





import torch

from accelerate import Accelerator, DistributedType






class  __A   :

			'''simple docstring'''







			def __enter__(   self							):



				'''simple docstring'''







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

				return self





			def __exit__(   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				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	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase = 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)'''							)
				lowerCamelCase__        =		enabled and accelerator.distributed_type != DistributedType.NO
				lowerCamelCase__        =		0
				if self.enabled:
					lowerCamelCase__        =		accelerator
					lowerCamelCase__        =		model
					lowerCamelCase__        =		local_sgd_steps





			def 		__lowerCamelCase    (   self							):



				'''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 		__lowerCamelCase    (   self							):



				'''simple docstring'''







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










from __future__ import annotations

import math
import random





def lowerCAmelCase__(__snake_case	,__snake_case	,__snake_case							)    ->				list[int]:







	'''simple docstring'''







	lowerCamelCase__        =		d * e - 1
	lowerCamelCase__        =		0
	lowerCamelCase__        =		0
	while p == 0:
		lowerCamelCase__        =		random.randint(2	,n - 1							)
		lowerCamelCase__        =		k
		while True:
			if t % 2 == 0:
				lowerCamelCase__        =		t // 2
				lowerCamelCase__        =		(g**t) % n
				lowerCamelCase__        =		math.gcd(x - 1	,__snake_case							)
				if x > 1 and y > 1:
					lowerCamelCase__        =		y
					lowerCamelCase__        =		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()



