






import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


SCREAMING_SNAKE_CASE__		:       Union[str, Any]   							=							logging.getLogger(__name__)





def    _lowerCamelCase	(	)   -> Optional[Any]:

     '''simple docstring'''
     UpperCAmelCase__			:     Any										= argparse.ArgumentParser(description="""Train a unigram tokenizer on the wikitext dataset."""					)
     parser.add_argument(
         """--dataset_name"""   ,       type=__lowerCamelCase   ,       default="""wikitext"""   ,       help="""Name of the training. Explore datasets at: hf.co/datasets."""   ,       )
     parser.add_argument(
         """--dataset_config"""   ,       type=__lowerCamelCase   ,       default="""wikitext-103-raw-v1"""   ,       help="""Configuration name of the dataset."""					)
     parser.add_argument(
         """--batch_size"""   ,       type=__lowerCamelCase   ,       default=1000   ,       help="""Batch size during training."""   ,       )
     parser.add_argument(
         """--vocab_size"""   ,       type=__lowerCamelCase   ,       default=1_0048   ,       help="""Size of the desired vocabulary."""   ,       )
     parser.add_argument(
         """--limit"""   ,       default=__lowerCamelCase   ,       type=__lowerCamelCase   ,       help="""Limit the number of shards (used for debugging)."""   ,       )
     parser.add_argument(
         """--export_to_hub"""   ,       action="""store_true"""   ,       )

     UpperCAmelCase__			:     Tuple										= parser.parse_args()
     return args





def    _lowerCamelCase	(	__lowerCamelCase					)   -> str:

     '''simple docstring'''
     UpperCAmelCase__			:     str										= datasets.load_dataset(args.dataset_name   ,       args.dataset_config   ,       split="""train"""					)

     if args.limit is not None:
          UpperCAmelCase__			:     Optional[int]										= min(len(__lowerCamelCase					)   ,       args.limit					)
          UpperCAmelCase__			:     List[Any]										= dataset.select(range(__lowerCamelCase					)					)
          logger.info(F"Limiting the dataset to {args.limit} entries."					)

     def batch_iterator():
          for i in range(0   ,       len(__lowerCamelCase					)   ,       args.batch_size					):
               yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
     UpperCAmelCase__			:     str										= Tokenizer(Unigram()					)
     UpperCAmelCase__			:     Any										= normalizers.Sequence([normalizers.Replace("""``"""   ,       """\""""					), normalizers.Replace("""''"""   ,       """\""""					)]					)
     UpperCAmelCase__			:     Union[str, Any]										= pre_tokenizers.Metaspace()

     # Prepare the trainer.
     UpperCAmelCase__			:     Optional[int]										= UnigramTrainer(
         unk_token="""<unk>"""   ,       special_tokens=["""[CLS]""", """[SEP]""", """<unk>""", """<pad>""", """[MASK]"""]   ,       vocab_size=args.vocab_size   ,       )

     logger.info("""Training the tokenizer."""					)
     tokenizer.train_from_iterator(batch_iterator()   ,       trainer=__lowerCamelCase					)
     logger.info("""Tokenizer training complete!"""					)

     UpperCAmelCase__			:     List[str]										= tokenizer.token_to_id("""[CLS]"""					)
     UpperCAmelCase__			:     int										= tokenizer.token_to_id("""[SEP]"""					)
     UpperCAmelCase__			:     List[Any]										= processors.TemplateProcessing(
         single="""[CLS]:0 $A:0 [SEP]:0"""   ,       pair="""[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1"""   ,       special_tokens=[
             ("""[CLS]""", cls_token_id),
             ("""[SEP]""", sep_token_id),
         ]   ,       )
     UpperCAmelCase__			:     str										= decoders.Metaspace()

     if args.export_to_hub:
          logger.info("""Exporting the trained tokenzier to Hub."""					)
          UpperCAmelCase__			:     Dict										= AlbertTokenizerFast(tokenizer_object=__lowerCamelCase					)
          new_tokenizer.push_to_hub("""unigram-tokenizer-dataset"""					)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE__		:       Any   							=							parse_args()
    main(args)














from ..utils import requires_backends


SCREAMING_SNAKE_CASE__		:       int   							=							None







class 							UpperCAmelCase_     :




     def __init__(							self  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
          requires_backends(self  ,	["""detectron2"""]		)




     @classmethod
     def      __UpperCAmelCase     (							cls  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
          requires_backends(cls  ,	["""detectron2"""]		)






