

'''simple docstring'''







import torch

from accelerate import Accelerator, DistributedType
class 					__a   :

						def __enter__(		self	:						Tuple)						->int:

							"""simple docstring"""



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

							return self

						def __exit__(		self	:						Optional[int]    ,			lowercase__	:						Union[str, Any]    ,			lowercase__	:						Any    ,			lowercase__	:						List[Any])						->Optional[int]:

							"""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	:						Optional[int]    ,			lowercase__	:						Accelerator    ,			lowercase__	:						torch.nn.Module    ,			lowercase__	:						int    ,			lowercase__	:						bool = True)						->List[Any]:

							"""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)""")
							_lowercase							=     enabled and accelerator.distributed_type != DistributedType.NO
							_lowercase							=     0
							if self.enabled:
								_lowercase							=     accelerator
								_lowercase							=     model
								_lowercase							=     local_sgd_steps

						def 	_UpperCAmelCase							(		self	:						Any)						->str:

							"""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 	_UpperCAmelCase							(		self	:						Optional[Any])						->Dict:

							"""simple docstring"""



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







'''simple docstring'''







from __future__ import annotations

import math
import random






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     d * e - 1
	_lowercase							=     0
	_lowercase							=     0
	while p == 0:
		_lowercase							=     random.randint(2							,       n - 1	)
		_lowercase							=     k
		while True:
			if t % 2 == 0:
				_lowercase							=     t // 2
				_lowercase							=     (g**t) % n
				_lowercase							=     math.gcd(x - 1							,       snake_case_	)
				if x > 1 and y > 1:
					_lowercase							=     y
					_lowercase							=     n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




