"""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  lowerCAmelCase__     (      _UpperCamelCase							:					Optional[Any]=1_0       )       ->					Dict:





						"""simple docstring"""






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


# Configuration
SCREAMING_SNAKE_CASE__       =	HfArgumentParser(TokenizerTrainingArguments)
SCREAMING_SNAKE_CASE__       =	parser.parse_args()

# Base tokenizer
SCREAMING_SNAKE_CASE__       =	AutoTokenizer.from_pretrained(args.base_tokenizer)
SCREAMING_SNAKE_CASE__       =	list(bytes_to_unicode().values())

# Load dataset
SCREAMING_SNAKE_CASE__       =	load_dataset(args.dataset_name, split="train", streaming=True)
SCREAMING_SNAKE_CASE__       =	iter(dataset)


# Training and saving
SCREAMING_SNAKE_CASE__       =	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  lowerCAmelCase__     (      _UpperCamelCase							:					list       )       ->					list:





						"""simple docstring"""






						if len(_UpperCamelCase       ) <= 1:
												return data
						else:
												return [
												    *quick_sort([e for e in data[1:] if e <= data[0]]       ),
												    data[0],
												    *quick_sort([e for e in data[1:] if e > data[0]]       ),
												]


if __name__ == "__main__":
					import doctest

					doctest.testmod()


