"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def 						a_		(      lowerCamelCase ):
				# 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 						a_		(      lowerCamelCase ):
				# 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 						a_		(      lowerCamelCase ):
				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 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				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 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				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 						a_		(      lowerCamelCase ):
				# 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__":
						lowerCAmelCase__						:						List[str]		  =			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__						:						Optional[int]		  =			parser.parse_args()
						main(args)




"""simple docstring"""






from __future__ import annotations

from math import ceil, floor, sqrt



def 						a_		(      lowerCamelCase = 2_0_0_0_0_0_0 ):
				UpperCAmelCase__    =							[0]
				UpperCAmelCase__    =							42

				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
				UpperCAmelCase__    =							0
				# the area corresponding to the grid that gives the product closest to target
				UpperCAmelCase__    =							0
				# an estimate of b, using the quadratic formula
				UpperCAmelCase__    =							42
				# the largest integer less than b_estimate
				UpperCAmelCase__    =							42
				# the largest integer less than b_estimate
				UpperCAmelCase__    =							42
				# the triangle number corresponding to b_floor
				UpperCAmelCase__    =							42
				# the triangle number corresponding to b_ceil
				UpperCAmelCase__    =							42

				for idx_a, triangle_a in enumerate(triangle_numbers[1:]      ,    1 ):
								UpperCAmelCase__    =							(-1 + sqrt(1 + 8 * target / triangle_a )) / 2
								UpperCAmelCase__    =							floor(lowerCamelCase )
								UpperCAmelCase__    =							ceil(lowerCamelCase )
								UpperCAmelCase__    =							triangle_numbers[b_floor]
								UpperCAmelCase__    =							triangle_numbers[b_ceil]

								if abs(target - triangle_b_first_guess * triangle_a ) < abs(
								    target - best_product ):
												UpperCAmelCase__    =							triangle_b_first_guess * triangle_a
												UpperCAmelCase__    =							idx_a * b_floor

								if abs(target - triangle_b_second_guess * triangle_a ) < abs(
								    target - best_product ):
												UpperCAmelCase__    =							triangle_b_second_guess * triangle_a
												UpperCAmelCase__    =							idx_a * b_ceil

				return area


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



