


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def 				__lowerCamelCase						( UpperCAmelCase_						:		int						):




      """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 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      for char in word:
            a						:List[str] 							=       ord(UpperCAmelCase_						)
            if not _is_chinese_char(UpperCAmelCase_						):
                  return 0
      return 1
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[str]						):




      """simple docstring"""





      a						:Any 							=       set()

      for token in tokens:
            a						:str 							=       len(UpperCAmelCase_						) > 1 and is_chinese(UpperCAmelCase_						)
            if chinese_word:
                  word_set.add(UpperCAmelCase_						)
      a						:Dict 							=       list(UpperCAmelCase_						)
      return word_list
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		set()						):




      """simple docstring"""





      if not chinese_word_set:
            return bert_tokens
      a						:List[str] 							=       max([len(UpperCAmelCase_						) for w in chinese_word_set]						)

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




      """simple docstring"""





      a						:List[str] 							=       []

      for i in range(0 ,  len(UpperCAmelCase_						) ,  100						):
            a						:Optional[int] 							=       ltp_tokenizer.pipeline(lines[i : i + 100] ,  tasks=['''cws''']						).cws
            a						:str 							=       [get_chinese_word(UpperCAmelCase_						) for r in res]
            ltp_res.extend(UpperCAmelCase_						)
      assert len(UpperCAmelCase_						) == len(UpperCAmelCase_						)

      a						:List[Any] 							=       []
      for i in range(0 ,  len(UpperCAmelCase_						) ,  100						):
            a						:Union[str, Any] 							=       bert_tokenizer(lines[i : i + 100] ,  add_special_tokens=UpperCAmelCase_ ,  truncation=UpperCAmelCase_ ,  max_length=512						)
            bert_res.extend(res['''input_ids''']						)
      assert len(UpperCAmelCase_						) == len(UpperCAmelCase_						)

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

      assert len(UpperCAmelCase_						) == len(UpperCAmelCase_						)

      return ref_ids
def 				__lowerCamelCase						( UpperCAmelCase_						:		int						):




      """simple docstring"""





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

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

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


if __name__ == "__main__":
     snake_case				:    Optional[Any]       				=     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''',
     )

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









from __future__ import annotations
def 				__lowerCamelCase						( UpperCAmelCase_						:		dict ,  UpperCAmelCase_						:		str						):




      """simple docstring"""





      a							,				a						:Optional[Any] 							=       set(UpperCAmelCase_						), [start]

      while stack:
            a						:Optional[int] 							=       stack.pop()
            explored.add(UpperCAmelCase_						)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]						):
                  if adj not in explored:
                        stack.append(UpperCAmelCase_						)
      return explored


snake_case				:    Optional[int]       				=     {
    '''A''': ['''B''', '''C''', '''D'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B''', '''D'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E''', '''G'''],
    '''G''': ['''F'''],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, '''A'''))





