


snake_case__     							= [
    ['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  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]				)       ->  str:
						__magic_name__				:							Optional[int]				=      set()
						for row in edge_array:
												for item in row:
																		distinct_edge.add(item[0]				)
						return list(lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :Tuple				)       ->  int:
						__magic_name__				:							Union[str, Any]				=      ['0'] * len(lowercase__				)
						for i, row in enumerate(lowercase__				):
												for item in row:
																		if distinct_edge in item[0]:
																								__magic_name__				:							Optional[int]				=      '1'
																								break
						return "".join(lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :int				)       ->  Tuple:
						__magic_name__				:							Dict				=      get_distinct_edge(lowercase__				)
						__magic_name__				:							List[str]				=      {}

						for item in distinct_edge:
												__magic_name__				:							str				=      get_bitcode(lowercase__      ,						lowercase__				)
												# print('bit',bit)
												# bt=''.join(bit)
												__magic_name__				:							Optional[Any]				=      bit.count('1'				)
												__magic_name__				:							Optional[int]				=      [s, bit]
						# Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
						__magic_name__				:							Optional[int]				=      [
						    [k, v[0], v[1]]
						    for k, v in sorted(frequency_table.items()      ,						key=lambda lowercase__				: v[1][0]      ,						reverse=lowercase__				)
						]
						return sorted_frequency_table
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[Any]				)       ->  Tuple:
						__magic_name__				:							int				=      {}
						for _, item in enumerate(lowercase__				):
												nodes.setdefault(item[2]      ,						[]				).append(item[0]				)
						return nodes
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]				)       ->  Optional[int]:
						__magic_name__				:							List[Any]				=      {}
						for key, value in nodes.items():
												__magic_name__				:							int				=      value
						return cluster
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Any				)       ->  Optional[Any]:
						return [i * 1_0_0 / len(lowercase__				) for i in cluster]
def  __SCREAMING_SNAKE_CASE       (				)       ->  None:
						print('\nNodes\n'				)
						for key, value in nodes.items():
												print(lowercase__      ,						lowercase__				)
						print('\nSupport\n'				)
						print(lowercase__				)
						print('\n Cluster \n'				)
						for key, value in sorted(cluster.items()      ,						reverse=lowercase__				):
												print(lowercase__      ,						lowercase__				)
						print('\n Graph\n'				)
						for key, value in graph.items():
												print(lowercase__      ,						lowercase__				)
						print('\n Edge List of Frequent subgraphs \n'				)
						for edge_list in freq_subgraph_edge_list:
												print(lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :Any      ,						lowercase__  :int      ,						lowercase__  :Any				)       ->  List[str]:
						for i in cluster[ca]:
												__magic_name__				:							Optional[int]				=      0
												__magic_name__				:							Dict				=      ca + 1
												while ca < max(cluster.keys()				):
																		for j in cluster[ca]:
																								if int(lowercase__      ,						2				) & int(lowercase__      ,						2				) == int(lowercase__      ,						2				):
																														if tuple(nodes[i]				) in graph:
																																				graph[tuple(nodes[i]				)].append(nodes[j]				)
																														else:
																																				__magic_name__				:							Any				=      [nodes[j]]
																														count += 1
																		if count == 0:
																								__magic_name__				:							List[Any]				=      ca + 1
																		else:
																								break
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Any      ,						lowercase__  :Optional[int]				)       ->  int:
						__magic_name__				:							Union[str, Any]				=      cluster[max(cluster.keys()				)]
						__magic_name__				:							Dict				=      'Header'
						__magic_name__				:							Tuple				=      {}
						for i in x:
												if (["Header"],) in graph:
																		graph[(["Header"],)].append(x[i]				)
												else:
																		__magic_name__				:							Union[str, Any]				=      [x[i]]
						for i in x:
												__magic_name__				:							Optional[Any]				=      [['Header']]
						__magic_name__				:							str				=      1
						while i < max(lowercase__				) - 1:
												create_edge(lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__				)
												__magic_name__				:							Dict				=      i + 1
						return graph
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Dict      ,						lowercase__  :Optional[Any]      ,						lowercase__  :Union[str, Any]      ,						lowercase__  :Dict=None				)       ->  Any:
						__magic_name__				:							List[Any]				=      (path or []) + [start]
						if start == end:
												paths.append(lowercase__				)
						for node in graph[start]:
												if tuple(lowercase__				) not in path:
																		my_dfs(lowercase__      ,						tuple(lowercase__				)      ,						lowercase__      ,						lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[Any]      ,						lowercase__  :Optional[int]      ,						lowercase__  :Union[str, Any]				)       ->  int:
						__magic_name__				:							Dict				=      int(s / 1_0_0 * (len(lowercase__				) - 1)				)
						for i in cluster[k]:
												my_dfs(lowercase__      ,						tuple(cluster[k][i]				)      ,						(['Header'],)				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Dict				)       ->  Union[str, Any]:
						__magic_name__				:							Union[str, Any]				=      []
						for edges in paths:
												__magic_name__				:							Dict				=      []
												for j in range(len(lowercase__				) - 1				):
																		__magic_name__				:							Dict				=      list(edges[j]				)
																		for e in temp:
																								__magic_name__				:							int				=      (e[0], e[1])
																								el.append(lowercase__				)
												freq_sub_el.append(lowercase__				)
						return freq_sub_el
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Dict				)       ->  List[Any]:
						for i in range(len(lowercase__				)				):
												for j in range(len(edge_array[i]				)				):
																		__magic_name__				:							Optional[int]				=      edge_array[i][j].split('-'				)
																		__magic_name__				:							List[Any]				=      t


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






import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :List[Any]      ,						lowercase__  :str      ,						lowercase__  :List[str]      ,						lowercase__  :List[Any]				)       ->  List[Any]:
						# Initialise PyTorch model
						__magic_name__				:							Any				=      BertConfig.from_pretrained(
						    'bert-large-cased'      ,						vocab_size=lowercase__      ,						max_position_embeddings=5_1_2      ,						is_decoder=lowercase__      ,						add_cross_attention=lowercase__      ,						)
						__magic_name__				:							Optional[int]				=      bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						__magic_name__				:							Optional[int]				=      BertGenerationConfig(**lowercase__				)
						if is_encoder:
												__magic_name__				:							Dict				=      BertGenerationEncoder(lowercase__				)
						else:
												__magic_name__				:							str				=      BertGenerationDecoder(lowercase__				)
						print(f"Building PyTorch model from configuration: {config}"				)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert_generation(
						    lowercase__      ,						lowercase__      ,						model_class='bert'      ,						is_encoder_named_decoder=lowercase__      ,						is_encoder=lowercase__      ,						)

						# Save pytorch-model
						print(f"Save PyTorch model and config to {pytorch_dump_path}"				)
						model.save_pretrained(lowercase__				)


if __name__ == "__main__":
			snake_case__     							= argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
			)
			parser.add_argument(
			    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			parser.add_argument(
			    '--is_encoder_named_decoder',
			    action='store_true',
			    help='If decoder has to be renamed to encoder in PyTorch model.',
			)
			parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
			parser.add_argument('--vocab_size', default=5_0358, type=int, help='Vocab size of model')
			snake_case__     							= parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.tf_hub_path,
			    args.pytorch_dump_path,
			    args.is_encoder_named_decoder,
			    args.vocab_size,
			    is_encoder=args.is_encoder,
			)


