






"""simple docstring"""





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 					a						( __snake_case		):
    SCREAMING_SNAKE_CASE						:					Optional[Any]						=			["""image_processor""", """tokenizer"""]
    SCREAMING_SNAKE_CASE						:					Optional[Any]						=			"""AutoImageProcessor"""
    SCREAMING_SNAKE_CASE						:					Dict						=			"""AutoTokenizer"""






    def __init__(	self					:							int	,						__SCREAMING_SNAKE_CASE					:							Optional[int]=None	,						__SCREAMING_SNAKE_CASE					:							Optional[Any]=None	,						**__SCREAMING_SNAKE_CASE					:							int )					->		Any:
      lowerCamelCase_   					=						None
      if "feature_extractor" in kwargs:
        warnings.warn(
            'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
            ' instead.'	,						__SCREAMING_SNAKE_CASE	,						)
        lowerCamelCase_   					=						kwargs.pop('feature_extractor' )

      lowerCamelCase_   					=						image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
        raise ValueError('You need to specify an `image_processor`.' )
      if tokenizer is None:
        raise ValueError('You need to specify a `tokenizer`.' )

      super().__init__(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						self.image_processor
      lowerCamelCase_   					=						False






    def __call__(	self					:							Tuple	,						*__SCREAMING_SNAKE_CASE					:							Any	,						**__SCREAMING_SNAKE_CASE					:							List[str] )					->		Union[str, Any]:
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE )

      lowerCamelCase_   					=						kwargs.pop('images'	,						__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						kwargs.pop('text'	,						__SCREAMING_SNAKE_CASE )
      if len(__SCREAMING_SNAKE_CASE ) > 0:
        lowerCamelCase_   					=						args[0]
        lowerCamelCase_   					=						args[1:]

      if images is None and text is None:
        raise ValueError('You need to specify either an `images` or `text` input to process.' )

      if images is not None:
        lowerCamelCase_   					=						self.image_processor(__SCREAMING_SNAKE_CASE	,						*__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE )
      if text is not None:
        lowerCamelCase_   					=						self.tokenizer(__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE )

      if text is None:
        return inputs
      elif images is None:
        return encodings
      else:
        lowerCamelCase_   					=						encodings['input_ids']
        return inputs






    def        UpperCamelCase	(	self					:							List[Any]	,						*__SCREAMING_SNAKE_CASE					:							Optional[Any]	,						**__SCREAMING_SNAKE_CASE					:							List[str] )					->		Optional[Any]:
      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							List[Any]	,						*__SCREAMING_SNAKE_CASE					:							Union[str, Any]	,						**__SCREAMING_SNAKE_CASE					:							Optional[int] )					->		Tuple:
      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE )






    @contextmanager
    def        UpperCamelCase	(	self					:							int )					->		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 images inputs, or in a separate call.' )
      lowerCamelCase_   					=						True
      lowerCamelCase_   					=						self.tokenizer
      yield
      lowerCamelCase_   					=						self.image_processor
      lowerCamelCase_   					=						False






    @property
    def        UpperCamelCase	(	self					:							List[str] )					->		Optional[int]:
      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'	,						__SCREAMING_SNAKE_CASE	,						)
      return self.image_processor_class






    @property
    def        UpperCamelCase	(	self					:							Union[str, Any] )					->		List[Any]:
      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'	,						__SCREAMING_SNAKE_CASE	,						)
      return self.image_processor












"""simple docstring"""





def 					lowerCamelCase__	(							_lowerCamelCase	:		dict		,    _lowerCamelCase	:		int			)				->    int:
  if not root or root not in binary_tree_mirror_dictionary:
    return
  lowerCamelCase_					,     lowerCamelCase_   					=						binary_tree_mirror_dictionary[root][:2]
  lowerCamelCase_   					=						[right_child, left_child]
  binary_tree_mirror_dict(_lowerCamelCase		,    _lowerCamelCase			)
  binary_tree_mirror_dict(_lowerCamelCase		,    _lowerCamelCase			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		dict		,    _lowerCamelCase	:		int = 1			)				->    dict:
  if not binary_tree:
    raise ValueError('binary tree cannot be empty'			)
  if root not in binary_tree:
    lowerCamelCase_   					=						F'''root {root} is not present in the binary_tree'''
    raise ValueError(_lowerCamelCase			)
  lowerCamelCase_   					=						dict(_lowerCamelCase			)
  binary_tree_mirror_dict(_lowerCamelCase		,    _lowerCamelCase			)
  return binary_tree_mirror_dictionary


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




