text: # This file is autogenerated by the command `make fix-copies`, do not edit.
  # flake8: noqa
  from ..file_utils import DummyObject, requires_backends
  
  
  class PyTorchBenchmark(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class PyTorchBenchmarkArguments(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class GlueDataset(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class GlueDataTrainingArguments(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LineByLineTextDataset(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LineByLineWithRefDataset(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LineByLineWithSOPTextDataset(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SquadDataset(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SquadDataTrainingArguments(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class TextDataset(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class TextDatasetForNextSentencePrediction(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BeamScorer(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BeamSearchScorer(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ForcedBOSTokenLogitsProcessor(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ForcedEOSTokenLogitsProcessor(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class HammingDiversityLogitsProcessor(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class InfNanRemoveLogitsProcessor(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LogitsProcessor(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LogitsProcessorList(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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"])
  
  
  def top_k_top_p_filtering(*args, **kwargs):
      requires_backends(top_k_top_p_filtering, ["torch"])
  
  
  class Conv1D(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class PreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  def apply_chunking_to_forward(*args, **kwargs):
      requires_backends(apply_chunking_to_forward, ["torch"])
  
  
  def prune_layer(*args, **kwargs):
      requires_backends(prune_layer, ["torch"])
  
  
  ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class AlbertForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AlbertForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AlbertForPreTraining(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AlbertForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AlbertForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AlbertForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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"])
  
  
  def load_tf_weights_in_albert(*args, **kwargs):
      requires_backends(load_tf_weights_in_albert, ["torch"])
  
  
  MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING = None
  
  
  MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING = None
  
  
  MODEL_FOR_CAUSAL_LM_MAPPING = None
  
  
  MODEL_FOR_CTC_MAPPING = None
  
  
  MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = None
  
  
  MODEL_FOR_IMAGE_SEGMENTATION_MAPPING = None
  
  
  MODEL_FOR_MASKED_LM_MAPPING = None
  
  
  MODEL_FOR_MULTIPLE_CHOICE_MAPPING = None
  
  
  MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = None
  
  
  MODEL_FOR_OBJECT_DETECTION_MAPPING = None
  
  
  MODEL_FOR_PRETRAINING_MAPPING = None
  
  
  MODEL_FOR_QUESTION_ANSWERING_MAPPING = None
  
  
  MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = None
  
  
  MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = None
  
  
  MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = None
  
  
  MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = None
  
  
  MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = None
  
  
  MODEL_FOR_VISION_2_SEQ_MAPPING = None
  
  
  MODEL_MAPPING = None
  
  
  MODEL_WITH_LM_HEAD_MAPPING = None
  
  
  class AutoModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForAudioClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForAudioFrameClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForAudioXVector(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForCTC(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForImageClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForImageSegmentation(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForNextSentencePrediction(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForObjectDetection(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForPreTraining(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForSemanticSegmentation(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForSeq2SeqLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForSpeechSeq2Seq(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForTableQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelForVision2Seq(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AutoModelWithLMHead(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  BART_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class BartForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BartForConditionalGeneration(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BartForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BartForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BartModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BartPretrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class PretrainedBartModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  BEIT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class BeitForImageClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BeitForMaskedImageModeling(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BeitForSemanticSegmentation(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BeitModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BeitPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class BertForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BertForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BertForNextSentencePrediction(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BertForPreTraining(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BertForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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"])
  
  
  def load_tf_weights_in_bert(*args, **kwargs):
      requires_backends(load_tf_weights_in_bert, ["torch"])
  
  
  class BertGenerationDecoder(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BertGenerationEncoder(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BertGenerationPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(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"])
  
  
  BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class BigBirdForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BigBirdForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BigBirdForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BigBirdForPreTraining(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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"])
  
  
  def load_tf_weights_in_big_bird(*args, **kwargs):
      requires_backends(load_tf_weights_in_big_bird, ["torch"])
  
  
  BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class BigBirdPegasusForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BigBirdPegasusForConditionalGeneration(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BigBirdPegasusForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BigBirdPegasusForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BigBirdPegasusModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BigBirdPegasusPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class BlenderbotForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BlenderbotForConditionalGeneration(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BlenderbotModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BlenderbotPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class BlenderbotSmallForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BlenderbotSmallForConditionalGeneration(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BlenderbotSmallModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class BlenderbotSmallPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class CamembertForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class CamembertForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class CamembertForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class CamembertForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class CamembertForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class CamembertForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class CamembertModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class CanineForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class CanineForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class CanineForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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"])
  
  
  def load_tf_weights_in_canine(*args, **kwargs):
      requires_backends(load_tf_weights_in_canine, ["torch"])
  
  
  CLIP_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class CLIPModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class CLIPPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class CLIPTextModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class CLIPVisionModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class ConvBertForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ConvBertForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ConvBertForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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"])
  
  
  def load_tf_weights_in_convbert(*args, **kwargs):
      requires_backends(load_tf_weights_in_convbert, ["torch"])
  
  
  CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class ConvNextForImageClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ConvNextModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ConvNextPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  CTRL_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class CTRLForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class CTRLLMHeadModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class CTRLModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class CTRLPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class DebertaForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DebertaForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DebertaForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DebertaForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DebertaModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DebertaPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class DebertaV2ForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DebertaV2ForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DebertaV2ForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DebertaV2ForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DebertaV2Model(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DebertaV2PreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  DEIT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class DeiTForImageClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DeiTForImageClassificationWithTeacher(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DeiTModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DeiTPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class DistilBertForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DistilBertForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DistilBertForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DistilBertForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DistilBertForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DistilBertModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DistilBertPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class DPRContextEncoder(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DPRPretrainedContextEncoder(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DPRPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DPRPretrainedQuestionEncoder(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class DPRPretrainedReader(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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"])
  
  
  ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class ElectraForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ElectraForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ElectraForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ElectraForPreTraining(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ElectraForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ElectraForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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"])
  
  
  def load_tf_weights_in_electra(*args, **kwargs):
      requires_backends(load_tf_weights_in_electra, ["torch"])
  
  
  class EncoderDecoderModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class FlaubertForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class FlaubertForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class FlaubertForQuestionAnsweringSimple(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class FlaubertForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class FlaubertForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class FlaubertModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class FlaubertWithLMHeadModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  FNET_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class FNetForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class FNetForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class FNetForNextSentencePrediction(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class FNetForPreTraining(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class FNetForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class FNetForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class FNetForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class FunnelForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class FunnelForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class FunnelForPreTraining(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class FunnelForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class FunnelForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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"])
  
  
  def load_tf_weights_in_funnel(*args, **kwargs):
      requires_backends(load_tf_weights_in_funnel, ["torch"])
  
  
  GPT2_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class GPT2DoubleHeadsModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class GPT2ForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class GPT2ForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class GPT2LMHeadModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class GPT2Model(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class GPT2PreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  def load_tf_weights_in_gpt2(*args, **kwargs):
      requires_backends(load_tf_weights_in_gpt2, ["torch"])
  
  
  GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class GPTNeoForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class GPTNeoForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class GPTNeoModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class GPTNeoPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(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"])
  
  
  GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class GPTJForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class GPTJForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class GPTJForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class GPTJModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class GPTJPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class HubertForCTC(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class HubertForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class HubertModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class HubertPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  IBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class IBertForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class IBertForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class IBertForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class IBertForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class IBertForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class IBertModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class IBertPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class ImageGPTForCausalImageModeling(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ImageGPTForImageClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ImageGPTModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ImageGPTPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  def load_tf_weights_in_imagegpt(*args, **kwargs):
      requires_backends(load_tf_weights_in_imagegpt, ["torch"])
  
  
  LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class LayoutLMForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LayoutLMForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LayoutLMForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LayoutLMModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LayoutLMPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class LayoutLMv2ForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LayoutLMv2ForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LayoutLMv2ForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LayoutLMv2Model(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LayoutLMv2PreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  LED_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class LEDForConditionalGeneration(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LEDForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LEDForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LEDModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LEDPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class LongformerForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LongformerForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LongformerForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LongformerForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LongformerForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LongformerModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LongformerPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LongformerSelfAttention(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  LUKE_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class LukeForEntityClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LukeForEntityPairClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LukeForEntitySpanClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LukeForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LukeModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class LukePreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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"])
  
  
  M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class M2M100ForConditionalGeneration(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class M2M100Model(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class M2M100PreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MarianForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MarianModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MarianMTModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MBartForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MBartForConditionalGeneration(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MBartForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MBartForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MBartModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MBartPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class MegatronBertForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MegatronBertForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MegatronBertForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MegatronBertForNextSentencePrediction(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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"])
  
  
  MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class MobileBertForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MobileBertForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MobileBertForNextSentencePrediction(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MobileBertForPreTraining(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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"])
  
  
  def load_tf_weights_in_mobilebert(*args, **kwargs):
      requires_backends(load_tf_weights_in_mobilebert, ["torch"])
  
  
  MPNET_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class MPNetForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MPNetForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MPNetForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MPNetForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MPNetForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class MPNetLayer(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class NystromformerForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class NystromformerForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class OpenAIGPTForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class OpenAIGPTLMHeadModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class OpenAIGPTModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class OpenAIGPTPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(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(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class PegasusForConditionalGeneration(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class PegasusModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class PegasusPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class PerceiverForImageClassificationConvProcessing(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class PerceiverForImageClassificationFourier(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class PerceiverForImageClassificationLearned(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class PerceiverForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class PerceiverForMultimodalAutoencoding(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class PerceiverForOpticalFlow(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class PerceiverForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class PerceiverLayer(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ProphetNetEncoder(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ProphetNetForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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"])
  
  
  REALM_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class RealmEmbedder(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class RealmForOpenQA(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class RealmKnowledgeAugEncoder(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class RealmPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class RealmReader(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class RealmRetriever(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class RealmScorer(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  def load_tf_weights_in_realm(*args, **kwargs):
      requires_backends(load_tf_weights_in_realm, ["torch"])
  
  
  REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class ReformerAttention(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ReformerForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ReformerForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ReformerForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ReformerLayer(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class RemBertForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class RemBertForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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"])
  
  
  def load_tf_weights_in_rembert(*args, **kwargs):
      requires_backends(load_tf_weights_in_rembert, ["torch"])
  
  
  RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class RetriBertModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class RetriBertPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class RobertaForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class RobertaForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class RobertaForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class RobertaForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class RobertaForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class RobertaForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class RobertaModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class RobertaPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class RoFormerForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class RoFormerForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class RoFormerForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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"])
  
  
  def load_tf_weights_in_roformer(*args, **kwargs):
      requires_backends(load_tf_weights_in_roformer, ["torch"])
  
  
  SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class SegformerDecodeHead(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SegformerForImageClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SegformerForSemanticSegmentation(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SegformerLayer(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SegformerModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SegformerPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  SEW_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class SEWForCTC(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SEWForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SEWModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SEWPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class SEWDForCTC(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SEWDForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SEWDModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SEWDPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SpeechEncoderDecoderModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class Speech2TextForConditionalGeneration(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class Speech2TextModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class Speech2TextPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class Speech2Text2ForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class Speech2Text2PreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class SplinterForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SplinterLayer(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SplinterModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SplinterPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class SqueezeBertForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SqueezeBertForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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"])
  
  
  SWIN_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class SwinForImageClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SwinModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class SwinPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  T5_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class T5EncoderModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class T5ForConditionalGeneration(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class T5Model(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class T5PreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  def load_tf_weights_in_t5(*args, **kwargs):
      requires_backends(load_tf_weights_in_t5, ["torch"])
  
  
  TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class AdaptiveEmbedding(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class TransfoXLForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class TransfoXLLMHeadModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class TransfoXLModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class TransfoXLPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(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"])
  
  
  TROCR_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class TrOCRForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class TrOCRPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class UniSpeechForCTC(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class UniSpeechForPreTraining(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class UniSpeechForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class UniSpeechModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class UniSpeechPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class UniSpeechSatForAudioFrameClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class UniSpeechSatForCTC(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class UniSpeechSatForPreTraining(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class UniSpeechSatForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class UniSpeechSatForXVector(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class UniSpeechSatModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class UniSpeechSatPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  VILT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class ViltForImageAndTextRetrieval(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ViltForImagesAndTextClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ViltForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ViltForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ViltLayer(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ViltModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ViltPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class VisionEncoderDecoderModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class VisionTextDualEncoderModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class VisualBertForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class VisualBertForPreTraining(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class VisualBertForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class VisualBertForRegionToPhraseAlignment(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class VisualBertForVisualReasoning(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class VisualBertLayer(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ViTModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ViTPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class ViTMAEForPreTraining(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ViTMAELayer(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ViTMAEModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class ViTMAEPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class Wav2Vec2ForAudioFrameClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class Wav2Vec2ForCTC(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class Wav2Vec2ForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class Wav2Vec2ForPreTraining(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class Wav2Vec2ForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class Wav2Vec2ForXVector(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  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(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class WavLMForCTC(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class WavLMForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class WavLMForXVector(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class WavLMModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class WavLMPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  XGLM_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class XGLMForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XGLMModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XGLMPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  XLM_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class XLMForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMForQuestionAnsweringSimple(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMWithLMHeadModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class XLMProphetNetDecoder(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMProphetNetEncoder(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMProphetNetForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMProphetNetForConditionalGeneration(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMProphetNetModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class XLMRobertaForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMRobertaForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMRobertaForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMRobertaForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMRobertaForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMRobertaForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMRobertaModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class XLMRobertaXLForCausalLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMRobertaXLForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMRobertaXLForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMRobertaXLForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMRobertaXLForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMRobertaXLForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMRobertaXLModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLMRobertaXLPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  XLNET_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class XLNetForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLNetForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLNetForQuestionAnsweringSimple(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLNetForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLNetForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLNetLMHeadModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLNetModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class XLNetPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  def load_tf_weights_in_xlnet(*args, **kwargs):
      requires_backends(load_tf_weights_in_xlnet, ["torch"])
  
  
  YOSO_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class YosoForMaskedLM(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class YosoForMultipleChoice(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class YosoForQuestionAnswering(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class YosoForSequenceClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class YosoForTokenClassification(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class YosoLayer(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class YosoModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class YosoPreTrainedModel(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class Adafactor(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  class AdamW(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  def get_constant_schedule(*args, **kwargs):
      requires_backends(get_constant_schedule, ["torch"])
  
  
  def get_constant_schedule_with_warmup(*args, **kwargs):
      requires_backends(get_constant_schedule_with_warmup, ["torch"])
  
  
  def get_cosine_schedule_with_warmup(*args, **kwargs):
      requires_backends(get_cosine_schedule_with_warmup, ["torch"])
  
  
  def get_cosine_with_hard_restarts_schedule_with_warmup(*args, **kwargs):
      requires_backends(get_cosine_with_hard_restarts_schedule_with_warmup, ["torch"])
  
  
  def get_linear_schedule_with_warmup(*args, **kwargs):
      requires_backends(get_linear_schedule_with_warmup, ["torch"])
  
  
  def get_polynomial_decay_schedule_with_warmup(*args, **kwargs):
      requires_backends(get_polynomial_decay_schedule_with_warmup, ["torch"])
  
  
  def get_scheduler(*args, **kwargs):
      requires_backends(get_scheduler, ["torch"])
  
  
  class Trainer(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  
  
  def torch_distributed_zero_first(*args, **kwargs):
      requires_backends(torch_distributed_zero_first, ["torch"])
  
  
  class Seq2SeqTrainer(metaclass=DummyObject):
      _backends = ["torch"]
  
      def __init__(self, *args, **kwargs):
          requires_backends(self, ["torch"])
  