





import re

from filelock import FileLock


try:
     import nltk

     __lowercase	: str       =True
except (ImportError, ModuleNotFoundError):
     __lowercase	: Optional[Any]       =False

if NLTK_AVAILABLE:
     with FileLock(""".lock""") as lock:
          nltk.download("""punkt""", quiet=True)




def      a__			(       lowercase__	):






   '''simple docstring'''






   re.sub("<n>"	,     ""	,     lowercase__	)  # remove pegasus newline char
   assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
   return "\n".join(nltk.sent_tokenize(lowercase__	)	)






'''simple docstring'''




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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase     :							int       						=     logging.get_logger(__name__)

lowercase     :							Dict       						=     {'vocab_file': 'spiece.model'}

lowercase     :							Tuple       						=     {
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

lowercase     :							Tuple       						=     {'bert_for_seq_generation': 5_12}





class 					A							(				__snake_case		):
     __magic_name__          =						VOCAB_FILES_NAMES
     __magic_name__          =						PRETRAINED_VOCAB_FILES_MAP
     __magic_name__          =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__          =						[]
     __magic_name__          =						['''input_ids''', '''attention_mask''']





     def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE="<s>"		,     SCREAMING_SNAKE_CASE="</s>"		,     SCREAMING_SNAKE_CASE="<unk>"		,     SCREAMING_SNAKE_CASE="<pad>"		,     SCREAMING_SNAKE_CASE="<::::>"		,     SCREAMING_SNAKE_CASE = None		,     **SCREAMING_SNAKE_CASE		,     )					->							None:






         """simple docstring"""






         A			:  str   =		{} if sp_model_kwargs is None else sp_model_kwargs

         # Add extra_ids to the special token list
         super().__init__(
             bos_token=SCREAMING_SNAKE_CASE		,     eos_token=SCREAMING_SNAKE_CASE		,     unk_token=SCREAMING_SNAKE_CASE		,     pad_token=SCREAMING_SNAKE_CASE		,     sep_token=SCREAMING_SNAKE_CASE		,     sp_model_kwargs=self.sp_model_kwargs		,     **SCREAMING_SNAKE_CASE		,     )

         A			:  List[Any]   =		vocab_file

         A			:  List[Any]   =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
         self.sp_model.Load(SCREAMING_SNAKE_CASE      )





     @property
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         return self.sp_model.get_piece_size()





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  Any   =		{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE      ): i for i in range(self.vocab_size      )}
         vocab.update(self.added_tokens_encoder      )
         return vocab





     def __getstate__(				self      )					->							Tuple:






         """simple docstring"""






         A			:  Dict   =		self.__dict__.copy()
         A			:  int   =		None
         return state





     def __setstate__(				self		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  Union[str, Any]   =		d

         # for backward compatibility
         if not hasattr(self		,     '''sp_model_kwargs'''      ):
             A			:  Optional[int]   =		{}

         A			:  Optional[int]   =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
         self.sp_model.Load(self.vocab_file      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							List[str]:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Optional[int]:






         """simple docstring"""






         return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Optional[Any]:






         """simple docstring"""






         A			:  Tuple   =		self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE      )
         return token





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Tuple:






         """simple docstring"""






         A			:  List[Any]   =		[]
         A			:  Dict   =		''''''
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE      ) + token
                 A			:  str   =		[]
             else:
                 current_sub_tokens.append(SCREAMING_SNAKE_CASE      )
         out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE      )
         return out_string.strip()





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE = None      )					->							Tuple[str]:






         """simple docstring"""






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

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

         return (out_vocab_file,)




