


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer







def    _snake_case (       __snake_case    )     ->     str:






      '''simple docstring'''







      if (
          (cp >= 0X4E00 and cp <= 0X9FFF)
          or (cp >= 0X3400 and cp <= 0X4DBF)  #
          or (cp >= 0X2_0000 and cp <= 0X2_A6DF)  #
          or (cp >= 0X2_A700 and cp <= 0X2_B73F)  #
          or (cp >= 0X2_B740 and cp <= 0X2_B81F)  #
          or (cp >= 0X2_B820 and cp <= 0X2_CEAF)  #
          or (cp >= 0XF900 and cp <= 0XFAFF)
          or (cp >= 0X2_F800 and cp <= 0X2_FA1F)  #
      ):  #
            return True

      return False







def    _snake_case (       __snake_case    )     ->     Union[str, Any]:






      '''simple docstring'''







      for char in word:
            UpperCAmelCase_  :	Tuple				      =		ord(__snake_case    )
            if not _is_chinese_char(__snake_case    ):
                  return 0
      return 1







def    _snake_case (       __snake_case    )     ->     Dict:






      '''simple docstring'''







      UpperCAmelCase_  :	Dict				      =		set()

      for token in tokens:
            UpperCAmelCase_  :	Union[str, Any]				      =		len(__snake_case    ) > 1 and is_chinese(__snake_case    )
            if chinese_word:
                  word_set.add(__snake_case    )
      UpperCAmelCase_  :	List[str]				      =		list(__snake_case    )
      return word_list







def    _snake_case (       __snake_case					,  __snake_case    )     ->     List[str]:






      '''simple docstring'''







      if not chinese_word_set:
            return bert_tokens
      UpperCAmelCase_  :	Union[str, Any]				      =		max([len(__snake_case    ) for w in chinese_word_set]    )

      UpperCAmelCase_  :	List[Any]				      =		bert_tokens
      UpperCAmelCase_  :	Optional[Any]				      =		0, len(__snake_case    )
      while start < end:
            UpperCAmelCase_  :	List[str]				      =		True
            if is_chinese(bert_word[start]    ):
                  UpperCAmelCase_  :	str				      =		min(end - start					,  __snake_case    )
                  for i in range(__snake_case					,  1					,  -1    ):
                        UpperCAmelCase_  :	Optional[Any]				      =		"".join(bert_word[start : start + i]    )
                        if whole_word in chinese_word_set:
                              for j in range(start + 1					,  start + i    ):
                                    UpperCAmelCase_  :	str				      =		"##" + bert_word[j]
                              UpperCAmelCase_  :	Union[str, Any]				      =		start + i
                              UpperCAmelCase_  :	Any				      =		False
                              break
            if single_word:
                  start += 1
      return bert_word







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     Any:






      '''simple docstring'''







      UpperCAmelCase_  :	List[str]				      =		[]

      for i in range(0					,  len(__snake_case    )					,  1_0_0    ):
            UpperCAmelCase_  :	Dict				      =		ltp_tokenizer.pipeline(lines[i : i + 1_0_0]					,  tasks=["cws"]    ).cws
            UpperCAmelCase_  :	int				      =		[get_chinese_word(__snake_case    ) for r in res]
            ltp_res.extend(__snake_case    )
      assert len(__snake_case    ) == len(__snake_case    )

      UpperCAmelCase_  :	Any				      =		[]
      for i in range(0					,  len(__snake_case    )					,  1_0_0    ):
            UpperCAmelCase_  :	Tuple				      =		bert_tokenizer(lines[i : i + 1_0_0]					,  add_special_tokens=__snake_case					,  truncation=__snake_case					,  max_length=5_1_2    )
            bert_res.extend(res["input_ids"]    )
      assert len(__snake_case    ) == len(__snake_case    )

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

      assert len(__snake_case    ) == len(__snake_case    )

      return ref_ids







def    _snake_case (       __snake_case    )     ->     int:






      '''simple docstring'''







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

      UpperCAmelCase_  :	List[str]				      =		prepare_ref(__snake_case					,  __snake_case					,  __snake_case    )

      with open(args.save_path					,  "w"					,  encoding="utf-8"    ) as f:
            UpperCAmelCase_  :	List[Any]				      =		[json.dumps(__snake_case    ) + "\n" for ref in ref_ids]
            f.writelines(__snake_case    )


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

   __lowerCamelCase         =			parser.parse_args()
   main(args)





def    _snake_case (       __snake_case					,  __snake_case    )     ->     str:






      '''simple docstring'''







      if a < 0 or b < 0:
            raise ValueError("the value of both inputs must be positive"    )

      UpperCAmelCase_  :	List[Any]				      =		str(bin(__snake_case    )    )[2:]  # remove the leading "0b"
      UpperCAmelCase_  :	int				      =		str(bin(__snake_case    )    )[2:]  # remove the leading "0b"

      UpperCAmelCase_  :	Optional[int]				      =		max(len(__snake_case    )					,  len(__snake_case    )    )

      return "0b" + "".join(
          str(int(char_a == "1" and char_b == "1"    )    )
          for char_a, char_b in zip(a_binary.zfill(__snake_case    )					,  b_binary.zfill(__snake_case    )    )    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
