




"""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	,   SCREAMING_SNAKE_CASE__						)						):
		yield [next(SCREAMING_SNAKE_CASE__						)[args.text_column] for _ in range(SCREAMING_SNAKE_CASE__						)]


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

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

# Load dataset
_lowerCamelCase	:      Tuple   =							load_dataset(args.dataset_name, split='train', streaming=True)
_lowerCamelCase	:      Any   =							iter(dataset)


# Training and saving
_lowerCamelCase	:      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)












'''simple docstring'''







from __future__ import annotations





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      list[int | float] )			->						int | float:
						if len(SCREAMING_SNAKE_CASE__ ) == 0:
												raise ValueError('''find_min() arg is an empty sequence''' )
						UpperCAmelCase_      :   Optional[int]      =	nums[0]
						for num in nums:
												UpperCAmelCase_      :   Tuple      =	min(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
						return min_num


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)





