



"""simple docstring"""





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						if (
						    (cp >= 0X4E00 and cp <= 0X9FFF)
						    or (cp >= 0X3400 and cp <= 0X4DBF)  #
						    or (cp >= 0X20000 and cp <= 0X2A6DF)  #
						    or (cp >= 0X2A700 and cp <= 0X2B73F)  #
						    or (cp >= 0X2B740 and cp <= 0X2B81F)  #
						    or (cp >= 0X2B820 and cp <= 0X2CEAF)  #
						    or (cp >= 0XF900 and cp <= 0XFAFF)
						    or (cp >= 0X2F800 and cp <= 0X2FA1F)  #
						):  #
												return True

						return False
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						for char in word:
												snake_case_   : str						=			ord(__UpperCamelCase    )
												if not _is_chinese_char(__UpperCamelCase    ):
																		return 0
						return 1
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]    ):


						'''simple docstring'''






						snake_case_   : Any						=			set()

						for token in tokens:
												snake_case_   : List[Any]						=			len(__UpperCamelCase    ) > 1 and is_chinese(__UpperCamelCase    )
												if chinese_word:
																		word_set.add(__UpperCamelCase    )
						snake_case_   : Optional[Any]						=			list(__UpperCamelCase    )
						return word_list
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			Optional[Any]    ):


						'''simple docstring'''






						if not chinese_word_set:
												return bert_tokens
						snake_case_   : int						=			max([len(__UpperCamelCase    ) for w in chinese_word_set]    )

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


						'''simple docstring'''






						snake_case_   : Any						=			[]

						for i in range(0					, len(__UpperCamelCase    )					, 1_0_0    ):
												snake_case_   : Dict						=			ltp_tokenizer.pipeline(lines[i : i + 1_0_0]					, tasks=["""cws"""]    ).cws
												snake_case_   : Optional[Any]						=			[get_chinese_word(__UpperCamelCase    ) for r in res]
												ltp_res.extend(__UpperCamelCase    )
						assert len(__UpperCamelCase    ) == len(__UpperCamelCase    )

						snake_case_   : List[Any]						=			[]
						for i in range(0					, len(__UpperCamelCase    )					, 1_0_0    ):
												snake_case_   : Optional[Any]						=			bert_tokenizer(lines[i : i + 1_0_0]					, add_special_tokens=__UpperCamelCase					, truncation=__UpperCamelCase					, max_length=5_1_2    )
												bert_res.extend(res["""input_ids"""]    )
						assert len(__UpperCamelCase    ) == len(__UpperCamelCase    )

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

						assert len(__UpperCamelCase    ) == len(__UpperCamelCase    )

						return ref_ids
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






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

						snake_case_   : Dict						=			prepare_ref(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )

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


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






"""simple docstring"""





def 	__lowerCAmelCase						(		__UpperCamelCase      :			str    ):


						'''simple docstring'''






						return " ".join(input_str.split()[::-1]    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()

