"""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


UpperCAmelCase__			=    logging.get_logger(__name__)

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

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

UpperCAmelCase__			=    {
    """google/rembert""": 2_5_6,
}


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	List[str]  				=				VOCAB_FILES_NAMES
    _snake_case  :	Union[str, Any]  				=				PRETRAINED_VOCAB_FILES_MAP
    _snake_case  :	Any  				=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


    def __init__(       self						:	Tuple ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Union[str, Any]=False ,					__lowerCAmelCase						:	List[Any]=True ,					__lowerCAmelCase						:	Any=True ,					__lowerCAmelCase						:	Optional[Any]="[CLS]" ,					__lowerCAmelCase						:	List[str]="[SEP]" ,					__lowerCAmelCase						:	int="[UNK]" ,					__lowerCAmelCase						:	List[Any]="[SEP]" ,					__lowerCAmelCase						:	Optional[int]="[PAD]" ,					__lowerCAmelCase						:	Tuple="[CLS]" ,					__lowerCAmelCase						:	str="[MASK]" ,					**__lowerCAmelCase						:	Dict ,					):
      super().__init__(
          do_lower_case=__lowerCAmelCase ,					remove_space=__lowerCAmelCase ,					keep_accents=__lowerCAmelCase ,					bos_token=__lowerCAmelCase ,					eos_token=__lowerCAmelCase ,					unk_token=__lowerCAmelCase ,					sep_token=__lowerCAmelCase ,					pad_token=__lowerCAmelCase ,					cls_token=__lowerCAmelCase ,					mask_token=__lowerCAmelCase ,					**__lowerCAmelCase ,					)

      _UpperCAmelCase    			=   do_lower_case
      _UpperCAmelCase    			=   remove_space
      _UpperCAmelCase    			=   keep_accents
      _UpperCAmelCase    			=   vocab_file

      _UpperCAmelCase    			=   spm.SentencePieceProcessor()
      self.sp_model.Load(__lowerCAmelCase       )


    @property
    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      return len(self.sp_model       )


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   {self.convert_ids_to_tokens(__lowerCAmelCase       ): i for i in range(self.vocab_size       )}
      vocab.update(self.added_tokens_encoder       )
      return vocab


    def __getstate__(       self						:	int       ):
      _UpperCAmelCase    			=   self.__dict__.copy()
      _UpperCAmelCase    			=   None
      return state


    def __setstate__(       self						:	Optional[int] ,					__lowerCAmelCase						:	Optional[int]       ):
      _UpperCAmelCase    			=   d
      _UpperCAmelCase    			=   spm.SentencePieceProcessor()
      self.sp_model.Load(self.vocab_file       )


    def   lowerCAmelCase_	(       self						:	List[Any] ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	Optional[int]=False       ):
      _UpperCAmelCase    			=   self.sp_model.EncodeAsPieces(__lowerCAmelCase       )
      return pieces


    def   lowerCAmelCase_	(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	int       ):
      return self.sp_model.PieceToId(__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	int ,					__lowerCAmelCase						:	Dict       ):
      return self.sp_model.IdToPiece(__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	Tuple       ):
      _UpperCAmelCase    			=   self.sp_model.decode_pieces(__lowerCAmelCase       )
      return out_string


    def   lowerCAmelCase_	(       self						:	Optional[int] ,					__lowerCAmelCase						:	List[int] ,					__lowerCAmelCase						:	Optional[List[int]] = None       ):
      _UpperCAmelCase    			=   [self.sep_token_id]
      _UpperCAmelCase    			=   [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   lowerCAmelCase_	(       self						:	int ,					__lowerCAmelCase						:	List[int] ,					__lowerCAmelCase						:	Optional[List[int]] = None ,					__lowerCAmelCase						:	bool = False       ):

      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(__lowerCAmelCase       )) + [1] + ([0] * len(__lowerCAmelCase       )) + [1]
      return [1] + ([0] * len(__lowerCAmelCase       )) + [1]


    def   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	List[int] ,					__lowerCAmelCase						:	Optional[List[int]] = None       ):
      _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       ) * [0] + len(token_ids_a + sep       ) * [1]


    def   lowerCAmelCase_	(       self						:	Optional[Any] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Optional[str] = None       ):
      if not os.path.isdir(__lowerCAmelCase       ):
        logger.error("""Vocabulary path ({}) should be a directory""".format(__lowerCAmelCase       )       )
        return
      _UpperCAmelCase    			=   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       ):
        copyfile(self.vocab_file ,					__lowerCAmelCase       )

      return (out_vocab_file,)

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase__			=    {"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ViTMAEForPreTraining""",
        """ViTMAELayer""",
        """ViTMAEModel""",
        """ViTMAEPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """TFViTMAEForPreTraining""",
        """TFViTMAEModel""",
        """TFViTMAEPreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vit_mae import (
            VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
            ViTMAEForPreTraining,
            ViTMAELayer,
            ViTMAEModel,
            ViTMAEPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
    import sys

    UpperCAmelCase__			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
