
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"						)






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()




