
"""simple docstring"""
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_a   :      Optional[int]       =				False






@nightly
@require_torch_gpu
class   _UpperCAmelCase					(       unittest.TestCase):
    def        lowerCamelCase__						(     self  ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"  )
      # remove text_unet
      pipe.remove_unused_weights()
      pipe.to(snake_case_  )
      pipe.set_progress_bar_config(disable=snake_case_  )

      _snake_case  :    str            = load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"  )

      _snake_case  :    List[Any]            = torch.manual_seed(0  )
      _snake_case  :    Dict            = pipe(
          prompt="first prompt"    ,     image=snake_case_    ,     text_to_image_strength=0.75    ,     generator=snake_case_    ,     guidance_scale=7.5    ,     num_inference_steps=2    ,     output_type="numpy"    ,     ).images

      with tempfile.TemporaryDirectory() as tmpdirname:
        pipe.save_pretrained(snake_case_  )
        _snake_case  :    Union[str, Any]            = VersatileDiffusionDualGuidedPipeline.from_pretrained(snake_case_  )

      pipe.to(snake_case_  )
      pipe.set_progress_bar_config(disable=snake_case_  )

      _snake_case  :    List[str]            = generator.manual_seed(0  )
      _snake_case  :    Optional[int]            = pipe(
          prompt="first prompt"    ,     image=snake_case_    ,     text_to_image_strength=0.75    ,     generator=snake_case_    ,     guidance_scale=7.5    ,     num_inference_steps=2    ,     output_type="numpy"    ,     ).images

      assert np.abs(image - new_image  ).sum() < 1E-5, "Models don't have the same forward pass"


    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"  )
      pipe.remove_unused_weights()
      pipe.to(snake_case_  )
      pipe.set_progress_bar_config(disable=snake_case_  )

      _snake_case  :    Dict            = "cyberpunk 2077"
      _snake_case  :    Optional[Any]            = load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"  )
      _snake_case  :    List[Any]            = torch.manual_seed(0  )
      _snake_case  :    List[str]            = pipe(
          prompt=snake_case_    ,     image=snake_case_    ,     text_to_image_strength=0.75    ,     generator=snake_case_    ,     guidance_scale=7.5    ,     num_inference_steps=50    ,     output_type="numpy"    ,     ).images

      _snake_case  :    int            = image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      _snake_case  :    int            = np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]  )

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



"""simple docstring"""
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
_a   :      List[str]       =				{
    """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 					a__ ( a :					dict[str, list[str]]		):

  """simple docstring"""







  _snake_case  :    List[Any]            = {node: {node} for node in graph}

  _snake_case  :    str            = {node: graph[node][:] for node in graph}

  while len(a		) > 2:
    # Choose a random edge.
    _snake_case  :    str            = random.choice(list(graph_copy.keys()		)		)
    _snake_case  :    Union[str, Any]            = random.choice(graph_copy[u]		)

    # Contract edge (u, v) to new node uv
    _snake_case  :    Union[str, Any]            = u + v
    _snake_case  :    Union[str, Any]            = list(set(graph_copy[u] + graph_copy[v]		)		)
    uv_neighbors.remove(a		)
    uv_neighbors.remove(a		)
    _snake_case  :    Union[str, Any]            = uv_neighbors
    for neighbor in uv_neighbors:
      graph_copy[neighbor].append(a		)

    _snake_case  :    Dict            = 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(a		)
      if v in graph_copy[neighbor]:
        graph_copy[neighbor].remove(a		)

    # Find cutset.
  _snake_case  :    List[Any]            = [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))

