





from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class      UpperCamelCase    (						__a     ):
	lowerCAmelCase	:   int				=	["""image_processor""", """tokenizer"""]
	lowerCAmelCase	:   Union[str, Any]				=	"""BlipImageProcessor"""
	lowerCAmelCase	:   List[str]				=	"""AutoTokenizer"""



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       False
							super().__init__(a__			,	a__		)
							A__							=       self.image_processor



	def __call__(       self			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = 0			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = False			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							if images is None and text is None:
													raise ValueError("You have to specify either images or text."		)

							# Get only text
							if images is None:
													A__							=       self.tokenizer
													A__							=       self.tokenizer(
													    text=a__			,	add_special_tokens=a__			,	padding=a__			,	truncation=a__			,	max_length=a__			,	stride=a__			,	pad_to_multiple_of=a__			,	return_attention_mask=a__			,	return_overflowing_tokens=a__			,	return_special_tokens_mask=a__			,	return_offsets_mapping=a__			,	return_token_type_ids=a__			,	return_length=a__			,	verbose=a__			,	return_tensors=a__			,	**a__			,	)
													return text_encoding

							# add pixel_values
							A__							=       self.image_processor(a__			,	return_tensors=a__		)

							if text is not None:
													A__							=       self.tokenizer(
													    text=a__			,	add_special_tokens=a__			,	padding=a__			,	truncation=a__			,	max_length=a__			,	stride=a__			,	pad_to_multiple_of=a__			,	return_attention_mask=a__			,	return_overflowing_tokens=a__			,	return_special_tokens_mask=a__			,	return_offsets_mapping=a__			,	return_token_type_ids=a__			,	return_length=a__			,	verbose=a__			,	return_tensors=a__			,	**a__			,	)
							else:
													A__							=       None

							if text_encoding is not None:
													encoding_image_processor.update(a__		)

							return encoding_image_processor



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



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



	@property
	# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
	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		)		)










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()





