
def  SCREAMING_SNAKE_CASE_		(    __A				:    Any						,					__A				:    Any						,					__A				:    Optional[int]						,					__A				:    Dict       )			->	Dict:

       """simple docstring"""

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







def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    int						,					__A				:    Optional[Any]						,					__A				:    Union[str, Any]       )			->	Dict:

       """simple docstring"""

       a_      :	Optional[Any]							=						[[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_      :	List[Any]							=						dp[i - 1][w_]

       return dp[n][w_], dp







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    Tuple						,					__A				:    Optional[int]       )			->	List[Any]:

       """simple docstring"""

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

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

       a_      :	Any							=						knapsack(__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase       )
       a_      :	set							=						set()
       _construct_solution(__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase						,					__lowerCAmelCase       )

       return optimal_val, example_optional_set







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    int						,					__A				:    Dict						,					__A				:    Tuple						,					__A				:    Optional[int]       )			->	int:

       """simple docstring"""

       if i > 0 and j > 0:
              if dp[i - 1][j] == dp[i][j]:
                     _construct_solution(__lowerCAmelCase						,					__lowerCAmelCase						,					i - 1						,					__lowerCAmelCase						,					__lowerCAmelCase       )
              else:
                     optimal_set.add(__lowerCAmelCase       )
                     _construct_solution(__lowerCAmelCase						,					__lowerCAmelCase						,					i - 1						,					j - wt[i - 1]						,					__lowerCAmelCase       )


if __name__ == "__main__":
   UpperCAmelCase_   :				List[Any]						      = [3, 2, 4, 4]
   UpperCAmelCase_   :				int						      = [4, 3, 2, 3]
   UpperCAmelCase_   :				List[str]						      = 4
   UpperCAmelCase_   :				List[str]						      = 6
   UpperCAmelCase_   :				Dict						      = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
   UpperCAmelCase_   :				Optional[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
   UpperCAmelCase_   :				Optional[int]						      = 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 typing import List, Optional

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


UpperCAmelCase_   :				Dict						      = logging.get_logger(__name__)

UpperCAmelCase_   :				int						      = {
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Union[str, Any]       =		'''autoformer'''
      snake_case__       :  Any       =		{
          '''hidden_size''': '''d_model''',
          '''num_attention_heads''': '''encoder_attention_heads''',
          '''num_hidden_layers''': '''encoder_layers''',
      }
      def __init__( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Optional[int] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[int] = None  ,							SCREAMING_SNAKE_CASE__       : str = "student_t"  ,							SCREAMING_SNAKE_CASE__       : str = "nll"  ,							SCREAMING_SNAKE_CASE__       : int = 1  ,							SCREAMING_SNAKE_CASE__       : List[int] = [1, 2, 3, 4, 5, 6, 7]  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							SCREAMING_SNAKE_CASE__       : int = 0  ,							SCREAMING_SNAKE_CASE__       : int = 0  ,							SCREAMING_SNAKE_CASE__       : int = 0  ,							SCREAMING_SNAKE_CASE__       : int = 0  ,							SCREAMING_SNAKE_CASE__       : Optional[List[int]] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[List[int]] = None  ,							SCREAMING_SNAKE_CASE__       : int = 6_4  ,							SCREAMING_SNAKE_CASE__       : int = 2  ,							SCREAMING_SNAKE_CASE__       : int = 2  ,							SCREAMING_SNAKE_CASE__       : int = 2  ,							SCREAMING_SNAKE_CASE__       : int = 2  ,							SCREAMING_SNAKE_CASE__       : int = 3_2  ,							SCREAMING_SNAKE_CASE__       : int = 3_2  ,							SCREAMING_SNAKE_CASE__       : str = "gelu"  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : int = 1_0_0  ,							SCREAMING_SNAKE_CASE__       : float = 0.02  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							SCREAMING_SNAKE_CASE__       : List[str]=True  ,							SCREAMING_SNAKE_CASE__       : int = 1_0  ,							SCREAMING_SNAKE_CASE__       : int = 2_5  ,							SCREAMING_SNAKE_CASE__       : int = 3  ,							**SCREAMING_SNAKE_CASE__       : int  ,							)	->    Union[str, Any]:
             # time series specific configuration
             a_      :	Optional[int]							=						prediction_length
             a_      :	Tuple							=						context_length if context_length is not None else prediction_length
             a_      :	Tuple							=						distribution_output
             a_      :	int							=						loss
             a_      :	Tuple							=						input_size
             a_      :	int							=						num_time_features
             a_      :	Dict							=						lags_sequence
             a_      :	List[str]							=						scaling
             a_      :	Tuple							=						num_dynamic_real_features
             a_      :	Dict							=						num_static_real_features
             a_      :	Dict							=						num_static_categorical_features
             if cardinality is not None and num_static_categorical_features > 0:
                    if len(SCREAMING_SNAKE_CASE__  ) != num_static_categorical_features:
                           raise ValueError(
                               'The cardinality should be a list of the same length as `num_static_categorical_features`'  )
                    a_      :	Any							=						cardinality
             else:
                    a_      :	Tuple							=						[0]
             if embedding_dimension is not None and num_static_categorical_features > 0:
                    if len(SCREAMING_SNAKE_CASE__  ) != num_static_categorical_features:
                           raise ValueError(
                               'The embedding dimension should be a list of the same length as `num_static_categorical_features`'  )
                    a_      :	Tuple							=						embedding_dimension
             else:
                    a_      :	Optional[Any]							=						[min(5_0  ,							(cat + 1) // 2  ) for cat in self.cardinality]
             a_      :	Optional[Any]							=						num_parallel_samples

             # Transformer architecture configuration
             a_      :	Dict							=						input_size * len(self.lags_sequence  ) + self._number_of_features
             a_      :	Tuple							=						d_model
             a_      :	int							=						encoder_attention_heads
             a_      :	Optional[int]							=						decoder_attention_heads
             a_      :	str							=						encoder_ffn_dim
             a_      :	List[str]							=						decoder_ffn_dim
             a_      :	Any							=						encoder_layers
             a_      :	Any							=						decoder_layers

             a_      :	List[Any]							=						dropout
             a_      :	Tuple							=						attention_dropout
             a_      :	Union[str, Any]							=						activation_dropout
             a_      :	List[Any]							=						encoder_layerdrop
             a_      :	List[str]							=						decoder_layerdrop

             a_      :	Optional[Any]							=						activation_function
             a_      :	Union[str, Any]							=						init_std

             a_      :	List[str]							=						use_cache

             # Autoformer
             a_      :	Tuple							=						label_length
             a_      :	List[Any]							=						moving_average
             a_      :	Optional[Any]							=						autocorrelation_factor

             super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )

      @property
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    int:
             return (
                 sum(self.embedding_dimension  )
                 + self.num_dynamic_real_features
                 + self.num_time_features
                 + self.num_static_real_features
                 + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
             )


