code: 
  
  
  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__ 	=				logging.get_logger(__name__)
  lowerCAmelCase__ 	=				{'vocab_file': 'spiece.model'}
  
  lowerCAmelCase__ 	=				{
      '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__ 	=				{
      'google/fnet-base': 5_12,
      'google/fnet-large': 5_12,
  }
  
  lowerCAmelCase__ 	=				'▁'
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       _a):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				VOCAB_FILES_NAMES
       __SCREAMING_SNAKE_CASE						    =				PRETRAINED_VOCAB_FILES_MAP
       __SCREAMING_SNAKE_CASE						    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __SCREAMING_SNAKE_CASE						    =				['input_ids', 'token_type_ids']
  
  
       def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase=False				,		__lowerCamelCase=True				,		__lowerCamelCase=True				,		__lowerCamelCase="<unk>"				,		__lowerCamelCase="[SEP]"				,		__lowerCamelCase="<pad>"				,		__lowerCamelCase="[CLS]"				,		__lowerCamelCase="[MASK]"				,		__lowerCamelCase = None				,		**__lowerCamelCase				,		)    ->       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   : str       					=				(
                  AddedToken(__lowerCamelCase				,		lstrip=__lowerCamelCase				,		rstrip=__lowerCamelCase				,		normalized=__lowerCamelCase)
                  if isinstance(__lowerCamelCase				,		__lowerCamelCase)
                  else mask_token
              )
  
              _A   : Optional[Any]       					=				{} if sp_model_kwargs is None else sp_model_kwargs
  
              super().__init__(
                  do_lower_case=__lowerCamelCase				,		remove_space=__lowerCamelCase				,		keep_accents=__lowerCamelCase				,		unk_token=__lowerCamelCase				,		sep_token=__lowerCamelCase				,		pad_token=__lowerCamelCase				,		cls_token=__lowerCamelCase				,		mask_token=__lowerCamelCase				,		sp_model_kwargs=self.sp_model_kwargs				,		**__lowerCamelCase				,		)
  
              _A   : str       					=				do_lower_case
              _A   : List[Any]       					=				remove_space
              _A   : Optional[Any]       					=				keep_accents
              _A   : Any       					=				vocab_file
  
              _A   : str       					=				spm.SentencePieceProcessor(**self.sp_model_kwargs)
              self.sp_model.Load(__lowerCamelCase)
  
  
       @property
       def 				_lowerCamelCase				(		self)    ->       Optional[int]:
              return len(self.sp_model)
  
  
       def 				_lowerCamelCase				(		self)    ->       List[Any]:
              _A   : List[str]       					=				{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:
              _A   : Optional[int]       					=				self.__dict__.copy()
              _A   : Any       					=				None
              return state
  
  
       def __setstate__(		self				,		__lowerCamelCase)    ->       str:
              _A   : Optional[Any]       					=				d
  
              # for backward compatibility
              if not hasattr(self				,		"sp_model_kwargs"):
                     _A   : str       					=				{}
  
              _A   : str       					=				spm.SentencePieceProcessor(**self.sp_model_kwargs)
              self.sp_model.Load(self.vocab_file)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Tuple:
              if self.remove_space:
                     _A   : Union[str, Any]       					=				" ".join(inputs.strip().split())
              else:
                     _A   : Optional[int]       					=				inputs
              _A   : Dict       					=				outputs.replace("``"				,		"\"").replace("''"				,		"\"")
  
              if not self.keep_accents:
                     _A   : str       					=				unicodedata.normalize("NFKD"				,		__lowerCamelCase)
                     _A   : List[Any]       					=				"".join([c for c in outputs if not unicodedata.combining(__lowerCamelCase)])
              if self.do_lower_case:
                     _A   : Union[str, Any]       					=				outputs.lower()
  
              return outputs
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       List[str]:
              _A   : List[str]       					=				self.preprocess_text(__lowerCamelCase)
              _A   : Optional[int]       					=				self.sp_model.encode(__lowerCamelCase				,		out_type=__lowerCamelCase)
              _A   : Union[str, Any]       					=				[]
              for piece in pieces:
                     if len(__lowerCamelCase) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
                            _A   : List[str]       					=				self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCamelCase				,		""))
                            if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                   if len(cur_pieces[0]) == 1:
                                          _A   : Optional[int]       					=				cur_pieces[1:]
                                   else:
                                          _A   : Optional[Any]       					=				cur_pieces[0][1:]
                            cur_pieces.append(piece[-1])
                            new_pieces.extend(__lowerCamelCase)
                     else:
                            new_pieces.append(__lowerCamelCase)
  
              return new_pieces
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Any:
              return self.sp_model.PieceToId(__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Any:
              return self.sp_model.IdToPiece(__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       int:
              _A   : int       					=				[]
              _A   : Dict       					=				""
              _A   : Optional[Any]       					=				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(__lowerCamelCase) + token
                            _A   : Tuple       					=				True
                            _A   : Dict       					=				[]
                     else:
                            current_sub_tokens.append(__lowerCamelCase)
                            _A   : str       					=				False
              out_string += self.sp_model.decode(__lowerCamelCase)
              return out_string.strip()
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = False				,		__lowerCamelCase = None				,		__lowerCamelCase = True				,		**__lowerCamelCase				,		)    ->       str:
              _A   : List[str]       					=				kwargs.pop("use_source_tokenizer"				,		__lowerCamelCase)
  
              _A   : Union[str, Any]       					=				self.convert_ids_to_tokens(__lowerCamelCase				,		skip_special_tokens=__lowerCamelCase)
  
              # 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   : int       					=				[]
              _A   : List[Any]       					=				[]
              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(__lowerCamelCase))
                                   _A   : Dict       					=				[]
                            sub_texts.append(__lowerCamelCase)
                     else:
                            current_sub_text.append(__lowerCamelCase)
              if current_sub_text:
                     sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase))
  
              # Mimic the behavior of the Rust tokenizer:
              # No space after <unk>
              if spaces_between_special_tokens:
                     _A   : int       					=				re.sub(r"(<unk>) "				,		r"\1"				,		" ".join(__lowerCamelCase))
              else:
                     _A   : Optional[Any]       					=				"".join(__lowerCamelCase)
  
              _A   : Dict       					=				(
                  clean_up_tokenization_spaces
                  if clean_up_tokenization_spaces is not None
                  else self.clean_up_tokenization_spaces
              )
              if clean_up_tokenization_spaces:
                     _A   : Optional[Any]       					=				self.clean_up_tokenization(__lowerCamelCase)
                     return clean_text
              else:
                     return text
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = None)    ->       List[int]:
              _A   : List[Any]       					=				[self.sep_token_id]
              _A   : List[str]       					=				[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				,		__lowerCamelCase				,		__lowerCamelCase = None				,		__lowerCamelCase = False)    ->       List[int]:
  
              if already_has_special_tokens:
                     return super().get_special_tokens_mask(
                         token_ids_a=__lowerCamelCase				,		token_ids_a=__lowerCamelCase				,		already_has_special_tokens=__lowerCamelCase)
  
              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				,		__lowerCamelCase				,		__lowerCamelCase = None)    ->       List[int]:
              _A   : Optional[Any]       					=				[self.sep_token_id]
              _A   : int       					=				[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				,		__lowerCamelCase				,		__lowerCamelCase = None)    ->       Tuple[str]:
              if not os.path.isdir(__lowerCamelCase):
                     logger.error(F"Vocabulary path ({save_directory}) should be a directory")
                     return
              _A   : List[str]       					=				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) and os.path.isfile(self.vocab_file):
                     copyfile(self.vocab_file				,		__lowerCamelCase)
              elif not os.path.isfile(self.vocab_file):
                     with open(__lowerCamelCase				,		"wb") as fi:
                            _A   : Dict       					=				self.sp_model.serialized_model_proto()
                            fi.write(__lowerCamelCase)
  
              return (out_vocab_file,)
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowerCAmelCase__   	=				{
      'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
  }
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				['SegformerFeatureExtractor']
      lowerCAmelCase__   	=				['SegformerImageProcessor']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'SegformerDecodeHead',
          'SegformerForImageClassification',
          'SegformerForSemanticSegmentation',
          'SegformerLayer',
          'SegformerModel',
          'SegformerPreTrainedModel',
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSegformerDecodeHead',
          'TFSegformerForImageClassification',
          'TFSegformerForSemanticSegmentation',
          'TFSegformerModel',
          'TFSegformerPreTrainedModel',
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig
  
      try:
          if not is_vision_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .feature_extraction_segformer import SegformerFeatureExtractor
          from .image_processing_segformer import SegformerImageProcessor
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_segformer import (
              SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              SegformerDecodeHead,
              SegformerForImageClassification,
              SegformerForSemanticSegmentation,
              SegformerLayer,
              SegformerModel,
              SegformerPreTrainedModel,
          )
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_segformer import (
              TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFSegformerDecodeHead,
              TFSegformerForImageClassification,
              TFSegformerForSemanticSegmentation,
              TFSegformerModel,
              TFSegformerPreTrainedModel,
          )
  
  else:
      import sys
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  