
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


__lowerCAmelCase			:     Any           =   logging.get_logger(__name__)


__lowerCAmelCase			:     Dict           =   '▁'

__lowerCAmelCase			:     Union[str, Any]           =   {'vocab_file': 'spiece.model'}

__lowerCAmelCase			:     int           =   {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

__lowerCAmelCase			:     str           =   {
    'google/reformer-crime-and-punishment': 524288,
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		VOCAB_FILES_NAMES
  a__           =		PRETRAINED_VOCAB_FILES_MAP
  a__           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a__           =		["""input_ids""", """attention_mask"""]
  def __init__( self			: Optional[int]			,							UpperCamelCase__			: Any			,							UpperCamelCase__			: List[Any]="</s>"			,							UpperCamelCase__			: str="<unk>"			,							UpperCamelCase__			: List[Any]=[]			,							UpperCamelCase__			: Optional[Dict[str, Any]] = None			,							**UpperCamelCase__			: Optional[int]			,							)			->	None:







    """simple docstring"""






    __magic_name__				     =       {} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        eos_token=UpperCamelCase__			,							unk_token=UpperCamelCase__			,							additional_special_tokens=UpperCamelCase__			,							sp_model_kwargs=self.sp_model_kwargs			,							**UpperCamelCase__			,							)

    __magic_name__				     =       vocab_file
    __magic_name__				     =       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
    self.sp_model.Load(UpperCamelCase__    )
  @property
  def 							_lowercase							( self			: List[str]    )			->	Optional[Any]:







    """simple docstring"""






    return self.sp_model.get_piece_size()
  def 							_lowercase							( self			: str    )			->	Dict[str, int]:







    """simple docstring"""






    __magic_name__				     =       {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[int]    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       self.__dict__.copy()
    __magic_name__				     =       None
    return state
  def __setstate__( self			: Any			,							UpperCamelCase__			: Optional[int]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       d

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

    __magic_name__				     =       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
    self.sp_model.Load(self.vocab_file    )
  def 							_lowercase							( self			: str			,							UpperCamelCase__			: str    )			->	List[str]:







    """simple docstring"""






    return self.sp_model.encode(UpperCamelCase__			,							out_type=UpperCamelCase__    )
  def 							_lowercase							( self			: Union[str, Any]			,							UpperCamelCase__			: Optional[int]    )			->	List[str]:







    """simple docstring"""






    return self.sp_model.piece_to_id(UpperCamelCase__    )
  def 							_lowercase							( self			: Any			,							UpperCamelCase__			: int    )			->	Union[str, Any]:







    """simple docstring"""






    if index < self.sp_model.get_piece_size():
      __magic_name__				     =       self.sp_model.IdToPiece(UpperCamelCase__    )
    return token
  def 							_lowercase							( self			: Dict			,							UpperCamelCase__			: Optional[Any]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       []
    __magic_name__				     =       """"""
    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(UpperCamelCase__    ) + token
        __magic_name__				     =       []
      else:
        current_sub_tokens.append(UpperCamelCase__    )
    out_string += self.sp_model.decode(UpperCamelCase__    )
    return out_string.strip()





  def 							_lowercase							( self			: List[str]			,							UpperCamelCase__			: str			,							UpperCamelCase__			: Optional[str] = None    )			->	Tuple[str]:







    """simple docstring"""






    if not os.path.isdir(UpperCamelCase__    ):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
      return
    __magic_name__				     =       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__    ) and os.path.isfile(self.vocab_file    ):
      copyfile(self.vocab_file			,							UpperCamelCase__    )
    elif not os.path.isfile(self.vocab_file    ):
      with open(UpperCamelCase__			,							"""wb"""    ) as fi:
        __magic_name__				     =       self.sp_model.serialized_model_proto()
        fi.write(UpperCamelCase__    )

    return (out_vocab_file,)


'''simple docstring'''




from typing import TYPE_CHECKING

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


_lowercase						: Dict						=						{"configuration_mmbt": ["MMBTConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase						: Dict						=						["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]


if TYPE_CHECKING:
						from .configuration_mmbt import MMBTConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
						import sys

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





