import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_fnet import FNetTokenizer
else:
      UpperCAmelCase_								=							None

UpperCAmelCase_								=							logging.get_logger(__name__)
UpperCAmelCase_								=							{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_								=							{
    '''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''',
    },
    '''tokenizer_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json''',
    },
}

UpperCAmelCase_								=							{
    '''google/fnet-base''': 512,
    '''google/fnet-large''': 512,
}

UpperCAmelCase_								=							'''▁'''





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[Any]		=VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE     :     List[Any]		=PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE     :     int		=["""input_ids""", """token_type_ids"""]
     _SCREAMING_SNAKE_CASE     :     str		=FNetTokenizer



     def __init__(					self				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      lowerCAmelCase__=False				,      lowerCAmelCase__=True				,      lowerCAmelCase__=True				,      lowerCAmelCase__="<unk>"				,      lowerCAmelCase__="[SEP]"				,      lowerCAmelCase__="<pad>"				,      lowerCAmelCase__="[CLS]"				,      lowerCAmelCase__="[MASK]"				,      **lowerCAmelCase__				,      ):
            # 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(lowerCAmelCase__				,      lstrip=lowerCAmelCase__				,      rstrip=lowerCAmelCase__				,      normalized=lowerCAmelCase__       )
                if isinstance(lowerCAmelCase__				,      lowerCAmelCase__       )
                else mask_token
            )

            super().__init__(
                lowerCAmelCase__				,      tokenizer_file=lowerCAmelCase__				,      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__				,      **lowerCAmelCase__				,      )

            _A=	do_lower_case
            _A=	remove_space
            _A=	keep_accents
            _A=	vocab_file
            _A=	False if not self.vocab_file else True



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__ = None       ):
            _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 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__ = None       ):
            _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 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__ = None       ):
            if not os.path.isdir(lowerCAmelCase__       ):
                   logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
                   return
            _A=	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__       ):
                   copyfile(self.vocab_file				,      lowerCAmelCase__       )

            return (out_vocab_file,)
import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase_								=							logging.get_logger(__name__)





class      lowerCAmelCase						(					_a			):



     def __init__(					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            warnings.warn(
                'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
                ' Please use PerceiverImageProcessor instead.'				,      lowerCAmelCase__				,      )
            super().__init__(*lowerCAmelCase__				,      **lowerCAmelCase__       )