code: 
  
  
  
  """simple docstring"""
  
  
  
  
  _lowerCAmelCase       :      List[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 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Tuple       )						->     Union[str, Any]:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Any              =					set()
       for row in edge_array:
            for item in row:
                 distinct_edge.add(item[0]       )
       return list(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str		,						SCREAMING_SNAKE_CASE__			:		List[Any]       )						->     str:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Optional[int]              =					["0"] * len(SCREAMING_SNAKE_CASE__       )
       for i, row in enumerate(SCREAMING_SNAKE_CASE__       ):
            for item in row:
                 if distinct_edge in item[0]:
                      _UpperCAmelCase    :			List[str]              =					"1"
                      break
       return "".join(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Any       )						->     Union[str, Any]:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Optional[int]              =					get_distinct_edge(SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			Optional[int]              =					{}
  
       for item in distinct_edge:
            _UpperCAmelCase    :			str              =					get_bitcode(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
            # print('bit',bit)
            # bt=''.join(bit)
            _UpperCAmelCase    :			Union[str, Any]              =					bit.count("1"       )
            _UpperCAmelCase    :			Tuple              =					[s, bit]
       # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
       _UpperCAmelCase    :			Optional[int]              =					[
           [k, v[0], v[1]]
           for k, v in sorted(frequency_table.items()		,						key=lambda SCREAMING_SNAKE_CASE__       : v[1][0]		,						reverse=SCREAMING_SNAKE_CASE__       )
       ]
       return sorted_frequency_table
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[int]       )						->     str:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Optional[Any]              =					{}
       for _, item in enumerate(SCREAMING_SNAKE_CASE__       ):
            nodes.setdefault(item[2]		,						[]       ).append(item[0]       )
       return nodes
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]       )						->     Any:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			int              =					{}
       for key, value in nodes.items():
            _UpperCAmelCase    :			Dict              =					value
       return cluster
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Union[str, Any]       )						->     List[Any]:
  
  
  
  
  
       '''simple docstring'''
  
       return [i * 100 / len(SCREAMING_SNAKE_CASE__       ) for i in cluster]
  
  def 		__snake_case		(   )						->     None:
  
  
  
  
  
       '''simple docstring'''
  
       print("\nNodes\n"       )
       for key, value in nodes.items():
            print(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
       print("\nSupport\n"       )
       print(SCREAMING_SNAKE_CASE__       )
       print("\n Cluster \n"       )
       for key, value in sorted(cluster.items()		,						reverse=SCREAMING_SNAKE_CASE__       ):
            print(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
       print("\n Graph\n"       )
       for key, value in graph.items():
            print(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
       print("\n Edge List of Frequent subgraphs \n"       )
       for edge_list in freq_subgraph_edge_list:
            print(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Dict		,						SCREAMING_SNAKE_CASE__			:		Tuple		,						SCREAMING_SNAKE_CASE__			:		Union[str, Any]		,						SCREAMING_SNAKE_CASE__			:		Optional[Any]       )						->     List[str]:
  
  
  
  
  
       '''simple docstring'''
  
       for i in cluster[ca]:
            _UpperCAmelCase    :			List[Any]              =					0
            _UpperCAmelCase    :			List[Any]              =					ca + 1
            while ca < max(cluster.keys()       ):
                 for j in cluster[ca]:
                      if int(SCREAMING_SNAKE_CASE__		,						2       ) & int(SCREAMING_SNAKE_CASE__		,						2       ) == int(SCREAMING_SNAKE_CASE__		,						2       ):
                           if tuple(nodes[i]       ) in graph:
                                graph[tuple(nodes[i]       )].append(nodes[j]       )
                           else:
                                _UpperCAmelCase    :			Union[str, Any]              =					[nodes[j]]
                           count += 1
                 if count == 0:
                      _UpperCAmelCase    :			List[Any]              =					ca + 1
                 else:
                      break
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[str]		,						SCREAMING_SNAKE_CASE__			:		Dict       )						->     Union[str, Any]:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Dict              =					cluster[max(cluster.keys()       )]
       _UpperCAmelCase    :			Dict              =					"Header"
       _UpperCAmelCase    :			Dict              =					{}
       for i in x:
            if (["Header"],) in graph:
                 graph[(["Header"],)].append(x[i]       )
            else:
                 _UpperCAmelCase    :			List[Any]              =					[x[i]]
       for i in x:
            _UpperCAmelCase    :			Dict              =					[["Header"]]
       _UpperCAmelCase    :			List[Any]              =					1
       while i < max(SCREAMING_SNAKE_CASE__       ) - 1:
            create_edge(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
            _UpperCAmelCase    :			Union[str, Any]              =					i + 1
       return graph
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		Optional[Any]		,						SCREAMING_SNAKE_CASE__			:		Any=None       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			str              =					(path or []) + [start]
       if start == end:
            paths.append(SCREAMING_SNAKE_CASE__       )
       for node in graph[start]:
            if tuple(SCREAMING_SNAKE_CASE__       ) not in path:
                 my_dfs(SCREAMING_SNAKE_CASE__		,						tuple(SCREAMING_SNAKE_CASE__       )		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		Union[str, Any]		,						SCREAMING_SNAKE_CASE__			:		List[str]       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Union[str, Any]              =					int(s / 100 * (len(SCREAMING_SNAKE_CASE__       ) - 1)       )
       for i in cluster[k]:
            my_dfs(SCREAMING_SNAKE_CASE__		,						tuple(cluster[k][i]       )		,						(["Header"],)       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Union[str, Any]       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Any              =					[]
       for edges in paths:
            _UpperCAmelCase    :			Tuple              =					[]
            for j in range(len(SCREAMING_SNAKE_CASE__       ) - 1       ):
                 _UpperCAmelCase    :			int              =					list(edges[j]       )
                 for e in temp:
                      _UpperCAmelCase    :			str              =					(e[0], e[1])
                      el.append(SCREAMING_SNAKE_CASE__       )
            freq_sub_el.append(SCREAMING_SNAKE_CASE__       )
       return freq_sub_el
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[int]       )						->     str:
  
  
  
  
  
       '''simple docstring'''
  
       for i in range(len(SCREAMING_SNAKE_CASE__       )       ):
            for j in range(len(edge_array[i]       )       ):
                 _UpperCAmelCase    :			Optional[Any]              =					edge_array[i][j].split("-"       )
                 _UpperCAmelCase    :			List[Any]              =					t
  
  
  if __name__ == "__main__":
   preprocess(edge_array)
   _lowerCAmelCase       :      Tuple							=							get_frequency_table(edge_array)
   _lowerCAmelCase       :      Union[str, Any]							=							get_nodes(frequency_table)
   _lowerCAmelCase       :      Union[str, Any]							=							get_cluster(nodes)
   _lowerCAmelCase       :      str							=							get_support(cluster)
   _lowerCAmelCase       :      Optional[Any]							=							construct_graph(cluster, nodes)
   find_freq_subgraph_given_support(60, cluster, graph)
   _lowerCAmelCase       :      list							=							[]
   _lowerCAmelCase       :      List[Any]							=							freq_subgraphs_edge_list(paths)
   print_all()
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  from arguments import TokenizerTrainingArguments
  from datasets import load_dataset
  from tqdm import tqdm
  
  from transformers import AutoTokenizer, HfArgumentParser
  from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str=10       )						->     List[Any]:
  
  
  
  
  
       '''simple docstring'''
  
       for _ in tqdm(range(0		,						args.n_examples		,						SCREAMING_SNAKE_CASE__       )       ):
            yield [next(SCREAMING_SNAKE_CASE__       )[args.text_column] for _ in range(SCREAMING_SNAKE_CASE__       )]
  
  
  # Configuration
  _lowerCAmelCase       :      Optional[Any]							=							HfArgumentParser(TokenizerTrainingArguments)
  _lowerCAmelCase       :      Dict							=							parser.parse_args()
  
  # Base tokenizer
  _lowerCAmelCase       :      int							=							AutoTokenizer.from_pretrained(args.base_tokenizer)
  _lowerCAmelCase       :      List[str]							=							list(bytes_to_unicode().values())
  
  # Load dataset
  _lowerCAmelCase       :      Dict							=							load_dataset(args.dataset_name, split="train", streaming=True)
  _lowerCAmelCase       :      List[Any]							=							iter(dataset)
  
  
  # Training and saving
  _lowerCAmelCase       :      List[str]							=							tokenizer.train_new_from_iterator(
      batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
  )
  new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
  