


"""simple docstring"""







def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




						'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




						'''simple docstring'''


						__SCREAMING_SNAKE_CASE						       =    [[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_:
																								__SCREAMING_SNAKE_CASE						       =    max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]  ,						dp[i - 1][w_]       )
																		else:
																								__SCREAMING_SNAKE_CASE						       =    dp[i - 1][w_]

						return dp[n][w_], dp




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




						'''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"       )

						__SCREAMING_SNAKE_CASE						       =    len(lowerCAmelCase_       )
						if num_items != len(lowerCAmelCase_       ):
												__SCREAMING_SNAKE_CASE						       =    (
												    "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_       ):
																		__SCREAMING_SNAKE_CASE						       =    (
																		    "All weights must be integers but got weight of "
																		    f"""type {type(wt[i]       )} at index {i}"""
																		)
																		raise TypeError(lowerCAmelCase_       )

						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    knapsack(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )
						__SCREAMING_SNAKE_CASE						       =    set()
						_construct_solution(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )

						return optimal_val, example_optional_set




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




						'''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__":
		a__    :							Optional[Any]            =					[3, 2, 4, 4]
		a__    :							Any            =					[4, 3, 2, 3]
		a__    :							Union[str, Any]            =					4
		a__    :							Optional[Any]            =					6
		a__    :							List[Any]            =					[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
		a__    :							Union[str, 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
		a__    :							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)




"""simple docstring"""







from typing import List

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


a__    :							Tuple            =					logging.get_logger(__name__)

a__    :							List[Any]            =					{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Any    =      "efficientformer"




	def __init__(			self      :    Any      ,      UpperCAmelCase__      :    List[int] = [3, 2, 6, 4]      ,      UpperCAmelCase__      :    List[int] = [4_8, 9_6, 2_2_4, 4_4_8]      ,      UpperCAmelCase__      :    List[bool] = [True, True, True, True]      ,      UpperCAmelCase__      :    int = 4_4_8      ,      UpperCAmelCase__      :    int = 3_2      ,      UpperCAmelCase__      :    int = 4      ,      UpperCAmelCase__      :    int = 7      ,      UpperCAmelCase__      :    int = 5      ,      UpperCAmelCase__      :    int = 8      ,      UpperCAmelCase__      :    int = 4      ,      UpperCAmelCase__      :    float = 0.0      ,      UpperCAmelCase__      :    int = 1_6      ,      UpperCAmelCase__      :    int = 3      ,      UpperCAmelCase__      :    int = 3      ,      UpperCAmelCase__      :    int = 3      ,      UpperCAmelCase__      :    int = 2      ,      UpperCAmelCase__      :    int = 1      ,      UpperCAmelCase__      :    float = 0.0      ,      UpperCAmelCase__      :    int = 1      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    float = 1E-5      ,      UpperCAmelCase__      :    str = "gelu"      ,      UpperCAmelCase__      :    float = 0.02      ,      UpperCAmelCase__      :    float = 1E-12      ,      UpperCAmelCase__      :    int = 2_2_4      ,      UpperCAmelCase__      :    float = 1E-05      ,      **UpperCAmelCase__      :    Tuple      ,      )	->     None:
						super().__init__(**UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    hidden_act
						__SCREAMING_SNAKE_CASE						       =    hidden_dropout_prob
						__SCREAMING_SNAKE_CASE						       =    hidden_sizes
						__SCREAMING_SNAKE_CASE						       =    num_hidden_layers
						__SCREAMING_SNAKE_CASE						       =    num_attention_heads
						__SCREAMING_SNAKE_CASE						       =    initializer_range
						__SCREAMING_SNAKE_CASE						       =    layer_norm_eps
						__SCREAMING_SNAKE_CASE						       =    patch_size
						__SCREAMING_SNAKE_CASE						       =    num_channels
						__SCREAMING_SNAKE_CASE						       =    depths
						__SCREAMING_SNAKE_CASE						       =    mlp_expansion_ratio
						__SCREAMING_SNAKE_CASE						       =    downsamples
						__SCREAMING_SNAKE_CASE						       =    dim
						__SCREAMING_SNAKE_CASE						       =    key_dim
						__SCREAMING_SNAKE_CASE						       =    attention_ratio
						__SCREAMING_SNAKE_CASE						       =    resolution
						__SCREAMING_SNAKE_CASE						       =    pool_size
						__SCREAMING_SNAKE_CASE						       =    downsample_patch_size
						__SCREAMING_SNAKE_CASE						       =    downsample_stride
						__SCREAMING_SNAKE_CASE						       =    downsample_pad
						__SCREAMING_SNAKE_CASE						       =    drop_path_rate
						__SCREAMING_SNAKE_CASE						       =    num_metaad_blocks
						__SCREAMING_SNAKE_CASE						       =    distillation
						__SCREAMING_SNAKE_CASE						       =    use_layer_scale
						__SCREAMING_SNAKE_CASE						       =    layer_scale_init_value
						__SCREAMING_SNAKE_CASE						       =    image_size
						__SCREAMING_SNAKE_CASE						       =    batch_norm_eps
