





'''simple docstring'''







import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer






def  _lowerCamelCase				( lowercase						:      List[str]						)	->	Optional[Any]:
				# 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 >= 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  _lowerCamelCase				( lowercase						:      str						)	->	Tuple:
				# word like '180' or '身高' or '神'
				for char in word:
								_a								= ord(lowercase						)
								if not _is_chinese_char(lowercase						):
												return 0
				return 1






def  _lowerCamelCase				( lowercase						:      List[str]						)	->	List[Any]:
				_a								= set()

				for token in tokens:
								_a								= len(lowercase						) > 1 and is_chinese(lowercase						)
								if chinese_word:
												word_set.add(lowercase						)
				_a								= list(lowercase						)
				return word_list






def  _lowerCamelCase				( lowercase						:      List[str]    ,							lowercase						:      set()						)	->	Dict:
				if not chinese_word_set:
								return bert_tokens
				_a								= max([len(lowercase						) for w in chinese_word_set]						)

				_a								= bert_tokens
				_a	,					_a								= 0, len(lowercase						)
				while start < end:
								_a								= True
								if is_chinese(bert_word[start]						):
												_a								= min(end - start    ,							lowercase						)
												for i in range(lowercase    ,							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  _lowerCamelCase				( lowercase						:      List[str]    ,							lowercase						:      LTP    ,							lowercase						:      BertTokenizer						)	->	int:
				_a								= []

				for i in range(0    ,							len(lowercase						)    ,							100						):
								_a								= ltp_tokenizer.pipeline(lines[i : i + 100]    ,							tasks=["cws"]						).cws
								_a								= [get_chinese_word(lowercase						) for r in res]
								ltp_res.extend(lowercase						)
				assert len(lowercase						) == len(lowercase						)

				_a								= []
				for i in range(0    ,							len(lowercase						)    ,							100						):
								_a								= bert_tokenizer(lines[i : i + 100]    ,							add_special_tokens=lowercase    ,							truncation=lowercase    ,							max_length=512						)
								bert_res.extend(res["input_ids"]						)
				assert len(lowercase						) == len(lowercase						)

				_a								= []
				for input_ids, chinese_word in zip(lowercase    ,							lowercase						):
								_a								= []
								for id in input_ids:
												_a								= bert_tokenizer._convert_id_to_token(lowercase						)
												input_tokens.append(lowercase						)
								_a								= add_sub_symbol(lowercase    ,							lowercase						)
								_a								= []
								# 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] == "##":
																_a								= 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  _lowerCamelCase				( lowercase						:      str						)	->	Tuple:
				# 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								= f.readlines()
				_a								= [line.strip() for line in data if len(lowercase						) > 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(lowercase    ,							lowercase    ,							lowercase						)

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


if __name__ == "__main__":
					lowerCAmelCase_					:    Tuple      	=			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',
					)

					lowerCAmelCase_					:    Tuple      	=			parser.parse_args()
					main(args)







"""simple docstring"""
def      __lowerCamelCase   (							a_     :		int ,   a_     :		str							)			->      Optional[int]:
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  [1]
      for i in range(2 ,   a_							):
            factorials.append(factorials[-1] * i							)
      assert 0 <= k < factorials[-1] * n, "k out of bounds"

      __SCREAMING_SNAKE_CASE   :List[str]						=  []
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  list(range(a_							)							)

      # Find permutation
      while factorials:
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  factorials.pop()
            __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Dict						=  divmod(a_ ,   a_							)
            permutation.append(elements[number]							)
            elements.remove(elements[number]							)
      permutation.append(elements[0]							)

      return permutation


if __name__ == "__main__":
  import doctest

  doctest.testmod()