



"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Optional[int]:  # noqa: E741
		a_     :      int 						=     len(SCREAMING_SNAKE_CASE__	)
		a_     :      Union[str, Any] 						=     0
		a_     :      Dict 						=     [0] * n
		a_     :      Union[str, Any] 						=     [False] * n
		a_     :      str 						=     [False] * n

		def dfs(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
				if parent == root:
						out_edge_count += 1
				a_     :      str 						=     True
				a_     :      Optional[int] 						=     at

				for to in l[at]:
						if to == parent:
								pass
						elif not visited[to]:
								a_     :      str 						=     dfs(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
								a_     :      List[str] 						=     min(low[at], low[to]	)

								# AP found via bridge
								if at < low[to]:
										a_     :      Optional[int] 						=     True
								# AP found via cycle
								if at == low[to]:
										a_     :      Dict 						=     True
						else:
								a_     :      Union[str, Any] 						=     min(low[at], SCREAMING_SNAKE_CASE__	)
				return out_edge_count

		for i in range(SCREAMING_SNAKE_CASE__	):
				if not visited[i]:
						a_     :      Optional[Any] 						=     0
						a_     :      Tuple 						=     dfs(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, -1, SCREAMING_SNAKE_CASE__	)
						a_     :      Tuple 						=     out_edge_count > 1

		for x in range(len(SCREAMING_SNAKE_CASE__	)	):
				if is_art[x] is True:
						print(SCREAMING_SNAKE_CASE__	)


# Adjacency list of graph
SCREAMING_SNAKE_CASE_				= {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}
compute_ap(data)




"""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


SCREAMING_SNAKE_CASE_				= False




class 			snake_case_						(						unittest.TestCase				):
							pass




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






							def snake_case_  (   self      ):
									a_     :      Tuple 						=     VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"      )
									pipe.to(a_      )
									pipe.set_progress_bar_config(disable=a_      )

									a_     :      Optional[Any] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"      )
									a_     :      Optional[Any] 						=     torch.manual_seed(0      )
									a_     :      Dict 						=     pipe(
									    image=a_			,     generator=a_			,     guidance_scale=7.5			,     num_inference_steps=5_0			,     output_type="numpy"			,     ).images

									a_     :      List[Any] 						=     image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									a_     :      Any 						=     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