





"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class       a							(   unittest.TestCase  ):



 @property
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  torch.manual_seed(0     )
  lowerCAmelCase    				= 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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.dummy_uncond_unet
  lowerCAmelCase    				= ScoreSdeVeScheduler()

  lowerCAmelCase    				= ScoreSdeVePipeline(unet=_snake_case				,       scheduler=_snake_case     )
  sde_ve.to(_snake_case     )
  sde_ve.set_progress_bar_config(disable=_snake_case     )

  lowerCAmelCase    				= torch.manual_seed(0     )
  lowerCAmelCase    				= sde_ve(num_inference_steps=2				,       output_type='numpy'				,       generator=_snake_case     ).images

  lowerCAmelCase    				= torch.manual_seed(0     )
  lowerCAmelCase    				= sde_ve(num_inference_steps=2				,       output_type='numpy'				,       generator=_snake_case				,       return_dict=_snake_case     )[
      0
  ]

  lowerCAmelCase    				= image[0, -3:, -3:, -1]
  lowerCAmelCase    				= image_from_tuple[0, -3:, -3:, -1]

  assert image.shape == (1, 32, 32, 3)
  lowerCAmelCase    				= 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       a							(   unittest.TestCase  ):



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= 'google/ncsnpp-church-256'
  lowerCAmelCase    				= UNetaDModel.from_pretrained(_snake_case     )

  lowerCAmelCase    				= ScoreSdeVeScheduler.from_pretrained(_snake_case     )

  lowerCAmelCase    				= ScoreSdeVePipeline(unet=_snake_case				,       scheduler=_snake_case     )
  sde_ve.to(_snake_case     )
  sde_ve.set_progress_bar_config(disable=_snake_case     )

  lowerCAmelCase    				= torch.manual_seed(0     )
  lowerCAmelCase    				= sde_ve(num_inference_steps=10				,       output_type='numpy'				,       generator=_snake_case     ).images

  lowerCAmelCase    				= image[0, -3:, -3:, -1]

  assert image.shape == (1, 2_56, 2_56, 3)

  lowerCAmelCase    				= np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]     )

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












"""simple docstring"""
from __future__ import annotations

import os
from collections.abc import Mapping

__UpperCamelCase     :						Optional[Any]						       =  tuple[int, int]







class       a							:



 def __init__(     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= vertices
  lowerCAmelCase    				= {
      (min(_snake_case     ), max(_snake_case     )): weight for edge, weight in edges.items()
  }



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  self.vertices.add(edge[0]     )
  self.vertices.add(edge[1]     )
  lowerCAmelCase    				= weight



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= Graph({min(self.vertices     )}				,       {}     )
  lowerCAmelCase    				= 42
  lowerCAmelCase    				= 42
  lowerCAmelCase    				= 42
  lowerCAmelCase    				= 42

  while len(subgraph.vertices     ) < len(self.vertices     ):
   lowerCAmelCase    				= 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:
      lowerCAmelCase    				= edge
      lowerCAmelCase    				= weight

   subgraph.add_edge(_snake_case				,       _snake_case     )

  return subgraph


def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str = "p107_network.txt"			):
 lowerCAmelCase    				= os.path.abspath(os.path.dirname(_UpperCAmelCase			)			)
 lowerCAmelCase    				= os.path.join(_UpperCAmelCase  ,     _UpperCAmelCase			)
 lowerCAmelCase    				= {}
 lowerCAmelCase    				= 42
 lowerCAmelCase    				= 42
 lowerCAmelCase    				= 42

 with open(_UpperCAmelCase			) as f:
  lowerCAmelCase    				= f.read().strip().split('\n'			)

 lowerCAmelCase    				= [line.split(','			) for line in data]

 for edgea in range(1  ,     len(_UpperCAmelCase			)			):
  for edgea in range(_UpperCAmelCase			):
   if adjaceny_matrix[edgea][edgea] != "-":
    lowerCAmelCase    				= int(adjaceny_matrix[edgea][edgea]			)

 lowerCAmelCase    				= Graph(set(range(len(_UpperCAmelCase			)			)			)  ,     _UpperCAmelCase			)

 lowerCAmelCase    				= graph.prims_algorithm()

 lowerCAmelCase    				= sum(graph.edges.values()			)
 lowerCAmelCase    				= sum(subgraph.edges.values()			)

 return initial_total - optimal_total


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





