



"""simple docstring"""

import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





def       __magic_name__    (   _lowerCamelCase				: Tuple					):
							# 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_e00 and cp <= 0x9_fff)
							    or (cp >= 0x3_400 and cp <= 0x4_dbf)  #
							    or (cp >= 0x20_000 and cp <= 0x2a_6df)  #
							    or (cp >= 0x2a_700 and cp <= 0x2b_73f)  #
							    or (cp >= 0x2b_740 and cp <= 0x2b_81f)  #
							    or (cp >= 0x2b_820 and cp <= 0x2c_eaf)  #
							    or (cp >= 0xf_900 and cp <= 0xf_aff)
							    or (cp >= 0x2f_800 and cp <= 0x2f_a1f)  #
							):  #
														return True

							return False





def       __magic_name__    (   _lowerCamelCase				: str					):
							# word like '180' or '身高' or '神'
							for char in word:
														__a		:		List[str]									=       ord(_lowerCamelCase					)
														if not _is_chinese_char(_lowerCamelCase					):
																					return 0
							return 1





def       __magic_name__    (   _lowerCamelCase				: List[str]					):
							__a		:		Tuple									=       set()

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





def       __magic_name__    (   _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: set()					):
							if not chinese_word_set:
														return bert_tokens
							__a		:		Any									=       max([len(_lowerCamelCase					) for w in chinese_word_set]					)

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





def       __magic_name__    (   _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: LTP  ,       _lowerCamelCase				: BertTokenizer					):
							__a		:		Optional[int]									=       []

							for i in range(0  ,       len(_lowerCamelCase					)  ,       1_0_0					):
														__a		:		Tuple									=       ltp_tokenizer.pipeline(lines[i : i + 1_0_0]  ,       tasks=["""cws"""]					).cws
														__a		:		Union[str, Any]									=       [get_chinese_word(_lowerCamelCase					) for r in res]
														ltp_res.extend(_lowerCamelCase					)
							assert len(_lowerCamelCase					) == len(_lowerCamelCase					)

							__a		:		Tuple									=       []
							for i in range(0  ,       len(_lowerCamelCase					)  ,       1_0_0					):
														__a		:		Optional[int]									=       bert_tokenizer(lines[i : i + 1_0_0]  ,       add_special_tokens=_lowerCamelCase  ,       truncation=_lowerCamelCase  ,       max_length=5_1_2					)
														bert_res.extend(res["""input_ids"""]					)
							assert len(_lowerCamelCase					) == len(_lowerCamelCase					)

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

							__a		:		Optional[int]									=       prepare_ref(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)

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


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

	lowercase__			   =  parser.parse_args()
	main(args)






"""simple docstring"""

def       __magic_name__    (   _lowerCamelCase				: list[int]					):
							if not nums:  # Makes sure that the list is not empty
														raise ValueError("""List is empty"""					)

							__a		:		Any									=       sum(_lowerCamelCase					) / len(_lowerCamelCase					)  # Calculate the average
							return sum(abs(x - average					) for x in nums					) / len(_lowerCamelCase					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

