code: 
  
  
  
  
  
  '''simple docstring'''
  
  __lowerCAmelCase							:			Optional[Any]				=[
      ["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       (							_lowerCamelCase      :      Tuple ):
      A__       =    set()
      for row in edge_array:
          for item in row:
              distinct_edge.add(item[0] )
      return list(_lowerCamelCase )
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Union[str, Any]		, _lowerCamelCase      :      int ):
      A__       =    ["0"] * len(_lowerCamelCase )
      for i, row in enumerate(_lowerCamelCase ):
          for item in row:
              if distinct_edge in item[0]:
                  A__       =    "1"
                  break
      return "".join(_lowerCamelCase )
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      str ):
      A__       =    get_distinct_edge(_lowerCamelCase )
      A__       =    {}
  
      for item in distinct_edge:
          A__       =    get_bitcode(_lowerCamelCase		, _lowerCamelCase )
          # 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 _lowerCamelCase : v[1][0]		, reverse=_lowerCamelCase )
      ]
      return sorted_frequency_table
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int] ):
      A__       =    {}
      for _, item in enumerate(_lowerCamelCase ):
          nodes.setdefault(item[2]		, [] ).append(item[0] )
      return nodes
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Tuple ):
      A__       =    {}
      for key, value in nodes.items():
          A__       =    value
      return cluster
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int] ):
      return [i * 1_00 / len(_lowerCamelCase ) for i in cluster]
  
  
  
  
  
  def 		UpperCamelCase       (							):
      print("\nNodes\n" )
      for key, value in nodes.items():
          print(_lowerCamelCase		, _lowerCamelCase )
      print("\nSupport\n" )
      print(_lowerCamelCase )
      print("\n Cluster \n" )
      for key, value in sorted(cluster.items()		, reverse=_lowerCamelCase ):
          print(_lowerCamelCase		, _lowerCamelCase )
      print("\n Graph\n" )
      for key, value in graph.items():
          print(_lowerCamelCase		, _lowerCamelCase )
      print("\n Edge List of Frequent subgraphs \n" )
      for edge_list in freq_subgraph_edge_list:
          print(_lowerCamelCase )
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Dict		, _lowerCamelCase      :      List[str]		, _lowerCamelCase      :      int		, _lowerCamelCase      :      int ):
      for i in cluster[ca]:
          A__       =    0
          A__       =    ca + 1
          while ca < max(cluster.keys() ):
              for j in cluster[ca]:
                  if int(_lowerCamelCase		, 2 ) & int(_lowerCamelCase		, 2 ) == int(_lowerCamelCase		, 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 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      str ):
      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(_lowerCamelCase ) - 1:
          create_edge(_lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase )
          A__       =    i + 1
      return graph
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[str]		, _lowerCamelCase      :      Any		, _lowerCamelCase      :      List[str]		, _lowerCamelCase      :      List[Any]=None ):
      A__       =    (path or []) + [start]
      if start == end:
          paths.append(_lowerCamelCase )
      for node in graph[start]:
          if tuple(_lowerCamelCase ) not in path:
              my_dfs(_lowerCamelCase		, tuple(_lowerCamelCase )		, _lowerCamelCase		, _lowerCamelCase )
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[str]		, _lowerCamelCase      :      Dict		, _lowerCamelCase      :      Union[str, Any] ):
      A__       =    int(s / 1_00 * (len(_lowerCamelCase ) - 1) )
      for i in cluster[k]:
          my_dfs(_lowerCamelCase		, tuple(cluster[k][i] )		, (["Header"],) )
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Union[str, Any] ):
      A__       =    []
      for edges in paths:
          A__       =    []
          for j in range(len(_lowerCamelCase ) - 1 ):
              A__       =    list(edges[j] )
              for e in temp:
                  A__       =    (e[0], e[1])
                  el.append(_lowerCamelCase )
          freq_sub_el.append(_lowerCamelCase )
      return freq_sub_el
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      int ):
      for i in range(len(_lowerCamelCase ) ):
          for j in range(len(edge_array[i] ) ):
              A__       =    edge_array[i][j].split("-" )
              A__       =    t
  
  
  if __name__ == "__main__":
    preprocess(edge_array)
    __lowerCAmelCase							:			int				=get_frequency_table(edge_array)
    __lowerCAmelCase							:			Dict				=get_nodes(frequency_table)
    __lowerCAmelCase							:			Any				=get_cluster(nodes)
    __lowerCAmelCase							:			int				=get_support(cluster)
    __lowerCAmelCase							:			Tuple				=construct_graph(cluster, nodes)
    find_freq_subgraph_given_support(60, cluster, graph)
    __lowerCAmelCase							:			list				=[]
    __lowerCAmelCase							:			int				=freq_subgraphs_edge_list(paths)
    print_all()
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import torch
  
  from transformers import AutoTokenizer
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :Any		, lowercase_   :Dict							)->							Any:
       A__       =    AutoTokenizer.from_pretrained(lowercase_							)
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :int							)->							Optional[int]:
  
       if isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
           A__       =    None
           for l in x:
               A__       =    self.tokenizer(
                   lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, return_tensors="pt"		, )
               A__       =    torch.tensor([len(lowercase_							)]							)
               if d is not None:
                   for k in d.keys():
                       A__       =    torch.cat((d[k], t[k])		, 0							)
               else:
                   A__       =    t
  
           A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"							)							)
           A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"							)							)
  
       elif isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
           A__       =    self.tokenizer(
               lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, return_tensors="pt"		, )
  
       else:
           raise Exception(
               "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
               " strings` for supports are supported."							)
  
       return d
  
  
  
   def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :List[str]		, lowercase_   :str		, lowercase_   :str		, lowercase_   :List[Any]		, lowercase_   :Dict=0.7_0							)->							int:
       A__       =    []
       for idx in range(len(W_query["input_ids"]							)							):
           A__       =    A__       =    range(p_start.shape[1]							)
  
           A__       =    []
           for start_id in start_indexes:
               for end_id in end_indexes:
                   if start_id < end_id:
                       output.append(
                           (
                               start_id,
                               end_id,
                               p_start[idx][start_id].item(),
                               p_end[idx][end_id].item(),
                           )							)
  
           output.sort(key=lambda lowercase_							: (tup[2] * tup[3])		, reverse=lowercase_							)
           A__       =    []
           for k in range(len(lowercase_							)							):
               if output[k][2] * output[k][3] >= thresh:
                   A__,			A__       =    output[k][0], output[k][1]
                   A__       =    self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]							)
                   temp.append((decoded, output[k][2] * output[k][3])							)
  
           final_outputs.append(lowercase_							)
  
       return final_outputs
  
  
  
  