






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__A      =				logging.get_logger(__name__)

__A      =				{
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					"deta"
							A_						  =					{
							    "hidden_size": "d_model",
							    "num_attention_heads": "encoder_attention_heads",
							}

							def __init__(  self:   int		,     __A:   List[str]=None		,     __A:   Optional[int]=9_00		,     __A:   Dict=20_48		,     __A:   Union[str, Any]=6		,     __A:   Union[str, Any]=20_48		,     __A:   Tuple=8		,     __A:   Optional[int]=6		,     __A:   str=10_24		,     __A:   Optional[Any]=8		,     __A:   Union[str, Any]=0.0		,     __A:   Optional[Any]=True		,     __A:   List[Any]="relu"		,     __A:   List[str]=2_56		,     __A:   Any=0.1		,     __A:   str=0.0		,     __A:   List[str]=0.0		,     __A:   List[Any]=0.02		,     __A:   List[str]=1.0		,     __A:   Any=True		,     __A:   Dict=False		,     __A:   List[str]="sine"		,     __A:   Optional[Any]=5		,     __A:   Union[str, Any]=4		,     __A:   List[str]=4		,     __A:   Optional[Any]=True		,     __A:   List[str]=3_00		,     __A:   List[Any]=True		,     __A:   str=True		,     __A:   Optional[Any]=1		,     __A:   Dict=5		,     __A:   int=2		,     __A:   List[Any]=1		,     __A:   Any=1		,     __A:   Tuple=5		,     __A:   List[Any]=2		,     __A:   Any=0.1		,     __A:   Any=0.25		,     **__A:   int		,     )    ->				Any:
												if backbone_config is None:
																	logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''   )
																	_A								= CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']   )
												else:
																	if isinstance(__A		,     __A   ):
																						_A								= backbone_config.pop('''model_type'''   )
																						_A								= CONFIG_MAPPING[backbone_model_type]
																						_A								= config_class.from_dict(__A   )

												_A								= backbone_config
												_A								= num_queries
												_A								= max_position_embeddings
												_A								= d_model
												_A								= encoder_ffn_dim
												_A								= encoder_layers
												_A								= encoder_attention_heads
												_A								= decoder_ffn_dim
												_A								= decoder_layers
												_A								= decoder_attention_heads
												_A								= dropout
												_A								= attention_dropout
												_A								= activation_dropout
												_A								= activation_function
												_A								= init_std
												_A								= init_xavier_std
												_A								= encoder_layerdrop
												_A								= auxiliary_loss
												_A								= position_embedding_type
												# deformable attributes
												_A								= num_feature_levels
												_A								= encoder_n_points
												_A								= decoder_n_points
												_A								= two_stage
												_A								= two_stage_num_proposals
												_A								= with_box_refine
												_A								= assign_first_stage
												if two_stage is True and with_box_refine is False:
																	raise ValueError('''If two_stage is True, with_box_refine must be True.'''   )
												# Hungarian matcher
												_A								= class_cost
												_A								= bbox_cost
												_A								= giou_cost
												# Loss coefficients
												_A								= mask_loss_coefficient
												_A								= dice_loss_coefficient
												_A								= bbox_loss_coefficient
												_A								= giou_loss_coefficient
												_A								= eos_coefficient
												_A								= focal_alpha
												super().__init__(is_encoder_decoder=__A		,     **__A   )

							@property
							def       __A (  self:   List[Any]   )    ->				int:
												return self.encoder_attention_heads

							@property
							def       __A (  self:   Optional[Any]   )    ->				int:
												return self.d_model



							def       __A (  self:   Union[str, Any]   )    ->				Optional[int]:
												_A								= copy.deepcopy(self.__dict__   )
												_A								= self.backbone_config.to_dict()
												_A								= self.__class__.model_type
												return output











def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







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




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







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

					return dp[n][w_], dp




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







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

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

					_A							,_A								= knapsack(_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )
					_A								= set()
					_construct_solution(_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )

					return optimal_val, example_optional_set




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







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


if __name__ == "__main__":
				__A      =				[3, 2, 4, 4]
				__A      =				[4, 3, 2, 3]
				__A      =				4
				__A      =				6
				__A      =				[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
				__A ,  __A      =				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 ,  __A      =				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)



