






def 		a__			(		_UpperCamelCase			: dict							):
   __lowerCamelCase													=    set()
   # edges = list of graph's edges
   __lowerCamelCase													=    get_edges(_UpperCamelCase							)

   # 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							,__lowerCamelCase													=    edges.pop()
      chosen_vertices.add(_UpperCamelCase							)
      chosen_vertices.add(_UpperCamelCase							)
      for edge in edges.copy():
         if from_node in edge or to_node in edge:
            edges.discard(_UpperCamelCase							)
   return chosen_vertices





def 		a__			(		_UpperCamelCase			: dict							):
   __lowerCamelCase													=    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)}")











import torch

from diffusers import StableDiffusionPipeline


a_			=					"""path-to-your-trained-model"""
a_			=					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

a_			=					"""A photo of sks dog in a bucket"""
a_			=					pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")






