



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()



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		)