






"""simple docstring"""




def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  global f  # a global dp table for knapsack
  if f[i][j] < 0:
    if j < wt[i - 1]:
      _a  :       Optional[Any]             =	mf_knapsack(i - 1     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)
    else:
      _a  :       Any             =	max(
          mf_knapsack(i - 1     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)     ,   mf_knapsack(i - 1     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   j - wt[i - 1]				) + val[i - 1]     ,   )
    _a  :       int             =	val
  return f[i][j]

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       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_:
        _a  :       int             =	max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]     ,   dp[i - 1][w_]				)
      else:
        _a  :       Any             =	dp[i - 1][w_]

  return dp[n][w_], dp

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  if not (isinstance(UpperCamelCase__     ,   (list, tuple)				) and isinstance(UpperCamelCase__     ,   (list, tuple)				)):
    raise ValueError(
        """Both the weights and values vectors must be either lists or tuples"""				)

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

  _a      ,  _a  :       List[Any]             =	knapsack(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)
  _a  :       set             =	set()
  _construct_solution(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)

  return optimal_val, example_optional_set

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  # 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(UpperCamelCase__     ,   UpperCamelCase__     ,   i - 1     ,   UpperCamelCase__     ,   UpperCamelCase__				)
    else:
      optimal_set.add(UpperCamelCase__				)
      _construct_solution(UpperCamelCase__     ,   UpperCamelCase__     ,   i - 1     ,   j - wt[i - 1]     ,   UpperCamelCase__				)


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"""




import warnings

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


_snake_case          =   logging.get_logger(__name__)

_snake_case          =   {
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       int			=					'''mvp'''
 UpperCamelCase					:       Union[str, Any]			=					['''past_key_values''']
 UpperCamelCase					:       Any			=					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


 def __init__(     self		:						List[str]							,						UpperCAmelCase__		:						List[str]=50267							,						UpperCAmelCase__		:						Optional[Any]=1024							,						UpperCAmelCase__		:						Tuple=12							,						UpperCAmelCase__		:						Optional[Any]=4096							,						UpperCAmelCase__		:						int=16							,						UpperCAmelCase__		:						Tuple=12							,						UpperCAmelCase__		:						int=4096							,						UpperCAmelCase__		:						List[Any]=16							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Tuple="gelu"							,						UpperCAmelCase__		:						Union[str, Any]=1024							,						UpperCAmelCase__		:						List[str]=0.1							,						UpperCAmelCase__		:						Any=0.0							,						UpperCAmelCase__		:						Dict=0.0							,						UpperCAmelCase__		:						Tuple=0.0_2							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Optional[Any]=False							,						UpperCAmelCase__		:						int=True							,						UpperCAmelCase__		:						Tuple=1							,						UpperCAmelCase__		:						Dict=0							,						UpperCAmelCase__		:						Union[str, Any]=2							,						UpperCAmelCase__		:						Optional[int]=True							,						UpperCAmelCase__		:						Tuple=2							,						UpperCAmelCase__		:						Any=2							,						UpperCAmelCase__		:						Optional[Any]=False							,						UpperCAmelCase__		:						Dict=100							,						UpperCAmelCase__		:						Union[str, Any]=800							,						**UpperCAmelCase__		:						Dict							,						)					->       List[Any]:
   _a  :       Any             =	vocab_size
   _a  :       Any             =	max_position_embeddings
   _a  :       Union[str, Any]             =	d_model
   _a  :       List[str]             =	encoder_ffn_dim
   _a  :       List[Any]             =	encoder_layers
   _a  :       Dict             =	encoder_attention_heads
   _a  :       Tuple             =	decoder_ffn_dim
   _a  :       List[Any]             =	decoder_layers
   _a  :       Optional[Any]             =	decoder_attention_heads
   _a  :       Optional[Any]             =	dropout
   _a  :       str             =	attention_dropout
   _a  :       Dict             =	activation_dropout
   _a  :       Any             =	activation_function
   _a  :       Tuple             =	init_std
   _a  :       Dict             =	encoder_layerdrop
   _a  :       Optional[int]             =	decoder_layerdrop
   _a  :       Optional[Any]             =	classifier_dropout
   _a  :       List[Any]             =	use_cache
   _a  :       Dict             =	encoder_layers
   _a  :       str             =	scale_embedding  # scale factor will be sqrt(d_model) if True
   _a  :       int             =	use_prompt
   _a  :       Dict             =	prompt_length
   _a  :       Dict             =	prompt_mid_dim

   super().__init__(
       pad_token_id=UpperCAmelCase__							,						bos_token_id=UpperCAmelCase__							,						eos_token_id=UpperCAmelCase__							,						is_encoder_decoder=UpperCAmelCase__							,						decoder_start_token_id=UpperCAmelCase__							,						forced_eos_token_id=UpperCAmelCase__							,						**UpperCAmelCase__							,						)

   if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""							,						UpperCAmelCase__							):
     _a  :       List[str]             =	self.bos_token_id
     warnings.warn(
         f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
         """The config can simply be saved and uploaded again to be fixed."""							)

