

'''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


UpperCamelCase_						:      int       =						logging.get_logger(__name__)


UpperCamelCase_						:      Union[str, Any]       =						"""▁"""

UpperCamelCase_						:      str       =						{"""vocab_file""": """spiece.model"""}

UpperCamelCase_						:      List[str]       =						{
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

UpperCamelCase_						:      Any       =						{
    """google/reformer-crime-and-punishment""": 524288,
}

class        _a    (						__A							):
     SCREAMING_SNAKE_CASE_							:   Dict       =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_							:   str       =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_							:   Any       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_							:   Tuple       =						['input_ids', 'attention_mask']






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

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

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






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






     def     _lowercase     (			self   )							->      int:
          _snake_case							=				{self.convert_ids_to_tokens(__lowercase   ): i for i in range(self.vocab_size   )}
          vocab.update(self.added_tokens_encoder   )
          return vocab






     def __getstate__(			self   )							->      int:
          _snake_case							=				self.__dict__.copy()
          _snake_case							=				None
          return state






     def __setstate__(			self ,_SCREAMING_SNAKE_CASE   )							->      Tuple:
          _snake_case							=				d

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

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






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      str:
          return self.sp_model.encode(__lowercase ,out_type=__lowercase   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      List[str]:
          return self.sp_model.piece_to_id(__lowercase   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      str:
          if index < self.sp_model.get_piece_size():
               _snake_case							=				self.sp_model.IdToPiece(__lowercase   )
          return token






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      List[Any]:
          _snake_case							=				[]
          _snake_case							=				""
          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(__lowercase   ) + token
                    _snake_case							=				[]
               else:
                    current_sub_tokens.append(__lowercase   )
          out_string += self.sp_model.decode(__lowercase   )
          return out_string.strip()






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      Optional[Any]:
          if not os.path.isdir(__lowercase   ):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
               return
          _snake_case							=				os.path.join(
              __lowercase ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

          if os.path.abspath(self.vocab_file   ) != os.path.abspath(__lowercase   ) and os.path.isfile(self.vocab_file   ):
               copyfile(self.vocab_file ,__lowercase   )
          elif not os.path.isfile(self.vocab_file   ):
               with open(__lowercase ,"wb"   ) as fi:
                    _snake_case							=				self.sp_model.serialized_model_proto()
                    fi.write(__lowercase   )

          return (out_vocab_file,)








'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_						:      int       =						logging.get_logger(__name__)

UpperCamelCase_						:      Tuple       =						{
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   List[str]       =						"""vit_msn"""






     def __init__(			self ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=3_072 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0_2 ,_SCREAMING_SNAKE_CASE=1e-06 ,_SCREAMING_SNAKE_CASE=224 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=True ,**_SCREAMING_SNAKE_CASE ,)							->      int:
          super().__init__(**_SCREAMING_SNAKE_CASE   )

          _snake_case							=				hidden_size
          _snake_case							=				num_hidden_layers
          _snake_case							=				num_attention_heads
          _snake_case							=				intermediate_size
          _snake_case							=				hidden_act
          _snake_case							=				hidden_dropout_prob
          _snake_case							=				attention_probs_dropout_prob
          _snake_case							=				initializer_range
          _snake_case							=				layer_norm_eps
          _snake_case							=				image_size
          _snake_case							=				patch_size
          _snake_case							=				num_channels
          _snake_case							=				qkv_bias


