


"""simple docstring"""


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer






def      a__ (				__lowercase   )	->	Optional[Any]:
				# 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_e00 and cp <= 0x9_fff)
				    or (cp >= 0x3_400 and cp <= 0x4_dbf)  #
				    or (cp >= 0x20_000 and cp <= 0x2a_6df)  #
				    or (cp >= 0x2a_700 and cp <= 0x2b_73f)  #
				    or (cp >= 0x2b_740 and cp <= 0x2b_81f)  #
				    or (cp >= 0x2b_820 and cp <= 0x2c_eaf)  #
				    or (cp >= 0xf_900 and cp <= 0xf_aff)
				    or (cp >= 0x2f_800 and cp <= 0x2f_a1f)  #
				):  #
								return True

				return False






def      a__ (				__lowercase   )	->	List[str]:
				# word like '180' or '身高' or '神'
				for char in word:
								_A					  =			ord(__lowercase   )
								if not _is_chinese_char(__lowercase   ):
												return 0
				return 1






def      a__ (				__lowercase   )	->	Dict:
				_A					  =			set()

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






def      a__ (				__lowercase     ,			__lowercase   )	->	List[str]:
				if not chinese_word_set:
								return bert_tokens
				_A					  =			max([len(__lowercase   ) for w in chinese_word_set]   )

				_A					  =			bert_tokens
				_A  ,		_A					  =			0, len(__lowercase   )
				while start < end:
								_A					  =			True
								if is_chinese(bert_word[start]   ):
												_A					  =			min(end - start     ,			__lowercase   )
												for i in range(__lowercase     ,			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      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	List[Any]:
				_A					  =			[]

				for i in range(0     ,			len(__lowercase   )     ,			100   ):
								_A					  =			ltp_tokenizer.pipeline(lines[i : i + 100]     ,			tasks=["cws"]   ).cws
								_A					  =			[get_chinese_word(__lowercase   ) for r in res]
								ltp_res.extend(__lowercase   )
				assert len(__lowercase   ) == len(__lowercase   )

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

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

				assert len(__lowercase   ) == len(__lowercase   )

				return ref_ids






def      a__ (				__lowercase   )	->	Union[str, 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:
								_A					  =			f.readlines()
				_A					  =			[line.strip() for line in data if len(__lowercase   ) > 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(__lowercase     ,			__lowercase     ,			__lowercase   )

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


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)



"""simple docstring"""


import random






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	Optional[Any]:
				_A					  =			a[left_index]
				_A					  =			left_index + 1
				for j in range(left_index + 1     ,			__lowercase   ):
								if a[j] < pivot:
												_A  ,		_A					  =			a[i], a[j]
												i += 1
				_A  ,		_A					  =			a[i - 1], a[left_index]
				return i - 1






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	int:
				if left < right:
								_A					  =			random.randint(__lowercase     ,			right - 1   )
								_A  ,		_A					  =			(
								    a[left],
								    a[pivot],
								)  # switches the pivot with the left most bound
								_A					  =			partition(__lowercase     ,			__lowercase     ,			__lowercase   )
								quick_sort_random(
								    __lowercase     ,			__lowercase     ,			__lowercase   )  # recursive quicksort to the left of the pivot point
								quick_sort_random(
								    __lowercase     ,			pivot_index + 1     ,			__lowercase   )  # recursive quicksort to the right of the pivot point






def      a__ (				)	->	Dict:
				_A					  =			input("Enter numbers separated by a comma:\n"   ).strip()
				_A					  =			[int(__lowercase   ) for item in user_input.split(","   )]

				quick_sort_random(__lowercase     ,			0     ,			len(__lowercase   )   )

				print(__lowercase   )


if __name__ == "__main__":
		main()