
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  a_	( __lowercase     :			str=10				)				->						int:
			for _ in tqdm(range(0 ,      args.n_examples ,      __lowercase				)				):
						yield [next(__lowercase				)[args.text_column] for _ in range(__lowercase				)]


# Configuration
_lowerCamelCase :       Any     					=    HfArgumentParser(TokenizerTrainingArguments)
_lowerCamelCase :       List[str]     					=    parser.parse_args()

# Base tokenizer
_lowerCamelCase :       int     					=    AutoTokenizer.from_pretrained(args.base_tokenizer)
_lowerCamelCase :       List[Any]     					=    list(bytes_to_unicode().values())

# Load dataset
_lowerCamelCase :       Union[str, Any]     					=    load_dataset(args.dataset_name, split='''train''', streaming=True)
_lowerCamelCase :       List[str]     					=    iter(dataset)


# Training and saving
_lowerCamelCase :       Tuple     					=    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)

_lowerCamelCase :       Union[str, Any]     					=    {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

_lowerCamelCase :       Optional[Any]     					=    [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_lowerCamelCase :       Dict     					=    [0 for x in range(len(graph))]
_lowerCamelCase :       Dict     					=    []






def  a_	( __lowercase     :			Optional[int] ,      __lowercase     :			Dict				)				->						Optional[int]:
			_snake_case						=    1
			while stack:
						_snake_case						=    stack.pop()
						print(__lowercase ,      clothes[current_clothing]				)
						order += 1






def  a_	( __lowercase     :			Union[str, Any] ,      __lowercase     :			Dict ,      __lowercase     :			Union[str, Any]				)				->						List[Any]:
			_snake_case						=    1
			for v in graph[u]:
						if not visited[v]:
									depth_first_search(__lowercase ,      __lowercase ,      __lowercase				)

			stack.append(__lowercase				)






def  a_	( __lowercase     :			Dict ,      __lowercase     :			int				)				->						List[str]:
			for v in range(len(__lowercase				)				):
						if not visited[v]:
									depth_first_search(__lowercase ,      __lowercase ,      __lowercase				)


if __name__ == "__main__":
			topological_sort(graph, visited)
			print(stack)
			print_stack(stack, clothes)