

class A						:






      def __init__(      self		:  Union[str, Any]    ,     lowercase_		:  List[str]				)      ->		None:
             """simple docstring"""




             _lowerCamelCase :	List[Any]						=data
             _lowerCamelCase :	List[str]						=None
             _lowerCamelCase :	Optional[int]						=None




def 			a_   (					SCREAMING_SNAKE_CASE__      :   List[str]	,    SCREAMING_SNAKE_CASE__      :   int  ):
       '''simple docstring'''





       if node is None:
              _lowerCamelCase :	str						=BinaryTreeNode(SCREAMING_SNAKE_CASE__  )
              return node

       # binary search tree is not empty,
       # so we will insert it into the tree
       # if new_value is less than value of data in node,
       #  add it to left subtree and proceed recursively
       if new_value < node.data:
              _lowerCamelCase :	int						=insert(node.left_child	,    SCREAMING_SNAKE_CASE__  )
       else:
              # if new_value is greater than value of data in node,
              #  add it to right subtree and proceed recursively
              _lowerCamelCase :	Optional[Any]						=insert(node.right_child	,    SCREAMING_SNAKE_CASE__  )
       return node




def 			a_   (					SCREAMING_SNAKE_CASE__      :   List[Any]  ):  # if node is None,return
       '''simple docstring'''





       if node:
              _lowerCamelCase :	Union[str, Any]						=inorder(node.left_child  )
              _lowerCamelCase :	List[str]						=[*inorder_array, node.data]
              _lowerCamelCase :	List[Any]						=inorder_array + inorder(node.right_child  )
       else:
              _lowerCamelCase :	int						=[]
       return inorder_array




def 			a_   (					):
       '''simple docstring'''





       _lowerCamelCase :	int						=insert(SCREAMING_SNAKE_CASE__	,    15  )
       insert(SCREAMING_SNAKE_CASE__	,    10  )
       insert(SCREAMING_SNAKE_CASE__	,    25  )
       insert(SCREAMING_SNAKE_CASE__	,    6  )
       insert(SCREAMING_SNAKE_CASE__	,    14  )
       insert(SCREAMING_SNAKE_CASE__	,    20  )
       insert(SCREAMING_SNAKE_CASE__	,    60  )
       return root




def 			a_   (					):
       '''simple docstring'''





       _lowerCamelCase :	Tuple						=make_tree()
       print('Printing values of binary search tree in Inorder Traversal.'  )
       inorder(SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class A						(     UpperCamelCase_  ):
      UpperCamelCase__							:						Optional[Any]   ='AutoFeatureExtractor'
      UpperCamelCase__							:						List[str]   ='Speech2Text2Tokenizer'






      def __init__(      self		:  str    ,     lowercase_		:  Dict    ,     lowercase_		:  Optional[int]				)      ->		str:
             """simple docstring"""




             super().__init__(lowercase_    ,     lowercase_				)
             _lowerCamelCase :	Optional[Any]						=self.feature_extractor
             _lowerCamelCase :	Tuple						=False






      def __call__(      self		:  Tuple    ,     *lowercase_		:  str    ,     **lowercase_		:  List[Any]				)      ->		List[str]:
             """simple docstring"""




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

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

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






      def  lowerCamelCase (      self		:  str    ,     *lowercase_		:  List[Any]    ,     **lowercase_		:  Any				)      ->		Union[str, Any]:
             """simple docstring"""




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






      def  lowerCamelCase (      self		:  Optional[int]    ,     *lowercase_		:  Dict    ,     **lowercase_		:  Optional[int]				)      ->		str:
             """simple docstring"""




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






      @contextmanager
      def  lowerCamelCase (      self		:  Optional[Any]				)      ->		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.'				)
             _lowerCamelCase :	Optional[Any]						=True
             _lowerCamelCase :	List[Any]						=self.tokenizer
             yield
             _lowerCamelCase :	Optional[int]						=self.feature_extractor
             _lowerCamelCase :	List[str]						=False


