code: 
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
      lowerCAmelCase_							:		Optional[int]						=							"AutoFeatureExtractor"
      lowerCAmelCase_							:		int						=							"Speech2Text2Tokenizer"
  
  
  
  
  
  
      def __init__(	self			:    Optional[int]    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    Optional[int]    ):
          super().__init__(UpperCAmelCase_    ,				UpperCAmelCase_    )
          lowerCAmelCase				:			Union[str, Any]    			=      self.feature_extractor
          lowerCAmelCase				:			int    			=      False
  
  
  
  
  
  
      def __call__(	self			:    Optional[int]    ,				*UpperCAmelCase_			:    Union[str, Any]    ,				**UpperCAmelCase_			:    Tuple    ):
          # For backward compatibility
          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.'    )
              lowerCAmelCase				:			List[Any]    			=      kwargs.pop('raw_speech'    )
          else:
              lowerCAmelCase				:			Tuple    			=      kwargs.pop('audio'    ,				UpperCAmelCase_    )
          lowerCAmelCase				:			Any    			=      kwargs.pop('sampling_rate'    ,				UpperCAmelCase_    )
          lowerCAmelCase				:			Any    			=      kwargs.pop('text'    ,				UpperCAmelCase_    )
          if len(UpperCAmelCase_    ) > 0:
              lowerCAmelCase				:			Union[str, Any]    			=      args[0]
              lowerCAmelCase				:			List[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:
              lowerCAmelCase				:			Optional[int]    			=      self.feature_extractor(UpperCAmelCase_    ,				*UpperCAmelCase_    ,				sampling_rate=UpperCAmelCase_    ,				**UpperCAmelCase_    )
          if text is not None:
              lowerCAmelCase				:			str    			=      self.tokenizer(UpperCAmelCase_    ,				**UpperCAmelCase_    )
  
          if text is None:
              return inputs
          elif audio is None:
              return encodings
          else:
              lowerCAmelCase				:			Optional[Any]    			=      encodings['input_ids']
              return inputs
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ,				*UpperCAmelCase_			:    str    ,				**UpperCAmelCase_			:    Any    ):
          return self.tokenizer.batch_decode(*UpperCAmelCase_    ,				**UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    int    ,				*UpperCAmelCase_			:    int    ,				**UpperCAmelCase_			:    str    ):
          return self.tokenizer.decode(*UpperCAmelCase_    ,				**UpperCAmelCase_    )
  
  
  
  
  
  
      @contextmanager
      def 	lowercase__ (	self			:    int    ):
          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.'    )
          lowerCAmelCase				:			List[str]    			=      True
          lowerCAmelCase				:			Dict    			=      self.tokenizer
          yield
          lowerCAmelCase				:			Optional[int]    			=      self.feature_extractor
          lowerCAmelCase				:			Dict    			=      False
  
  
  
  
  
  
style_context: 
  
  from __future__ import annotations
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> set[int]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[str]    			=      {1}
      lowerCAmelCase				:			int    			=      [1]
      for _ in range(2,      depth + 1				):
          lowerCAmelCase				:			str    			=      [*previous_coefficients, 0]
          lowerCAmelCase				:			Optional[int]    			=      [0, *previous_coefficients]
          lowerCAmelCase				:			Tuple    			=      []
          for x, y in zip(_UpperCAmelCase,      _UpperCAmelCase				):
              coefficients.add(x + y				)
              previous_coefficients.append(x + y				)
      return coefficients
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> set[int]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			str    			=      set()
      for number in unique_coefficients:
          lowerCAmelCase				:			Dict    			=      2
          lowerCAmelCase				:			Tuple    			=      number
          while divisor**2 <= copy_number:
              lowerCAmelCase				:			List[str]    			=      0
              while copy_number % divisor == 0:
                  copy_number //= divisor
                  multiplicity += 1
              if multiplicity >= 2:
                  non_squarefrees.add(_UpperCAmelCase				)
                  break
              divisor += 1
  
      return unique_coefficients.difference(_UpperCAmelCase				)
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase = 51				)					-> int:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			int    			=      get_pascal_triangle_unique_coefficients(_UpperCAmelCase				)
      lowerCAmelCase				:			Optional[int]    			=      get_squarefrees(_UpperCAmelCase				)
      return sum(_UpperCAmelCase				)
  
  
  if __name__ == "__main__":
   print(F'{solution() = }')
  
  
  
  
  
  