code: import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  
  class 				_lowercase						(    lowerCamelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Union[str, Any]        =						['image_processor', 'tokenizer']
      SCREAMING_SNAKE_CASE:							str        =						'AutoImageProcessor'
      SCREAMING_SNAKE_CASE:							Optional[int]        =						'AutoTokenizer'
  
  
  
  
      def __init__(						self					,	lowerCamelCase__=None					,	lowerCamelCase__=None					,	**lowerCamelCase__   ):
         lowerCAmelCase_:       int         =  None
         if "feature_extractor" in kwargs:
            warnings.warn(
                "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                " instead."					,	lowercase_					,	)
            lowerCAmelCase_:       Optional[int]         =  kwargs.pop("feature_extractor"   )
  
         lowerCAmelCase_:       List[Any]         =  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__(lowercase_					,	lowercase_   )
         lowerCAmelCase_:       str         =  self.image_processor
         lowerCAmelCase_:       Union[str, Any]         =  False
  
  
  
  
      def __call__(						self					,	*lowerCamelCase__					,	**lowerCamelCase__   ):
  
  
  
  
  
  
         if self._in_target_context_manager:
            return self.current_processor(*lowercase_					,	**lowercase_   )
  
         lowerCAmelCase_:       List[str]         =  kwargs.pop("images"					,	lowercase_   )
         lowerCAmelCase_:       Optional[Any]         =  kwargs.pop("text"					,	lowercase_   )
         if len(lowercase_   ) > 0:
            lowerCAmelCase_:       Optional[Any]         =  args[0]
            lowerCAmelCase_:       str         =  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_:       List[Any]         =  self.image_processor(lowercase_					,	*lowercase_					,	**lowercase_   )
         if text is not None:
            lowerCAmelCase_:       Optional[int]         =  self.tokenizer(lowercase_					,	**lowercase_   )
  
         if text is None:
            return inputs
         elif images is None:
            return encodings
         else:
            lowerCAmelCase_:       Union[str, Any]         =  encodings["input_ids"]
            return inputs
  
  
  
  
      def     _a						(						self					,	*lowerCamelCase__					,	**lowerCamelCase__   ):
         return self.tokenizer.batch_decode(*lowercase_					,	**lowercase_   )
  
  
  
  
      def     _a						(						self					,	*lowerCamelCase__					,	**lowerCamelCase__   ):
         return self.tokenizer.decode(*lowercase_					,	**lowercase_   )
  
  
  
  
      @contextmanager
      def     _a						(						self   ):
         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_:       List[Any]         =  True
         lowerCAmelCase_:       List[Any]         =  self.tokenizer
         yield
         lowerCAmelCase_:       Union[str, Any]         =  self.image_processor
         lowerCAmelCase_:       Optional[Any]         =  False
  
  
  
  
      @property
      def     _a						(						self   ):
         warnings.warn(
             "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,	lowercase_					,	)
         return self.image_processor_class
  
  
  
  
      @property
      def     _a						(						self   ):
         warnings.warn(
             "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,	lowercase_					,	)
         return self.image_processor
  
  
  
  
style_context: import itertools
  def 		snake_case__  (		lowercase				):
     return (
         int("".join(combination[0:2]				)				) * int("".join(combination[2:5]				)				)
         == int("".join(combination[5:9]				)				)
     ) or (
         int("".join(combination[0]				)				) * int("".join(combination[1:5]				)				)
         == int("".join(combination[5:9]				)				)
     )
  def 		snake_case__  (		):
     return sum(
         {
             int("".join(pandigital[5:9]				)				)
             for pandigital in itertools.permutations("123456789"				)
             if is_combination_valid(lowercase				)
         }				)
  
  
  if __name__ == "__main__":
   print(solution())