'''simple docstring'''

from math import pow







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Optional[int]  ,			_A					:				Dict  ,			_A					:				List[str]  ,			_A					:				List[Any]  ,			):
   '''simple docstring'''
   if current_sum == needed_sum:
      # If the sum of the powers is equal to needed_sum, then we have a solution.
      solutions_count += 1
      return current_sum, solutions_count

   UpperCAmelCase__							:				Dict   				=  int(pow(_A  ,			_A    )    )
   if current_sum + i_to_n <= needed_sum:
      # If the sum of the powers is less than needed_sum, then continue adding powers.
      current_sum += i_to_n
      UpperCAmelCase__				,      UpperCAmelCase__							:				Tuple   				=  backtrack(
          _A  ,			_A  ,			current_number + 1  ,			_A  ,			_A    )
      current_sum -= i_to_n
   if i_to_n < needed_sum:
      # If the power of i is less than needed_sum, then try with the next power.
      UpperCAmelCase__				,      UpperCAmelCase__							:				Any   				=  backtrack(
          _A  ,			_A  ,			current_number + 1  ,			_A  ,			_A    )
   return current_sum, solutions_count







def __UpperCamelCase(   _A					:				int  ,			_A					:				Optional[Any]    ):
   '''simple docstring'''
   if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
      raise ValueError(
          '''Invalid input\n'''
          '''needed_sum must be between 1 and 1000, power between 2 and 10.'''    )

   return backtrack(_A  ,			_A  ,			1  ,			0  ,			0    )[1]  # Return the solutions_count


if __name__ == "__main__":
    import doctest

    doctest.testmod()





'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Tuple      							=   ['''image_processor''', '''tokenizer''']
      UpperCAmelCase_      :      str      							=   '''CLIPImageProcessor'''
      UpperCAmelCase_      :      Dict      							=   ('''CLIPTokenizer''', '''CLIPTokenizerFast''')




      def __init__(				self					,lowerCamelCase_=None					,lowerCamelCase_=None					,**lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''					,lowerCamelCase_					,)
            UpperCAmelCase__							:				str   				=  kwargs.pop('''feature_extractor'''							)

         UpperCAmelCase__							:				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__(lowerCamelCase_					,lowerCamelCase_							)




      def __call__(				self					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=None					,**lowerCamelCase_							)			->			Dict:



         '''simple docstring'''



         if text is None and images is None:
            raise ValueError('''You have to specify either text or images. Both cannot be none.'''							)

         if text is not None:
            UpperCAmelCase__							:				Tuple   				=  self.tokenizer(lowerCamelCase_					,return_tensors=lowerCamelCase_					,**lowerCamelCase_							)

         if images is not None:
            UpperCAmelCase__							:				Optional[int]   				=  self.image_processor(lowerCamelCase_					,return_tensors=lowerCamelCase_					,**lowerCamelCase_							)

         if text is not None and images is not None:
            UpperCAmelCase__							:				str   				=  image_features.pixel_values
            return encoding
         elif text is not None:
            return encoding
         else:
            return BatchEncoding(data=dict(**lowerCamelCase_							)					,tensor_type=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,*lowerCamelCase_					,**lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         return self.tokenizer.batch_decode(*lowerCamelCase_					,**lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,*lowerCamelCase_					,**lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         return self.tokenizer.decode(*lowerCamelCase_					,**lowerCamelCase_							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  self.tokenizer.model_input_names
         UpperCAmelCase__							:				Optional[Any]   				=  self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,lowerCamelCase_					,)
         return self.image_processor_class




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,lowerCamelCase_					,)
         return self.image_processor




