




import torch

from accelerate import Accelerator, DistributedType

class        _lowerCamelCase  :

      """simple docstring"""

      def __enter__(   self    :       Tuple ):
          if self.enabled:
              __UpperCamelCase     						=  self.model.no_sync()
              self.model_sync_obj.__enter__()

          return self







      def __exit__(   self    :       List[str]       ,				snake_case    :       Any       ,				snake_case    :       List[Any]       ,				snake_case    :       Optional[int] ):
          if self.enabled:
              # Average all models on exit
              self._sync_and_avg_model_params()
              self.model_sync_obj.__exit__(snake_case       ,				snake_case       ,				snake_case )







      def __init__(   self    :       Optional[int]       ,				snake_case    :       Accelerator       ,				snake_case    :       torch.nn.Module       ,				snake_case    :       int       ,				snake_case    :       bool = True ):
          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     						=  enabled and accelerator.distributed_type != DistributedType.NO
          __UpperCamelCase     						=  0
          if self.enabled:
              __UpperCamelCase     						=  accelerator
              __UpperCamelCase     						=  model
              __UpperCamelCase     						=  local_sgd_steps







      def 					snake_case		(   self    :       List[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 					snake_case		(   self    :       Dict ):
          self.accelerator.wait_for_everyone()
          with self.accelerator.autocast():
              for param in self.model.parameters():
                  __UpperCamelCase     						=  self.accelerator.reduce(param.data       ,				reduction='''mean''' )









def    UpperCamelCase(					lowercase_							)	->				int:





  '''simple docstring'''





  return int(lowercase_							) if x - int(lowercase_							) <= 0 else int(lowercase_							) + 1


if __name__ == "__main__":
    import doctest

    doctest.testmod()