
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


a__				:      int    		=     logging.getLogger(__name__)







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = argparse.ArgumentParser(description='''Train a unigram tokenizer on the wikitext dataset.'''   )
    parser.add_argument(
        '''--dataset_name''' ,						type=a__ ,						default='''wikitext''' ,						help='''Name of the training. Explore datasets at: hf.co/datasets.''' ,						)
    parser.add_argument(
        '''--dataset_config''' ,						type=a__ ,						default='''wikitext-103-raw-v1''' ,						help='''Configuration name of the dataset.'''   )
    parser.add_argument(
        '''--batch_size''' ,						type=a__ ,						default=1_000 ,						help='''Batch size during training.''' ,						)
    parser.add_argument(
        '''--vocab_size''' ,						type=a__ ,						default=10_048 ,						help='''Size of the desired vocabulary.''' ,						)
    parser.add_argument(
        '''--limit''' ,						default=a__ ,						type=a__ ,						help='''Limit the number of shards (used for debugging).''' ,						)
    parser.add_argument(
        '''--export_to_hub''' ,						action='''store_true''' ,						)

    SCREAMING_SNAKE_CASE				:				List[Any]				      = parser.parse_args()
    return args







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[Any]				      = datasets.load_dataset(args.dataset_name ,						args.dataset_config ,						split='''train'''   )

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

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

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

    # Prepare the trainer.
    SCREAMING_SNAKE_CASE				:				Tuple				      = 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=a__   )
    logger.info('''Tokenizer training complete!'''   )

    SCREAMING_SNAKE_CASE				:				Any				      = tokenizer.token_to_id('''[CLS]'''   )
    SCREAMING_SNAKE_CASE				:				Any				      = tokenizer.token_to_id('''[SEP]'''   )
    SCREAMING_SNAKE_CASE				:				Dict				      = 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),
        ] ,						)
    SCREAMING_SNAKE_CASE				:				List[str]				      = decoders.Metaspace()

    if args.export_to_hub:
        logger.info('''Exporting the trained tokenzier to Hub.'''   )
        SCREAMING_SNAKE_CASE				:				List[str]				      = AlbertTokenizerFast(tokenizer_object=a__   )
        new_tokenizer.push_to_hub('''unigram-tokenizer-dataset'''   )


if __name__ == "__main__":
      a__				:      Optional[int]    		=     parse_args()
      main(args)






'''simple docstring'''






from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

lowercase						:   Union[str, Any]      						=							TypeVar('''T''', bound=bool)

class 	SCREAMING_SNAKE_CASE_		(							Generic[T]					):



 def __init__(	self ,		A			):






        '''simple docstring'''




        lowercase	:		T    =   value
        lowercase	:		SkewNode[T] | None    =   None
        lowercase	:		SkewNode[T] | None    =   None



 @property
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        return self._value





 @staticmethod
 def  SCREAMING_SNAKE_CASE(	A ,		A			):






        '''simple docstring'''




        if not roota:
               return roota

        if not roota:
               return roota

        if roota.value > roota.value:
               lowercase,  lowercase	:		Union[str, Any]    =   roota, roota

        lowercase	:		Optional[int]    =   roota
        lowercase	:		Tuple    =   roota.right
        lowercase	:		Union[str, Any]    =   roota.left
        lowercase	:		Any    =   SkewNode.merge(A ,		A			)

        return result






class 	SCREAMING_SNAKE_CASE_		(							Generic[T]					):



 def __init__(	self ,		A = ()			):






        '''simple docstring'''




        lowercase	:		SkewNode[T] | None    =   None
        if data:
               for item in data:
                      self.insert(A			)



 def __bool__(	self			):






        '''simple docstring'''




        return self._root is not None



 def __iter__(	self			):






        '''simple docstring'''




        lowercase	:		list[Any]    =   []
        while self:
               result.append(self.pop()			)

        # Pushing items back to the heap not to clear it.
        for item in result:
               self.insert(A			)

        return iter(A			)



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        lowercase	:		List[str]    =   SkewNode.merge(self._root ,		SkewNode(A			)			)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		List[Any]    =   self.top()
        lowercase	:		int    =   (
            SkewNode.merge(self._root.left ,		self._root.right			) if self._root else None
        )

        return result



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        if not self._root:
               raise IndexError('''Can\'t get top element for the empty heap.'''			)
        return self._root.value





 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Optional[Any]    =   None


if __name__ == "__main__":
      import doctest

      doctest.testmod()




