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


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import TvltImageProcessor
def 			UpperCamelCase__						(    A__      ,			A__=10      ,			A__=10      ,			A__=False      ,			A__=False   )       -> Optional[int]:
				snake_case__ :     List[str] 							=				[]
				for i in range(image_processor_tester.num_frames   ):
								video.append(np.random.randint(255      ,			size=(image_processor_tester.num_channels, width, height)      ,			dtype=np.uinta   )   )

				if not numpify and not torchify:
								# PIL expects the channel dimension as last dimension
								snake_case__ :     Optional[int] 							=				[Image.fromarray(np.moveaxis(A__      ,			0      ,			-1   )   ) for frame in video]

				if torchify:
								snake_case__ :     Any 							=				[torch.from_numpy(A__   ) for frame in video]

				return video
def 			UpperCamelCase__						(    A__      ,			A__=False      ,			A__=False      ,			A__=False   )       -> Optional[Any]:
				assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

				snake_case__ :     str 							=				[]
				for i in range(image_processor_tester.batch_size   ):
								if equal_resolution:
												snake_case__ :     Optional[int] 							=				image_processor_tester.max_resolution
								else:
												snake_case__							,  snake_case__ :     Any 							=				np.random.choice(
												    np.arange(image_processor_tester.min_resolution      ,			image_processor_tester.max_resolution   )      ,			2   )
												snake_case__ :     Any 							=				prepare_video(
												    image_processor_tester=A__      ,			width=A__      ,			height=A__      ,			numpify=A__      ,			torchify=A__      ,			)
								video_inputs.append(A__   )

				return video_inputs





class 			__snake_case  (      unittest.TestCase     ):




					def __init__(       self   ,	__UpperCamelCase   ,	__UpperCamelCase=7   ,	__UpperCamelCase=3   ,	__UpperCamelCase=4   ,	__UpperCamelCase=18   ,	__UpperCamelCase=30   ,	__UpperCamelCase=400   ,	__UpperCamelCase=True   ,	__UpperCamelCase=None   ,	__UpperCamelCase=True   ,	__UpperCamelCase=[0.5, 0.5, 0.5]   ,	__UpperCamelCase=[0.5, 0.5, 0.5]   ,	__UpperCamelCase=True   ,	__UpperCamelCase=None   ,	)					->  int:



									'''simple docstring'''







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

									snake_case__ :     List[str] 							=				parent
									snake_case__ :     int 							=				batch_size
									snake_case__ :     Any 							=				num_channels
									snake_case__ :     Tuple 							=				num_frames
									snake_case__ :     Optional[int] 							=				image_size
									snake_case__ :     List[str] 							=				min_resolution
									snake_case__ :     Union[str, Any] 							=				max_resolution
									snake_case__ :     List[Any] 							=				do_resize
									snake_case__ :     Dict 							=				size
									snake_case__ :     Optional[int] 							=				do_normalize
									snake_case__ :     Any 							=				image_mean
									snake_case__ :     Any 							=				image_std
									snake_case__ :     List[Any] 							=				do_center_crop
									snake_case__ :     int 							=				crop_size




					def       __a      (       self		)					->  Tuple:



									'''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,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									}





@require_torch
@require_vision
class 			__snake_case  (      _lowerCamelCase   ,unittest.TestCase     ):
					__lowerCamelCase	 =       TvltImageProcessor if is_vision_available() else None




					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									snake_case__ :     Tuple 							=				TvltImageProcessorTester(self		)




					@property
					def       __a      (       self		)					->  Optional[int]:



									'''simple docstring'''







									return self.image_processor_tester.prepare_image_processor_dict()




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



									'''simple docstring'''







									snake_case__ :     Tuple 							=				self.image_processing_class(**self.image_processor_dict		)
									self.assertTrue(hasattr(__UpperCamelCase   ,	'image_mean'		)		)
									self.assertTrue(hasattr(__UpperCamelCase   ,	'image_std'		)		)
									self.assertTrue(hasattr(__UpperCamelCase   ,	'do_normalize'		)		)
									self.assertTrue(hasattr(__UpperCamelCase   ,	'do_resize'		)		)
									self.assertTrue(hasattr(__UpperCamelCase   ,	'do_center_crop'		)		)
									self.assertTrue(hasattr(__UpperCamelCase   ,	'size'		)		)




					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									snake_case__ :     Tuple 							=				self.image_processing_class(**self.image_processor_dict		)
									# create random PIL videos
									snake_case__ :     Optional[Any] 							=				prepare_video_inputs(self.image_processor_tester   ,	equal_resolution=__UpperCamelCase		)
									for video in video_inputs:
													self.assertIsInstance(__UpperCamelCase   ,	__UpperCamelCase		)
													self.assertIsInstance(video[0]   ,	Image.Image		)

									# Test not batched input
									snake_case__ :     Tuple 							=				image_processor(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
									snake_case__ :     int 							=				image_processor(__UpperCamelCase   ,	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       __a      (       self		)					->  List[Any]:



									'''simple docstring'''







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

									# Test not batched input
									snake_case__ :     Dict 							=				image_processor(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
									snake_case__ :     List[Any] 							=				image_processor(__UpperCamelCase   ,	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       __a      (       self		)					->  int:



									'''simple docstring'''







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

									# Test not batched input
									snake_case__ :     Any 							=				image_processor(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
									snake_case__ :     List[Any] 							=				image_processor(__UpperCamelCase   ,	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'],
									    )   ,	)



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__					:       Dict		=       {
    '''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__   )       -> set[tuple[str, str]]:
				snake_case__ :     Tuple 							=				{node: {node} for node in graph}

				snake_case__ :     Tuple 							=				{node: graph[node][:] for node in graph}

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

								# Contract edge (u, v) to new node uv
								snake_case__ :     Tuple 							=				u + v
								snake_case__ :     Dict 							=				list(set(graph_copy[u] + graph_copy[v]   )   )
								uv_neighbors.remove(A__   )
								uv_neighbors.remove(A__   )
								snake_case__ :     Optional[Any] 							=				uv_neighbors
								for neighbor in uv_neighbors:
												graph_copy[neighbor].append(A__   )

								snake_case__ :     Tuple 							=				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__ :     Tuple 							=				[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))


