




'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


lowercase__			=	{
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase__			=	[
           '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''MobileViTV2ForImageClassification''',
           '''MobileViTV2ForSemanticSegmentation''',
           '''MobileViTV2Model''',
           '''MobileViTV2PreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_mobilevitva import (
           MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
           MobileViTVaConfig,
           MobileViTVaOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mobilevitva import (
                  MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MobileViTVaForImageClassification,
                  MobileViTVaForSemanticSegmentation,
                  MobileViTVaModel,
                  MobileViTVaPreTrainedModel,
              )

else:
       import sys

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







'''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


lowercase__			=	logging.get_logger(__name__)
lowercase__			=	{'''vocab_file''': '''spiece.model'''}

lowercase__			=	{
    '''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''',
    },
}

lowercase__			=	{
    '''google/fnet-base''': 5_1_2,
    '''google/fnet-large''': 5_1_2,
}

lowercase__			=	'''▁'''






class 						lowercase     (   UpperCAmelCase_):
      __lowercase 		=  VOCAB_FILES_NAMES
      __lowercase 		=  PRETRAINED_VOCAB_FILES_MAP
      __lowercase 		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __lowercase 		=  ['''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.
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= (
             AddedToken(a__ ,  lstrip=a__ ,  rstrip=a__ ,  normalized=a__					)
             if isinstance(a__ ,  a__					)
             else mask_token
         )

         SCREAMING_SNAKE_CASE__			:				Any       						= {} 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__ ,  )

         SCREAMING_SNAKE_CASE__			:				Dict       						= do_lower_case
         SCREAMING_SNAKE_CASE__			:				Tuple       						= remove_space
         SCREAMING_SNAKE_CASE__			:				List[str]       						= keep_accents
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= vocab_file

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
         self.sp_model.Load(a__					)






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






      def    A__(self					)       ->   Any:
         SCREAMING_SNAKE_CASE__			:				int       						= {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					)       ->   Any:
         SCREAMING_SNAKE_CASE__			:				List[str]       						= self.__dict__.copy()
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= None
         return state






      def __setstate__(self ,  a__					)       ->   int:
         SCREAMING_SNAKE_CASE__			:				Any       						= d

         # for backward compatibility
         if not hasattr(self ,  '''sp_model_kwargs'''					):
            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= {}

         SCREAMING_SNAKE_CASE__			:				int       						= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
         self.sp_model.Load(self.vocab_file					)






      def    A__(self ,  a__					)       ->   str:
         if self.remove_space:
            SCREAMING_SNAKE_CASE__			:				List[str]       						= ''' '''.join(inputs.strip().split()					)
         else:
            SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= inputs
         SCREAMING_SNAKE_CASE__			:				Dict       						= outputs.replace('''``''' ,  '''"'''					).replace('''\'\'''' ,  '''"'''					)

         if not self.keep_accents:
            SCREAMING_SNAKE_CASE__			:				Dict       						= unicodedata.normalize('''NFKD''' ,  a__					)
            SCREAMING_SNAKE_CASE__			:				List[Any]       						= ''''''.join([c for c in outputs if not unicodedata.combining(a__					)]					)
         if self.do_lower_case:
            SCREAMING_SNAKE_CASE__			:				str       						= outputs.lower()

         return outputs






      def    A__(self ,  a__					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__			:				Dict       						= self.preprocess_text(a__					)
         SCREAMING_SNAKE_CASE__			:				List[str]       						= self.sp_model.encode(a__ ,  out_type=a__					)
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= []
         for piece in pieces:
            if len(a__					) > 1 and piece[-1] == str(''','''					) and piece[-2].isdigit():
               SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= 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:
                     SCREAMING_SNAKE_CASE__			:				List[str]       						= cur_pieces[1:]
                  else:
                     SCREAMING_SNAKE_CASE__			:				str       						= cur_pieces[0][1:]
               cur_pieces.append(piece[-1]					)
               new_pieces.extend(a__					)
            else:
               new_pieces.append(a__					)

         return new_pieces






      def    A__(self ,  a__					)       ->   str:
         return self.sp_model.PieceToId(a__					)






      def    A__(self ,  a__					)       ->   Optional[int]:
         return self.sp_model.IdToPiece(a__					)






      def    A__(self ,  a__					)       ->   Optional[Any]:
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= []
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= ''''''
         SCREAMING_SNAKE_CASE__			:				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(a__					) + token
               SCREAMING_SNAKE_CASE__			:				List[Any]       						= True
               SCREAMING_SNAKE_CASE__			:				int       						= []
            else:
               current_sub_tokens.append(a__					)
               SCREAMING_SNAKE_CASE__			:				Dict       						= False
         out_string += self.sp_model.decode(a__					)
         return out_string.strip()






      def    A__(self ,  a__ ,  a__ = False ,  a__ = None ,  a__ = True ,  **a__ ,  )       ->   str:
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= kwargs.pop('''use_source_tokenizer''' ,  a__					)

         SCREAMING_SNAKE_CASE__			:				int       						= 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
         SCREAMING_SNAKE_CASE__			:				Dict       						= []
         SCREAMING_SNAKE_CASE__			:				List[str]       						= []
         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__					)					)
                  SCREAMING_SNAKE_CASE__			:				List[str]       						= []
               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:
            SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= re.sub(R'''(<unk>) ''' ,  R'''\1''' ,  ''' '''.join(a__					)					)
         else:
            SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= ''''''.join(a__					)

         SCREAMING_SNAKE_CASE__			:				int       						= (
             clean_up_tokenization_spaces
             if clean_up_tokenization_spaces is not None
             else self.clean_up_tokenization_spaces
         )
         if clean_up_tokenization_spaces:
            SCREAMING_SNAKE_CASE__			:				str       						= self.clean_up_tokenization(a__					)
            return clean_text
         else:
            return text






      def    A__(self ,  a__ ,  a__ = None					)       ->   List[int]:
         SCREAMING_SNAKE_CASE__			:				str       						= [self.sep_token_id]
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= [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    A__(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    A__(self ,  a__ ,  a__ = None					)       ->   List[int]:
         SCREAMING_SNAKE_CASE__			:				List[str]       						= [self.sep_token_id]
         SCREAMING_SNAKE_CASE__			:				Tuple       						= [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    A__(self ,  a__ ,  a__ = None					)       ->   Tuple[str]:
         if not os.path.isdir(a__					):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
            return
         SCREAMING_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:
               SCREAMING_SNAKE_CASE__			:				int       						= self.sp_model.serialized_model_proto()
               fi.write(a__					)

         return (out_vocab_file,)

