

'''simple docstring'''
def  _A		(     snake_case		,							snake_case		,							snake_case		,							snake_case					)      ->     Optional[Any]:
   global f  # a global dp table for knapsack
   if f[i][j] < 0:
      if j < wt[i - 1]:
         _lowercase      :				Optional[Any]      					=       mf_knapsack(i - 1		,							snake_case		,							snake_case		,							snake_case					)
      else:
         _lowercase      :				Optional[int]      					=       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]		,							)
      _lowercase      :				Optional[Any]      					=       val
   return f[i][j]







def  _A		(     snake_case		,							snake_case		,							snake_case		,							snake_case					)      ->     List[Any]:
   _lowercase      :				Optional[int]      					=       [[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_:
            _lowercase      :				str      					=       max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]		,							dp[i - 1][w_]					)
         else:
            _lowercase      :				Tuple      					=       dp[i - 1][w_]

   return dp[n][w_], dp







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     Dict:
   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"					)

   _lowercase      :				List[str]      					=       len(snake_case					)
   if num_items != len(snake_case					):
      _lowercase      :				Optional[int]      					=       (
          "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					):
         _lowercase      :				Optional[Any]      					=       (
             "All weights must be integers but got weight of "
             F'''type {type(wt[i]					)} at index {i}'''
         )
         raise TypeError(snake_case					)

   _lowercase					,				_lowercase      :				int      					=       knapsack(snake_case		,							snake_case		,							snake_case		,							snake_case					)
   _lowercase      :				set      					=       set()
   _construct_solution(snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case					)

   return optimal_val, example_optional_set







def  _A		(     snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case					)      ->     List[Any]:
   # 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__":
    _snake_case         =  [3, 2, 4, 4]
    _snake_case         =  [4, 3, 2, 3]
    _snake_case         =  4
    _snake_case         =  6
    _snake_case         =  [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
    _snake_case  ,     _snake_case         =  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
    _snake_case  ,     _snake_case         =  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)









'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case         =  logging.get_logger(__name__)

_snake_case         =  {
    'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Any     	=							'roc_bert'


   def __init__(			self      ,    _UpperCamelCase=30522      ,    _UpperCamelCase=768      ,    _UpperCamelCase=12      ,    _UpperCamelCase=12      ,    _UpperCamelCase=3072      ,    _UpperCamelCase="gelu"      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=512      ,    _UpperCamelCase=2      ,    _UpperCamelCase=0.0_2      ,    _UpperCamelCase=1E-1_2      ,    _UpperCamelCase=True      ,    _UpperCamelCase=0      ,    _UpperCamelCase="absolute"      ,    _UpperCamelCase=None      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=768      ,    _UpperCamelCase=910      ,    _UpperCamelCase=512      ,    _UpperCamelCase=24858      ,    _UpperCamelCase=True      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				str      					=       vocab_size
      _lowercase      :				List[str]      					=       max_position_embeddings
      _lowercase      :				List[Any]      					=       hidden_size
      _lowercase      :				Dict      					=       num_hidden_layers
      _lowercase      :				str      					=       num_attention_heads
      _lowercase      :				int      					=       intermediate_size
      _lowercase      :				Optional[Any]      					=       hidden_act
      _lowercase      :				Union[str, Any]      					=       hidden_dropout_prob
      _lowercase      :				Dict      					=       attention_probs_dropout_prob
      _lowercase      :				Dict      					=       initializer_range
      _lowercase      :				List[Any]      					=       type_vocab_size
      _lowercase      :				Tuple      					=       layer_norm_eps
      _lowercase      :				Optional[int]      					=       use_cache
      _lowercase      :				Tuple      					=       enable_pronunciation
      _lowercase      :				Optional[int]      					=       enable_shape
      _lowercase      :				int      					=       pronunciation_embed_dim
      _lowercase      :				List[str]      					=       pronunciation_vocab_size
      _lowercase      :				int      					=       shape_embed_dim
      _lowercase      :				str      					=       shape_vocab_size
      _lowercase      :				str      					=       concat_input
      _lowercase      :				Dict      					=       position_embedding_type
      _lowercase      :				Optional[Any]      					=       classifier_dropout
      super().__init__(pad_token_id=_UpperCamelCase      ,    **_UpperCamelCase  )






