



'''simple docstring'''





from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowercase_		       =  {
    "vocab_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
    },
    "merges_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
    },
}

lowercase_		       =  {"allegro/herbert-base-cased": 514}
lowercase_		       =  {}






class __A			(  __lowerCamelCase						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       VOCAB_FILES_NAMES
 __lowerCamelCase    :       Dict          =       PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase    :       int          =       PRETRAINED_INIT_CONFIGURATION
 __lowerCamelCase    :       Any          =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase    :       str          =       HerbertTokenizer



 def __init__(self ,   A=None ,   A=None ,   A=None ,   A="<s>" ,   A="<unk>" ,   A="<pad>" ,   A="<mask>" ,   A="</s>" ,   **A ,   )    ->     List[Any]:


      """simple docstring"""



      super().__init__(
          A ,   A ,   tokenizer_file=A ,   cls_token=A ,   unk_token=A ,   pad_token=A ,   mask_token=A ,   sep_token=A ,   **A ,   )



 def 					a__  (self ,   A ,   A = None       )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  [self.cls_token_id]
      _a      =  [self.sep_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       )    ->     Optional[Any]:


      """simple docstring"""



      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 None:
           return [1] + ([0] * len(A       )) + [1]
      return [1] + ([0] * len(A       )) + [1] + ([0] * len(A       )) + [1]



 def 					a__  (self ,   A ,   A = None       )    ->     Optional[int]:


      """simple docstring"""



      _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 ,   A ,   A = None       )    ->     Tuple:


      """simple docstring"""



      _a      =  self._tokenizer.model.save(A ,   name=A       )
      return tuple(A       )










import warnings

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


__UpperCAmelCase       :   Tuple				       =							logging.get_logger(__name__)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Union[str, Any]							,				*A :    Union[str, Any]							,				**A :    Optional[int]		):
         warnings.warn(
             """The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
             """ Please use PerceiverImageProcessor instead."""							,				A							,				)
         super().__init__(*A							,				**A		)






