





from string import ascii_uppercase

def      __snake_case						(					_lowerCAmelCase : str				,				_lowerCAmelCase : str				,				_lowerCAmelCase : bool = False				,				_lowerCAmelCase : str = ascii_uppercase	) ->						str:
   A_					:				Union[str, Any]          =			keyword.upper()
   A_					:				int          =			plaintext.upper()
   A_					:				int          =			set(_lowerCAmelCase	)

   # create a list of unique characters in the keyword - their order matters
   # it determines how we will map plaintext characters to the ciphertext
   A_					:				int          =			[]
   for char in keyword:
      if char in alphabet_set and char not in unique_chars:
         unique_chars.append(_lowerCAmelCase	)
    # the number of those unique characters will determine the number of rows
   A_					:				Union[str, Any]          =			len(_lowerCAmelCase	)

   # create a shifted version of the alphabet
   A_					:				Optional[int]          =			unique_chars + [
       char for char in alphabet if char not in unique_chars
   ]

   # create a modified alphabet by splitting the shifted alphabet into rows
   A_					:				int          =			[
       shifted_alphabet[k : k + num_unique_chars_in_keyword]
       for k in range(0				,				26				,				_lowerCAmelCase	)
   ]

   # map the alphabet characters to the modified alphabet characters
   # going 'vertically' through the modified alphabet - consider columns first
   A_					:				List[str]          =			{}
   A_					:				Optional[int]          =			0
   for column in range(_lowerCAmelCase	):
      for row in modified_alphabet:
         # if current row (the last one) is too short, break out of loop
         if len(_lowerCAmelCase	) <= column:
            break

         # map current letter to letter in modified alphabet
         A_					:				List[Any]          =			row[column]
         letter_index += 1

   if verbose:
      print(_lowerCAmelCase	)
   # create the encrypted text by mapping the plaintext to the modified alphabet
   return "".join(mapping[char] if char in mapping else char for char in plaintext	)


if __name__ == "__main__":
 # example use
 print(mixed_keyword('''college''', '''UNIVERSITY'''))







import torch
from torch import autograd


class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Any			,      snake_case       :torch.tensor			,      snake_case       :float			,      snake_case       :bool  ):





        '''simple docstring'''




        A_					:				int          =			inputs.numel()
        A_					:				str          =			int(0.005 * nb_elems  ) + 1
        if sigmoid:
           A_					:				List[Any]          =			(torch.sigmoid(snake_case  ) > threshold).type(inputs.type()  )
        else:
           A_					:				Optional[Any]          =			(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
           A_					:				Tuple          =			inputs.flatten().kthvalue(max(nb_elems - nb_min			,      1  )  ).values
           A_					:				Optional[int]          =			(inputs > k_threshold).type(inputs.type()  )
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Optional[int]			,      snake_case       :int  ):





        '''simple docstring'''




        return gradOutput, None, None




class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Tuple			,      snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				Tuple          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.flatten().sort(descending=snake_case  )
        A_					:				Dict          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				str          =			mask.flatten()
        A_					:				List[Any]          =			0
        A_					:				List[str]          =			1
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :List[Any]			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        return gradOutput, None




class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				str          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.abs().flatten().sort(descending=snake_case  )
        A_					:				int          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				List[str]          =			mask.flatten()
        A_					:				List[str]          =			0
        A_					:				Union[str, Any]          =			1
        return mask
