





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer

def       A__(		__lowerCAmelCase ):
				# 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 >= 0X2_0000 and cp <= 0X2_a6df)  #
				    or (cp >= 0X2_a700 and cp <= 0X2_b73f)  #
				    or (cp >= 0X2_b740 and cp <= 0X2_b81f)  #
				    or (cp >= 0X2_b820 and cp <= 0X2_ceaf)  #
				    or (cp >= 0Xf900 and cp <= 0Xfaff)
				    or (cp >= 0X2_f800 and cp <= 0X2_fa1f)  #
				):  #
								return True

				return False

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

def       A__(		__lowerCAmelCase ):
				_snake_case :			List[str]  						=   set()

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

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				if not chinese_word_set:
								return bert_tokens
				_snake_case :			int  						=   max([len(__lowerCAmelCase ) for w in chinese_word_set] )

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

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			List[str]  						=   []

				for i in range(0	,			len(__lowerCAmelCase )	,			1_00 ):
								_snake_case :			List[Any]  						=   ltp_tokenizer.pipeline(lines[i : i + 1_00]	,			tasks=['cws'] ).cws
								_snake_case :			Dict  						=   [get_chinese_word(__lowerCAmelCase ) for r in res]
								ltp_res.extend(__lowerCAmelCase )
				assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )

				_snake_case :			Any  						=   []
				for i in range(0	,			len(__lowerCAmelCase )	,			1_00 ):
								_snake_case :			Union[str, Any]  						=   bert_tokenizer(lines[i : i + 1_00]	,			add_special_tokens=__lowerCAmelCase	,			truncation=__lowerCAmelCase	,			max_length=5_12 )
								bert_res.extend(res['input_ids'] )
				assert len(__lowerCAmelCase ) == len(__lowerCAmelCase )

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

				_snake_case :			List[str]  						=   prepare_ref(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase )

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


if __name__ == "__main__":
		lowercase_		:				Optional[int]				=						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_		:				List[str]				=						parser.parse_args()
		main(args)













import math

def       A__(		__lowerCAmelCase ):

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5	,			int(math.sqrt(__lowerCAmelCase ) + 1 )	,			6 ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True

def       A__(		__lowerCAmelCase = 1_00_01 ):

				try:
								_snake_case :			int  						=   int(__lowerCAmelCase )
				except (TypeError, ValueError):
								raise TypeError('Parameter nth must be int or castable to int.' ) from None
				if nth <= 0:
								raise ValueError('Parameter nth must be greater than or equal to one.' )
				_snake_case :			list[int]  						=   []
				_snake_case :			List[Any]  						=   2
				while len(__lowerCAmelCase ) < nth:
								if is_prime(__lowerCAmelCase ):
												primes.append(__lowerCAmelCase )
												num += 1
								else:
												num += 1
				return primes[len(__lowerCAmelCase ) - 1]


if __name__ == "__main__":
		print(F'''{solution() = }''')






