


'''simple docstring'''






import random


def       _snake_case     (  A				,					A				,					A )		->					Optional[int]:
							lowerCAmelCase__      =			a[left_index]
							lowerCAmelCase__      =			left_index + 1
							for j in range(left_index + 1				,					A ):
														if a[j] < pivot:
																					lowerCAmelCase__     ,      lowerCAmelCase__      =			a[i], a[j]
																					i += 1
							lowerCAmelCase__     ,      lowerCAmelCase__      =			a[i - 1], a[left_index]
							return i - 1


def       _snake_case     (  A				,					A				,					A )		->					int:
							if left < right:
														lowerCAmelCase__      =			random.randint(A				,					right - 1 )
														lowerCAmelCase__     ,      lowerCAmelCase__      =			(
														    a[left],
														    a[pivot],
														)  # switches the pivot with the left most bound
														lowerCAmelCase__      =			partition(A				,					A				,					A )
														quick_sort_random(
														    A				,					A				,					A )  # recursive quicksort to the left of the pivot point
														quick_sort_random(
														    A				,					pivot_index + 1				,					A )  # recursive quicksort to the right of the pivot point


def       _snake_case     (  )		->					Tuple:
							lowerCAmelCase__      =			input('''Enter numbers separated by a comma:\n''' ).strip()
							lowerCAmelCase__      =			[int(A ) for item in user_input.split(''',''' )]

							quick_sort_random(A				,					0				,					len(A ) )

							print(A )


if __name__ == "__main__":
		main()



'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


__UpperCAmelCase					       =							False




class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							pass




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





							'''simple docstring'''






							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''     )
														pipe.to(lowerCamelCase_     )
														pipe.set_progress_bar_config(disable=lowerCamelCase_     )

														lowerCAmelCase__      =			load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''     )
														lowerCAmelCase__      =			torch.manual_seed(0     )
														lowerCAmelCase__      =			pipe(
														    image=lowerCamelCase_      ,					generator=lowerCamelCase_      ,					guidance_scale=7.5      ,					num_inference_steps=50      ,					output_type='''numpy'''      ,					).images

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

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__      =			np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

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