



"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


_UpperCamelCase:				int    							=  logging.get_logger(__name__)

_UpperCamelCase:				Optional[Any]    							=  {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase:				Optional[int]    							=  {
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json',
    },
}


class   a__		(						SCREAMING_SNAKE_CASE__     ):


   def __init__(							self      :							List[str],     lowerCAmelCase      :							List[str]=None,     lowerCAmelCase      :							Any=None,     lowerCAmelCase      :							Dict=False,     lowerCAmelCase      :							int=True,     lowerCAmelCase      :							List[str]=False,     lowerCAmelCase      :							Any="<s>",     lowerCAmelCase      :							Union[str, Any]="</s>",     lowerCAmelCase      :							Tuple="<unk>",     lowerCAmelCase      :							Dict="<sep>",     lowerCAmelCase      :							Optional[int]="<pad>",     lowerCAmelCase      :							List[str]="<cls>",     lowerCAmelCase      :							int="<mask>",     lowerCAmelCase      :							int=["<eop>", "<eod>"],     **lowerCAmelCase      :							Optional[int],     )    ->       Union[str, Any]:
         lowercase  :      List[str]					=   AddedToken(lowerCAmelCase,     lstrip=lowerCAmelCase,     rstrip=lowerCAmelCase			) if isinstance(lowerCAmelCase,     lowerCAmelCase			) else mask_token

         super().__init__(
             vocab_file=lowerCAmelCase,     tokenizer_file=lowerCAmelCase,     do_lower_case=lowerCAmelCase,     remove_space=lowerCAmelCase,     keep_accents=lowerCAmelCase,     bos_token=lowerCAmelCase,     eos_token=lowerCAmelCase,     unk_token=lowerCAmelCase,     sep_token=lowerCAmelCase,     pad_token=lowerCAmelCase,     cls_token=lowerCAmelCase,     mask_token=lowerCAmelCase,     additional_special_tokens=lowerCAmelCase,     **lowerCAmelCase,     )

         lowercase  :      Optional[int]					=   3
         lowercase  :      List[str]					=   do_lower_case
         lowercase  :      Dict					=   remove_space
         lowercase  :      Union[str, Any]					=   keep_accents
         lowercase  :      Any					=   vocab_file
         lowercase  :      str					=   False if not self.vocab_file else True

         try:
               import jieba
         except ModuleNotFoundError as error:
               raise error.__class__(
                   'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
                   'See https://pypi.org/project/jieba/ for installation.'			)
         lowercase  :      Any					=   jieba
         lowercase  :      List[Any]					=   str.maketrans(' \n',     '\u2582\u2583'			)


   def        lowercase				(							self      :							Union[str, Any],     lowerCAmelCase      :							List[int],     lowerCAmelCase      :							Optional[List[int]] = None			)    ->       List[int]:
         lowercase  :      List[Any]					=   [self.sep_token_id]
         lowercase  :      List[Any]					=   [self.cls_token_id]
         if token_ids_a is None:
               return token_ids_a + sep + cls
         return token_ids_a + sep + token_ids_a + sep + cls


   def        lowercase				(							self      :							Dict,     lowerCAmelCase      :							List[int],     lowerCAmelCase      :							Optional[List[int]] = None			)    ->       List[int]:
         lowercase  :      Any					=   [self.sep_token_id]
         lowercase  :      List[Any]					=   [2]

         if token_ids_a is None:
               return len(token_ids_a + sep			) * [0] + cls_segment_id
         return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1] + cls_segment_id


   def        lowercase				(							self      :							List[Any],     lowerCAmelCase      :							str,     lowerCAmelCase      :							Optional[str] = None			)    ->       Tuple[str]:
         if not self.can_save_slow_tokenizer:
               raise ValueError(
                   'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                   'tokenizer.'			)

         if not os.path.isdir(lowerCAmelCase			):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
               return
         lowercase  :      Optional[Any]					=   os.path.join(
             lowerCAmelCase,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

         if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCAmelCase			):
               copyfile(self.vocab_file,     lowerCAmelCase			)

         return (out_vocab_file,)


   def        lowercase				(							self      :							Union[str, Any],     lowerCAmelCase      :							Optional[Any],     *lowerCAmelCase      :							str,     **lowerCAmelCase      :							List[str]			)    ->       Dict:
         lowercase  :      Optional[Any]					=   [
             ' '.join([x.translate(self.translator			) for x in self.jieba.cut(lowerCAmelCase,     cut_all=lowerCAmelCase			)]			)
             for text in batch_text_or_text_pairs
         ]
         return super()._batch_encode_plus(lowerCAmelCase,     *lowerCAmelCase,     **lowerCAmelCase			)






   def        lowercase				(							self      :							List[str],     *lowerCAmelCase      :							int,     **lowerCAmelCase      :							List[str]			)    ->       List[str]:
         lowercase  :      Union[str, Any]					=   super()._decode(*lowerCAmelCase,     **lowerCAmelCase			)
         lowercase  :      List[Any]					=   text.replace(' ',     ''			).replace('\u2582',     ' '			).replace('\u2583',     '\n'			)
         return text





"""simple docstring"""




from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode


def   lowercase__     (			_UpperCAmelCase=10				)     ->			Any:






      '''simple docstring'''


      for _ in tqdm(range(0	,      args.n_examples	,      _UpperCAmelCase				)				):
            yield [next(_UpperCAmelCase				)[args.text_column] for _ in range(_UpperCAmelCase				)]


# Configuration
_UpperCamelCase:				Union[str, Any]    							=  HfArgumentParser(TokenizerTrainingArguments)
_UpperCamelCase:				Dict    							=  parser.parse_args()

# Base tokenizer
_UpperCamelCase:				List[Any]    							=  AutoTokenizer.from_pretrained(args.base_tokenizer)
_UpperCamelCase:				int    							=  list(bytes_to_unicode().values())

# Load dataset
_UpperCamelCase:				Union[str, Any]    							=  load_dataset(args.dataset_name, split='train', streaming=True)
_UpperCamelCase:				Union[str, Any]    							=  iter(dataset)


# Training and saving
_UpperCamelCase:				Union[str, Any]    							=  tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)





new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
