"""simple docstring"""







import torch

from accelerate import Accelerator, DistributedType


class      _lowercase						:

				def __enter__(       self    :      Tuple       ):


										'''simple docstring'''






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

										return self

				def __exit__(       self    :      List[str]    ,      _lowercase    :      Optional[int]    ,      _lowercase    :      str    ,      _lowercase    :      List[str]       ):


										'''simple docstring'''






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

				def __init__(       self    :      Any    ,      _lowercase    :      Dict    ,      _lowercase    :      Any    ,      _lowercase    :      int    ,      _lowercase    :      List[Any] = True       ):


										'''simple docstring'''






										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     A	(       self    :      Optional[Any]       ):


										'''simple docstring'''
										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    :      List[str]       ):


										'''simple docstring'''
										self.accelerator.wait_for_everyone()
										with self.accelerator.autocast():
																for param in self.model.parameters():
																						UpperCAmelCase__   		=						self.accelerator.reduce(param.data    ,      reduction="""mean"""       )


"""simple docstring"""







from __future__ import annotations


def 					a__ (						UpperCAmelCase__    :     int     )->		list[int]:
						assert (
						    isinstance(UpperCAmelCase__						,     UpperCAmelCase__     ) and mask > 0
						), f'mask needs to be positive integer, your input {mask}'
						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						mask

						while submask:
												all_submasks.append(UpperCAmelCase__     )
												UpperCAmelCase__   		=						(submask - 1) & mask

						return all_submasks


if __name__ == "__main__":
						import doctest

						doctest.testmod()

