




'''simple docstring'''



import warnings

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





class 			lowerCAmelCase      (       __lowerCAmelCase):
       __lowercase     :					Union[str, Any]       				=	['''image_processor''', '''tokenizer''']
       __lowercase     :					Union[str, Any]       				=	'''AutoImageProcessor'''
       __lowercase     :					Optional[int]       				=	'''AutoTokenizer'''


       def __init__(      self			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     **__SCREAMING_SNAKE_CASE     )					->     Dict:



            '''simple docstring'''




            __snake_case			    =     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			,     )
                 __snake_case			    =     kwargs.pop('''feature_extractor'''     )

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

            super().__init__(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )
            __snake_case			    =     self.image_processor


       def __call__(      self			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     **__SCREAMING_SNAKE_CASE     )					->     Tuple:



            '''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:
                 __snake_case			    =     self.tokenizer(__SCREAMING_SNAKE_CASE			,     return_tensors=__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE     )

            if images is not None:
                 __snake_case			    =     self.image_processor(__SCREAMING_SNAKE_CASE			,     return_tensors=__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE     )

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


       def 					lowerCAmelCase					(      self			,     *__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE     )					->     Union[str, Any]:



            '''simple docstring'''




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


       def 					lowerCAmelCase					(      self			,     *__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE     )					->     Optional[Any]:



            '''simple docstring'''




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


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



            '''simple docstring'''




            __snake_case			    =     self.tokenizer.model_input_names
            __snake_case			    =     self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )


       @property
       def 					lowerCAmelCase					(      self     )					->     Union[str, Any]:



            '''simple docstring'''




            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 					lowerCAmelCase					(      self     )					->     Union[str, Any]:



            '''simple docstring'''




            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'''



from __future__ import annotations







def 	_UpperCamelCase       (_lowerCamelCase							:   int      ,		_lowerCamelCase							:   list[int]      ,		_lowerCamelCase							:   list[int]      ,		_lowerCamelCase							:   int		)->      int:




     '''simple docstring'''





     if counter == 0 or capacity == 0:
          return 0

     # If weight of the nth item is more than Knapsack of capacity,
     #   then this item cannot be included in the optimal solution,
     # else return the maximum of two cases:
     #   (1) nth item included
     #   (2) not included
     if weights[counter - 1] > capacity:
          return knapsack(_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		counter - 1		)
     else:
          __snake_case			    =     capacity - weights[counter - 1]
          __snake_case			    =     values[counter - 1] + knapsack(
              _lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		counter - 1		)
          __snake_case			    =     knapsack(_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		counter - 1		)
          return max(_lowerCamelCase      ,		_lowerCamelCase		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






