




'''simple docstring'''


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

from .haversine_distance import haversine_distance

_snake_case : Tuple  				=						637_8137.0
_snake_case : Optional[int]  				=						635_6752.31_4245
_snake_case : int  				=						6378137
def 						snake_case_       (UpperCamelCase			:    float					,		UpperCamelCase			:    float					,		UpperCamelCase			:    float					,		UpperCamelCase			:    float ):





							'''simple docstring'''


							_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(UpperCamelCase ) ) )
							_a      	=							atan((1 - flattening) * tan(radians(UpperCamelCase ) ) )

							# Compute central angle between two points
							# using haversine theta. sigma =  haversine_distance / equatorial radius
							_a      	=							haversine_distance(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase ) / 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(UpperCamelCase ) ** 2) * (cos(UpperCamelCase ) ** 2)
							_a      	=							cos(sigma / 2 ) ** 2
							_a      	=							(sigma - sin(UpperCamelCase )) * (x_numerator / x_demonimator)

							# Intermediate Y value
							# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
							_a      	=							(cos(UpperCamelCase ) ** 2) * (sin(UpperCamelCase ) ** 2)
							_a      	=							sin(sigma / 2 ) ** 2
							_a      	=							(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'''


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

@slow
@require_torch_gpu
class      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    int   )     ->				Any:


														"""simple docstring"""


														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				int:


														"""simple docstring"""


														_a      	=							StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														sd_pipe.set_scheduler('''sample_euler'''   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.manual_seed(0   )
														_a      	=							sd_pipe([prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=9.0				,     num_inference_steps=20				,     output_type='''np'''   )

														_a      	=							output.images

														_a      	=							image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														_a      	=							np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]   )

														assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2







							def   __lowerCAmelCase     (    self			:    Any   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														sd_pipe.set_scheduler('''sample_euler'''   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.manual_seed(0   )
														_a      	=							sd_pipe([prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=9.0				,     num_inference_steps=20				,     output_type='''np'''   )

														_a      	=							output.images

														_a      	=							image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														_a      	=							np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]   )

														assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5e-1







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														sd_pipe.set_scheduler('''sample_dpmpp_2m'''   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.manual_seed(0   )
														_a      	=							sd_pipe(
														    [prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=7.5				,     num_inference_steps=15				,     output_type='''np'''				,     use_karras_sigmas=lowerCAmelCase_				,     )

														_a      	=							output.images

														_a      	=							image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														_a      	=							np.array(
														    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]   )

														assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2

