'''simple docstring'''




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


snake_case  =			logging.getLogger(__name__)







def 			A_   ( ):
						_lowerCAmelCase		   =   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=1_000  ,  help='Batch size during training.'  ,  )
						parser.add_argument(
						    '--vocab_size'  ,  type=_lowerCamelCase  ,  default=10_048  ,  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'  ,  )

						_lowerCAmelCase		   =   parser.parse_args()
						return args







def 			A_   ( _lowerCamelCase			:							Dict    ):
						_lowerCAmelCase		   =   datasets.load_dataset(args.dataset_name  ,  args.dataset_config  ,  split='train'    )

						if args.limit is not None:
												_lowerCAmelCase		   =   min(len(_lowerCamelCase    )  ,  args.limit    )
												_lowerCAmelCase		   =   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.
						_lowerCAmelCase		   =   Tokenizer(Unigram()    )
						_lowerCAmelCase		   =   normalizers.Sequence([normalizers.Replace('``'  ,  '\"'    ), normalizers.Replace('\'\''  ,  '\"'    )]    )
						_lowerCAmelCase		   =   pre_tokenizers.Metaspace()

						# Prepare the trainer.
						_lowerCAmelCase		   =   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!'    )

						_lowerCAmelCase		   =   tokenizer.token_to_id('[CLS]'    )
						_lowerCAmelCase		   =   tokenizer.token_to_id('[SEP]'    )
						_lowerCAmelCase		   =   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),
						    ]  ,  )
						_lowerCAmelCase		   =   decoders.Metaspace()

						if args.export_to_hub:
												logger.info('Exporting the trained tokenzier to Hub.'    )
												_lowerCAmelCase		   =   AlbertTokenizerFast(tokenizer_object=_lowerCamelCase    )
												new_tokenizer.push_to_hub('unigram-tokenizer-dataset'    )


if __name__ == "__main__":
	snake_case  =			parse_args()
	main(args)






'''simple docstring'''




from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


snake_case  =			logging.get_logger(__name__)

snake_case  =			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case  =			{
    '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
    '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
    '''tokenizer_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'''},
}

snake_case  =			{
    '''openai-gpt''': 512,
}





class        SCREAMING_SNAKE_CASE   (				__a ):
  """simple docstring"""







  __A     =		VOCAB_FILES_NAMES
  __A     =		PRETRAINED_VOCAB_FILES_MAP
  __A     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __A     =		["input_ids", "attention_mask"]
  __A     =		OpenAIGPTTokenizer



  def __init__(  self   :					Union[str, Any]					,					__lowerCAmelCase   :					Union[str, Any]=None					,					__lowerCAmelCase   :					str=None					,					__lowerCAmelCase   :					Optional[int]=None					,					__lowerCAmelCase   :					Union[str, Any]="<unk>"					,					**__lowerCAmelCase   :					Union[str, Any]					):


        """simple docstring"""



        super().__init__(__lowerCAmelCase					,					__lowerCAmelCase					,					tokenizer_file=__lowerCAmelCase					,					unk_token=__lowerCAmelCase					,					**__lowerCAmelCase					)



  @property
  def   a   (  self   :					str					):


        """simple docstring"""



        return True



  def   a   (  self   :					Tuple					,					__lowerCAmelCase   :					str					,					__lowerCAmelCase   :					Optional[str] = None					):


        """simple docstring"""



        _lowerCAmelCase		   =   self._tokenizer.model.save(__lowerCAmelCase					,					name=__lowerCAmelCase					)
        return tuple(__lowerCAmelCase					)





