'''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__	:     Any      =    logging.getLogger(__name__)


def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Optional[int]  = 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=10048      ,	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_				:  Tuple  = datasets.load_dataset(args.dataset_name      ,	args.dataset_config      ,	split='train'  )

     if args.limit is not None:
          UpperCAmelCase_				:  Dict  = min(len(lowerCamelCase_  )      ,	args.limit  )
          UpperCAmelCase_				:  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.
     UpperCAmelCase_				:  Optional[int]  = Tokenizer(Unigram()  )
     UpperCAmelCase_				:  Optional[int]  = normalizers.Sequence([normalizers.Replace('``'      ,	'"'  ), normalizers.Replace('\'\''      ,	'"'  )]  )
     UpperCAmelCase_				:  Union[str, Any]  = pre_tokenizers.Metaspace()

     # Prepare the trainer.
     UpperCAmelCase_				:  str  = 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_				:  Optional[Any]  = tokenizer.token_to_id('[CLS]'  )
     UpperCAmelCase_				:  List[str]  = tokenizer.token_to_id('[SEP]'  )
     UpperCAmelCase_				:  List[str]  = 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_				:  List[str]  = decoders.Metaspace()

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


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







'''simple docstring'''


import io
import json
import subprocess


snake_case__	:     Any      =    [
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

snake_case__	:     Tuple      =    8


def    _lowerCamelCase       ( lowerCamelCase_   :				Tuple      ,	lowerCamelCase_   :				Union[str, Any]  ):



     """simple docstring"""





     UpperCAmelCase_				:  List[Any]  = {}
     for src, tgt in pairs:
          UpperCAmelCase_				:  str  = F'''{src}-{tgt}'''
          UpperCAmelCase_				:  List[str]  = F'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
          UpperCAmelCase_				:  Any  = subprocess.run(lowerCamelCase_      ,	stdout=subprocess.PIPE  ).stdout.decode('utf-8'  ).splitlines()
          UpperCAmelCase_				:  Any  = F'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
          UpperCAmelCase_				:  List[str]  = subprocess.run(lowerCamelCase_      ,	stdout=subprocess.PIPE  ).stdout.decode('utf-8'  ).splitlines()
          UpperCAmelCase_				:  str  = {'src': src_lines[:n_objs], 'tgt': tgt_lines[:n_objs]}
     return text


snake_case__	:     Union[str, Any]      =    get_all_data(pairs, n_objs)
snake_case__	:     Dict      =    '''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
 snake_case__	:     List[str]      =    json.dump(text, f, indent=2, ensure_ascii=False)






