




"""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 						lowercase_ (						_UpperCAmelCase=10		):




	"""simple docstring"""






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


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

# Base tokenizer
_lowerCamelCase					:     Any				       =    AutoTokenizer.from_pretrained(args.base_tokenizer)
_lowerCamelCase					:     str				       =    list(bytes_to_unicode().values())

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











"""simple docstring"""

def 						lowercase_ (						_UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Optional[int]						=						9

	while num > 0:
		if num % 10 != digit:
			return False
		num //= 100
		digit -= 1

	return True

def 						lowercase_ (						):




	"""simple docstring"""






	A_					:			Dict						=						1_3890_2663

	while not is_square_form(num * num		):
		if num % 10 == 3:
			num -= 6  # (3 - 6) % 10 = 7
		else:
			num -= 4  # (7 - 4) % 10 = 3

	return num * 10


if __name__ == "__main__":
		print(f'{solution() = }')





