






"""simple docstring"""


import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					'''MCTCTFeatureExtractor'''
    __magic_name__   					=					'''AutoTokenizer'''





    def __init__(							self     : List[Any]    ,		A     : Optional[Any]    ,		A     : Optional[int]      ):


      '''simple docstring'''




      super().__init__(A    ,		A      )
      a					:      Optional[int]    =       self.feature_extractor
      a					:      Any    =       False





    def __call__(							self     : List[Any]    ,		*A     : str    ,		**A     : Tuple      ):


      '''simple docstring'''




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

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

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





    def     lowerCamelCase__				(							self     : Union[str, Any]    ,		*A     : List[str]    ,		**A     : Dict      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : Dict    ,		*A     : str    ,		**A     : List[str]      ):


      '''simple docstring'''




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

      a					:      Tuple    =       kwargs.pop('input_features'    ,		A      )
      a					:      str    =       kwargs.pop('labels'    ,		A      )
      if len(A      ) > 0:
        a					:      Tuple    =       args[0]
        a					:      Optional[Any]    =       args[1:]

      if input_features is not None:
        a					:      Any    =       self.feature_extractor.pad(A    ,		*A    ,		**A      )
      if labels is not None:
        a					:      Dict    =       self.tokenizer.pad(A    ,		**A      )

      if labels is None:
        return input_features
      elif input_features is None:
        return labels
      else:
        a					:      str    =       labels['input_ids']
        return input_features





    def     lowerCamelCase__				(							self     : Optional[int]    ,		*A     : int    ,		**A     : Union[str, Any]      ):


      '''simple docstring'''




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





    @contextmanager
    def     lowerCamelCase__				(							self     : Tuple      ):


      '''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.'      )
      a					:      Tuple    =       True
      a					:      Any    =       self.tokenizer
      yield
      a					:      Union[str, Any]    =       self.feature_extractor
      a					:      Optional[Any]    =       False












"""simple docstring"""


def       snake_case  (A_				:int				):







  '''simple docstring'''

  if isinstance(A_     ,      A_				):
    raise TypeError('\'float\' object cannot be interpreted as an integer'				)
  if isinstance(A_     ,      A_				):
    raise TypeError('\'str\' object cannot be interpreted as an integer'				)

  if num == 0:
    return "0b0"

  a					:      List[Any]    =       False

  if num < 0:
    a					:      Optional[int]    =       True
    a					:      Dict    =       -num

  a					:      list[int]    =       []
  while num > 0:
    binary.insert(0     ,      num % 2				)
    num >>= 1

  if negative:
    return "-0b" + "".join(str(A_				) for e in binary				)

  return "0b" + "".join(str(A_				) for e in binary				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()




