


"""simple docstring"""





import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def      _snake_case						(						_snake_case				:			Any			):
      # 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 >= 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				:			str			):
      # word like '180' or '身高' or '神'
      for char in word:
            lowerCAmelCase					:	str       =   ord(_snake_case			)
            if not _is_chinese_char(_snake_case			):
                  return 0
      return 1


def      _snake_case						(						_snake_case				:			List[str]			):
      lowerCAmelCase					:	Tuple       =   set()

      for token in tokens:
            lowerCAmelCase					:	Tuple       =   len(_snake_case			) > 1 and is_chinese(_snake_case			)
            if chinese_word:
                  word_set.add(_snake_case			)
      lowerCAmelCase					:	str       =   list(_snake_case			)
      return word_list


def      _snake_case						(						_snake_case				:			List[str] ,   _snake_case				:			set()			):
      if not chinese_word_set:
            return bert_tokens
      lowerCAmelCase					:	int       =   max([len(_snake_case			) for w in chinese_word_set]			)

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


def      _snake_case						(						_snake_case				:			List[str] ,   _snake_case				:			LTP ,   _snake_case				:			BertTokenizer			):
      lowerCAmelCase					:	Union[str, Any]       =   []

      for i in range(0 ,   len(_snake_case			) ,   100			):
            lowerCAmelCase					:	Optional[int]       =   ltp_tokenizer.seg(lines[i : i + 100]			)[0]
            lowerCAmelCase					:	str       =   [get_chinese_word(_snake_case			) for r in res]
            ltp_res.extend(_snake_case			)
      assert len(_snake_case			) == len(_snake_case			)

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

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

      lowerCAmelCase					:	Dict       =   prepare_ref(_snake_case ,   _snake_case ,   _snake_case			)

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


if __name__ == "__main__":
 snake_case__		:     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''')

 snake_case__		:     Any    				=				parser.parse_args()
 main(args)






"""simple docstring"""





def      _snake_case						(						_snake_case				:			float ,   _snake_case				:			list[float]			):
      if discount_rate < 0:
            raise ValueError('''Discount rate cannot be negative'''			)
      if not cash_flows:
            raise ValueError('''Cash flows list cannot be empty'''			)
      lowerCAmelCase					:	List[str]       =   sum(
          cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_snake_case			)			)
      return round(_snake_case ,   ndigits=2			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


