
def       UpperCAmelCase__							(    __magic_name__  :       str							,					__magic_name__  :       str							,					__magic_name__  :       Optional[Any]							,					__magic_name__  :       int							,					__magic_name__  :       Dict					):


					'''simple docstring'''

					if target == 0:
										answer.append(path.copy()					)
					else:
										for index in range(SCREAMING_SNAKE_CASE_							,					len(SCREAMING_SNAKE_CASE_					)					):
															if target >= candidates[index]:
																				path.append(candidates[index]					)
																				backtrack(SCREAMING_SNAKE_CASE_							,					SCREAMING_SNAKE_CASE_							,					SCREAMING_SNAKE_CASE_							,					target - candidates[index]							,					SCREAMING_SNAKE_CASE_					)
																				path.pop(len(SCREAMING_SNAKE_CASE_					) - 1					)

def       UpperCAmelCase__							(    __magic_name__  :       List[Any]							,					__magic_name__  :       List[str]					):


					'''simple docstring'''

					lowerCAmelCase						:   List[Any]							=       []  # type: list[int]
					lowerCAmelCase						:   Tuple							=       []  # type: list[int]
					backtrack(SCREAMING_SNAKE_CASE_							,					SCREAMING_SNAKE_CASE_							,					SCREAMING_SNAKE_CASE_							,					SCREAMING_SNAKE_CASE_							,					0					)
					return answer

def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					print(combination_sum([-8, 2.3, 0]							,					1					)					)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()


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       UpperCAmelCase__							(    __magic_name__  :       str=10					):


					'''simple docstring'''

					for _ in tqdm(range(0							,					args.n_examples							,					__magic_name__					)					):
										yield [next(__magic_name__					)[args.text_column] for _ in range(__magic_name__					)]


# Configuration
__SCREAMING_SNAKE_CASE     :				Dict             =       HfArgumentParser(TokenizerTrainingArguments)
__SCREAMING_SNAKE_CASE     :				str             =       parser.parse_args()

# Base tokenizer
__SCREAMING_SNAKE_CASE     :				Tuple             =       AutoTokenizer.from_pretrained(args.base_tokenizer)
__SCREAMING_SNAKE_CASE     :				List[str]             =       list(bytes_to_unicode().values())

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


# Training and saving
__SCREAMING_SNAKE_CASE     :				Optional[int]             =       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)
