

'''simple docstring'''
import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def 	_lowerCAmelCase (						_UpperCamelCase						:					str    )				->      Optional[Any]:
     """simple docstring"""




     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 	_lowerCAmelCase (						_UpperCamelCase						:					str    )				->      Optional[Any]:
     """simple docstring"""




     for char in word:
          _SCREAMING_SNAKE_CASE							   =ord(_UpperCamelCase    )
          if not _is_chinese_char(_UpperCamelCase    ):
               return 0
     return 1


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[str]    )				->      Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =set()

     for token in tokens:
          _SCREAMING_SNAKE_CASE							   =len(_UpperCamelCase    ) > 1 and is_chinese(_UpperCamelCase    )
          if chinese_word:
               word_set.add(_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =list(_UpperCamelCase    )
     return word_list


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[str]							,			_UpperCamelCase						:					set()    )				->      Any:
     """simple docstring"""




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

     _SCREAMING_SNAKE_CASE							   =bert_tokens
     _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =0, len(_UpperCamelCase    )
     while start < end:
          _SCREAMING_SNAKE_CASE							   =True
          if is_chinese(bert_word[start]    ):
               _SCREAMING_SNAKE_CASE							   =min(end - start							,			_UpperCamelCase    )
               for i in range(_UpperCamelCase							,			1							,			-1    ):
                    _SCREAMING_SNAKE_CASE							   =''.join(bert_word[start : start + i]    )
                    if whole_word in chinese_word_set:
                         for j in range(start + 1							,			start + i    ):
                              _SCREAMING_SNAKE_CASE							   ='##' + bert_word[j]
                         _SCREAMING_SNAKE_CASE							   =start + i
                         _SCREAMING_SNAKE_CASE							   =False
                         break
          if single_word:
               start += 1
     return bert_word


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[str]							,			_UpperCamelCase						:					LTP							,			_UpperCamelCase						:					BertTokenizer    )				->      Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =[]

     for i in range(0							,			len(_UpperCamelCase    )							,			1_00    ):
          _SCREAMING_SNAKE_CASE							   =ltp_tokenizer.seg(lines[i : i + 1_00]    )[0]
          _SCREAMING_SNAKE_CASE							   =[get_chinese_word(_UpperCamelCase    ) for r in res]
          ltp_res.extend(_UpperCamelCase    )
     assert len(_UpperCamelCase    ) == len(_UpperCamelCase    )

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

     _SCREAMING_SNAKE_CASE							   =[]
     for input_ids, chinese_word in zip(_UpperCamelCase							,			_UpperCamelCase    ):
          _SCREAMING_SNAKE_CASE							   =[]
          for id in input_ids:
               _SCREAMING_SNAKE_CASE							   =bert_tokenizer._convert_id_to_token(_UpperCamelCase    )
               input_tokens.append(_UpperCamelCase    )
          _SCREAMING_SNAKE_CASE							   =add_sub_symbol(_UpperCamelCase							,			_UpperCamelCase    )
          _SCREAMING_SNAKE_CASE							   =[]
          # 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] == "##":
                    _SCREAMING_SNAKE_CASE							   =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						:					Optional[Any]    )				->      List[str]:
     """simple docstring"""




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

     _SCREAMING_SNAKE_CASE							   =prepare_ref(_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase    )

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


if __name__ == "__main__":
     lowerCamelCase			:				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")

     lowerCamelCase			:				Union[str, Any] 			=					parser.parse_args()
     main(args)



'''simple docstring'''
import os


def 	_lowerCAmelCase (						)				->      List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =os.path.dirname(os.path.realpath(_UpperCamelCase    )    )
     _SCREAMING_SNAKE_CASE							   =os.path.join(_UpperCamelCase							,			'triangle.txt'    )

     with open(_UpperCamelCase    ) as f:
          _SCREAMING_SNAKE_CASE							   =f.readlines()

     _SCREAMING_SNAKE_CASE							   =[]
     for line in triangle:
          _SCREAMING_SNAKE_CASE							   =[]
          for number in line.strip().split(' '    ):
               numbers_from_line.append(int(_UpperCamelCase    )    )
          a.append(_UpperCamelCase    )

     for i in range(1							,			len(_UpperCamelCase    )    ):
          for j in range(len(a[i]    )    ):
               _SCREAMING_SNAKE_CASE							   =a[i - 1][j] if j != len(a[i - 1]    ) else 0
               _SCREAMING_SNAKE_CASE							   =a[i - 1][j - 1] if j > 0 else 0
               a[i][j] += max(_UpperCamelCase							,			_UpperCamelCase    )
     return max(a[-1]    )


if __name__ == "__main__":
     print(solution())
