




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : List[str]			       ="""Speech2TextFeatureExtractor"""
  UpperCAmelCase__      : List[str]			       ="""Speech2TextTokenizer"""


  def __init__(			self    : Dict				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : int		)					->Tuple:
        """simple docstring"""







        super().__init__(UpperCAmelCase__				,				UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Dict       =   self.feature_extractor
        SCREAMING_SNAKE_CASE :      List[str]       =   False


  def __call__(			self    : str				,				*UpperCAmelCase__    : str				,				**UpperCAmelCase__    : Tuple		)					->Optional[int]:
        """simple docstring"""







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

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

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


  def  _lowercase      (			self    : List[Any]				,				*UpperCAmelCase__    : Dict				,				**UpperCAmelCase__    : Dict		)					->Tuple:
        """simple docstring"""







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


  def  _lowercase      (			self    : int				,				*UpperCAmelCase__    : List[Any]				,				**UpperCAmelCase__    : Dict		)					->int:
        """simple docstring"""







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




  @contextmanager
  def  _lowercase      (			self    : Tuple		)					->Optional[Any]:
        """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."""		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   True
        SCREAMING_SNAKE_CASE :      Tuple       =   self.tokenizer
        yield
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.feature_extractor
        SCREAMING_SNAKE_CASE :      Any       =   False







def 					__lowercase      (     _A = 50			)     ->						int:
      SCREAMING_SNAKE_CASE :      Tuple       =   [1] * (length + 1)

      for row_length in range(3						,    length + 1			):
            for block_length in range(3						,    row_length + 1			):
                  for block_start in range(row_length - block_length			):
                        ways_number[row_length] += ways_number[
                            row_length - block_start - block_length - 1
                        ]

                  ways_number[row_length] += 1

      return ways_number[length]


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




