



"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def     _lowercase       (						__snake_case			)		->     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 >= 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     _lowercase       (						__snake_case			)		->     Any:
			# word like '180' or '身高' or '神'
			for char in word:
						__lowerCAmelCase   :						Any				= ord(__snake_case			)
						if not _is_chinese_char(__snake_case			):
									return 0
			return 1


def     _lowercase       (						__snake_case			)		->     Any:
			__lowerCAmelCase   :						Union[str, Any]				= set()

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


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Union[str, Any]:
			if not chinese_word_set:
						return bert_tokens
			__lowerCAmelCase   :						Dict				= max([len(__snake_case			) for w in chinese_word_set]			)

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


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     List[str]:
			__lowerCAmelCase   :						Tuple				= []

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

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

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

			assert len(__snake_case			) == len(__snake_case			)

			return ref_ids


def     _lowercase       (						__snake_case			)		->     Dict:
			# 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:
						__lowerCAmelCase   :						int				= f.readlines()
			__lowerCAmelCase   :						Optional[Any]				= [line.strip() for line in data if len(__snake_case			) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
			__lowerCAmelCase   :						Optional[int]				= LTP(args.ltp			)  # faster in GPU device
			__lowerCAmelCase   :						Optional[Any]				= BertTokenizer.from_pretrained(args.bert			)

			__lowerCAmelCase   :						int				= prepare_ref(__snake_case      ,__snake_case      ,__snake_case			)

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


if __name__ == "__main__":
	__snake_case							:     Any   =					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',
	)

	__snake_case							:     List[str]   =					parser.parse_args()
	main(args)




"""simple docstring"""






from abc import ABC, abstractmethod
from argparse import ArgumentParser



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	@staticmethod
	@abstractmethod
	def 							_SCREAMING_SNAKE_CASE     (  _SCREAMING_SNAKE_CASE:	ArgumentParser)       -> str:

				"""simple docstring"""

				raise NotImplementedError()







	@abstractmethod
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> str:

				"""simple docstring"""

				raise NotImplementedError()