
import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer







def     A__   (  lowerCamelCase  )					->		int:
				# 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__   (  lowerCamelCase  )					->		Dict:
				# word like '180' or '身高' or '神'
				for char in word:
								UpperCamelCase_:							Union[str, Any]		=  ord(lowerCamelCase  )
								if not _is_chinese_char(lowerCamelCase  ):
												return 0
				return 1







def     A__   (  lowerCamelCase  )					->		List[Any]:
				UpperCamelCase_:							Tuple		=  set()

				for token in tokens:
								UpperCamelCase_:							List[str]		=  len(lowerCamelCase  ) > 1 and is_chinese(lowerCamelCase  )
								if chinese_word:
												word_set.add(lowerCamelCase  )
				UpperCamelCase_:							Dict		=  list(lowerCamelCase  )
				return word_list







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		List[Any]:
				if not chinese_word_set:
								return bert_tokens
				UpperCamelCase_:							Any		=  max([len(lowerCamelCase  ) for w in chinese_word_set]  )

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







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				UpperCamelCase_:							Dict		=  []

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

				UpperCamelCase_:							int		=  []
				for i in range(0       ,      len(lowerCamelCase  )       ,      1_00  ):
								UpperCamelCase_:							str		=  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  )

				UpperCamelCase_:							Optional[Any]		=  []
				for input_ids, chinese_word in zip(lowerCamelCase       ,      lowerCamelCase  ):
								UpperCamelCase_:							Tuple		=  []
								for id in input_ids:
												UpperCamelCase_:							Union[str, Any]		=  bert_tokenizer._convert_id_to_token(lowerCamelCase  )
												input_tokens.append(lowerCamelCase  )
								UpperCamelCase_:							Dict		=  add_sub_symbol(lowerCamelCase       ,      lowerCamelCase  )
								UpperCamelCase_:							Optional[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] == "##":
																UpperCamelCase_:							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     A__   (  lowerCamelCase  )					->		List[Any]:
				# 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_:							str		=  f.readlines()
				UpperCamelCase_:							List[Any]		=  [line.strip() for line in data if len(lowerCamelCase  ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
				UpperCamelCase_:							Optional[int]		=  LTP(args.ltp  )  # faster in GPU device
				UpperCamelCase_:							Any		=  BertTokenizer.from_pretrained(args.bert  )

				UpperCamelCase_:							str		=  prepare_ref(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )

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


if __name__ == "__main__":
							lowerCamelCase_  :	Union[str, Any] 				=					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_  :	Tuple 				=					parser.parse_args()
							main(args)








def     A__   (  lowerCamelCase  )					->		list:
				return [
				    txt[:a] + txt[a].upper() + txt[a + 1 :]
				    for a in range(len(lowerCamelCase  )  )
				    if txt[a].isalpha()
				]


if __name__ == "__main__":
							__import__("""doctest""").testmod()






