


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer



def UpperCamelCase_				(				__a      )	->   Optional[int]:
      # 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 >= 0X4e00 and cp <= 0X9fff)
          or (cp >= 0X3400 and cp <= 0X4dbf)  #
          or (cp >= 0X20000 and cp <= 0X2a6df)  #
          or (cp >= 0X2a700 and cp <= 0X2b73f)  #
          or (cp >= 0X2b740 and cp <= 0X2b81f)  #
          or (cp >= 0X2b820 and cp <= 0X2ceaf)  #
          or (cp >= 0Xf900 and cp <= 0Xfaff)
          or (cp >= 0X2f800 and cp <= 0X2fa1f)  #
      ):  #
            return True

      return False



def UpperCamelCase_				(				__a      )	->   Optional[int]:
      # word like '180' or '身高' or '神'
      for char in word:
            a__	:  Optional[Any]     =   ord(__a      )
            if not _is_chinese_char(__a      ):
                  return 0
      return 1



def UpperCamelCase_				(				__a      )	->   Optional[int]:
      a__	:  Optional[Any]     =   set()

      for token in tokens:
            a__	:  Optional[Any]     =   len(__a      ) > 1 and is_chinese(__a      )
            if chinese_word:
                  word_set.add(__a      )
      a__	:  Optional[Any]     =   list(__a      )
      return word_list



def UpperCamelCase_				(				__a			,				__a      )	->   Optional[int]:
      if not chinese_word_set:
            return bert_tokens
      a__	:  int     =   max([len(__a      ) for w in chinese_word_set]      )

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



def UpperCamelCase_				(				__a			,				__a			,				__a      )	->   Optional[Any]:
      a__	:  Any     =   []

      for i in range(0			,				len(__a      )			,				100      ):
            a__	:  List[str]     =   ltp_tokenizer.seg(lines[i : i + 100]      )[0]
            a__	:  Optional[Any]     =   [get_chinese_word(__a      ) for r in res]
            ltp_res.extend(__a      )
      assert len(__a      ) == len(__a      )

      a__	:  List[Any]     =   []
      for i in range(0			,				len(__a      )			,				100      ):
            a__	:  List[Any]     =   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      )

      a__	:  List[Any]     =   []
      for input_ids, chinese_word in zip(__a			,				__a      ):
            a__	:  List[Any]     =   []
            for id in input_ids:
                  a__	:  Tuple     =   bert_tokenizer._convert_id_to_token(__a      )
                  input_tokens.append(__a      )
            a__	:  List[str]     =   add_sub_symbol(__a			,				__a      )
            a__	:  Tuple     =   []
            # 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] == "##":
                        a__	:  Optional[Any]     =   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 UpperCamelCase_				(				__a      )	->   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:
            a__	:  int     =   f.readlines()
      a__	:  Dict     =   [line.strip() for line in data if len(__a      ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      a__	:  List[Any]     =   LTP(args.ltp      )  # faster in GPU device
      a__	:  List[str]     =   BertTokenizer.from_pretrained(args.bert      )

      a__	:  Optional[int]     =   prepare_ref(__a			,				__a			,				__a      )

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


if __name__ == "__main__":
    UpperCamelCase					:       List[str]			    =     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					:       Dict			    =     parser.parse_args()
    main(args)









def   __lowercase       (			snake_case			):







				"""simple docstring"""






				return "".join([hex(snake_case			)[2:].zfill(2			).upper() for byte in list(snake_case			)]			)

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				if (len(snake_case			) % 2) != 0:
								raise ValueError(
								    '''Base16 encoded data is invalid:
Data does not have an even number of hex digits.'''			)
				# Check the character set - the standard base16 alphabet
				# is uppercase according to RFC3548 section 6
				if not set(snake_case			) <= set('''0123456789ABCDEF'''			):
								raise ValueError(
								    '''Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.'''			)
				# For every two hexadecimal digits (= a byte), turn it into an integer.
				# Then, string the result together into bytes, and return it.
				return bytes(int(data[i] + data[i + 1],	1_6			) for i in range(0,	len(snake_case			),	2			)			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()






