





'''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_   (	SCREAMING_SNAKE_CASE__   :      str=10 )			->						List[str]:
						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
snake_case_  :							List[Any]            =	HfArgumentParser(TokenizerTrainingArguments)
snake_case_  :							Optional[int]            =	parser.parse_args()

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

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


# Training and saving
snake_case_  :							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)











from typing import Callable




def    lowerCamelCase__			(		A__			:   Callable      ):





       '''simple docstring'''



       def wrapper(A__			:   int      ):
              __lowerCamelCase  =		function_with_docstring.__doc__
              return target_function

       return wrapper

