1569,6 +1569,7 @@ class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
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with tf.name_scope(self.classifier.name):
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self.classifier.build([None, None, self.config.hidden_size])
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__all__ = [
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"TFAlbertPreTrainedModel",
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"TFAlbertModel",
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@ -1577,5 +1578,5 @@ __all__ = [
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"TFAlbertForSequenceClassification",
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"TFAlbertForTokenClassification",
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"TFAlbertForQuestionAnswering",
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"TFAlbertForMultipleChoice"
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"TFAlbertForMultipleChoice",
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]
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@ -347,6 +347,5 @@ class AlbertTokenizer(PreTrainedTokenizer):
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return (out_vocab_file,)
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__all__ = [
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"AlbertTokenizer"
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]
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__all__ = ["AlbertTokenizer"]
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@ -211,6 +211,5 @@ class AlbertTokenizerFast(PreTrainedTokenizerFast):
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return (out_vocab_file,)
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__all__ = [
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"AlbertTokenizerFast"
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]
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__all__ = ["AlbertTokenizerFast"]
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@ -384,8 +384,5 @@ class AlignConfig(PretrainedConfig):
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return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)
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__all__ = [
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"AlignTextConfig",
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"AlignVisionConfig",
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"AlignConfig"
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]
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__all__ = ["AlignTextConfig", "AlignVisionConfig", "AlignConfig"]
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@ -1642,9 +1642,5 @@ class AlignModel(AlignPreTrainedModel):
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vision_model_output=vision_outputs,
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)
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__all__ = [
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"AlignPreTrainedModel",
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"AlignTextModel",
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"AlignVisionModel",
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"AlignModel"
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]
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__all__ = ["AlignPreTrainedModel", "AlignTextModel", "AlignVisionModel", "AlignModel"]
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@ -122,6 +122,5 @@ class AlignProcessor(ProcessorMixin):
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image_processor_input_names = self.image_processor.model_input_names
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return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
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__all__ = [
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"AlignProcessor"
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]
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__all__ = ["AlignProcessor"]
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@ -402,8 +402,5 @@ class AltCLIPConfig(PretrainedConfig):
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return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)
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__all__ = [
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"AltCLIPTextConfig",
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"AltCLIPVisionConfig",
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"AltCLIPConfig"
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]
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__all__ = ["AltCLIPTextConfig", "AltCLIPVisionConfig", "AltCLIPConfig"]
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@ -1702,9 +1702,5 @@ def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_l
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incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
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return incremental_indices.long() + padding_idx
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__all__ = [
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"AltCLIPPreTrainedModel",
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"AltCLIPVisionModel",
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"AltCLIPTextModel",
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"AltCLIPModel"
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]
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__all__ = ["AltCLIPPreTrainedModel", "AltCLIPVisionModel", "AltCLIPTextModel", "AltCLIPModel"]
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@ -132,6 +132,5 @@ class AltCLIPProcessor(ProcessorMixin):
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image_processor_input_names = self.image_processor.model_input_names
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return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
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__all__ = [
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"AltCLIPProcessor"
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]
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__all__ = ["AltCLIPProcessor"]
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@ -122,6 +122,5 @@ class ASTConfig(PretrainedConfig):
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self.max_length = max_length
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self.num_mel_bins = num_mel_bins
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__all__ = [
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"ASTConfig"
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]
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__all__ = ["ASTConfig"]
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@ -237,6 +237,5 @@ class ASTFeatureExtractor(SequenceFeatureExtractor):
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return padded_inputs
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__all__ = [
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"ASTFeatureExtractor"
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]
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__all__ = ["ASTFeatureExtractor"]
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@ -613,8 +613,5 @@ class ASTForAudioClassification(ASTPreTrainedModel):
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attentions=outputs.attentions,
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)
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__all__ = [
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"ASTPreTrainedModel",
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"ASTModel",
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"ASTForAudioClassification"
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]
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__all__ = ["ASTPreTrainedModel", "ASTModel", "ASTForAudioClassification"]
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@ -243,6 +243,5 @@ class AutoformerConfig(PretrainedConfig):
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+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
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)
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__all__ = [
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"AutoformerConfig"
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]
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__all__ = ["AutoformerConfig"]
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@ -2155,8 +2155,5 @@ class AutoformerForPrediction(AutoformerPreTrainedModel):
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)
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)
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__all__ = [
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"AutoformerPreTrainedModel",
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"AutoformerModel",
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"AutoformerForPrediction"
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]
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__all__ = ["AutoformerPreTrainedModel", "AutoformerModel", "AutoformerForPrediction"]
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@ -329,9 +329,5 @@ class BarkConfig(PretrainedConfig):
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**kwargs,
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)
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__all__ = [
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"BarkSemanticConfig",
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"BarkCoarseConfig",
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"BarkFineConfig",
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"BarkConfig"
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]
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__all__ = ["BarkSemanticConfig", "BarkCoarseConfig", "BarkFineConfig", "BarkConfig"]
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@ -1023,7 +1023,7 @@ class BarkSemanticModel(BarkCausalModel):
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language modeling head on top.""",
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BARK_MODEL_START_DOCSTRING.format(config="BarkCoarseConfig"),
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)
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@register(backends=('torch',))
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@register(backends=("torch",))
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class BarkCoarseModel(BarkCausalModel):
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base_model_prefix = "coarse_acoustics"
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config_class = BarkCoarseConfig
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@ -1911,11 +1911,12 @@ class BarkModel(BarkPreTrainedModel):
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config.fine_acoustics_config._attn_implementation = config._attn_implementation
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return config
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__all__ = [
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"BarkPreTrainedModel",
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"BarkCausalModel",
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"BarkFineModel",
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"BarkCoarseModel",
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"BarkSemanticModel",
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"BarkModel"
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"BarkModel",
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]
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@ -287,6 +287,5 @@ class BarkProcessor(ProcessorMixin):
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return encoded_text
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__all__ = [
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"BarkProcessor"
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]
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__all__ = ["BarkProcessor"]
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@ -403,7 +403,5 @@ class BartOnnxConfig(OnnxSeq2SeqConfigWithPast):
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flattened_output, name, idx, t
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)
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__all__ = [
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"BartConfig",
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"BartOnnxConfig"
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]
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__all__ = ["BartConfig", "BartOnnxConfig"]
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|
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@ -2334,6 +2334,7 @@ class BartForCausalLM(BartPreTrainedModel):
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)
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return reordered_past
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__all__ = [
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"BartPreTrainedModel",
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"PretrainedBartModel",
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@ -2343,5 +2344,5 @@ __all__ = [
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"BartForConditionalGeneration",
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"BartForSequenceClassification",
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"BartForQuestionAnswering",
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"BartForCausalLM"
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"BartForCausalLM",
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]
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|
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@ -2009,5 +2009,5 @@ __all__ = [
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"FlaxBartForSequenceClassification",
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"FlaxBartForQuestionAnswering",
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"FlaxBartDecoderPreTrainedModel",
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"FlaxBartForCausalLM"
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"FlaxBartForCausalLM",
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]
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|
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@ -1716,9 +1716,5 @@ class TFBartForSequenceClassification(TFBartPretrainedModel, TFSequenceClassific
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with tf.name_scope(self.classification_head.name):
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self.classification_head.build(None)
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__all__ = [
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"TFBartPretrainedModel",
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"TFBartModel",
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"TFBartForConditionalGeneration",
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"TFBartForSequenceClassification"
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]
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__all__ = ["TFBartPretrainedModel", "TFBartModel", "TFBartForConditionalGeneration", "TFBartForSequenceClassification"]
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|
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@ -391,6 +391,5 @@ class BartTokenizer(PreTrainedTokenizer):
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text = " " + text
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return (text, kwargs)
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__all__ = [
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"BartTokenizer"
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]
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__all__ = ["BartTokenizer"]
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|
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@ -277,6 +277,5 @@ class BartTokenizerFast(PreTrainedTokenizerFast):
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return len(cls + token_ids_0 + sep) * [0]
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return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
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__all__ = [
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"BartTokenizerFast"
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]
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__all__ = ["BartTokenizerFast"]
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|
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@ -288,6 +288,5 @@ class BarthezTokenizer(PreTrainedTokenizer):
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return (out_vocab_file,)
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__all__ = [
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"BarthezTokenizer"
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]
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__all__ = ["BarthezTokenizer"]
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|
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@ -196,6 +196,5 @@ class BarthezTokenizerFast(PreTrainedTokenizerFast):
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return (out_vocab_file,)
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__all__ = [
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"BarthezTokenizerFast"
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]
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__all__ = ["BarthezTokenizerFast"]
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|
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@ -315,6 +315,5 @@ class BartphoTokenizer(PreTrainedTokenizer):
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return out_vocab_file, out_monolingual_vocab_file
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__all__ = [
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"BartphoTokenizer"
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]
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__all__ = ["BartphoTokenizer"]
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|
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@ -230,7 +230,5 @@ class BeitOnnxConfig(OnnxConfig):
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def atol_for_validation(self) -> float:
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return 1e-4
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__all__ = [
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"BeitConfig",
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"BeitOnnxConfig"
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]
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__all__ = ["BeitConfig", "BeitOnnxConfig"]
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|
|
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@ -34,6 +34,5 @@ class BeitFeatureExtractor(BeitImageProcessor):
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)
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super().__init__(*args, **kwargs)
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__all__ = [
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"BeitFeatureExtractor"
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]
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__all__ = ["BeitFeatureExtractor"]
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|
|
|
@ -532,6 +532,5 @@ class BeitImageProcessor(BaseImageProcessor):
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return semantic_segmentation
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__all__ = [
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"BeitImageProcessor"
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]
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__all__ = ["BeitImageProcessor"]
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|
|
|
@ -1429,11 +1429,12 @@ class BeitBackbone(BeitPreTrainedModel, BackboneMixin):
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attentions=outputs.attentions,
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)
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__all__ = [
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"BeitPreTrainedModel",
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"BeitModel",
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"BeitForMaskedImageModeling",
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"BeitForImageClassification",
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"BeitForSemanticSegmentation",
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"BeitBackbone"
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"BeitBackbone",
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]
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|
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@ -956,5 +956,5 @@ __all__ = [
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"FlaxBeitPreTrainedModel",
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"FlaxBeitModel",
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"FlaxBeitForMaskedImageModeling",
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"FlaxBeitForImageClassification"
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"FlaxBeitForImageClassification",
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]
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|
|
|
@ -152,7 +152,5 @@ class BertOnnxConfig(OnnxConfig):
|
|||
]
|
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)
|
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__all__ = [
|
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"BertConfig",
|
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"BertOnnxConfig"
|
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]
|
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|
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__all__ = ["BertConfig", "BertOnnxConfig"]
|
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|
|
|
@ -2030,6 +2030,7 @@ class BertForQuestionAnswering(BertPreTrainedModel):
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attentions=outputs.attentions,
|
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)
|
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|
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|
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__all__ = [
|
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"load_tf_weights_in_bert",
|
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"BertPreTrainedModel",
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|
@ -2041,5 +2042,5 @@ __all__ = [
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"BertForSequenceClassification",
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"BertForMultipleChoice",
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"BertForTokenClassification",
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"BertForQuestionAnswering"
|
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"BertForQuestionAnswering",
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]
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|
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@ -1733,5 +1733,5 @@ __all__ = [
|
|||
"FlaxBertForMultipleChoice",
|
||||
"FlaxBertForTokenClassification",
|
||||
"FlaxBertForQuestionAnswering",
|
||||
"FlaxBertForCausalLM"
|
||||
"FlaxBertForCausalLM",
|
||||
]
|
||||
|
|
|
@ -2121,6 +2121,7 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel, TFQuestionAnsweringLoss)
|
|||
with tf.name_scope(self.qa_outputs.name):
|
||||
self.qa_outputs.build([None, None, self.config.hidden_size])
|
||||
|
||||
|
||||
__all__ = [
|
||||
"TFBertPreTrainedModel",
|
||||
"TFBertModel",
|
||||
|
@ -2131,5 +2132,5 @@ __all__ = [
|
|||
"TFBertForSequenceClassification",
|
||||
"TFBertForMultipleChoice",
|
||||
"TFBertForTokenClassification",
|
||||
"TFBertForQuestionAnswering"
|
||||
"TFBertForQuestionAnswering",
|
||||
]
|
||||
|
|
|
@ -503,8 +503,5 @@ class WordpieceTokenizer(object):
|
|||
output_tokens.extend(sub_tokens)
|
||||
return output_tokens
|
||||
|
||||
__all__ = [
|
||||
"BertTokenizer",
|
||||
"BasicTokenizer",
|
||||
"WordpieceTokenizer"
|
||||
]
|
||||
|
||||
__all__ = ["BertTokenizer", "BasicTokenizer", "WordpieceTokenizer"]
|
||||
|
|
|
@ -173,6 +173,5 @@ class BertTokenizerFast(PreTrainedTokenizerFast):
|
|||
files = self._tokenizer.model.save(save_directory, name=filename_prefix)
|
||||
return tuple(files)
|
||||
|
||||
__all__ = [
|
||||
"BertTokenizerFast"
|
||||
]
|
||||
|
||||
__all__ = ["BertTokenizerFast"]
|
||||
|
|
|
@ -125,6 +125,5 @@ class BertGenerationConfig(PretrainedConfig):
|
|||
self.position_embedding_type = position_embedding_type
|
||||
self.use_cache = use_cache
|
||||
|
||||
__all__ = [
|
||||
"BertGenerationConfig"
|
||||
]
|
||||
|
||||
__all__ = ["BertGenerationConfig"]
|
||||
|
|
|
@ -1024,9 +1024,10 @@ class BertGenerationDecoder(BertGenerationPreTrainedModel):
|
|||
)
|
||||
return reordered_past
|
||||
|
||||
|
||||
__all__ = [
|
||||
"load_tf_weights_in_bert_generation",
|
||||
"BertGenerationPreTrainedModel",
|
||||
"BertGenerationEncoder",
|
||||
"BertGenerationDecoder"
|
||||
"BertGenerationDecoder",
|
||||
]
|
||||
|
|
|
@ -174,6 +174,5 @@ class BertGenerationTokenizer(PreTrainedTokenizer):
|
|||
|
||||
return (out_vocab_file,)
|
||||
|
||||
__all__ = [
|
||||
"BertGenerationTokenizer"
|
||||
]
|
||||
|
||||
__all__ = ["BertGenerationTokenizer"]
|
||||
|
|
|
@ -983,8 +983,5 @@ class SentencepieceTokenizer(object):
|
|||
|
||||
return new_pieces
|
||||
|
||||
__all__ = [
|
||||
"BertJapaneseTokenizer",
|
||||
"CharacterTokenizer",
|
||||
"MecabTokenizer"
|
||||
]
|
||||
|
||||
__all__ = ["BertJapaneseTokenizer", "CharacterTokenizer", "MecabTokenizer"]
|
||||
|
|
|
@ -768,6 +768,4 @@ def casual_tokenize(text, preserve_case=True, reduce_len=False, strip_handles=Fa
|
|||
|
||||
###############################################################################
|
||||
|
||||
__all__ = [
|
||||
"BertweetTokenizer"
|
||||
]
|
||||
__all__ = ["BertweetTokenizer"]
|
||||
|
|
|
@ -174,7 +174,5 @@ class BigBirdOnnxConfig(OnnxConfig):
|
|||
]
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"BigBirdConfig",
|
||||
"BigBirdOnnxConfig"
|
||||
]
|
||||
|
||||
__all__ = ["BigBirdConfig", "BigBirdOnnxConfig"]
|
||||
|
|
|
@ -3162,6 +3162,7 @@ class BigBirdForQuestionAnswering(BigBirdPreTrainedModel):
|
|||
mask = torch.where(mask < q_lengths, 1, 0)
|
||||
return mask
|
||||
|
||||
|
||||
__all__ = [
|
||||
"load_tf_weights_in_big_bird",
|
||||
"BigBirdPreTrainedModel",
|
||||
|
@ -3172,5 +3173,5 @@ __all__ = [
|
|||
"BigBirdForSequenceClassification",
|
||||
"BigBirdForMultipleChoice",
|
||||
"BigBirdForTokenClassification",
|
||||
"BigBirdForQuestionAnswering"
|
||||
"BigBirdForQuestionAnswering",
|
||||
]
|
||||
|
|
|
@ -2653,5 +2653,5 @@ __all__ = [
|
|||
"FlaxBigBirdForMultipleChoice",
|
||||
"FlaxBigBirdForTokenClassification",
|
||||
"FlaxBigBirdForQuestionAnswering",
|
||||
"FlaxBigBirdForCausalLM"
|
||||
"FlaxBigBirdForCausalLM",
|
||||
]
|
||||
|
|
|
@ -323,6 +323,5 @@ class BigBirdTokenizer(PreTrainedTokenizer):
|
|||
return len(cls + token_ids_0 + sep) * [0]
|
||||
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
|
||||
|
||||
__all__ = [
|
||||
"BigBirdTokenizer"
|
||||
]
|
||||
|
||||
__all__ = ["BigBirdTokenizer"]
|
||||
|
|
|
@ -231,6 +231,5 @@ class BigBirdTokenizerFast(PreTrainedTokenizerFast):
|
|||
|
||||
return (out_vocab_file,)
|
||||
|
||||
__all__ = [
|
||||
"BigBirdTokenizerFast"
|
||||
]
|
||||
|
||||
__all__ = ["BigBirdTokenizerFast"]
|
||||
|
|
|
@ -411,7 +411,5 @@ class BigBirdPegasusOnnxConfig(OnnxSeq2SeqConfigWithPast):
|
|||
flattened_output, name, idx, t
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"BigBirdPegasusConfig",
|
||||
"BigBirdPegasusOnnxConfig"
|
||||
]
|
||||
|
||||
__all__ = ["BigBirdPegasusConfig", "BigBirdPegasusOnnxConfig"]
|
||||
|
|
|
@ -3093,11 +3093,12 @@ class BigBirdPegasusForCausalLM(BigBirdPegasusPreTrainedModel):
|
|||
)
|
||||
return reordered_past
|
||||
|
||||
|
||||
__all__ = [
|
||||
"BigBirdPegasusPreTrainedModel",
|
||||
"BigBirdPegasusModel",
|
||||
"BigBirdPegasusForConditionalGeneration",
|
||||
"BigBirdPegasusForSequenceClassification",
|
||||
"BigBirdPegasusForQuestionAnswering",
|
||||
"BigBirdPegasusForCausalLM"
|
||||
"BigBirdPegasusForCausalLM",
|
||||
]
|
||||
|
|
|
@ -132,6 +132,5 @@ class BioGptConfig(PretrainedConfig):
|
|||
self.activation_dropout = activation_dropout
|
||||
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
|
||||
|
||||
__all__ = [
|
||||
"BioGptConfig"
|
||||
]
|
||||
|
||||
__all__ = ["BioGptConfig"]
|
||||
|
|
|
@ -942,10 +942,11 @@ class BioGptForSequenceClassification(BioGptPreTrainedModel):
|
|||
def set_input_embeddings(self, value):
|
||||
self.biogpt.embed_tokens = value
|
||||
|
||||
|
||||
__all__ = [
|
||||
"BioGptPreTrainedModel",
|
||||
"BioGptModel",
|
||||
"BioGptForCausalLM",
|
||||
"BioGptForTokenClassification",
|
||||
"BioGptForSequenceClassification"
|
||||
"BioGptForSequenceClassification",
|
||||
]
|
||||
|
|
|
@ -358,6 +358,5 @@ class BioGptTokenizer(PreTrainedTokenizer):
|
|||
|
||||
self.sm = sacremoses
|
||||
|
||||
__all__ = [
|
||||
"BioGptTokenizer"
|
||||
]
|
||||
|
||||
__all__ = ["BioGptTokenizer"]
|
||||
|
|
|
@ -134,6 +134,5 @@ class BitConfig(BackboneConfigMixin, PretrainedConfig):
|
|||
out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"BitConfig"
|
||||
]
|
||||
|
||||
__all__ = ["BitConfig"]
|
||||
|
|
|
@ -346,6 +346,5 @@ class BitImageProcessor(BaseImageProcessor):
|
|||
data = {"pixel_values": images}
|
||||
return BatchFeature(data=data, tensor_type=return_tensors)
|
||||
|
||||
__all__ = [
|
||||
"BitImageProcessor"
|
||||
]
|
||||
|
||||
__all__ = ["BitImageProcessor"]
|
||||
|
|
|
@ -900,9 +900,5 @@ class BitBackbone(BitPreTrainedModel, BackboneMixin):
|
|||
attentions=None,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"BitPreTrainedModel",
|
||||
"BitModel",
|
||||
"BitForImageClassification",
|
||||
"BitBackbone"
|
||||
]
|
||||
|
||||
__all__ = ["BitPreTrainedModel", "BitModel", "BitForImageClassification", "BitBackbone"]
|
||||
|
|
|
@ -394,7 +394,5 @@ class BlenderbotOnnxConfig(OnnxSeq2SeqConfigWithPast):
|
|||
inputs_or_outputs[f"{name}.{i}.encoder.key"] = {0: "batch", 2: encoder_sequence}
|
||||
inputs_or_outputs[f"{name}.{i}.encoder.value"] = {0: "batch", 2: encoder_sequence}
|
||||
|
||||
__all__ = [
|
||||
"BlenderbotConfig",
|
||||
"BlenderbotOnnxConfig"
|
||||
]
|
||||
|
||||
__all__ = ["BlenderbotConfig", "BlenderbotOnnxConfig"]
|
||||
|
|
|
@ -1616,9 +1616,10 @@ class BlenderbotForCausalLM(BlenderbotPreTrainedModel):
|
|||
)
|
||||
return reordered_past
|
||||
|
||||
|
||||
__all__ = [
|
||||
"BlenderbotPreTrainedModel",
|
||||
"BlenderbotModel",
|
||||
"BlenderbotForConditionalGeneration",
|
||||
"BlenderbotForCausalLM"
|
||||
"BlenderbotForCausalLM",
|
||||
]
|
||||
|
|
|
@ -1508,8 +1508,4 @@ append_replace_return_docstrings(
|
|||
FlaxBlenderbotForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"FlaxBlenderbotPreTrainedModel",
|
||||
"FlaxBlenderbotModel",
|
||||
"FlaxBlenderbotForConditionalGeneration"
|
||||
]
|
||||
__all__ = ["FlaxBlenderbotPreTrainedModel", "FlaxBlenderbotModel", "FlaxBlenderbotForConditionalGeneration"]
|
||||
|
|
|
@ -1559,8 +1559,5 @@ class TFBlenderbotForConditionalGeneration(TFBlenderbotPreTrainedModel, TFCausal
|
|||
with tf.name_scope(self.bias_layer.name):
|
||||
self.bias_layer.build(None)
|
||||
|
||||
__all__ = [
|
||||
"TFBlenderbotPreTrainedModel",
|
||||
"TFBlenderbotModel",
|
||||
"TFBlenderbotForConditionalGeneration"
|
||||
]
|
||||
|
||||
__all__ = ["TFBlenderbotPreTrainedModel", "TFBlenderbotModel", "TFBlenderbotForConditionalGeneration"]
|
||||
|
|
|
@ -422,6 +422,5 @@ class BlenderbotTokenizer(PreTrainedTokenizer):
|
|||
"{{ eos_token }}"
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"BlenderbotTokenizer"
|
||||
]
|
||||
|
||||
__all__ = ["BlenderbotTokenizer"]
|
||||
|
|
|
@ -304,6 +304,5 @@ class BlenderbotTokenizerFast(PreTrainedTokenizerFast):
|
|||
"{{ eos_token }}"
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"BlenderbotTokenizerFast"
|
||||
]
|
||||
|
||||
__all__ = ["BlenderbotTokenizerFast"]
|
||||
|
|
|
@ -389,7 +389,5 @@ class BlenderbotSmallOnnxConfig(OnnxSeq2SeqConfigWithPast):
|
|||
flattened_output, name, idx, t
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"BlenderbotSmallConfig",
|
||||
"BlenderbotSmallOnnxConfig"
|
||||
]
|
||||
|
||||
__all__ = ["BlenderbotSmallConfig", "BlenderbotSmallOnnxConfig"]
|
||||
|
|
|
@ -1568,9 +1568,10 @@ class BlenderbotSmallForCausalLM(BlenderbotSmallPreTrainedModel):
|
|||
)
|
||||
return reordered_past
|
||||
|
||||
|
||||
__all__ = [
|
||||
"BlenderbotSmallPreTrainedModel",
|
||||
"BlenderbotSmallModel",
|
||||
"BlenderbotSmallForConditionalGeneration",
|
||||
"BlenderbotSmallForCausalLM"
|
||||
"BlenderbotSmallForCausalLM",
|
||||
]
|
||||
|
|
|
@ -1528,5 +1528,5 @@ append_replace_return_docstrings(
|
|||
__all__ = [
|
||||
"FlaxBlenderbotSmallPreTrainedModel",
|
||||
"FlaxBlenderbotSmallModel",
|
||||
"FlaxBlenderbotSmallForConditionalGeneration"
|
||||
"FlaxBlenderbotSmallForConditionalGeneration",
|
||||
]
|
||||
|
|
|
@ -1529,8 +1529,5 @@ class TFBlenderbotSmallForConditionalGeneration(TFBlenderbotSmallPreTrainedModel
|
|||
with tf.name_scope(self.bias_layer.name):
|
||||
self.bias_layer.build(None)
|
||||
|
||||
__all__ = [
|
||||
"TFBlenderbotSmallPreTrainedModel",
|
||||
"TFBlenderbotSmallModel",
|
||||
"TFBlenderbotSmallForConditionalGeneration"
|
||||
]
|
||||
|
||||
__all__ = ["TFBlenderbotSmallPreTrainedModel", "TFBlenderbotSmallModel", "TFBlenderbotSmallForConditionalGeneration"]
|
||||
|
|
|
@ -235,6 +235,5 @@ class BlenderbotSmallTokenizer(PreTrainedTokenizer):
|
|||
"{{ eos_token }}"
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"BlenderbotSmallTokenizer"
|
||||
]
|
||||
|
||||
__all__ = ["BlenderbotSmallTokenizer"]
|
||||
|
|
|
@ -115,6 +115,5 @@ class BlenderbotSmallTokenizerFast(PreTrainedTokenizerFast):
|
|||
"{{ eos_token }}"
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"BlenderbotSmallTokenizerFast"
|
||||
]
|
||||
|
||||
__all__ = ["BlenderbotSmallTokenizerFast"]
|
||||
|
|
|
@ -365,8 +365,5 @@ class BlipConfig(PretrainedConfig):
|
|||
|
||||
return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)
|
||||
|
||||
__all__ = [
|
||||
"BlipTextConfig",
|
||||
"BlipVisionConfig",
|
||||
"BlipConfig"
|
||||
]
|
||||
|
||||
__all__ = ["BlipTextConfig", "BlipVisionConfig", "BlipConfig"]
|
||||
|
|
|
@ -313,6 +313,5 @@ class BlipImageProcessor(BaseImageProcessor):
|
|||
|
||||
return encoded_outputs
|
||||
|
||||
__all__ = [
|
||||
"BlipImageProcessor"
|
||||
]
|
||||
|
||||
__all__ = ["BlipImageProcessor"]
|
||||
|
|
|
@ -1489,11 +1489,12 @@ class BlipForImageTextRetrieval(BlipPreTrainedModel):
|
|||
question_embeds=question_embeds,
|
||||
)
|
||||
|
||||
|
||||
__all__ = [
|
||||
"BlipPreTrainedModel",
|
||||
"BlipVisionModel",
|
||||
"BlipModel",
|
||||
"BlipForConditionalGeneration",
|
||||
"BlipForQuestionAnswering",
|
||||
"BlipForImageTextRetrieval"
|
||||
"BlipForImageTextRetrieval",
|
||||
]
|
||||
|
|
|
@ -951,6 +951,5 @@ class BlipTextLMHeadModel(BlipTextPreTrainedModel):
|
|||
)
|
||||
return reordered_past
|
||||
|
||||
__all__ = [
|
||||
"BlipTextModel"
|
||||
]
|
||||
|
||||
__all__ = ["BlipTextModel"]
|
||||
|
|
|
@ -1704,11 +1704,12 @@ class TFBlipForImageTextRetrieval(TFBlipPreTrainedModel):
|
|||
with tf.name_scope(self.itm_head.name):
|
||||
self.itm_head.build([None, None, self.config.text_config.hidden_size])
|
||||
|
||||
|
||||
__all__ = [
|
||||
"TFBlipPreTrainedModel",
|
||||
"TFBlipVisionModel",
|
||||
"TFBlipModel",
|
||||
"TFBlipForConditionalGeneration",
|
||||
"TFBlipForQuestionAnswering",
|
||||
"TFBlipForImageTextRetrieval"
|
||||
"TFBlipForImageTextRetrieval",
|
||||
]
|
||||
|
|
|
@ -1123,6 +1123,5 @@ class TFBlipTextLMHeadModel(TFBlipTextPreTrainedModel):
|
|||
with tf.name_scope(self.cls.name):
|
||||
self.cls.build(None)
|
||||
|
||||
__all__ = [
|
||||
"TFBlipTextModel"
|
||||
]
|
||||
|
||||
__all__ = ["TFBlipTextModel"]
|
||||
|
|
|
@ -151,6 +151,5 @@ class BlipProcessor(ProcessorMixin):
|
|||
image_processor_input_names = self.image_processor.model_input_names
|
||||
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
|
||||
|
||||
__all__ = [
|
||||
"BlipProcessor"
|
||||
]
|
||||
|
||||
__all__ = ["BlipProcessor"]
|
||||
|
|
|
@ -355,8 +355,5 @@ class Blip2Config(PretrainedConfig):
|
|||
**kwargs,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"Blip2VisionConfig",
|
||||
"Blip2QFormerConfig",
|
||||
"Blip2Config"
|
||||
]
|
||||
|
||||
__all__ = ["Blip2VisionConfig", "Blip2QFormerConfig", "Blip2Config"]
|
||||
|
|
|
@ -1863,10 +1863,11 @@ class Blip2ForConditionalGeneration(Blip2PreTrainedModel):
|
|||
outputs = torch.cat([bos_tokens, outputs], dim=-1)
|
||||
return outputs
|
||||
|
||||
|
||||
__all__ = [
|
||||
"Blip2PreTrainedModel",
|
||||
"Blip2VisionModel",
|
||||
"Blip2QFormerModel",
|
||||
"Blip2Model",
|
||||
"Blip2ForConditionalGeneration"
|
||||
"Blip2ForConditionalGeneration",
|
||||
]
|
||||
|
|
|
@ -156,6 +156,5 @@ class Blip2Processor(ProcessorMixin):
|
|||
image_processor_input_names = self.image_processor.model_input_names
|
||||
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
|
||||
|
||||
__all__ = [
|
||||
"Blip2Processor"
|
||||
]
|
||||
|
||||
__all__ = ["Blip2Processor"]
|
||||
|
|
|
@ -236,7 +236,5 @@ class BloomOnnxConfig(OnnxConfigWithPast):
|
|||
def default_onnx_opset(self) -> int:
|
||||
return 13
|
||||
|
||||
__all__ = [
|
||||
"BloomConfig",
|
||||
"BloomOnnxConfig"
|
||||
]
|
||||
|
||||
__all__ = ["BloomConfig", "BloomOnnxConfig"]
|
||||
|
|
|
@ -1246,11 +1246,12 @@ class BloomForQuestionAnswering(BloomPreTrainedModel):
|
|||
attentions=outputs.attentions,
|
||||
)
|
||||
|
||||
|
||||
__all__ = [
|
||||
"BloomPreTrainedModel",
|
||||
"BloomModel",
|
||||
"BloomForCausalLM",
|
||||
"BloomForSequenceClassification",
|
||||
"BloomForTokenClassification",
|
||||
"BloomForQuestionAnswering"
|
||||
"BloomForQuestionAnswering",
|
||||
]
|
||||
|
|
|
@ -737,8 +737,4 @@ class FlaxBloomForCausalLM(FlaxBloomPreTrainedModel):
|
|||
|
||||
append_call_sample_docstring(FlaxBloomForCausalLM, _CHECKPOINT_FOR_DOC, FlaxCausalLMOutput, _CONFIG_FOR_DOC)
|
||||
|
||||
__all__ = [
|
||||
"FlaxBloomPreTrainedModel",
|
||||
"FlaxBloomModel",
|
||||
"FlaxBloomForCausalLM"
|
||||
]
|
||||
__all__ = ["FlaxBloomPreTrainedModel", "FlaxBloomModel", "FlaxBloomForCausalLM"]
|
||||
|
|
|
@ -159,6 +159,5 @@ class BloomTokenizerFast(PreTrainedTokenizerFast):
|
|||
"""
|
||||
return "{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}"
|
||||
|
||||
__all__ = [
|
||||
"BloomTokenizerFast"
|
||||
]
|
||||
|
||||
__all__ = ["BloomTokenizerFast"]
|
||||
|
|
|
@ -349,8 +349,5 @@ class BridgeTowerConfig(PretrainedConfig):
|
|||
|
||||
return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)
|
||||
|
||||
__all__ = [
|
||||
"BridgeTowerVisionConfig",
|
||||
"BridgeTowerTextConfig",
|
||||
"BridgeTowerConfig"
|
||||
]
|
||||
|
||||
__all__ = ["BridgeTowerVisionConfig", "BridgeTowerTextConfig", "BridgeTowerConfig"]
|
||||
|
|
|
@ -562,6 +562,5 @@ class BridgeTowerImageProcessor(BaseImageProcessor):
|
|||
|
||||
return encoded_outputs
|
||||
|
||||
__all__ = [
|
||||
"BridgeTowerImageProcessor"
|
||||
]
|
||||
|
||||
__all__ = ["BridgeTowerImageProcessor"]
|
||||
|
|
|
@ -1912,10 +1912,11 @@ class BridgeTowerForContrastiveLearning(BridgeTowerPreTrainedModel):
|
|||
attentions=outputs.attentions,
|
||||
)
|
||||
|
||||
|
||||
__all__ = [
|
||||
"BridgeTowerPreTrainedModel",
|
||||
"BridgeTowerModel",
|
||||
"BridgeTowerForMaskedLM",
|
||||
"BridgeTowerForImageAndTextRetrieval",
|
||||
"BridgeTowerForContrastiveLearning"
|
||||
"BridgeTowerForContrastiveLearning",
|
||||
]
|
||||
|
|
|
@ -120,6 +120,5 @@ class BridgeTowerProcessor(ProcessorMixin):
|
|||
image_processor_input_names = self.image_processor.model_input_names
|
||||
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
|
||||
|
||||
__all__ = [
|
||||
"BridgeTowerProcessor"
|
||||
]
|
||||
|
||||
__all__ = ["BridgeTowerProcessor"]
|
||||
|
|
|
@ -136,6 +136,5 @@ class BrosConfig(PretrainedConfig):
|
|||
self.dim_bbox_projection = self.hidden_size // self.num_attention_heads
|
||||
self.classifier_dropout_prob = classifier_dropout_prob
|
||||
|
||||
__all__ = [
|
||||
"BrosConfig"
|
||||
]
|
||||
|
||||
__all__ = ["BrosConfig"]
|
||||
|
|
|
@ -1320,10 +1320,11 @@ class BrosSpadeELForTokenClassification(BrosPreTrainedModel):
|
|||
attentions=outputs.attentions,
|
||||
)
|
||||
|
||||
|
||||
__all__ = [
|
||||
"BrosPreTrainedModel",
|
||||
"BrosModel",
|
||||
"BrosForTokenClassification",
|
||||
"BrosSpadeEEForTokenClassification",
|
||||
"BrosSpadeELForTokenClassification"
|
||||
"BrosSpadeELForTokenClassification",
|
||||
]
|
||||
|
|
|
@ -110,6 +110,5 @@ class BrosProcessor(ProcessorMixin):
|
|||
tokenizer_input_names = self.tokenizer.model_input_names
|
||||
return list(dict.fromkeys(tokenizer_input_names))
|
||||
|
||||
__all__ = [
|
||||
"BrosProcessor"
|
||||
]
|
||||
|
||||
__all__ = ["BrosProcessor"]
|
||||
|
|
|
@ -235,6 +235,5 @@ class ByT5Tokenizer(PreTrainedTokenizer):
|
|||
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
||||
return ()
|
||||
|
||||
__all__ = [
|
||||
"ByT5Tokenizer"
|
||||
]
|
||||
|
||||
__all__ = ["ByT5Tokenizer"]
|
||||
|
|
|
@ -154,7 +154,5 @@ class CamembertOnnxConfig(OnnxConfig):
|
|||
]
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"CamembertConfig",
|
||||
"CamembertOnnxConfig"
|
||||
]
|
||||
|
||||
__all__ = ["CamembertConfig", "CamembertOnnxConfig"]
|
||||
|
|
|
@ -1583,6 +1583,7 @@ def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_l
|
|||
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
|
||||
return incremental_indices.long() + padding_idx
|
||||
|
||||
|
||||
__all__ = [
|
||||
"CamembertPreTrainedModel",
|
||||
"CamembertModel",
|
||||
|
@ -1591,5 +1592,5 @@ __all__ = [
|
|||
"CamembertForMultipleChoice",
|
||||
"CamembertForTokenClassification",
|
||||
"CamembertForQuestionAnswering",
|
||||
"CamembertForCausalLM"
|
||||
"CamembertForCausalLM",
|
||||
]
|
||||
|
|
|
@ -1798,6 +1798,7 @@ class TFCamembertForCausalLM(TFCamembertPreTrainedModel, TFCausalLanguageModelin
|
|||
with tf.name_scope(self.lm_head.name):
|
||||
self.lm_head.build(None)
|
||||
|
||||
|
||||
__all__ = [
|
||||
"TFCamembertPreTrainedModel",
|
||||
"TFCamembertModel",
|
||||
|
@ -1806,5 +1807,5 @@ __all__ = [
|
|||
"TFCamembertForTokenClassification",
|
||||
"TFCamembertForMultipleChoice",
|
||||
"TFCamembertForQuestionAnswering",
|
||||
"TFCamembertForCausalLM"
|
||||
"TFCamembertForCausalLM",
|
||||
]
|
||||
|
|
|
@ -320,6 +320,5 @@ class CamembertTokenizer(PreTrainedTokenizer):
|
|||
return len(cls + token_ids_0 + sep) * [0]
|
||||
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
|
||||
|
||||
__all__ = [
|
||||
"CamembertTokenizer"
|
||||
]
|
||||
|
||||
__all__ = ["CamembertTokenizer"]
|
||||
|
|
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue