



'''simple docstring'''




from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst



def    __lowerCamelCase						(      )   ->    Optional[int]:
							UpperCAmelCase  ,  UpperCAmelCase  :	Tuple							  =	9, 1_4  # noqa: F841
							UpperCAmelCase  :	Any							  =	[
							    [0, 1, 4],
							    [0, 7, 8],
							    [1, 2, 8],
							    [7, 8, 7],
							    [7, 6, 1],
							    [2, 8, 2],
							    [8, 6, 6],
							    [2, 3, 7],
							    [2, 5, 4],
							    [6, 5, 2],
							    [3, 5, 1_4],
							    [3, 4, 9],
							    [5, 4, 1_0],
							    [1, 7, 1_1],
							]

							UpperCAmelCase  :	int							  =	defaultdict(_lowercase	)
							for nodea, nodea, cost in edges:
														adjancency[nodea].append([nodea, cost]	)
														adjancency[nodea].append([nodea, cost]	)

							UpperCAmelCase  :	Tuple							  =	mst(_lowercase	)

							UpperCAmelCase  :	Optional[int]							  =	[
							    [7, 6, 1],
							    [2, 8, 2],
							    [6, 5, 2],
							    [0, 1, 4],
							    [2, 5, 4],
							    [2, 3, 7],
							    [0, 7, 8],
							    [3, 4, 9],
							]

							for answer in expected:
														UpperCAmelCase  :	int							  =	tuple(answer[:2]	)
														UpperCAmelCase  :	Optional[Any]							  =	tuple(edge[::-1]	)
														assert edge in result or reverse in result






'''simple docstring'''




from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
							from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

