






'''simple docstring'''







def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> set:
							lowerCamelCase__      :			List[str] 						= set()
							# edges = list of graph's edges
							lowerCamelCase__      :			Optional[int] 						= get_edges(lowerCamelCase_   )

							# While there are still elements in edges list, take an arbitrary edge
							# (from_node, to_node) and add his extremity to chosen_vertices and then
							# remove all arcs adjacent to the from_node and to_node
							while edges:
														lowerCamelCase__      :			List[str] 						= edges.pop()
														chosen_vertices.add(lowerCamelCase_   )
														chosen_vertices.add(lowerCamelCase_   )
														for edge in edges.copy():
																					if from_node in edge or to_node in edge:
																												edges.discard(lowerCamelCase_   )
							return chosen_vertices





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> set:
							lowerCamelCase__      :			Optional[int] 						= set()
							for from_node, to_nodes in graph.items():
														for to_node in to_nodes:
																					edges.add((from_node, to_node)   )
							return edges


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
			# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")







'''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

