
"""simple docstring"""

from __future__ import annotations




def      lowercase__    (       lowerCAmelCase					:       list[int]			,					lowerCAmelCase					:       list[int]			,					lowerCAmelCase					:       int			)			->   tuple[float, list[float]]:






    """simple docstring"""




    UpperCAmelCase          =       list(range(len(lowerCAmelCase			)			)			)
    UpperCAmelCase          =       [v / w for v, w in zip(lowerCAmelCase			,					lowerCAmelCase			)]
    index.sort(key=lambda lowerCAmelCase			: ratio[i]			,					reverse=lowerCAmelCase			)

    UpperCAmelCase          =       0
    UpperCAmelCase          =       [0] * len(lowerCAmelCase			)
    for i in index:
        if weight[i] <= capacity:
            UpperCAmelCase          =       1
            max_value += value[i]
            capacity -= weight[i]
        else:
            UpperCAmelCase          =       capacity / weight[i]
            max_value += value[i] * capacity / weight[i]
            break

    return max_value, fractions


if __name__ == "__main__":
   import doctest

   doctest.testmod()





"""simple docstring"""

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						["image_processor", "tokenizer"]
      __SCREAMING_SNAKE_CASE :				Dict				   =						"AutoImageProcessor"
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						"AutoTokenizer"




      def __init__(				self ,							lowercase_ ,							lowercase_				)    ->       List[str]:
          super().__init__(lowercase_ ,							lowercase_				)
          UpperCAmelCase          =       self.image_processor




      def __call__(				self ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							**lowercase_				)    ->       Any:

          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          =       self.tokenizer(lowercase_ ,							return_tensors=lowercase_ ,							**lowercase_				)

          if images is not None:
              UpperCAmelCase          =       self.image_processor(lowercase_ ,							return_tensors=lowercase_ ,							**lowercase_				)

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




      def      a_   (				self ,							*lowercase_ ,							**lowercase_				)    ->       Optional[Any]:
          return self.tokenizer.batch_decode(*lowercase_ ,							**lowercase_				)




      def      a_   (				self ,							*lowercase_ ,							**lowercase_				)    ->       Optional[int]:
          return self.tokenizer.decode(*lowercase_ ,							**lowercase_				)




      @property
      def      a_   (				self				)    ->       Optional[int]:
          return ["input_ids", "attention_mask", "pixel_values"]



