






"""simple docstring"""







import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):







					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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):







					for char in word:
										a__          =  ord(__UpperCamelCase)
										if not _is_chinese_char(__UpperCamelCase):
															return 0
					return 1





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):







					if not chinese_word_set:
										return bert_tokens
					a__          =  max([len(__UpperCamelCase) for w in chinese_word_set])

					a__          =  bert_tokens
					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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):







					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__":
	__a       :	Tuple             = 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       :	int             = parser.parse_args()
	main(args)










"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_albert import AlbertTokenizer
else:
	__a       :	int             = None

__a       :	List[str]             = logging.get_logger(__name__)
__a       :	Union[str, Any]             = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__a       :	str             = {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

__a       :	Optional[int]             = {
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

__a       :	Union[str, Any]             = '▁'







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =VOCAB_FILES_NAMES
							_SCREAMING_SNAKE_CASE    =PRETRAINED_VOCAB_FILES_MAP
							_SCREAMING_SNAKE_CASE    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_SCREAMING_SNAKE_CASE    =AlbertTokenizer




							def __init__(   self:     List[Any]				,		__A:     List[Any]=None				,		__A:     Tuple=None				,		__A:     Union[str, Any]=True				,		__A:     int=True				,		__A:     Optional[int]=False				,		__A:     List[Any]="[CLS]"				,		__A:     Optional[int]="[SEP]"				,		__A:     Union[str, Any]="<unk>"				,		__A:     int="[SEP]"				,		__A:     Optional[Any]="<pad>"				,		__A:     Optional[int]="[CLS]"				,		__A:     str="[MASK]"				,		**__A:     List[Any]				,		):

												'''simple docstring'''

												a__          =  (
												    AddedToken(__A				,		lstrip=__A				,		rstrip=__A				,		normalized=__A    )
												    if isinstance(__A				,		__A    )
												    else mask_token
												)

												super().__init__(
												    __A				,		tokenizer_file=__A				,		do_lower_case=__A				,		remove_space=__A				,		keep_accents=__A				,		bos_token=__A				,		eos_token=__A				,		unk_token=__A				,		sep_token=__A				,		pad_token=__A				,		cls_token=__A				,		mask_token=__A				,		**__A				,		)

												a__          =  do_lower_case
												a__          =  remove_space
												a__          =  keep_accents
												a__          =  vocab_file
												a__          =  False if not self.vocab_file else True




							def  lowercase		(   self:     Union[str, Any]				,		__A:     List[int]				,		__A:     Optional[List[int]] = None    ):

												'''simple docstring'''

												a__          =  [self.sep_token_id]
												a__          =  [self.cls_token_id]
												if token_ids_a is None:
																	return cls + token_ids_a + sep
												return cls + token_ids_a + sep + token_ids_a + sep




							def  lowercase		(   self:     List[Any]				,		__A:     List[int]				,		__A:     Optional[List[int]] = None    ):

												'''simple docstring'''

												a__          =  [self.sep_token_id]
												a__          =  [self.cls_token_id]

												if token_ids_a is None:
																	return len(cls + token_ids_a + sep    ) * [0]
												return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]





							def  lowercase		(   self:     Union[str, Any]				,		__A:     str				,		__A:     Optional[str] = None    ):

												'''simple docstring'''

												if not self.can_save_slow_tokenizer:
																	raise ValueError(
																	    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																	    '''tokenizer.'''    )

												if not os.path.isdir(__A    ):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
																	return
												a__          =  os.path.join(
												    __A				,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

												if os.path.abspath(self.vocab_file    ) != os.path.abspath(__A    ):
																	copyfile(self.vocab_file				,		__A    )

												return (out_vocab_file,)


