


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


A      = logging.get_logger(__name__)

A      = "▁"

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

A      = {
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

A      = {
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  VOCAB_FILES_NAMES
  A__=  PRETRAINED_VOCAB_FILES_MAP
  A__=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A__=  ['input_ids', 'attention_mask']






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




         """simple docstring"""







         UpperCAmelCase__										=     AddedToken(_lowercase		,						lstrip=_lowercase		,						rstrip=_lowercase ) if isinstance(_lowercase		,						_lowercase ) else mask_token

         UpperCAmelCase__										=     {} if sp_model_kwargs is None else sp_model_kwargs

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

         UpperCAmelCase__										=     spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(str(_lowercase ) )
         UpperCAmelCase__										=     vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # Mimic fairseq token-to-id alignment for the first 4 token
         UpperCAmelCase__										=     {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

         # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
         UpperCAmelCase__										=     1

         UpperCAmelCase__										=     len(self.sp_model ) + self.fairseq_offset
         UpperCAmelCase__										=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}






  def __getstate__(					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.__dict__.copy()
         UpperCAmelCase__										=     None
         UpperCAmelCase__										=     self.sp_model.serialized_model_proto()
         return state






  def __setstate__(					self      :					List[Any]		,						_lowercase      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     d

         # for backward compatibility
         if not hasattr(self		,						"sp_model_kwargs" ):
                UpperCAmelCase__										=     {}

         UpperCAmelCase__										=     spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto )






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					List[int]		,						_lowercase      :					Optional[List[int]] = None ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					List[int]		,						_lowercase      :					Optional[List[int]] = None		,						_lowercase      :					bool = False ):




         """simple docstring"""







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

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






  def 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					List[int]		,						_lowercase      :					Optional[List[int]] = None ):




         """simple docstring"""







         UpperCAmelCase__										=     [self.sep_token_id]
         UpperCAmelCase__										=     [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 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         return len(self.sp_model ) + self.fairseq_offset + 1  # Add the <mask> token






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     {self.convert_ids_to_tokens(_lowercase ): i for i in range(self.vocab_size )}
         vocab.update(self.added_tokens_encoder )
         return vocab






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					str ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					List[str] ):




         """simple docstring"""







         if token in self.fairseq_tokens_to_ids:
                return self.fairseq_tokens_to_ids[token]
         UpperCAmelCase__										=     self.sp_model.PieceToId(_lowercase )

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					Union[str, Any] ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					str		,						_lowercase      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     "".join(_lowercase ).replace(_lowercase		,						" " ).strip()
         return out_string






  def 			_UpperCAmelCase  (					self      :					List[Any]		,						_lowercase      :					str		,						_lowercase      :					Optional[str] = None ):




         """simple docstring"""







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

         if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ) and os.path.isfile(self.vocab_file ):
                copyfile(self.vocab_file		,						_lowercase )
         elif not os.path.isfile(self.vocab_file ):
                with open(_lowercase		,						"wb" ) as fi:
                       UpperCAmelCase__										=     self.sp_model.serialized_model_proto()
                       fi.write(_lowercase )

         return (out_vocab_file,)









import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer





def __UpperCAmelCase       (		__A			)						->					Union[str, Any]:



       '''simple docstring'''





       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 __UpperCAmelCase       (		__A			)						->					List[str]:



       '''simple docstring'''





       for char in word:
              UpperCAmelCase__										=     ord(__A			)
              if not _is_chinese_char(__A			):
                     return 0
       return 1





def __UpperCAmelCase       (		__A			)						->					str:



       '''simple docstring'''





       UpperCAmelCase__										=     set()

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





def __UpperCAmelCase       (		__A ,						__A			)						->					Optional[Any]:



       '''simple docstring'''





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

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





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					List[Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     []

       for i in range(0 ,						len(__A			) ,						1_0_0			):
              UpperCAmelCase__										=     ltp_tokenizer.seg(lines[i : i + 1_0_0]			)[0]
              UpperCAmelCase__										=     [get_chinese_word(__A			) for r in res]
              ltp_res.extend(__A			)
       assert len(__A			) == len(__A			)

       UpperCAmelCase__										=     []
       for i in range(0 ,						len(__A			) ,						1_0_0			):
              UpperCAmelCase__										=     bert_tokenizer(lines[i : i + 1_0_0] ,						add_special_tokens=__A ,						truncation=__A ,						max_length=5_1_2			)
              bert_res.extend(res["input_ids"]			)
       assert len(__A			) == len(__A			)

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

       assert len(__A			) == len(__A			)

       return ref_ids





def __UpperCAmelCase       (		__A			)						->					Optional[int]:



       '''simple docstring'''





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

       UpperCAmelCase__										=     prepare_ref(__A ,						__A ,						__A			)

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


if __name__ == "__main__":
    A      = 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")

    A      = parser.parse_args()
    main(args)





