


"""simple docstring"""






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


__snake_case		=       logging.get_logger(__name__)


__snake_case		=       '''▁'''

__snake_case		=       {'''vocab_file''': '''spiece.model'''}

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

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


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	List[Any]    =		VOCAB_FILES_NAMES
     A_       :	Any    =		PRETRAINED_VOCAB_FILES_MAP
     A_       :	str    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_       :	Optional[Any]    =		['input_ids', 'attention_mask']


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase="</s>"    ,				__UpperCAmelCase="<unk>"    ,				__UpperCAmelCase=[]    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							None:
          _a    	=				{} 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    ,				)

          _a    	=				vocab_file
          _a    	=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
          self.sp_model.Load(__UpperCAmelCase							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          return self.sp_model.get_piece_size()


     def 							_UpperCAmelCase      (      self							)       ->							Dict[str, int]:
          _a    	=				{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							)       ->							Union[str, Any]:
          _a    	=				self.__dict__.copy()
          _a    	=				None
          return state


     def __setstate__(      self    ,				__UpperCAmelCase							)       ->							Optional[Any]:
          _a    	=				d

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

          _a    	=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
          self.sp_model.Load(self.vocab_file							)


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


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Any:
          return self.sp_model.piece_to_id(__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Tuple:
          if index < self.sp_model.get_piece_size():
               _a    	=				self.sp_model.IdToPiece(__UpperCAmelCase							)
          return token


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[Any]:
          _a    	=				[]
          _a    	=				''''''
          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
                    _a    	=				[]
               else:
                    current_sub_tokens.append(__UpperCAmelCase							)
          out_string += self.sp_model.decode(__UpperCAmelCase							)
          return out_string.strip()





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							Tuple[str]:
          if not os.path.isdir(__UpperCAmelCase							):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
               return
          _a    	=				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:
                    _a    	=				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_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case		=       {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

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

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''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

   __snake_case		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)