code: 
  import torch
  
  from accelerate import Accelerator, DistributedType
  
  
  
  
  class      __lowercase	:
  
  
  
  
  
  
  	def __enter__(self							:   str						)   -> List[str]:
  							if self.enabled:
  													_lowercase					:  List[str]    			=      self.model.no_sync()
  													self.model_sync_obj.__enter__()
  
  							return self
  
  
  
  
  
  
  	def __exit__(self							:   List[Any]			,  snake_case							:   List[str]			,  snake_case							:   List[Any]			,  snake_case							:   Dict						)   -> Optional[Any]:
  							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							:   int			,  snake_case							:   Accelerator			,  snake_case							:   torch.nn.Module			,  snake_case							:   int			,  snake_case							:   bool = True						)   -> Any:
  							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)"						)
  							_lowercase					:  List[Any]    			=      enabled and accelerator.distributed_type != DistributedType.NO
  							_lowercase					:  List[Any]    			=      0
  							if self.enabled:
  													_lowercase					:  Tuple    			=      accelerator
  													_lowercase					:  str    			=      model
  													_lowercase					:  Optional[Any]    			=      local_sgd_steps
  
  
  
  
  
  
  	def 			_a(self							:   Optional[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 			_a(self							:   Dict						)   -> Optional[Any]:
  							self.accelerator.wait_for_everyone()
  							with self.accelerator.autocast():
  													for param in self.model.parameters():
  																			_lowercase					:  List[Any]    			=      self.accelerator.reduce(param.data			,  reduction="mean"						)
  
  
  
  
  
style_context: 
  def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> bool:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						if not isinstance(_UpperCAmelCase   , _UpperCAmelCase     ):
  												_lowercase					:  Optional[int]    			=      f"""Input value of [number={number}] must be an integer"""
  												raise TypeError(_UpperCAmelCase     )
  						if number < 0 or number % 2 == 1:
  												return False
  						_lowercase					:  List[str]    			=      int(number**0.5     )
  						return number == number_sqrt * (number_sqrt + 1)
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  
  
  
  
  