





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


__lowercase  :   Union[str, Any]						      =logging.get_logger(__name__)

__lowercase  :   str						      ={"""vocab_file""": """sentencepiece.model"""}

__lowercase  :   List[str]						      ={
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

__lowercase  :   Tuple						      ={
    """google/rembert""": 256,
}

class      A       (	__lowercase						):
      _snake_case							     =VOCAB_FILES_NAMES
      _snake_case							     =PRETRAINED_VOCAB_FILES_MAP
      _snake_case							     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



      def __init__(	self:						str		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						str=False		,  _lowerCAmelCase:						Dict=True		,  _lowerCAmelCase:						Dict=True		,  _lowerCAmelCase:						Tuple="[CLS]"		,  _lowerCAmelCase:						List[Any]="[SEP]"		,  _lowerCAmelCase:						List[str]="[UNK]"		,  _lowerCAmelCase:						Dict="[SEP]"		,  _lowerCAmelCase:						Dict="[PAD]"		,  _lowerCAmelCase:						Tuple="[CLS]"		,  _lowerCAmelCase:						Tuple="[MASK]"		,  **_lowerCAmelCase:						Any		,  )						->    int:
         '''simple docstring'''

         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:						str       )						->    Tuple:
         '''simple docstring'''

         return len(self.sp_model       )



      def 	lowerCAmelCase__    (	self:						List[str]       )						->    str:
         '''simple docstring'''

         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:						Any       )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.__dict__.copy()
         UpperCAmelCase_											=None
         return state



      def __setstate__(	self:						Union[str, Any]		,  _lowerCAmelCase:						Dict       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=d
         UpperCAmelCase_											=spm.SentencePieceProcessor()
         self.sp_model.Load(self.vocab_file       )



      def 	lowerCAmelCase__    (	self:						str		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						List[str]=False       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=self.sp_model.EncodeAsPieces(_lowerCAmelCase       )
         return pieces



      def 	lowerCAmelCase__    (	self:						int		,  _lowerCAmelCase:						List[Any]       )						->    Optional[Any]:
         '''simple docstring'''

         return self.sp_model.PieceToId(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Optional[int]		,  _lowerCAmelCase:						List[Any]       )						->    Any:
         '''simple docstring'''

         return self.sp_model.IdToPiece(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						List[str]		,  _lowerCAmelCase:						Any       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_											=self.sp_model.decode_pieces(_lowerCAmelCase       )
         return out_string



      def 	lowerCAmelCase__    (	self:						str		,  _lowerCAmelCase:						List[int]		,  _lowerCAmelCase:						Optional[List[int]] = None       )						->    List[int]:
         '''simple docstring'''

         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:						str		,  _lowerCAmelCase:						List[int]		,  _lowerCAmelCase:						Optional[List[int]] = None		,  _lowerCAmelCase:						bool = False       )						->    List[int]:
         '''simple docstring'''

         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:						Optional[int]		,  _lowerCAmelCase:						List[int]		,  _lowerCAmelCase:						Optional[List[int]] = None       )						->    List[int]:
         '''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       ) * [0] + len(token_ids_a + sep       ) * [1]






      def 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Optional[str] = None       )						->    Tuple[str]:
         '''simple docstring'''

         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,)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase  :   Any						      ={
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   Tuple						      =[
         """UperNetForSemanticSegmentation""",
         """UperNetPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_upernet import UperNetConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
     import sys

     __lowercase  :   Any						      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




