




'''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







'''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() = }")

