

'''simple docstring'''





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 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				lowerCamelCase_							=						['image_processor', 'tokenizer']
				lowerCamelCase_							=						'BlipImageProcessor'
				lowerCamelCase_							=						('BertTokenizer', 'BertTokenizerFast')







				def __init__(					self    :		str		,				UpperCAmelCase__    :		Optional[Any]		,				UpperCAmelCase__    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=False
										super().__init__(UpperCAmelCase__		,				UpperCAmelCase__      )
										lowercase   :       Union[str, Any]								=self.image_processor







				def __call__(					self    :		List[str]		,				UpperCAmelCase__    :		ImageInput = None		,				UpperCAmelCase__    :		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,				UpperCAmelCase__    :		bool = True		,				UpperCAmelCase__    :		Union[bool, str, PaddingStrategy] = False		,				UpperCAmelCase__    :		Union[bool, str, TruncationStrategy] = None		,				UpperCAmelCase__    :		Optional[int] = None		,				UpperCAmelCase__    :		int = 0		,				UpperCAmelCase__    :		Optional[int] = None		,				UpperCAmelCase__    :		Optional[bool] = None		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		bool = True		,				UpperCAmelCase__    :		Optional[Union[str, TensorType]] = None		,				**UpperCAmelCase__    :		List[Any]		,				):
										'''simple docstring'''
										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:
																lowercase   :       str								=self.tokenizer
																lowercase   :       Optional[Any]								=self.tokenizer(
																    text=UpperCAmelCase__		,				add_special_tokens=UpperCAmelCase__		,				padding=UpperCAmelCase__		,				truncation=UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				stride=UpperCAmelCase__		,				pad_to_multiple_of=UpperCAmelCase__		,				return_attention_mask=UpperCAmelCase__		,				return_overflowing_tokens=UpperCAmelCase__		,				return_special_tokens_mask=UpperCAmelCase__		,				return_offsets_mapping=UpperCAmelCase__		,				return_token_type_ids=UpperCAmelCase__		,				return_length=UpperCAmelCase__		,				verbose=UpperCAmelCase__		,				return_tensors=UpperCAmelCase__		,				**UpperCAmelCase__		,				)
																return text_encoding

										# add pixel_values
										lowercase   :       List[Any]								=self.image_processor(UpperCAmelCase__		,				return_tensors=UpperCAmelCase__      )

										if text is not None:
																lowercase   :       Tuple								=self.tokenizer(
																    text=UpperCAmelCase__		,				add_special_tokens=UpperCAmelCase__		,				padding=UpperCAmelCase__		,				truncation=UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				stride=UpperCAmelCase__		,				pad_to_multiple_of=UpperCAmelCase__		,				return_attention_mask=UpperCAmelCase__		,				return_overflowing_tokens=UpperCAmelCase__		,				return_special_tokens_mask=UpperCAmelCase__		,				return_offsets_mapping=UpperCAmelCase__		,				return_token_type_ids=UpperCAmelCase__		,				return_length=UpperCAmelCase__		,				verbose=UpperCAmelCase__		,				return_tensors=UpperCAmelCase__		,				**UpperCAmelCase__		,				)
										else:
																lowercase   :       Optional[Any]								=None

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

										return encoding_image_processor







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				*UpperCAmelCase__    :		int		,				**UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										return self.tokenizer.batch_decode(*UpperCAmelCase__		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Tuple		,				*UpperCAmelCase__    :		List[Any]		,				**UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										return self.tokenizer.decode(*UpperCAmelCase__		,				**UpperCAmelCase__      )







				@property
				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=self.tokenizer.model_input_names
										lowercase   :       Union[str, Any]								=self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )








'''simple docstring'''





import math






def 							_lowerCAmelCase (      __magic_name__	:    float      ,   __magic_name__	:    float	)							->     float:
						if (
						    not isinstance(__magic_name__      ,   (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 							_lowerCAmelCase (      __magic_name__	:    float      ,   __magic_name__	:    float	)							->     float:
						if (
						    not isinstance(__magic_name__      ,   (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()





