


import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


_snake_case             =     logging.get_logger(__name__)
class 	UpperCAmelCase_					(					lowercase__    ):

   '''simple docstring'''




   def __init__(		self						,				*__A						,				**__A ):






     """simple docstring"""





     warnings.warn(
         "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
         " use BeitImageProcessor instead."						,				_a						,				)
     super().__init__(*_a						,				**_a )





'''simple docstring'''







import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer





def 					UpperCAmelCase_		(__a      :    List[Any] ):






   """simple docstring"""

   if (
       (cp >= 0x4E_00 and cp <= 0x9F_FF)
       or (cp >= 0x34_00 and cp <= 0x4D_BF)  #
       or (cp >= 0x2_00_00 and cp <= 0x2_A6_DF)  #
       or (cp >= 0x2_A7_00 and cp <= 0x2_B7_3F)  #
       or (cp >= 0x2_B7_40 and cp <= 0x2_B8_1F)  #
       or (cp >= 0x2_B8_20 and cp <= 0x2_CE_AF)  #
       or (cp >= 0xF9_00 and cp <= 0xFA_FF)
       or (cp >= 0x2_F8_00 and cp <= 0x2_FA_1F)  #
   ):  #
      return True

   return False





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   for char in word:
      _a				:	Union[str, Any]			=   ord(__a )
      if not _is_chinese_char(__a ):
         return 0
   return 1





def 					UpperCAmelCase_		(__a      :    List[str] ):






   """simple docstring"""

   _a				:	Dict			=   set()

   for token in tokens:
      _a				:	str			=   len(__a ) > 1 and is_chinese(__a )
      if chinese_word:
         word_set.add(__a )
   _a				:	Optional[Any]			=   list(__a )
   return word_list





def 					UpperCAmelCase_		(__a      :    List[str] ,	__a      :    set() ):






   """simple docstring"""

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

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





def 					UpperCAmelCase_		(__a      :    List[str] ,	__a      :    LTP ,	__a      :    BertTokenizer ):






   """simple docstring"""

   _a				:	int			=   []

   for i in range(0 ,	len(__a ) ,	1_0_0 ):
      _a				:	Union[str, Any]			=   ltp_tokenizer.seg(lines[i : i + 1_0_0] )[0]
      _a				:	Optional[Any]			=   [get_chinese_word(__a ) for r in res]
      ltp_res.extend(__a )
   assert len(__a ) == len(__a )

   _a				:	str			=   []
   for i in range(0 ,	len(__a ) ,	1_0_0 ):
      _a				:	List[str]			=   bert_tokenizer(lines[i : i + 1_0_0] ,	add_special_tokens=__a ,	truncation=__a ,	max_length=5_1_2 )
      bert_res.extend(res['input_ids'] )
   assert len(__a ) == len(__a )

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

   assert len(__a ) == len(__a )

   return ref_ids





def 					UpperCAmelCase_		(__a      :    Optional[Any] ):






   """simple docstring"""

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

   _a				:	int			=   prepare_ref(__a ,	__a ,	__a )

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


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



