
"""simple docstring"""







from math import atan, cos, radians, sin, tan

from .haversine_distance import haversine_distance

lowerCamelCase__           =	6_3_7_8_1_3_7.0
lowerCamelCase__           =	6_3_5_6_7_5_2.3_1_4_2_4_5
lowerCamelCase__           =	6_378_137
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[Any]		    =   (AXIS_A - AXIS_B) / AXIS_A
	# Parametric latitudes
	# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
	__lowerCAmelCase    :		Tuple		    =   atan((1 - flattening) * tan(radians(_UpperCamelCase	)	)	)
	__lowerCAmelCase    :		Any		    =   atan((1 - flattening) * tan(radians(_UpperCamelCase	)	)	)

	# Compute central angle between two points
	# using haversine theta. sigma =  haversine_distance / equatorial radius
	__lowerCAmelCase    :		Union[str, Any]		    =   haversine_distance(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	) / EQUATORIAL_RADIUS

	# Intermediate P and Q values
	__lowerCAmelCase    :		Optional[int]		    =   (b_lata + b_lata) / 2
	__lowerCAmelCase    :		Tuple		    =   (b_lata - b_lata) / 2

	# Intermediate X value
	# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
	__lowerCAmelCase    :		str		    =   (sin(_UpperCamelCase	) ** 2) * (cos(_UpperCamelCase	) ** 2)
	__lowerCAmelCase    :		List[Any]		    =   cos(sigma / 2	) ** 2
	__lowerCAmelCase    :		List[str]		    =   (sigma - sin(_UpperCamelCase	)) * (x_numerator / x_demonimator)

	# Intermediate Y value
	# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
	__lowerCAmelCase    :		str		    =   (cos(_UpperCamelCase	) ** 2) * (sin(_UpperCamelCase	) ** 2)
	__lowerCAmelCase    :		Optional[Any]		    =   sin(sigma / 2	) ** 2
	__lowerCAmelCase    :		Union[str, Any]		    =   (sigma + sin(_UpperCamelCase	)) * (y_numerator / y_denominator)

	return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))


if __name__ == "__main__":
							import doctest

							doctest.testmod()

"""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 				A__ (			_lowerCamelCase):
		A_		: Any            =			['image_processor', 'tokenizer']
		A_		: Optional[Any]            =			'BlipImageProcessor'
		A_		: int            =			'AutoTokenizer'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Union[str, Any]		    =   False
			super().__init__(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[Any]		    =   self.image_processor

		def __call__(   self ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = 0 ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			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:
				__lowerCAmelCase    :		Optional[int]		    =   self.tokenizer
				__lowerCAmelCase    :		List[str]		    =   self.tokenizer(
				    text=_SCREAMING_SNAKE_CASE ,	add_special_tokens=_SCREAMING_SNAKE_CASE ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=_SCREAMING_SNAKE_CASE ,	stride=_SCREAMING_SNAKE_CASE ,	pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,	return_attention_mask=_SCREAMING_SNAKE_CASE ,	return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,	return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE ,	return_token_type_ids=_SCREAMING_SNAKE_CASE ,	return_length=_SCREAMING_SNAKE_CASE ,	verbose=_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)
				return text_encoding

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

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

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

			return encoding_image_processor

		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)







		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   self.tokenizer.model_input_names
			__lowerCAmelCase    :		Union[str, Any]		    =   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)