


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case				=			logging.get_logger(__name__)

_snake_case				=			{
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}




class  A_ (			a_  ):







 """simple docstring"""





 _UpperCamelCase					:			int     	=       '''vilt'''
 def __init__(      self       :			str							,     _a       :			Optional[Any]=3_0_5_2_2							,     _a       :			Any=2							,     _a       :			Tuple=2							,     _a       :			Optional[Any]=4_0							,     _a       :			Any=7_6_8							,     _a       :			List[str]=1_2							,     _a       :			List[str]=1_2							,     _a       :			List[Any]=3_0_7_2							,     _a       :			str="gelu"							,     _a       :			str=0.0							,     _a       :			Any=0.0							,     _a       :			Optional[Any]=0.0_2							,     _a       :			Optional[int]=1E-12							,     _a       :			int=3_8_4							,     _a       :			List[str]=3_2							,     _a       :			Dict=3							,     _a       :			int=True							,     _a       :			Optional[Any]=-1							,     _a       :			Optional[int]=False							,     _a       :			List[Any]=-1							,     **_a       :			Tuple							,     )     ->      int:
      super().__init__(tie_word_embeddings=_a							,     **_a					)

      UpperCAmelCase    :					Optional[Any]			=      vocab_size
      UpperCAmelCase    :					Optional[Any]			=      type_vocab_size
      UpperCAmelCase    :					List[str]			=      modality_type_vocab_size
      UpperCAmelCase    :					Tuple			=      max_position_embeddings

      UpperCAmelCase    :					List[Any]			=      hidden_size
      UpperCAmelCase    :					Optional[int]			=      num_hidden_layers
      UpperCAmelCase    :					Optional[int]			=      num_attention_heads
      UpperCAmelCase    :					str			=      intermediate_size
      UpperCAmelCase    :					Dict			=      hidden_act
      UpperCAmelCase    :					str			=      hidden_dropout_prob
      UpperCAmelCase    :					Union[str, Any]			=      attention_probs_dropout_prob
      UpperCAmelCase    :					Any			=      initializer_range
      UpperCAmelCase    :					List[Any]			=      layer_norm_eps

      UpperCAmelCase    :					Dict			=      image_size
      UpperCAmelCase    :					Dict			=      patch_size
      UpperCAmelCase    :					List[str]			=      num_channels
      UpperCAmelCase    :					str			=      qkv_bias
      UpperCAmelCase    :					Any			=      max_image_length
      UpperCAmelCase    :					List[str]			=      num_images







from __future__ import annotations







def 	A (		A__		,		A__		,		A__		,		A__			):

     # Base Case
     if counter == 0 or capacity == 0:
          return 0

     # If weight of the nth item is more than Knapsack of capacity,
     #   then this item cannot be included in the optimal solution,
     # else return the maximum of two cases:
     #   (1) nth item included
     #   (2) not included
     if weights[counter - 1] > capacity:
          return knapsack(A__		,		A__		,		A__		,		counter - 1			)
     else:
          UpperCAmelCase    :					List[str]			=      capacity - weights[counter - 1]
          UpperCAmelCase    :					Tuple			=      values[counter - 1] + knapsack(
              A__		,		A__		,		A__		,		counter - 1			)
          UpperCAmelCase    :					Optional[int]			=      knapsack(A__		,		A__		,		A__		,		counter - 1			)
          return max(A__		,		A__			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



