






from cva import destroyAllWindows, imread, imshow, waitKey
def 		_a					(   a				:Any			,							a				:int				)							->    str:
 a						,  a							=   img.shape[0], img.shape[1]

 def to_grayscale(a				:Tuple			,							a				:Dict			,							a				:Optional[int]				):
  return 0.2_126 * red + 0.587 * green + 0.114 * blue

 def normalize(a				:Optional[Any]				):
  return min(a			,							255				)

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

 return img


if __name__ == "__main__":
     # read original image
     UpperCAmelCase__            =	{
         percentage: imread("image_data/lena.jpg", 1) for percentage in (10, 20, 30, 40)
     }

     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()










import warnings

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






class 	lowercase_      (			lowercase     ):






      '''simple docstring'''




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







      def __init__(   self   :		Any				,      __UpperCAmelCase   :		Union[str, Any]=None				,      __UpperCAmelCase   :		int=None				,      **__UpperCAmelCase   :		Union[str, Any]						)					->Dict:




       """simple docstring"""


       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.'''				,      __UpperCAmelCase				,      )
        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 have to specify an image_processor.'''						)
       if tokenizer is None:
        raise ValueError('''You have to specify a tokenizer.'''						)

       super().__init__(__UpperCAmelCase				,      __UpperCAmelCase						)
       a							=   self.image_processor







      def __call__(   self   :		List[Any]				,      __UpperCAmelCase   :		Optional[Any]=None				,      __UpperCAmelCase   :		Optional[int]=None				,      __UpperCAmelCase   :		str=None				,      **__UpperCAmelCase   :		List[Any]						)					->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:
        a							=   self.tokenizer(__UpperCAmelCase				,      return_tensors=__UpperCAmelCase				,      **__UpperCAmelCase						)

       if images is not None:
        a							=   self.image_processor(__UpperCAmelCase				,      return_tensors=__UpperCAmelCase				,      **__UpperCAmelCase						)

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







      def    __lowerCAmelCase      (   self   :		List[Any]				,      *__UpperCAmelCase   :		Optional[Any]				,      **__UpperCAmelCase   :		List[str]						)					->Optional[Any]:




       """simple docstring"""


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







      def    __lowerCAmelCase      (   self   :		str				,      *__UpperCAmelCase   :		List[Any]				,      **__UpperCAmelCase   :		Optional[Any]						)					->Union[str, Any]:




       """simple docstring"""


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







      @property
      def    __lowerCAmelCase      (   self   :		str						)					->str:




       """simple docstring"""


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







      @property
      def    __lowerCAmelCase      (   self   :		str						)					->Optional[Any]:




       """simple docstring"""


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







      @property
      def    __lowerCAmelCase      (   self   :		int						)					->Optional[Any]:




       """simple docstring"""


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


