



_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)





"""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)
