



"""simple docstring"""




from string import ascii_uppercase


def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase = False	,      _UpperCAmelCase = ascii_uppercase				)     ->			str:






      '''simple docstring'''


      lowercase  :      List[Any]					=   keyword.upper()
      lowercase  :      int					=   plaintext.upper()
      lowercase  :      Optional[int]					=   set(_UpperCAmelCase				)

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

      # create a shifted version of the alphabet
      lowercase  :      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
      lowercase  :      int					=   [
          shifted_alphabet[k : k + num_unique_chars_in_keyword]
          for k in range(0	,      26	,      _UpperCAmelCase				)
      ]

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

                  # map current letter to letter in modified alphabet
                  lowercase  :      str					=   row[column]
                  letter_index += 1

      if verbose:
            print(_UpperCAmelCase				)
      # 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'))





"""simple docstring"""




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				'AutoFeatureExtractor'
   _lowerCamelCase												=				'Speech2Text2Tokenizer'


   def __init__(							self      :							str,     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							Optional[Any]			)    ->       Dict:
         super().__init__(lowerCAmelCase,     lowerCAmelCase			)
         lowercase  :      Tuple					=   self.feature_extractor
         lowercase  :      Any					=   False


   def __call__(							self      :							Tuple,     *lowerCAmelCase      :							Tuple,     **lowerCAmelCase      :							Tuple			)    ->       Dict:
         # For backward compatibility
         if self._in_target_context_manager:
               return self.current_processor(*lowerCAmelCase,     **lowerCAmelCase			)

         if "raw_speech" in kwargs:
               warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'			)
               lowercase  :      str					=   kwargs.pop('raw_speech'			)
         else:
               lowercase  :      str					=   kwargs.pop('audio',     lowerCAmelCase			)
         lowercase  :      Dict					=   kwargs.pop('sampling_rate',     lowerCAmelCase			)
         lowercase  :      Optional[Any]					=   kwargs.pop('text',     lowerCAmelCase			)
         if len(lowerCAmelCase			) > 0:
               lowercase  :      Any					=   args[0]
               lowercase  :      int					=   args[1:]

         if audio is None and text is None:
               raise ValueError('You need to specify either an `audio` or `text` input to process.'			)

         if audio is not None:
               lowercase  :      List[Any]					=   self.feature_extractor(lowerCAmelCase,     *lowerCAmelCase,     sampling_rate=lowerCAmelCase,     **lowerCAmelCase			)
         if text is not None:
               lowercase  :      str					=   self.tokenizer(lowerCAmelCase,     **lowerCAmelCase			)

         if text is None:
               return inputs
         elif audio is None:
               return encodings
         else:
               lowercase  :      Tuple					=   encodings['input_ids']
               return inputs


   def        lowercase				(							self      :							str,     *lowerCAmelCase      :							List[str],     **lowerCAmelCase      :							Union[str, Any]			)    ->       str:
         return self.tokenizer.batch_decode(*lowerCAmelCase,     **lowerCAmelCase			)


   def        lowercase				(							self      :							Tuple,     *lowerCAmelCase      :							Union[str, Any],     **lowerCAmelCase      :							int			)    ->       Dict:
         return self.tokenizer.decode(*lowerCAmelCase,     **lowerCAmelCase			)






   @contextmanager
   def        lowercase				(							self      :							str			)    ->       Any:
         warnings.warn(
             '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
             'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
             'your audio inputs, or in a separate call.'			)
         lowercase  :      Optional[Any]					=   True
         lowercase  :      Any					=   self.tokenizer
         yield
         lowercase  :      Any					=   self.feature_extractor
         lowercase  :      Any					=   False
