
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
						# 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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
						# word like '180' or '身高' or '神'
						for char in word:
												UpperCamelCase__    :   Optional[Any]					=				ord(UpperCamelCase__		)
												if not _is_chinese_char(UpperCamelCase__		):
																		return 0
						return 1


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
						UpperCamelCase__    :   Dict					=				set()

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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
						if not chinese_word_set:
												return bert_tokens
						UpperCamelCase__    :   str					=				max([len(UpperCamelCase__		) for w in chinese_word_set]		)

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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
						UpperCamelCase__    :   str					=				[]

						for i in range(0 ,		len(UpperCamelCase__		) ,		1_0_0		):
												UpperCamelCase__    :   Optional[Any]					=				ltp_tokenizer.pipeline(lines[i : i + 1_0_0] ,		tasks=['''cws''']		).cws
												UpperCamelCase__    :   List[str]					=				[get_chinese_word(UpperCamelCase__		) for r in res]
												ltp_res.extend(UpperCamelCase__		)
						assert len(UpperCamelCase__		) == len(UpperCamelCase__		)

						UpperCamelCase__    :   str					=				[]
						for i in range(0 ,		len(UpperCamelCase__		) ,		1_0_0		):
												UpperCamelCase__    :   Dict					=				bert_tokenizer(lines[i : i + 1_0_0] ,		add_special_tokens=UpperCamelCase__ ,		truncation=UpperCamelCase__ ,		max_length=5_1_2		)
												bert_res.extend(res['''input_ids''']		)
						assert len(UpperCamelCase__		) == len(UpperCamelCase__		)

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

						UpperCamelCase__    :   Dict					=				prepare_ref(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)

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


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

				lowerCamelCase      =parser.parse_args()
				main(args)




def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ = 5_0_0_0_0_0_0_0		):
      UpperCamelCase__    :   Any					=				set()
      UpperCamelCase__    :   Any					=				int((limit - 2_4) ** (1 / 2)		)

      UpperCamelCase__    :   Dict					=				set(range(3 ,		prime_square_limit + 1 ,		2		)		)
      primes.add(2		)
      for p in range(3 ,		prime_square_limit + 1 ,		2		):
            if p not in primes:
                  continue
            primes.difference_update(set(range(p * p ,		prime_square_limit + 1 ,		UpperCamelCase__		)		)		)

      for primea in primes:
            UpperCamelCase__    :   Dict					=				primea * primea
            for primea in primes:
                  UpperCamelCase__    :   str					=				primea * primea * primea
                  if square + cube >= limit - 1_6:
                        break
                  for primea in primes:
                        UpperCamelCase__    :   Dict					=				primea * primea * primea * primea
                        UpperCamelCase__    :   Tuple					=				square + cube + tetr
                        if total >= limit:
                              break
                        ret.add(UpperCamelCase__		)

      return len(UpperCamelCase__		)


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


