






'''simple docstring'''







import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 					a__    (			lowerCamelCase						):
     '''simple docstring'''







     _snake_case             =  ['image_processor', 'tokenizer']
     _snake_case             =  'AutoImageProcessor'
     _snake_case             =  'AutoTokenizer'






     def __init__(self    :	Tuple				,				lowercase__    :	Union[str, Any]=None				,				lowercase__    :	Optional[Any]=None				,				**lowercase__    :	str					)  ->       Tuple:
           __A		   =      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__				,				)
                 __A		   =      kwargs.pop("feature_extractor"					)

           __A		   =      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__					)
           __A		   =      self.image_processor
           __A		   =      False






     def __call__(self    :	List[str]				,				*lowercase__    :	List[Any]				,				**lowercase__    :	List[str]					)  ->       str:
           # For backward compatibility
           if self._in_target_context_manager:
                 return self.current_processor(*lowercase__				,				**lowercase__					)

           __A		   =      kwargs.pop("images"				,				lowercase__					)
           __A		   =      kwargs.pop("text"				,				lowercase__					)
           if len(lowercase__					) > 0:
                 __A		   =      args[0]
                 __A		   =      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:
                 __A		   =      self.image_processor(lowercase__				,				*lowercase__				,				**lowercase__					)
           if text is not None:
                 __A		   =      self.tokenizer(lowercase__				,				**lowercase__					)

           if text is None:
                 return inputs
           elif images is None:
                 return encodings
           else:
                 __A		   =      encodings["input_ids"]
                 return inputs






     def     _UpperCamelCase   (self    :	Tuple				,				*lowercase__    :	List[Any]				,				**lowercase__    :	List[Any]					)  ->       Union[str, Any]:
           return self.tokenizer.batch_decode(*lowercase__				,				**lowercase__					)






     def     _UpperCamelCase   (self    :	List[str]				,				*lowercase__    :	Optional[Any]				,				**lowercase__    :	Any					)  ->       List[Any]:
           return self.tokenizer.decode(*lowercase__				,				**lowercase__					)






     @contextmanager
     def     _UpperCamelCase   (self    :	Dict					)  ->       Dict:
           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."					)
           __A		   =      True
           __A		   =      self.tokenizer
           yield
           __A		   =      self.image_processor
           __A		   =      False






     @property
     def     _UpperCamelCase   (self    :	Union[str, Any]					)  ->       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     _UpperCamelCase   (self    :	int					)  ->       Any:
           warnings.warn(
               "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,				lowercase__				,				)
           return self.image_processor










'''simple docstring'''







from cva import destroyAllWindows, imread, imshow, waitKey


def 						_UpperCamelCase							(     snake_case__							,			snake_case__							):
      __A   ,				__A		   =      img.shape[0], img.shape[1]

      def to_grayscale(snake_case__							,			snake_case__							,			snake_case__							):
            return 0.2126 * red + 0.587 * green + 0.114 * blue

      def normalize(snake_case__							):
            return min(snake_case__							,			255							)

      for i in range(snake_case__							):
            for j in range(snake_case__							):
                  __A		   =      int(to_grayscale(*img[i][j]							)							)
                  __A		   =      [
                      normalize(snake_case__							),
                      normalize(greyscale + factor							),
                      normalize(greyscale + 2 * factor							),
                  ]

      return img


if __name__ == "__main__":
       # read original image
       __A      :       Dict      =	{
           percentage: imread('image_data/lena.jpg', 1) for percentage in (1_0, 2_0, 3_0, 4_0)
       }

       for percentage, img in images.items():
              make_sepia(img, percentage)

       for percentage, img in images.items():
              imshow(F"""Original image with sepia (factor: {percentage})""", img)

       waitKey(0)
       destroyAllWindows()


