

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 _a					(			SCREAMING_SNAKE_CASE						:     Optional[Any]=10						)       ->       Dict:

 """simple docstring"""






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


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

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

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


# Training and saving
_a									=  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 typing import TYPE_CHECKING

from ...utils import _LazyModule


SCREAMING_SNAKE_CASE__       =	{"tokenization_bert_japanese": ["BertJapaneseTokenizer", "CharacterTokenizer", "MecabTokenizer"]}


if TYPE_CHECKING:
					from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer

else:
					import sys

					SCREAMING_SNAKE_CASE__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


