




from typing import List, Optional, Tuple

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


lowerCamelCase	:	Any				=   logging.get_logger(__name__)

lowerCamelCase	:	str				=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase	:	Union[str, Any]				=   {
    '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'
    },
}

lowerCamelCase	:	Dict				=   {'allegro/herbert-base-cased': 5_1_4}
lowerCamelCase	:	str				=   {}




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      _snake_case												=						VOCAB_FILES_NAMES
      _snake_case												=						PRETRAINED_VOCAB_FILES_MAP
      _snake_case												=						PRETRAINED_INIT_CONFIGURATION
      _snake_case												=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case												=						HerbertTokenizer

      def __init__(				self    ,				A=None    ,				A=None    ,				A=None    ,				A="<s>"    ,				A="<unk>"    ,				A="<pad>"    ,				A="<mask>"    ,				A="</s>"    ,				**A    ,				)		->    List[str]:
         super().__init__(
             A    ,				A    ,				tokenizer_file=A    ,				cls_token=A    ,				unk_token=A    ,				pad_token=A    ,				mask_token=A    ,				sep_token=A    ,				**A    ,				)

      def      UpperCAmelCase				(				self    ,				A    ,				A = None     )		->    List[int]:
         snake_case				:    Optional[Any]           =	[self.cls_token_id]
         snake_case				:    str           =	[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      UpperCAmelCase				(				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 None:
            return [1] + ([0] * len(A     )) + [1]
         return [1] + ([0] * len(A     )) + [1] + ([0] * len(A     )) + [1]

      def      UpperCAmelCase				(				self    ,				A    ,				A = None     )		->    List[int]:
         snake_case				:    Optional[Any]           =	[self.sep_token_id]
         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      UpperCAmelCase				(				self    ,				A    ,				A = None     )		->    Tuple[str]:
         snake_case				:    Dict           =	self._tokenizer.model.save(A    ,				name=A     )
         return tuple(A     )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase	:	List[str]				=   {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Optional[Any]				=   [
           'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
           'Swinv2ForImageClassification',
           'Swinv2ForMaskedImageModeling',
           'Swinv2Model',
           'Swinv2PreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_swinva import (
                  SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SwinvaForImageClassification,
                  SwinvaForMaskedImageModeling,
                  SwinvaModel,
                  SwinvaPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase	:	Optional[int]				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





