





import warnings

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






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   List[str]				=	["""image_processor""", """tokenizer"""]
	lowerCAmelCase	:   str				=	"""CLIPImageProcessor"""
	lowerCAmelCase	:   str				=	("""CLIPTokenizer""", """CLIPTokenizerFast""")



	def __init__(       self			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	**UpperCAmelCase__		):
							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 need to specify an `image_processor`."		)
							if tokenizer is None:
													raise ValueError("You need to specify a `tokenizer`."		)

							super().__init__(UpperCAmelCase__			,	UpperCAmelCase__		)



	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__							=       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 			__A							(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							return self.tokenizer.batch_decode(*UpperCAmelCase__			,	**UpperCAmelCase__		)



	def 			__A							(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							return self.tokenizer.decode(*UpperCAmelCase__			,	**UpperCAmelCase__		)



	@property
	def 			__A							(       self		):
							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 			__A							(       self		):
							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 			__A							(       self		):
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,	UpperCAmelCase__			,	)
							return self.image_processor










import math
def  UpperCamelCase						(       _A			:				float			,							_A			:				float    )->       float:





						"""simple docstring"""





						if (
						    not isinstance(_A			,							(int, float)    )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("power_factor must be a valid float value between -1 and 1."    )
						return apparent_power * power_factor
def  UpperCamelCase						(       _A			:				float			,							_A			:				float    )->       float:





						"""simple docstring"""





						if (
						    not isinstance(_A			,							(int, float)    )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("power_factor must be a valid float value between -1 and 1."    )
						return apparent_power * math.sqrt(1 - power_factor**2    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





