def  __UpperCamelCase    (						A ,							A ,							A ,							A ,							A							):
     if index == number_of_items:
          return 0
     UpperCamelCase__										=      0
     UpperCamelCase__										=      0
     UpperCamelCase__										=      knapsack(A ,							A ,							A ,							A ,							index + 1							)
     if weights[index] <= max_weight:
          UpperCamelCase__										=      values[index] + knapsack(
              A ,							A ,							A ,							max_weight - weights[index] ,							index + 1							)
     return max(A ,							A							)


if __name__ == "__main__":
     import doctest

     doctest.testmod()




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   (    __UpperCamelCase	, __UpperCamelCase							):






  @register_to_config
  def __init__(self   ,				SCREAMING_SNAKE_CASE_ = 768   ,				)		->      Tuple:





       '''simple docstring'''





       super().__init__()

       UpperCamelCase__										=      nn.Parameter(torch.zeros(1   ,				SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      nn.Parameter(torch.ones(1   ,				SCREAMING_SNAKE_CASE_	)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      nn.Parameter(self.mean.to(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      nn.Parameter(self.std.to(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)	)
       return self






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





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



