






"""simple docstring"""


lowercase_								=	[
    ['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'bh-e12', 'cd-e2', 'ce-e4',
     'de-e1', 'df-e8', 'dg-e5', 'dh-e10', 'ef-e3', 'eg-e2', 'fg-e6', 'gh-e6', 'hi-e3'],
    ['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'be-e6', 'cd-e2', 'de-e1', 'df-e8',
     'ef-e3', 'eg-e2', 'fg-e6'],
    ['ab-e1', 'ac-e3', 'bc-e4', 'bd-e2', 'de-e1', 'df-e8', 'dg-e5', 'ef-e3', 'eg-e2',
     'eh-e12', 'fg-e6', 'fh-e10', 'gh-e6'],
    ['ab-e1', 'ac-e3', 'bc-e4', 'bd-e2', 'bh-e12', 'cd-e2', 'df-e8', 'dh-e10'],
    ['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'cd-e2', 'ce-e4', 'de-e1', 'df-e8',
     'dg-e5', 'ef-e3', 'eg-e2', 'fg-e6']
]



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  __A		     =					set()
  for row in edge_array:
    for item in row:
      distinct_edge.add(item[0]					)
  return list(lowercase__					)



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					['0'] * len(lowercase__					)
  for i, row in enumerate(lowercase__					):
    for item in row:
      if distinct_edge in item[0]:
        __A		     =					'1'
        break
  return "".join(lowercase__					)



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  __A		     =					get_distinct_edge(lowercase__					)
  __A		     =					{}

  for item in distinct_edge:
    __A		     =					get_bitcode(lowercase__						,			lowercase__					)
    # print('bit',bit)
    # bt=''.join(bit)
    __A		     =					bit.count('''1'''					)
    __A		     =					[s, bit]
  # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
  __A		     =					[
      [k, v[0], v[1]]
      for k, v in sorted(frequency_table.items()						,			key=lambda __UpperCamelCase					: v[1][0]						,			reverse=lowercase__					)
  ]
  return sorted_frequency_table



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  __A		     =					{}
  for _, item in enumerate(lowercase__					):
    nodes.setdefault(item[2]						,			[]					).append(item[0]					)
  return nodes



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  __A		     =					{}
  for key, value in nodes.items():
    __A		     =					value
  return cluster



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  return [i * 1_0_0 / len(lowercase__					) for i in cluster]



def    lowerCAmelCase							(       ):




  """simple docstring"""







  print('''\nNodes\n'''					)
  for key, value in nodes.items():
    print(lowercase__						,			lowercase__					)
  print('''\nSupport\n'''					)
  print(lowercase__					)
  print('''\n Cluster \n'''					)
  for key, value in sorted(cluster.items()						,			reverse=lowercase__					):
    print(lowercase__						,			lowercase__					)
  print('''\n Graph\n'''					)
  for key, value in graph.items():
    print(lowercase__						,			lowercase__					)
  print('''\n Edge List of Frequent subgraphs \n'''					)
  for edge_list in freq_subgraph_edge_list:
    print(lowercase__					)



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  for i in cluster[ca]:
    __A		     =					0
    __A		     =					ca + 1
    while ca < max(cluster.keys()					):
      for j in cluster[ca]:
        if int(lowercase__						,			2					) & int(lowercase__						,			2					) == int(lowercase__						,			2					):
          if tuple(nodes[i]					) in graph:
            graph[tuple(nodes[i]					)].append(nodes[j]					)
          else:
            __A		     =					[nodes[j]]
          count += 1
      if count == 0:
        __A		     =					ca + 1
      else:
        break



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					cluster[max(cluster.keys()					)]
  __A		     =					'Header'
  __A		     =					{}
  for i in x:
    if (["Header"],) in graph:
      graph[(["Header"],)].append(x[i]					)
    else:
      __A		     =					[x[i]]
  for i in x:
    __A		     =					[['Header']]
  __A		     =					1
  while i < max(lowercase__					) - 1:
    create_edge(lowercase__						,			lowercase__						,			lowercase__						,			lowercase__					)
    __A		     =					i + 1
  return graph



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase=None					):




  """simple docstring"""







  __A		     =					(path or []) + [start]
  if start == end:
    paths.append(lowercase__					)
  for node in graph[start]:
    if tuple(lowercase__					) not in path:
      my_dfs(lowercase__						,			tuple(lowercase__					)						,			lowercase__						,			lowercase__					)



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					int(s / 1_0_0 * (len(lowercase__					) - 1)					)
  for i in cluster[k]:
    my_dfs(lowercase__						,			tuple(cluster[k][i]					)						,			(['''Header'''],)					)



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  __A		     =					[]
  for edges in paths:
    __A		     =					[]
    for j in range(len(lowercase__					) - 1					):
      __A		     =					list(edges[j]					)
      for e in temp:
        __A		     =					(e[0], e[1])
        el.append(lowercase__					)
    freq_sub_el.append(lowercase__					)
  return freq_sub_el



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  for i in range(len(lowercase__					)					):
    for j in range(len(edge_array[i]					)					):
      __A		     =					edge_array[i][j].split('''-'''					)
      __A		     =					t


if __name__ == "__main__":
       preprocess(edge_array)
       lowercase_								=	get_frequency_table(edge_array)
       lowercase_								=	get_nodes(frequency_table)
       lowercase_								=	get_cluster(nodes)
       lowercase_								=	get_support(cluster)
       lowercase_								=	construct_graph(cluster, nodes)
       find_freq_subgraph_given_support(60, cluster, graph)
       lowercase_								=	[]
       lowercase_								=	freq_subgraphs_edge_list(paths)
       print_all()











'''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       lowerCamelCase       :


							@staticmethod
							def  SCREAMING_SNAKE_CASE_		(       *__snake_case						:	Optional[Any]							,  **__snake_case						:	str							):





													'''simple docstring'''





													pass







@is_pipeline_test
@require_torch_or_tf
@require_vision
class       lowerCamelCase       (    unittest.TestCase	):
					_SCREAMING_SNAKE_CASE				 =     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
					_SCREAMING_SNAKE_CASE				 =     TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	Any							,  __snake_case						:	Tuple							,  __snake_case						:	Union[str, Any]							):





											'''simple docstring'''





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


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	Any							,  __snake_case						:	str							):





											'''simple docstring'''





											_snake_case: Dict	       =		image_classifier('./tests/fixtures/tests_samples/COCO/000000039769.png'							)

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

											import datasets

											_snake_case: str	       =		datasets.load_dataset('hf-internal-testing/fixtures_image_utils'							,  'image'							,  split='test'							)

											# Accepts URL + PIL.Image + lists
											_snake_case: List[str]	       =		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(
											    __snake_case							,  [
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											    ]							,  )


					@require_torch
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: Any	       =		'hf-internal-testing/tiny-random-vit'
											_snake_case: List[str]	       =		pipeline('image-classification'							,  model=__snake_case							)

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

											_snake_case: Dict	       =		image_classifier(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ]							,  top_k=2							,  )
											self.assertEqual(
											    nested_simplify(__snake_case							,  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  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





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

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

											_snake_case: str	       =		image_classifier(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ]							,  top_k=2							,  )
											self.assertEqual(
											    nested_simplify(__snake_case							,  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  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		PreTrainedTokenizer()

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

											self.assertIs(image_classifier.tokenizer							,  __snake_case							)




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





											'''simple docstring'''





											_snake_case: Any	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-conv'							)
											_snake_case: int	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.4_385, 'label': 'tabby, tabby cat'},
											        {'score': 0.321, 'label': 'tiger cat'},
											        {'score': 0.0_502, 'label': 'Egyptian cat'},
											        {'score': 0.0_137, 'label': 'crib, cot'},
											        {'score': 0.007, 'label': 'radiator'},
											    ]							,  )

											_snake_case: Union[str, Any]	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-fourier'							)
											_snake_case: int	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.5_658, 'label': 'tabby, tabby cat'},
											        {'score': 0.1_309, 'label': 'tiger cat'},
											        {'score': 0.0_722, 'label': 'Egyptian cat'},
											        {'score': 0.0_707, 'label': 'remote control, remote'},
											        {'score': 0.0_082, 'label': 'computer keyboard, keypad'},
											    ]							,  )

											_snake_case: Optional[int]	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-learned'							)
											_snake_case: str	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.3_022, 'label': 'tabby, tabby cat'},
											        {'score': 0.2_362, 'label': 'Egyptian cat'},
											        {'score': 0.1_856, 'label': 'tiger cat'},
											        {'score': 0.0_324, 'label': 'remote control, remote'},
											        {'score': 0.0_096, 'label': 'quilt, comforter, comfort, puff'},
											    ]							,  )





