

'''simple docstring'''

import torch

from accelerate import Accelerator, DistributedType




class 			A_					:






     '''simple docstring'''



     def __enter__(						self					:	Tuple			)		->      str:




      if self.enabled:
       UpperCAmelCase			:      Dict		   =					self.model.no_sync()
       self.model_sync_obj.__enter__()

      return self


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




      if self.enabled:
       # Average all models on exit
       self._sync_and_avg_model_params()
       self.model_sync_obj.__exit__(__lowerCamelCase	,     __lowerCamelCase	,     __lowerCamelCase			)


     def __init__(						self					:	Union[str, Any]	,     lowercase_					:	Accelerator	,     lowercase_					:	torch.nn.Module	,     lowercase_					:	int	,     lowercase_					:	bool = True			)		->      str:




      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			:      Optional[Any]		   =					enabled and accelerator.distributed_type != DistributedType.NO
      UpperCAmelCase			:      List[Any]		   =					0
      if self.enabled:
       UpperCAmelCase			:      Optional[int]		   =					accelerator
       UpperCAmelCase			:      List[Any]		   =					model
       UpperCAmelCase			:      int		   =					local_sgd_steps


     def   UpperCAmelCase_			(						self					:	Union[str, Any]			)		->      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					:	Any			)		->      str:
      self.accelerator.wait_for_everyone()
      with self.accelerator.autocast():
       for param in self.model.parameters():
        UpperCAmelCase			:      Optional[int]		   =					self.accelerator.reduce(param.data	,     reduction='mean'			)












'''simple docstring'''





def  lowerCamelCase_  (     lowercase__					,  lowercase__):
 lowerCamelCase__	=  len(lowercase__)
 lowerCamelCase__	=  len(lowercase__)
 lowerCamelCase__	=  (
     first_input_list_length
     if first_input_list_length > second_input_list_length
     else second_input_list_length
 )
 lowerCamelCase__	=  []
 for char_count in range(lowercase__):
  if char_count < first_input_list_length:
   output_result_list.append(first_input_list[char_count])
  if char_count < second_input_list_length:
   output_result_list.append(second_input_list[char_count])

 return output_result_list


if __name__ == "__main__":
 print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)

