






'''simple docstring'''

import warnings

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






class     a_						(      lowerCamelCase    ):
  lowercase							=				["""image_processor""", """tokenizer"""]
  lowercase							=				"""CLIPImageProcessor"""
  lowercase							=				("""CLIPTokenizer""", """CLIPTokenizerFast""")

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



        """simple docstring"""






        UpperCamelCase      = 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      ,		)
              UpperCamelCase      = kwargs.pop("""feature_extractor"""				)

        UpperCamelCase      = 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__(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

  def __call__(				self      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=None      ,		**_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """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      = self.tokenizer(_SCREAMING_SNAKE_CASE      ,		return_tensors=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

        if images is not None:
              UpperCamelCase      = self.image_processor(_SCREAMING_SNAKE_CASE      ,		return_tensors=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

        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(**_SCREAMING_SNAKE_CASE				)      ,		tensor_type=_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






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

  def   A__						(				self      ,		*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)  ->				Dict:



        """simple docstring"""






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

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



        """simple docstring"""






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

  @property
  def   A__						(				self				)  ->				int:



        """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   A__						(				self				)  ->				List[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 PIL import Image







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			Image:

      def brightness(__UpperCamelCase   ) -> float:
            return 128 + level + (c - 128)

      if not -255.0 <= level <= 255.0:
            raise ValueError("""level must be between -255.0 (black) and 255.0 (white)"""   )
      return img.point(__UpperCamelCase   )


if __name__ == "__main__":
     # Load image
     with Image.open('image_data/lena.jpg') as img:
          # Change brightness to 100
          SCREAMING_SNAKE_CASE__     =       change_brightness(img, 1_0_0)
          brigt_img.save('image_data/lena_brightness.png', format='png')
