code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import os
  import re
  import unicodedata
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple
  
  import sentencepiece as spm
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  lowerCamelCase_     :     str    		=						logging.get_logger(__name__)
  lowerCamelCase_     :     Union[str, Any]    		=						{"""vocab_file""": """spiece.model"""}
  
  lowerCamelCase_     :     List[Any]    		=						{
      """vocab_file""": {
          """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
          """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
      },
  }
  
  lowerCamelCase_     :     Any    		=						{
      """google/fnet-base""": 5_1_2,
      """google/fnet-large""": 5_1_2,
  }
  
  lowerCamelCase_     :     Optional[int]    		=						"""▁"""
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						VOCAB_FILES_NAMES
   __lowerCAmelCase				      =						PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase				      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase				      =						["input_ids", "token_type_ids"]
  
  
   def __init__(	self				,					__A				,					__A=False				,					__A=True				,					__A=True				,					__A="<unk>"				,					__A="[SEP]"				,					__A="<pad>"				,					__A="[CLS]"				,					__A="[MASK]"				,					__A = None				,					**__A				,					)      ->  None:
       # Mask token behave like a normal word, i.e. include the space before it and
       # is included in the raw text, there should be a match in a non-normalized sentence.
       a					=(
           AddedToken(__A				,					lstrip=__A				,					rstrip=__A				,					normalized=__A		)
           if isinstance(__A				,					__A		)
           else mask_token
       )
  
       a					={} if sp_model_kwargs is None else sp_model_kwargs
  
       super().__init__(
           do_lower_case=__A				,					remove_space=__A				,					keep_accents=__A				,					unk_token=__A				,					sep_token=__A				,					pad_token=__A				,					cls_token=__A				,					mask_token=__A				,					sp_model_kwargs=self.sp_model_kwargs				,					**__A				,					)
  
       a					=do_lower_case
       a					=remove_space
       a					=keep_accents
       a					=vocab_file
  
       a					=spm.SentencePieceProcessor(**self.sp_model_kwargs		)
       self.sp_model.Load(__A		)
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       return len(self.sp_model		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
       a					={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		)      ->  List[Any]:
       a					=self.__dict__.copy()
       a					=None
       return state
  
  
   def __setstate__(	self				,					__A		)      ->  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    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Optional[Any]:
       if self.remove_space:
           a					=''' '''.join(inputs.strip().split()		)
       else:
           a					=inputs
       a					=outputs.replace('''``'''				,					'''"'''		).replace('''\'\''''				,					'''"'''		)
  
       if not self.keep_accents:
           a					=unicodedata.normalize('''NFKD'''				,					__A		)
           a					=''''''.join([c for c in outputs if not unicodedata.combining(__A		)]		)
       if self.do_lower_case:
           a					=outputs.lower()
  
       return outputs
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  List[str]:
       a					=self.preprocess_text(__A		)
       a					=self.sp_model.encode(__A				,					out_type=__A		)
       a					=[]
       for piece in pieces:
           if len(__A		) > 1 and piece[-1] == str(''','''		) and piece[-2].isdigit():
               a					=self.sp_model.EncodeAsPieces(piece[:-1].replace(__A				,					''''''		)		)
               if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                   if len(cur_pieces[0]		) == 1:
                       a					=cur_pieces[1:]
                   else:
                       a					=cur_pieces[0][1:]
               cur_pieces.append(piece[-1]		)
               new_pieces.extend(__A		)
           else:
               new_pieces.append(__A		)
  
       return new_pieces
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Dict:
       return self.sp_model.PieceToId(__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Dict:
       return self.sp_model.IdToPiece(__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  int:
       a					=[]
       a					=''''''
       a					=False
       for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
               if not prev_is_special:
                   out_string += " "
               out_string += self.sp_model.decode(__A		) + token
               a					=True
               a					=[]
           else:
               current_sub_tokens.append(__A		)
               a					=False
       out_string += self.sp_model.decode(__A		)
       return out_string.strip()
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A = False				,					__A = None				,					__A = True				,					**__A				,					)      ->  str:
       a					=kwargs.pop('''use_source_tokenizer'''				,					__A		)
  
       a					=self.convert_ids_to_tokens(__A				,					skip_special_tokens=__A		)
  
       # To avoid mixing byte-level and unicode for byte-level BPT
       # we need to build string separately for added tokens and byte-level tokens
       # cf. https://github.com/huggingface/transformers/issues/1133
       a					=[]
       a					=[]
       for token in filtered_tokens:
           if skip_special_tokens and token in self.all_special_ids:
               continue
           if token in self.added_tokens_encoder:
               if current_sub_text:
                   sub_texts.append(self.convert_tokens_to_string(__A		)		)
                   a					=[]
               sub_texts.append(__A		)
           else:
               current_sub_text.append(__A		)
       if current_sub_text:
           sub_texts.append(self.convert_tokens_to_string(__A		)		)
  
       # Mimic the behavior of the Rust tokenizer:
       # No space after <unk>
       if spaces_between_special_tokens:
           a					=re.sub(r'''(<unk>) '''				,					r'''\1'''				,					''' '''.join(__A		)		)
       else:
           a					=''''''.join(__A		)
  
       a					=(
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
           a					=self.clean_up_tokenization(__A		)
           return clean_text
       else:
           return text
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A = None		)      ->  List[int]:
       a					=[self.sep_token_id]
       a					=[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    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A = None				,					__A = False		)      ->  List[int]:
  
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__A				,					token_ids_a=__A				,					already_has_special_tokens=__A		)
  
       if token_ids_a is not None:
           return [1] + ([0] * len(__A		)) + [1] + ([0] * len(__A		)) + [1]
       return [1] + ([0] * len(__A		)) + [1]
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A = None		)      ->  List[int]:
       a					=[self.sep_token_id]
       a					=[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    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A = None		)      ->  Tuple[str]:
       if not os.path.isdir(__A		):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
           return
       a					=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:
               a					=self.sp_model.serialized_model_proto()
               fi.write(__A		)
  
       return (out_vocab_file,)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  lowerCamelCase_     :     Any    		=						{"""configuration_dinat""": ["""DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DinatConfig"""]}
  
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase_     :     List[str]    		=						[
             """DINAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
             """DinatForImageClassification""",
             """DinatModel""",
             """DinatPreTrainedModel""",
             """DinatBackbone""",
         ]
  
  if TYPE_CHECKING:
         from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_dinat import (
                    DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                    DinatBackbone,
                    DinatForImageClassification,
                    DinatModel,
                    DinatPreTrainedModel,
                )
  
  else:
         import sys
  
         lowerCamelCase_     :     List[Any]    		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)