



class      UpperCamelCase :







		"""simple docstring"""



		def __init__(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   int:


						'''simple docstring'''
						A	 =      n
						A	 =      [None] * self.n
						A	 =      0  # index of the first element
						A	 =      0
						A	 =      0
		def __len__(  self		:       Optional[int]		)    ->   int:


						'''simple docstring'''
						return self.size
		def       A(  self		:       Optional[Any]		)    ->   bool:


						'''simple docstring'''
						return self.size == 0
		def       A(  self		:       Optional[int]		)    ->   int:


						'''simple docstring'''
						return False if self.is_empty() else self.array[self.front]
		def       A(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						if self.size >= self.n:
										raise Exception('QUEUE IS FULL'		)

						A	 =      data
						A	 =      (self.rear + 1) % self.n
						self.size += 1
						return self







		def       A(  self		:       int		)    ->   List[str]:


						'''simple docstring'''
						if self.size == 0:
										raise Exception('UNDERFLOW'		)

						A	 =      self.array[self.front]
						A	 =      None
						A	 =      (self.front + 1) % self.n
						self.size -= 1
						return temp













import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer







def      snake_case		(							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 >= 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      snake_case		(							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      snake_case		(							UpperCAmelCase	:       List[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      snake_case		(							UpperCAmelCase	:       List[str],						UpperCAmelCase	:       set()   ):
				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      snake_case		(							UpperCAmelCase	:       List[str],						UpperCAmelCase	:       LTP,						UpperCAmelCase	:       BertTokenizer   ):
				A	 =      []

				for i in range(0,						len(UpperCAmelCase   ),						1_00   ):
								A	 =      ltp_tokenizer.seg(lines[i : i + 1_00]   )[0]
								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   ),						1_00   ):
								A	 =      bert_tokenizer(lines[i : i + 1_00],						add_special_tokens=UpperCAmelCase,						truncation=UpperCAmelCase,						max_length=5_12   )
								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      snake_case		(							UpperCAmelCase	:       List[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(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__":
					lowerCAmelCase_           = 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')

					lowerCAmelCase_           = parser.parse_args()
					main(args)





