






"""simple docstring"""
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class 						a_ (      snake_case_					):







      '''simple docstring'''

      lowerCamelCase__			:							Union[str, Any]					=      'AutoFeatureExtractor'
      lowerCamelCase__			:							Optional[int]					=      'Speech2Text2Tokenizer'




      def __init__(self,	lowerCamelCase_,	lowerCamelCase_   ):

       '''simple docstring'''



       super().__init__(lowerCamelCase_,	lowerCamelCase_   )
       lowerCamelCase__	:     Optional[int]								=		self.feature_extractor
       lowerCamelCase__	:     str								=		False




      def __call__(self,	*lowerCamelCase_,	**lowerCamelCase_   ):

       '''simple docstring'''



       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.'   )
        lowerCamelCase__	:     str								=		kwargs.pop('raw_speech'   )
       else:
        lowerCamelCase__	:     str								=		kwargs.pop('audio',	lowerCamelCase_   )
       lowerCamelCase__	:     Optional[int]								=		kwargs.pop('sampling_rate',	lowerCamelCase_   )
       lowerCamelCase__	:     Union[str, Any]								=		kwargs.pop('text',	lowerCamelCase_   )
       if len(lowerCamelCase_   ) > 0:
        lowerCamelCase__	:     Tuple								=		args[0]
        lowerCamelCase__	:     Union[str, 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__	:     int								=		self.feature_extractor(lowerCamelCase_,	*lowerCamelCase_,	sampling_rate=lowerCamelCase_,	**lowerCamelCase_   )
       if text is not None:
        lowerCamelCase__	:     Dict								=		self.tokenizer(lowerCamelCase_,	**lowerCamelCase_   )

       if text is None:
        return inputs
       elif audio is None:
        return encodings
       else:
        lowerCamelCase__	:     Optional[Any]								=		encodings['input_ids']
        return inputs




      def      a__				(self,	*lowerCamelCase_,	**lowerCamelCase_   ):

       '''simple docstring'''



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




      def      a__				(self,	*lowerCamelCase_,	**lowerCamelCase_   ):

       '''simple docstring'''



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





      @contextmanager
      def      a__				(self   ):

       '''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__	:     Tuple								=		True
       lowerCamelCase__	:     Dict								=		self.tokenizer
       yield
       lowerCamelCase__	:     List[Any]								=		self.feature_extractor
       lowerCamelCase__	:     Optional[Any]								=		False









"""simple docstring"""
class 						a_ :







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     Dict								=		data
       lowerCamelCase__	:     BinaryTreeNode | None								=		None
       lowerCamelCase__	:     BinaryTreeNode | None								=		None






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase							):
 if node is None:
  lowerCamelCase__	:     List[str]								=		BinaryTreeNode(_lowerCamelCase							)
  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__	:     str								=		insert(node.left_child     ,  _lowerCamelCase							)
 else:
  # if new_value is greater than value of data in node,
  #  add it to right subtree and proceed recursively
  lowerCamelCase__	:     str								=		insert(node.right_child     ,  _lowerCamelCase							)
 return node






def 			lowerCamelCase_    (		_lowerCamelCase							):  # if node is None,return
 if node:
  lowerCamelCase__	:     Any								=		inorder(node.left_child							)
  lowerCamelCase__	:     Tuple								=		[*inorder_array, node.data]
  lowerCamelCase__	:     List[Any]								=		inorder_array + inorder(node.right_child							)
 else:
  lowerCamelCase__	:     Any								=		[]
 return inorder_array






def 			lowerCamelCase_    (		):
 lowerCamelCase__	:     Tuple								=		insert(_lowerCamelCase     ,  15							)
 insert(_lowerCamelCase     ,  10							)
 insert(_lowerCamelCase     ,  25							)
 insert(_lowerCamelCase     ,  6							)
 insert(_lowerCamelCase     ,  14							)
 insert(_lowerCamelCase     ,  20							)
 insert(_lowerCamelCase     ,  60							)
 return root






def 			lowerCamelCase_    (		):
 # main function
 lowerCamelCase__	:     List[Any]								=		make_tree()
 print('Printing values of binary search tree in Inorder Traversal.'							)
 inorder(_lowerCamelCase							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()

