



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 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  ["image_processor", "tokenizer"]
						a__   =  "BlipImageProcessor"
						a__   =  ("BertTokenizer", "BertTokenizerFast")







						def __init__(	self      :			int	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Optional[Any] )      ->				List[Any]:
									A							:					Dict              =						False
									super().__init__(__lowerCamelCase	,	__lowerCamelCase )
									A							:					int              =						self.image_processor







						def __call__(	self      :			Tuple	,	__lowerCamelCase      :			ImageInput = None	,	__lowerCamelCase      :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			Union[bool, str, PaddingStrategy] = False	,	__lowerCamelCase      :			Union[bool, str, TruncationStrategy] = None	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			int = 0	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			Optional[bool] = None	,	__lowerCamelCase      :			bool = False	,	__lowerCamelCase      :			bool = False	,	__lowerCamelCase      :			bool = False	,	__lowerCamelCase      :			bool = False	,	__lowerCamelCase      :			bool = False	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			Optional[Union[str, TensorType]] = None	,	**__lowerCamelCase      :			List[str]	,	)      ->				BatchEncoding:
									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							:					Optional[int]              =						self.tokenizer
												A							:					List[str]              =						self.tokenizer(
												    text=__lowerCamelCase	,	add_special_tokens=__lowerCamelCase	,	padding=__lowerCamelCase	,	truncation=__lowerCamelCase	,	max_length=__lowerCamelCase	,	stride=__lowerCamelCase	,	pad_to_multiple_of=__lowerCamelCase	,	return_attention_mask=__lowerCamelCase	,	return_overflowing_tokens=__lowerCamelCase	,	return_special_tokens_mask=__lowerCamelCase	,	return_offsets_mapping=__lowerCamelCase	,	return_token_type_ids=__lowerCamelCase	,	return_length=__lowerCamelCase	,	verbose=__lowerCamelCase	,	return_tensors=__lowerCamelCase	,	**__lowerCamelCase	,	)
												return text_encoding

									# add pixel_values
									A							:					Any              =						self.image_processor(__lowerCamelCase	,	return_tensors=__lowerCamelCase )

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

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

									return encoding_image_processor







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	*__lowerCamelCase      :			Union[str, Any]	,	**__lowerCamelCase      :			List[Any] )      ->				List[str]:
									return self.tokenizer.batch_decode(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	*__lowerCamelCase      :			Dict	,	**__lowerCamelCase      :			Optional[Any] )      ->				Dict:
									return self.tokenizer.decode(*__lowerCamelCase	,	**__lowerCamelCase )







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Dict:
									A							:					Optional[int]              =						self.tokenizer.model_input_names
									A							:					Any              =						self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )




from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__SCREAMING_SNAKE_CASE           =  1.0_5_4_5_7_1_8_1_7e-3_4  # unit of ℏ : J * s

__SCREAMING_SNAKE_CASE           =  3e8  # unit of c : m * s^-1




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):

			if (force, area, distance).count(0    ) != 1:
						raise ValueError("One and only one argument must be 0"    )
			if force < 0:
						raise ValueError("Magnitude of force can not be negative"    )
			if distance < 0:
						raise ValueError("Distance can not be negative"    )
			if area < 0:
						raise ValueError("Area can not be negative"    )
			if force == 0:
						A							:					int              =						(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
						    240 * (distance) ** 4
						)
						return {"force": force}
			elif area == 0:
						A							:					Tuple              =						(240 * force * (distance) ** 4) / (
						    REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
						)
						return {"area": area}
			elif distance == 0:
						A							:					Dict              =						(
						    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
						) ** (1 / 4)
						return {"distance": distance}
			raise ValueError("One and only one argument must be 0"    )


# Run doctest
if __name__ == "__main__":
							import doctest

							doctest.testmod()