code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  
  
  
  
  
  
  class 						lowercase     (   lowerCamelCase__):
        __lowercase 		=  ['image_processor', 'tokenizer']
        __lowercase 		=  'AutoImageProcessor'
        __lowercase 		=  'AutoTokenizer'
  
  
  
  
  
  
        def __init__(self ,  a__=None ,  a__=None ,  **a__					)       ->   Tuple:
           SCREAMING_SNAKE_CASE__			:				str       						= 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_ ,  )
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= kwargs.pop('''feature_extractor'''					)
  
           SCREAMING_SNAKE_CASE__			:				Optional[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_					)
           SCREAMING_SNAKE_CASE__			:				str       						= self.image_processor
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= False
  
  
  
  
  
  
        def __call__(self ,  *a__ ,  **a__					)       ->   str:
  
  
  
  
  
  
           if self._in_target_context_manager:
              return self.current_processor(*lowercase_ ,  **lowercase_					)
  
           SCREAMING_SNAKE_CASE__			:				Any       						= kwargs.pop('''images''' ,  lowercase_					)
           SCREAMING_SNAKE_CASE__			:				str       						= kwargs.pop('''text''' ,  lowercase_					)
           if len(lowercase_					) > 0:
              SCREAMING_SNAKE_CASE__			:				Any       						= args[0]
              SCREAMING_SNAKE_CASE__			:				List[Any]       						= 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:
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= self.image_processor(lowercase_ ,  *lowercase_ ,  **lowercase_					)
           if text is not None:
              SCREAMING_SNAKE_CASE__			:				List[str]       						= self.tokenizer(lowercase_ ,  **lowercase_					)
  
           if text is None:
              return inputs
           elif images is None:
              return encodings
           else:
              SCREAMING_SNAKE_CASE__			:				List[Any]       						= encodings['''input_ids''']
              return inputs
  
  
  
  
  
  
        def    A__(self ,  *a__ ,  **a__					)       ->   List[str]:
           return self.tokenizer.batch_decode(*lowercase_ ,  **lowercase_					)
  
  
  
  
  
  
        def    A__(self ,  *a__ ,  **a__					)       ->   List[Any]:
           return self.tokenizer.decode(*lowercase_ ,  **lowercase_					)
  
  
  
  
  
  
        @contextmanager
        def    A__(self					)       ->   Union[str, Any]:
           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.'''					)
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= True
           SCREAMING_SNAKE_CASE__			:				int       						= self.tokenizer
           yield
           SCREAMING_SNAKE_CASE__			:				Dict       						= self.image_processor
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= False
  
  
  
  
  
  
        @property
        def    A__(self					)       ->   List[str]:
           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					)       ->   Dict:
           warnings.warn(
               '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,  lowercase_ ,  )
           return self.image_processor
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  def 				UpperCamelCase	(  snake_case = 99_80_01   ):
  
  
  
  
     '''simple docstring'''
  
     for number in range(n - 1	,     99_99	,     -1   ):
        SCREAMING_SNAKE_CASE__			:				int       						= str(snake_case   )
  
        # checks whether 'str_number' is a palindrome.
        if str_number == str_number[::-1]:
           SCREAMING_SNAKE_CASE__			:				Any       						= 9_99
  
           # if 'number' is a product of two 3-digit numbers
           # then number is the answer otherwise fetch next number.
           while divisor != 99:
              if (number % divisor == 0) and (len(str(number // divisor   )   ) == 3.0):
                 return number
              divisor -= 1
     raise ValueError('''That number is larger than our acceptable range.'''   )
  
  
  if __name__ == "__main__":
         print(F"{solution() = }")
  
  