





'''simple docstring'''







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

from .haversine_distance import haversine_distance

lowerCAmelCase_					:    List[Any]      	=			6_378_137.0
lowerCAmelCase_					:    List[Any]      	=			6_356_752.314_245
lowerCAmelCase_					:    int      	=			6_37_81_37






def  _lowerCamelCase				( lowercase						:      float    ,							lowercase						:      float    ,							lowercase						:      float    ,							lowercase						:      float						)	->	float:
				_a								= (AXIS_A - AXIS_B) / AXIS_A
				# Parametric latitudes
				# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
				_a								= atan((1 - flattening) * tan(radians(lowercase						)						)						)
				_a								= atan((1 - flattening) * tan(radians(lowercase						)						)						)

				# Compute central angle between two points
				# using haversine theta. sigma =  haversine_distance / equatorial radius
				_a								= haversine_distance(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						) / EQUATORIAL_RADIUS

				# Intermediate P and Q values
				_a								= (b_lata + b_lata) / 2
				_a								= (b_lata - b_lata) / 2

				# Intermediate X value
				# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
				_a								= (sin(lowercase						) ** 2) * (cos(lowercase						) ** 2)
				_a								= cos(sigma / 2						) ** 2
				_a								= (sigma - sin(lowercase						)) * (x_numerator / x_demonimator)

				# Intermediate Y value
				# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
				_a								= (cos(lowercase						) ** 2) * (sin(lowercase						) ** 2)
				_a								= sin(sigma / 2						) ** 2
				_a								= (sigma + sin(lowercase						)) * (y_numerator / y_denominator)

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()







'''simple docstring'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase_					:    Dict      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Optional[int]      	=			{
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='deta'
				__a            ={
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				}




				def __init__(  self     :					List[str]					,				__a     :					List[str]=None					,				__a     :					Dict=9_00					,				__a     :					str=20_48					,				__a     :					Tuple=6					,				__a     :					List[str]=20_48					,				__a     :					str=8					,				__a     :					Union[str, Any]=6					,				__a     :					int=10_24					,				__a     :					List[Any]=8					,				__a     :					Dict=0.0					,				__a     :					Tuple=True					,				__a     :					Optional[Any]="relu"					,				__a     :					Tuple=2_56					,				__a     :					Optional[Any]=0.1					,				__a     :					int=0.0					,				__a     :					List[Any]=0.0					,				__a     :					Optional[int]=0.02					,				__a     :					str=1.0					,				__a     :					Dict=True					,				__a     :					Dict=False					,				__a     :					Optional[int]="sine"					,				__a     :					Any=5					,				__a     :					List[str]=4					,				__a     :					Optional[int]=4					,				__a     :					List[str]=True					,				__a     :					str=3_00					,				__a     :					int=True					,				__a     :					int=True					,				__a     :					Tuple=1					,				__a     :					Optional[int]=5					,				__a     :					Tuple=2					,				__a     :					Dict=1					,				__a     :					Optional[int]=1					,				__a     :					Any=5					,				__a     :					Optional[int]=2					,				__a     :					Dict=0.1					,				__a     :					str=0.25					,				**__a     :					Tuple					,				):
								if backbone_config is None:
												logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."					)
												_a								= CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]					)
								else:
												if isinstance(__a					,				__a					):
																_a								= backbone_config.pop("model_type"					)
																_a								= CONFIG_MAPPING[backbone_model_type]
																_a								= config_class.from_dict(__a					)

								_a								= backbone_config
								_a								= num_queries
								_a								= max_position_embeddings
								_a								= d_model
								_a								= encoder_ffn_dim
								_a								= encoder_layers
								_a								= encoder_attention_heads
								_a								= decoder_ffn_dim
								_a								= decoder_layers
								_a								= decoder_attention_heads
								_a								= dropout
								_a								= attention_dropout
								_a								= activation_dropout
								_a								= activation_function
								_a								= init_std
								_a								= init_xavier_std
								_a								= encoder_layerdrop
								_a								= auxiliary_loss
								_a								= position_embedding_type
								# deformable attributes
								_a								= num_feature_levels
								_a								= encoder_n_points
								_a								= decoder_n_points
								_a								= two_stage
								_a								= two_stage_num_proposals
								_a								= with_box_refine
								_a								= assign_first_stage
								if two_stage is True and with_box_refine is False:
												raise ValueError("If two_stage is True, with_box_refine must be True."					)
								# Hungarian matcher
								_a								= class_cost
								_a								= bbox_cost
								_a								= giou_cost
								# Loss coefficients
								_a								= mask_loss_coefficient
								_a								= dice_loss_coefficient
								_a								= bbox_loss_coefficient
								_a								= giou_loss_coefficient
								_a								= eos_coefficient
								_a								= focal_alpha
								super().__init__(is_encoder_decoder=__a					,				**__a					)




				@property
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								return self.encoder_attention_heads




				@property
				def   UpperCamelCase__   (  self     :					Dict					):
								return self.d_model




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= copy.deepcopy(self.__dict__					)
								_a								= self.backbone_config.to_dict()
								_a								= self.__class__.model_type
								return output
