






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   SCREAMING_SNAKE_CASE__						(   snake_case_=1_0  )   ->  Optional[int]:


	"""simple docstring"""



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


# Configuration
UpperCamelCase__						:      Union[str, Any]								=					HfArgumentParser(TokenizerTrainingArguments)
UpperCamelCase__						:      Tuple								=					parser.parse_args()

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

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


# Training and saving
UpperCamelCase__						:      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)













def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  int:


	"""simple docstring"""



	return n * (2 * n - 1)





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  bool:


	"""simple docstring"""



	a		=			(1 + 2_4 * n) ** 0.5
	return ((1 + root) / 6) % 1 == 0





def   SCREAMING_SNAKE_CASE__						(   snake_case_ = 1_4_4  )   ->  int:


	"""simple docstring"""



	a		=			start
	a		=			hexagonal_num(snake_case_  )
	while not is_pentagonal(snake_case_  ):
		n += 1
		a		=			hexagonal_num(snake_case_  )
	return num


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





