





"""simple docstring"""




UpperCamelCase_           =[
    ["""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        a_				(							_lowercase   ):
						_UpperCamelCase			:					str     =    set()
						for row in edge_array:
												for item in row:
																		distinct_edge.add(item[0]   )
						return list(SCREAMING_SNAKE_CASE_   )





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Tuple     =    ['''0'''] * len(SCREAMING_SNAKE_CASE_   )
						for i, row in enumerate(SCREAMING_SNAKE_CASE_   ):
												for item in row:
																		if distinct_edge in item[0]:
																								_UpperCamelCase			:					Optional[int]     =    '''1'''
																								break
						return "".join(SCREAMING_SNAKE_CASE_   )





def        a_				(							_lowercase   ):
						_UpperCamelCase			:					List[Any]     =    get_distinct_edge(SCREAMING_SNAKE_CASE_   )
						_UpperCamelCase			:					Any     =    {}

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





def        a_				(							_lowercase   ):
						_UpperCamelCase			:					Tuple     =    {}
						for _, item in enumerate(SCREAMING_SNAKE_CASE_   ):
												nodes.setdefault(item[2]       ,						[]   ).append(item[0]   )
						return nodes





def        a_				(							_lowercase   ):
						_UpperCamelCase			:					Dict     =    {}
						for key, value in nodes.items():
												_UpperCamelCase			:					Union[str, Any]     =    value
						return cluster





def        a_				(							_lowercase   ):
						return [i * 100 / len(SCREAMING_SNAKE_CASE_   ) for i in cluster]





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





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase   ):






						for i in cluster[ca]:
												_UpperCamelCase			:					List[str]     =    0
												_UpperCamelCase			:					List[Any]     =    ca + 1
												while ca < max(cluster.keys()   ):
																		for j in cluster[ca]:
																								if int(SCREAMING_SNAKE_CASE_       ,						2   ) & int(SCREAMING_SNAKE_CASE_       ,						2   ) == int(SCREAMING_SNAKE_CASE_       ,						2   ):
																														if tuple(nodes[i]   ) in graph:
																																				graph[tuple(nodes[i]   )].append(nodes[j]   )
																														else:
																																				_UpperCamelCase			:					Dict     =    [nodes[j]]
																														count += 1
																		if count == 0:
																								_UpperCamelCase			:					Tuple     =    ca + 1
																		else:
																								break





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					int     =    cluster[max(cluster.keys()   )]
						_UpperCamelCase			:					List[str]     =    '''Header'''
						_UpperCamelCase			:					str     =    {}
						for i in x:
												if (["Header"],) in graph:
																		graph[(["Header"],)].append(x[i]   )
												else:
																		_UpperCamelCase			:					int     =    [x[i]]
						for i in x:
												_UpperCamelCase			:					Any     =    [['''Header''']]
						_UpperCamelCase			:					Dict     =    1
						while i < max(SCREAMING_SNAKE_CASE_   ) - 1:
												create_edge(SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_   )
												_UpperCamelCase			:					List[Any]     =    i + 1
						return graph





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase=None   ):
						_UpperCamelCase			:					List[Any]     =    (path or []) + [start]
						if start == end:
												paths.append(SCREAMING_SNAKE_CASE_   )
						for node in graph[start]:
												if tuple(SCREAMING_SNAKE_CASE_   ) not in path:
																		my_dfs(SCREAMING_SNAKE_CASE_       ,						tuple(SCREAMING_SNAKE_CASE_   )       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_   )





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Tuple     =    int(s / 100 * (len(SCREAMING_SNAKE_CASE_   ) - 1)   )
						for i in cluster[k]:
												my_dfs(SCREAMING_SNAKE_CASE_       ,						tuple(cluster[k][i]   )       ,						(['''Header'''],)   )





def        a_				(							_lowercase   ):
						_UpperCamelCase			:					List[str]     =    []
						for edges in paths:
												_UpperCamelCase			:					Any     =    []
												for j in range(len(SCREAMING_SNAKE_CASE_   ) - 1   ):
																		_UpperCamelCase			:					str     =    list(edges[j]   )
																		for e in temp:
																								_UpperCamelCase			:					List[Any]     =    (e[0], e[1])
																								el.append(SCREAMING_SNAKE_CASE_   )
												freq_sub_el.append(SCREAMING_SNAKE_CASE_   )
						return freq_sub_el





def        a_				(							_lowercase   ):






						for i in range(len(SCREAMING_SNAKE_CASE_   )   ):
												for j in range(len(edge_array[i]   )   ):
																		_UpperCamelCase			:					Optional[Any]     =    edge_array[i][j].split('''-'''   )
																		_UpperCamelCase			:					Any     =    t


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






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 		__magic_name__ :






      @staticmethod
      def      UpperCAmelCase_							(							*_lowercase						,		**_lowercase     )->			Tuple:
          pass






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






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase     )->			List[Any]:
         UpperCamelCase_          =		ImageClassificationPipeline(model=_lowercase						,		image_processor=_lowercase						,		top_k=2     )
         UpperCamelCase_          =		[
             Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     ),
             "http://images.cocodataset.org/val2017/000000039769.jpg",
         ]
         return image_classifier, examples






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase     )->			List[str]:
         UpperCamelCase_          =		image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"     )

         self.assertEqual(
             _lowercase						,		[
                 {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
             ]						,		)

         import datasets

         UpperCamelCase_          =		datasets.load_dataset("hf-internal-testing/fixtures_image_utils"						,		"image"						,		split="test"     )

         # Accepts URL + PIL.Image + lists
         UpperCamelCase_          =		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(
             _lowercase						,		[
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
             ]						,		)






     @require_torch
     def      UpperCAmelCase_							(							self     )->			str:
         UpperCamelCase_          =		"hf-internal-testing/tiny-random-vit"
         UpperCamelCase_          =		pipeline("image-classification"						,		model=_lowercase     )

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

         UpperCamelCase_          =		image_classifier(
             [
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
             ]						,		top_k=2						,		)
         self.assertEqual(
             nested_simplify(_lowercase						,		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      UpperCAmelCase_							(							self     )->			Tuple:
         UpperCamelCase_          =		"hf-internal-testing/tiny-random-vit"
         UpperCamelCase_          =		pipeline("image-classification"						,		model=_lowercase						,		framework="tf"     )

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

         UpperCamelCase_          =		image_classifier(
             [
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
             ]						,		top_k=2						,		)
         self.assertEqual(
             nested_simplify(_lowercase						,		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      UpperCAmelCase_							(							self     )->			Optional[Any]:
         UpperCamelCase_          =		PreTrainedTokenizer()

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

         self.assertIs(image_classifier.tokenizer						,		_lowercase     )






     @slow
     @require_torch
     def      UpperCAmelCase_							(							self     )->			int:
         # Perceiver is not tested by `run_pipeline_test` properly.
         # That is because the type of feature_extractor and model preprocessor need to be kept
         # in sync, which is not the case in the current design
         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-conv"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		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"},
             ]						,		)

         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-fourier"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		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"},
             ]						,		)

         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-learned"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		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"},
             ]						,		)
