dummy_pt_objects.py 70.8 KB
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# This file is autogenerated by the command `make fix-copies`, do not edit.
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from ..file_utils import requires_backends
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class PyTorchBenchmark:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class PyTorchBenchmarkArguments:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DataCollator:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DataCollatorForLanguageModeling:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DataCollatorForPermutationLanguageModeling:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DataCollatorForSeq2Seq:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DataCollatorForSOP:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DataCollatorForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DataCollatorForWholeWordMask:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DataCollatorWithPadding:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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def default_data_collator(*args, **kwargs):
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    requires_backends(default_data_collator, ["torch"])
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class GlueDataset:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class GlueDataTrainingArguments:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LineByLineTextDataset:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LineByLineWithRefDataset:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LineByLineWithSOPTextDataset:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class SquadDataset:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class SquadDataTrainingArguments:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class TextDataset:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class TextDatasetForNextSentencePrediction:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BeamScorer:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BeamSearchScorer:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class ForcedBOSTokenLogitsProcessor:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class ForcedEOSTokenLogitsProcessor:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class HammingDiversityLogitsProcessor:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class InfNanRemoveLogitsProcessor:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LogitsProcessor:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LogitsProcessorList:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LogitsWarper:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MinLengthLogitsProcessor:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class NoBadWordsLogitsProcessor:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class NoRepeatNGramLogitsProcessor:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class PrefixConstrainedLogitsProcessor:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class RepetitionPenaltyLogitsProcessor:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class TemperatureLogitsWarper:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class TopKLogitsWarper:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class TopPLogitsWarper:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MaxLengthCriteria:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MaxTimeCriteria:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class StoppingCriteria:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class StoppingCriteriaList:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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def top_k_top_p_filtering(*args, **kwargs):
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    requires_backends(top_k_top_p_filtering, ["torch"])
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class Conv1D:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class PreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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def apply_chunking_to_forward(*args, **kwargs):
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    requires_backends(apply_chunking_to_forward, ["torch"])
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def prune_layer(*args, **kwargs):
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    requires_backends(prune_layer, ["torch"])
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ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None


class AlbertForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class AlbertForMultipleChoice:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class AlbertForPreTraining:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class AlbertForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class AlbertForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class AlbertForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class AlbertModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class AlbertPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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def load_tf_weights_in_albert(*args, **kwargs):
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    requires_backends(load_tf_weights_in_albert, ["torch"])
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MODEL_FOR_CAUSAL_LM_MAPPING = None


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MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = None


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MODEL_FOR_MASKED_LM_MAPPING = None


MODEL_FOR_MULTIPLE_CHOICE_MAPPING = None


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MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = None


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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


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MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = None


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MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = None


MODEL_MAPPING = None


MODEL_WITH_LM_HEAD_MAPPING = None


class AutoModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class AutoModelForCausalLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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class AutoModelForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class AutoModelForMultipleChoice:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class AutoModelForNextSentencePrediction:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class AutoModelForPreTraining:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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class AutoModelForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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class AutoModelForSeq2SeqLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class AutoModelForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class AutoModelForTableQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class AutoModelForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class AutoModelWithLMHead:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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BART_PRETRAINED_MODEL_ARCHIVE_LIST = None


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class BartForCausalLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BartForConditionalGeneration:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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class BartForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BartForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BartModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BartPretrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class PretrainedBartModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None


class BertForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BertForMultipleChoice:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BertForNextSentencePrediction:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BertForPreTraining:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BertForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BertForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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class BertForTokenClassification:
    def __init__(self, *args, **kwargs):
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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class BertLayer:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BertLMHeadModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BertModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BertPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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def load_tf_weights_in_bert(*args, **kwargs):
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    requires_backends(load_tf_weights_in_bert, ["torch"])
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class BertGenerationDecoder:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BertGenerationEncoder:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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def load_tf_weights_in_bert_generation(*args, **kwargs):
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    requires_backends(load_tf_weights_in_bert_generation, ["torch"])
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BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST = None


class BigBirdForCausalLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BigBirdForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BigBirdForMultipleChoice:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BigBirdForPreTraining:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BigBirdForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BigBirdForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BigBirdForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BigBirdLayer:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BigBirdModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Vasudev Gupta's avatar
Vasudev Gupta committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BigBirdPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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def load_tf_weights_in_big_bird(*args, **kwargs):
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    requires_backends(load_tf_weights_in_big_bird, ["torch"])
Vasudev Gupta's avatar
Vasudev Gupta committed
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Sam Shleifer's avatar
Sam Shleifer committed
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BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST = None


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class BlenderbotForCausalLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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Sam Shleifer's avatar
Sam Shleifer committed
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class BlenderbotForConditionalGeneration:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Sam Shleifer's avatar
Sam Shleifer committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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Sam Shleifer committed
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class BlenderbotModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST = None


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class BlenderbotSmallForCausalLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BlenderbotSmallForConditionalGeneration:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class BlenderbotSmallModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None


class CamembertForCausalLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class CamembertForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class CamembertForMultipleChoice:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class CamembertForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class CamembertForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class CamembertForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class CamembertModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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abhishek thakur's avatar
abhishek thakur committed
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CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None


class ConvBertForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
abhishek thakur's avatar
abhishek thakur committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
abhishek thakur's avatar
abhishek thakur committed
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class ConvBertForMultipleChoice:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
abhishek thakur's avatar
abhishek thakur committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
abhishek thakur's avatar
abhishek thakur committed
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class ConvBertForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
abhishek thakur's avatar
abhishek thakur committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
abhishek thakur's avatar
abhishek thakur committed
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class ConvBertForSequenceClassification:
    def __init__(self, *args, **kwargs):
870
        requires_backends(self, ["torch"])
abhishek thakur's avatar
abhishek thakur committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
abhishek thakur's avatar
abhishek thakur committed
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class ConvBertForTokenClassification:
    def __init__(self, *args, **kwargs):
879
        requires_backends(self, ["torch"])
abhishek thakur's avatar
abhishek thakur committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
abhishek thakur's avatar
abhishek thakur committed
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class ConvBertLayer:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
abhishek thakur's avatar
abhishek thakur committed
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class ConvBertModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
abhishek thakur's avatar
abhishek thakur committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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abhishek thakur committed
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class ConvBertPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
abhishek thakur's avatar
abhishek thakur committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
abhishek thakur's avatar
abhishek thakur committed
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def load_tf_weights_in_convbert(*args, **kwargs):
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    requires_backends(load_tf_weights_in_convbert, ["torch"])
abhishek thakur's avatar
abhishek thakur committed
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CTRL_PRETRAINED_MODEL_ARCHIVE_LIST = None


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class CTRLForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class CTRLLMHeadModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class CTRLModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class CTRLPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None


NielsRogge's avatar
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class DebertaForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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NielsRogge committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DebertaForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
NielsRogge's avatar
NielsRogge committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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NielsRogge committed
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class DebertaForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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NielsRogge's avatar
NielsRogge committed
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class DebertaForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
NielsRogge's avatar
NielsRogge committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DebertaModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DebertaPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST = None


class DebertaV2ForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DebertaV2ForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DebertaV2ForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DebertaV2ForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DebertaV2Model:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DebertaV2PreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None


class DistilBertForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DistilBertForMultipleChoice:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DistilBertForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DistilBertForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DistilBertForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DistilBertModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DistilBertPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
1129
        requires_backends(self, ["torch"])
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Ratthachat (Jung)'s avatar
Ratthachat (Jung) committed
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DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST = None


DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST = None


DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST = None


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class DPRContextEncoder:
    def __init__(self, *args, **kwargs):
1143
        requires_backends(self, ["torch"])
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class DPRPretrainedContextEncoder:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DPRPretrainedQuestionEncoder:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DPRPretrainedReader:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DPRQuestionEncoder:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class DPRReader:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST = None


class ElectraForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class ElectraForMultipleChoice:
    def __init__(self, *args, **kwargs):
1185
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
1189
        requires_backends(self, ["torch"])
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class ElectraForPreTraining:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class ElectraForQuestionAnswering:
    def __init__(self, *args, **kwargs):
1199
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
1203
        requires_backends(self, ["torch"])
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class ElectraForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class ElectraForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class ElectraModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class ElectraPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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def load_tf_weights_in_electra(*args, **kwargs):
1243
    requires_backends(load_tf_weights_in_electra, ["torch"])
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class EncoderDecoderModel:
    def __init__(self, *args, **kwargs):
1248
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None


class FlaubertForMultipleChoice:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class FlaubertForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class FlaubertForQuestionAnsweringSimple:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class FlaubertForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class FlaubertForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class FlaubertModel:
    def __init__(self, *args, **kwargs):
1305
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
1309
        requires_backends(self, ["torch"])
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class FlaubertWithLMHeadModel:
    def __init__(self, *args, **kwargs):
1314
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
1318
        requires_backends(self, ["torch"])
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class FSMTForConditionalGeneration:
    def __init__(self, *args, **kwargs):
1323
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class FSMTModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class PretrainedFSMTModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST = None


class FunnelBaseModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class FunnelForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class FunnelForMultipleChoice:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class FunnelForPreTraining:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class FunnelForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class FunnelForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class FunnelForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class FunnelModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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def load_tf_weights_in_funnel(*args, **kwargs):
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    requires_backends(load_tf_weights_in_funnel, ["torch"])
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GPT2_PRETRAINED_MODEL_ARCHIVE_LIST = None


class GPT2DoubleHeadsModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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Lysandre's avatar
Lysandre committed
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class GPT2ForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Lysandre's avatar
Lysandre committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Lysandre's avatar
Lysandre committed
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class GPT2LMHeadModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class GPT2Model:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class GPT2PreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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def load_tf_weights_in_gpt2(*args, **kwargs):
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    requires_backends(load_tf_weights_in_gpt2, ["torch"])
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Suraj Patil's avatar
Suraj Patil committed
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GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST = None


class GPTNeoForCausalLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Suraj Patil's avatar
Suraj Patil committed
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class GPTNeoModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Suraj Patil's avatar
Suraj Patil committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Suraj Patil's avatar
Suraj Patil committed
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class GPTNeoPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Suraj Patil's avatar
Suraj Patil committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Suraj Patil's avatar
Suraj Patil committed
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def load_tf_weights_in_gpt_neo(*args, **kwargs):
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    requires_backends(load_tf_weights_in_gpt_neo, ["torch"])
Suraj Patil's avatar
Suraj Patil committed
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Sehoon Kim's avatar
Sehoon Kim committed
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IBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None


class IBertForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Sehoon Kim's avatar
Sehoon Kim committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Sehoon Kim's avatar
Sehoon Kim committed
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class IBertForMultipleChoice:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Sehoon Kim's avatar
Sehoon Kim committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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Sehoon Kim committed
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class IBertForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Sehoon Kim's avatar
Sehoon Kim committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Sehoon Kim's avatar
Sehoon Kim committed
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class IBertForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Sehoon Kim's avatar
Sehoon Kim committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Sehoon Kim's avatar
Sehoon Kim committed
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class IBertForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Sehoon Kim's avatar
Sehoon Kim committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Sehoon Kim's avatar
Sehoon Kim committed
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class IBertModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Sehoon Kim's avatar
Sehoon Kim committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Sehoon Kim's avatar
Sehoon Kim committed
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class IBertPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = None


class LayoutLMForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
NielsRogge's avatar
NielsRogge committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
NielsRogge's avatar
NielsRogge committed
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class LayoutLMForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LayoutLMForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LayoutLMModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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Patrick von Platen's avatar
Patrick von Platen committed
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LED_PRETRAINED_MODEL_ARCHIVE_LIST = None


class LEDForConditionalGeneration:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Patrick von Platen's avatar
Patrick von Platen committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Patrick von Platen's avatar
Patrick von Platen committed
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class LEDForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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Patrick von Platen committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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Patrick von Platen committed
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class LEDForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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Patrick von Platen committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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Patrick von Platen committed
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class LEDModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Patrick von Platen's avatar
Patrick von Platen committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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Patrick von Platen committed
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LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None


class LongformerForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LongformerForMultipleChoice:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LongformerForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LongformerForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LongformerForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LongformerModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LongformerSelfAttention:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LxmertEncoder:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LxmertForPreTraining:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LxmertForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LxmertModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LxmertPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LxmertVisualFeatureEncoder:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class LxmertXLayer:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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Suraj Patil's avatar
Suraj Patil committed
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M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST = None


class M2M100ForConditionalGeneration:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Suraj Patil's avatar
Suraj Patil committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Suraj Patil's avatar
Suraj Patil committed
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class M2M100Model:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Suraj Patil's avatar
Suraj Patil committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Suraj Patil's avatar
Suraj Patil committed
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class MarianForCausalLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MarianModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MarianMTModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MBartForCausalLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MBartForConditionalGeneration:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MBartForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MBartForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MBartModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None


class MegatronBertForCausalLM:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])


class MegatronBertForMaskedLM:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

    @classmethod
    def from_pretrained(self, *args, **kwargs):
        requires_backends(self, ["torch"])


class MegatronBertForMultipleChoice:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

    @classmethod
    def from_pretrained(self, *args, **kwargs):
        requires_backends(self, ["torch"])


class MegatronBertForNextSentencePrediction:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])


class MegatronBertForPreTraining:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])


class MegatronBertForQuestionAnswering:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

    @classmethod
    def from_pretrained(self, *args, **kwargs):
        requires_backends(self, ["torch"])


class MegatronBertForSequenceClassification:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

    @classmethod
    def from_pretrained(self, *args, **kwargs):
        requires_backends(self, ["torch"])


class MegatronBertForTokenClassification:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

    @classmethod
    def from_pretrained(self, *args, **kwargs):
        requires_backends(self, ["torch"])


class MegatronBertModel:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

    @classmethod
    def from_pretrained(self, *args, **kwargs):
        requires_backends(self, ["torch"])


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class MMBTForClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MMBTModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class ModalEmbeddings:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None


class MobileBertForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MobileBertForMultipleChoice:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MobileBertForNextSentencePrediction:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MobileBertForPreTraining:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MobileBertForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MobileBertForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MobileBertForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MobileBertLayer:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MobileBertModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MobileBertPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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def load_tf_weights_in_mobilebert(*args, **kwargs):
2016
    requires_backends(load_tf_weights_in_mobilebert, ["torch"])
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2018


StillKeepTry's avatar
StillKeepTry committed
2019
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2023
MPNET_PRETRAINED_MODEL_ARCHIVE_LIST = None


class MPNetForMaskedLM:
    def __init__(self, *args, **kwargs):
2024
        requires_backends(self, ["torch"])
StillKeepTry's avatar
StillKeepTry committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2028
        requires_backends(self, ["torch"])
StillKeepTry's avatar
StillKeepTry committed
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class MPNetForMultipleChoice:
    def __init__(self, *args, **kwargs):
2033
        requires_backends(self, ["torch"])
StillKeepTry's avatar
StillKeepTry committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2037
        requires_backends(self, ["torch"])
StillKeepTry's avatar
StillKeepTry committed
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class MPNetForQuestionAnswering:
    def __init__(self, *args, **kwargs):
2042
        requires_backends(self, ["torch"])
StillKeepTry's avatar
StillKeepTry committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2046
        requires_backends(self, ["torch"])
StillKeepTry's avatar
StillKeepTry committed
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class MPNetForSequenceClassification:
    def __init__(self, *args, **kwargs):
2051
        requires_backends(self, ["torch"])
StillKeepTry's avatar
StillKeepTry committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2055
        requires_backends(self, ["torch"])
StillKeepTry's avatar
StillKeepTry committed
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class MPNetForTokenClassification:
    def __init__(self, *args, **kwargs):
2060
        requires_backends(self, ["torch"])
StillKeepTry's avatar
StillKeepTry committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2064
        requires_backends(self, ["torch"])
StillKeepTry's avatar
StillKeepTry committed
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class MPNetLayer:
    def __init__(self, *args, **kwargs):
2069
        requires_backends(self, ["torch"])
StillKeepTry's avatar
StillKeepTry committed
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class MPNetModel:
    def __init__(self, *args, **kwargs):
2074
        requires_backends(self, ["torch"])
StillKeepTry's avatar
StillKeepTry committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2078
        requires_backends(self, ["torch"])
StillKeepTry's avatar
StillKeepTry committed
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class MPNetPreTrainedModel:
    def __init__(self, *args, **kwargs):
2083
        requires_backends(self, ["torch"])
StillKeepTry's avatar
StillKeepTry committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2087
        requires_backends(self, ["torch"])
StillKeepTry's avatar
StillKeepTry committed
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class MT5EncoderModel:
    def __init__(self, *args, **kwargs):
2092
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2096
        requires_backends(self, ["torch"])
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Patrick von Platen's avatar
Patrick von Platen committed
2099
2100
class MT5ForConditionalGeneration:
    def __init__(self, *args, **kwargs):
2101
        requires_backends(self, ["torch"])
Patrick von Platen's avatar
Patrick von Platen committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2105
        requires_backends(self, ["torch"])
Patrick von Platen's avatar
Patrick von Platen committed
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class MT5Model:
    def __init__(self, *args, **kwargs):
2110
        requires_backends(self, ["torch"])
Patrick von Platen's avatar
Patrick von Platen committed
2111
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2114
        requires_backends(self, ["torch"])
Patrick von Platen's avatar
Patrick von Platen committed
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OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST = None


class OpenAIGPTDoubleHeadsModel:
    def __init__(self, *args, **kwargs):
2122
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2126
        requires_backends(self, ["torch"])
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class OpenAIGPTForSequenceClassification:
    def __init__(self, *args, **kwargs):
2131
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2135
        requires_backends(self, ["torch"])
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class OpenAIGPTLMHeadModel:
    def __init__(self, *args, **kwargs):
2140
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2144
        requires_backends(self, ["torch"])
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class OpenAIGPTModel:
    def __init__(self, *args, **kwargs):
2149
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2153
        requires_backends(self, ["torch"])
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class OpenAIGPTPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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def load_tf_weights_in_openai_gpt(*args, **kwargs):
2166
    requires_backends(load_tf_weights_in_openai_gpt, ["torch"])
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class PegasusForCausalLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class PegasusForConditionalGeneration:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class PegasusModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2189
        requires_backends(self, ["torch"])
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Weizhen's avatar
Weizhen committed
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2196
PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST = None


class ProphetNetDecoder:
    def __init__(self, *args, **kwargs):
2197
        requires_backends(self, ["torch"])
Weizhen's avatar
Weizhen committed
2198
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2201


class ProphetNetEncoder:
    def __init__(self, *args, **kwargs):
2202
        requires_backends(self, ["torch"])
Weizhen's avatar
Weizhen committed
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class ProphetNetForCausalLM:
    def __init__(self, *args, **kwargs):
2207
        requires_backends(self, ["torch"])
Weizhen's avatar
Weizhen committed
2208
2209
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class ProphetNetForConditionalGeneration:
    def __init__(self, *args, **kwargs):
2212
        requires_backends(self, ["torch"])
Weizhen's avatar
Weizhen committed
2213
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2216
        requires_backends(self, ["torch"])
Weizhen's avatar
Weizhen committed
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class ProphetNetModel:
    def __init__(self, *args, **kwargs):
2221
        requires_backends(self, ["torch"])
Weizhen's avatar
Weizhen committed
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2224

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2225
        requires_backends(self, ["torch"])
Weizhen's avatar
Weizhen committed
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class ProphetNetPreTrainedModel:
    def __init__(self, *args, **kwargs):
2230
        requires_backends(self, ["torch"])
Weizhen's avatar
Weizhen committed
2231
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2233

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2234
        requires_backends(self, ["torch"])
Weizhen's avatar
Weizhen committed
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class RagModel:
    def __init__(self, *args, **kwargs):
2239
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2243
        requires_backends(self, ["torch"])
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class RagSequenceForGeneration:
    def __init__(self, *args, **kwargs):
2248
        requires_backends(self, ["torch"])
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class RagTokenForGeneration:
    def __init__(self, *args, **kwargs):
2253
        requires_backends(self, ["torch"])
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REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None


class ReformerAttention:
    def __init__(self, *args, **kwargs):
2261
        requires_backends(self, ["torch"])
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class ReformerForMaskedLM:
    def __init__(self, *args, **kwargs):
2266
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2270
        requires_backends(self, ["torch"])
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class ReformerForQuestionAnswering:
    def __init__(self, *args, **kwargs):
2275
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2279
        requires_backends(self, ["torch"])
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class ReformerForSequenceClassification:
    def __init__(self, *args, **kwargs):
2284
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2288
        requires_backends(self, ["torch"])
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class ReformerLayer:
    def __init__(self, *args, **kwargs):
2293
        requires_backends(self, ["torch"])
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class ReformerModel:
    def __init__(self, *args, **kwargs):
2298
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2302
        requires_backends(self, ["torch"])
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class ReformerModelWithLMHead:
    def __init__(self, *args, **kwargs):
2307
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2311
        requires_backends(self, ["torch"])
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RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None


class RetriBertModel:
    def __init__(self, *args, **kwargs):
2319
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2323
        requires_backends(self, ["torch"])
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class RetriBertPreTrainedModel:
    def __init__(self, *args, **kwargs):
2328
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2332
        requires_backends(self, ["torch"])
2333
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ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None


class RobertaForCausalLM:
    def __init__(self, *args, **kwargs):
2340
        requires_backends(self, ["torch"])
2341
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2344


class RobertaForMaskedLM:
    def __init__(self, *args, **kwargs):
2345
        requires_backends(self, ["torch"])
2346
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2348

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2349
        requires_backends(self, ["torch"])
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class RobertaForMultipleChoice:
    def __init__(self, *args, **kwargs):
2354
        requires_backends(self, ["torch"])
2355
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2357

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2358
        requires_backends(self, ["torch"])
2359
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class RobertaForQuestionAnswering:
    def __init__(self, *args, **kwargs):
2363
        requires_backends(self, ["torch"])
2364
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2367
        requires_backends(self, ["torch"])
2368
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class RobertaForSequenceClassification:
    def __init__(self, *args, **kwargs):
2372
        requires_backends(self, ["torch"])
2373
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2376
        requires_backends(self, ["torch"])
2377
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class RobertaForTokenClassification:
    def __init__(self, *args, **kwargs):
2381
        requires_backends(self, ["torch"])
2382
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2384

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2385
        requires_backends(self, ["torch"])
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class RobertaModel:
    def __init__(self, *args, **kwargs):
2390
        requires_backends(self, ["torch"])
2391
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2393

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2394
        requires_backends(self, ["torch"])
2395
2396


Suraj Patil's avatar
Suraj Patil committed
2397
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2400
2401
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST = None


class Speech2TextForConditionalGeneration:
    def __init__(self, *args, **kwargs):
2402
        requires_backends(self, ["torch"])
Suraj Patil's avatar
Suraj Patil committed
2403
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2406
        requires_backends(self, ["torch"])
Suraj Patil's avatar
Suraj Patil committed
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class Speech2TextModel:
    def __init__(self, *args, **kwargs):
2411
        requires_backends(self, ["torch"])
Suraj Patil's avatar
Suraj Patil committed
2412
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2414

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2415
        requires_backends(self, ["torch"])
Suraj Patil's avatar
Suraj Patil committed
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Sylvain Gugger's avatar
Sylvain Gugger committed
2418
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2422
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None


class SqueezeBertForMaskedLM:
    def __init__(self, *args, **kwargs):
2423
        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
2424
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2426

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2427
        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
2428
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2430
2431


class SqueezeBertForMultipleChoice:
    def __init__(self, *args, **kwargs):
2432
        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
2433
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2435

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2436
        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
2437
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2440


class SqueezeBertForQuestionAnswering:
    def __init__(self, *args, **kwargs):
2441
        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
2442
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2444

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2445
        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
2446
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2449


class SqueezeBertForSequenceClassification:
    def __init__(self, *args, **kwargs):
2450
        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
2451
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2453

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2454
        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
2455
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2457
2458


class SqueezeBertForTokenClassification:
    def __init__(self, *args, **kwargs):
2459
        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
2460
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2462

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2463
        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
2464
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2466
2467


class SqueezeBertModel:
    def __init__(self, *args, **kwargs):
2468
        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
2469
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2471

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2472
        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
2473
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2476


class SqueezeBertModule:
    def __init__(self, *args, **kwargs):
2477
        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
2478
2479
2480
2481


class SqueezeBertPreTrainedModel:
    def __init__(self, *args, **kwargs):
2482
        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
2483
2484
2485

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2486
        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
2487
2488


2489
2490
2491
T5_PRETRAINED_MODEL_ARCHIVE_LIST = None


2492
2493
class T5EncoderModel:
    def __init__(self, *args, **kwargs):
2494
        requires_backends(self, ["torch"])
2495
2496
2497

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2498
        requires_backends(self, ["torch"])
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2500


2501
2502
class T5ForConditionalGeneration:
    def __init__(self, *args, **kwargs):
2503
        requires_backends(self, ["torch"])
2504
2505
2506

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2507
        requires_backends(self, ["torch"])
2508
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2511


class T5Model:
    def __init__(self, *args, **kwargs):
2512
        requires_backends(self, ["torch"])
2513
2514
2515

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2516
        requires_backends(self, ["torch"])
2517
2518
2519
2520


class T5PreTrainedModel:
    def __init__(self, *args, **kwargs):
2521
        requires_backends(self, ["torch"])
2522
2523
2524

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2525
        requires_backends(self, ["torch"])
2526
2527
2528


def load_tf_weights_in_t5(*args, **kwargs):
2529
    requires_backends(load_tf_weights_in_t5, ["torch"])
2530
2531


NielsRogge's avatar
NielsRogge committed
2532
2533
2534
2535
2536
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST = None


class TapasForMaskedLM:
    def __init__(self, *args, **kwargs):
2537
        requires_backends(self, ["torch"])
NielsRogge's avatar
NielsRogge committed
2538
2539
2540

    @classmethod
    def from_pretrained(self, *args, **kwargs):
2541
        requires_backends(self, ["torch"])
NielsRogge's avatar
NielsRogge committed
2542
2543
2544
2545


class TapasForQuestionAnswering:
    def __init__(self, *args, **kwargs):
2546
        requires_backends(self, ["torch"])
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NielsRogge committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2550
        requires_backends(self, ["torch"])
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NielsRogge committed
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class TapasForSequenceClassification:
    def __init__(self, *args, **kwargs):
2555
        requires_backends(self, ["torch"])
NielsRogge's avatar
NielsRogge committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2559
        requires_backends(self, ["torch"])
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class TapasModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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NielsRogge committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2568
        requires_backends(self, ["torch"])
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NielsRogge committed
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TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST = None


class AdaptiveEmbedding:
    def __init__(self, *args, **kwargs):
2576
        requires_backends(self, ["torch"])
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sandip's avatar
sandip committed
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class TransfoXLForSequenceClassification:
    def __init__(self, *args, **kwargs):
2581
        requires_backends(self, ["torch"])
sandip's avatar
sandip committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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sandip committed
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class TransfoXLLMHeadModel:
    def __init__(self, *args, **kwargs):
2590
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class TransfoXLModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class TransfoXLPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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def load_tf_weights_in_transfo_xl(*args, **kwargs):
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    requires_backends(load_tf_weights_in_transfo_xl, ["torch"])
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VIT_PRETRAINED_MODEL_ARCHIVE_LIST = None


class ViTForImageClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class ViTModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class ViTPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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Patrick von Platen's avatar
Patrick von Platen committed
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WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST = None


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class Wav2Vec2ForCTC:
    def __init__(self, *args, **kwargs):
2650
        requires_backends(self, ["torch"])
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Patrick von Platen's avatar
Patrick von Platen committed
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class Wav2Vec2ForMaskedLM:
    def __init__(self, *args, **kwargs):
2655
        requires_backends(self, ["torch"])
Patrick von Platen's avatar
Patrick von Platen committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2659
        requires_backends(self, ["torch"])
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Patrick von Platen committed
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class Wav2Vec2Model:
    def __init__(self, *args, **kwargs):
2664
        requires_backends(self, ["torch"])
Patrick von Platen's avatar
Patrick von Platen committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2668
        requires_backends(self, ["torch"])
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Patrick von Platen committed
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class Wav2Vec2PreTrainedModel:
    def __init__(self, *args, **kwargs):
2673
        requires_backends(self, ["torch"])
Patrick von Platen's avatar
Patrick von Platen committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2677
        requires_backends(self, ["torch"])
Patrick von Platen's avatar
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XLM_PRETRAINED_MODEL_ARCHIVE_LIST = None


class XLMForMultipleChoice:
    def __init__(self, *args, **kwargs):
2685
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class XLMForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class XLMForQuestionAnsweringSimple:
    def __init__(self, *args, **kwargs):
2703
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class XLMForSequenceClassification:
    def __init__(self, *args, **kwargs):
2712
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class XLMForTokenClassification:
    def __init__(self, *args, **kwargs):
2721
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2725
        requires_backends(self, ["torch"])
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class XLMModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2734
        requires_backends(self, ["torch"])
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class XLMPreTrainedModel:
    def __init__(self, *args, **kwargs):
2739
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class XLMWithLMHeadModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2752
        requires_backends(self, ["torch"])
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Weizhen's avatar
Weizhen committed
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XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST = None


class XLMProphetNetDecoder:
    def __init__(self, *args, **kwargs):
2760
        requires_backends(self, ["torch"])
Weizhen's avatar
Weizhen committed
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class XLMProphetNetEncoder:
    def __init__(self, *args, **kwargs):
2765
        requires_backends(self, ["torch"])
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class XLMProphetNetForCausalLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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Weizhen committed
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class XLMProphetNetForConditionalGeneration:
    def __init__(self, *args, **kwargs):
2775
        requires_backends(self, ["torch"])
Weizhen's avatar
Weizhen committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2779
        requires_backends(self, ["torch"])
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Weizhen committed
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class XLMProphetNetModel:
    def __init__(self, *args, **kwargs):
2784
        requires_backends(self, ["torch"])
Weizhen's avatar
Weizhen committed
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2788
        requires_backends(self, ["torch"])
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XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None


class XLMRobertaForCausalLM:
    def __init__(self, *args, **kwargs):
2796
        requires_backends(self, ["torch"])
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class XLMRobertaForMaskedLM:
    def __init__(self, *args, **kwargs):
2801
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class XLMRobertaForMultipleChoice:
    def __init__(self, *args, **kwargs):
2810
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2814
        requires_backends(self, ["torch"])
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class XLMRobertaForQuestionAnswering:
    def __init__(self, *args, **kwargs):
2819
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2823
        requires_backends(self, ["torch"])
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class XLMRobertaForSequenceClassification:
    def __init__(self, *args, **kwargs):
2828
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2832
        requires_backends(self, ["torch"])
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class XLMRobertaForTokenClassification:
    def __init__(self, *args, **kwargs):
2837
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2841
        requires_backends(self, ["torch"])
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class XLMRobertaModel:
    def __init__(self, *args, **kwargs):
2846
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2850
        requires_backends(self, ["torch"])
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XLNET_PRETRAINED_MODEL_ARCHIVE_LIST = None


class XLNetForMultipleChoice:
    def __init__(self, *args, **kwargs):
2858
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2862
        requires_backends(self, ["torch"])
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class XLNetForQuestionAnswering:
    def __init__(self, *args, **kwargs):
2867
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2871
        requires_backends(self, ["torch"])
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class XLNetForQuestionAnsweringSimple:
    def __init__(self, *args, **kwargs):
2876
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2880
        requires_backends(self, ["torch"])
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class XLNetForSequenceClassification:
    def __init__(self, *args, **kwargs):
2885
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2889
        requires_backends(self, ["torch"])
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class XLNetForTokenClassification:
    def __init__(self, *args, **kwargs):
2894
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2898
        requires_backends(self, ["torch"])
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class XLNetLMHeadModel:
    def __init__(self, *args, **kwargs):
2903
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2907
        requires_backends(self, ["torch"])
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class XLNetModel:
    def __init__(self, *args, **kwargs):
2912
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2916
        requires_backends(self, ["torch"])
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class XLNetPreTrainedModel:
    def __init__(self, *args, **kwargs):
2921
        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(self, *args, **kwargs):
2925
        requires_backends(self, ["torch"])
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def load_tf_weights_in_xlnet(*args, **kwargs):
2929
    requires_backends(load_tf_weights_in_xlnet, ["torch"])
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class Adafactor:
    def __init__(self, *args, **kwargs):
2934
        requires_backends(self, ["torch"])
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class AdamW:
    def __init__(self, *args, **kwargs):
2939
        requires_backends(self, ["torch"])
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def get_constant_schedule(*args, **kwargs):
2943
    requires_backends(get_constant_schedule, ["torch"])
2944
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def get_constant_schedule_with_warmup(*args, **kwargs):
2947
    requires_backends(get_constant_schedule_with_warmup, ["torch"])
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def get_cosine_schedule_with_warmup(*args, **kwargs):
2951
    requires_backends(get_cosine_schedule_with_warmup, ["torch"])
2952
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def get_cosine_with_hard_restarts_schedule_with_warmup(*args, **kwargs):
2955
    requires_backends(get_cosine_with_hard_restarts_schedule_with_warmup, ["torch"])
2956
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def get_linear_schedule_with_warmup(*args, **kwargs):
2959
    requires_backends(get_linear_schedule_with_warmup, ["torch"])
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def get_polynomial_decay_schedule_with_warmup(*args, **kwargs):
2963
    requires_backends(get_polynomial_decay_schedule_with_warmup, ["torch"])
2964
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Sylvain Gugger's avatar
Sylvain Gugger committed
2966
def get_scheduler(*args, **kwargs):
2967
    requires_backends(get_scheduler, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
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class Trainer:
    def __init__(self, *args, **kwargs):
2972
        requires_backends(self, ["torch"])
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2975


def torch_distributed_zero_first(*args, **kwargs):
2976
    requires_backends(torch_distributed_zero_first, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
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class Seq2SeqTrainer:
    def __init__(self, *args, **kwargs):
2981
        requires_backends(self, ["torch"])