From 1b730c3d11fdad0180ee9f9d3da9cff933c3b264 Mon Sep 17 00:00:00 2001 From: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Date: Fri, 14 Jan 2022 10:59:41 -0500 Subject: [PATCH] Better dummies (#15148) * Better dummies * See if this fixes the issue * Fix quality * Style * Add doc for DummyObject --- src/transformers/file_utils.py | 12 + src/transformers/utils/dummy_flax_objects.py | 1188 +--- src/transformers/utils/dummy_pt_objects.py | 5109 ++++++----------- ..._pytorch_quantization_and_torch_objects.py | 142 +- .../utils/dummy_scatter_objects.py | 50 +- .../dummy_sentencepiece_and_speech_objects.py | 11 +- ...my_sentencepiece_and_tokenizers_objects.py | 3 +- .../utils/dummy_sentencepiece_objects.py | 137 +- .../utils/dummy_speech_objects.py | 7 +- src/transformers/utils/dummy_tf_objects.py | 3029 ++++------ .../utils/dummy_timm_and_vision_objects.py | 41 +- .../utils/dummy_tokenizers_objects.py | 269 +- .../utils/dummy_vision_objects.py | 87 +- utils/check_dummies.py | 77 +- utils/check_repo.py | 1 + 15 files changed, 3287 insertions(+), 6876 deletions(-) diff --git a/src/transformers/file_utils.py b/src/transformers/file_utils.py index aa910fdf83..408df727ac 100644 --- a/src/transformers/file_utils.py +++ b/src/transformers/file_utils.py @@ -831,6 +831,18 @@ def requires_backends(obj, backends): raise ImportError("".join([BACKENDS_MAPPING[backend][1].format(name) for backend in backends])) +class DummyObject(type): + """ + Metaclass for the dummy objects. Any class inheriting from it will return the ImportError generated by + `requires_backend` each time a user tries to access any method of that class. + """ + + def __getattr__(cls, key): + if key.startswith("_"): + return super().__getattr__(cls, key) + requires_backends(cls, cls._backends) + + def add_start_docstrings(*docstr): def docstring_decorator(fn): fn.__doc__ = "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "") diff --git a/src/transformers/utils/dummy_flax_objects.py b/src/transformers/utils/dummy_flax_objects.py index 52c0e5e124..675c7c5088 100644 --- a/src/transformers/utils/dummy_flax_objects.py +++ b/src/transformers/utils/dummy_flax_objects.py @@ -1,170 +1,131 @@ # This file is autogenerated by the command `make fix-copies`, do not edit. -from ..file_utils import requires_backends +# flake8: noqa +from ..file_utils import DummyObject, requires_backends -class FlaxForcedBOSTokenLogitsProcessor: - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxForcedBOSTokenLogitsProcessor(metaclass=DummyObject): + _backends = ["flax"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - -class FlaxForcedEOSTokenLogitsProcessor: - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - -class FlaxLogitsProcessor: - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - -class FlaxLogitsProcessorList: - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - -class FlaxLogitsWarper: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) -class FlaxMinLengthLogitsProcessor: - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxForcedEOSTokenLogitsProcessor(metaclass=DummyObject): + _backends = ["flax"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - -class FlaxTemperatureLogitsWarper: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) -class FlaxTopKLogitsWarper: +class FlaxLogitsProcessor(metaclass=DummyObject): + _backends = ["flax"] + def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) -class FlaxTopPLogitsWarper: +class FlaxLogitsProcessorList(metaclass=DummyObject): + _backends = ["flax"] + def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) -class FlaxPreTrainedModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxLogitsWarper(metaclass=DummyObject): + _backends = ["flax"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAlbertForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAlbertForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - - -class FlaxAlbertForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) -class FlaxAlbertForQuestionAnswering: +class FlaxMinLengthLogitsProcessor(metaclass=DummyObject): + _backends = ["flax"] + def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxTemperatureLogitsWarper(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxAlbertForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxTopKLogitsWarper(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxAlbertForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxTopPLogitsWarper(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxAlbertModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxAlbertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): +class FlaxAlbertForMaskedLM(metaclass=DummyObject): + _backends = ["flax"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["flax"]) + + +class FlaxAlbertForMultipleChoice(metaclass=DummyObject): + _backends = ["flax"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["flax"]) + + +class FlaxAlbertForPreTraining(metaclass=DummyObject): + _backends = ["flax"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["flax"]) + + +class FlaxAlbertForQuestionAnswering(metaclass=DummyObject): + _backends = ["flax"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["flax"]) + + +class FlaxAlbertForSequenceClassification(metaclass=DummyObject): + _backends = ["flax"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["flax"]) + + +class FlaxAlbertForTokenClassification(metaclass=DummyObject): + _backends = ["flax"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["flax"]) + + +class FlaxAlbertModel(metaclass=DummyObject): + _backends = ["flax"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["flax"]) + + +class FlaxAlbertPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) @@ -204,1320 +165,799 @@ FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING = None FLAX_MODEL_MAPPING = None -class FlaxAutoModel: +class FlaxAutoModel(metaclass=DummyObject): + _backends = ["flax"] + def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxAutoModelForCausalLM(metaclass=DummyObject): + _backends = ["flax"] -class FlaxAutoModelForCausalLM: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxAutoModelForImageClassification(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxAutoModelForImageClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxAutoModelForMaskedLM(metaclass=DummyObject): + _backends = ["flax"] -class FlaxAutoModelForMaskedLM: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxAutoModelForMultipleChoice(metaclass=DummyObject): + _backends = ["flax"] -class FlaxAutoModelForMultipleChoice: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxAutoModelForNextSentencePrediction(metaclass=DummyObject): + _backends = ["flax"] -class FlaxAutoModelForNextSentencePrediction: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxAutoModelForPreTraining(metaclass=DummyObject): + _backends = ["flax"] -class FlaxAutoModelForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxAutoModelForQuestionAnswering(metaclass=DummyObject): + _backends = ["flax"] -class FlaxAutoModelForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxAutoModelForSeq2SeqLM(metaclass=DummyObject): + _backends = ["flax"] -class FlaxAutoModelForSeq2SeqLM: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxAutoModelForSequenceClassification(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxAutoModelForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxAutoModelForTokenClassification(metaclass=DummyObject): + _backends = ["flax"] -class FlaxAutoModelForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxAutoModelForVision2Seq(metaclass=DummyObject): + _backends = ["flax"] -class FlaxAutoModelForVision2Seq: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBartForConditionalGeneration(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBartForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBartForQuestionAnswering(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBartForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBartForSequenceClassification(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBartForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBartModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBartModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBartPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxBartPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBeitForImageClassification(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBeitForImageClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) -class FlaxBeitForMaskedImageModeling: +class FlaxBeitForMaskedImageModeling(metaclass=DummyObject): + _backends = ["flax"] + def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBeitModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBeitModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBeitPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxBeitPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBertForMaskedLM(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBertForMaskedLM: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBertForMultipleChoice(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBertForMultipleChoice: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBertForNextSentencePrediction(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBertForNextSentencePrediction: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBertForPreTraining(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBertForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) -class FlaxBertForQuestionAnswering: +class FlaxBertForQuestionAnswering(metaclass=DummyObject): + _backends = ["flax"] + def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBertForSequenceClassification(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBertForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBertForTokenClassification(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBertForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBertModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBertModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBertPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBigBirdForMaskedLM(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBigBirdForMaskedLM: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBigBirdForMultipleChoice(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxBigBirdForMultipleChoice: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBigBirdForPreTraining(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBigBirdForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) -class FlaxBigBirdForQuestionAnswering: +class FlaxBigBirdForQuestionAnswering(metaclass=DummyObject): + _backends = ["flax"] + def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBigBirdForSequenceClassification(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBigBirdForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBigBirdForTokenClassification(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxBigBirdForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBigBirdModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBigBirdModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBigBirdPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBigBirdPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBlenderbotForConditionalGeneration(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBlenderbotForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBlenderbotModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBlenderbotModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBlenderbotPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBlenderbotPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBlenderbotSmallForConditionalGeneration(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBlenderbotSmallForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBlenderbotSmallModel(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxBlenderbotSmallModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxBlenderbotSmallPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxBlenderbotSmallPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxCLIPModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxCLIPModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxCLIPPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxCLIPPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxCLIPTextModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxCLIPTextModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxCLIPTextPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxCLIPTextPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxCLIPVisionModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxCLIPVisionModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxCLIPVisionPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxCLIPVisionPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxDistilBertForMaskedLM(metaclass=DummyObject): + _backends = ["flax"] -class FlaxDistilBertForMaskedLM: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxDistilBertForMultipleChoice(metaclass=DummyObject): + _backends = ["flax"] -class FlaxDistilBertForMultipleChoice: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxDistilBertForQuestionAnswering(metaclass=DummyObject): + _backends = ["flax"] -class FlaxDistilBertForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxDistilBertForSequenceClassification(metaclass=DummyObject): + _backends = ["flax"] -class FlaxDistilBertForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxDistilBertForTokenClassification(metaclass=DummyObject): + _backends = ["flax"] -class FlaxDistilBertForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxDistilBertModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxDistilBertModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxDistilBertPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxDistilBertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxElectraForMaskedLM(metaclass=DummyObject): + _backends = ["flax"] -class FlaxElectraForMaskedLM: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxElectraForMultipleChoice(metaclass=DummyObject): + _backends = ["flax"] -class FlaxElectraForMultipleChoice: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxElectraForPreTraining(metaclass=DummyObject): + _backends = ["flax"] -class FlaxElectraForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) -class FlaxElectraForQuestionAnswering: +class FlaxElectraForQuestionAnswering(metaclass=DummyObject): + _backends = ["flax"] + def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxElectraForSequenceClassification(metaclass=DummyObject): + _backends = ["flax"] -class FlaxElectraForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxElectraForTokenClassification(metaclass=DummyObject): + _backends = ["flax"] -class FlaxElectraForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxElectraModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxElectraModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxElectraPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxElectraPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxEncoderDecoderModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxEncoderDecoderModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxGPT2LMHeadModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxGPT2LMHeadModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxGPT2Model(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxGPT2Model: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxGPT2PreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxGPT2PreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxGPTNeoForCausalLM(metaclass=DummyObject): + _backends = ["flax"] -class FlaxGPTNeoForCausalLM: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxGPTNeoModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxGPTNeoModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxGPTNeoPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxGPTNeoPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxGPTJForCausalLM(metaclass=DummyObject): + _backends = ["flax"] -class FlaxGPTJForCausalLM: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxGPTJModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxGPTJModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxGPTJPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxGPTJPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxMarianModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxMarianModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxMarianMTModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxMarianMTModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxMarianPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxMarianPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxMBartForConditionalGeneration(metaclass=DummyObject): + _backends = ["flax"] -class FlaxMBartForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxMBartForQuestionAnswering(metaclass=DummyObject): + _backends = ["flax"] -class FlaxMBartForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxMBartForSequenceClassification(metaclass=DummyObject): + _backends = ["flax"] -class FlaxMBartForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxMBartModel(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxMBartModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxMBartPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxMBartPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxMT5ForConditionalGeneration(metaclass=DummyObject): + _backends = ["flax"] -class FlaxMT5ForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxMT5Model(metaclass=DummyObject): + _backends = ["flax"] -class FlaxMT5Model: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxPegasusForConditionalGeneration(metaclass=DummyObject): + _backends = ["flax"] -class FlaxPegasusForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxPegasusModel(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxPegasusModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxPegasusPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxPegasusPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxRobertaForMaskedLM(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxRobertaForMaskedLM: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxRobertaForMultipleChoice(metaclass=DummyObject): + _backends = ["flax"] -class FlaxRobertaForMultipleChoice: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxRobertaForQuestionAnswering(metaclass=DummyObject): + _backends = ["flax"] -class FlaxRobertaForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxRobertaForSequenceClassification(metaclass=DummyObject): + _backends = ["flax"] -class FlaxRobertaForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxRobertaForTokenClassification(metaclass=DummyObject): + _backends = ["flax"] -class FlaxRobertaForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxRobertaModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxRobertaModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxRobertaPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxRobertaPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxRoFormerForMaskedLM(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxRoFormerForMaskedLM: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxRoFormerForMultipleChoice(metaclass=DummyObject): + _backends = ["flax"] -class FlaxRoFormerForMultipleChoice: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxRoFormerForQuestionAnswering(metaclass=DummyObject): + _backends = ["flax"] -class FlaxRoFormerForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxRoFormerForSequenceClassification(metaclass=DummyObject): + _backends = ["flax"] -class FlaxRoFormerForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxRoFormerForTokenClassification(metaclass=DummyObject): + _backends = ["flax"] -class FlaxRoFormerForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxRoFormerModel(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxRoFormerModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxRoFormerPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxRoFormerPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxT5ForConditionalGeneration(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxT5ForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxT5Model(metaclass=DummyObject): + _backends = ["flax"] -class FlaxT5Model: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxT5PreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxT5PreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxVisionEncoderDecoderModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxVisionEncoderDecoderModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxVisionTextDualEncoderModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxVisionTextDualEncoderModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxViTForImageClassification(metaclass=DummyObject): + _backends = ["flax"] - -class FlaxViTForImageClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) -class FlaxViTModel: +class FlaxViTModel(metaclass=DummyObject): + _backends = ["flax"] + def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxViTPreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] -class FlaxViTPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxWav2Vec2ForCTC(metaclass=DummyObject): + _backends = ["flax"] -class FlaxWav2Vec2ForCTC: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) -class FlaxWav2Vec2ForPreTraining: - def __init__(self, *args, **kwargs): - requires_backends(self, ["flax"]) - +class FlaxWav2Vec2ForPreTraining(metaclass=DummyObject): + _backends = ["flax"] -class FlaxWav2Vec2Model: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - - def __call__(self, *args, **kwargs): - requires_backends(self, ["flax"]) +class FlaxWav2Vec2Model(metaclass=DummyObject): + _backends = ["flax"] -class FlaxWav2Vec2PreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["flax"]) - def __call__(self, *args, **kwargs): +class FlaxWav2Vec2PreTrainedModel(metaclass=DummyObject): + _backends = ["flax"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["flax"]) diff --git a/src/transformers/utils/dummy_pt_objects.py b/src/transformers/utils/dummy_pt_objects.py index a5e576b884..57b43ab363 100644 --- a/src/transformers/utils/dummy_pt_objects.py +++ b/src/transformers/utils/dummy_pt_objects.py @@ -1,214 +1,228 @@ # This file is autogenerated by the command `make fix-copies`, do not edit. -from ..file_utils import requires_backends +# flake8: noqa +from ..file_utils import DummyObject, requires_backends -class PyTorchBenchmark: +class PyTorchBenchmark(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class PyTorchBenchmarkArguments: +class PyTorchBenchmarkArguments(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class GlueDataset: +class GlueDataset(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class GlueDataTrainingArguments: +class GlueDataTrainingArguments(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class LineByLineTextDataset: +class LineByLineTextDataset(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class LineByLineWithRefDataset: +class LineByLineWithRefDataset(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class LineByLineWithSOPTextDataset: +class LineByLineWithSOPTextDataset(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class SquadDataset: +class SquadDataset(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class SquadDataTrainingArguments: +class SquadDataTrainingArguments(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class TextDataset: +class TextDataset(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class TextDatasetForNextSentencePrediction: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class TextDatasetForNextSentencePrediction(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BeamScorer: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class BeamSearchScorer: +class BeamScorer(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class ForcedBOSTokenLogitsProcessor: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BeamSearchScorer(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class ForcedEOSTokenLogitsProcessor: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class HammingDiversityLogitsProcessor: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class InfNanRemoveLogitsProcessor: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class LogitsProcessor: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class LogitsProcessorList: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class LogitsWarper: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class MinLengthLogitsProcessor: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class ForcedBOSTokenLogitsProcessor(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class NoBadWordsLogitsProcessor: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class NoRepeatNGramLogitsProcessor: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class PrefixConstrainedLogitsProcessor: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class RepetitionPenaltyLogitsProcessor: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class TemperatureLogitsWarper: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class TopKLogitsWarper: +class ForcedEOSTokenLogitsProcessor(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class TopPLogitsWarper: +class HammingDiversityLogitsProcessor(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class MaxLengthCriteria: +class InfNanRemoveLogitsProcessor(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class MaxTimeCriteria: +class LogitsProcessor(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class StoppingCriteria: +class LogitsProcessorList(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class StoppingCriteriaList: +class LogitsWarper(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MinLengthLogitsProcessor(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class NoBadWordsLogitsProcessor(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class NoRepeatNGramLogitsProcessor(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class PrefixConstrainedLogitsProcessor(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class RepetitionPenaltyLogitsProcessor(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class TemperatureLogitsWarper(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class TopKLogitsWarper(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class TopPLogitsWarper(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MaxLengthCriteria(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MaxTimeCriteria(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class StoppingCriteria(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class StoppingCriteriaList(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -217,22 +231,19 @@ def top_k_top_p_filtering(*args, **kwargs): requires_backends(top_k_top_p_filtering, ["torch"]) -class Conv1D: +class Conv1D(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class PreTrainedModel: +class PreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - def apply_chunking_to_forward(*args, **kwargs): requires_backends(apply_chunking_to_forward, ["torch"]) @@ -245,92 +256,59 @@ def prune_layer(*args, **kwargs): ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class AlbertForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AlbertForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AlbertForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class AlbertForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class AlbertForQuestionAnswering: +class AlbertForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AlbertForPreTraining(metaclass=DummyObject): + _backends = ["torch"] - -class AlbertForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AlbertForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class AlbertForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AlbertForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class AlbertModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AlbertForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] - -class AlbertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class AlbertModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class AlbertPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -398,512 +376,320 @@ MODEL_MAPPING = None MODEL_WITH_LM_HEAD_MAPPING = None -class AutoModel: +class AutoModel(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForAudioClassification(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForAudioClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForAudioFrameClassification(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForAudioFrameClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForAudioXVector(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForAudioXVector: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForCausalLM(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForCausalLM: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForCTC(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForCTC: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForImageClassification(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForImageClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForImageSegmentation(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForImageSegmentation: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForMaskedLM: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForMultipleChoice: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForNextSentencePrediction(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForNextSentencePrediction: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForObjectDetection(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForObjectDetection: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForPreTraining(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForSeq2SeqLM(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForSeq2SeqLM: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForSpeechSeq2Seq(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForSpeechSeq2Seq: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForTableQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForTableQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelForVision2Seq(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelForVision2Seq: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class AutoModelWithLMHead(metaclass=DummyObject): + _backends = ["torch"] - -class AutoModelWithLMHead: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - BART_PRETRAINED_MODEL_ARCHIVE_LIST = None -class BartForCausalLM: +class BartForCausalLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BartForConditionalGeneration(metaclass=DummyObject): + _backends = ["torch"] - -class BartForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BartForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class BartForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BartForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class BartForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BartModel(metaclass=DummyObject): + _backends = ["torch"] - -class BartModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BartPretrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class BartPretrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class PretrainedBartModel(metaclass=DummyObject): + _backends = ["torch"] - -class PretrainedBartModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - BEIT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class BeitForImageClassification: +class BeitForImageClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class BeitForMaskedImageModeling: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BeitForMaskedImageModeling(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BeitForSemanticSegmentation: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class BeitModel: +class BeitForSemanticSegmentation(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BeitModel(metaclass=DummyObject): + _backends = ["torch"] - -class BeitPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class BeitPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class BertForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BertForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BertForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BertForNextSentencePrediction: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BertForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class BertForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BertForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BertForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BertForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BertLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class BertLMHeadModel: +class BertForNextSentencePrediction(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BertForPreTraining(metaclass=DummyObject): + _backends = ["torch"] - -class BertModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BertForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class BertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class BertForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class BertForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class BertLayer(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class BertLMHeadModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class BertModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class BertPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -911,27 +697,26 @@ def load_tf_weights_in_bert(*args, **kwargs): requires_backends(load_tf_weights_in_bert, ["torch"]) -class BertGenerationDecoder: +class BertGenerationDecoder(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class BertGenerationEncoder: +class BertGenerationEncoder(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class BertGenerationPreTrainedModel: +class BertGenerationPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - def load_tf_weights_in_bert_generation(*args, **kwargs): requires_backends(load_tf_weights_in_bert_generation, ["torch"]) @@ -940,109 +725,73 @@ def load_tf_weights_in_bert_generation(*args, **kwargs): BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST = None -class BigBirdForCausalLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BigBirdForCausalLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class BigBirdForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BigBirdForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class BigBirdLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class BigBirdModel: +class BigBirdForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BigBirdForPreTraining(metaclass=DummyObject): + _backends = ["torch"] - -class BigBirdPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class BigBirdForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class BigBirdForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class BigBirdForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class BigBirdLayer(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class BigBirdModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class BigBirdPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -1053,344 +802,211 @@ def load_tf_weights_in_big_bird(*args, **kwargs): BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST = None -class BigBirdPegasusForCausalLM: +class BigBirdPegasusForCausalLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BigBirdPegasusForConditionalGeneration(metaclass=DummyObject): + _backends = ["torch"] - -class BigBirdPegasusForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BigBirdPegasusForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class BigBirdPegasusForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BigBirdPegasusForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class BigBirdPegasusForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BigBirdPegasusModel(metaclass=DummyObject): + _backends = ["torch"] - -class BigBirdPegasusModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BigBirdPegasusPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class BigBirdPegasusPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class BlenderbotForCausalLM: +class BlenderbotForCausalLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BlenderbotForConditionalGeneration(metaclass=DummyObject): + _backends = ["torch"] - -class BlenderbotForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BlenderbotModel(metaclass=DummyObject): + _backends = ["torch"] - -class BlenderbotModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BlenderbotPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class BlenderbotPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST = None -class BlenderbotSmallForCausalLM: +class BlenderbotSmallForCausalLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BlenderbotSmallForConditionalGeneration(metaclass=DummyObject): + _backends = ["torch"] - -class BlenderbotSmallForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BlenderbotSmallModel(metaclass=DummyObject): + _backends = ["torch"] - -class BlenderbotSmallModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class BlenderbotSmallPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class BlenderbotSmallPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class CamembertForCausalLM: +class CamembertForCausalLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class CamembertForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] - -class CamembertForMaskedLM: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class CamembertForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] - -class CamembertForMultipleChoice: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class CamembertForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class CamembertForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class CamembertForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class CamembertForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class CamembertForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] - -class CamembertForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class CamembertModel(metaclass=DummyObject): + _backends = ["torch"] - -class CamembertModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - CANINE_PRETRAINED_MODEL_ARCHIVE_LIST = None -class CanineForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class CanineForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CanineForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CanineForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CanineForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class CanineLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class CanineModel: +class CanineForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class CanineForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class CaninePreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class CanineForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class CanineLayer(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class CanineModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class CaninePreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -1401,143 +1017,90 @@ def load_tf_weights_in_canine(*args, **kwargs): CLIP_PRETRAINED_MODEL_ARCHIVE_LIST = None -class CLIPModel: +class CLIPModel(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class CLIPPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class CLIPPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class CLIPTextModel(metaclass=DummyObject): + _backends = ["torch"] - -class CLIPTextModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class CLIPVisionModel(metaclass=DummyObject): + _backends = ["torch"] - -class CLIPVisionModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class ConvBertForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class ConvBertForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvBertForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvBertForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvBertForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvBertForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ConvBertLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class ConvBertModel: +class ConvBertForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class ConvBertForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class ConvBertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class ConvBertForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class ConvBertForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class ConvBertLayer(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class ConvBertModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class ConvBertPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -1548,327 +1111,206 @@ def load_tf_weights_in_convbert(*args, **kwargs): CTRL_PRETRAINED_MODEL_ARCHIVE_LIST = None -class CTRLForSequenceClassification: +class CTRLForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class CTRLLMHeadModel(metaclass=DummyObject): + _backends = ["torch"] - -class CTRLLMHeadModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class CTRLModel(metaclass=DummyObject): + _backends = ["torch"] - -class CTRLModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class CTRLPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class CTRLPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None -class DebertaForMaskedLM: +class DebertaForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class DebertaForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class DebertaForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class DebertaForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class DebertaForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class DebertaForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] - -class DebertaForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class DebertaModel(metaclass=DummyObject): + _backends = ["torch"] - -class DebertaModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class DebertaPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class DebertaPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST = None -class DebertaV2ForMaskedLM: +class DebertaV2ForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class DebertaV2ForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class DebertaV2ForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class DebertaV2ForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class DebertaV2ForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class DebertaV2ForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] - -class DebertaV2ForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class DebertaV2Model(metaclass=DummyObject): + _backends = ["torch"] - -class DebertaV2Model: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class DebertaV2PreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class DebertaV2PreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - DEIT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class DeiTForImageClassification: +class DeiTForImageClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class DeiTForImageClassificationWithTeacher: +class DeiTForImageClassificationWithTeacher(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class DeiTModel: +class DeiTModel(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class DeiTPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class DeiTPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class DistilBertForMaskedLM: +class DistilBertForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class DistilBertForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] - -class DistilBertForMultipleChoice: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class DistilBertForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class DistilBertForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class DistilBertForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class DistilBertForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class DistilBertForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] - -class DistilBertForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class DistilBertModel(metaclass=DummyObject): + _backends = ["torch"] - -class DistilBertModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class DistilBertPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class DistilBertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST = None @@ -1879,44 +1321,51 @@ DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST = None DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST = None -class DPRContextEncoder: +class DPRContextEncoder(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class DPRPretrainedContextEncoder: +class DPRPretrainedContextEncoder(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class DPRPreTrainedModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class DPRPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class DPRPretrainedQuestionEncoder: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class DPRPretrainedReader: +class DPRPretrainedQuestionEncoder(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class DPRQuestionEncoder: +class DPRPretrainedReader(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class DPRReader: +class DPRQuestionEncoder(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class DPRReader(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -1924,104 +1373,66 @@ class DPRReader: ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST = None -class ElectraForCausalLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class ElectraForCausalLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ElectraForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ElectraForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ElectraForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class ElectraForQuestionAnswering: +class ElectraForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class ElectraForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] - -class ElectraForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class ElectraForPreTraining(metaclass=DummyObject): + _backends = ["torch"] - -class ElectraForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class ElectraForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class ElectraModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class ElectraForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class ElectraPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class ElectraForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class ElectraModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class ElectraPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -2029,351 +1440,222 @@ def load_tf_weights_in_electra(*args, **kwargs): requires_backends(load_tf_weights_in_electra, ["torch"]) -class EncoderDecoderModel: +class EncoderDecoderModel(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class FlaubertForMultipleChoice: +class FlaubertForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class FlaubertForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class FlaubertForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class FlaubertForQuestionAnsweringSimple(metaclass=DummyObject): + _backends = ["torch"] - -class FlaubertForQuestionAnsweringSimple: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class FlaubertForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class FlaubertForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class FlaubertForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] - -class FlaubertForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class FlaubertModel(metaclass=DummyObject): + _backends = ["torch"] - -class FlaubertModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class FlaubertWithLMHeadModel(metaclass=DummyObject): + _backends = ["torch"] - -class FlaubertWithLMHeadModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - FNET_PRETRAINED_MODEL_ARCHIVE_LIST = None -class FNetForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class FNetForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FNetForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FNetForNextSentencePrediction: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FNetForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class FNetForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class FNetForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FNetForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FNetForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FNetLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class FNetModel: +class FNetForNextSentencePrediction(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class FNetForPreTraining(metaclass=DummyObject): + _backends = ["torch"] - -class FNetPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class FNetForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class FSMTForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class FNetForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class FSMTModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class FNetForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] - -class PretrainedFSMTModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class FNetLayer(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class FNetModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class FNetPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class FSMTForConditionalGeneration(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class FSMTModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class PretrainedFSMTModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST = None -class FunnelBaseModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class FunnelBaseModel(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FunnelForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FunnelForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class FunnelForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class FunnelForQuestionAnswering: +class FunnelForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class FunnelForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] - -class FunnelForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class FunnelForPreTraining(metaclass=DummyObject): + _backends = ["torch"] - -class FunnelForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class FunnelForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class FunnelModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class FunnelForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class FunnelPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class FunnelForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class FunnelModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class FunnelPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -2384,77 +1666,47 @@ def load_tf_weights_in_funnel(*args, **kwargs): GPT2_PRETRAINED_MODEL_ARCHIVE_LIST = None -class GPT2DoubleHeadsModel: +class GPT2DoubleHeadsModel(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class GPT2ForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class GPT2ForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class GPT2ForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] - -class GPT2ForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class GPT2LMHeadModel(metaclass=DummyObject): + _backends = ["torch"] - -class GPT2LMHeadModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class GPT2Model(metaclass=DummyObject): + _backends = ["torch"] - -class GPT2Model: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class GPT2PreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class GPT2PreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - def load_tf_weights_in_gpt2(*args, **kwargs): requires_backends(load_tf_weights_in_gpt2, ["torch"]) @@ -2463,53 +1715,33 @@ def load_tf_weights_in_gpt2(*args, **kwargs): GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST = None -class GPTNeoForCausalLM: +class GPTNeoForCausalLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class GPTNeoForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class GPTNeoForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class GPTNeoModel(metaclass=DummyObject): + _backends = ["torch"] - -class GPTNeoModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class GPTNeoPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class GPTNeoPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - def load_tf_weights_in_gpt_neo(*args, **kwargs): requires_backends(load_tf_weights_in_gpt_neo, ["torch"]) @@ -2518,238 +1750,152 @@ def load_tf_weights_in_gpt_neo(*args, **kwargs): GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST = None -class GPTJForCausalLM: +class GPTJForCausalLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class GPTJForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class GPTJForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class GPTJForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class GPTJForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class GPTJModel(metaclass=DummyObject): + _backends = ["torch"] - -class GPTJModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class GPTJPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class GPTJPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class HubertForCTC: +class HubertForCTC(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class HubertForSequenceClassification: +class HubertForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class HubertModel(metaclass=DummyObject): + _backends = ["torch"] - -class HubertModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class HubertPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class HubertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - IBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class IBertForMaskedLM: +class IBertForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class IBertForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] - -class IBertForMultipleChoice: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class IBertForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class IBertForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class IBertForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class IBertForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class IBertForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] - -class IBertForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class IBertModel(metaclass=DummyObject): + _backends = ["torch"] - -class IBertModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class IBertPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class IBertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class ImageGPTForCausalImageModeling: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class ImageGPTForCausalImageModeling(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ImageGPTForImageClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class ImageGPTModel: +class ImageGPTForImageClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class ImageGPTModel(metaclass=DummyObject): + _backends = ["torch"] - -class ImageGPTPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class ImageGPTPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -2760,280 +1906,172 @@ def load_tf_weights_in_imagegpt(*args, **kwargs): LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = None -class LayoutLMForMaskedLM: +class LayoutLMForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LayoutLMForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class LayoutLMForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LayoutLMForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] - -class LayoutLMForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LayoutLMModel(metaclass=DummyObject): + _backends = ["torch"] - -class LayoutLMModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LayoutLMPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class LayoutLMPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST = None -class LayoutLMv2ForQuestionAnswering: +class LayoutLMv2ForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LayoutLMv2ForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class LayoutLMv2ForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LayoutLMv2ForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] - -class LayoutLMv2ForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LayoutLMv2Model(metaclass=DummyObject): + _backends = ["torch"] - -class LayoutLMv2Model: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LayoutLMv2PreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class LayoutLMv2PreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - LED_PRETRAINED_MODEL_ARCHIVE_LIST = None -class LEDForConditionalGeneration: +class LEDForConditionalGeneration(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LEDForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class LEDForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LEDForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class LEDForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LEDModel(metaclass=DummyObject): + _backends = ["torch"] - -class LEDModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LEDPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class LEDPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None -class LongformerForMaskedLM: +class LongformerForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LongformerForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] - -class LongformerForMultipleChoice: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LongformerForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class LongformerForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LongformerForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class LongformerForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LongformerForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] - -class LongformerForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LongformerModel(metaclass=DummyObject): + _backends = ["torch"] - -class LongformerModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LongformerPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class LongformerPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LongformerSelfAttention(metaclass=DummyObject): + _backends = ["torch"] - -class LongformerSelfAttention: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -3041,109 +2079,93 @@ class LongformerSelfAttention: LUKE_PRETRAINED_MODEL_ARCHIVE_LIST = None -class LukeForEntityClassification: +class LukeForEntityClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class LukeForEntityPairClassification: +class LukeForEntityPairClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class LukeForEntitySpanClassification: +class LukeForEntitySpanClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class LukeForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LukeForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LukeModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LukePreTrainedModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LxmertEncoder: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class LxmertForPreTraining: +class LukeModel(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class LxmertForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class LukePreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LxmertModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LxmertPreTrainedModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class LxmertVisualFeatureEncoder: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class LxmertXLayer: +class LxmertEncoder(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class LxmertForPreTraining(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class LxmertForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class LxmertModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class LxmertPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class LxmertVisualFeatureEncoder(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class LxmertXLayer(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -3151,284 +2173,180 @@ class LxmertXLayer: M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST = None -class M2M100ForConditionalGeneration: +class M2M100ForConditionalGeneration(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class M2M100Model(metaclass=DummyObject): + _backends = ["torch"] - -class M2M100Model: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class M2M100PreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class M2M100PreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MarianForCausalLM(metaclass=DummyObject): + _backends = ["torch"] - -class MarianForCausalLM: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MarianModel(metaclass=DummyObject): + _backends = ["torch"] - -class MarianModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MarianMTModel(metaclass=DummyObject): + _backends = ["torch"] - -class MarianMTModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MBartForCausalLM(metaclass=DummyObject): + _backends = ["torch"] - -class MBartForCausalLM: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MBartForConditionalGeneration(metaclass=DummyObject): + _backends = ["torch"] - -class MBartForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MBartForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class MBartForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MBartForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class MBartForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MBartModel(metaclass=DummyObject): + _backends = ["torch"] - -class MBartModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MBartPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class MBartPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class MegatronBertForCausalLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MegatronBertForCausalLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegatronBertForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegatronBertForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegatronBertForNextSentencePrediction: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegatronBertForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class MegatronBertForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MegatronBertForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegatronBertForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegatronBertForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegatronBertModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MegatronBertPreTrainedModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MMBTForClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class MMBTModel: +class MegatronBertForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class MegatronBertForNextSentencePrediction(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class ModalEmbeddings: +class MegatronBertForPreTraining(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MegatronBertForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MegatronBertForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MegatronBertForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MegatronBertModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MegatronBertPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MMBTForClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MMBTModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class ModalEmbeddings(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -3436,109 +2354,73 @@ class ModalEmbeddings: MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class MobileBertForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MobileBertForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileBertForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileBertForNextSentencePrediction: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileBertForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class MobileBertForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MobileBertForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileBertForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileBertForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MobileBertLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class MobileBertModel: +class MobileBertForNextSentencePrediction(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MobileBertForPreTraining(metaclass=DummyObject): + _backends = ["torch"] - -class MobileBertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class MobileBertForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MobileBertForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MobileBertForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MobileBertLayer(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MobileBertModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MobileBertPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -3549,510 +2431,354 @@ def load_tf_weights_in_mobilebert(*args, **kwargs): MPNET_PRETRAINED_MODEL_ARCHIVE_LIST = None -class MPNetForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MPNetForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MPNetForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MPNetForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MPNetForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MPNetForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class MPNetLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class MPNetModel: +class MPNetForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MPNetForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class MPNetPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MPNetForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class MT5EncoderModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MPNetForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] - -class MT5ForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class MPNetLayer(metaclass=DummyObject): + _backends = ["torch"] - -class MT5Model: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class MPNetModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MPNetPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MT5EncoderModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MT5ForConditionalGeneration(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class MT5Model(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None -class NystromformerForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class NystromformerForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NystromformerForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NystromformerForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NystromformerForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NystromformerForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class NystromformerLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class NystromformerModel: +class NystromformerForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class NystromformerForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class NystromformerPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class NystromformerForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class NystromformerForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class NystromformerLayer(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class NystromformerModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class NystromformerPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class OpenAIGPTDoubleHeadsModel: +class OpenAIGPTDoubleHeadsModel(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class OpenAIGPTForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class OpenAIGPTForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class OpenAIGPTLMHeadModel(metaclass=DummyObject): + _backends = ["torch"] - -class OpenAIGPTLMHeadModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class OpenAIGPTModel(metaclass=DummyObject): + _backends = ["torch"] - -class OpenAIGPTModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class OpenAIGPTPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class OpenAIGPTPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - def load_tf_weights_in_openai_gpt(*args, **kwargs): requires_backends(load_tf_weights_in_openai_gpt, ["torch"]) -class PegasusForCausalLM: +class PegasusForCausalLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class PegasusForConditionalGeneration(metaclass=DummyObject): + _backends = ["torch"] - -class PegasusForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class PegasusModel(metaclass=DummyObject): + _backends = ["torch"] - -class PegasusModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class PegasusPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class PegasusPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST = None -class PerceiverForImageClassificationConvProcessing: +class PerceiverForImageClassificationConvProcessing(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class PerceiverForImageClassificationFourier: +class PerceiverForImageClassificationFourier(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class PerceiverForImageClassificationLearned: +class PerceiverForImageClassificationLearned(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class PerceiverForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class PerceiverForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PerceiverForMultimodalAutoencoding: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class PerceiverForOpticalFlow: +class PerceiverForMultimodalAutoencoding(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class PerceiverForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class PerceiverForOpticalFlow(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class PerceiverLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class PerceiverModel: +class PerceiverForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class PerceiverLayer(metaclass=DummyObject): + _backends = ["torch"] - -class PerceiverPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class PerceiverModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class PerceiverPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST = None -class ProphetNetDecoder: +class ProphetNetDecoder(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class ProphetNetEncoder: +class ProphetNetEncoder(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class ProphetNetForCausalLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class ProphetNetForCausalLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ProphetNetForConditionalGeneration: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ProphetNetModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ProphetNetPreTrainedModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RagModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RagPreTrainedModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RagSequenceForGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class RagTokenForGeneration: +class ProphetNetForConditionalGeneration(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class ProphetNetModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class ProphetNetPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class RagModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class RagPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class RagSequenceForGeneration(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class RagTokenForGeneration(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -4060,189 +2786,125 @@ class RagTokenForGeneration: REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None -class ReformerAttention: +class ReformerAttention(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class ReformerForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class ReformerForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ReformerForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ReformerForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class ReformerLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class ReformerModel: +class ReformerForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class ReformerForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class ReformerModelWithLMHead: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class ReformerLayer(metaclass=DummyObject): + _backends = ["torch"] - -class ReformerPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class ReformerModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class ReformerModelWithLMHead(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class ReformerPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class RemBertForCausalLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class RemBertForCausalLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RemBertForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RemBertForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RemBertForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RemBertForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RemBertForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RemBertLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class RemBertModel: +class RemBertForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class RemBertForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] - -class RemBertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class RemBertForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class RemBertForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class RemBertForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class RemBertLayer(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class RemBertModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class RemBertPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -4253,230 +2915,142 @@ def load_tf_weights_in_rembert(*args, **kwargs): RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class RetriBertModel: +class RetriBertModel(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class RetriBertPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class RetriBertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None -class RobertaForCausalLM: +class RobertaForCausalLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class RobertaForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] - -class RobertaForMaskedLM: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class RobertaForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] - -class RobertaForMultipleChoice: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class RobertaForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class RobertaForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class RobertaForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class RobertaForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class RobertaForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] - -class RobertaForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class RobertaModel(metaclass=DummyObject): + _backends = ["torch"] - -class RobertaModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class RobertaPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class RobertaPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None -class RoFormerForCausalLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class RoFormerForCausalLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoFormerForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoFormerForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoFormerForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoFormerForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoFormerForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class RoFormerLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class RoFormerModel: +class RoFormerForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class RoFormerForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] - -class RoFormerPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class RoFormerForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class RoFormerForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class RoFormerForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class RoFormerLayer(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class RoFormerModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class RoFormerPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -4487,399 +3061,275 @@ def load_tf_weights_in_roformer(*args, **kwargs): SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None -class SegformerDecodeHead: +class SegformerDecodeHead(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class SegformerForImageClassification: +class SegformerForImageClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class SegformerForSemanticSegmentation: +class SegformerForSemanticSegmentation(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class SegformerLayer: +class SegformerLayer(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class SegformerModel: +class SegformerModel(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class SegformerPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class SegformerPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - SEW_PRETRAINED_MODEL_ARCHIVE_LIST = None -class SEWForCTC: +class SEWForCTC(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class SEWForSequenceClassification: +class SEWForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class SEWModel(metaclass=DummyObject): + _backends = ["torch"] - -class SEWModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class SEWPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class SEWPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST = None -class SEWDForCTC: +class SEWDForCTC(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class SEWDForSequenceClassification: +class SEWDForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class SEWDModel(metaclass=DummyObject): + _backends = ["torch"] - -class SEWDModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class SEWDPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class SEWDPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class SpeechEncoderDecoderModel(metaclass=DummyObject): + _backends = ["torch"] - -class SpeechEncoderDecoderModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class Speech2TextForConditionalGeneration: +class Speech2TextForConditionalGeneration(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class Speech2TextModel(metaclass=DummyObject): + _backends = ["torch"] - -class Speech2TextModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class Speech2TextPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class Speech2TextPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class Speech2Text2ForCausalLM(metaclass=DummyObject): + _backends = ["torch"] - -class Speech2Text2ForCausalLM: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class Speech2Text2PreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class Speech2Text2PreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST = None -class SplinterForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class SplinterForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SplinterLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class SplinterModel: +class SplinterLayer(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class SplinterModel(metaclass=DummyObject): + _backends = ["torch"] - -class SplinterPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class SplinterPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class SqueezeBertForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class SqueezeBertForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SqueezeBertForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SqueezeBertForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SqueezeBertForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SqueezeBertForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SqueezeBertModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class SqueezeBertModule: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class SqueezeBertPreTrainedModel: +class SqueezeBertForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class SqueezeBertForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class SqueezeBertForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class SqueezeBertForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class SqueezeBertModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class SqueezeBertModule(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class SqueezeBertPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) T5_PRETRAINED_MODEL_ARCHIVE_LIST = None -class T5EncoderModel: +class T5EncoderModel(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class T5ForConditionalGeneration(metaclass=DummyObject): + _backends = ["torch"] - -class T5ForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class T5Model(metaclass=DummyObject): + _backends = ["torch"] - -class T5Model: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class T5PreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class T5PreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - def load_tf_weights_in_t5(*args, **kwargs): requires_backends(load_tf_weights_in_t5, ["torch"]) @@ -4888,58 +3338,40 @@ def load_tf_weights_in_t5(*args, **kwargs): TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST = None -class AdaptiveEmbedding: +class AdaptiveEmbedding(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class TransfoXLForSequenceClassification: +class TransfoXLForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class TransfoXLLMHeadModel(metaclass=DummyObject): + _backends = ["torch"] - -class TransfoXLLMHeadModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class TransfoXLModel(metaclass=DummyObject): + _backends = ["torch"] - -class TransfoXLModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class TransfoXLPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class TransfoXLPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - def load_tf_weights_in_transfo_xl(*args, **kwargs): requires_backends(load_tf_weights_in_transfo_xl, ["torch"]) @@ -4948,734 +3380,533 @@ def load_tf_weights_in_transfo_xl(*args, **kwargs): TROCR_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TrOCRForCausalLM: +class TrOCRForCausalLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class TrOCRPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class TrOCRPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST = None -class UniSpeechForCTC: +class UniSpeechForCTC(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class UniSpeechForPreTraining: +class UniSpeechForPreTraining(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class UniSpeechForSequenceClassification: +class UniSpeechForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class UniSpeechModel(metaclass=DummyObject): + _backends = ["torch"] - -class UniSpeechModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class UniSpeechPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class UniSpeechPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class UniSpeechSatForAudioFrameClassification: +class UniSpeechSatForAudioFrameClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class UniSpeechSatForCTC: +class UniSpeechSatForCTC(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class UniSpeechSatForPreTraining: +class UniSpeechSatForPreTraining(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class UniSpeechSatForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class UniSpeechSatForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class UniSpeechSatForXVector: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class UniSpeechSatModel: +class UniSpeechSatForXVector(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class UniSpeechSatModel(metaclass=DummyObject): + _backends = ["torch"] - -class UniSpeechSatPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class UniSpeechSatPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class VisionEncoderDecoderModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class VisionEncoderDecoderModel(metaclass=DummyObject): + _backends = ["torch"] - -class VisionTextDualEncoderModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class VisionTextDualEncoderModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class VisualBertForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class VisualBertForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VisualBertForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class VisualBertForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class VisualBertForPreTraining(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class VisualBertForRegionToPhraseAlignment: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class VisualBertForVisualReasoning: +class VisualBertForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class VisualBertLayer: +class VisualBertForRegionToPhraseAlignment(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class VisualBertModel: +class VisualBertForVisualReasoning(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class VisualBertLayer(metaclass=DummyObject): + _backends = ["torch"] - -class VisualBertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class VisualBertModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class VisualBertPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) VIT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class ViTForImageClassification: +class ViTForImageClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class ViTModel: +class ViTModel(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class ViTPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class ViTPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST = None -class Wav2Vec2ForAudioFrameClassification: +class Wav2Vec2ForAudioFrameClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class Wav2Vec2ForCTC: +class Wav2Vec2ForCTC(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class Wav2Vec2ForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class Wav2Vec2ForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2ForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class Wav2Vec2ForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class Wav2Vec2ForPreTraining(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class Wav2Vec2ForXVector: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class Wav2Vec2Model: +class Wav2Vec2ForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class Wav2Vec2ForXVector(metaclass=DummyObject): + _backends = ["torch"] - -class Wav2Vec2PreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class Wav2Vec2Model(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class Wav2Vec2PreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST = None -class WavLMForAudioFrameClassification: +class WavLMForAudioFrameClassification(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class WavLMForCTC: +class WavLMForCTC(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class WavLMForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class WavLMForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - -class WavLMForXVector: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class WavLMModel: +class WavLMForXVector(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class WavLMModel(metaclass=DummyObject): + _backends = ["torch"] - -class WavLMPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): +class WavLMPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) XLM_PRETRAINED_MODEL_ARCHIVE_LIST = None -class XLMForMultipleChoice: +class XLMForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLMForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class XLMForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLMForQuestionAnsweringSimple(metaclass=DummyObject): + _backends = ["torch"] - -class XLMForQuestionAnsweringSimple: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLMForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class XLMForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLMForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] - -class XLMForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLMModel(metaclass=DummyObject): + _backends = ["torch"] - -class XLMModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLMPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class XLMPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLMWithLMHeadModel(metaclass=DummyObject): + _backends = ["torch"] - -class XLMWithLMHeadModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST = None -class XLMProphetNetDecoder: +class XLMProphetNetDecoder(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class XLMProphetNetEncoder: +class XLMProphetNetEncoder(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class XLMProphetNetForCausalLM: +class XLMProphetNetForCausalLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLMProphetNetForConditionalGeneration(metaclass=DummyObject): + _backends = ["torch"] - -class XLMProphetNetForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLMProphetNetModel(metaclass=DummyObject): + _backends = ["torch"] - -class XLMProphetNetModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None -class XLMRobertaForCausalLM: +class XLMRobertaForCausalLM(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLMRobertaForMaskedLM(metaclass=DummyObject): + _backends = ["torch"] - -class XLMRobertaForMaskedLM: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLMRobertaForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] - -class XLMRobertaForMultipleChoice: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLMRobertaForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class XLMRobertaForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLMRobertaForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class XLMRobertaForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLMRobertaForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] - -class XLMRobertaForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLMRobertaModel(metaclass=DummyObject): + _backends = ["torch"] - -class XLMRobertaModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - XLNET_PRETRAINED_MODEL_ARCHIVE_LIST = None -class XLNetForMultipleChoice: +class XLNetForMultipleChoice(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLNetForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] - -class XLNetForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLNetForQuestionAnsweringSimple(metaclass=DummyObject): + _backends = ["torch"] - -class XLNetForQuestionAnsweringSimple: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLNetForSequenceClassification(metaclass=DummyObject): + _backends = ["torch"] - -class XLNetForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLNetForTokenClassification(metaclass=DummyObject): + _backends = ["torch"] - -class XLNetForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLNetLMHeadModel(metaclass=DummyObject): + _backends = ["torch"] - -class XLNetLMHeadModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLNetModel(metaclass=DummyObject): + _backends = ["torch"] - -class XLNetModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) +class XLNetPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] - -class XLNetPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["torch"]) - def load_tf_weights_in_xlnet(*args, **kwargs): requires_backends(load_tf_weights_in_xlnet, ["torch"]) -class Adafactor: +class Adafactor(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) -class AdamW: +class AdamW(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -5708,7 +3939,9 @@ def get_scheduler(*args, **kwargs): requires_backends(get_scheduler, ["torch"]) -class Trainer: +class Trainer(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) @@ -5717,6 +3950,8 @@ def torch_distributed_zero_first(*args, **kwargs): requires_backends(torch_distributed_zero_first, ["torch"]) -class Seq2SeqTrainer: +class Seq2SeqTrainer(metaclass=DummyObject): + _backends = ["torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) diff --git a/src/transformers/utils/dummy_pytorch_quantization_and_torch_objects.py b/src/transformers/utils/dummy_pytorch_quantization_and_torch_objects.py index 9f036f9e1a..5612b769de 100644 --- a/src/transformers/utils/dummy_pytorch_quantization_and_torch_objects.py +++ b/src/transformers/utils/dummy_pytorch_quantization_and_torch_objects.py @@ -1,120 +1,78 @@ # This file is autogenerated by the command `make fix-copies`, do not edit. -from ..file_utils import requires_backends +# flake8: noqa +from ..file_utils import DummyObject, requires_backends QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class QDQBertForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["pytorch_quantization", "torch"]) +class QDQBertForMaskedLM(metaclass=DummyObject): + _backends = ["pytorch_quantization", "torch"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["pytorch_quantization", "torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["pytorch_quantization", "torch"]) - - -class QDQBertForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["pytorch_quantization", "torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["pytorch_quantization", "torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["pytorch_quantization", "torch"]) - - -class QDQBertForNextSentencePrediction: - def __init__(self, *args, **kwargs): - requires_backends(self, ["pytorch_quantization", "torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["pytorch_quantization", "torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["pytorch_quantization", "torch"]) - - -class QDQBertForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["pytorch_quantization", "torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["pytorch_quantization", "torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["pytorch_quantization", "torch"]) - - -class QDQBertForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["pytorch_quantization", "torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["pytorch_quantization", "torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["pytorch_quantization", "torch"]) - - -class QDQBertForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["pytorch_quantization", "torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["pytorch_quantization", "torch"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["pytorch_quantization", "torch"]) - - -class QDQBertLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["pytorch_quantization", "torch"]) -class QDQBertLMHeadModel: +class QDQBertForMultipleChoice(metaclass=DummyObject): + _backends = ["pytorch_quantization", "torch"] + def __init__(self, *args, **kwargs): requires_backends(self, ["pytorch_quantization", "torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["pytorch_quantization", "torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["pytorch_quantization", "torch"]) +class QDQBertForNextSentencePrediction(metaclass=DummyObject): + _backends = ["pytorch_quantization", "torch"] - -class QDQBertModel: def __init__(self, *args, **kwargs): requires_backends(self, ["pytorch_quantization", "torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["pytorch_quantization", "torch"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["pytorch_quantization", "torch"]) +class QDQBertForQuestionAnswering(metaclass=DummyObject): + _backends = ["pytorch_quantization", "torch"] - -class QDQBertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["pytorch_quantization", "torch"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["pytorch_quantization", "torch"]) - def forward(self, *args, **kwargs): +class QDQBertForSequenceClassification(metaclass=DummyObject): + _backends = ["pytorch_quantization", "torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["pytorch_quantization", "torch"]) + + +class QDQBertForTokenClassification(metaclass=DummyObject): + _backends = ["pytorch_quantization", "torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["pytorch_quantization", "torch"]) + + +class QDQBertLayer(metaclass=DummyObject): + _backends = ["pytorch_quantization", "torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["pytorch_quantization", "torch"]) + + +class QDQBertLMHeadModel(metaclass=DummyObject): + _backends = ["pytorch_quantization", "torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["pytorch_quantization", "torch"]) + + +class QDQBertModel(metaclass=DummyObject): + _backends = ["pytorch_quantization", "torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["pytorch_quantization", "torch"]) + + +class QDQBertPreTrainedModel(metaclass=DummyObject): + _backends = ["pytorch_quantization", "torch"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["pytorch_quantization", "torch"]) diff --git a/src/transformers/utils/dummy_scatter_objects.py b/src/transformers/utils/dummy_scatter_objects.py index 6a53b0f963..abe9be04d1 100644 --- a/src/transformers/utils/dummy_scatter_objects.py +++ b/src/transformers/utils/dummy_scatter_objects.py @@ -1,69 +1,45 @@ # This file is autogenerated by the command `make fix-copies`, do not edit. -from ..file_utils import requires_backends +# flake8: noqa +from ..file_utils import DummyObject, requires_backends TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TapasForMaskedLM: +class TapasForMaskedLM(metaclass=DummyObject): + _backends = ["scatter"] + def __init__(self, *args, **kwargs): requires_backends(self, ["scatter"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["scatter"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["scatter"]) +class TapasForQuestionAnswering(metaclass=DummyObject): + _backends = ["scatter"] - -class TapasForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["scatter"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["scatter"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["scatter"]) +class TapasForSequenceClassification(metaclass=DummyObject): + _backends = ["scatter"] - -class TapasForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["scatter"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["scatter"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["scatter"]) +class TapasModel(metaclass=DummyObject): + _backends = ["scatter"] - -class TapasModel: def __init__(self, *args, **kwargs): requires_backends(self, ["scatter"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["scatter"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["scatter"]) +class TapasPreTrainedModel(metaclass=DummyObject): + _backends = ["scatter"] - -class TapasPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["scatter"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["scatter"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["scatter"]) - def load_tf_weights_in_tapas(*args, **kwargs): requires_backends(load_tf_weights_in_tapas, ["scatter"]) diff --git a/src/transformers/utils/dummy_sentencepiece_and_speech_objects.py b/src/transformers/utils/dummy_sentencepiece_and_speech_objects.py index 42727619d9..53e2502dab 100644 --- a/src/transformers/utils/dummy_sentencepiece_and_speech_objects.py +++ b/src/transformers/utils/dummy_sentencepiece_and_speech_objects.py @@ -1,11 +1,10 @@ # This file is autogenerated by the command `make fix-copies`, do not edit. -from ..file_utils import requires_backends +# flake8: noqa +from ..file_utils import DummyObject, requires_backends -class Speech2TextProcessor: +class Speech2TextProcessor(metaclass=DummyObject): + _backends = ["sentencepiece", "speech"] + def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece", "speech"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece", "speech"]) diff --git a/src/transformers/utils/dummy_sentencepiece_and_tokenizers_objects.py b/src/transformers/utils/dummy_sentencepiece_and_tokenizers_objects.py index 0cb93ec194..89efff7123 100644 --- a/src/transformers/utils/dummy_sentencepiece_and_tokenizers_objects.py +++ b/src/transformers/utils/dummy_sentencepiece_and_tokenizers_objects.py @@ -1,5 +1,6 @@ # This file is autogenerated by the command `make fix-copies`, do not edit. -from ..file_utils import requires_backends +# flake8: noqa +from ..file_utils import DummyObject, requires_backends SLOW_TO_FAST_CONVERTERS = None diff --git a/src/transformers/utils/dummy_sentencepiece_objects.py b/src/transformers/utils/dummy_sentencepiece_objects.py index 9bdc03411a..90ad8a8967 100644 --- a/src/transformers/utils/dummy_sentencepiece_objects.py +++ b/src/transformers/utils/dummy_sentencepiece_objects.py @@ -1,200 +1,157 @@ # This file is autogenerated by the command `make fix-copies`, do not edit. -from ..file_utils import requires_backends +# flake8: noqa +from ..file_utils import DummyObject, requires_backends -class AlbertTokenizer: +class AlbertTokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] + def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class BarthezTokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class BarthezTokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class BartphoTokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class BartphoTokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class BertGenerationTokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class BertGenerationTokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class BigBirdTokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class BigBirdTokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class CamembertTokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class CamembertTokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class DebertaV2Tokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class DebertaV2Tokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class LayoutXLMTokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class LayoutXLMTokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class M2M100Tokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class M2M100Tokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class MarianTokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class MarianTokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class MBart50Tokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class MBart50Tokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class MBartTokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class MBartTokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class MLukeTokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class MLukeTokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class MT5Tokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class MT5Tokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class PegasusTokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class PegasusTokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class ReformerTokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class ReformerTokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class RemBertTokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class RemBertTokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class Speech2TextTokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class Speech2TextTokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class T5Tokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class T5Tokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class XLMProphetNetTokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class XLMProphetNetTokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class XLMRobertaTokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class XLMRobertaTokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) +class XLNetTokenizer(metaclass=DummyObject): + _backends = ["sentencepiece"] -class XLNetTokenizer: def __init__(self, *args, **kwargs): requires_backends(self, ["sentencepiece"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["sentencepiece"]) diff --git a/src/transformers/utils/dummy_speech_objects.py b/src/transformers/utils/dummy_speech_objects.py index 9dd744f199..a1fd102aab 100644 --- a/src/transformers/utils/dummy_speech_objects.py +++ b/src/transformers/utils/dummy_speech_objects.py @@ -1,7 +1,10 @@ # This file is autogenerated by the command `make fix-copies`, do not edit. -from ..file_utils import requires_backends +# flake8: noqa +from ..file_utils import DummyObject, requires_backends -class Speech2TextFeatureExtractor: +class Speech2TextFeatureExtractor(metaclass=DummyObject): + _backends = ["speech"] + def __init__(self, *args, **kwargs): requires_backends(self, ["speech"]) diff --git a/src/transformers/utils/dummy_tf_objects.py b/src/transformers/utils/dummy_tf_objects.py index 98bb7afbe9..c099da8924 100644 --- a/src/transformers/utils/dummy_tf_objects.py +++ b/src/transformers/utils/dummy_tf_objects.py @@ -1,13 +1,18 @@ # This file is autogenerated by the command `make fix-copies`, do not edit. -from ..file_utils import requires_backends +# flake8: noqa +from ..file_utils import DummyObject, requires_backends -class TensorFlowBenchmarkArguments: +class TensorFlowBenchmarkArguments(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TensorFlowBenchmark: +class TensorFlowBenchmark(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) @@ -16,7 +21,9 @@ def tf_top_k_top_p_filtering(*args, **kwargs): requires_backends(tf_top_k_top_p_filtering, ["tf"]) -class PushToHubCallback: +class PushToHubCallback(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) @@ -24,89 +31,65 @@ class PushToHubCallback: TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFLayoutLMForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFLayoutLMForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLayoutLMForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLayoutLMForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLayoutLMMainLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFLayoutLMModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFLayoutLMForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLayoutLMPreTrainedModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFPreTrainedModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFSequenceSummary: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFSharedEmbeddings: +class TFLayoutLMForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFLayoutLMMainLayer(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFLayoutLMModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFLayoutLMPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFSequenceSummary(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFSharedEmbeddings(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) @@ -118,97 +101,66 @@ def shape_list(*args, **kwargs): TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFAlbertForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFAlbertForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAlbertForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAlbertForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFAlbertForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFAlbertForMultipleChoice(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAlbertForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAlbertForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFAlbertMainLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFAlbertModel: +class TFAlbertForPreTraining(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFAlbertForQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] - -class TFAlbertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): +class TFAlbertForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFAlbertForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFAlbertMainLayer(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFAlbertModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFAlbertPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) @@ -254,904 +206,559 @@ TF_MODEL_MAPPING = None TF_MODEL_WITH_LM_HEAD_MAPPING = None -class TFAutoModel: +class TFAutoModel(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFAutoModelForCausalLM(metaclass=DummyObject): + _backends = ["tf"] - -class TFAutoModelForCausalLM: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFAutoModelForImageClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFAutoModelForImageClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFAutoModelForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] - -class TFAutoModelForMaskedLM: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFAutoModelForMultipleChoice(metaclass=DummyObject): + _backends = ["tf"] - -class TFAutoModelForMultipleChoice: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFAutoModelForPreTraining(metaclass=DummyObject): + _backends = ["tf"] - -class TFAutoModelForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFAutoModelForQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] - -class TFAutoModelForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFAutoModelForSeq2SeqLM(metaclass=DummyObject): + _backends = ["tf"] - -class TFAutoModelForSeq2SeqLM: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFAutoModelForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFAutoModelForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFAutoModelForTableQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] - -class TFAutoModelForTableQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFAutoModelForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFAutoModelForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFAutoModelForVision2Seq(metaclass=DummyObject): + _backends = ["tf"] - -class TFAutoModelForVision2Seq: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFAutoModelWithLMHead(metaclass=DummyObject): + _backends = ["tf"] - -class TFAutoModelWithLMHead: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFBartForConditionalGeneration(metaclass=DummyObject): + _backends = ["tf"] - -class TFBartForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFBartModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFBartModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFBartPretrainedModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFBartPretrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFBertEmbeddings: +class TFBertEmbeddings(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFBertForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFBertForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBertForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBertForNextSentencePrediction: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBertForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFBertForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFBertForMultipleChoice(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBertForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBertForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBertLMHeadModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFBertMainLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFBertModel: +class TFBertForNextSentencePrediction(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFBertForPreTraining(metaclass=DummyObject): + _backends = ["tf"] - -class TFBertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFBertForQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] - -class TFBlenderbotForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFBertForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFBlenderbotModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFBertForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFBlenderbotPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFBertLMHeadModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFBlenderbotSmallForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFBertMainLayer(metaclass=DummyObject): + _backends = ["tf"] - -class TFBlenderbotSmallModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFBertModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFBlenderbotSmallPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): +class TFBertPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFBlenderbotForConditionalGeneration(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFBlenderbotModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFBlenderbotPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFBlenderbotSmallForConditionalGeneration(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFBlenderbotSmallModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFBlenderbotSmallPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFCamembertForMaskedLM: +class TFCamembertForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFCamembertForMultipleChoice(metaclass=DummyObject): + _backends = ["tf"] - -class TFCamembertForMultipleChoice: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFCamembertForQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] - -class TFCamembertForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFCamembertForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFCamembertForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFCamembertForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFCamembertForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFCamembertModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFCamembertModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFCLIPModel: +class TFCLIPModel(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFCLIPPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFCLIPPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFCLIPTextModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFCLIPTextModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFCLIPVisionModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFCLIPVisionModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFConvBertForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFConvBertForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFConvBertForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFConvBertForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFConvBertForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFConvBertForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFConvBertLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFConvBertModel: +class TFConvBertForMultipleChoice(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFConvBertForQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] - -class TFConvBertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): +class TFConvBertForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFConvBertForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFConvBertLayer(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFConvBertModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFConvBertPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFCTRLForSequenceClassification: +class TFCTRLForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFCTRLLMHeadModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFCTRLLMHeadModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFCTRLModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFCTRLModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFCTRLPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFCTRLPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFDebertaForMaskedLM: +class TFDebertaForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFDebertaForQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] - -class TFDebertaForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFDebertaForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFDebertaForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFDebertaForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFDebertaForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFDebertaModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFDebertaModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFDebertaPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFDebertaPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFDebertaV2ForMaskedLM: +class TFDebertaV2ForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFDebertaV2ForQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] - -class TFDebertaV2ForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFDebertaV2ForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFDebertaV2ForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFDebertaV2ForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFDebertaV2ForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFDebertaV2Model(metaclass=DummyObject): + _backends = ["tf"] - -class TFDebertaV2Model: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFDebertaV2PreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFDebertaV2PreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFDistilBertForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFDistilBertForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDistilBertForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDistilBertForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDistilBertForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDistilBertForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFDistilBertMainLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFDistilBertModel: +class TFDistilBertForMultipleChoice(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFDistilBertForQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] - -class TFDistilBertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): +class TFDistilBertForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFDistilBertForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFDistilBertMainLayer(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFDistilBertModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFDistilBertPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) @@ -1164,32 +771,44 @@ TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST = None TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFDPRContextEncoder: +class TFDPRContextEncoder(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFDPRPretrainedContextEncoder: +class TFDPRPretrainedContextEncoder(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFDPRPretrainedQuestionEncoder: +class TFDPRPretrainedQuestionEncoder(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFDPRPretrainedReader: +class TFDPRPretrainedReader(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFDPRQuestionEncoder: +class TFDPRQuestionEncoder(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFDPRReader: +class TFDPRReader(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) @@ -1197,522 +816,332 @@ class TFDPRReader: TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFElectraForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFElectraForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFElectraForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFElectraForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFElectraForQuestionAnswering: +class TFElectraForMultipleChoice(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFElectraForPreTraining(metaclass=DummyObject): + _backends = ["tf"] - -class TFElectraForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFElectraForQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] - -class TFElectraForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFElectraForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFElectraModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFElectraForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFElectraPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFElectraModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFEncoderDecoderModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): +class TFElectraPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFEncoderDecoderModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFFlaubertForMultipleChoice: +class TFFlaubertForMultipleChoice(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFFlaubertForQuestionAnsweringSimple(metaclass=DummyObject): + _backends = ["tf"] - -class TFFlaubertForQuestionAnsweringSimple: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFFlaubertForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFFlaubertForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFFlaubertForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFFlaubertForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFFlaubertModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFFlaubertModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFFlaubertPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFFlaubertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFFlaubertWithLMHeadModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFFlaubertWithLMHeadModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFFunnelBaseModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFFunnelBaseModel(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFunnelForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFunnelForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFFunnelForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFFunnelForQuestionAnswering: +class TFFunnelForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFFunnelForMultipleChoice(metaclass=DummyObject): + _backends = ["tf"] - -class TFFunnelForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFFunnelForPreTraining(metaclass=DummyObject): + _backends = ["tf"] - -class TFFunnelForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFFunnelForQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] - -class TFFunnelModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFFunnelForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFFunnelPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): +class TFFunnelForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFFunnelModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFFunnelPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFGPT2DoubleHeadsModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFGPT2DoubleHeadsModel(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFGPT2ForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFGPT2LMHeadModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFGPT2MainLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFGPT2Model: +class TFGPT2ForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFGPT2LMHeadModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFGPT2PreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): +class TFGPT2MainLayer(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFGPT2Model(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFGPT2PreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFHubertForCTC: +class TFHubertForCTC(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFHubertModel: +class TFHubertModel(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFHubertPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFHubertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFLEDForConditionalGeneration(metaclass=DummyObject): + _backends = ["tf"] - -class TFLEDForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFLEDModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFLEDModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFLEDPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFLEDPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFLongformerForMaskedLM: +class TFLongformerForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFLongformerForMultipleChoice(metaclass=DummyObject): + _backends = ["tf"] - -class TFLongformerForMultipleChoice: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFLongformerForQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] - -class TFLongformerForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFLongformerForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFLongformerForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFLongformerForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFLongformerForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFLongformerModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFLongformerModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFLongformerPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFLongformerPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFLongformerSelfAttention(metaclass=DummyObject): + _backends = ["tf"] - -class TFLongformerSelfAttention: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) @@ -1720,488 +1149,326 @@ class TFLongformerSelfAttention: TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFLxmertForPreTraining: +class TFLxmertForPreTraining(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFLxmertMainLayer: +class TFLxmertMainLayer(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFLxmertModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFLxmertModel(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLxmertPreTrainedModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFLxmertVisualFeatureEncoder: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFMarianModel: +class TFLxmertPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFLxmertVisualFeatureEncoder(metaclass=DummyObject): + _backends = ["tf"] - -class TFMarianMTModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFMarianModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFMarianPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFMarianMTModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFMBartForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFMarianPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFMBartModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFMBartForConditionalGeneration(metaclass=DummyObject): + _backends = ["tf"] - -class TFMBartPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): +class TFMBartModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFMBartPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFMobileBertForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFMobileBertForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileBertForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileBertForNextSentencePrediction: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileBertForPreTraining: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFMobileBertForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFMobileBertForMultipleChoice(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileBertForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileBertForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMobileBertMainLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFMobileBertModel: +class TFMobileBertForNextSentencePrediction(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFMobileBertForPreTraining(metaclass=DummyObject): + _backends = ["tf"] - -class TFMobileBertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): +class TFMobileBertForQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFMobileBertForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFMobileBertForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFMobileBertMainLayer(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFMobileBertModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFMobileBertPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFMPNetForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFMPNetForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMPNetForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMPNetForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMPNetForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMPNetForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFMPNetMainLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFMPNetModel: +class TFMPNetForMultipleChoice(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFMPNetForQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] - -class TFMPNetPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFMPNetForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFMT5EncoderModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFMPNetForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFMT5ForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFMPNetMainLayer(metaclass=DummyObject): + _backends = ["tf"] - -class TFMT5Model: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): +class TFMPNetModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFMPNetPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFMT5EncoderModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFMT5ForConditionalGeneration(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFMT5Model(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFOpenAIGPTDoubleHeadsModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFOpenAIGPTDoubleHeadsModel(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFOpenAIGPTForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFOpenAIGPTLMHeadModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFOpenAIGPTMainLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFOpenAIGPTModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFOpenAIGPTForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFOpenAIGPTPreTrainedModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFPegasusForConditionalGeneration: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFPegasusModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFPegasusPreTrainedModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRagModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRagPreTrainedModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRagSequenceForGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFRagTokenForGeneration: +class TFOpenAIGPTLMHeadModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFOpenAIGPTMainLayer(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFOpenAIGPTModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFOpenAIGPTPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFPegasusForConditionalGeneration(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFPegasusModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFPegasusPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRagModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRagPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRagSequenceForGeneration(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRagTokenForGeneration(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) @@ -2209,833 +1476,547 @@ class TFRagTokenForGeneration: TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFRemBertForCausalLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFRemBertForCausalLM(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRemBertForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRemBertForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRemBertForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRemBertForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRemBertForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRemBertLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFRemBertModel: +class TFRemBertForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFRemBertForMultipleChoice(metaclass=DummyObject): + _backends = ["tf"] - -class TFRemBertPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): +class TFRemBertForQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRemBertForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRemBertForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRemBertLayer(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRemBertModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRemBertPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFRobertaForCausalLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFRobertaForCausalLM(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRobertaMainLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFRobertaModel: +class TFRobertaForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFRobertaForMultipleChoice(metaclass=DummyObject): + _backends = ["tf"] - -class TFRobertaPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): +class TFRobertaForQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRobertaForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRobertaForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRobertaMainLayer(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRobertaModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRobertaPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFRoFormerForCausalLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFRoFormerForCausalLM(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRoFormerForMaskedLM: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRoFormerForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRoFormerForQuestionAnswering: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRoFormerForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRoFormerForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFRoFormerLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFRoFormerModel: +class TFRoFormerForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFRoFormerForMultipleChoice(metaclass=DummyObject): + _backends = ["tf"] - -class TFRoFormerPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): +class TFRoFormerForQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRoFormerForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRoFormerForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRoFormerLayer(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRoFormerModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFRoFormerPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFT5EncoderModel: +class TFT5EncoderModel(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFT5ForConditionalGeneration(metaclass=DummyObject): + _backends = ["tf"] - -class TFT5ForConditionalGeneration: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFT5Model(metaclass=DummyObject): + _backends = ["tf"] - -class TFT5Model: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFT5PreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFT5PreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFTapasForMaskedLM: +class TFTapasForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFTapasForQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] - -class TFTapasForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFTapasForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFTapasForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFTapasModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFTapasModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFTapasPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFTapasPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFAdaptiveEmbedding: +class TFAdaptiveEmbedding(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFTransfoXLForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFTransfoXLForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFTransfoXLLMHeadModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFTransfoXLMainLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFTransfoXLModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFTransfoXLLMHeadModel(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFTransfoXLPreTrainedModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFVisionEncoderDecoderModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFViTForImageClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFViTModel: +class TFTransfoXLMainLayer(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFTransfoXLModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFViTPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): +class TFTransfoXLPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFVisionEncoderDecoderModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFViTForImageClassification(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFViTModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFViTPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFWav2Vec2ForCTC: +class TFWav2Vec2ForCTC(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFWav2Vec2Model: +class TFWav2Vec2Model(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFWav2Vec2PreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFWav2Vec2PreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFXLMForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFXLMForMultipleChoice(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMForQuestionAnsweringSimple: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLMMainLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFXLMModel: +class TFXLMForQuestionAnsweringSimple(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFXLMForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFXLMPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFXLMForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFXLMWithLMHeadModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): +class TFXLMMainLayer(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFXLMModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFXLMPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFXLMWithLMHeadModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFXLMRobertaForMaskedLM: +class TFXLMRobertaForMaskedLM(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFXLMRobertaForMultipleChoice(metaclass=DummyObject): + _backends = ["tf"] - -class TFXLMRobertaForMultipleChoice: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFXLMRobertaForQuestionAnswering(metaclass=DummyObject): + _backends = ["tf"] - -class TFXLMRobertaForQuestionAnswering: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFXLMRobertaForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFXLMRobertaForSequenceClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFXLMRobertaForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] - -class TFXLMRobertaForTokenClassification: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFXLMRobertaModel(metaclass=DummyObject): + _backends = ["tf"] - -class TFXLMRobertaModel: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST = None -class TFXLNetForMultipleChoice: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFXLNetForMultipleChoice(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLNetForQuestionAnsweringSimple: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLNetForSequenceClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLNetForTokenClassification: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLNetLMHeadModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLNetMainLayer: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class TFXLNetModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) +class TFXLNetForQuestionAnsweringSimple(metaclass=DummyObject): + _backends = ["tf"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class TFXLNetPreTrainedModel: - def __init__(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tf"]) - - def call(self, *args, **kwargs): - requires_backends(self, ["tf"]) - - -class AdamWeightDecay: def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class GradientAccumulator: +class TFXLNetForSequenceClassification(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) -class WarmUp: +class TFXLNetForTokenClassification(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFXLNetLMHeadModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFXLNetMainLayer(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFXLNetModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class TFXLNetPreTrainedModel(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class AdamWeightDecay(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class GradientAccumulator(metaclass=DummyObject): + _backends = ["tf"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["tf"]) + + +class WarmUp(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) @@ -3044,6 +2025,8 @@ def create_optimizer(*args, **kwargs): requires_backends(create_optimizer, ["tf"]) -class TFTrainer: +class TFTrainer(metaclass=DummyObject): + _backends = ["tf"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tf"]) diff --git a/src/transformers/utils/dummy_timm_and_vision_objects.py b/src/transformers/utils/dummy_timm_and_vision_objects.py index baa0563cc2..86badb8746 100644 --- a/src/transformers/utils/dummy_timm_and_vision_objects.py +++ b/src/transformers/utils/dummy_timm_and_vision_objects.py @@ -1,53 +1,34 @@ # This file is autogenerated by the command `make fix-copies`, do not edit. -from ..file_utils import requires_backends +# flake8: noqa +from ..file_utils import DummyObject, requires_backends DETR_PRETRAINED_MODEL_ARCHIVE_LIST = None -class DetrForObjectDetection: +class DetrForObjectDetection(metaclass=DummyObject): + _backends = ["timm", "vision"] + def __init__(self, *args, **kwargs): requires_backends(self, ["timm", "vision"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["timm", "vision"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["timm", "vision"]) +class DetrForSegmentation(metaclass=DummyObject): + _backends = ["timm", "vision"] - -class DetrForSegmentation: def __init__(self, *args, **kwargs): requires_backends(self, ["timm", "vision"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["timm", "vision"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["timm", "vision"]) +class DetrModel(metaclass=DummyObject): + _backends = ["timm", "vision"] - -class DetrModel: def __init__(self, *args, **kwargs): requires_backends(self, ["timm", "vision"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["timm", "vision"]) - def forward(self, *args, **kwargs): - requires_backends(self, ["timm", "vision"]) +class DetrPreTrainedModel(metaclass=DummyObject): + _backends = ["timm", "vision"] - -class DetrPreTrainedModel: def __init__(self, *args, **kwargs): requires_backends(self, ["timm", "vision"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["timm", "vision"]) - - def forward(self, *args, **kwargs): - requires_backends(self, ["timm", "vision"]) diff --git a/src/transformers/utils/dummy_tokenizers_objects.py b/src/transformers/utils/dummy_tokenizers_objects.py index d641b49ead..28897493ce 100644 --- a/src/transformers/utils/dummy_tokenizers_objects.py +++ b/src/transformers/utils/dummy_tokenizers_objects.py @@ -1,398 +1,311 @@ # This file is autogenerated by the command `make fix-copies`, do not edit. -from ..file_utils import requires_backends +# flake8: noqa +from ..file_utils import DummyObject, requires_backends -class AlbertTokenizerFast: +class AlbertTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] + def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class BartTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class BartTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class BarthezTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class BarthezTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class BertTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class BertTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class BigBirdTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class BigBirdTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class BlenderbotTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class BlenderbotTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class BlenderbotSmallTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class BlenderbotSmallTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class CamembertTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class CamembertTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class CLIPTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class CLIPTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class ConvBertTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class ConvBertTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class DebertaTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class DebertaTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class DistilBertTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class DistilBertTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class DPRContextEncoderTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class DPRContextEncoderTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class DPRQuestionEncoderTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class DPRQuestionEncoderTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class DPRReaderTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class DPRReaderTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class ElectraTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class ElectraTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class FNetTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class FNetTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class FunnelTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class FunnelTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class GPT2TokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class GPT2TokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class HerbertTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class HerbertTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class LayoutLMTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class LayoutLMTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class LayoutLMv2TokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class LayoutLMv2TokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class LayoutXLMTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class LayoutXLMTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class LEDTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class LEDTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class LongformerTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class LongformerTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class LxmertTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class LxmertTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class MBartTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class MBartTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class MBart50TokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class MBart50TokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class MobileBertTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class MobileBertTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class MPNetTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class MPNetTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class MT5TokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class MT5TokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class OpenAIGPTTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class OpenAIGPTTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class PegasusTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class PegasusTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class ReformerTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class ReformerTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class RemBertTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class RemBertTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class RetriBertTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class RetriBertTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class RobertaTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class RobertaTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class RoFormerTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class RoFormerTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class SplinterTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class SplinterTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class SqueezeBertTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class SqueezeBertTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class T5TokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class T5TokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class XLMRobertaTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class XLMRobertaTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class XLNetTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class XLNetTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) +class PreTrainedTokenizerFast(metaclass=DummyObject): + _backends = ["tokenizers"] -class PreTrainedTokenizerFast: def __init__(self, *args, **kwargs): requires_backends(self, ["tokenizers"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["tokenizers"]) diff --git a/src/transformers/utils/dummy_vision_objects.py b/src/transformers/utils/dummy_vision_objects.py index 5347757624..b7408bf3db 100644 --- a/src/transformers/utils/dummy_vision_objects.py +++ b/src/transformers/utils/dummy_vision_objects.py @@ -1,79 +1,94 @@ # This file is autogenerated by the command `make fix-copies`, do not edit. -from ..file_utils import requires_backends +# flake8: noqa +from ..file_utils import DummyObject, requires_backends -class ImageFeatureExtractionMixin: +class ImageFeatureExtractionMixin(metaclass=DummyObject): + _backends = ["vision"] + def __init__(self, *args, **kwargs): requires_backends(self, ["vision"]) -class BeitFeatureExtractor: +class BeitFeatureExtractor(metaclass=DummyObject): + _backends = ["vision"] + def __init__(self, *args, **kwargs): requires_backends(self, ["vision"]) -class CLIPFeatureExtractor: +class CLIPFeatureExtractor(metaclass=DummyObject): + _backends = ["vision"] + def __init__(self, *args, **kwargs): requires_backends(self, ["vision"]) -class CLIPProcessor: - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) +class CLIPProcessor(metaclass=DummyObject): + _backends = ["vision"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["vision"]) - - -class DeiTFeatureExtractor: def __init__(self, *args, **kwargs): requires_backends(self, ["vision"]) -class DetrFeatureExtractor: +class DeiTFeatureExtractor(metaclass=DummyObject): + _backends = ["vision"] + def __init__(self, *args, **kwargs): requires_backends(self, ["vision"]) -class ImageGPTFeatureExtractor: +class DetrFeatureExtractor(metaclass=DummyObject): + _backends = ["vision"] + def __init__(self, *args, **kwargs): requires_backends(self, ["vision"]) -class LayoutLMv2FeatureExtractor: +class ImageGPTFeatureExtractor(metaclass=DummyObject): + _backends = ["vision"] + def __init__(self, *args, **kwargs): requires_backends(self, ["vision"]) -class LayoutLMv2Processor: - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) +class LayoutLMv2FeatureExtractor(metaclass=DummyObject): + _backends = ["vision"] - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["vision"]) - - -class LayoutXLMProcessor: - def __init__(self, *args, **kwargs): - requires_backends(self, ["vision"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["vision"]) - - -class PerceiverFeatureExtractor: def __init__(self, *args, **kwargs): requires_backends(self, ["vision"]) -class SegformerFeatureExtractor: +class LayoutLMv2Processor(metaclass=DummyObject): + _backends = ["vision"] + def __init__(self, *args, **kwargs): requires_backends(self, ["vision"]) -class ViTFeatureExtractor: +class LayoutXLMProcessor(metaclass=DummyObject): + _backends = ["vision"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["vision"]) + + +class PerceiverFeatureExtractor(metaclass=DummyObject): + _backends = ["vision"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["vision"]) + + +class SegformerFeatureExtractor(metaclass=DummyObject): + _backends = ["vision"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["vision"]) + + +class ViTFeatureExtractor(metaclass=DummyObject): + _backends = ["vision"] + def __init__(self, *args, **kwargs): requires_backends(self, ["vision"]) diff --git a/utils/check_dummies.py b/utils/check_dummies.py index 2084b32f13..e082b4b859 100644 --- a/utils/check_dummies.py +++ b/utils/check_dummies.py @@ -33,46 +33,15 @@ DUMMY_CONSTANT = """ {0} = None """ -DUMMY_PRETRAINED_CLASS = """ -class {0}: - def __init__(self, *args, **kwargs): - requires_backends(self, {1}) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, {1}) -""" - -PT_DUMMY_PRETRAINED_CLASS = ( - DUMMY_PRETRAINED_CLASS - + """ - def forward(self, *args, **kwargs): - requires_backends(self, {1}) -""" -) - -TF_DUMMY_PRETRAINED_CLASS = ( - DUMMY_PRETRAINED_CLASS - + """ - def call(self, *args, **kwargs): - requires_backends(self, {1}) -""" -) - -FLAX_DUMMY_PRETRAINED_CLASS = ( - DUMMY_PRETRAINED_CLASS - + """ - def __call__(self, *args, **kwargs): - requires_backends(self, {1}) -""" -) - DUMMY_CLASS = """ -class {0}: +class {0}(metaclass=DummyObject): + _backends = {1} + def __init__(self, *args, **kwargs): requires_backends(self, {1}) """ + DUMMY_FUNCTION = """ def {0}(*args, **kwargs): requires_backends({0}, {1}) @@ -126,45 +95,12 @@ def read_init(): def create_dummy_object(name, backend_name): """Create the code for the dummy object corresponding to `name`.""" - _models = [ - "ForCausalLM", - "ForConditionalGeneration", - "ForMaskedLM", - "ForMultipleChoice", - "ForNextSentencePrediction", - "ForObjectDetection", - "ForQuestionAnswering", - "ForSegmentation", - "ForSequenceClassification", - "ForTokenClassification", - "Model", - ] - _pretrained = ["Config", "Tokenizer", "Processor"] if name.isupper(): return DUMMY_CONSTANT.format(name) elif name.islower(): return DUMMY_FUNCTION.format(name, backend_name) else: - is_model = False - for part in _models: - if part in name: - is_model = True - break - if is_model: - if name.startswith("TF"): - return TF_DUMMY_PRETRAINED_CLASS.format(name, backend_name) - if name.startswith("Flax"): - return FLAX_DUMMY_PRETRAINED_CLASS.format(name, backend_name) - return PT_DUMMY_PRETRAINED_CLASS.format(name, backend_name) - is_pretrained = False - for part in _pretrained: - if part in name: - is_pretrained = True - break - if is_pretrained: - return DUMMY_PRETRAINED_CLASS.format(name, backend_name) - else: - return DUMMY_CLASS.format(name, backend_name) + return DUMMY_CLASS.format(name, backend_name) def create_dummy_files(): @@ -176,7 +112,8 @@ def create_dummy_files(): for backend, objects in backend_specific_objects.items(): backend_name = "[" + ", ".join(f'"{b}"' for b in backend.split("_and_")) + "]" dummy_file = "# This file is autogenerated by the command `make fix-copies`, do not edit.\n" - dummy_file += "from ..file_utils import requires_backends\n\n" + dummy_file += "# flake8: noqa\n" + dummy_file += "from ..file_utils import DummyObject, requires_backends\n\n" dummy_file += "\n".join([create_dummy_object(o, backend_name) for o in objects]) dummy_files[backend] = dummy_file diff --git a/utils/check_repo.py b/utils/check_repo.py index d15fc88801..0f1e624f47 100644 --- a/utils/check_repo.py +++ b/utils/check_repo.py @@ -522,6 +522,7 @@ UNDOCUMENTED_OBJECTS = [ "BasicTokenizer", # Internal, should never have been in the main init. "CharacterTokenizer", # Internal, should never have been in the main init. "DPRPretrainedReader", # Like an Encoder. + "DummyObject", # Just picked by mistake sometimes. "MecabTokenizer", # Internal, should never have been in the main init. "ModelCard", # Internal type. "SqueezeBertModule", # Internal building block (should have been called SqueezeBertLayer)