code: 
  
  
  
  A_    :					List[str]             =[
      ["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(UpperCAmelCase__      )
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		["""0"""] * len(UpperCAmelCase__      )
  						for i, row in enumerate(UpperCAmelCase__      ):
  												for item in row:
  																		if distinct_edge in item[0]:
  																								a_					=		"""1"""
  																								break
  						return "".join(UpperCAmelCase__      )
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		get_distinct_edge(UpperCAmelCase__      )
  						a_					=		{}
  
  						for item in distinct_edge:
  												a_					=		get_bitcode(UpperCAmelCase__       ,    UpperCAmelCase__      )
  												# 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=UpperCAmelCase__      )
  						]
  						return sorted_frequency_table
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		{}
  						for _, item in enumerate(UpperCAmelCase__      ):
  												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 * 100 / len(UpperCAmelCase__      ) for i in cluster]
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""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   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(UpperCAmelCase__       ,    2      ) & int(UpperCAmelCase__       ,    2      ) == int(UpperCAmelCase__       ,    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(UpperCAmelCase__      ) - 1:
  												create_edge(UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      )
  												a_					=		i + 1
  						return graph
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__=None      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		(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   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		int(s / 100 * (len(UpperCAmelCase__      ) - 1)      )
  						for i in cluster[k]:
  												my_dfs(UpperCAmelCase__       ,    tuple(cluster[k][i]      )       ,    (["""Header"""],)      )
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		[]
  						for edges in paths:
  												a_					=		[]
  												for j in range(len(UpperCAmelCase__      ) - 1      ):
  																		a_					=		list(edges[j]      )
  																		for e in temp:
  																								a_					=		(e[0], e[1])
  																								el.append(UpperCAmelCase__      )
  												freq_sub_el.append(UpperCAmelCase__      )
  						return freq_sub_el
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						for i in range(len(UpperCAmelCase__      )      ):
  												for j in range(len(edge_array[i]      )      ):
  																		a_					=		edge_array[i][j].split("""-"""      )
  																		a_					=		t
  
  
  if __name__ == "__main__":
  	preprocess(edge_array)
  	A_    :					Optional[int]             =get_frequency_table(edge_array)
  	A_    :					Optional[Any]             =get_nodes(frequency_table)
  	A_    :					Optional[int]             =get_cluster(nodes)
  	A_    :					Dict             =get_support(cluster)
  	A_    :					Optional[Any]             =construct_graph(cluster, nodes)
  	find_freq_subgraph_given_support(60, cluster, graph)
  	A_    :					list             =[]
  	A_    :					Optional[int]             =freq_subgraphs_edge_list(paths)
  	print_all()
style_context: 
  
  
  from typing import List
  
  from ...processing_utils import ProcessorMixin
  from ...utils import is_torch_available
  
  
  if is_torch_available():
  	import torch
  
  
  
  
  
  class        lowercase_				( UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		['''image_processor''', '''tokenizer''']
  							snake_case_        =		'''OneFormerImageProcessor'''
  							snake_case_        =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')
  
  
  							def __init__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase = 77			,    _UpperCAmelCase = 77			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if image_processor is None:
  																			raise ValueError("""You need to specify an `image_processor`."""		)
  													if tokenizer is None:
  																			raise ValueError("""You need to specify a `tokenizer`."""		)
  
  													a_					=		max_seq_length
  													a_					=		task_seq_length
  
  													super().__init__(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=77		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if text_list is None:
  																			raise ValueError("""tokens cannot be None."""		)
  
  													a_					=		self.tokenizer(_UpperCAmelCase			,    padding="""max_length"""			,    max_length=_UpperCAmelCase			,    truncation=_UpperCAmelCase		)
  
  													a_			,   a_					=		tokens["""attention_mask"""], tokens["""input_ids"""]
  
  													a_					=		[]
  													for attn_mask, input_id in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		torch.tensor(_UpperCAmelCase		) * torch.tensor(_UpperCAmelCase		)
  																			token_inputs.append(token.unsqueeze(0		)		)
  
  													a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  													return token_inputs
  
  
  							def __call__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if task_inputs is None:
  																			raise ValueError("""You have to specify the task_input. Found None."""		)
  													elif images is None:
  																			raise ValueError("""You have to specify the image. Found None."""		)
  
  													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
  																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)
  
  													a_					=		self.image_processor(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		[task_inputs]
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
  																			a_					=		[]
  																			for task in task_inputs:
  																									a_					=		f"the task is {task}"
  																									task_token_inputs.append(_UpperCAmelCase		)
  																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
  													else:
  																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)
  
  													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
  																			a_					=		encoded_inputs.text_inputs
  
  																			a_					=		[]
  																			for texts in texts_list:
  																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
  																									text_inputs.append(text_input_list.unsqueeze(0		)		)
  
  																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  
  													return encoded_inputs
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if task_inputs is None:
  																			raise ValueError("""You have to specify the task_input. Found None."""		)
  													elif images is None:
  																			raise ValueError("""You have to specify the image. Found None."""		)
  
  													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
  																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)
  
  													a_					=		self.image_processor.encode_inputs(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		[task_inputs]
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
  																			a_					=		[]
  																			for task in task_inputs:
  																									a_					=		f"the task is {task}"
  																									task_token_inputs.append(_UpperCAmelCase		)
  																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
  													else:
  																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)
  
  													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
  																			a_					=		encoded_inputs.text_inputs
  
  																			a_					=		[]
  																			for texts in texts_list:
  																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
  																									text_inputs.append(text_input_list.unsqueeze(0		)		)
  
  																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  
  													return encoded_inputs
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_semantic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_instance_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_panoptic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)