




'''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


lowercase						:   int      						=							logging.getLogger(__name__)




def 		a_(			)					->Dict:






       '''simple docstring'''




       lowercase	:		Optional[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''' ,					)

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




def 		a_(			lowerCamelCase   )					->str:






       '''simple docstring'''




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

       if args.limit is not None:
              lowercase	:		List[str]    =   min(len(lowerCamelCase   ) ,					args.limit   )
              lowercase	:		Optional[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.
       lowercase	:		Tuple    =   Tokenizer(Unigram()   )
       lowercase	:		Tuple    =   normalizers.Sequence([normalizers.Replace('''``''' ,					'''"'''   ), normalizers.Replace('''\'\'''' ,					'''"'''   )]   )
       lowercase	:		str    =   pre_tokenizers.Metaspace()

       # Prepare the trainer.
       lowercase	:		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=lowerCamelCase   )
       logger.info('''Tokenizer training complete!'''   )

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

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


if __name__ == "__main__":
      lowercase						:   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()




