"""simple docstring"""







def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Tuple	,     __SCREAMING_SNAKE_CASE				:	str	,     __SCREAMING_SNAKE_CASE				:	str	,     __SCREAMING_SNAKE_CASE				:	Dict ):
     global f  # a global dp table for knapsack
     if f[i][j] < 0:
          if j < wt[i - 1]:
               lowercase_      :							Optional[int]						=      mf_knapsack(i - 1	,     A__	,     A__	,     A__ )
          else:
               lowercase_      :							str						=      max(
                   mf_knapsack(i - 1	,     A__	,     A__	,     A__ )	,     mf_knapsack(i - 1	,     A__	,     A__	,     j - wt[i - 1] ) + val[i - 1]	,     )
          lowercase_      :							int						=      val
     return f[i][j]



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Dict	,     __SCREAMING_SNAKE_CASE				:	int	,     __SCREAMING_SNAKE_CASE				:	Optional[Any]	,     __SCREAMING_SNAKE_CASE				:	Optional[Any] ):
     lowercase_      :							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_:
                    lowercase_      :							Tuple						=      max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]	,     dp[i - 1][w_] )
               else:
                    lowercase_      :							Optional[int]						=      dp[i - 1][w_]

     return dp[n][w_], dp



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Any	,     __SCREAMING_SNAKE_CASE				:	List[str]	,     __SCREAMING_SNAKE_CASE				:	Optional[Any] ):
     if not (isinstance(A__	,     (list, tuple) ) and isinstance(A__	,     (list, tuple) )):
          raise ValueError(
              'Both the weights and values vectors must be either lists or tuples' )

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

     lowercase_      :							Tuple						=      knapsack(A__	,     A__	,     A__	,     A__ )
     lowercase_      :							set						=      set()
     _construct_solution(A__	,     A__	,     A__	,     A__	,     A__ )

     return optimal_val, example_optional_set



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Optional[int]	,     __SCREAMING_SNAKE_CASE				:	Optional[Any]	,     __SCREAMING_SNAKE_CASE				:	Tuple	,     __SCREAMING_SNAKE_CASE				:	Any	,     __SCREAMING_SNAKE_CASE				:	str ):
     # 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(A__	,     A__	,     i - 1	,     A__	,     A__ )
          else:
               optimal_set.add(A__ )
               _construct_solution(A__	,     A__	,     i - 1	,     j - wt[i - 1]	,     A__ )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE             =[3, 2, 4, 4]
       __SCREAMING_SNAKE_CASE             =[4, 3, 2, 3]
       __SCREAMING_SNAKE_CASE             =4
       __SCREAMING_SNAKE_CASE             =6
       __SCREAMING_SNAKE_CASE             =[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
       __SCREAMING_SNAKE_CASE      ,			__SCREAMING_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
       __SCREAMING_SNAKE_CASE      ,			__SCREAMING_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 typing import List, Optional, Union

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


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {
    '''huggingface/informer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''informer'''
	__magic_name__       =							{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	    '''num_hidden_layers''': '''encoder_layers''',
	}





	def __init__( self   :					Optional[Any]      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					str = "student_t"      ,      lowerCAmelCase_   :					str = "nll"      ,      lowerCAmelCase_   :					int = 1      ,      lowerCAmelCase_   :					List[int] = None      ,      lowerCAmelCase_   :					Optional[Union[str, bool]] = "mean"      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					Optional[List[int]] = None      ,      lowerCAmelCase_   :					Optional[List[int]] = None      ,      lowerCAmelCase_   :					int = 64      ,      lowerCAmelCase_   :					int = 32      ,      lowerCAmelCase_   :					int = 32      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					str = "gelu"      ,      lowerCAmelCase_   :					float = 0.0_5      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					int = 100      ,      lowerCAmelCase_   :					float = 0.0_2      ,      lowerCAmelCase_   :					List[Any]=True      ,      lowerCAmelCase_   :					str = "prob"      ,      lowerCAmelCase_   :					int = 5      ,      lowerCAmelCase_   :					bool = True      ,      **lowerCAmelCase_   :					Tuple      ,      )		->     Tuple:
				# time series specific configuration
				UpperCAmelCase_	:					str     	=   prediction_length
				UpperCAmelCase_	:					Tuple     	=   context_length or prediction_length
				UpperCAmelCase_	:					Any     	=   distribution_output
				UpperCAmelCase_	:					Union[str, Any]     	=   loss
				UpperCAmelCase_	:					Any     	=   input_size
				UpperCAmelCase_	:					int     	=   num_time_features
				UpperCAmelCase_	:					Union[str, Any]     	=   lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
				UpperCAmelCase_	:					List[Any]     	=   scaling
				UpperCAmelCase_	:					List[str]     	=   num_dynamic_real_features
				UpperCAmelCase_	:					int     	=   num_static_real_features
				UpperCAmelCase_	:					Any     	=   num_static_categorical_features

				# set cardinality
				if cardinality and num_static_categorical_features > 0:
							if len(lowerCAmelCase_		) != num_static_categorical_features:
										raise ValueError(
										    "The cardinality should be a list of the same length as `num_static_categorical_features`"		)
							UpperCAmelCase_	:					Optional[Any]     	=   cardinality
				else:
							UpperCAmelCase_	:					Optional[int]     	=   [0]

				# set embedding_dimension
				if embedding_dimension and num_static_categorical_features > 0:
							if len(lowerCAmelCase_		) != num_static_categorical_features:
										raise ValueError(
										    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"		)
							UpperCAmelCase_	:					Any     	=   embedding_dimension
				else:
							UpperCAmelCase_	:					Optional[Any]     	=   [min(50      ,      (cat + 1) // 2		) for cat in self.cardinality]

				UpperCAmelCase_	:					Dict     	=   num_parallel_samples

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

				UpperCAmelCase_	:					List[str]     	=   dropout
				UpperCAmelCase_	:					Optional[Any]     	=   attention_dropout
				UpperCAmelCase_	:					Any     	=   activation_dropout
				UpperCAmelCase_	:					Union[str, Any]     	=   encoder_layerdrop
				UpperCAmelCase_	:					Optional[int]     	=   decoder_layerdrop

				UpperCAmelCase_	:					Union[str, Any]     	=   activation_function
				UpperCAmelCase_	:					int     	=   init_std

				UpperCAmelCase_	:					Optional[Any]     	=   use_cache

				# Informer
				UpperCAmelCase_	:					int     	=   attention_type
				UpperCAmelCase_	:					List[str]     	=   sampling_factor
				UpperCAmelCase_	:					Union[str, Any]     	=   distil

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





	@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
				)



