




'''simple docstring'''

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Union[str, Any]       =							'ClapFeatureExtractor'
		lowercase   :      List[Any]       =							('RobertaTokenizer', 'RobertaTokenizerFast')






		def __init__(   self		,						__A		,						__A				) ->     Union[str, Any]:
								super().__init__(__A		,						__A				)






		def __call__(   self		,						__A=None		,						__A=None		,						__A=None		,						**__A				) ->     List[Any]:
								_lowerCAmelCase										=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:
														_lowerCAmelCase										=self.tokenizer(__A		,						return_tensors=__A		,						**__A				)

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

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






		def 		UpperCamelCase__       (   self		,						*__A		,						**__A				) ->     int:
								return self.tokenizer.batch_decode(*__A		,						**__A				)






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






		@property
		def 		UpperCamelCase__       (   self				) ->     str:
								_lowerCAmelCase										=self.tokenizer.model_input_names
								_lowerCAmelCase										=self.feature_extractor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names				)				)






'''simple docstring'''

from PIL import Image



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						def brightness(a__      ) -> float:
												return 1_2_8 + level + (c - 1_2_8)

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


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''')





