import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def    a__       (						__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 >= 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    a__       (						__UpperCamelCase ):
      # word like '180' or '身高' or '神'
      for char in word:
            SCREAMING_SNAKE_CASE_      =     ord(__UpperCamelCase )
            if not _is_chinese_char(__UpperCamelCase ):
                  return 0
      return 1


def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     set()

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


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

      SCREAMING_SNAKE_CASE_      =     bert_tokens
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     0, len(__UpperCamelCase )
      while start < end:
            SCREAMING_SNAKE_CASE_      =     True
            if is_chinese(bert_word[start] ):
                  SCREAMING_SNAKE_CASE_      =     min(end - start   ,					__UpperCamelCase )
                  for i in range(__UpperCamelCase   ,					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    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     []

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

      SCREAMING_SNAKE_CASE_      =     []
      for i in range(0   ,					len(__UpperCamelCase )   ,					1_0_0 ):
            SCREAMING_SNAKE_CASE_      =     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 )

      SCREAMING_SNAKE_CASE_      =     []
      for input_ids, chinese_word in zip(__UpperCamelCase   ,					__UpperCamelCase ):
            SCREAMING_SNAKE_CASE_      =     []
            for id in input_ids:
                  SCREAMING_SNAKE_CASE_      =     bert_tokenizer._convert_id_to_token(__UpperCamelCase )
                  input_tokens.append(__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     add_sub_symbol(__UpperCamelCase   ,					__UpperCamelCase )
            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(__UpperCamelCase ):
                  if token[:2] == "##":
                        SCREAMING_SNAKE_CASE_      =     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    a__       (						__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:
            SCREAMING_SNAKE_CASE_      =     f.readlines()
      SCREAMING_SNAKE_CASE_      =     [line.strip() for line in data if len(__UpperCamelCase ) > 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(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )

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


if __name__ == "__main__":
      A   :     Union[str, 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",
      )

      A   :     int       =				parser.parse_args()
      main(args)



def    a__       (						__UpperCamelCase = 1_0_0_0 ):
      SCREAMING_SNAKE_CASE_      =     -1
      SCREAMING_SNAKE_CASE_      =     0
      for a in range(1   ,					n // 3 ):
            # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
            SCREAMING_SNAKE_CASE_      =     (n * n - 2 * a * n) // (2 * n - 2 * a)
            SCREAMING_SNAKE_CASE_      =     n - a - b
            if c * c == (a * a + b * b):
                  SCREAMING_SNAKE_CASE_      =     a * b * c
                  if candidate >= product:
                        SCREAMING_SNAKE_CASE_      =     candidate
      return product


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


