import warnings

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


class _a  (			_lowerCamelCase   ):

	'''simple docstring'''



	lowerCamelCase_       :     int          =			["""image_processor""", """tokenizer"""]
	lowerCamelCase_       :     int          =			"""ChineseCLIPImageProcessor"""
	lowerCamelCase_       :     Dict          =			("""BertTokenizer""", """BertTokenizerFast""")

	def __init__(  self				,					__UpperCAmelCase=None				,					__UpperCAmelCase=None				,					**__UpperCAmelCase       ):
			__A				:       int			    = 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				:       int			    = kwargs.pop("feature_extractor"       )

			__A				:       Any			    = 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				:       Union[str, Any]			    = self.image_processor

	def __call__(  self				,					__UpperCAmelCase=None				,					__UpperCAmelCase=None				,					__UpperCAmelCase=None				,					**__UpperCAmelCase       ):

			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				:       Dict			    = self.tokenizer(lowercase_				,					return_tensors=lowercase_				,					**lowercase_       )

			if images is not None:
					__A				:       Any			    = self.image_processor(lowercase_				,					return_tensors=lowercase_				,					**lowercase_       )

			if text is not None and images is not None:
					__A				:       Optional[int]			    = image_features.pixel_values
					return encoding
			elif text is not None:
					return encoding
			else:
					return BatchEncoding(data=dict(**lowercase_       )				,					tensor_type=lowercase_       )

	def 	__UpperCAmelCase(  self				,					*__UpperCAmelCase				,					**__UpperCAmelCase       ):
			return self.tokenizer.batch_decode(*lowercase_				,					**lowercase_       )

	def 	__UpperCAmelCase(  self				,					*__UpperCAmelCase				,					**__UpperCAmelCase       ):
			return self.tokenizer.decode(*lowercase_				,					**lowercase_       )

	@property
	def 	__UpperCAmelCase(  self       ):
			__A				:       Dict			    = self.tokenizer.model_input_names
			__A				:       Dict			    = self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )







	@property
	def 	__UpperCAmelCase(  self       ):
			warnings.warn(
			    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,					lowercase_				,					)
			return self.image_processor_class











'''simple docstring'''

from PIL import Image


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Image:

					'''simple docstring'''

					def brightness(__UpperCAmelCase      ) -> 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(__UpperCAmelCase      )


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

