



"""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()





"""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)
