'''simple docstring'''





import sys

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			int:
							if not numbers:
														return 0
							if target == 0 and target in numbers:
														return 0
							if not isinstance(snake_case__     ,						(list, tuple)							) or not all(
							    isinstance(snake_case__     ,						snake_case__							) for number in numbers							):
														raise ValueError('''numbers must be an iterable of integers'''							)

							lowerCAmelCase				=	lowerCAmelCase				=	lowerCAmelCase				=	0
							lowerCAmelCase				=	sys.maxsize

							while right < len(snake_case__							):
														curr_sum += numbers[right]
														while curr_sum >= target and left <= right:
																					lowerCAmelCase				=	min(snake_case__     ,						right - left + 1							)
																					curr_sum -= numbers[left]
																					left += 1
														right += 1

							return 0 if min_len == sys.maxsize else min_len




import torch

from accelerate import Accelerator, DistributedType




class      lowercase_						:



	"""simple docstring"""





	def __enter__(							self       )		->Dict:
								if self.enabled:
															lowerCAmelCase				=	self.model.no_sync()
															self.model_sync_obj.__enter__()

								return self


	def __exit__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->Any:
								if self.enabled:
															# Average all models on exit
															self._sync_and_avg_model_params()
															self.model_sync_obj.__exit__(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )


	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = True       )		->Tuple:
								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)'''       )
								lowerCAmelCase				=	enabled and accelerator.distributed_type != DistributedType.NO
								lowerCAmelCase				=	0
								if self.enabled:
															lowerCAmelCase				=	accelerator
															lowerCAmelCase				=	model
															lowerCAmelCase				=	local_sgd_steps


	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[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     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[int]:
								self.accelerator.wait_for_everyone()
								with self.accelerator.autocast():
															for param in self.model.parameters():
																						lowerCAmelCase				=	self.accelerator.reduce(param.data	,    reduction='''mean'''       )



