

from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
lowerCamelCase 	=   {
    "1": ["2", "3", "4", "5"],
    "2": ["1", "3", "4", "5"],
    "3": ["1", "2", "4", "5", "10"],
    "4": ["1", "2", "3", "5", "6"],
    "5": ["1", "2", "3", "4", "7"],
    "6": ["7", "8", "9", "10", "4"],
    "7": ["6", "8", "9", "10", "5"],
    "8": ["6", "7", "9", "10"],
    "9": ["6", "7", "8", "10"],
    "10": ["6", "7", "8", "9", "3"],
}




def      UpperCAmelCase__							(     _A :					Union[str, Any]				):





  '''simple docstring'''







  a__    ={node: {node} for node in graph}

  a__    ={node: graph[node][:] for node in graph}

  while len(lowerCAmelCase__				) > 2:
    # Choose a random edge.
    a__    =random.choice(list(graph_copy.keys()				)				)
    a__    =random.choice(graph_copy[u]				)

    # Contract edge (u, v) to new node uv
    a__    =u + v
    a__    =list(set(graph_copy[u] + graph_copy[v]				)				)
    uv_neighbors.remove(lowerCAmelCase__				)
    uv_neighbors.remove(lowerCAmelCase__				)
    a__    =uv_neighbors
    for neighbor in uv_neighbors:
      graph_copy[neighbor].append(lowerCAmelCase__				)

    a__    =set(contracted_nodes[u].union(contracted_nodes[v]				)				)

    # Remove nodes u and v.
    del graph_copy[u]
    del graph_copy[v]
    for neighbor in uv_neighbors:
      if u in graph_copy[neighbor]:
        graph_copy[neighbor].remove(lowerCAmelCase__				)
      if v in graph_copy[neighbor]:
        graph_copy[neighbor].remove(lowerCAmelCase__				)

    # Find cutset.
  a__    =[contracted_nodes[node] for node in graph_copy]
  return {
      (node, neighbor)
      for node in groups[0]
      for neighbor in graph[node]
      if neighbor in groups[1]
  }


if __name__ == "__main__":
       print(partition_graph(TEST_GRAPH))


import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference







@slow
@require_torch_gpu
class 							lowercase						(   unittest.TestCase						):




							def 					A__ (							self):
												lowercase		      =		'''runwayml/stable-diffusion-v1-5'''
												lowercase		      =		TextToVideoZeroPipeline.from_pretrained(A__  ,torch_dtype=torch.floataa).to('''cuda''')
												lowercase		      =		DDIMScheduler.from_config(pipe.scheduler.config)
												lowercase		      =		torch.Generator(device='''cuda''').manual_seed(0)

												lowercase		      =		'''A bear is playing a guitar on Times Square'''
												lowercase		      =		pipe(prompt=A__  ,generator=A__).images

												lowercase		      =		load_pt(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt''')

												assert_mean_pixel_difference(A__  ,A__)




