


UpperCamelCase :				Dict						    =       [
    ['''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 							UpperCamelCase_  (				__snake_case			:						str     )->      Optional[Any]:




						'''simple docstring'''
						__magic_name__ 		=			set()
						for row in edge_array:
												for item in row:
																		distinct_edge.add(item[0]     )
						return list(UpperCamelCase__     )
def 							UpperCamelCase_  (				__snake_case			:						Union[str, Any]	,			__snake_case			:						List[Any]     )->      Dict:




						'''simple docstring'''
						__magic_name__ 		=			['0'] * len(UpperCamelCase__     )
						for i, row in enumerate(UpperCamelCase__     ):
												for item in row:
																		if distinct_edge in item[0]:
																								__magic_name__ 		=			'1'
																								break
						return "".join(UpperCamelCase__     )
def 							UpperCamelCase_  (				__snake_case			:						Any     )->      List[str]:




						'''simple docstring'''
						__magic_name__ 		=			get_distinct_edge(UpperCamelCase__     )
						__magic_name__ 		=			{}

						for item in distinct_edge:
												__magic_name__ 		=			get_bitcode(UpperCamelCase__	,			UpperCamelCase__     )
												# print('bit',bit)
												# bt=''.join(bit)
												__magic_name__ 		=			bit.count('1'     )
												__magic_name__ 		=			[s, bit]
						# Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
						__magic_name__ 		=			[
						    [k, v[0], v[1]]
						    for k, v in sorted(frequency_table.items()	,			key=lambda __snake_case     : v[1][0]	,			reverse=UpperCamelCase__     )
						]
						return sorted_frequency_table
def 							UpperCamelCase_  (				__snake_case			:						Dict     )->      Dict:




						'''simple docstring'''
						__magic_name__ 		=			{}
						for _, item in enumerate(UpperCamelCase__     ):
												nodes.setdefault(item[2]	,			[]     ).append(item[0]     )
						return nodes
def 							UpperCamelCase_  (				__snake_case			:						Tuple     )->      Tuple:




						'''simple docstring'''
						__magic_name__ 		=			{}
						for key, value in nodes.items():
												__magic_name__ 		=			value
						return cluster
def 							UpperCamelCase_  (				__snake_case			:						Any     )->      List[Any]:




						'''simple docstring'''
						return [i * 1_00 / len(UpperCamelCase__     ) for i in cluster]
def 							UpperCamelCase_  (				)->      Optional[Any]:




						'''simple docstring'''
						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 							UpperCamelCase_  (				__snake_case			:						Dict	,			__snake_case			:						Union[str, Any]	,			__snake_case			:						str	,			__snake_case			:						Optional[Any]     )->      Any:




						'''simple docstring'''






						for i in cluster[ca]:
												__magic_name__ 		=			0
												__magic_name__ 		=			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:
																																				__magic_name__ 		=			[nodes[j]]
																														count += 1
																		if count == 0:
																								__magic_name__ 		=			ca + 1
																		else:
																								break
def 							UpperCamelCase_  (				__snake_case			:						Optional[Any]	,			__snake_case			:						Any     )->      List[Any]:




						'''simple docstring'''
						__magic_name__ 		=			cluster[max(cluster.keys()     )]
						__magic_name__ 		=			'Header'
						__magic_name__ 		=			{}
						for i in x:
												if (["Header"],) in graph:
																		graph[(["Header"],)].append(x[i]     )
												else:
																		__magic_name__ 		=			[x[i]]
						for i in x:
												__magic_name__ 		=			[['Header']]
						__magic_name__ 		=			1
						while i < max(UpperCamelCase__     ) - 1:
												create_edge(UpperCamelCase__	,			UpperCamelCase__	,			UpperCamelCase__	,			UpperCamelCase__     )
												__magic_name__ 		=			i + 1
						return graph
def 							UpperCamelCase_  (				__snake_case			:						Tuple	,			__snake_case			:						str	,			__snake_case			:						str	,			__snake_case			:						List[Any]=None     )->      Tuple:




						'''simple docstring'''
						__magic_name__ 		=			(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 							UpperCamelCase_  (				__snake_case			:						List[str]	,			__snake_case			:						Optional[Any]	,			__snake_case			:						Optional[Any]     )->      str:




						'''simple docstring'''
						__magic_name__ 		=			int(s / 1_00 * (len(UpperCamelCase__     ) - 1)     )
						for i in cluster[k]:
												my_dfs(UpperCamelCase__	,			tuple(cluster[k][i]     )	,			(['Header'],)     )
def 							UpperCamelCase_  (				__snake_case			:						Tuple     )->      Tuple:




						'''simple docstring'''
						__magic_name__ 		=			[]
						for edges in paths:
												__magic_name__ 		=			[]
												for j in range(len(UpperCamelCase__     ) - 1     ):
																		__magic_name__ 		=			list(edges[j]     )
																		for e in temp:
																								__magic_name__ 		=			(e[0], e[1])
																								el.append(UpperCamelCase__     )
												freq_sub_el.append(UpperCamelCase__     )
						return freq_sub_el
def 							UpperCamelCase_  (				__snake_case			:						str     )->      Tuple:




						'''simple docstring'''






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


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



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class     __snake_case							(      _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:					Union[str, Any] 				= ["image_processor", "tokenizer"]
					SCREAMING_SNAKE_CASE_		:					Tuple 				= "AutoImageProcessor"
					SCREAMING_SNAKE_CASE_		:					Dict 				= "AutoTokenizer"
					def __init__(	self					:	List[str]  ,			_lowerCAmelCase					:	str=None  ,			_lowerCAmelCase					:	Dict=None  ,			**_lowerCAmelCase					:	List[str]     )       ->				Optional[int]:



											'''simple docstring'''





											__magic_name__ 		=			None
											if "feature_extractor" in kwargs:
																	warnings.warn(
																	    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																	    ' instead.'  ,			_lowerCAmelCase  ,			)
																	__magic_name__ 		=			kwargs.pop('feature_extractor'     )

											__magic_name__ 		=			image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																	raise ValueError('You have to specify an image_processor.'     )
											if tokenizer is None:
																	raise ValueError('You have to specify a tokenizer.'     )

											super().__init__(_lowerCAmelCase  ,			_lowerCAmelCase     )
											__magic_name__ 		=			self.image_processor
					def __call__(	self					:	str  ,			_lowerCAmelCase					:	Any=None  ,			_lowerCAmelCase					:	int=None  ,			_lowerCAmelCase					:	List[str]=None  ,			**_lowerCAmelCase					:	int     )       ->				int:



											'''simple docstring'''





											if text is None and images is None:
																	raise ValueError('You have to specify either text or images. Both cannot be none.'     )

											if text is not None:
																	__magic_name__ 		=			self.tokenizer(_lowerCAmelCase  ,			return_tensors=_lowerCAmelCase  ,			**_lowerCAmelCase     )

											if images is not None:
																	__magic_name__ 		=			self.image_processor(_lowerCAmelCase  ,			return_tensors=_lowerCAmelCase  ,			**_lowerCAmelCase     )

											if text is not None and images is not None:
																	__magic_name__ 		=			image_features.pixel_values
																	return encoding
											elif text is not None:
																	return encoding
											else:
																	return BatchEncoding(data=dict(**_lowerCAmelCase     )  ,			tensor_type=_lowerCAmelCase     )
					def   a (	self					:	Optional[int]  ,			*_lowerCAmelCase					:	Optional[int]  ,			**_lowerCAmelCase					:	Optional[Any]     )       ->				Optional[int]:



											'''simple docstring'''





											return self.tokenizer.batch_decode(*_lowerCAmelCase  ,			**_lowerCAmelCase     )
					def   a (	self					:	Union[str, Any]  ,			*_lowerCAmelCase					:	str  ,			**_lowerCAmelCase					:	Tuple     )       ->				int:



											'''simple docstring'''





											return self.tokenizer.decode(*_lowerCAmelCase  ,			**_lowerCAmelCase     )
					@property
					def   a (	self					:	List[Any]     )       ->				Tuple:



											'''simple docstring'''





											__magic_name__ 		=			self.tokenizer.model_input_names
											__magic_name__ 		=			self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
					@property
					def   a (	self					:	Optional[Any]     )       ->				Dict:



											'''simple docstring'''





											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,			_lowerCAmelCase  ,			)
											return self.image_processor_class







					@property
					def   a (	self					:	Dict     )       ->				Tuple:



											'''simple docstring'''





											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,			_lowerCAmelCase  ,			)
											return self.image_processor

