






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer

def 	__lowerCamelCase      (		snake_case__		)							->  Optional[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 >= 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 	__lowerCamelCase      (		snake_case__		)							->  List[Any]:
						# word like '180' or '身高' or '神'
						for char in word:
												_SCREAMING_SNAKE_CASE     				=  ord(lowerCAmelCase__		)
												if not _is_chinese_char(lowerCAmelCase__		):
																		return 0
						return 1

def 	__lowerCamelCase      (		snake_case__		)							->  Tuple:
						_SCREAMING_SNAKE_CASE     				=  set()

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

def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  List[str]:
						if not chinese_word_set:
												return bert_tokens
						_SCREAMING_SNAKE_CASE     				=  max([len(lowerCAmelCase__		) for w in chinese_word_set]		)

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

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  Optional[Any]:
						_SCREAMING_SNAKE_CASE     				=  []

						for i in range(0	,len(lowerCAmelCase__		)	,1_00		):
												_SCREAMING_SNAKE_CASE     				=  ltp_tokenizer.seg(lines[i : i + 1_00]		)[0]
												_SCREAMING_SNAKE_CASE     				=  [get_chinese_word(lowerCAmelCase__		) for r in res]
												ltp_res.extend(lowerCAmelCase__		)
						assert len(lowerCAmelCase__		) == len(lowerCAmelCase__		)

						_SCREAMING_SNAKE_CASE     				=  []
						for i in range(0	,len(lowerCAmelCase__		)	,1_00		):
												_SCREAMING_SNAKE_CASE     				=  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__		)

						_SCREAMING_SNAKE_CASE     				=  []
						for input_ids, chinese_word in zip(lowerCAmelCase__	,lowerCAmelCase__		):
												_SCREAMING_SNAKE_CASE     				=  []
												for id in input_ids:
																		_SCREAMING_SNAKE_CASE     				=  bert_tokenizer._convert_id_to_token(lowerCAmelCase__		)
																		input_tokens.append(lowerCAmelCase__		)
												_SCREAMING_SNAKE_CASE     				=  add_sub_symbol(lowerCAmelCase__	,lowerCAmelCase__		)
												_SCREAMING_SNAKE_CASE     				=  []
												# 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] == "##":
																								_SCREAMING_SNAKE_CASE     				=  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 	__lowerCamelCase      (		snake_case__		)							->  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:
												_SCREAMING_SNAKE_CASE     				=  f.readlines()
						_SCREAMING_SNAKE_CASE     				=  [line.strip() for line in data if len(lowerCAmelCase__		) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
						_SCREAMING_SNAKE_CASE     				=  LTP(args.ltp		)  # faster in GPU device
						_SCREAMING_SNAKE_CASE     				=  BertTokenizer.from_pretrained(args.bert		)

						_SCREAMING_SNAKE_CASE     				=  prepare_ref(lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__		)

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


if __name__ == "__main__":
		UpperCamelCase			    =     argparse.ArgumentParser(description='''prepare_chinese_ref''')
		parser.add_argument(
		    '''--file_name''',
		    type=str,
		    default='''./resources/chinese-demo.txt''',
		    help='''file need process, same as training data in lm''',
		)
		parser.add_argument(
		    '''--ltp''', type=str, default='''./resources/ltp''', help='''resources for LTP tokenizer, usually a path'''
		)
		parser.add_argument('''--bert''', type=str, default='''./resources/robert''', help='''resources for Bert tokenizer''')
		parser.add_argument('''--save_path''', type=str, default='''./resources/ref.txt''', help='''path to save res''')

		UpperCamelCase			    =     parser.parse_args()
		main(args)









from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors

def 	__lowerCamelCase      (		snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  prime_factors(snake_case__		)
						if is_square_free(snake_case__		):
												return -1 if len(snake_case__		) % 2 else 1
						return 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()

