
import torch

from accelerate import Accelerator, DistributedType
class    _UpperCamelCase:

						def __enter__(					self		:			str			):
													if self.enabled:
																				_UpperCAmelCase  :			int	       =		self.model.no_sync()
																				self.model_sync_obj.__enter__()

													return self

						def __exit__(					self		:			str	,						_lowerCamelCase		:			Union[str, Any]	,						_lowerCamelCase		:			Tuple	,						_lowerCamelCase		:			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		:			str	,						_lowerCamelCase		:			Accelerator	,						_lowerCamelCase		:			torch.nn.Module	,						_lowerCamelCase		:			int	,						_lowerCamelCase		:			bool = True			):
													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  :			Any	       =		enabled and accelerator.distributed_type != DistributedType.NO
													_UpperCAmelCase  :			str	       =		0
													if self.enabled:
																				_UpperCAmelCase  :			Tuple	       =		accelerator
																				_UpperCAmelCase  :			List[str]	       =		model
																				_UpperCAmelCase  :			Dict	       =		local_sgd_steps

						def 						a__ (					self		:			Optional[int]			):
													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		:			Tuple			):
													self.accelerator.wait_for_everyone()
													with self.accelerator.autocast():
																				for param in self.model.parameters():
																											_UpperCAmelCase  :			Optional[Any]	       =		self.accelerator.reduce(param.data	,						reduction="mean"			)






import math




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE  )   ->      list[int]:

							"""simple docstring"""






							if not isinstance(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  ):
														_UpperCAmelCase  :			Optional[Any]	       =		F"""Input value of [number={number}] must be an integer"""
														raise TypeError(_SCREAMING_SNAKE_CASE  )
							if number < 1:
														_UpperCAmelCase  :			Dict	       =		F"""Input value of [number={number}] must be a positive integer"""
														raise ValueError(_SCREAMING_SNAKE_CASE  )
							_UpperCAmelCase  :			List[Any]	       =		[number]
							while number != 1:
														if number % 2 == 0:
																					_UpperCAmelCase  :			List[str]	       =		math.floor(math.sqrt(_SCREAMING_SNAKE_CASE  )  )
														else:
																					_UpperCAmelCase  :			Dict	       =		math.floor(
																					    math.sqrt(_SCREAMING_SNAKE_CASE  ) * math.sqrt(_SCREAMING_SNAKE_CASE  ) * math.sqrt(_SCREAMING_SNAKE_CASE  )  )
														sequence.append(_SCREAMING_SNAKE_CASE  )
							return sequence


if __name__ == "__main__":
				import doctest

				doctest.testmod()




