


"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def       A_   (   _lowerCAmelCase     :					List[Any]       ):






     """simple docstring"""

     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       A_   (   _lowerCAmelCase     :					str       ):






     """simple docstring"""

     for char in word:
          _a    	=				ord(_lowerCAmelCase       )
          if not _is_chinese_char(_lowerCAmelCase       ):
               return 0
     return 1
def       A_   (   _lowerCAmelCase     :					List[str]       ):






     """simple docstring"""

     _a    	=				set()

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






     """simple docstring"""

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

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






     """simple docstring"""

     _a    	=				[]

     for i in range(0,     len(_lowerCAmelCase       ),     1_00       ):
          _a    	=				ltp_tokenizer.pipeline(lines[i : i + 1_00],     tasks=['''cws''']       ).cws
          _a    	=				[get_chinese_word(_lowerCAmelCase       ) for r in res]
          ltp_res.extend(_lowerCAmelCase       )
     assert len(_lowerCAmelCase       ) == len(_lowerCAmelCase       )

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

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

     assert len(_lowerCAmelCase       ) == len(_lowerCAmelCase       )

     return ref_ids
def       A_   (   _lowerCAmelCase     :					List[str]       ):






     """simple docstring"""

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

     _a    	=				prepare_ref(_lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase       )

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


if __name__ == "__main__":
   __snake_case		=       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		=       parser.parse_args()
   main(args)



"""simple docstring"""






from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels