




class 			_lowerCamelCase    :

      """simple docstring"""

      def __init__(  self       :			Optional[Any]			,							snake_case       :			int					):
          __UpperCamelCase			=    data
          __UpperCamelCase			=    42
          __UpperCamelCase			=    42







def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						None:

    """simple docstring"""


    __UpperCamelCase			=    0
    __UpperCamelCase			=    x




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						None:

    """simple docstring"""


    __UpperCamelCase		,   __UpperCamelCase			=    find_set(lowercase_   ), find_set(lowercase_   )
    if x == y:
        return

    elif x.rank > y.rank:
        __UpperCamelCase			=    x
    else:
        __UpperCamelCase			=    y
        if x.rank == y.rank:
            y.rank += 1




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Node:

    """simple docstring"""


    if x != x.parent:
        __UpperCamelCase			=    find_set(x.parent   )
    return x.parent




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						set:

    """simple docstring"""


    __UpperCamelCase			=    ({0, 1, 2}, {3, 4, 5})
    for s in sets:
        if node.data in s:
            return s
    __UpperCamelCase			=    F"{node.data} is not in {sets}"
    raise ValueError(lowercase_   )




def        __SCREAMING_SNAKE_CASE (	)							->						None:

    """simple docstring"""


    __UpperCamelCase			=    [Node(lowercase_   ) for i in range(6   )]
    for v in vertex:
        make_set(lowercase_   )

    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(lowercase_   ).isdisjoint(find_python_set(lowercase_   )   ):
                assert find_set(lowercase_   ) != find_set(lowercase_   )
            else:
                assert find_set(lowercase_   ) == find_set(lowercase_   )


if __name__ == "__main__":
 test_disjoint_set()












'''simple docstring'''


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 						lowerCAmelCase_	(       UpperCAmelCase_							):







  '''simple docstring'''

  UpperCamelCase_			:      Optional[int]										=				"""AutoFeatureExtractor"""
  UpperCamelCase_			:      List[Any]										=				"""Speech2Text2Tokenizer"""



  def __init__( self				:							str					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							int	)			->		Any:







       '''simple docstring'''



       super().__init__(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       A:		Union[str, Any]          =		self.feature_extractor
       A:		Dict          =		False



  def __call__( self				:							Any					,  *SCREAMING_SNAKE_CASE_				:							Any					,  **SCREAMING_SNAKE_CASE_				:							Optional[int]	)			->		Optional[Any]:







       '''simple docstring'''



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

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

       if text is None:
            return inputs
       elif audio is None:
            return encodings
       else:
            A:		str          =		encodings['''input_ids''']
            return inputs



  def 					_snake_case     ( self				:							Tuple					,  *SCREAMING_SNAKE_CASE_				:							int					,  **SCREAMING_SNAKE_CASE_				:							Union[str, Any]	)			->		str:







       '''simple docstring'''



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



  def 					_snake_case     ( self				:							Optional[int]					,  *SCREAMING_SNAKE_CASE_				:							str					,  **SCREAMING_SNAKE_CASE_				:							int	)			->		Union[str, Any]:







       '''simple docstring'''



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




  @contextmanager
  def 					_snake_case     ( self				:							str	)			->		int:







       '''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:		List[Any]          =		True
       A:		List[str]          =		self.tokenizer
       yield
       A:		List[Any]          =		self.feature_extractor
       A:		int          =		False






