


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





def 							A     (					_UpperCAmelCase    :       Tuple   )    ->			int:







			'''simple docstring'''
			# 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 							A     (					_UpperCAmelCase    :       str   )    ->			Optional[int]:







			'''simple docstring'''
			# word like '180' or '身高' or '神'
			for char in word:
						_UpperCAmelCase			=       ord(_UpperCAmelCase   )
						if not _is_chinese_char(_UpperCAmelCase   ):
									return 0
			return 1





def 							A     (					_UpperCAmelCase    :       List[str]   )    ->			Tuple:







			'''simple docstring'''
			_UpperCAmelCase			=       set()

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





def 							A     (					_UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       set()   )    ->			int:







			'''simple docstring'''
			if not chinese_word_set:
						return bert_tokens
			_UpperCAmelCase			=       max([len(_UpperCAmelCase   ) for w in chinese_word_set]   )

			_UpperCAmelCase			=       bert_tokens
			_UpperCAmelCase	,					_UpperCAmelCase			=       0, len(_UpperCAmelCase   )
			while start < end:
						_UpperCAmelCase			=       True
						if is_chinese(bert_word[start]   ):
									_UpperCAmelCase			=       min(end - start							,       _UpperCAmelCase   )
									for i in range(_UpperCAmelCase							,       1							,       -1   ):
												_UpperCAmelCase			=       ''.join(bert_word[start : start + i]   )
												if whole_word in chinese_word_set:
															for j in range(start + 1							,       start + i   ):
																		_UpperCAmelCase			=       '##' + bert_word[j]
															_UpperCAmelCase			=       start + i
															_UpperCAmelCase			=       False
															break
						if single_word:
									start += 1
			return bert_word





def 							A     (					_UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       LTP							,       _UpperCAmelCase    :       BertTokenizer   )    ->			Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase			=       []

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

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

			_UpperCAmelCase			=       []
			for input_ids, chinese_word in zip(_UpperCAmelCase							,       _UpperCAmelCase   ):
						_UpperCAmelCase			=       []
						for id in input_ids:
									_UpperCAmelCase			=       bert_tokenizer._convert_id_to_token(_UpperCAmelCase   )
									input_tokens.append(_UpperCAmelCase   )
						_UpperCAmelCase			=       add_sub_symbol(_UpperCAmelCase							,       _UpperCAmelCase   )
						_UpperCAmelCase			=       []
						# 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] == "##":
												_UpperCAmelCase			=       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 							A     (					_UpperCAmelCase    :       Optional[int]   )    ->			Dict:







			'''simple docstring'''
			# 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			=       f.readlines()
			_UpperCAmelCase			=       [line.strip() for line in data if len(_UpperCAmelCase   ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
			_UpperCAmelCase			=       LTP(args.ltp   )  # faster in GPU device
			_UpperCAmelCase			=       BertTokenizer.from_pretrained(args.bert   )

			_UpperCAmelCase			=       prepare_ref(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase   )

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


if __name__ == "__main__":
							UpperCAmelCase__       =	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",
							)

							UpperCAmelCase__       =	parser.parse_args()
							main(args)





from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase__       =	{"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
							from .tokenization_bertweet import BertweetTokenizer

else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
