


import torch

from accelerate import Accelerator, DistributedType




class  A_ :







 """simple docstring"""





 def __enter__(      self       :			str					)     ->      List[str]:






      if self.enabled:
           UpperCAmelCase    :					Optional[int]			=      self.model.no_sync()
           self.model_sync_obj.__enter__()

      return self
 def __exit__(      self       :			Optional[Any]							,     _a       :			List[Any]							,     _a       :			List[str]							,     _a       :			Tuple					)     ->      Union[str, Any]:






      if self.enabled:
           # Average all models on exit
           self._sync_and_avg_model_params()
           self.model_sync_obj.__exit__(_lowercase							,     _lowercase							,     _lowercase					)
 def __init__(      self       :			Tuple							,     _a       :			Accelerator							,     _a       :			torch.nn.Module							,     _a       :			int							,     _a       :			bool = True					)     ->      Dict:






      if accelerator.distributed_type not in [
          DistributedType.NO,
          DistributedType.MULTI_CPU,
          DistributedType.MULTI_GPU,
      ]:
           raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"					)
      UpperCAmelCase    :					Dict			=      enabled and accelerator.distributed_type != DistributedType.NO
      UpperCAmelCase    :					Optional[Any]			=      0
      if self.enabled:
           UpperCAmelCase    :					Tuple			=      accelerator
           UpperCAmelCase    :					Union[str, Any]			=      model
           UpperCAmelCase    :					Dict			=      local_sgd_steps
 def 	_UpperCamelCase     (      self       :			int					)     ->      Any:
      self.num_steps += 1
      if not self.enabled:
           return

      if self.num_steps % self.local_sgd_steps == 0:
           self._sync_and_avg_model_params()

 def 	_UpperCamelCase     (      self       :			Tuple					)     ->      Dict:
      self.accelerator.wait_for_everyone()
      with self.accelerator.autocast():
           for param in self.model.parameters():
                UpperCAmelCase    :					List[str]			=      self.accelerator.reduce(param.data							,     reduction="mean"					)




from __future__ import annotations



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			str      )				->   bool:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= 42
  SCREAMING_SNAKE_CASE__     				= list(__UpperCamelCase      )
  SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= _vector[:-1], int(_vector[-1]      )
  SCREAMING_SNAKE_CASE__     				= [int(__UpperCamelCase      ) for digit in __vector]

  vector.reverse()
  for i, digit in enumerate(__UpperCamelCase      ):
    if i & 1 == 0:
      SCREAMING_SNAKE_CASE__     				= digit * 2
      if doubled > 9:
        doubled -= 9
      check_digit += doubled
    else:
      check_digit += digit

  return check_digit % 10 == 0


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   assert is_luhn('''79927398713''')
   assert not is_luhn('''79927398714''')
