


'''simple docstring'''



import torch

from accelerate import Accelerator, DistributedType





class      UpperCAmelCase_						:





    """simple docstring"""






    def __enter__(							self			)       ->  List[Any]:
         if self.enabled:
              __lowerCamelCase     :			Optional[Any]              =	self.model.no_sync()
              self.model_sync_obj.__enter__()

         return self







    def __exit__(							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  int:
         if self.enabled:
              # Average all models on exit
              self._sync_and_avg_model_params()
              self.model_sync_obj.__exit__(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)







    def __init__(							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = True			)       ->  int:
         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)'			)
         __lowerCamelCase     :			Dict              =	enabled and accelerator.distributed_type != DistributedType.NO
         __lowerCamelCase     :			int              =	0
         if self.enabled:
              __lowerCamelCase     :			Optional[Any]              =	accelerator
              __lowerCamelCase     :			Tuple              =	model
              __lowerCamelCase     :			Tuple              =	local_sgd_steps







    def    lowercase_       (							self			)       ->  List[str]:
         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    lowercase_       (							self			)       ->  List[Any]:
         self.accelerator.wait_for_everyone()
         with self.accelerator.autocast():
              for param in self.model.parameters():
                   __lowerCamelCase     :			List[Any]              =	self.accelerator.reduce(param.data    ,  reduction='mean'			)










'''simple docstring'''



def     UpperCAmelCase__      (			UpperCAmelCase_					:       str							)  ->	int:
     __lowerCamelCase     :			Tuple              =	str(UpperCAmelCase_							).strip()
     if not bin_string:
          raise ValueError('Empty string was passed to the function'							)
     __lowerCamelCase     :			Optional[int]              =	bin_string[0] == '-'
     if is_negative:
          __lowerCamelCase     :			List[Any]              =	bin_string[1:]
     if not all(char in '01' for char in bin_string							):
          raise ValueError('Non-binary value was passed to the function'							)
     __lowerCamelCase     :			Optional[int]              =	0
     for char in bin_string:
          __lowerCamelCase     :			str              =	2 * decimal_number + int(UpperCAmelCase_							)
     return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
      from doctest import testmod

      testmod()






