





"""simple docstring"""





import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin




class 							snake_case							(			UpperCamelCase_ ):

      '''simple docstring'''



      _A     :       List[str]         =       'MCTCTFeatureExtractor'
      _A     :       Dict         =       'AutoTokenizer'

      def __init__(							self       :      str						,				__lowercase       :      Optional[Any]						,				__lowercase       :      str ):


        '''simple docstring'''




        super().__init__(__lowercase						,				__lowercase )
        __UpperCAmelCase    :						Tuple       =						self.feature_extractor
        __UpperCAmelCase    :						Union[str, Any]       =						False

      def __call__(							self       :      Optional[int]						,				*__lowercase       :      Optional[int]						,				**__lowercase       :      int ):


        '''simple docstring'''




        if self._in_target_context_manager:
          return self.current_processor(*__lowercase						,				**__lowercase )

        if "raw_speech" in kwargs:
          warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''' )
          __UpperCAmelCase    :						List[Any]       =						kwargs.pop('''raw_speech''' )
        else:
          __UpperCAmelCase    :						List[str]       =						kwargs.pop('''audio'''						,				__lowercase )
        __UpperCAmelCase    :						Union[str, Any]       =						kwargs.pop('''sampling_rate'''						,				__lowercase )
        __UpperCAmelCase    :						List[str]       =						kwargs.pop('''text'''						,				__lowercase )
        if len(__lowercase ) > 0:
          __UpperCAmelCase    :						Optional[Any]       =						args[0]
          __UpperCAmelCase    :						Optional[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:
          __UpperCAmelCase    :						Optional[Any]       =						self.feature_extractor(__lowercase						,				*__lowercase						,				sampling_rate=__lowercase						,				**__lowercase )
        if text is not None:
          __UpperCAmelCase    :						Optional[Any]       =						self.tokenizer(__lowercase						,				**__lowercase )

        if text is None:
          return inputs
        elif audio is None:
          return encodings
        else:
          __UpperCAmelCase    :						Optional[int]       =						encodings['''input_ids''']
          return inputs

      def       A_					(							self       :      str						,				*__lowercase       :      Union[str, Any]						,				**__lowercase       :      Union[str, Any] ):


        '''simple docstring'''




        return self.tokenizer.batch_decode(*__lowercase						,				**__lowercase )

      def       A_					(							self       :      List[str]						,				*__lowercase       :      List[str]						,				**__lowercase       :      Tuple ):


        '''simple docstring'''




        if self._in_target_context_manager:
          return self.current_processor.pad(*__lowercase						,				**__lowercase )

        __UpperCAmelCase    :						Optional[Any]       =						kwargs.pop('''input_features'''						,				__lowercase )
        __UpperCAmelCase    :						int       =						kwargs.pop('''labels'''						,				__lowercase )
        if len(__lowercase ) > 0:
          __UpperCAmelCase    :						List[Any]       =						args[0]
          __UpperCAmelCase    :						Tuple       =						args[1:]

        if input_features is not None:
          __UpperCAmelCase    :						List[str]       =						self.feature_extractor.pad(__lowercase						,				*__lowercase						,				**__lowercase )
        if labels is not None:
          __UpperCAmelCase    :						Tuple       =						self.tokenizer.pad(__lowercase						,				**__lowercase )

        if labels is None:
          return input_features
        elif input_features is None:
          return labels
        else:
          __UpperCAmelCase    :						Union[str, Any]       =						labels['''input_ids''']
          return input_features

      def       A_					(							self       :      Any						,				*__lowercase       :      int						,				**__lowercase       :      List[Any] ):


        '''simple docstring'''




        return self.tokenizer.decode(*__lowercase						,				**__lowercase )







      @contextmanager
      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        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.''' )
        __UpperCAmelCase    :						Any       =						True
        __UpperCAmelCase    :						int       =						self.tokenizer
        yield
        __UpperCAmelCase    :						Optional[int]       =						self.feature_extractor
        __UpperCAmelCase    :						Tuple       =						False






"""simple docstring"""





from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowercase__				:str				   =       (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase__				:list[int]				   =       [ord(letter) for letter in string.ascii_lowercase]
lowercase__				:set[int]				   =       {ord(char) for char in VALID_CHARS}

lowercase__				:list[str]				   =       ["the", "be", "to", "of", "and", "in", "that", "have"]


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->str | None:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						""
  __UpperCAmelCase    :						int
  __UpperCAmelCase    :						int
  __UpperCAmelCase    :						int

  for keychar, cipherchar in zip(cycle(UpperCAmelCase_  )  ,			UpperCAmelCase_  ):
    __UpperCAmelCase    :						Optional[Any]       =						cipherchar ^ keychar
    if decodedchar not in VALID_INTS:
      return None
    decoded += chr(UpperCAmelCase_  )

  return decoded


def       lowerCamelCase_       (   UpperCAmelCase_  )						->list[str]:





  """simple docstring"""


  __UpperCAmelCase    :						list[str]       =						[]
  for key in product(UpperCAmelCase_  ,			repeat=3  ):
    __UpperCAmelCase    :						List[Any]       =						try_key(UpperCAmelCase_  ,			UpperCAmelCase_  )
    if encoded is not None:
      possibles.append(UpperCAmelCase_  )
  return possibles


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->list[str]:





  """simple docstring"""


  return [possible for possible in possibles if common_word in possible.lower()]


def       lowerCamelCase_       (   UpperCAmelCase_ = "p059_cipher.txt"  )						->int:





  """simple docstring"""


  __UpperCAmelCase    :						list[int]
  __UpperCAmelCase    :						list[str]
  __UpperCAmelCase    :						str
  __UpperCAmelCase    :						str
  __UpperCAmelCase    :						str       =						Path(UpperCAmelCase_  ).parent.joinpath(UpperCAmelCase_  ).read_text(encoding='''utf-8'''  )

  __UpperCAmelCase    :						List[str]       =						[int(UpperCAmelCase_  ) for number in data.strip().split(''','''  )]

  __UpperCAmelCase    :						List[Any]       =						filter_valid_chars(UpperCAmelCase_  )
  for common_word in COMMON_WORDS:
    __UpperCAmelCase    :						Optional[int]       =						filter_common_word(UpperCAmelCase_  ,			UpperCAmelCase_  )
    if len(UpperCAmelCase_  ) == 1:
      break

  __UpperCAmelCase    :						Optional[Any]       =						possibles[0]
  return sum(ord(UpperCAmelCase_  ) for char in decoded_text  )


if __name__ == "__main__":
  print(f"""{solution() = }""")