





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





def        _lowercase   (  _UpperCAmelCase			)  ->					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_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        _lowercase   (  _UpperCAmelCase			)  ->					int:
				# word like '180' or '身高' or '神'
				for char in word:
								lowerCamelCase			=ord(_UpperCAmelCase			)
								if not _is_chinese_char(_UpperCAmelCase			):
												return 0
				return 1





def        _lowercase   (  _UpperCAmelCase			)  ->					Any:
				lowerCamelCase			=set()

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





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					List[str]:
				if not chinese_word_set:
								return bert_tokens
				lowerCamelCase			=max([len(_UpperCAmelCase			) for w in chinese_word_set]			)

				lowerCamelCase			=bert_tokens
				lowerCamelCase	,					lowerCamelCase			=0, len(_UpperCAmelCase			)
				while start < end:
								lowerCamelCase			=True
								if is_chinese(bert_word[start]			):
												lowerCamelCase			=min(end - start     , _UpperCAmelCase			)
												for i in range(_UpperCAmelCase     , 1     , -1			):
																lowerCamelCase			="""""".join(bert_word[start : start + i]			)
																if whole_word in chinese_word_set:
																				for j in range(start + 1     , start + i			):
																								lowerCamelCase			="""##""" + bert_word[j]
																				lowerCamelCase			=start + i
																				lowerCamelCase			=False
																				break
								if single_word:
												start += 1
				return bert_word





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					Any:
				lowerCamelCase			=[]

				for i in range(0     , len(_UpperCAmelCase			)     , 1_00			):
								lowerCamelCase			=ltp_tokenizer.pipeline(lines[i : i + 1_00]     , tasks=["""cws"""]			).cws
								lowerCamelCase			=[get_chinese_word(_UpperCAmelCase			) for r in res]
								ltp_res.extend(_UpperCAmelCase			)
				assert len(_UpperCAmelCase			) == len(_UpperCAmelCase			)

				lowerCamelCase			=[]
				for i in range(0     , len(_UpperCAmelCase			)     , 1_00			):
								lowerCamelCase			=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			)

				lowerCamelCase			=[]
				for input_ids, chinese_word in zip(_UpperCAmelCase     , _UpperCAmelCase			):
								lowerCamelCase			=[]
								for id in input_ids:
												lowerCamelCase			=bert_tokenizer._convert_id_to_token(_UpperCAmelCase			)
												input_tokens.append(_UpperCAmelCase			)
								lowerCamelCase			=add_sub_symbol(_UpperCAmelCase     , _UpperCAmelCase			)
								lowerCamelCase			=[]
								# 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] == "##":
																lowerCamelCase			=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        _lowercase   (  _UpperCAmelCase			)  ->					Optional[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:
								lowerCamelCase			=f.readlines()
				lowerCamelCase			=[line.strip() for line in data if len(_UpperCAmelCase			) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
				lowerCamelCase			=LTP(args.ltp			)  # faster in GPU device
				lowerCamelCase			=BertTokenizer.from_pretrained(args.bert			)

				lowerCamelCase			=prepare_ref(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)

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


if __name__ == "__main__":
							UpperCAmelCase__  :     Any   			=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''',
							)

							UpperCAmelCase__  :     str   			=parser.parse_args()
							main(args)







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


UpperCAmelCase__  :     Optional[int]   			=logging.get_logger(__name__)

UpperCAmelCase__  :     int   			={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__  :     List[str]   			={
    '''vocab_file''': {'''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt'''},
    '''tokenizer_file''': {
        '''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json'''
    },
}

UpperCAmelCase__  :     Any   			={'''mobilebert-uncased''': 5_12}


UpperCAmelCase__  :     Any   			={}

class  __A       (  a      ):
				__A								=			VOCAB_FILES_NAMES
				__A								=			PRETRAINED_VOCAB_FILES_MAP
				__A								=			PRETRAINED_INIT_CONFIGURATION
				__A								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A								=			MobileBertTokenizer


				def __init__(						self				,	UpperCAmelCase_=None				,	UpperCAmelCase_=None				,	UpperCAmelCase_=True				,	UpperCAmelCase_="[UNK]"				,	UpperCAmelCase_="[SEP]"				,	UpperCAmelCase_="[PAD]"				,	UpperCAmelCase_="[CLS]"				,	UpperCAmelCase_="[MASK]"				,	UpperCAmelCase_=True				,	UpperCAmelCase_=None				,	**UpperCAmelCase_				,	):
								super().__init__(
								    UpperCAmelCase_				,	tokenizer_file=UpperCAmelCase_				,	do_lower_case=UpperCAmelCase_				,	unk_token=UpperCAmelCase_				,	sep_token=UpperCAmelCase_				,	pad_token=UpperCAmelCase_				,	cls_token=UpperCAmelCase_				,	mask_token=UpperCAmelCase_				,	tokenize_chinese_chars=UpperCAmelCase_				,	strip_accents=UpperCAmelCase_				,	**UpperCAmelCase_				,	)

								lowerCamelCase			=json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
								if (
								    normalizer_state.get("""lowercase"""				,	UpperCAmelCase_							) != do_lower_case
								    or normalizer_state.get("""strip_accents"""				,	UpperCAmelCase_							) != strip_accents
								    or normalizer_state.get("""handle_chinese_chars"""				,	UpperCAmelCase_							) != tokenize_chinese_chars
								):
												lowerCamelCase			=getattr(UpperCAmelCase_				,	normalizer_state.pop("""type"""							)							)
												lowerCamelCase			=do_lower_case
												lowerCamelCase			=strip_accents
												lowerCamelCase			=tokenize_chinese_chars
												lowerCamelCase			=normalizer_class(**UpperCAmelCase_							)

								lowerCamelCase			=do_lower_case


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_=None							):
								lowerCamelCase			=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
												output += token_ids_a + [self.sep_token_id]

								return output


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_ = None							):
								lowerCamelCase			=[self.sep_token_id]
								lowerCamelCase			=[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 _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_ = None							):
								lowerCamelCase			=self._tokenizer.model.save(UpperCAmelCase_				,	name=UpperCAmelCase_							)
								return tuple(UpperCAmelCase_							)





