



"""simple docstring"""







from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean

def       _lowerCamelCase				(							UpperCAmelCase_ :       int     )	-> Optional[Any]:






	"""simple docstring"""





	def is_in_circle(UpperCAmelCase_ :       float,	UpperCAmelCase_ :       float     ) -> bool:
		A__											=					sqrt((x**2) + (y**2)     )
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	A__											=					mean(
	    int(is_in_circle(uniform(-1.0,	1.0     ),	uniform(-1.0,	1.0     )     )     )
	    for _ in range(UpperCAmelCase_     )     )
	# The ratio of the area for circle to square is pi/4.
	A__											=					proportion * 4
	print(F"""The estimated value of pi is {pi_estimate}"""     )
	print(F"""The numpy value of pi is {pi}"""     )
	print(F"""The total error is {abs(pi - pi_estimate     )}"""     )

def       _lowerCamelCase				(							UpperCAmelCase_ :       int,	UpperCAmelCase_ :       Callable[[float], float],	UpperCAmelCase_ :       float = 0.0,	UpperCAmelCase_ :       float = 1.0,	)	-> float:






	"""simple docstring"""





	return mean(
	    function_to_integrate(uniform(UpperCAmelCase_,	UpperCAmelCase_     )     ) for _ in range(UpperCAmelCase_     )     ) * (max_value - min_value)

def       _lowerCamelCase				(							UpperCAmelCase_ :       int,	UpperCAmelCase_ :       float = 0.0,	UpperCAmelCase_ :       float = 1.0     )	-> None:






	"""simple docstring"""





	def identity_function(UpperCAmelCase_ :       float     ) -> float:
		return x

	A__											=					area_under_curve_estimator(
	    UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_     )
	A__											=					(max_value * max_value - min_value * min_value) / 2

	print("******************"     )
	print(F"""Estimating area under y=x where x varies from {min_value} to {max_value}"""     )
	print(F"""Estimated value is {estimated_value}"""     )
	print(F"""Expected value is {expected_value}"""     )
	print(F"""Total error is {abs(estimated_value - expected_value     )}"""     )
	print("******************"     )

def       _lowerCamelCase				(							UpperCAmelCase_ :       int     )	-> None:






	"""simple docstring"""





	def function_to_integrate(UpperCAmelCase_ :       float     ) -> float:
		return sqrt(4.0 - x * x     )

	A__											=					area_under_curve_estimator(
	    UpperCAmelCase_,	UpperCAmelCase_,	0.0,	2.0     )

	print("******************"     )
	print("Estimating pi using area_under_curve_estimator"     )
	print(F"""Estimated value is {estimated_value}"""     )
	print(F"""Expected value is {pi}"""     )
	print(F"""Total error is {abs(estimated_value - pi     )}"""     )
	print("******************"     )


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

