



UpperCAmelCase__			:   Tuple      			=    [
    ['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	(   _UpperCamelCase			):
				_UpperCAmelCase						:  Tuple     					=	set()
				for row in edge_array:
								for item in row:
												distinct_edge.add(item[0]			)
				return list(_UpperCamelCase			)


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase			):
				_UpperCAmelCase						:  str     					=	['''0'''] * len(_UpperCamelCase			)
				for i, row in enumerate(_UpperCamelCase			):
								for item in row:
												if distinct_edge in item[0]:
																_UpperCAmelCase						:  Union[str, Any]     					=	'''1'''
																break
				return "".join(_UpperCamelCase			)


def 	_A	(   _UpperCamelCase			):
				_UpperCAmelCase						:  int     					=	get_distinct_edge(_UpperCamelCase			)
				_UpperCAmelCase						:  str     					=	{}

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


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


def 	_A	(   _UpperCamelCase			):
				_UpperCAmelCase						:  List[Any]     					=	{}
				for key, value in nodes.items():
								_UpperCAmelCase						:  Optional[Any]     					=	value
				return cluster


def 	_A	(   _UpperCamelCase			):
				return [i * 100 / len(_UpperCamelCase			) for i in cluster]


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


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


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


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase=None			):
				_UpperCAmelCase						:  str     					=	(path or []) + [start]
				if start == end:
								paths.append(_UpperCamelCase			)
				for node in graph[start]:
								if tuple(_UpperCamelCase			) not in path:
												my_dfs(_UpperCamelCase      ,					tuple(_UpperCamelCase			)      ,					_UpperCamelCase      ,					_UpperCamelCase			)


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			):
				_UpperCAmelCase						:  str     					=	int(s / 100 * (len(_UpperCamelCase			) - 1)			)
				for i in cluster[k]:
								my_dfs(_UpperCamelCase      ,					tuple(cluster[k][i]			)      ,					(['''Header'''],)			)


def 	_A	(   _UpperCamelCase			):
				_UpperCAmelCase						:  Union[str, Any]     					=	[]
				for edges in paths:
								_UpperCAmelCase						:  Tuple     					=	[]
								for j in range(len(_UpperCamelCase			) - 1			):
												_UpperCAmelCase						:  Union[str, Any]     					=	list(edges[j]			)
												for e in temp:
																_UpperCAmelCase						:  Optional[int]     					=	(e[0], e[1])
																el.append(_UpperCamelCase			)
								freq_sub_el.append(_UpperCamelCase			)
				return freq_sub_el


def 	_A	(   _UpperCamelCase			):
				for i in range(len(_UpperCamelCase			)			):
								for j in range(len(edge_array[i]			)			):
												_UpperCAmelCase						:  Tuple     					=	edge_array[i][j].split('''-'''			)
												_UpperCAmelCase						:  Tuple     					=	t


if __name__ == "__main__":
					preprocess(edge_array)
					UpperCAmelCase__			:   List[str]      			=    get_frequency_table(edge_array)
					UpperCAmelCase__			:   int      			=    get_nodes(frequency_table)
					UpperCAmelCase__			:   List[Any]      			=    get_cluster(nodes)
					UpperCAmelCase__			:   List[Any]      			=    get_support(cluster)
					UpperCAmelCase__			:   Optional[int]      			=    construct_graph(cluster, nodes)
					find_freq_subgraph_given_support(60, cluster, graph)
					UpperCAmelCase__			:   list      			=    []
					UpperCAmelCase__			:   int      			=    freq_subgraphs_edge_list(paths)
					print_all()





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__			:   Optional[int]      			=    {
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__			:   List[Any]      			=    ['ErnieMTokenizer']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__			:   Dict      			=    [
					    'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'ErnieMForMultipleChoice',
					    'ErnieMForQuestionAnswering',
					    'ErnieMForSequenceClassification',
					    'ErnieMForTokenClassification',
					    'ErnieMModel',
					    'ErnieMPreTrainedModel',
					    'ErnieMForInformationExtraction',
					]


if TYPE_CHECKING:
					from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ernie_m import ErnieMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ernie_m import (
										    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ErnieMForInformationExtraction,
										    ErnieMForMultipleChoice,
										    ErnieMForQuestionAnswering,
										    ErnieMForSequenceClassification,
										    ErnieMForTokenClassification,
										    ErnieMModel,
										    ErnieMPreTrainedModel,
										)


else:
					import sys

					UpperCAmelCase__			:   Union[str, Any]      			=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
