"""simple docstring"""




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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase_									=logging.get_logger(__name__)

UpperCamelCase_									={"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCamelCase_									={
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

UpperCamelCase_									={
    """moussaKam/mbarthez""": 1_024,
    """moussaKam/barthez""": 1_024,
    """moussaKam/barthez-orangesum-title""": 1_024,
}

UpperCamelCase_									="""▁"""




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   VOCAB_FILES_NAMES
				UpperCamelCase								=   PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase								=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase								=   ['''input_ids''', '''attention_mask''']






				def __init__(			self		:   List[Any],			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   Union[str, Any]="<s>",			lowerCAmelCase__		:   str="</s>",			lowerCAmelCase__		:   Optional[Any]="</s>",			lowerCAmelCase__		:   int="<s>",			lowerCAmelCase__		:   Union[str, Any]="<unk>",			lowerCAmelCase__		:   Union[str, Any]="<pad>",			lowerCAmelCase__		:   Optional[Any]="<mask>",			lowerCAmelCase__		:   Optional[Dict[str, Any]] = None,			**lowerCAmelCase__		:   Optional[int],			)     ->				None:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   AddedToken(lowerCAmelCase__,			lstrip=lowerCAmelCase__,			rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__,			lowerCAmelCase__							) else mask_token

										_UpperCamelCase      : int         =   {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=lowerCAmelCase__,			eos_token=lowerCAmelCase__,			unk_token=lowerCAmelCase__,			sep_token=lowerCAmelCase__,			cls_token=lowerCAmelCase__,			pad_token=lowerCAmelCase__,			mask_token=lowerCAmelCase__,			sp_model_kwargs=self.sp_model_kwargs,			**lowerCAmelCase__,			)

										_UpperCamelCase      : Union[str, Any]         =   vocab_file
										_UpperCamelCase      : Union[str, Any]         =   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(str(lowerCAmelCase__							)							)

										_UpperCamelCase      : Dict         =   {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

										_UpperCamelCase      : Dict         =   len(self.sp_model							) - 1
										_UpperCamelCase      : Dict         =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}






				def     snake_case     (			self		:   List[str],			lowerCAmelCase__		:   List[int],			lowerCAmelCase__		:   Optional[List[int]] = None							)     ->				List[int]:




										'''simple docstring'''



										if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										_UpperCamelCase      : Union[str, Any]         =   [self.cls_token_id]
										_UpperCamelCase      : Dict         =   [self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep






				def     snake_case     (			self		:   Tuple,			lowerCAmelCase__		:   List[int],			lowerCAmelCase__		:   Optional[List[int]] = None,			lowerCAmelCase__		:   bool = False							)     ->				List[int]:




										'''simple docstring'''



										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=lowerCAmelCase__,			token_ids_a=lowerCAmelCase__,			already_has_special_tokens=lowerCAmelCase__							)

										if token_ids_a is None:
																return [1] + ([0] * len(lowerCAmelCase__							)) + [1]
										return [1] + ([0] * len(lowerCAmelCase__							)) + [1, 1] + ([0] * len(lowerCAmelCase__							)) + [1]






				def     snake_case     (			self		:   Optional[int],			lowerCAmelCase__		:   List[int],			lowerCAmelCase__		:   Optional[List[int]] = None							)     ->				List[int]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   [self.sep_token_id]
										_UpperCamelCase      : Any         =   [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 + sep + token_ids_a + sep							) * [0]






				@property
				def     snake_case     (			self		:   List[str]							)     ->				int:




										'''simple docstring'''



										return len(self.sp_model							)






				def     snake_case     (			self		:   Union[str, Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   {self.convert_ids_to_tokens(lowerCAmelCase__							): i for i in range(self.vocab_size							)}
										vocab.update(self.added_tokens_encoder							)
										return vocab






				def     snake_case     (			self		:   Optional[int],			lowerCAmelCase__		:   str							)     ->				List[str]:




										'''simple docstring'''



										return self.sp_model.encode(lowerCAmelCase__,			out_type=lowerCAmelCase__							)






				def     snake_case     (			self		:   str,			lowerCAmelCase__		:   Dict							)     ->				List[Any]:




										'''simple docstring'''



										if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
										_UpperCamelCase      : Optional[int]         =   self.sp_model.PieceToId(lowerCAmelCase__							)

										return spm_id if spm_id else self.unk_token_id






				def     snake_case     (			self		:   Tuple,			lowerCAmelCase__		:   List[str]							)     ->				Optional[int]:




										'''simple docstring'''



										if index in self.fairseq_ids_to_tokens:
																return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(lowerCAmelCase__							)






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   Optional[int]							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : int         =   []
										_UpperCamelCase      : str         =   ''''''
										_UpperCamelCase      : Dict         =   False
										for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																						if not prev_is_special:
																												out_string += " "
																						out_string += self.sp_model.decode(lowerCAmelCase__							) + token
																						_UpperCamelCase      : Optional[int]         =   True
																						_UpperCamelCase      : Tuple         =   []
																else:
																						current_sub_tokens.append(lowerCAmelCase__							)
																						_UpperCamelCase      : str         =   False
										out_string += self.sp_model.decode(lowerCAmelCase__							)
										return out_string.strip()






				def __getstate__(			self		:   Tuple							)     ->				Optional[int]:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.__dict__.copy()
										_UpperCamelCase      : List[str]         =   None
										return state






				def __setstate__(			self		:   Any,			lowerCAmelCase__		:   List[Any]							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : str         =   d

										# for backward compatibility
										if not hasattr(self,			'''sp_model_kwargs'''							):
																_UpperCamelCase      : Any         =   {}

										_UpperCamelCase      : int         =   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(self.vocab_file							)






				def     snake_case     (			self		:   Union[str, Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Optional[str] = None							)     ->				Tuple[str]:




										'''simple docstring'''



										if not os.path.isdir(lowerCAmelCase__							):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
																return
										_UpperCamelCase      : Optional[Any]         =   os.path.join(
										    lowerCAmelCase__,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

										if os.path.abspath(self.vocab_file							) != os.path.abspath(lowerCAmelCase__							) and os.path.isfile(self.vocab_file							):
																copyfile(self.vocab_file,			lowerCAmelCase__							)
										elif not os.path.isfile(self.vocab_file							):
																with open(lowerCAmelCase__,			'''wb'''							) as fi:
																						_UpperCamelCase      : Any         =   self.sp_model.serialized_model_proto()
																						fi.write(lowerCAmelCase__							)

										return (out_vocab_file,)










"""simple docstring"""




import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





def 	a_    (							_lowercase       ):
						# 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_    (							_lowercase       ):
						# word like '180' or '身高' or '神'
						for char in word:
												_UpperCamelCase      : Dict         =   ord(_lowercase       )
												if not _is_chinese_char(_lowercase       ):
																		return 0
						return 1





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : List[str]         =   set()

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





def 	a_    (							_lowercase	,						_lowercase       ):
						if not chinese_word_set:
												return bert_tokens
						_UpperCamelCase      : Tuple         =   max([len(_lowercase       ) for w in chinese_word_set]       )

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





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : List[Any]         =   []

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

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

						_UpperCamelCase      : Optional[Any]         =   []
						for input_ids, chinese_word in zip(_lowercase	,						_lowercase       ):
												_UpperCamelCase      : str         =   []
												for id in input_ids:
																		_UpperCamelCase      : Dict         =   bert_tokenizer._convert_id_to_token(_lowercase       )
																		input_tokens.append(_lowercase       )
												_UpperCamelCase      : str         =   add_sub_symbol(_lowercase	,						_lowercase       )
												_UpperCamelCase      : List[str]         =   []
												# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
												for i, token in enumerate(_lowercase       ):
																		if token[:2] == "##":
																								_UpperCamelCase      : int         =   token[2:]
																								# save chinese tokens' pos
																								if len(_lowercase       ) == 1 and _is_chinese_char(ord(_lowercase       )       ):
																														ref_id.append(_lowercase       )
												ref_ids.append(_lowercase       )

						assert len(_lowercase       ) == len(_lowercase       )

						return ref_ids





def 	a_    (							_lowercase       ):
						# 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:
												_UpperCamelCase      : Union[str, Any]         =   f.readlines()
						_UpperCamelCase      : Tuple         =   [line.strip() for line in data if len(_lowercase       ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
						_UpperCamelCase      : List[Any]         =   LTP(args.ltp       )  # faster in GPU device
						_UpperCamelCase      : int         =   BertTokenizer.from_pretrained(args.bert       )

						_UpperCamelCase      : List[str]         =   prepare_ref(_lowercase	,						_lowercase	,						_lowercase       )

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


if __name__ == "__main__":
						UpperCamelCase_									=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_									=parser.parse_args()
						main(args)



