





import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Union[str, Any]:




    """simple docstring"""




    if (
        (cp >= 0X4E_00 and cp <= 0X9F_FF)
        or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
        or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
        or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
        or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
        or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
        or (cp >= 0XF9_00 and cp <= 0XFA_FF)
        or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
    ):  #
        return True

    return False





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    for char in word:
        _A     =							ord(_SCREAMING_SNAKE_CASE				)
        if not _is_chinese_char(_SCREAMING_SNAKE_CASE				):
            return 0
    return 1





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Dict:




    """simple docstring"""




    _A     =							set()

    for token in tokens:
        _A     =							len(_SCREAMING_SNAKE_CASE				) > 1 and is_chinese(_SCREAMING_SNAKE_CASE				)
        if chinese_word:
            word_set.add(_SCREAMING_SNAKE_CASE				)
    _A     =							list(_SCREAMING_SNAKE_CASE				)
    return word_list





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Tuple:




    """simple docstring"""




    if not chinese_word_set:
        return bert_tokens
    _A     =							max([len(_SCREAMING_SNAKE_CASE				) for w in chinese_word_set]				)

    _A     =							bert_tokens
    _A,			_A     =							0, len(_SCREAMING_SNAKE_CASE				)
    while start < end:
        _A     =							True
        if is_chinese(bert_word[start]				):
            _A     =							min(end - start							,     _SCREAMING_SNAKE_CASE				)
            for i in range(_SCREAMING_SNAKE_CASE							,     1							,     -1				):
                _A     =							''.join(bert_word[start : start + i]				)
                if whole_word in chinese_word_set:
                    for j in range(start + 1							,     start + i				):
                        _A     =							'##' + bert_word[j]
                    _A     =							start + i
                    _A     =							False
                    break
        if single_word:
            start += 1
    return bert_word





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> List[Any]:




    """simple docstring"""




    _A     =							[]

    for i in range(0							,     len(_SCREAMING_SNAKE_CASE				)							,     100				):
        _A     =							ltp_tokenizer.seg(lines[i : i + 100]				)[0]
        _A     =							[get_chinese_word(_SCREAMING_SNAKE_CASE				) for r in res]
        ltp_res.extend(_SCREAMING_SNAKE_CASE				)
    assert len(_SCREAMING_SNAKE_CASE				) == len(_SCREAMING_SNAKE_CASE				)

    _A     =							[]
    for i in range(0							,     len(_SCREAMING_SNAKE_CASE				)							,     100				):
        _A     =							bert_tokenizer(lines[i : i + 100]							,     add_special_tokens=_SCREAMING_SNAKE_CASE							,     truncation=_SCREAMING_SNAKE_CASE							,     max_length=512				)
        bert_res.extend(res['input_ids']				)
    assert len(_SCREAMING_SNAKE_CASE				) == len(_SCREAMING_SNAKE_CASE				)

    _A     =							[]
    for input_ids, chinese_word in zip(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							[]
        for id in input_ids:
            _A     =							bert_tokenizer._convert_id_to_token(_SCREAMING_SNAKE_CASE				)
            input_tokens.append(_SCREAMING_SNAKE_CASE				)
        _A     =							add_sub_symbol(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
        _A     =							[]
        # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
        for i, token in enumerate(_SCREAMING_SNAKE_CASE				):
            if token[:2] == "##":
                _A     =							token[2:]
                # save chinese tokens' pos
                if len(_SCREAMING_SNAKE_CASE				) == 1 and _is_chinese_char(ord(_SCREAMING_SNAKE_CASE				)				):
                    ref_id.append(_SCREAMING_SNAKE_CASE				)
        ref_ids.append(_SCREAMING_SNAKE_CASE				)

    assert len(_SCREAMING_SNAKE_CASE				) == len(_SCREAMING_SNAKE_CASE				)

    return ref_ids





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Union[str, Any]:




    """simple docstring"""




    with open(args.file_name							,     'r'							,     encoding='utf-8'				) as f:
        _A     =							f.readlines()
    _A     =							[line.strip() for line in data if len(_SCREAMING_SNAKE_CASE				) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
    _A     =							LTP(args.ltp				)  # faster in GPU device
    _A     =							BertTokenizer.from_pretrained(args.bert				)

    _A     =							prepare_ref(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)

    with open(args.save_path							,     'w'							,     encoding='utf-8'				) as f:
        _A     =							[json.dumps(_SCREAMING_SNAKE_CASE				) + '\n' for ref in ref_ids]
        f.writelines(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
 __A							:			Union[str, Any]         =			argparse.ArgumentParser(description="prepare_chinese_ref")
 parser.add_argument(
     "--file_name",
     type=str,
     default="./resources/chinese-demo.txt",
     help="file need process, same as training data in lm",
 )
 parser.add_argument(
     "--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
 )
 parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
 parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")

 __A							:			Union[str, Any]         =			parser.parse_args()
 main(args)






'''simple docstring'''

import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
UpperCamelCase__  :						Dict					=      logging.getLogger(__name__)







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[Any] 					=	argparse.ArgumentParser(
       description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'''     )
   parser.add_argument('''--file_path'''	,      type=_A	,      default='''data/dump.txt'''	,      help='''The path to the data.'''     )
   parser.add_argument('''--tokenizer_type'''	,      type=_A	,      default='''bert'''	,      choices=['''bert''', '''roberta''', '''gpt2''']     )
   parser.add_argument('''--tokenizer_name'''	,      type=_A	,      default='''bert-base-uncased'''	,      help='''The tokenizer to use.'''     )
   parser.add_argument('''--dump_file'''	,      type=_A	,      default='''data/dump'''	,      help='''The dump file prefix.'''     )
   UpperCAmelCase__				:					Optional[int] 					=	parser.parse_args()

   logger.info(F'''Loading Tokenizer ({args.tokenizer_name})'''     )
   if args.tokenizer_type == "bert":
      UpperCAmelCase__				:					List[str] 					=	BertTokenizer.from_pretrained(args.tokenizer_name     )
      UpperCAmelCase__				:					Optional[Any] 					=	tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
      UpperCAmelCase__				:					Union[str, Any] 					=	tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
   elif args.tokenizer_type == "roberta":
      UpperCAmelCase__				:					Union[str, Any] 					=	RobertaTokenizer.from_pretrained(args.tokenizer_name     )
      UpperCAmelCase__				:					List[Any] 					=	tokenizer.special_tokens_map['''cls_token''']  # `<s>`
      UpperCAmelCase__				:					Dict 					=	tokenizer.special_tokens_map['''sep_token''']  # `</s>`
   elif args.tokenizer_type == "gpt2":
      UpperCAmelCase__				:					List[str] 					=	GPTaTokenizer.from_pretrained(args.tokenizer_name     )
      UpperCAmelCase__				:					List[Any] 					=	tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
      UpperCAmelCase__				:					Optional[Any] 					=	tokenizer.special_tokens_map['''eos_token''']  # `<|endoftext|>`

   logger.info(F'''Loading text from {args.file_path}'''     )
   with open(args.file_path	,      '''r'''	,      encoding='''utf8'''     ) as fp:
      UpperCAmelCase__				:					List[Any] 					=	fp.readlines()

   logger.info('''Start encoding'''     )
   logger.info(F'''{len(_A     )} examples to process.'''     )

   UpperCAmelCase__				:					List[str] 					=	[]
   UpperCAmelCase__				:					Union[str, Any] 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	1_00_00
   UpperCAmelCase__				:					Tuple 					=	time.time()
   for text in data:
      UpperCAmelCase__				:					Any 					=	F'''{bos} {text.strip()} {sep}'''
      UpperCAmelCase__				:					int 					=	tokenizer.encode(_A	,      add_special_tokens=_A     )
      rslt.append(_A     )

      iter += 1
      if iter % interval == 0:
         UpperCAmelCase__				:					int 					=	time.time()
         logger.info(F'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''     )
         UpperCAmelCase__				:					Optional[Any] 					=	time.time()
   logger.info('''Finished binarization'''     )
   logger.info(F'''{len(_A     )} examples processed.'''     )

   UpperCAmelCase__				:					Dict 					=	F'''{args.dump_file}.{args.tokenizer_name}.pickle'''
   UpperCAmelCase__				:					Dict 					=	tokenizer.vocab_size
   if vocab_size < (1 << 16):
      UpperCAmelCase__				:					Any 					=	[np.uintaa(_A     ) for d in rslt]
   else:
      UpperCAmelCase__				:					str 					=	[np.intaa(_A     ) for d in rslt]
   random.shuffle(rslt_     )
   logger.info(F'''Dump to {dp_file}'''     )
   with open(_A	,      '''wb'''     ) as handle:
      pickle.dump(rslt_	,      _A	,      protocol=pickle.HIGHEST_PROTOCOL     )


if __name__ == "__main__":
  main()




