






"""simple docstring"""





import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer



def     __lowercase					(    a :				Union[str, Any] )					->					Dict:
      # This defines a "chinese character" as anything in the CJK Unicode block:
      #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
      #
      # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
      # despite its name. The modern Korean Hangul alphabet is a different block,
      # as is Japanese Hiragana and Katakana. Those alphabets are used to write
      # space-separated words, so they are not treated specially and handled
      # like the all of the other languages.
      if (
          (cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
          or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F)  #
          or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F)  #
          or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F)  #
          or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F)  #
          or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F)  #
          or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
          or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F)  #
      ):  #
            return True

      return False



def     __lowercase					(    a :				str )					->					str:
      # word like '180' or '身高' or '神'
      for char in word:
            __snake_case      :    List[Any]  			=ord(a )
            if not _is_chinese_char(a ):
                  return 0
      return 1



def     __lowercase					(    a :				List[str] )					->					List[Any]:
      __snake_case      :    Union[str, Any]  			=set()

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



def     __lowercase					(    a :				List[str]       ,		a :				set() )					->					Any:
      if not chinese_word_set:
            return bert_tokens
      __snake_case      :    Dict  			=max([len(a ) for w in chinese_word_set] )

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



def     __lowercase					(    a :				List[str]       ,		a :				LTP       ,		a :				BertTokenizer )					->					Optional[int]:
      __snake_case      :    List[Any]  			=[]

      for i in range(0       ,		len(a )       ,		100 ):
            __snake_case      :    List[Any]  			=ltp_tokenizer.seg(lines[i : i + 100] )[0]
            __snake_case      :    Dict  			=[get_chinese_word(a ) for r in res]
            ltp_res.extend(a )
      assert len(a ) == len(a )

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

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

      assert len(a ) == len(a )

      return ref_ids



def     __lowercase					(    a :				Dict )					->					Dict:
      # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
      # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
      with open(args.file_name       ,		'''r'''       ,		encoding='''utf-8''' ) as f:
            __snake_case      :    int  			=f.readlines()
      __snake_case      :    Optional[Any]  			=[line.strip() for line in data if len(a ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      __snake_case      :    Dict  			=LTP(args.ltp )  # faster in GPU device
      __snake_case      :    Tuple  			=BertTokenizer.from_pretrained(args.bert )

      __snake_case      :    Union[str, Any]  			=prepare_ref(a       ,		a       ,		a )

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


if __name__ == "__main__":
    UpperCamelCase_	:				List[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""")

    UpperCamelCase_	:				Tuple										= parser.parse_args()
    main(args)










"""simple docstring"""





def     __lowercase					(    a :				str       ,		a :				str )					->					str:
      __snake_case      :    int  			=len(a )
      __snake_case      :    int  			=len(a )
      __snake_case      :    int  			=(
          first_str_length if first_str_length > second_str_length else second_str_length
      )
      __snake_case      :    list  			=[]
      for char_count in range(a ):
            if char_count < first_str_length:
                  output_list.append(first_str[char_count] )
            if char_count < second_str_length:
                  output_list.append(second_str[char_count] )
      return "".join(a )


if __name__ == "__main__":
    print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)


