| |
| from ..utils import DummyObject, requires_backends |
|
|
|
|
| class Cache(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class DynamicCache(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class EncoderDecoderCache(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class HQQQuantizedCache(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class HybridCache(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class OffloadedCache(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class OffloadedStaticCache(metaclass=DummyObject): |
| _backends = ["torch"] |
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|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class QuantizedCache(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class QuantoQuantizedCache(metaclass=DummyObject): |
| _backends = ["torch"] |
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|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class SinkCache(metaclass=DummyObject): |
| _backends = ["torch"] |
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|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class SlidingWindowCache(metaclass=DummyObject): |
| _backends = ["torch"] |
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|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class StaticCache(metaclass=DummyObject): |
| _backends = ["torch"] |
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|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class GlueDataset(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class GlueDataTrainingArguments(metaclass=DummyObject): |
| _backends = ["torch"] |
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|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class LineByLineTextDataset(metaclass=DummyObject): |
| _backends = ["torch"] |
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|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class LineByLineWithRefDataset(metaclass=DummyObject): |
| _backends = ["torch"] |
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|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class LineByLineWithSOPTextDataset(metaclass=DummyObject): |
| _backends = ["torch"] |
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|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class SquadDataset(metaclass=DummyObject): |
| _backends = ["torch"] |
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|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class SquadDataTrainingArguments(metaclass=DummyObject): |
| _backends = ["torch"] |
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|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class TextDataset(metaclass=DummyObject): |
| _backends = ["torch"] |
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|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class TextDatasetForNextSentencePrediction(metaclass=DummyObject): |
| _backends = ["torch"] |
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|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class AlternatingCodebooksLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class BayesianDetectorConfig(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class BayesianDetectorModel(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class BeamScorer(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class BeamSearchScorer(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
| class ClassifierFreeGuidanceLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class ConstrainedBeamSearchScorer(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class Constraint(metaclass=DummyObject): |
| _backends = ["torch"] |
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|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
| class ConstraintListState(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class DisjunctiveConstraint(metaclass=DummyObject): |
| _backends = ["torch"] |
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|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class EncoderNoRepeatNGramLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class EncoderRepetitionPenaltyLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class EosTokenCriteria(metaclass=DummyObject): |
| _backends = ["torch"] |
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|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class EpsilonLogitsWarper(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class EtaLogitsWarper(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class ExponentialDecayLengthPenalty(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class ForcedBOSTokenLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class ForcedEOSTokenLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class GenerationMixin(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class HammingDiversityLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class InfNanRemoveLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class LogitNormalization(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class LogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class LogitsProcessorList(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class MaxLengthCriteria(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class MaxTimeCriteria(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class MinLengthLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class MinNewTokensLengthLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class MinPLogitsWarper(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class NoBadWordsLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class NoRepeatNGramLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class PhrasalConstraint(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class PrefixConstrainedLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class RepetitionPenaltyLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class SequenceBiasLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class StoppingCriteria(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class StoppingCriteriaList(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class StopStringCriteria(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
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|
|
|
| class SuppressTokensAtBeginLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class SuppressTokensLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class SynthIDTextWatermarkDetector(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class SynthIDTextWatermarkingConfig(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class SynthIDTextWatermarkLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class TemperatureLogitsWarper(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class TopKLogitsWarper(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class TopPLogitsWarper(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class TypicalLogitsWarper(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class UnbatchedClassifierFreeGuidanceLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class WatermarkDetector(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class WatermarkLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class WhisperTimeStampLogitsProcessor(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| class TorchExportableModuleWithStaticCache(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| def convert_and_export_with_cache(*args, **kwargs): |
| requires_backends(convert_and_export_with_cache, ["torch"]) |
|
|
|
|
| class AttentionMaskInterface(metaclass=DummyObject): |
| _backends = ["torch"] |
|
|
| def __init__(self, *args, **kwargs): |
| requires_backends(self, ["torch"]) |
|
|
|
|
| def model_addition_debugger_context(*args, **kwargs): |
| requires_backends(model_addition_debugger_context, ["torch"]) |
|
|
|
|
| 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"]) |
|
|
