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dummy_pt_objects.py 92.5 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 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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    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])

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

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

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

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

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

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

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

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

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class RepetitionPenaltyLogitsProcessor:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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_AUDIO_CLASSIFICATION_MAPPING = None


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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_OBJECT_DETECTION_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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class AutoModelForAudioClassification:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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

    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class AutoModelForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class AutoModelForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class AutoModelForSeq2SeqLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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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    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class BartForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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BEIT_PRETRAINED_MODEL_ARCHIVE_LIST = None


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


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

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


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

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


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

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class BertForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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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class BertGenerationPreTrainedModel:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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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    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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"])
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BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST = None


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

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

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

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

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

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

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

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


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

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class BlenderbotForConditionalGeneration:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class BlenderbotModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class BlenderbotPreTrainedModel:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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

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

    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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CANINE_PRETRAINED_MODEL_ARCHIVE_LIST = None


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

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


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

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


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

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


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

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


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


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

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


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

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


def load_tf_weights_in_canine(*args, **kwargs):
    requires_backends(load_tf_weights_in_canine, ["torch"])


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CLIP_PRETRAINED_MODEL_ARCHIVE_LIST = None


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

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

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

    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class CLIPVisionModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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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"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class ConvBertForMultipleChoice:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class ConvBertForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class ConvBertForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class ConvBertForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class ConvBertLayer:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class ConvBertModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class ConvBertPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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def load_tf_weights_in_convbert(*args, **kwargs):
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    requires_backends(load_tf_weights_in_convbert, ["torch"])
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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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None


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class DebertaForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class DebertaForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class DebertaForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class DebertaForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class DebertaV2PreTrainedModel:
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    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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DEIT_PRETRAINED_MODEL_ARCHIVE_LIST = None


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


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


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

    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class DeiTPreTrainedModel:
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    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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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):
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        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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class ElectraForMultipleChoice:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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def load_tf_weights_in_electra(*args, **kwargs):
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    requires_backends(load_tf_weights_in_electra, ["torch"])
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class EncoderDecoderModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class FlaubertModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class FlaubertWithLMHeadModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class FSMTForConditionalGeneration:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class FunnelPreTrainedModel:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class GPT2ForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class GPT2ForTokenClassification:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


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class GPT2LMHeadModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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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GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST = None


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

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

    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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Suraj Patil's avatar
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class GPTNeoModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Suraj Patil's avatar
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class GPTNeoPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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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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Stella Biderman's avatar
Stella Biderman committed
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GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST = None


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

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


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

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


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

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


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

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


Patrick von Platen's avatar
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HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None


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


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

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


Patrick von Platen's avatar
Patrick von Platen committed
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class HubertModel:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


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

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


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"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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"])
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Sehoon Kim committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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Sehoon Kim committed
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class IBertForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class IBertForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class IBertModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class IBertPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class LayoutLMForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class LayoutLMPreTrainedModel:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


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LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST = None


class LayoutLMv2ForQuestionAnswering:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


class LayoutLMv2ForSequenceClassification:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


class LayoutLMv2ForTokenClassification:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


class LayoutLMv2Model:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


class LayoutLMv2PreTrainedModel:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


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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"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class LEDForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class LEDForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class LEDModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class LEDPreTrainedModel:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


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

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


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class LongformerSelfAttention:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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LUKE_PRETRAINED_MODEL_ARCHIVE_LIST = None


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


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


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


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

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

    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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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M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST = None


class M2M100ForConditionalGeneration:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class M2M100Model:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class M2M100PreTrainedModel:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


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class MarianForCausalLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class MBartForCausalLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class MBartPreTrainedModel:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


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MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None


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

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

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

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

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

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

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

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

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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def load_tf_weights_in_mobilebert(*args, **kwargs):
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    requires_backends(load_tf_weights_in_mobilebert, ["torch"])
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StillKeepTry's avatar
StillKeepTry committed
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MPNET_PRETRAINED_MODEL_ARCHIVE_LIST = None


class MPNetForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
StillKeepTry's avatar
StillKeepTry committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class MPNetForMultipleChoice:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class MPNetForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class MPNetForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class MPNetForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class MPNetLayer:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class MPNetModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class MPNetPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class MT5EncoderModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class MT5ForConditionalGeneration:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class MT5Model:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST = None


class OpenAIGPTDoubleHeadsModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class OpenAIGPTForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class OpenAIGPTLMHeadModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class OpenAIGPTModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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def load_tf_weights_in_openai_gpt(*args, **kwargs):
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    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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    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class PegasusPreTrainedModel:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


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PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST = None


class ProphetNetDecoder:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class ProphetNetEncoder:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class ProphetNetForCausalLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])

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class ProphetNetForConditionalGeneration:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class ProphetNetModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class ProphetNetPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class RagModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class RagPreTrainedModel:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


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class RagSequenceForGeneration:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class RagTokenForGeneration:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None


class ReformerAttention:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class ReformerForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class ReformerForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class ReformerForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class ReformerLayer:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class ReformerModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class ReformerModelWithLMHead:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class ReformerPreTrainedModel:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


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REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None


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

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


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

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


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

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


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

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


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

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


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

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


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


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

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


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

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


def load_tf_weights_in_rembert(*args, **kwargs):
    requires_backends(load_tf_weights_in_rembert, ["torch"])


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RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None


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


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

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class RobertaForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class RobertaForMultipleChoice:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class RobertaForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class RobertaForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class RobertaForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class RobertaModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class RobertaPreTrainedModel:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


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ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None


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

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

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

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

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

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

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

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


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

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

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


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

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


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SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST = None


class Speech2TextForConditionalGeneration:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class Speech2TextModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class Speech2TextPreTrainedModel:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


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class Speech2Text2ForCausalLM:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


class Speech2Text2PreTrainedModel:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


Ori Ram's avatar
Ori Ram committed
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SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST = None


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

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


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


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

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


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

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


Sylvain Gugger's avatar
Sylvain Gugger committed
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SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None


class SqueezeBertForMaskedLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
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class SqueezeBertForMultipleChoice:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
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class SqueezeBertForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
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class SqueezeBertForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
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class SqueezeBertForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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Sylvain Gugger committed
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class SqueezeBertModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Sylvain Gugger's avatar
Sylvain Gugger committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class SqueezeBertModule:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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Sylvain Gugger committed
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class SqueezeBertPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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Sylvain Gugger committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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Sylvain Gugger committed
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T5_PRETRAINED_MODEL_ARCHIVE_LIST = None


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class T5EncoderModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class T5ForConditionalGeneration:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class T5Model:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class T5PreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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def load_tf_weights_in_t5(*args, **kwargs):
3358
    requires_backends(load_tf_weights_in_t5, ["torch"])
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NielsRogge's avatar
NielsRogge committed
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TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST = None


class TapasForMaskedLM:
    def __init__(self, *args, **kwargs):
3366
        requires_backends(self, ["torch"])
NielsRogge's avatar
NielsRogge committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
NielsRogge's avatar
NielsRogge committed
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class TapasForQuestionAnswering:
    def __init__(self, *args, **kwargs):
3375
        requires_backends(self, ["torch"])
NielsRogge's avatar
NielsRogge committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
NielsRogge's avatar
NielsRogge committed
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class TapasForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
NielsRogge's avatar
NielsRogge committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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NielsRogge committed
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class TapasModel:
    def __init__(self, *args, **kwargs):
3393
        requires_backends(self, ["torch"])
NielsRogge's avatar
NielsRogge committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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NielsRogge committed
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class TapasPreTrainedModel:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

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


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def load_tf_weights_in_tapas(*args, **kwargs):
    requires_backends(load_tf_weights_in_tapas, ["torch"])


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TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST = None


class AdaptiveEmbedding:
    def __init__(self, *args, **kwargs):
3418
        requires_backends(self, ["torch"])
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sandip's avatar
sandip committed
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class TransfoXLForSequenceClassification:
    def __init__(self, *args, **kwargs):
3423
        requires_backends(self, ["torch"])
sandip's avatar
sandip committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
sandip's avatar
sandip committed
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class TransfoXLLMHeadModel:
    def __init__(self, *args, **kwargs):
3432
        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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def load_tf_weights_in_transfo_xl(*args, **kwargs):
3458
    requires_backends(load_tf_weights_in_transfo_xl, ["torch"])
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Gunjan Chhablani's avatar
Gunjan Chhablani committed
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VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None


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

    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
Gunjan Chhablani's avatar
Gunjan Chhablani committed
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class VisualBertForPreTraining:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])


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

    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
Gunjan Chhablani's avatar
Gunjan Chhablani committed
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class VisualBertForRegionToPhraseAlignment:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])


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


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


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

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

    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
Gunjan Chhablani's avatar
Gunjan Chhablani committed
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VIT_PRETRAINED_MODEL_ARCHIVE_LIST = None


class ViTForImageClassification:
    def __init__(self, *args, **kwargs):
3525
        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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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):
3551
        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):
3556
        requires_backends(self, ["torch"])
Patrick von Platen's avatar
Patrick von Platen committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
Patrick von Platen's avatar
Patrick von Platen committed
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Anton Lozhkov's avatar
Anton Lozhkov committed
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class Wav2Vec2ForPreTraining:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
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class Wav2Vec2ForSequenceClassification:
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
Anton Lozhkov's avatar
Anton Lozhkov committed
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Patrick von Platen's avatar
Patrick von Platen committed
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class Wav2Vec2Model:
    def __init__(self, *args, **kwargs):
3579
        requires_backends(self, ["torch"])
Patrick von Platen's avatar
Patrick von Platen committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
Patrick von Platen's avatar
Patrick von Platen committed
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class Wav2Vec2PreTrainedModel:
    def __init__(self, *args, **kwargs):
3588
        requires_backends(self, ["torch"])
Patrick von Platen's avatar
Patrick von Platen committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
Patrick von Platen's avatar
Patrick von Platen committed
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XLM_PRETRAINED_MODEL_ARCHIVE_LIST = None


class XLMForMultipleChoice:
    def __init__(self, *args, **kwargs):
3600
        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class XLMForQuestionAnsweringSimple:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class XLMForSequenceClassification:
    def __init__(self, *args, **kwargs):
3627
        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class XLMForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class XLMPreTrainedModel:
    def __init__(self, *args, **kwargs):
3654
        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["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):
3675
        requires_backends(self, ["torch"])
Weizhen's avatar
Weizhen committed
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class XLMProphetNetEncoder:
    def __init__(self, *args, **kwargs):
3680
        requires_backends(self, ["torch"])
Weizhen's avatar
Weizhen committed
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class XLMProphetNetForCausalLM:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
Weizhen's avatar
Weizhen committed
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    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])

Weizhen's avatar
Weizhen committed
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class XLMProphetNetForConditionalGeneration:
    def __init__(self, *args, **kwargs):
3694
        requires_backends(self, ["torch"])
Weizhen's avatar
Weizhen committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
Weizhen's avatar
Weizhen committed
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class XLMProphetNetModel:
    def __init__(self, *args, **kwargs):
3703
        requires_backends(self, ["torch"])
Weizhen's avatar
Weizhen committed
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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Weizhen committed
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XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None


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

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class XLMRobertaForMaskedLM:
    def __init__(self, *args, **kwargs):
3724
        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class XLMRobertaForMultipleChoice:
    def __init__(self, *args, **kwargs):
3733
        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class XLMRobertaForQuestionAnswering:
    def __init__(self, *args, **kwargs):
3742
        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class XLMRobertaForSequenceClassification:
    def __init__(self, *args, **kwargs):
3751
        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class XLMRobertaForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class XLMRobertaModel:
    def __init__(self, *args, **kwargs):
3769
        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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XLNET_PRETRAINED_MODEL_ARCHIVE_LIST = None


class XLNetForMultipleChoice:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class XLNetForQuestionAnswering:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class XLNetForQuestionAnsweringSimple:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class XLNetForSequenceClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class XLNetForTokenClassification:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class XLNetLMHeadModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class XLNetModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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class XLNetPreTrainedModel:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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    @classmethod
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    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
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def load_tf_weights_in_xlnet(*args, **kwargs):
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    requires_backends(load_tf_weights_in_xlnet, ["torch"])
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class Adafactor:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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class AdamW:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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def get_constant_schedule(*args, **kwargs):
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    requires_backends(get_constant_schedule, ["torch"])
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def get_constant_schedule_with_warmup(*args, **kwargs):
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    requires_backends(get_constant_schedule_with_warmup, ["torch"])
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def get_cosine_schedule_with_warmup(*args, **kwargs):
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    requires_backends(get_cosine_schedule_with_warmup, ["torch"])
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def get_cosine_with_hard_restarts_schedule_with_warmup(*args, **kwargs):
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    requires_backends(get_cosine_with_hard_restarts_schedule_with_warmup, ["torch"])
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def get_linear_schedule_with_warmup(*args, **kwargs):
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    requires_backends(get_linear_schedule_with_warmup, ["torch"])
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def get_polynomial_decay_schedule_with_warmup(*args, **kwargs):
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    requires_backends(get_polynomial_decay_schedule_with_warmup, ["torch"])
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Sylvain Gugger's avatar
Sylvain Gugger committed
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def get_scheduler(*args, **kwargs):
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    requires_backends(get_scheduler, ["torch"])
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class Trainer:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])
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def torch_distributed_zero_first(*args, **kwargs):
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    requires_backends(torch_distributed_zero_first, ["torch"])
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class Seq2SeqTrainer:
    def __init__(self, *args, **kwargs):
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        requires_backends(self, ["torch"])