text: # This file is autogenerated by the command `make fix-copies`, do not edit.
  from ..file_utils import requires_pytorch
  
  
  class PyTorchBenchmark:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class PyTorchBenchmarkArguments:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DataCollator:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DataCollatorForLanguageModeling:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DataCollatorForPermutationLanguageModeling:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DataCollatorForSeq2Seq:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DataCollatorForSOP:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DataCollatorForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DataCollatorForWholeWordMask:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DataCollatorWithPadding:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  def default_data_collator(*args, **kwargs):
      requires_pytorch(default_data_collator)
  
  
  class GlueDataset:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class GlueDataTrainingArguments:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LineByLineTextDataset:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LineByLineWithRefDataset:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LineByLineWithSOPTextDataset:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class SquadDataset:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class SquadDataTrainingArguments:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class TextDataset:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class TextDatasetForNextSentencePrediction:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BeamScorer:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BeamSearchScorer:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class HammingDiversityLogitsProcessor:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LogitsProcessor:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LogitsProcessorList:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LogitsWarper:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MinLengthLogitsProcessor:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class NoBadWordsLogitsProcessor:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class NoRepeatNGramLogitsProcessor:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class PrefixConstrainedLogitsProcessor:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class RepetitionPenaltyLogitsProcessor:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class TemperatureLogitsWarper:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class TopKLogitsWarper:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class TopPLogitsWarper:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  def top_k_top_p_filtering(*args, **kwargs):
      requires_pytorch(top_k_top_p_filtering)
  
  
  class Conv1D:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class PreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  def apply_chunking_to_forward(*args, **kwargs):
      requires_pytorch(apply_chunking_to_forward)
  
  
  def prune_layer(*args, **kwargs):
      requires_pytorch(prune_layer)
  
  
  ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class AlbertForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AlbertForMultipleChoice:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AlbertForPreTraining:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AlbertForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AlbertForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AlbertForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AlbertModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AlbertPreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  def load_tf_weights_in_albert(*args, **kwargs):
      requires_pytorch(load_tf_weights_in_albert)
  
  
  MODEL_FOR_CAUSAL_LM_MAPPING = None
  
  
  MODEL_FOR_MASKED_LM_MAPPING = None
  
  
  MODEL_FOR_MULTIPLE_CHOICE_MAPPING = None
  
  
  MODEL_FOR_NEXT_SENTENCE_PREDICTION_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_TABLE_QUESTION_ANSWERING_MAPPING = None
  
  
  MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = None
  
  
  MODEL_MAPPING = None
  
  
  MODEL_WITH_LM_HEAD_MAPPING = None
  
  
  class AutoModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AutoModelForCausalLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AutoModelForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AutoModelForMultipleChoice:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AutoModelForNextSentencePrediction:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AutoModelForPreTraining:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AutoModelForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AutoModelForSeq2SeqLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AutoModelForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AutoModelForTableQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AutoModelForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AutoModelWithLMHead:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  BART_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class BartForConditionalGeneration:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BartForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BartForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BartModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BartPretrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class PretrainedBartModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class BertForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BertForMultipleChoice:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BertForNextSentencePrediction:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BertForPreTraining:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BertForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BertForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BertForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BertLayer:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BertLMHeadModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BertModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BertPreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  def load_tf_weights_in_bert(*args, **kwargs):
      requires_pytorch(load_tf_weights_in_bert)
  
  
  class BertGenerationDecoder:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BertGenerationEncoder:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  def load_tf_weights_in_bert_generation(*args, **kwargs):
      requires_pytorch(load_tf_weights_in_bert_generation)
  
  
  BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class BlenderbotForConditionalGeneration:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BlenderbotModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class BlenderbotSmallForConditionalGeneration:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class BlenderbotSmallModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class CamembertForCausalLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class CamembertForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class CamembertForMultipleChoice:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class CamembertForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class CamembertForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class CamembertForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class CamembertModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class ConvBertForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ConvBertForMultipleChoice:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ConvBertForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ConvBertForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ConvBertForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ConvBertLayer:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ConvBertModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ConvBertPreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  def load_tf_weights_in_convbert(*args, **kwargs):
      requires_pytorch(load_tf_weights_in_convbert)
  
  
  CTRL_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class CTRLForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class CTRLLMHeadModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class CTRLModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class CTRLPreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class DebertaForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DebertaForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DebertaForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DebertaForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DebertaModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DebertaPreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class DistilBertForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DistilBertForMultipleChoice:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DistilBertForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DistilBertForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DistilBertForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DistilBertModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DistilBertPreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  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:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DPRPretrainedContextEncoder:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DPRPretrainedQuestionEncoder:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DPRPretrainedReader:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DPRQuestionEncoder:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class DPRReader:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class ElectraForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ElectraForMultipleChoice:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ElectraForPreTraining:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ElectraForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ElectraForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ElectraForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ElectraModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ElectraPreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  def load_tf_weights_in_electra(*args, **kwargs):
      requires_pytorch(load_tf_weights_in_electra)
  
  
  class EncoderDecoderModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class FlaubertForMultipleChoice:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class FlaubertForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class FlaubertForQuestionAnsweringSimple:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class FlaubertForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class FlaubertForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class FlaubertModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class FlaubertWithLMHeadModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class FSMTForConditionalGeneration:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class FSMTModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class PretrainedFSMTModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class FunnelBaseModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class FunnelForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class FunnelForMultipleChoice:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class FunnelForPreTraining:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class FunnelForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class FunnelForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class FunnelForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class FunnelModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  def load_tf_weights_in_funnel(*args, **kwargs):
      requires_pytorch(load_tf_weights_in_funnel)
  
  
  GPT2_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class GPT2DoubleHeadsModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class GPT2ForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class GPT2LMHeadModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class GPT2Model:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class GPT2PreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  def load_tf_weights_in_gpt2(*args, **kwargs):
      requires_pytorch(load_tf_weights_in_gpt2)
  
  
  LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class LayoutLMForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LayoutLMForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LayoutLMForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LayoutLMModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  LED_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class LEDForConditionalGeneration:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LEDForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LEDForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LEDModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class LongformerForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LongformerForMultipleChoice:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LongformerForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LongformerForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LongformerForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LongformerModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LongformerSelfAttention:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LxmertEncoder:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LxmertForPreTraining:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LxmertForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LxmertModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LxmertPreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LxmertVisualFeatureEncoder:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class LxmertXLayer:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MarianModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MarianMTModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MBartForConditionalGeneration:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MBartForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MBartForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MBartModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MMBTForClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MMBTModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ModalEmbeddings:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class MobileBertForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MobileBertForMultipleChoice:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MobileBertForNextSentencePrediction:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MobileBertForPreTraining:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MobileBertForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MobileBertForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MobileBertForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MobileBertLayer:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MobileBertModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MobileBertPreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  def load_tf_weights_in_mobilebert(*args, **kwargs):
      requires_pytorch(load_tf_weights_in_mobilebert)
  
  
  MPNET_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class MPNetForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MPNetForMultipleChoice:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MPNetForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MPNetForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MPNetForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MPNetLayer:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MPNetModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MPNetPreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MT5EncoderModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MT5ForConditionalGeneration:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class MT5Model:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class OpenAIGPTDoubleHeadsModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class OpenAIGPTForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class OpenAIGPTLMHeadModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class OpenAIGPTModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class OpenAIGPTPreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  def load_tf_weights_in_openai_gpt(*args, **kwargs):
      requires_pytorch(load_tf_weights_in_openai_gpt)
  
  
  class PegasusForConditionalGeneration:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class PegasusModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class ProphetNetDecoder:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ProphetNetEncoder:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ProphetNetForCausalLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ProphetNetForConditionalGeneration:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ProphetNetModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ProphetNetPreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class RagModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class RagSequenceForGeneration:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class RagTokenForGeneration:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class ReformerAttention:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ReformerForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ReformerForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ReformerForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ReformerLayer:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ReformerModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class ReformerModelWithLMHead:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class RetriBertModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class RetriBertPreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class RobertaForCausalLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class RobertaForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class RobertaForMultipleChoice:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class RobertaForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class RobertaForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class RobertaForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class RobertaModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class SqueezeBertForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class SqueezeBertForMultipleChoice:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class SqueezeBertForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class SqueezeBertForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class SqueezeBertForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class SqueezeBertModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class SqueezeBertModule:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class SqueezeBertPreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  T5_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class T5EncoderModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class T5ForConditionalGeneration:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class T5Model:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class T5PreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  def load_tf_weights_in_t5(*args, **kwargs):
      requires_pytorch(load_tf_weights_in_t5)
  
  
  TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class TapasForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class TapasForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class TapasForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class TapasModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class AdaptiveEmbedding:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class TransfoXLForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class TransfoXLLMHeadModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class TransfoXLModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class TransfoXLPreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  def load_tf_weights_in_transfo_xl(*args, **kwargs):
      requires_pytorch(load_tf_weights_in_transfo_xl)
  
  
  WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class Wav2Vec2ForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class Wav2Vec2Model:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class Wav2Vec2PreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  XLM_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class XLMForMultipleChoice:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLMForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLMForQuestionAnsweringSimple:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLMForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLMForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLMModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLMPreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLMWithLMHeadModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class XLMProphetNetDecoder:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLMProphetNetEncoder:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLMProphetNetForCausalLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLMProphetNetForConditionalGeneration:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLMProphetNetModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class XLMRobertaForCausalLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLMRobertaForMaskedLM:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLMRobertaForMultipleChoice:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLMRobertaForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLMRobertaForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLMRobertaForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLMRobertaModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  XLNET_PRETRAINED_MODEL_ARCHIVE_LIST = None
  
  
  class XLNetForMultipleChoice:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLNetForQuestionAnswering:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLNetForQuestionAnsweringSimple:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLNetForSequenceClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLNetForTokenClassification:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLNetLMHeadModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLNetModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class XLNetPreTrainedModel:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
      @classmethod
      def from_pretrained(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  def load_tf_weights_in_xlnet(*args, **kwargs):
      requires_pytorch(load_tf_weights_in_xlnet)
  
  
  class Adafactor:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  class AdamW:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  def get_constant_schedule(*args, **kwargs):
      requires_pytorch(get_constant_schedule)
  
  
  def get_constant_schedule_with_warmup(*args, **kwargs):
      requires_pytorch(get_constant_schedule_with_warmup)
  
  
  def get_cosine_schedule_with_warmup(*args, **kwargs):
      requires_pytorch(get_cosine_schedule_with_warmup)
  
  
  def get_cosine_with_hard_restarts_schedule_with_warmup(*args, **kwargs):
      requires_pytorch(get_cosine_with_hard_restarts_schedule_with_warmup)
  
  
  def get_linear_schedule_with_warmup(*args, **kwargs):
      requires_pytorch(get_linear_schedule_with_warmup)
  
  
  def get_polynomial_decay_schedule_with_warmup(*args, **kwargs):
      requires_pytorch(get_polynomial_decay_schedule_with_warmup)
  
  
  def get_scheduler(*args, **kwargs):
      requires_pytorch(get_scheduler)
  
  
  class Trainer:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  
  
  def torch_distributed_zero_first(*args, **kwargs):
      requires_pytorch(torch_distributed_zero_first)
  
  
  class Seq2SeqTrainer:
      def __init__(self, *args, **kwargs):
          requires_pytorch(self)
  