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646
venv/Lib/site-packages/transformers/utils/dummy_pt_objects.py
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646
venv/Lib/site-packages/transformers/utils/dummy_pt_objects.py
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# This file is autogenerated by the command `make fix-copies`, do not edit.
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from ..utils import DummyObject, requires_backends
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class Cache(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class CacheConfig(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class DynamicCache(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class EncoderDecoderCache(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class HQQQuantizedCache(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class HybridCache(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class OffloadedCache(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class OffloadedStaticCache(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class QuantizedCache(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class QuantizedCacheConfig(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class QuantoQuantizedCache(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class SinkCache(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class SlidingWindowCache(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class StaticCache(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class GlueDataset(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class GlueDataTrainingArguments(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class LineByLineTextDataset(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class LineByLineWithRefDataset(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class LineByLineWithSOPTextDataset(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class SquadDataset(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class SquadDataTrainingArguments(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class TextDataset(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class TextDatasetForNextSentencePrediction(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class AlternatingCodebooksLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class BayesianDetectorConfig(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class BayesianDetectorModel(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class BeamScorer(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class BeamSearchScorer(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class ClassifierFreeGuidanceLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class ConstrainedBeamSearchScorer(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class Constraint(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class ConstraintListState(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class DisjunctiveConstraint(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class EncoderNoRepeatNGramLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class EncoderRepetitionPenaltyLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class EosTokenCriteria(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class EpsilonLogitsWarper(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class EtaLogitsWarper(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class ExponentialDecayLengthPenalty(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class ForcedBOSTokenLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class ForcedEOSTokenLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class GenerationMixin(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class HammingDiversityLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class InfNanRemoveLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class LogitNormalization(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class LogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class LogitsProcessorList(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class MaxLengthCriteria(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class MaxTimeCriteria(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class MinLengthLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class MinNewTokensLengthLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class MinPLogitsWarper(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class NoBadWordsLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class NoRepeatNGramLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class PhrasalConstraint(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class PrefixConstrainedLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class RepetitionPenaltyLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class SequenceBiasLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class StoppingCriteria(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class StoppingCriteriaList(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class StopStringCriteria(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class SuppressTokensAtBeginLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class SuppressTokensLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class SynthIDTextWatermarkDetector(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class SynthIDTextWatermarkingConfig(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class SynthIDTextWatermarkLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class TemperatureLogitsWarper(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class TopKLogitsWarper(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class TopPLogitsWarper(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class TypicalLogitsWarper(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class UnbatchedClassifierFreeGuidanceLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class WatermarkDetector(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class WatermarkLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class WhisperTimeStampLogitsProcessor(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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class TorchExportableModuleWithStaticCache(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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def convert_and_export_with_cache(*args, **kwargs):
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requires_backends(convert_and_export_with_cache, ["torch"])
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class AttentionMaskInterface(metaclass=DummyObject):
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_backends = ["torch"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["torch"])
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def model_addition_debugger_context(*args, **kwargs):
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requires_backends(model_addition_debugger_context, ["torch"])
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||||
|
||||
class GradientCheckpointingLayer(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
ROPE_INIT_FUNCTIONS = None
|
||||
|
||||
|
||||
def dynamic_rope_update(*args, **kwargs):
|
||||
requires_backends(dynamic_rope_update, ["torch"])
|
||||
|
||||
|
||||
class AttentionInterface(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class PreTrainedModel(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class Adafactor(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
def get_constant_schedule(*args, **kwargs):
|
||||
requires_backends(get_constant_schedule, ["torch"])
|
||||
|
||||
|
||||
def get_constant_schedule_with_warmup(*args, **kwargs):
|
||||
requires_backends(get_constant_schedule_with_warmup, ["torch"])
|
||||
|
||||
|
||||
def get_cosine_schedule_with_warmup(*args, **kwargs):
|
||||
requires_backends(get_cosine_schedule_with_warmup, ["torch"])
|
||||
|
||||
|
||||
def get_cosine_with_hard_restarts_schedule_with_warmup(*args, **kwargs):
|
||||
requires_backends(get_cosine_with_hard_restarts_schedule_with_warmup, ["torch"])
|
||||
|
||||
|
||||
def get_inverse_sqrt_schedule(*args, **kwargs):
|
||||
requires_backends(get_inverse_sqrt_schedule, ["torch"])
|
||||
|
||||
|
||||
def get_linear_schedule_with_warmup(*args, **kwargs):
|
||||
requires_backends(get_linear_schedule_with_warmup, ["torch"])
|
||||
|
||||
|
||||
def get_polynomial_decay_schedule_with_warmup(*args, **kwargs):
|
||||
requires_backends(get_polynomial_decay_schedule_with_warmup, ["torch"])
|
||||
|
||||
|
||||
def get_scheduler(*args, **kwargs):
|
||||
requires_backends(get_scheduler, ["torch"])
|
||||
|
||||
|
||||
def get_wsd_schedule(*args, **kwargs):
|
||||
requires_backends(get_wsd_schedule, ["torch"])
|
||||
|
||||
|
||||
class Conv1D(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
def apply_chunking_to_forward(*args, **kwargs):
|
||||
requires_backends(apply_chunking_to_forward, ["torch"])
|
||||
|
||||
|
||||
def prune_layer(*args, **kwargs):
|
||||
requires_backends(prune_layer, ["torch"])
|
||||
|
||||
|
||||
class Trainer(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
def torch_distributed_zero_first(*args, **kwargs):
|
||||
requires_backends(torch_distributed_zero_first, ["torch"])
|
||||
|
||||
|
||||
class Seq2SeqTrainer(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
Loading…
Add table
Add a link
Reference in a new issue