from __future__ import annotations

import os
from collections.abc import Mapping

SCREAMING_SNAKE_CASE		:     Dict         =	tuple[int, int]

class        UpperCamelCase						:





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase						)				->				List[Any]:
												UpperCamelCase_		:							List[Any]  =		vertices
												UpperCamelCase_		:							int  =		{
												    (min(__lowerCAmelCase						), max(__lowerCAmelCase						)): weight for edge, weight in edges.items()
												}





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase						)				->				Dict:
												self.vertices.add(edge[0]						)
												self.vertices.add(edge[1]						)
												UpperCamelCase_		:							Dict  =		weight





						def        A_							(self						)				->				Dict:
												UpperCamelCase_		:							Union[str, Any]  =		Graph({min(self.vertices						)}			,     {}						)
												UpperCamelCase_		:							Dict  =		42
												UpperCamelCase_		:							Optional[int]  =		42
												UpperCamelCase_		:							Any  =		42
												UpperCamelCase_		:							Union[str, Any]  =		42

												while len(subgraph.vertices						) < len(self.vertices						):
																		UpperCamelCase_		:							int  =		max(self.edges.values()						) + 1
																		for edge, weight in self.edges.items():
																								if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
																														if weight < min_weight:
																																				UpperCamelCase_		:							Tuple  =		edge
																																				UpperCamelCase_		:							Any  =		weight

																		subgraph.add_edge(__lowerCAmelCase			,     __lowerCAmelCase						)

												return subgraph






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str = "p107_network.txt"			):
						UpperCamelCase_		:							int  =		os.path.abspath(os.path.dirname(lowerCAmelCase__			)			)
						UpperCamelCase_		:							str  =		os.path.join(lowerCAmelCase__		, lowerCAmelCase__			)
						UpperCamelCase_		:							Optional[int]  =		{}
						UpperCamelCase_		:							str  =		42
						UpperCamelCase_		:							int  =		42
						UpperCamelCase_		:							int  =		42

						with open(lowerCAmelCase__			) as f:
												UpperCamelCase_		:							Any  =		f.read().strip().split("""\n"""			)

						UpperCamelCase_		:							Tuple  =		[line.split(""","""			) for line in data]

						for edgea in range(1		, len(lowerCAmelCase__			)			):
												for edgea in range(lowerCAmelCase__			):
																		if adjaceny_matrix[edgea][edgea] != "-":
																								UpperCamelCase_		:							Tuple  =		int(adjaceny_matrix[edgea][edgea]			)

						UpperCamelCase_		:							List[str]  =		Graph(set(range(len(lowerCAmelCase__			)			)			)		, lowerCAmelCase__			)

						UpperCamelCase_		:							Optional[Any]  =		graph.prims_algorithm()

						UpperCamelCase_		:							Optional[Any]  =		sum(graph.edges.values()			)
						UpperCamelCase_		:							str  =		sum(subgraph.edges.values()			)

						return initial_total - optimal_total


if __name__ == "__main__":
	print(F'''{solution() = }''')







"""simple docstring"""






import torch

from diffusers import StableDiffusionPipeline


__lowerCAmelCase       :			str   	="""path-to-your-trained-model"""
__lowerCAmelCase       :			int   	=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

__lowerCAmelCase       :			Optional[int]   	="""A photo of sks dog in a bucket"""
__lowerCAmelCase       :			int   	=pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

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



