



'''simple docstring'''




from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass

@dataclass
class     UpperCamelCase_				:
	lowercase    =				42
	lowercase    =				42




class     UpperCamelCase_				:





	def __init__(						self	,	A					)    ->    Any:
								UpperCAmelCase  :	list[list[Edge]]							  =	[[] for _ in range(A					)]
								UpperCAmelCase  :	Dict							  =	size





	def __getitem__(						self	,	A					)    ->    Iterator[Edge]:
								return iter(self._graph[vertex]					)





	@property
	def 				_lowercase(						self					)    ->    Optional[int]:
								return self._size





	def 				_lowercase(						self	,	A	,	A	,	A					)    ->    List[str]:
								if weight not in (0, 1):
															raise ValueError("""Edge weight must be either 0 or 1."""					)

								if to_vertex < 0 or to_vertex >= self.size:
															raise ValueError("""Vertex indexes must be in [0; size)."""					)

								self._graph[from_vertex].append(Edge(A	,	A					)					)






	def 				_lowercase(						self	,	A	,	A					)    ->    int | None:
								UpperCAmelCase  :	List[str]							  =	deque([start_vertex]					)
								UpperCAmelCase  :	list[int | None]							  =	[None] * self.size
								UpperCAmelCase  :	Optional[int]							  =	0

								while queue:
															UpperCAmelCase  :	Optional[Any]							  =	queue.popleft()
															UpperCAmelCase  :	int							  =	distances[current_vertex]
															if current_distance is None:
																						continue

															for edge in self[current_vertex]:
																						UpperCAmelCase  :	List[Any]							  =	current_distance + edge.weight
																						UpperCAmelCase  :	Dict							  =	distances[edge.destination_vertex]
																						if (
																						    isinstance(A	,	A					)
																						    and new_distance >= dest_vertex_distance
																						):
																													continue
																						UpperCAmelCase  :	Union[str, Any]							  =	new_distance
																						if edge.weight == 0:
																													queue.appendleft(edge.destination_vertex					)
																						else:
																													queue.append(edge.destination_vertex					)

								if distances[finish_vertex] is None:
															raise ValueError("""No path from start_vertex to finish_vertex."""					)

								return distances[finish_vertex]


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     :    Tuple      						=					False

class     UpperCamelCase_				(							unittest.TestCase    ):
	pass




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





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	Any							  =	VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion"""					)
								pipe.to(A					)
								pipe.set_progress_bar_config(disable=A					)

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

								UpperCAmelCase  :	Tuple							  =	image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								UpperCAmelCase  :	List[str]							  =	np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]					)

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

