



'''simple docstring'''





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

import sentencepiece as spm

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


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {"vocab_file": "sentencepiece.model"}

SCREAMING_SNAKE_CASE__      					=     {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

SCREAMING_SNAKE_CASE__      					=     {
    "google/rembert": 256,
}


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Tuple						   =					VOCAB_FILES_NAMES
       lowerCAmelCase__  :Any						   =					PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__  :List[Any]						   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_=False     ,UpperCAmelCase_=True     ,UpperCAmelCase_=True     ,UpperCAmelCase_="[CLS]"     ,UpperCAmelCase_="[SEP]"     ,UpperCAmelCase_="[UNK]"     ,UpperCAmelCase_="[SEP]"     ,UpperCAmelCase_="[PAD]"     ,UpperCAmelCase_="[CLS]"     ,UpperCAmelCase_="[MASK]"     ,**UpperCAmelCase_     ,)  ->							Any:
           super().__init__(
               do_lower_case=UpperCAmelCase_     ,remove_space=UpperCAmelCase_     ,keep_accents=UpperCAmelCase_     ,bos_token=UpperCAmelCase_     ,eos_token=UpperCAmelCase_     ,unk_token=UpperCAmelCase_     ,sep_token=UpperCAmelCase_     ,pad_token=UpperCAmelCase_     ,cls_token=UpperCAmelCase_     ,mask_token=UpperCAmelCase_     ,**UpperCAmelCase_     ,)

           lowercase__		       =						do_lower_case
           lowercase__		       =						remove_space
           lowercase__		       =						keep_accents
           lowercase__		       =						vocab_file

           lowercase__		       =						spm.SentencePieceProcessor()
           self.sp_model.Load(UpperCAmelCase_      )



       @property
       def 							_a      (				self      )  ->							Optional[Any]:
           return len(self.sp_model      )



       def 							_a      (				self      )  ->							Dict:
           lowercase__		       =						{self.convert_ids_to_tokens(UpperCAmelCase_      ): i for i in range(self.vocab_size      )}
           vocab.update(self.added_tokens_encoder      )
           return vocab



       def __getstate__(				self      )  ->							Optional[Any]:
           lowercase__		       =						self.__dict__.copy()
           lowercase__		       =						None
           return state



       def __setstate__(				self     ,UpperCAmelCase_      )  ->							List[str]:
           lowercase__		       =						d
           lowercase__		       =						spm.SentencePieceProcessor()
           self.sp_model.Load(self.vocab_file      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=False      )  ->							str:
           lowercase__		       =						self.sp_model.EncodeAsPieces(UpperCAmelCase_      )
           return pieces



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							Any:
           return self.sp_model.PieceToId(UpperCAmelCase_      )



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							str:
           return self.sp_model.IdToPiece(UpperCAmelCase_      )



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							str:
           lowercase__		       =						self.sp_model.decode_pieces(UpperCAmelCase_      )
           return out_string



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = None      )  ->							List[int]:
           lowercase__		       =						[self.sep_token_id]
           lowercase__		       =						[self.cls_token_id]
           if token_ids_a is None:
               return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = False      )  ->							List[int]:

           if already_has_special_tokens:
               if token_ids_a is not None:
                   raise ValueError(
                       "You should not supply a second sequence if the provided sequence of "
                       "ids is already formatted with special tokens for the model."      )
               return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

           if token_ids_a is not None:
               return [1] + ([0] * len(UpperCAmelCase_      )) + [1] + ([0] * len(UpperCAmelCase_      )) + [1]
           return [1] + ([0] * len(UpperCAmelCase_      )) + [1]



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = None      )  ->							List[int]:
           lowercase__		       =						[self.sep_token_id]
           lowercase__		       =						[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 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = None      )  ->							Tuple[str]:
           if not os.path.isdir(UpperCAmelCase_      ):
               logger.error("Vocabulary path ({}) should be a directory".format(UpperCAmelCase_      )      )
               return
           lowercase__		       =						os.path.join(
               UpperCAmelCase_     ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

           if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCAmelCase_      ):
               copyfile(self.vocab_file     ,UpperCAmelCase_      )

           return (out_vocab_file,)









'''simple docstring'''





print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))




