

import torch
from torch import autograd





class 			__lowerCAmelCase					( autograd.Function							):




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






        _lowercase     	=inputs.numel()
        _lowercase     	=int(0.005 * nb_elems ) + 1
        if sigmoid:
              _lowercase     	=(torch.sigmoid(lowerCAmelCase ) > threshold).type(inputs.type() )
        else:
              _lowercase     	=(inputs > threshold).type(inputs.type() )
        if mask.sum() < nb_min:
              # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
              _lowercase     	=inputs.flatten().kthvalue(max(nb_elems - nb_min							,       1 ) ).values
              _lowercase     	=(inputs > k_threshold).type(inputs.type() )
        return mask




  @staticmethod
  def   A__					(    lowerCAmelCase							,       lowerCAmelCase )  ->			Optional[Any]:
        '''simple docstring'''






        return gradOutput, None, None





class 			__lowerCAmelCase					( autograd.Function							):




  @staticmethod
  def   A__					(    lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=inputs.clone()
        _lowercase	,	_lowercase     	=inputs.flatten().sort(descending=lowerCAmelCase )
        _lowercase     	=int(threshold * inputs.numel() )

        # flat_out and mask access the same memory.
        _lowercase     	=mask.flatten()
        _lowercase     	=0
        _lowercase     	=1
        return mask




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






        return gradOutput, None





class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE							):




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






        _lowercase     	=inputs.clone()
        _lowercase	,	_lowercase     	=inputs.abs().flatten().sort(descending=lowerCAmelCase )
        _lowercase     	=int(threshold * inputs.numel() )

        # flat_out and mask access the same memory.
        _lowercase     	=mask.flatten()
        _lowercase     	=0
        _lowercase     	=1
        return mask





from __future__ import annotations

import random
import string





class 			__lowerCAmelCase					:




  def __init__(    self							,       lowerCAmelCase = None )  ->			None:
        '''simple docstring'''






        _lowercase     	=passcode or self.__passcode_creator()
        _lowercase     	=self.__make_key_list()
        _lowercase     	=self.__make_shift_key()




  def __str__(    self )  ->			str:
        '''simple docstring'''






        return "".join(self.__passcode )




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






        for i in range(1							,       len(lowerCAmelCase )							,       2 ):
              iterlist[i] *= -1
        return iterlist




  def   A__					(    self )  ->			list[str]:
        '''simple docstring'''






        _lowercase     	=string.ascii_letters + string.digits
        _lowercase     	=[random.choice(lowerCAmelCase ) for _ in range(random.randint(10							,       20 ) )]
        return password




  def   A__					(    self )  ->			list[str]:
        '''simple docstring'''






        _lowercase     	=(
            string.ascii_letters + string.digits + string.punctuation + ' \t\n'
        )

        _lowercase     	=[]

        # creates points known as breakpoints to break the key_list_options at those
        # points and pivot each substring
        _lowercase     	=sorted(set(self.__passcode ) )
        _lowercase     	=[]

        # algorithm for creating a new shuffled list, keys_l, out of key_list_options
        for i in key_list_options:
              temp_list.extend(lowerCAmelCase )

              # checking breakpoints at which to pivot temporary sublist and add it into
              # keys_l
              if i in breakpoints or i == key_list_options[-1]:
                    keys_l.extend(temp_list[::-1] )
                    temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
        return keys_l




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






        _lowercase     	=sum(self.__neg_pos([ord(lowerCAmelCase ) for x in self.__passcode] ) )
        return num if num > 0 else len(self.__passcode )




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






        _lowercase     	=''

        # decoding shift like Caesar cipher algorithm implementing negative shift or
        # reverse shift or left shift
        for i in encoded_message:
              _lowercase     	=self.__key_list.index(lowerCAmelCase )
              decoded_message += self.__key_list[
                  (position - self.__shift_key) % -len(self.__key_list )
              ]

        return decoded_message




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






        _lowercase     	=''

        # encoding shift like Caesar cipher algorithm implementing positive shift or
        # forward shift or right shift
        for i in plaintext:
              _lowercase     	=self.__key_list.index(lowerCAmelCase )
              encoded_message += self.__key_list[
                  (position + self.__shift_key) % len(self.__key_list )
              ]

        return encoded_message
def 					a				(      A__     :       str = "Hello, this is a modified Caesar cipher"					)   ->				str:





      """simple docstring"""



      _lowercase     	=ShuffledShiftCipher()
      return cipa.decrypt(cipa.encrypt(A__					)					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()


