




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	:	Optional[Any]				=   logging.get_logger(__name__)

lowerCamelCase	:	str				=   {'vocab_file': 'spiece.model'}

lowerCamelCase	:	Optional[int]				=   {
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

lowerCamelCase	:	Dict				=   {'bert_for_seq_generation': 5_1_2}




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      _snake_case												=						VOCAB_FILES_NAMES
      _snake_case												=						PRETRAINED_VOCAB_FILES_MAP
      _snake_case												=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case												=						[]
      _snake_case												=						["""input_ids""", """attention_mask"""]

      def __init__(				self    ,				A    ,				A="<s>"    ,				A="</s>"    ,				A="<unk>"    ,				A="<pad>"    ,				A="<::::>"    ,				A = None    ,				**A    ,				)		->    None:
         snake_case				:    List[str]           =	{} if sp_model_kwargs is None else sp_model_kwargs

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

         snake_case				:    List[Any]           =	vocab_file

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

      @property
      def      UpperCAmelCase				(				self     )		->    List[str]:
         return self.sp_model.get_piece_size()

      def      UpperCAmelCase				(				self     )		->    List[Any]:
         snake_case				:    Any           =	{self.convert_ids_to_tokens(A     ): i for i in range(self.vocab_size     )}
         vocab.update(self.added_tokens_encoder     )
         return vocab

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

      def __setstate__(				self    ,				A     )		->    Dict:
         snake_case				:    int           =	d

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

         snake_case				:    Any           =	spm.SentencePieceProcessor(**self.sp_model_kwargs     )
         self.sp_model.Load(self.vocab_file     )

      def      UpperCAmelCase				(				self    ,				A     )		->    List[str]:
         return self.sp_model.encode(A    ,				out_type=A     )

      def      UpperCAmelCase				(				self    ,				A     )		->    Tuple:
         return self.sp_model.piece_to_id(A     )

      def      UpperCAmelCase				(				self    ,				A     )		->    str:
         snake_case				:    Optional[Any]           =	self.sp_model.IdToPiece(A     )
         return token

      def      UpperCAmelCase				(				self    ,				A     )		->    List[Any]:
         snake_case				:    List[Any]           =	[]
         snake_case				:    List[Any]           =	""""""
         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(A     ) + token
               snake_case				:    Optional[Any]           =	[]
            else:
               current_sub_tokens.append(A     )
         out_string += self.sp_model.decode(A     )
         return out_string.strip()

      def      UpperCAmelCase				(				self    ,				A    ,				A = None     )		->    Tuple[str]:
         if not os.path.isdir(A     ):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""     )
            return
         snake_case				:    str           =	os.path.join(
             A    ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

         if os.path.abspath(self.vocab_file     ) != os.path.abspath(A     ) and os.path.isfile(self.vocab_file     ):
            copyfile(self.vocab_file    ,				A     )
         elif not os.path.isfile(self.vocab_file     ):
            with open(A    ,				"""wb"""     ) as fi:
               snake_case				:    Optional[int]           =	self.sp_model.serialized_model_proto()
               fi.write(A     )

         return (out_vocab_file,)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase	:	List[str]				=   {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Optional[Any]				=   [
           'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
           'Swinv2ForImageClassification',
           'Swinv2ForMaskedImageModeling',
           'Swinv2Model',
           'Swinv2PreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_swinva import (
                  SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SwinvaForImageClassification,
                  SwinvaForMaskedImageModeling,
                  SwinvaModel,
                  SwinvaPreTrainedModel,
              )


else:
       import sys

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





