





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def      A__       (    SCREAMING_SNAKE_CASE_   :      Dict						)  ->  Any:






   """simple docstring"""







   if (
       (cp >= 0x4_e00 and cp <= 0x9_fff)
       or (cp >= 0x3_400 and cp <= 0x4_dbf)  #
       or (cp >= 0x20_000 and cp <= 0x2a_6df)  #
       or (cp >= 0x2a_700 and cp <= 0x2b_73f)  #
       or (cp >= 0x2b_740 and cp <= 0x2b_81f)  #
       or (cp >= 0x2b_820 and cp <= 0x2c_eaf)  #
       or (cp >= 0xf_900 and cp <= 0xf_aff)
       or (cp >= 0x2f_800 and cp <= 0x2f_a1f)  #
   ):  #
      return True

   return False
def      A__       (    SCREAMING_SNAKE_CASE_   :      str						)  ->  str:






   """simple docstring"""







   for char in word:
      _UpperCAmelCase           =   ord(SCREAMING_SNAKE_CASE_						)
      if not _is_chinese_char(SCREAMING_SNAKE_CASE_						):
         return 0
   return 1
def      A__       (    SCREAMING_SNAKE_CASE_   :      List[str]						)  ->  int:






   """simple docstring"""







   _UpperCAmelCase           =   set()

   for token in tokens:
      _UpperCAmelCase           =   len(SCREAMING_SNAKE_CASE_						) > 1 and is_chinese(SCREAMING_SNAKE_CASE_						)
      if chinese_word:
         word_set.add(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   list(SCREAMING_SNAKE_CASE_						)
   return word_list
def      A__       (    SCREAMING_SNAKE_CASE_   :      List[str]				,	SCREAMING_SNAKE_CASE_   :      set()						)  ->  Tuple:






   """simple docstring"""







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

   _UpperCAmelCase           =   bert_tokens
   _UpperCAmelCase       ,      _UpperCAmelCase           =   0, len(SCREAMING_SNAKE_CASE_						)
   while start < end:
      _UpperCAmelCase           =   True
      if is_chinese(bert_word[start]						):
         _UpperCAmelCase           =   min(end - start				,	SCREAMING_SNAKE_CASE_						)
         for i in range(SCREAMING_SNAKE_CASE_				,	1				,	-1						):
            _UpperCAmelCase           =   ''''''.join(bert_word[start : start + i]						)
            if whole_word in chinese_word_set:
               for j in range(start + 1				,	start + i						):
                  _UpperCAmelCase           =   '''##''' + bert_word[j]
               _UpperCAmelCase           =   start + i
               _UpperCAmelCase           =   False
               break
      if single_word:
         start += 1
   return bert_word
def      A__       (    SCREAMING_SNAKE_CASE_   :      List[str]				,	SCREAMING_SNAKE_CASE_   :      LTP				,	SCREAMING_SNAKE_CASE_   :      BertTokenizer						)  ->  str:






   """simple docstring"""







   _UpperCAmelCase           =   []

   for i in range(0				,	len(SCREAMING_SNAKE_CASE_						)				,	1_00						):
      _UpperCAmelCase           =   ltp_tokenizer.pipeline(lines[i : i + 1_00]				,	tasks=['''cws''']						).cws
      _UpperCAmelCase           =   [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_						)

   _UpperCAmelCase           =   []
   for i in range(0				,	len(SCREAMING_SNAKE_CASE_						)				,	1_00						):
      _UpperCAmelCase           =   bert_tokenizer(lines[i : i + 1_00]				,	add_special_tokens=SCREAMING_SNAKE_CASE_				,	truncation=SCREAMING_SNAKE_CASE_				,	max_length=5_12						)
      bert_res.extend(res['''input_ids''']						)
   assert len(SCREAMING_SNAKE_CASE_						) == len(SCREAMING_SNAKE_CASE_						)

   _UpperCAmelCase           =   []
   for input_ids, chinese_word in zip(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						):
      _UpperCAmelCase           =   []
      for id in input_ids:
         _UpperCAmelCase           =   bert_tokenizer._convert_id_to_token(SCREAMING_SNAKE_CASE_						)
         input_tokens.append(SCREAMING_SNAKE_CASE_						)
      _UpperCAmelCase           =   add_sub_symbol(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)
      _UpperCAmelCase           =   []
      # 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] == "##":
            _UpperCAmelCase           =   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      A__       (    SCREAMING_SNAKE_CASE_   :      Tuple						)  ->  Dict:






   """simple docstring"""







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

   _UpperCAmelCase           =   prepare_ref(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)

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


if __name__ == "__main__":
   UpperCAmelCase_           =	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",
   )

   UpperCAmelCase_           =	parser.parse_args()
   main(args)






import numpy as np
def      A__       (    SCREAMING_SNAKE_CASE_   :      np.ndarray				,	SCREAMING_SNAKE_CASE_   :      float						)  ->  np.ndarray:






   """simple docstring"""







   return np.where(vector > 0				,	SCREAMING_SNAKE_CASE_				,	(alpha * (np.exp(SCREAMING_SNAKE_CASE_						) - 1))						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()