


"""simple docstring"""


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






def      a__ (				__lowercase   )	->	int:

				# A local function to see if a dot lands in the circle.
				def is_in_circle(__lowercase     ,			__lowercase   ) -> 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(_SCREAMING_SNAKE_CASE   )   )
				# 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      a__ (				__lowercase     ,			__lowercase     ,			__lowercase = 0.0     ,			__lowercase = 1.0     ,			)	->	float:
				return mean(
				    function_to_integrate(uniform(_SCREAMING_SNAKE_CASE     ,			_SCREAMING_SNAKE_CASE   )   ) for _ in range(_SCREAMING_SNAKE_CASE   )   ) * (max_value - min_value)






def      a__ (				__lowercase     ,			__lowercase = 0.0     ,			__lowercase = 1.0   )	->	None:

				def identity_function(__lowercase   ) -> float:
								return x

				_A					  =			area_under_curve_estimator(
				    _SCREAMING_SNAKE_CASE     ,			_SCREAMING_SNAKE_CASE     ,			_SCREAMING_SNAKE_CASE     ,			_SCREAMING_SNAKE_CASE   )
				_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      a__ (				__lowercase   )	->	None:

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

				_A					  =			area_under_curve_estimator(
				    _SCREAMING_SNAKE_CASE     ,			_SCREAMING_SNAKE_CASE     ,			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 tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a_       							=    False
class snake_case      (  unittest.TestCase):
		pass





@nightly
@require_torch_gpu
class snake_case      (  unittest.TestCase):







		def  a_ (					self      :       Optional[int]		)       ->						str:





						'''simple docstring'''







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







		def  a_ (					self      :       Tuple		)       ->						Any:





						'''simple docstring'''







						_A					  =			VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"    ,   torch_dtype=torch.floataa		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)

						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe.dual_guided(
						    prompt="first prompt"    ,   image=a__    ,   text_to_image_strength=0.7_5    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type="numpy"    ,   ).images

						with tempfile.TemporaryDirectory() as tmpdirname:
										pipe.save_pretrained(a__		)
										_A					  =			VersatileDiffusionPipeline.from_pretrained(a__    ,   torch_dtype=torch.floataa		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)

						_A					  =			generator.manual_seed(0		)
						_A					  =			pipe.dual_guided(
						    prompt="first prompt"    ,   image=a__    ,   text_to_image_strength=0.7_5    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type="numpy"    ,   ).images

						assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"







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





						'''simple docstring'''







						_A					  =			VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"    ,   torch_dtype=torch.floataa		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)

						_A					  =			"cyberpunk 2077"
						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)
						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe.dual_guided(
						    prompt=a__    ,   image=a__    ,   text_to_image_strength=0.7_5    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=50    ,   output_type="numpy"    ,   ).images

						_A					  =			image[0, 2_53:2_56, 2_53:2_56, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						_A					  =			np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]		)

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

						_A					  =			"A painting of a squirrel eating a burger "
						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe.text_to_image(
						    prompt=a__    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=50    ,   output_type="numpy"		).images

						_A					  =			image[0, 2_53:2_56, 2_53:2_56, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						_A					  =			np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]		)

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

						_A					  =			pipe.image_variation(a__    ,   generator=a__    ,   output_type="numpy"		).images

						_A					  =			image[0, 2_53:2_56, 2_53:2_56, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						_A					  =			np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]		)

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