





from typing import List, Optional, Tuple

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


SCREAMING_SNAKE_CASE__			      =						logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE__			      =						{
    """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"""
    },
}

SCREAMING_SNAKE_CASE__			      =						{"""allegro/herbert-base-cased""": 5_1_4}
SCREAMING_SNAKE_CASE__			      =						{}







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						List[str]     		=				VOCAB_FILES_NAMES
   _UpperCamelCase :						Tuple     		=				PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase :						Optional[int]     		=				PRETRAINED_INIT_CONFIGURATION
   _UpperCamelCase :						Tuple     		=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase :						Union[str, Any]     		=				HerbertTokenizer





   def __init__( self					,			snake_case=None					,			snake_case=None					,			snake_case=None					,			snake_case="<s>"					,			snake_case="<unk>"					,			snake_case="<pad>"					,			snake_case="<mask>"					,			snake_case="</s>"					,			**snake_case					,			)				->  str:
      """simple docstring"""





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





   def 	_snake_case ( self					,			snake_case					,			snake_case = None      )				->  List[int]:
      """simple docstring"""





      a__	:						Optional[int]      		=  [self.cls_token_id]
      a__	:						Optional[Any]      		=  [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 	_snake_case ( self					,			snake_case					,			snake_case = None					,			snake_case = False      )				->  List[int]:
      """simple docstring"""





      if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=snake_case					,			token_ids_a=snake_case					,			already_has_special_tokens=snake_case      )

      if token_ids_a is None:
         return [1] + ([0] * len(snake_case      )) + [1]
      return [1] + ([0] * len(snake_case      )) + [1] + ([0] * len(snake_case      )) + [1]





   def 	_snake_case ( self					,			snake_case					,			snake_case = None      )				->  List[int]:
      """simple docstring"""





      a__	:						Optional[int]      		=  [self.sep_token_id]
      a__	:						Any      		=  [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 	_snake_case ( self					,			snake_case					,			snake_case = None      )				->  Tuple[str]:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  self._tokenizer.model.save(snake_case					,			name=snake_case      )
      return tuple(snake_case      )








from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__       :			List[str]			      =						{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						[
      """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """VanForImageClassification""",
      """VanModel""",
      """VanPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_van import (
        VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
        VanForImageClassification,
        VanModel,
        VanPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE__       :			List[str]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)

