




import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")

class 		__lowerCAmelCase	:
							def __init__(						self						:Optional[int]		,      __magic_name__						:List[Any]		,      __magic_name__						:str		,      __magic_name__						:Any = True		,      __magic_name__						:Optional[int] = False				):







								'''simple docstring'''







								a										=							scheduler
								a										=							optimizers if isinstance(__UpperCAmelCase		,      (list, tuple)				) else [optimizers]
								a										=							split_batches
								a										=							step_with_optimizer
								a										=							GradientState()
							def       lowerCamelCase__							(						self						:int		,      *__magic_name__						:int		,      **__magic_name__						:Tuple				):







								'''simple docstring'''







								if not self.step_with_optimizer:
									# No link between scheduler and optimizer -> just step
									self.scheduler.step(*__UpperCAmelCase		,      **__UpperCAmelCase				)
									return

								# Otherwise, first make sure the optimizer was stepped.
								if not self.gradient_state.sync_gradients:
									if self.gradient_state.adjust_scheduler:
										self.scheduler._step_count += 1
									return

								for opt in self.optimizers:
									if opt.step_was_skipped:
										return
								if self.split_batches:
									# Split batches -> the training dataloader batch size is not changed so one step per training step
									self.scheduler.step(*__UpperCAmelCase		,      **__UpperCAmelCase				)
								else:
									# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
									# num_processes steps per training step
									a										=							AcceleratorState().num_processes
									for _ in range(__UpperCAmelCase				):
										# Special case when using OneCycle and `drop_last` was not used
										if hasattr(self.scheduler		,      """total_steps"""				):
											if self.scheduler._step_count <= self.scheduler.total_steps:
												self.scheduler.step(*__UpperCAmelCase		,      **__UpperCAmelCase				)
										else:
											self.scheduler.step(*__UpperCAmelCase		,      **__UpperCAmelCase				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								return self.scheduler.get_last_lr()
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								return self.scheduler.state_dict()
							def       lowerCamelCase__							(						self						:int		,      __magic_name__						:Optional[Any]				):







								'''simple docstring'''







								self.scheduler.load_state_dict(__UpperCAmelCase				)
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								return self.scheduler.get_lr()





							def       lowerCamelCase__							(						self						:Any		,      *__magic_name__						:Optional[int]		,      **__magic_name__						:str				):







								'''simple docstring'''







								return self.scheduler.print_lr(*__UpperCAmelCase		,      **__UpperCAmelCase				)








"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__A											=		{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		["""ReformerTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		["""ReformerTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		[
      """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ReformerAttention""",
      """ReformerForMaskedLM""",
      """ReformerForQuestionAnswering""",
      """ReformerForSequenceClassification""",
      """ReformerLayer""",
      """ReformerModel""",
      """ReformerModelWithLMHead""",
      """ReformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_reformer import ReformerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_reformer_fast import ReformerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_reformer import (
        REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        ReformerAttention,
        ReformerForMaskedLM,
        ReformerForQuestionAnswering,
        ReformerForSequenceClassification,
        ReformerLayer,
        ReformerModel,
        ReformerModelWithLMHead,
        ReformerPreTrainedModel,
    )

else:
  import sys

  __A											=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
