





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Tuple				      = 'ClapFeatureExtractor'
   _UpperCamelCase					:	Union[str, Any]				      = ('RobertaTokenizer', 'RobertaTokenizerFast')


   def __init__(					self    :       List[Any]    ,  a    :       int    ,  a    :       str )->     Any:





     """simple docstring"""







     super().__init__(a    ,  a )


   def __call__(					self    :       Any    ,  a    :       Tuple=None    ,  a    :       Optional[int]=None    ,  a    :       int=None    ,  **a    :       Optional[int] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						kwargs.pop('sampling_rate'    ,  a )

     if text is None and audios is None:
       raise ValueError('You have to specify either text or audios. Both cannot be none.' )

     if text is not None:
       lowercase__			    =						self.tokenizer(a    ,  return_tensors=a    ,  **a )

     if audios is not None:
       lowercase__			    =						self.feature_extractor(
           a    ,  sampling_rate=a    ,  return_tensors=a    ,  **a )

     if text is not None and audios is not None:
       lowercase__			    =						audio_features.input_features
       return encoding
     elif text is not None:
       return encoding
     else:
       return BatchEncoding(data=dict(**a )    ,  tensor_type=a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       str    ,  *a    :       Dict    ,  **a    :       int )->     Optional[int]:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  *a    :       int    ,  **a    :       Dict )->     Dict:





     """simple docstring"""







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


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						self.tokenizer.model_input_names
     lowercase__			    =						self.feature_extractor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )









from PIL import Image




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Image:

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

  if not -2_5_5.0 <= level <= 2_5_5.0:
    raise ValueError('level must be between -255.0 (black) and 255.0 (white)'	)
  return img.point(_SCREAMING_SNAKE_CASE	)


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


