


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def   _UpperCamelCase				(						lowerCAmelCase_   )						->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 >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
       or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
       or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
       or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
       or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
       or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
       or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
       or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
   ):  #
      return True

   return False
def   _UpperCamelCase				(						lowerCAmelCase_   )						->List[Any]:
   # word like '180' or '身高' or '神'
   for char in word:
      UpperCAmelCase							=     ord(lowerCAmelCase_   )
      if not _is_chinese_char(lowerCAmelCase_   ):
         return 0
   return 1
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Dict:
   UpperCAmelCase							=     set()

   for token in tokens:
      UpperCAmelCase							=     len(lowerCAmelCase_   ) > 1 and is_chinese(lowerCAmelCase_   )
      if chinese_word:
         word_set.add(lowerCAmelCase_   )
   UpperCAmelCase							=     list(lowerCAmelCase_   )
   return word_list
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Any:
   if not chinese_word_set:
      return bert_tokens
   UpperCAmelCase							=     max([len(lowerCAmelCase_   ) for w in chinese_word_set]   )

   UpperCAmelCase							=     bert_tokens
   UpperCAmelCase				,  UpperCAmelCase							=     0, len(lowerCAmelCase_   )
   while start < end:
      UpperCAmelCase							=     True
      if is_chinese(bert_word[start]   ):
         UpperCAmelCase							=     min(end - start  ,						lowerCAmelCase_   )
         for i in range(lowerCAmelCase_  ,						1  ,						-1   ):
            UpperCAmelCase							=     """""".join(bert_word[start : start + i]   )
            if whole_word in chinese_word_set:
               for j in range(start + 1  ,						start + i   ):
                  UpperCAmelCase							=     """##""" + bert_word[j]
               UpperCAmelCase							=     start + i
               UpperCAmelCase							=     False
               break
      if single_word:
         start += 1
   return bert_word
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Tuple:
   UpperCAmelCase							=     []

   for i in range(0  ,						len(lowerCAmelCase_   )  ,						1_0_0   ):
      UpperCAmelCase							=     ltp_tokenizer.pipeline(lines[i : i + 1_0_0]  ,						tasks=["""cws"""]   ).cws
      UpperCAmelCase							=     [get_chinese_word(lowerCAmelCase_   ) for r in res]
      ltp_res.extend(lowerCAmelCase_   )
   assert len(lowerCAmelCase_   ) == len(lowerCAmelCase_   )

   UpperCAmelCase							=     []
   for i in range(0  ,						len(lowerCAmelCase_   )  ,						1_0_0   ):
      UpperCAmelCase							=     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_   )

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

   UpperCAmelCase							=     prepare_ref(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )

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


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

 __a     =			parser.parse_args()
 main(args)







from math import sqrt
def   _UpperCamelCase				(						lowerCAmelCase_ = 1_0_0_0_0_0_0   )						->int:
   UpperCAmelCase							=     0
   UpperCAmelCase							=     0
   UpperCAmelCase							=     42

   while num_cuboids <= limit:
      max_cuboid_size += 1
      for sum_shortest_sides in range(2  ,						2 * max_cuboid_size + 1   ):
         if sqrt(sum_shortest_sides**2 + max_cuboid_size**2   ).is_integer():
            num_cuboids += (
                min(lowerCAmelCase_  ,						sum_shortest_sides // 2   )
                - max(1  ,						sum_shortest_sides - max_cuboid_size   )
                + 1
            )

   return max_cuboid_size


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



