






'''simple docstring'''







import random





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase = False      )      ->       dict:
							lowerCamelCase__					:	dict         =       {i: [] for i in range(UpperCamelCase      )}

							# if probability is greater or equal than 1, then generate a complete graph
							if probability >= 1:
														return complete_graph(UpperCamelCase      )
							# if probability is lower or equal than 0, then return a graph without edges
							if probability <= 0:
														return graph

							# for each couple of nodes, add an edge from u to v
							# if the number randomly generated is greater than probability probability
							for i in range(UpperCamelCase      ):
														for j in range(i + 1				,			UpperCamelCase      ):
																					if random.random() < probability:
																												graph[i].append(UpperCamelCase      )
																												if not directed:
																																			# if the graph is undirected, add an edge in from j to i, either
																																			graph[j].append(UpperCamelCase      )
							return graph





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       dict:
							return {
							    i: [j for j in range(UpperCamelCase      ) if i != j] for i in range(UpperCamelCase      )
							}


if __name__ == "__main__":
			import doctest

			doctest.testmod()









'''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


_A	:			Union[str, Any]         =False

class _lowercase	(				unittest.TestCase		):
							pass



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




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Tuple         =       VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion"""     )
														pipe.to(UpperCamelCase__     )
														pipe.set_progress_bar_config(disable=UpperCamelCase__     )

														lowerCamelCase__					:	Optional[int]         =       load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""     )
														lowerCamelCase__					:	List[Any]         =       torch.manual_seed(0     )
														lowerCamelCase__					:	List[Any]         =       pipe(
														    image=UpperCamelCase__			,							generator=UpperCamelCase__			,							guidance_scale=7.5			,							num_inference_steps=50			,							output_type="""numpy"""			,							).images

														lowerCamelCase__					:	List[str]         =       image[0, 253:256, 253:256, -1]

														assert image.shape == (1, 512, 512, 3)
														lowerCamelCase__					:	Tuple         =       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

