



"""simple docstring"""


def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]				):






				'''simple docstring'''



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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



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

				return dp[n][w_], dp







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						list					,     _SCREAMING_SNAKE_CASE   :						list				):






				'''simple docstring'''



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

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

				_UpperCAmelCase		,						_UpperCAmelCase					=    knapsack(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    set()
				_construct_solution(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

				return optimal_val, example_optional_set







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						list					,     _SCREAMING_SNAKE_CASE   :						list					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						set				):






				'''simple docstring'''



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


if __name__ == "__main__":
					__A   :							Optional[Any]										=   [3, 2, 4, 4]
					__A   :							Tuple										=   [4, 3, 2, 3]
					__A   :							Union[str, Any]										=   4
					__A   :							int										=   6
					__A   :							Optional[int]										=   [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					__A   :							int										=   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   :							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 ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A   :							Optional[int]										=   logging.get_logger(__name__)

__A   :							Optional[Any]										=   {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class 			_a (					lowerCAmelCase			,							lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""bit"""
			UpperCamelCase__			      =	["""preactivation""", """bottleneck"""]
			UpperCamelCase__			      =	["""SAME""", """VALID"""]



			def __init__(		self     :    List[Any]				,   __UpperCamelCase     :    Optional[int]=3				,   __UpperCamelCase     :    Optional[int]=6_4				,   __UpperCamelCase     :    List[Any]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,   __UpperCamelCase     :    Any=[3, 4, 6, 3]				,   __UpperCamelCase     :    str="preactivation"				,   __UpperCamelCase     :    Union[str, Any]="relu"				,   __UpperCamelCase     :    str=None				,   __UpperCamelCase     :    Dict=3_2				,   __UpperCamelCase     :    List[str]=0.0				,   __UpperCamelCase     :    Any=False				,   __UpperCamelCase     :    Union[str, Any]=3_2				,   __UpperCamelCase     :    List[str]=1				,   __UpperCamelCase     :    List[str]=None				,   __UpperCamelCase     :    Optional[Any]=None				,   **__UpperCamelCase     :    List[str]				,   )->List[Any]:
							super().__init__(**__UpperCamelCase   )
							if layer_type not in self.layer_types:
											raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types   )}'   )
							if global_padding is not None:
											if global_padding.upper() in self.supported_padding:
															_UpperCAmelCase					=    global_padding.upper()
											else:
															raise ValueError(F'Padding strategy {global_padding} not supported'   )
							_UpperCAmelCase					=    num_channels
							_UpperCAmelCase					=    embedding_size
							_UpperCAmelCase					=    hidden_sizes
							_UpperCAmelCase					=    depths
							_UpperCAmelCase					=    layer_type
							_UpperCAmelCase					=    hidden_act
							_UpperCAmelCase					=    global_padding
							_UpperCAmelCase					=    num_groups
							_UpperCAmelCase					=    drop_path_rate
							_UpperCAmelCase					=    embedding_dynamic_padding
							_UpperCAmelCase					=    output_stride
							_UpperCAmelCase					=    width_factor

							_UpperCAmelCase					=    ['''stem'''] + [F'stage{idx}' for idx in range(1				,   len(__UpperCamelCase   ) + 1   )]
							_UpperCAmelCase		,						_UpperCAmelCase					=    get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase				,   out_indices=__UpperCamelCase				,   stage_names=self.stage_names   )



