



import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Dict				)  ->     str:
   # 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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str				)  ->     Optional[int]:
   # word like '180' or '身高' or '神'
   for char in word:
      _A      =       ord(_snake_case				)
      if not _is_chinese_char(_snake_case				):
         return 0
   return 1

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]				)  ->     List[Any]:
   _A      =       set()

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

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]	,   _snake_case	:set()				)  ->     Optional[int]:
   if not chinese_word_set:
      return bert_tokens
   _A      =       max([len(_snake_case				) for w in chinese_word_set]				)

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

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]	,   _snake_case	:LTP	,   _snake_case	:BertTokenizer				)  ->     int:
   _A      =       []

   for i in range(0	,   len(_snake_case				)	,   100				):
      _A      =       ltp_tokenizer.seg(lines[i : i + 100]				)[0]
      _A      =       [get_chinese_word(_snake_case				) for r in res]
      ltp_res.extend(_snake_case				)
   assert len(_snake_case				) == len(_snake_case				)

   _A      =       []
   for i in range(0	,   len(_snake_case				)	,   100				):
      _A      =       bert_tokenizer(lines[i : i + 100]	,   add_special_tokens=_snake_case	,   truncation=_snake_case	,   max_length=512				)
      bert_res.extend(res['''input_ids''']				)
   assert len(_snake_case				) == len(_snake_case				)

   _A      =       []
   for input_ids, chinese_word in zip(_snake_case	,   _snake_case				):
      _A      =       []
      for id in input_ids:
         _A      =       bert_tokenizer._convert_id_to_token(_snake_case				)
         input_tokens.append(_snake_case				)
      _A      =       add_sub_symbol(_snake_case	,   _snake_case				)
      _A      =       []
      # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
      for i, token in enumerate(_snake_case				):
         if token[:2] == "##":
            _A      =       token[2:]
            # save chinese tokens' pos
            if len(_snake_case				) == 1 and _is_chinese_char(ord(_snake_case				)				):
               ref_id.append(_snake_case				)
      ref_ids.append(_snake_case				)

   assert len(_snake_case				) == len(_snake_case				)

   return ref_ids

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Any				)  ->     Optional[int]:
   # 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:
      _A      =       f.readlines()
   _A      =       [line.strip() for line in data if len(_snake_case				) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
   _A      =       LTP(args.ltp				)  # faster in GPU device
   _A      =       BertTokenizer.from_pretrained(args.bert				)

   _A      =       prepare_ref(_snake_case	,   _snake_case	,   _snake_case				)

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


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)





'''simple docstring'''
from __future__ import annotations

from math import ceil, floor, sqrt




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ = 200_0000			)  ->							int:


		'''simple docstring'''


		snake_case  :       list[int]     			=						[0]
		snake_case  :       int

		for idx in range(1 ,       ceil(sqrt(target * 2			) * 1.1			)			):
				triangle_numbers.append(triangle_numbers[-1] + idx			)

		# we want this to be as close as possible to target
		snake_case  :       int     			=						0
		# the area corresponding to the grid that gives the product closest to target
		snake_case  :       int     			=						0
		# an estimate of b, using the quadratic formula
		snake_case  :       float
		# the largest integer less than b_estimate
		snake_case  :       int
		# the largest integer less than b_estimate
		snake_case  :       int
		# the triangle number corresponding to b_floor
		snake_case  :       int
		# the triangle number corresponding to b_ceil
		snake_case  :       int

		for idx_a, triangle_a in enumerate(triangle_numbers[1:] ,       1			):
				snake_case  :       int     			=						(-1 + sqrt(1 + 8 * target / triangle_a			)) / 2
				snake_case  :       Optional[int]     			=						floor(SCREAMING_SNAKE_CASE__			)
				snake_case  :       int     			=						ceil(SCREAMING_SNAKE_CASE__			)
				snake_case  :       Optional[Any]     			=						triangle_numbers[b_floor]
				snake_case  :       Union[str, Any]     			=						triangle_numbers[b_ceil]

				if abs(target - triangle_b_first_guess * triangle_a			) < abs(
				    target - best_product			):
						snake_case  :       str     			=						triangle_b_first_guess * triangle_a
						snake_case  :       int     			=						idx_a * b_floor

				if abs(target - triangle_b_second_guess * triangle_a			) < abs(
				    target - best_product			):
						snake_case  :       List[Any]     			=						triangle_b_second_guess * triangle_a
						snake_case  :       Any     			=						idx_a * b_ceil

		return area


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


