
"""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))







"""simple docstring"""

import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
		from PIL import Image
else:

		class 							lowercase   :







						@staticmethod
						def        a_	(				*_lowerCamelCase :       Tuple   ,  **_lowerCamelCase :       str							):



							"""simple docstring"""

							pass

@is_pipeline_test
@require_torch_or_tf
@require_vision
class 							lowercase   (     unittest.TestCase):
				__lowerCAmelCase     :     str								= MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
				__lowerCAmelCase     :     Union[str, Any]								= TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING







				def        a_	(				self :       Tuple   ,  _lowerCamelCase :       Any   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Optional[Any]							):



					"""simple docstring"""

					A_	:					List[str]						 =		ImageClassificationPipeline(model=_lowerCamelCase   ,  image_processor=_lowerCamelCase   ,  top_k=2							)
					A_	:					Tuple						 =		[
					    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							),
					    '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					]
					return image_classifier, examples







				def        a_	(				self :       Optional[Any]   ,  _lowerCamelCase :       Optional[int]   ,  _lowerCamelCase :       Optional[Any]							):



					"""simple docstring"""

					A_	:					int						 =		image_classifier('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)

					self.assertEqual(
					    _lowerCamelCase   ,  [
					        {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					        {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					    ]   ,  )

					import datasets

					A_	:					Optional[Any]						 =		datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''   ,  '''image'''   ,  split='''test'''							)

					# Accepts URL + PIL.Image + lists
					A_	:					Union[str, Any]						 =		image_classifier(
					    [
					        Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							),
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					        # RGBA
					        dataset[0]['''file'''],
					        # LA
					        dataset[1]['''file'''],
					        # L
					        dataset[2]['''file'''],
					    ]							)
					self.assertEqual(
					    _lowerCamelCase   ,  [
					        [
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					        ],
					        [
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					        ],
					        [
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					        ],
					        [
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					        ],
					        [
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					        ],
					    ]   ,  )







				@require_torch
				def        a_	(				self :       Dict							):



					"""simple docstring"""

					A_	:					Optional[int]						 =		'''hf-internal-testing/tiny-random-vit'''
					A_	:					Tuple						 =		pipeline('''image-classification'''   ,  model=_lowerCamelCase							)

					A_	:					Optional[int]						 =		image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''							)
					self.assertEqual(
					    nested_simplify(_lowerCamelCase   ,  decimals=4							)   ,  [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}]   ,  )

					A_	:					str						 =		image_classifier(
					    [
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					    ]   ,  top_k=2   ,  )
					self.assertEqual(
					    nested_simplify(_lowerCamelCase   ,  decimals=4							)   ,  [
					        [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}],
					        [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}],
					    ]   ,  )







				@require_tf
				def        a_	(				self :       Tuple							):



					"""simple docstring"""

					A_	:					List[str]						 =		'''hf-internal-testing/tiny-random-vit'''
					A_	:					Optional[int]						 =		pipeline('''image-classification'''   ,  model=_lowerCamelCase   ,  framework='''tf'''							)

					A_	:					Tuple						 =		image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''							)
					self.assertEqual(
					    nested_simplify(_lowerCamelCase   ,  decimals=4							)   ,  [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}]   ,  )

					A_	:					Optional[Any]						 =		image_classifier(
					    [
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					    ]   ,  top_k=2   ,  )
					self.assertEqual(
					    nested_simplify(_lowerCamelCase   ,  decimals=4							)   ,  [
					        [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}],
					        [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}],
					    ]   ,  )







				def        a_	(				self :       Dict							):



					"""simple docstring"""

					A_	:					Any						 =		PreTrainedTokenizer()

					# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
					A_	:					List[Any]						 =		pipeline(
					    '''image-classification'''   ,  model='''hf-internal-testing/tiny-random-vit'''   ,  tokenizer=_lowerCamelCase							)

					self.assertIs(image_classifier.tokenizer   ,  _lowerCamelCase							)







				@slow
				@require_torch
				def        a_	(				self :       List[str]							):



					"""simple docstring"""

					A_	:					List[Any]						 =		pipeline('''image-classification'''   ,  model='''deepmind/vision-perceiver-conv'''							)
					A_	:					Optional[Any]						 =		image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''							)
					self.assertEqual(
					    nested_simplify(_lowerCamelCase   ,  decimals=4							)   ,  [
					        {'''score''': 0.4385, '''label''': '''tabby, tabby cat'''},
					        {'''score''': 0.321, '''label''': '''tiger cat'''},
					        {'''score''': 0.0502, '''label''': '''Egyptian cat'''},
					        {'''score''': 0.0137, '''label''': '''crib, cot'''},
					        {'''score''': 0.007, '''label''': '''radiator'''},
					    ]   ,  )

					A_	:					Dict						 =		pipeline('''image-classification'''   ,  model='''deepmind/vision-perceiver-fourier'''							)
					A_	:					str						 =		image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''							)
					self.assertEqual(
					    nested_simplify(_lowerCamelCase   ,  decimals=4							)   ,  [
					        {'''score''': 0.5658, '''label''': '''tabby, tabby cat'''},
					        {'''score''': 0.1309, '''label''': '''tiger cat'''},
					        {'''score''': 0.0722, '''label''': '''Egyptian cat'''},
					        {'''score''': 0.0707, '''label''': '''remote control, remote'''},
					        {'''score''': 0.0082, '''label''': '''computer keyboard, keypad'''},
					    ]   ,  )

					A_	:					Optional[int]						 =		pipeline('''image-classification'''   ,  model='''deepmind/vision-perceiver-learned'''							)
					A_	:					int						 =		image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''							)
					self.assertEqual(
					    nested_simplify(_lowerCamelCase   ,  decimals=4							)   ,  [
					        {'''score''': 0.3022, '''label''': '''tabby, tabby cat'''},
					        {'''score''': 0.2362, '''label''': '''Egyptian cat'''},
					        {'''score''': 0.1856, '''label''': '''tiger cat'''},
					        {'''score''': 0.0324, '''label''': '''remote control, remote'''},
					        {'''score''': 0.0096, '''label''': '''quilt, comforter, comfort, puff'''},
					    ]   ,  )





