


"""simple docstring"""



from __future__ import annotations

import os
from collections.abc import Mapping

__snake_case             =   tuple[int, int]





class _lowerCAmelCase    :







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		None:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   vertices
       snake_case   :				List[Any]				 =   {
           (min(UpperCamelCase__						), max(UpperCamelCase__						)): weight for edge, weight in edges.items()
       }







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		None:

       '''simple docstring'''




       self.vertices.add(edge[0]						)
       self.vertices.add(edge[1]						)
       snake_case   :				str				 =   weight







   def 			lowerCamelCase		(     self						)     ->		Graph:

       '''simple docstring'''




       snake_case   :				Dict				 =   Graph({min(self.vertices						)}						,     {}						)
       snake_case   :				List[str]				 =   42
       snake_case   :				List[Any]				 =   42
       snake_case   :				Union[str, Any]				 =   42
       snake_case   :				str				 =   42

       while len(subgraph.vertices						) < len(self.vertices						):
           snake_case   :				Optional[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:
                       snake_case   :				str				 =   edge
                       snake_case   :				Any				 =   weight

           subgraph.add_edge(UpperCamelCase__						,     UpperCamelCase__						)

       return subgraph







def   __lowerCAmelCase						(						lowercase  :  str = "p107_network.txt"			)     ->						str:


    """simple docstring"""





    snake_case   :				int				 =   os.path.abspath(os.path.dirname(__UpperCamelCase			)			)
    snake_case   :				Union[str, Any]				 =   os.path.join(__UpperCamelCase						,							__UpperCamelCase			)
    snake_case   :				int				 =   {}
    snake_case   :				List[str]				 =   42
    snake_case   :				Tuple				 =   42
    snake_case   :				Tuple				 =   42

    with open(__UpperCamelCase			) as f:
        snake_case   :				List[Any]				 =   f.read().strip().split("\n"			)

    snake_case   :				Dict				 =   [line.split(","			) for line in data]

    for edgea in range(1						,							len(__UpperCamelCase			)			):
        for edgea in range(__UpperCamelCase			):
            if adjaceny_matrix[edgea][edgea] != "-":
                snake_case   :				Optional[int]				 =   int(adjaceny_matrix[edgea][edgea]			)

    snake_case   :				Union[str, Any]				 =   Graph(set(range(len(__UpperCamelCase			)			)			)						,							__UpperCamelCase			)

    snake_case   :				List[Any]				 =   graph.prims_algorithm()

    snake_case   :				int				 =   sum(graph.edges.values()			)
    snake_case   :				int				 =   sum(subgraph.edges.values()			)

    return initial_total - optimal_total


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









"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class _lowerCAmelCase    (				unittest.TestCase     ):







   @property
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       torch.manual_seed(0						)
       snake_case   :				int				 =   UNetaDModel(
           block_out_channels=(32, 64)						,     layers_per_block=2						,     sample_size=32						,     in_channels=3						,     out_channels=3						,     down_block_types=("DownBlock2D", "AttnDownBlock2D")						,     up_block_types=("AttnUpBlock2D", "UpBlock2D")						,     )
       return model







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				Any				 =   self.dummy_uncond_unet
       snake_case   :				Tuple				 =   KarrasVeScheduler()

       snake_case   :				int				 =   KarrasVePipeline(unet=UpperCamelCase__						,     scheduler=UpperCamelCase__						)
       pipe.to(UpperCamelCase__						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case   :				Optional[Any]				 =   torch.manual_seed(0						)
       snake_case   :				List[Any]				 =   pipe(num_inference_steps=2						,     generator=UpperCamelCase__						,     output_type="numpy"						).images

       snake_case   :				Dict				 =   torch.manual_seed(0						)
       snake_case   :				Dict				 =   pipe(num_inference_steps=2						,     generator=UpperCamelCase__						,     output_type="numpy"						,     return_dict=UpperCamelCase__						)[0]

       snake_case   :				Tuple				 =   image[0, -3:, -3:, -1]
       snake_case   :				Tuple				 =   image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case   :				int				 =   np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]						)

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







@slow
@require_torch
class _lowerCAmelCase    (				unittest.TestCase     ):







   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   "google/ncsnpp-celebahq-256"
       snake_case   :				List[str]				 =   UNetaDModel.from_pretrained(UpperCamelCase__						)
       snake_case   :				Optional[Any]				 =   KarrasVeScheduler()

       snake_case   :				Optional[int]				 =   KarrasVePipeline(unet=UpperCamelCase__						,     scheduler=UpperCamelCase__						)
       pipe.to(UpperCamelCase__						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case   :				Dict				 =   torch.manual_seed(0						)
       snake_case   :				Union[str, Any]				 =   pipe(num_inference_steps=20						,     generator=UpperCamelCase__						,     output_type="numpy"						).images

       snake_case   :				int				 =   image[0, -3:, -3:, -1]
       assert image.shape == (1, 256, 256, 3)
       snake_case   :				Any				 =   np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]						)

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





