




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class 							__lowercase   (			a__    ):
     _lowerCAmelCase		       =						"AutoFeatureExtractor"
     _lowerCAmelCase		       =						"Speech2Text2Tokenizer"



     def __init__(  self   :							Dict      ,		lowercase__   :							Any      ,		lowercase__   :							List[str]       ):
       super().__init__(lowercase__      ,		lowercase__       )
       a_		    =  self.feature_extractor
       a_		    =  False



     def __call__(  self   :							str      ,		*lowercase__   :							Union[str, Any]      ,		**lowercase__   :							Any       ):
       # For backward compatibility
       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.'''       )
         a_		    =  kwargs.pop('''raw_speech'''       )
       else:
         a_		    =  kwargs.pop('''audio'''      ,		lowercase__       )
       a_		    =  kwargs.pop('''sampling_rate'''      ,		lowercase__       )
       a_		    =  kwargs.pop('''text'''      ,		lowercase__       )
       if len(lowercase__       ) > 0:
         a_		    =  args[0]
         a_		    =  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_		    =  self.feature_extractor(lowercase__      ,		*lowercase__      ,		sampling_rate=lowercase__      ,		**lowercase__       )
       if text is not None:
         a_		    =  self.tokenizer(lowercase__      ,		**lowercase__       )

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



     def 				__magic_name__		(  self   :							Any      ,		*lowercase__   :							str      ,		**lowercase__   :							Optional[Any]       ):
       return self.tokenizer.batch_decode(*lowercase__      ,		**lowercase__       )



     def 				__magic_name__		(  self   :							Any      ,		*lowercase__   :							Union[str, Any]      ,		**lowercase__   :							Any       ):
       return self.tokenizer.decode(*lowercase__      ,		**lowercase__       )



     @contextmanager
     def 				__magic_name__		(  self   :							Optional[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.'''       )
       a_		    =  True
       a_		    =  self.tokenizer
       yield
       a_		    =  self.feature_extractor
       a_		    =  False






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if not root or root not in binary_tree_mirror_dictionary:
    return
  a_    , a_		    =  binary_tree_mirror_dictionary[root][:2]
  a_		    =  [right_child, left_child]
  binary_tree_mirror_dict(_A      ,      _A			)
  binary_tree_mirror_dict(_A      ,      _A			)






def 				UpperCAmelCase__  (							_A      ,      _A = 1			):






  """simple docstring"""


  if not binary_tree:
    raise ValueError('''binary tree cannot be empty'''			)
  if root not in binary_tree:
    a_		    =  f"root {root} is not present in the binary_tree"
    raise ValueError(_A			)
  a_		    =  dict(_A			)
  binary_tree_mirror_dict(_A      ,      _A			)
  return binary_tree_mirror_dictionary


if __name__ == "__main__":
  UpperCamelCase__       				=	{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F"""Binary tree: {binary_tree}""")
  UpperCamelCase__       				=	binary_tree_mirror(binary_tree, 5)
  print(F"""Binary tree mirror: {binary_tree_mirror_dictionary}""")
