




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_	(     lowerCamelCase:				Tuple=10  )    ->	List[Any]:


      '''simple docstring'''




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


# Configuration
__SCREAMING_SNAKE_CASE			:      List[str]      			=HfArgumentParser(TokenizerTrainingArguments)
__SCREAMING_SNAKE_CASE			:      Tuple      			=parser.parse_args()

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

# Load dataset
__SCREAMING_SNAKE_CASE			:      Optional[int]      			=load_dataset(args.dataset_name, split="""train""", streaming=True)
__SCREAMING_SNAKE_CASE			:      str      			=iter(dataset)


# Training and saving
__SCREAMING_SNAKE_CASE			:      Union[str, Any]      			=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)


from .prime_check import Test

Test()


