
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ):
 # This defines a "chinese character" as anything in the CJK Unicode block:
 #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
 #
 # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
 # despite its name. The modern Korean Hangul alphabet is a different block,
 # as is Japanese Hiragana and Katakana. Those alphabets are used to write
 # space-separated words, so they are not treated specially and handled
 # like the all of the other languages.
 if (
     (cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
     or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
     or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
     or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
     or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
     or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
     or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
     or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
 ):  #
  return True

 return False




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ):
 # word like '180' or '身高' or '神'
 for char in word:
  __a			:  str		    =  ord(lowerCAmelCase__  )
  if not _is_chinese_char(lowerCAmelCase__  ):
   return 0
 return 1




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[str]  ):
 __a			:  str		    =  set()

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




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    set()  ):
 if not chinese_word_set:
  return bert_tokens
 __a			:  str		    =  max([len(lowerCAmelCase__  ) for w in chinese_word_set]  )

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




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    LTP  ,       lowerCAmelCase__							:    BertTokenizer  ):
 __a			:  Tuple		    =  []

 for i in range(0  ,       len(lowerCAmelCase__  )  ,       1_0_0  ):
  __a			:  Optional[int]		    =  ltp_tokenizer.pipeline(lines[i : i + 1_0_0]  ,       tasks=['''cws''']  ).cws
  __a			:  int		    =  [get_chinese_word(lowerCAmelCase__  ) for r in res]
  ltp_res.extend(lowerCAmelCase__  )
 assert len(lowerCAmelCase__  ) == len(lowerCAmelCase__  )

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

 __a			:  str		    =  []
 for input_ids, chinese_word in zip(lowerCAmelCase__  ,       lowerCAmelCase__  ):
  __a			:  Optional[int]		    =  []
  for id in input_ids:
   __a			:  Optional[int]		    =  bert_tokenizer._convert_id_to_token(lowerCAmelCase__  )
   input_tokens.append(lowerCAmelCase__  )
  __a			:  Optional[int]		    =  add_sub_symbol(lowerCAmelCase__  ,       lowerCAmelCase__  )
  __a			:  Union[str, Any]		    =  []
  # 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			:  Optional[Any]		    =  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        __UpperCamelCase					(    lowerCAmelCase__							:    Dict  ):
 # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
 # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
 with open(args.file_name  ,       '''r'''  ,       encoding='''utf-8'''  ) as f:
  __a			:  Optional[int]		    =  f.readlines()
 __a			:  Union[str, Any]		    =  [line.strip() for line in data if len(lowerCAmelCase__  ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
 __a			:  Optional[Any]		    =  LTP(args.ltp  )  # faster in GPU device
 __a			:  int		    =  BertTokenizer.from_pretrained(args.bert  )

 __a			:  Optional[Any]		    =  prepare_ref(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )

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


if __name__ == "__main__":
      lowercase__          =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',
      )

      lowercase__          =parser.parse_args()
      main(args)


from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				CustomTokenizer
   pass
