





"""simple docstring"""




lowerCamelCase__			=   [
    ['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							( UpperCamelCase     :	List[str]    ):
  A__											=							set()
  for row in edge_array:
    for item in row:
      distinct_edge.add(item[0]    )
  return list(lowercase__    )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Optional[Any]		,				UpperCamelCase     :	Dict    ):
  A__											=							["""0"""] * len(lowercase__    )
  for i, row in enumerate(lowercase__    ):
    for item in row:
      if distinct_edge in item[0]:
        A__											=							"""1"""
        break
  return "".join(lowercase__    )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Optional[int]    ):
  A__											=							get_distinct_edge(lowercase__    )
  A__											=							{}

  for item in distinct_edge:
    A__											=							get_bitcode(lowercase__		,				lowercase__    )
    # 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=lowercase__    )
  ]
  return sorted_frequency_table






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Tuple    ):
  A__											=							{}
  for _, item in enumerate(lowercase__    ):
    nodes.setdefault(item[2]		,				[]    ).append(item[0]    )
  return nodes






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Optional[int]    ):
  A__											=							{}
  for key, value in nodes.items():
    A__											=							value
  return cluster






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Dict    ):
  return [i * 100 / len(lowercase__    ) for i in cluster]






def 						_SCREAMING_SNAKE_CASE							( ):
  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							( UpperCamelCase     :	Any		,				UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	List[Any]		,				UpperCamelCase     :	Union[str, Any]    ):
  for i in cluster[ca]:
    A__											=							0
    A__											=							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:
            A__											=							[nodes[j]]
          count += 1
      if count == 0:
        A__											=							ca + 1
      else:
        break






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Any		,				UpperCamelCase     :	Union[str, Any]    ):
  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(lowercase__    ) - 1:
    create_edge(lowercase__		,				lowercase__		,				lowercase__		,				lowercase__    )
    A__											=							i + 1
  return graph






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Optional[Any]		,				UpperCamelCase     :	int		,				UpperCamelCase     :	Union[str, Any]		,				UpperCamelCase     :	Union[str, Any]=None    ):
  A__											=							(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							( UpperCamelCase     :	Optional[Any]		,				UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	List[str]    ):
  A__											=							int(s / 100 * (len(lowercase__    ) - 1)    )
  for i in cluster[k]:
    my_dfs(lowercase__		,				tuple(cluster[k][i]    )		,				(["""Header"""],)    )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	str    ):
  A__											=							[]
  for edges in paths:
    A__											=							[]
    for j in range(len(lowercase__    ) - 1    ):
      A__											=							list(edges[j]    )
      for e in temp:
        A__											=							(e[0], e[1])
        el.append(lowercase__    )
    freq_sub_el.append(lowercase__    )
  return freq_sub_el






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Optional[int]    ):
  for i in range(len(lowercase__    )    ):
    for j in range(len(edge_array[i]    )    ):
      A__											=							edge_array[i][j].split("""-"""    )
      A__											=							t


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












"""simple docstring"""




import argparse

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


logging.set_verbosity_info()






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[str]		,				UpperCamelCase     :	Tuple		,				UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	Union[str, Any]		,				UpperCamelCase     :	Optional[int]    ):
  # Initialise PyTorch model
  A__											=							BertConfig.from_pretrained(
      """bert-large-cased"""		,				vocab_size=UpperCamelCase		,				max_position_embeddings=512		,				is_decoder=UpperCamelCase		,				add_cross_attention=UpperCamelCase		,				)
  A__											=							bert_config.to_dict()
  del bert_config_dict["type_vocab_size"]
  A__											=							BertGenerationConfig(**UpperCamelCase    )
  if is_encoder:
    A__											=							BertGenerationEncoder(UpperCamelCase    )
  else:
    A__											=							BertGenerationDecoder(UpperCamelCase    )
  print(F"""Building PyTorch model from configuration: {config}"""    )

  # Load weights from tf checkpoint
  load_tf_weights_in_bert_generation(
      UpperCamelCase		,				UpperCamelCase		,				model_class="""bert"""		,				is_encoder_named_decoder=UpperCamelCase		,				is_encoder=UpperCamelCase		,				)

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


if __name__ == "__main__":
      lowerCamelCase__			=   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=50_358, type=int, help="Vocab size of model")
      lowerCamelCase__			=   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,
      )




