code: 
  
  
  
  _lowerCAmelCase       :      str							=							{
      0: '''underwear''',
      1: '''pants''',
      2: '''belt''',
      3: '''suit''',
      4: '''shoe''',
      5: '''socks''',
      6: '''shirt''',
      7: '''tie''',
      8: '''watch''',
  }
  
  _lowerCAmelCase       :      List[str]							=							[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]
  
  _lowerCAmelCase       :      Any							=							[0 for x in range(len(graph))]
  _lowerCAmelCase       :      str							=							[]
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Dict		,						SCREAMING_SNAKE_CASE__			:		List[str]       )						->     Tuple:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Optional[int]              =					1
       while stack:
            _UpperCAmelCase    :			Union[str, Any]              =					stack.pop()
            print(__lowerCAmelCase		,						clothes[current_clothing]       )
            order += 1
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]		,						SCREAMING_SNAKE_CASE__			:		Any		,						SCREAMING_SNAKE_CASE__			:		str       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			List[Any]              =					1
       for v in graph[u]:
            if not visited[v]:
                 depth_first_search(__lowerCAmelCase		,						__lowerCAmelCase		,						__lowerCAmelCase       )
  
       stack.append(__lowerCAmelCase       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Union[str, Any]:
  
  
  
  
  
       '''simple docstring'''
  
       for v in range(len(__lowerCAmelCase       )       ):
            if not visited[v]:
                 depth_first_search(__lowerCAmelCase		,						__lowerCAmelCase		,						__lowerCAmelCase       )
  
  
  if __name__ == "__main__":
   topological_sort(graph, visited)
   print(stack)
   print_stack(stack, clothes)
  
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)
  