






def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case       ):
     global f  # a global dp table for knapsack
     if f[i][j] < 0:
          if j < wt[i - 1]:
               __lowerCAmelCase								=					mf_knapsack(i - 1					, __snake_case					, __snake_case					, __snake_case       )
          else:
               __lowerCAmelCase								=					max(
                   mf_knapsack(i - 1					, __snake_case					, __snake_case					, __snake_case       )					, mf_knapsack(i - 1					, __snake_case					, __snake_case					, j - wt[i - 1]       ) + val[i - 1]					, )
          __lowerCAmelCase								=					val
     return f[i][j]





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					[[0] * (w + 1) for _ in range(n + 1       )]

     for i in range(1					, n + 1       ):
          for w_ in range(1					, w + 1       ):
               if wt[i - 1] <= w_:
                    __lowerCAmelCase								=					max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]					, dp[i - 1][w_]       )
               else:
                    __lowerCAmelCase								=					dp[i - 1][w_]

     return dp[n][w_], dp





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     if not (isinstance(__snake_case					, (list, tuple)       ) and isinstance(__snake_case					, (list, tuple)       )):
          raise ValueError(
              "Both the weights and values vectors must be either lists or tuples"       )

     __lowerCAmelCase								=					len(__snake_case       )
     if num_items != len(__snake_case       ):
          __lowerCAmelCase								=					(
              "The number of weights must be the same as the number of values.\n"
              F"""But got {num_items} weights and {len(__snake_case       )} values"""
          )
          raise ValueError(__snake_case       )
     for i in range(__snake_case       ):
          if not isinstance(wt[i]					, __snake_case       ):
               __lowerCAmelCase								=					(
                   "All weights must be integers but got weight of "
                   F"""type {type(wt[i]       )} at index {i}"""
               )
               raise TypeError(__snake_case       )

     __lowerCAmelCase			, __lowerCAmelCase								=					knapsack(__snake_case					, __snake_case					, __snake_case					, __snake_case       )
     __lowerCAmelCase								=					set()
     _construct_solution(__snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case       )

     return optimal_val, example_optional_set





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case       ):
     # for the current item i at a maximum weight j to be part of an optimal subset,
     # the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
     # where i - 1 means considering only the previous items at the given maximum weight
     if i > 0 and j > 0:
          if dp[i - 1][j] == dp[i][j]:
               _construct_solution(__snake_case					, __snake_case					, i - 1					, __snake_case					, __snake_case       )
          else:
               optimal_set.add(__snake_case       )
               _construct_solution(__snake_case					, __snake_case					, i - 1					, j - wt[i - 1]					, __snake_case       )


if __name__ == "__main__":
 lowerCamelCase			:					Optional[Any]         =	[3, 2, 4, 4]
 lowerCamelCase			:					Any         =	[4, 3, 2, 3]
 lowerCamelCase			:					Union[str, Any]         =	4
 lowerCamelCase			:					Optional[int]         =	6
 lowerCamelCase			:					Any         =	[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
 lowerCamelCase      ,					lowerCamelCase			:					Any         =	knapsack(w, wt, val, n)
 print(optimal_solution)
 print(mf_knapsack(n, wt, val, w))  # switched the n and w

 # testing the dynamic programming problem with example
 # the optimal subset for the above example are items 3 and 4
 lowerCamelCase      ,					lowerCamelCase			:					List[str]         =	knapsack_with_example_solution(w, wt, val)
 assert optimal_solution == 8
 assert optimal_subset == {3, 4}
 print('''optimal_value = ''', optimal_solution)
 print('''An optimal subset corresponding to the optimal value''', optimal_subset)














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


lowerCamelCase			:					Any         =	logging.get_logger(__name__)

lowerCamelCase			:					Dict         =	{
    '''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
    '''microsoft/markuplm-large''': '''https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json''',
}



class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""markuplm"""


      def __init__(	self					,						__UpperCamelCase=3_0_5_2_2					,						__UpperCamelCase=7_6_8					,						__UpperCamelCase=1_2					,						__UpperCamelCase=1_2					,						__UpperCamelCase=3_0_7_2					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.1					,						__UpperCamelCase=5_1_2					,						__UpperCamelCase=2					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=1e-12					,						__UpperCamelCase=0					,						__UpperCamelCase=0					,						__UpperCamelCase=2					,						__UpperCamelCase=2_5_6					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=2_1_6					,						__UpperCamelCase=1_0_0_1					,						__UpperCamelCase=3_2					,						__UpperCamelCase=5_0					,						__UpperCamelCase="absolute"					,						__UpperCamelCase=True					,						__UpperCamelCase=None					,						**__UpperCamelCase					,						)->	Dict:
           super().__init__(
               pad_token_id=__UpperCamelCase					,						bos_token_id=__UpperCamelCase					,						eos_token_id=__UpperCamelCase					,						**__UpperCamelCase					,						)
           __lowerCAmelCase								=					vocab_size
           __lowerCAmelCase								=					hidden_size
           __lowerCAmelCase								=					num_hidden_layers
           __lowerCAmelCase								=					num_attention_heads
           __lowerCAmelCase								=					hidden_act
           __lowerCAmelCase								=					intermediate_size
           __lowerCAmelCase								=					hidden_dropout_prob
           __lowerCAmelCase								=					attention_probs_dropout_prob
           __lowerCAmelCase								=					max_position_embeddings
           __lowerCAmelCase								=					type_vocab_size
           __lowerCAmelCase								=					initializer_range
           __lowerCAmelCase								=					layer_norm_eps
           __lowerCAmelCase								=					position_embedding_type
           __lowerCAmelCase								=					use_cache
           __lowerCAmelCase								=					classifier_dropout
           # additional properties
           __lowerCAmelCase								=					max_depth
           __lowerCAmelCase								=					max_xpath_tag_unit_embeddings
           __lowerCAmelCase								=					max_xpath_subs_unit_embeddings
           __lowerCAmelCase								=					tag_pad_id
           __lowerCAmelCase								=					subs_pad_id
           __lowerCAmelCase								=					xpath_unit_hidden_size






