
import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[str]  )   ->			str:






						'''simple docstring'''

						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 		UpperCamelCase     (       _UpperCAmelCase  :   str  )   ->			Any:






						'''simple docstring'''

						for char in word:
												_lowercase       :	Optional[Any]           =   ord(_UpperCAmelCase  )
												if not _is_chinese_char(_UpperCAmelCase  ):
																		return 0
						return 1



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[str]  )   ->			Any:






						'''simple docstring'''

						_lowercase       :	Union[str, Any]           =   set()

						for token in tokens:
												_lowercase       :	Optional[int]           =   len(_UpperCAmelCase  ) > 1 and is_chinese(_UpperCAmelCase  )
												if chinese_word:
																		word_set.add(_UpperCAmelCase  )
						_lowercase       :	str           =   list(_UpperCAmelCase  )
						return word_list



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[str] ,			_UpperCAmelCase  :   set()  )   ->			List[Any]:






						'''simple docstring'''

						if not chinese_word_set:
												return bert_tokens
						_lowercase       :	Optional[Any]           =   max([len(_UpperCAmelCase  ) for w in chinese_word_set]  )

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



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[str] ,			_UpperCAmelCase  :   LTP ,			_UpperCAmelCase  :   BertTokenizer  )   ->			Optional[int]:






						'''simple docstring'''

						_lowercase       :	Union[str, Any]           =   []

						for i in range(0 ,			len(_UpperCAmelCase  ) ,			100  ):
												_lowercase       :	Tuple           =   ltp_tokenizer.seg(lines[i : i + 100]  )[0]
												_lowercase       :	List[str]           =   [get_chinese_word(_UpperCAmelCase  ) for r in res]
												ltp_res.extend(_UpperCAmelCase  )
						assert len(_UpperCAmelCase  ) == len(_UpperCAmelCase  )

						_lowercase       :	Union[str, Any]           =   []
						for i in range(0 ,			len(_UpperCAmelCase  ) ,			100  ):
												_lowercase       :	Optional[int]           =   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  )

						_lowercase       :	Any           =   []
						for input_ids, chinese_word in zip(_UpperCAmelCase ,			_UpperCAmelCase  ):
												_lowercase       :	str           =   []
												for id in input_ids:
																		_lowercase       :	List[str]           =   bert_tokenizer._convert_id_to_token(_UpperCAmelCase  )
																		input_tokens.append(_UpperCAmelCase  )
												_lowercase       :	str           =   add_sub_symbol(_UpperCAmelCase ,			_UpperCAmelCase  )
												_lowercase       :	List[Any]           =   []
												# 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] == "##":
																								_lowercase       :	Any           =   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 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[Any]  )   ->			Tuple:






						'''simple docstring'''

						with open(args.file_name ,			"r" ,			encoding="utf-8"  ) as f:
												_lowercase       :	int           =   f.readlines()
						_lowercase       :	List[str]           =   [line.strip() for line in data if len(_UpperCAmelCase  ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
						_lowercase       :	int           =   LTP(args.ltp  )  # faster in GPU device
						_lowercase       :	Any           =   BertTokenizer.from_pretrained(args.bert  )

						_lowercase       :	Dict           =   prepare_ref(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )

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


if __name__ == "__main__":
							UpperCamelCase_						:     Optional[Any]	       =							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""")

							UpperCamelCase_						:     Any	       =							parser.parse_args()
							main(args)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase_						:     int	       =							{
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Any	       =							[
							    """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """AdaptiveEmbedding""",
							    """TransfoXLForSequenceClassification""",
							    """TransfoXLLMHeadModel""",
							    """TransfoXLModel""",
							    """TransfoXLPreTrainedModel""",
							    """load_tf_weights_in_transfo_xl""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Tuple	       =							[
							    """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFAdaptiveEmbedding""",
							    """TFTransfoXLForSequenceClassification""",
							    """TFTransfoXLLMHeadModel""",
							    """TFTransfoXLMainLayer""",
							    """TFTransfoXLModel""",
							    """TFTransfoXLPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
							from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_transfo_xl import (
														    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AdaptiveEmbedding,
														    TransfoXLForSequenceClassification,
														    TransfoXLLMHeadModel,
														    TransfoXLModel,
														    TransfoXLPreTrainedModel,
														    load_tf_weights_in_transfo_xl,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_transfo_xl import (
														    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFAdaptiveEmbedding,
														    TFTransfoXLForSequenceClassification,
														    TFTransfoXLLMHeadModel,
														    TFTransfoXLMainLayer,
														    TFTransfoXLModel,
														    TFTransfoXLPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase_						:     Optional[int]	       =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




