
"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)





class 				a__					(						A__       ):



     def __init__(							self :int      ,  *_lowerCamelCase :str      ,  **_lowerCamelCase :Optional[Any]   ):







      '''simple docstring'''



      warnings.warn(
          'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use GLPNImageProcessor instead.'      ,  _lowerCamelCase      ,  )
      super().__init__(*_lowerCamelCase      ,  **_lowerCamelCase   )


"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer




def       A_  (				__lowercase	):
 # 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       A_  (				__lowercase	):
 # word like '180' or '身高' or '神'
 for char in word:
  UpperCamelCase_     :					Union[str, Any]   	=ord(__lowercase	)
  if not _is_chinese_char(__lowercase	):
   return 0
 return 1




def       A_  (				__lowercase	):
 UpperCamelCase_     :					List[str]   	=set()

 for token in tokens:
  UpperCamelCase_     :					Optional[int]   	=len(__lowercase	) > 1 and is_chinese(__lowercase	)
  if chinese_word:
   word_set.add(__lowercase	)
 UpperCamelCase_     :					Tuple   	=list(__lowercase	)
 return word_list




def       A_  (				__lowercase						,	__lowercase	):
 if not chinese_word_set:
  return bert_tokens
 UpperCamelCase_     :					List[str]   	=max([len(__lowercase	) for w in chinese_word_set]	)

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




def       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 UpperCamelCase_     :					Tuple   	=[]

 for i in range(0						,	len(__lowercase	)						,	1_00	):
  UpperCamelCase_     :					Union[str, Any]   	=ltp_tokenizer.seg(lines[i : i + 1_00]	)[0]
  UpperCamelCase_     :					int   	=[get_chinese_word(__lowercase	) for r in res]
  ltp_res.extend(__lowercase	)
 assert len(__lowercase	) == len(__lowercase	)

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

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

 assert len(__lowercase	) == len(__lowercase	)

 return ref_ids




def       A_  (				__lowercase	):
 # 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:
  UpperCamelCase_     :					Tuple   	=f.readlines()
 UpperCamelCase_     :					Optional[int]   	=[line.strip() for line in data if len(__lowercase	) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
 UpperCamelCase_     :					Optional[Any]   	=LTP(args.ltp	)  # faster in GPU device
 UpperCamelCase_     :					Dict   	=BertTokenizer.from_pretrained(args.bert	)

 UpperCamelCase_     :					int   	=prepare_ref(__lowercase						,	__lowercase						,	__lowercase	)

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


if __name__ == "__main__":
 __SCREAMING_SNAKE_CASE							=     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')

 __SCREAMING_SNAKE_CASE							=     parser.parse_args()
 main(args)
