



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


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


def A    (							__UpperCamelCase       )    ->   str:
			A__	  =     set()

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


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   str:
			if not chinese_word_set:
						return bert_tokens
			A__	  =     max([len(__UpperCamelCase       ) for w in chinese_word_set]       )

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

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

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

			A__	  =     []
			for input_ids, chinese_word in zip(__UpperCamelCase			,						__UpperCamelCase       ):
						A__	  =     []
						for id in input_ids:
									A__	  =     bert_tokenizer._convert_id_to_token(__UpperCamelCase       )
									input_tokens.append(__UpperCamelCase       )
						A__	  =     add_sub_symbol(__UpperCamelCase			,						__UpperCamelCase       )
						A__	  =     []
						# 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] == "##":
												A__	  =     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       )    ->   List[str]:
			# 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(__UpperCamelCase       ) > 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(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   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''',
							)

							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()
							main(args)









from __future__ import annotations

from typing import Any


def A    (							__UpperCamelCase       )    ->   int:
			if not postfix_notation:
						return 0

			A__	  =     {'+', '-', '*', '/'}
			A__	  =     []

			for token in postfix_notation:
						if token in operations:
									A__  ,			A__	  =     stack.pop(), stack.pop()
									if token == "+":
												stack.append(a + b       )
									elif token == "-":
												stack.append(a - b       )
									elif token == "*":
												stack.append(a * b       )
									else:
												if a * b < 0 and a % b != 0:
															stack.append(a // b + 1       )
												else:
															stack.append(a // b       )
						else:
									stack.append(int(__UpperCamelCase       )       )

			return stack.pop()


if __name__ == "__main__":
							import doctest

							doctest.testmod()




