

'''simple docstring'''
import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu







@require_cpu
class 							A__    (       unittest.TestCase  ):





  def 						A  (		self      :	Dict )	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =torch.nn.Linear(10							,					10 )
       _SCREAMING_SNAKE_CASE							    =torch.optim.SGD(model.parameters()							,					0.1 )
       _SCREAMING_SNAKE_CASE							    =Accelerator()
       _SCREAMING_SNAKE_CASE							    =accelerator.prepare(_a )
       try:
            pickle.loads(pickle.dumps(_a ) )
       except Exception as e:
            self.fail(f"Accelerated optimizer pickling failed with {e}" )
       AcceleratorState._reset_state()



'''simple docstring'''
from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin







class 							A__    (       A__     ,   A__  ):





  @register_to_config
  def __init__(		self      :	Dict							,					_a      :	int = 768							,					)	->    Union[str, Any]:





       '''simple docstring'''






       super().__init__()

       _SCREAMING_SNAKE_CASE							    =nn.Parameter(torch.zeros(1							,					_a ) )
       _SCREAMING_SNAKE_CASE							    =nn.Parameter(torch.ones(1							,					_a ) )





  def 						A  (		self      :	Tuple							,					_a      :	Optional[Union[str, torch.device]] = None							,					_a      :	Optional[torch.dtype] = None							,					)	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =nn.Parameter(self.mean.to(_a ).to(_a ) )
       _SCREAMING_SNAKE_CASE							    =nn.Parameter(self.std.to(_a ).to(_a ) )
       return self





  def 						A  (		self      :	Tuple							,					_a      :	str )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =(embeds - self.mean) * 1.0 / self.std
       return embeds





  def 						A  (		self      :	List[str]							,					_a      :	Optional[Any] )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =(embeds * self.std) + self.mean
       return embeds
