


class 	a__						:

       def __init__( self   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  Dict				      =     data
            a_     :  int
            a_     :  Node



def 			_a      (    __UpperCamelCase			):
     a_     :  int				      =     0
     a_     :  Optional[Any]				      =     x



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_      ,      a_     :  Dict				      =     find_set(__UpperCamelCase			), find_set(__UpperCamelCase			)
     if x == y:
          return

     elif x.rank > y.rank:
          a_     :  Tuple				      =     x
     else:
          a_     :  Optional[Any]				      =     y
          if x.rank == y.rank:
               y.rank += 1



def 			_a      (    __UpperCamelCase			):
     if x != x.parent:
          a_     :  Tuple				      =     find_set(x.parent			)
     return x.parent



def 			_a      (    __UpperCamelCase			):
     a_     :  List[Any]				      =     ({0, 1, 2}, {3, 4, 5})
     for s in sets:
          if node.data in s:
               return s
     a_     :  Union[str, Any]				      =     F'''{node.data} is not in {sets}'''
     raise ValueError(__UpperCamelCase			)



def 			_a      (    ):
     a_     :  int				      =     [Node(__UpperCamelCase			) for i in range(6			)]
     for v in vertex:
          make_set(__UpperCamelCase			)

     union_set(vertex[0]	,	vertex[1]			)
     union_set(vertex[1]	,	vertex[2]			)
     union_set(vertex[3]	,	vertex[4]			)
     union_set(vertex[3]	,	vertex[5]			)

     for nodea in vertex:
          for nodea in vertex:
               if find_python_set(__UpperCamelCase			).isdisjoint(find_python_set(__UpperCamelCase			)			):
                    assert find_set(__UpperCamelCase			) != find_set(__UpperCamelCase			)
               else:
                    assert find_set(__UpperCamelCase			) == find_set(__UpperCamelCase			)


if __name__ == "__main__":
       test_disjoint_set()






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 					__lowerCamelCase							(      __snake_case      ):
      lowerCamelCase_      : Dict  			=   'AutoFeatureExtractor'
      lowerCamelCase_      : Optional[int]  			=   'Speech2Text2Tokenizer'







      def __init__(	self				,    lowerCamelCase				,    lowerCamelCase  )		->			List[str]:
        super().__init__(lowerCamelCase				,    lowerCamelCase  )
        snake_case_					= self.feature_extractor
        snake_case_					= False







      def __call__(	self				,    *lowerCamelCase				,    **lowerCamelCase  )		->			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."""  )
          snake_case_					= kwargs.pop("""raw_speech"""  )
        else:
          snake_case_					= kwargs.pop("""audio"""				,    lowerCamelCase  )
        snake_case_					= kwargs.pop("""sampling_rate"""				,    lowerCamelCase  )
        snake_case_					= kwargs.pop("""text"""				,    lowerCamelCase  )
        if len(lowerCamelCase  ) > 0:
          snake_case_					= args[0]
          snake_case_					= 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:
          snake_case_					= self.feature_extractor(lowerCamelCase				,    *lowerCamelCase				,    sampling_rate=lowerCamelCase				,    **lowerCamelCase  )
        if text is not None:
          snake_case_					= self.tokenizer(lowerCamelCase				,    **lowerCamelCase  )

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







      def       lowerCAmelCase_       (	self				,    *lowerCamelCase				,    **lowerCamelCase  )		->			int:
        return self.tokenizer.batch_decode(*lowerCamelCase				,    **lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    *lowerCamelCase				,    **lowerCamelCase  )		->			Union[str, Any]:
        return self.tokenizer.decode(*lowerCamelCase				,    **lowerCamelCase  )







      @contextmanager
      def       lowerCAmelCase_       (	self  )		->			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."""  )
        snake_case_					= True
        snake_case_					= self.tokenizer
        yield
        snake_case_					= self.feature_extractor
        snake_case_					= False