

import random





class 					__lowerCAmelCase	:




  @staticmethod
  def     A__	(							lowerCAmelCase   )    ->					tuple[list[int], list[int]]:
        '''simple docstring'''






        _lowercase      			=[ord(lowerCAmelCase   ) for i in text]
        _lowercase      			=[]
        _lowercase      			=[]
        for i in plain:
              _lowercase      			=random.randint(1				,  300   )
              _lowercase      			=(i + k) * k
              cipher.append(lowerCAmelCase   )
              key.append(lowerCAmelCase   )
        return cipher, key




  @staticmethod
  def     A__	(							lowerCAmelCase				,  lowerCAmelCase   )    ->					str:
        '''simple docstring'''






        _lowercase      			=[]
        for i in range(len(lowerCAmelCase   )   ):
              _lowercase      			=int((cipher[i] - (key[i]) ** 2) / key[i]   )
              plain.append(chr(lowerCAmelCase   )   )
        return "".join(lowerCAmelCase   )


if __name__ == "__main__":
  lowercase_							,		lowercase_   							=  Onepad().encrypt('Hello')
  print(c, k)
  print(Onepad().decrypt(c, k))





import flax.linen as nn
import jax
import jax.numpy as jnp





class 					__lowerCAmelCase	(   nn.Module				):
  _a		     =    42
  _a		     =    jnp.floataa




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase      			=nn.Conv(
            self.out_channels				,  kernel_size=(3, 3)				,  strides=(1, 1)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )




  def __call__(							self				,  lowerCAmelCase   )    ->					Any:
        '''simple docstring'''






        _lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=hidden_states.shape
        _lowercase      			=jax.image.resize(
            lowerCAmelCase				,  shape=(batch, height * 2, width * 2, channels)				,  method='nearest'				,  )
        _lowercase      			=self.conv(lowerCAmelCase   )
        return hidden_states





class 					__lowerCAmelCase	(   nn.Module				):
  _a		     =    42
  _a		     =    jnp.floataa




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=nn.Conv(
            self.out_channels				,  kernel_size=(3, 3)				,  strides=(2, 2)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )




  def __call__(							self				,  lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.conv(lowerCAmelCase   )
        return hidden_states





class 					__lowerCAmelCase	(   nn.Module				):
  _a		     =    42
  _a		     =    None
  _a		     =    0.0
  _a		     =    None
  _a		     =    jnp.floataa




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.in_channels if self.out_channels is None else self.out_channels

        _lowercase      			=nn.GroupNorm(num_groups=32				,  epsilon=1e-5   )
        _lowercase      			=nn.Conv(
            lowerCAmelCase				,  kernel_size=(3, 3)				,  strides=(1, 1)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )

        _lowercase      			=nn.Dense(lowerCAmelCase				,  dtype=self.dtype   )

        _lowercase      			=nn.GroupNorm(num_groups=32				,  epsilon=1e-5   )
        _lowercase      			=nn.Dropout(self.dropout_prob   )
        _lowercase      			=nn.Conv(
            lowerCAmelCase				,  kernel_size=(3, 3)				,  strides=(1, 1)				,  padding=((1, 1), (1, 1))				,  dtype=self.dtype				,  )

        _lowercase      			=self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

        _lowercase      			=None
        if use_nin_shortcut:
              _lowercase      			=nn.Conv(
                  lowerCAmelCase				,  kernel_size=(1, 1)				,  strides=(1, 1)				,  padding='VALID'				,  dtype=self.dtype				,  )




  def __call__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase=True   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=hidden_states
        _lowercase      			=self.norma(lowerCAmelCase   )
        _lowercase      			=nn.swish(lowerCAmelCase   )
        _lowercase      			=self.conva(lowerCAmelCase   )

        _lowercase      			=self.time_emb_proj(nn.swish(lowerCAmelCase   )   )
        _lowercase      			=jnp.expand_dims(jnp.expand_dims(lowerCAmelCase				,  1   )				,  1   )
        _lowercase      			=hidden_states + temb

        _lowercase      			=self.norma(lowerCAmelCase   )
        _lowercase      			=nn.swish(lowerCAmelCase   )
        _lowercase      			=self.dropout(lowerCAmelCase				,  lowerCAmelCase   )
        _lowercase      			=self.conva(lowerCAmelCase   )

        if self.conv_shortcut is not None:
              _lowercase      			=self.conv_shortcut(lowerCAmelCase   )

        return hidden_states + residual


