
'''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__		:   Union[str, Any]            =       {
    "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        snake_case							(		UpperCAmelCase	)->			set[tuple[str, str]]:







      """simple docstring"""
      __A       	=			{node: {node} for node in graph}

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

      while len(UpperCAmelCase	) > 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(UpperCAmelCase	)
            uv_neighbors.remove(UpperCAmelCase	)
            __A       	=			uv_neighbors
            for neighbor in uv_neighbors:
                  graph_copy[neighbor].append(UpperCAmelCase	)

            __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(UpperCAmelCase	)
                  if v in graph_copy[neighbor]:
                        graph_copy[neighbor].remove(UpperCAmelCase	)

    # 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))








'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import VideoMAEImageProcessor


class  UpperCamelCase__  (    unittest.TestCase):






 def __init__(			self  :Dict	,				_A  :Optional[Any]	,				_A  :Any=7	,				_A  :Dict=3	,				_A  :Optional[Any]=10	,				_A  :List[Any]=18	,				_A  :int=30	,				_A  :List[Any]=400	,				_A  :Any=True	,				_A  :Any=None	,				_A  :Optional[int]=True	,				_A  :Tuple=[0.5, 0.5, 0.5]	,				_A  :int=[0.5, 0.5, 0.5]	,				_A  :List[Any]=None	,				)				->			int:







       '''simple docstring'''



       __A       	=			size if size is not None else {'shortest_edge': 18}
       __A       	=			crop_size if crop_size is not None else {'height': 18, 'width': 18}

       __A       	=			parent
       __A       	=			batch_size
       __A       	=			num_channels
       __A       	=			num_frames
       __A       	=			image_size
       __A       	=			min_resolution
       __A       	=			max_resolution
       __A       	=			do_resize
       __A       	=			size
       __A       	=			do_normalize
       __A       	=			image_mean
       __A       	=			image_std
       __A       	=			crop_size






 def 	lowercase_						(			self  :Any			)				->			Union[str, Any]:







       '''simple docstring'''



       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_size": self.crop_size,
       }


@require_torch
@require_vision
class  UpperCamelCase__  (    SCREAMING_SNAKE_CASE      ,     unittest.TestCase):
 UpperCAmelCase__    :    Tuple										=					VideoMAEImageProcessor if is_vision_available() else None






 def 	lowercase_						(			self  :Dict			)				->			Dict:







       '''simple docstring'''



       __A       	=			VideoMAEImageProcessingTester(self			)






 @property
 def 	lowercase_						(			self  :Dict			)				->			List[str]:







       '''simple docstring'''



       return self.image_processor_tester.prepare_image_processor_dict()






 def 	lowercase_						(			self  :Dict			)				->			Optional[int]:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       self.assertTrue(hasattr(_A	,				'image_mean'			)			)
       self.assertTrue(hasattr(_A	,				'image_std'			)			)
       self.assertTrue(hasattr(_A	,				'do_normalize'			)			)
       self.assertTrue(hasattr(_A	,				'do_resize'			)			)
       self.assertTrue(hasattr(_A	,				'do_center_crop'			)			)
       self.assertTrue(hasattr(_A	,				'size'			)			)






 def 	lowercase_						(			self  :Optional[int]			)				->			List[Any]:







       '''simple docstring'''



       __A       	=			self.image_processing_class.from_dict(self.image_processor_dict			)
       self.assertEqual(image_processor.size	,				{'shortest_edge': 18}			)
       self.assertEqual(image_processor.crop_size	,				{'height': 18, 'width': 18}			)

       __A       	=			self.image_processing_class.from_dict(self.image_processor_dict	,				size=42	,				crop_size=84			)
       self.assertEqual(image_processor.size	,				{'shortest_edge': 42}			)
       self.assertEqual(image_processor.crop_size	,				{'height': 84, 'width': 84}			)






 def 	lowercase_						(			self  :Any			)				->			Dict:







       '''simple docstring'''



       pass






 def 	lowercase_						(			self  :Optional[int]			)				->			Tuple:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       # create random PIL videos
       __A       	=			prepare_video_inputs(self.image_processor_tester	,				equal_resolution=_A			)
       for video in video_inputs:
             self.assertIsInstance(_A	,				_A			)
             self.assertIsInstance(video[0]	,				Image.Image			)

       # Test not batched input
       __A       	=			image_processing(video_inputs[0]	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_videos.shape	,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)

       # Test batched
       __A       	=			image_processing(_A	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_videos.shape	,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)






 def 	lowercase_						(			self  :Dict			)				->			List[Any]:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       # create random numpy tensors
       __A       	=			prepare_video_inputs(self.image_processor_tester	,				equal_resolution=_A	,				numpify=_A			)
       for video in video_inputs:
             self.assertIsInstance(_A	,				_A			)
             self.assertIsInstance(video[0]	,				np.ndarray			)

       # Test not batched input
       __A       	=			image_processing(video_inputs[0]	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_videos.shape	,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)

       # Test batched
       __A       	=			image_processing(_A	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_videos.shape	,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)






 def 	lowercase_						(			self  :int			)				->			List[str]:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       # create random PyTorch tensors
       __A       	=			prepare_video_inputs(self.image_processor_tester	,				equal_resolution=_A	,				torchify=_A			)
       for video in video_inputs:
             self.assertIsInstance(_A	,				_A			)
             self.assertIsInstance(video[0]	,				torch.Tensor			)

       # Test not batched input
       __A       	=			image_processing(video_inputs[0]	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_videos.shape	,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)

       # Test batched
       __A       	=			image_processing(_A	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_videos.shape	,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)






