'''simple docstring'''




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






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)	->    List[Any]:
    __lowerCamelCase		=  [[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_:
                __lowerCamelCase		=  max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]				,  dp[i - 1][w_]				)
            else:
                __lowerCamelCase		=  dp[i - 1][w_]

    return dp[n][w_], dp






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

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

    __lowerCamelCase	,  __lowerCamelCase		=  knapsack(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)
    __lowerCamelCase		=  set()
    _construct_solution(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)

    return optimal_val, example_optional_set






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)	->    Union[str, Any]:
    # 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(UpperCamelCase__				,  UpperCamelCase__				,  i - 1				,  UpperCamelCase__				,  UpperCamelCase__				)
        else:
            optimal_set.add(UpperCamelCase__				)
            _construct_solution(UpperCamelCase__				,  UpperCamelCase__				,  i - 1				,  j - wt[i - 1]				,  UpperCamelCase__				)


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





import copy
from typing import Dict, List, Optional

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


A__  :  Optional[Any]								=					{
    '''facebook/mask2former-swin-small-coco-instance''': (
        '''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

A__  :  Dict								=					logging.get_logger(__name__)



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''mask2former'''
							_a			      = ['''swin''']
							_a			      = {'''hidden_size''': '''hidden_dim'''}




							def __init__(       self     :       Any				,				A_     :       Optional[Dict] = None				,				A_     :       int = 2_5_6				,				A_     :       int = 2_5_6				,				A_     :       int = 2_5_6				,				A_     :       int = 1_0_2_4				,				A_     :       str = "relu"				,				A_     :       int = 6				,				A_     :       int = 1_0				,				A_     :       int = 8				,				A_     :       float = 0.0				,				A_     :       int = 2_0_4_8				,				A_     :       bool = False				,				A_     :       bool = False				,				A_     :       int = 4				,				A_     :       int = 2_5_5				,				A_     :       int = 1_0_0				,				A_     :       float = 0.1				,				A_     :       float = 2.0				,				A_     :       float = 5.0				,				A_     :       float = 5.0				,				A_     :       int = 1_2_5_4_4				,				A_     :       float = 3.0				,				A_     :       float = 0.75				,				A_     :       float = 0.02				,				A_     :       float = 1.0				,				A_     :       bool = True				,				A_     :       List[int] = [4, 8, 1_6, 3_2]				,				A_     :       bool = None				,				**A_     :       Dict				,				):
									if backbone_config is None:
											logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.''')
											lowerCAmelCase_			:     int									= CONFIG_MAPPING['''swin'''](
											    image_size=2_2_4				,				in_channels=3				,				patch_size=4				,				embed_dim=9_6				,				depths=[2, 2, 1_8, 2]				,				num_heads=[3, 6, 1_2, 2_4]				,				window_size=7				,				drop_path_rate=0.3				,				use_absolute_embeddings=A_				,				out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				,				)

									if isinstance(A_				,				A_):
											lowerCAmelCase_			:     List[Any]									= backbone_config.pop('''model_type''')
											lowerCAmelCase_			:     Optional[Any]									= CONFIG_MAPPING[backbone_model_type]
											lowerCAmelCase_			:     List[Any]									= config_class.from_dict(A_)

									# verify that the backbone is supported
									if backbone_config.model_type not in self.backbones_supported:
											logger.warning_once(
											    F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
											    F"""Supported model types: {",".join(self.backbones_supported)}""")

									lowerCAmelCase_			:     List[Any]									= backbone_config
									lowerCAmelCase_			:     str									= feature_size
									lowerCAmelCase_			:     Optional[Any]									= mask_feature_size
									lowerCAmelCase_			:     int									= hidden_dim
									lowerCAmelCase_			:     int									= encoder_feedforward_dim
									lowerCAmelCase_			:     Optional[int]									= activation_function
									lowerCAmelCase_			:     Any									= encoder_layers
									lowerCAmelCase_			:     Optional[Any]									= decoder_layers
									lowerCAmelCase_			:     Optional[Any]									= num_attention_heads
									lowerCAmelCase_			:     Optional[int]									= dropout
									lowerCAmelCase_			:     List[str]									= dim_feedforward
									lowerCAmelCase_			:     Optional[Any]									= pre_norm
									lowerCAmelCase_			:     List[str]									= enforce_input_projection
									lowerCAmelCase_			:     Tuple									= common_stride
									lowerCAmelCase_			:     Optional[Any]									= ignore_value
									lowerCAmelCase_			:     Optional[Any]									= num_queries
									lowerCAmelCase_			:     int									= no_object_weight
									lowerCAmelCase_			:     Tuple									= class_weight
									lowerCAmelCase_			:     int									= mask_weight
									lowerCAmelCase_			:     Dict									= dice_weight
									lowerCAmelCase_			:     str									= train_num_points
									lowerCAmelCase_			:     Dict									= oversample_ratio
									lowerCAmelCase_			:     Tuple									= importance_sample_ratio
									lowerCAmelCase_			:     List[str]									= init_std
									lowerCAmelCase_			:     List[str]									= init_xavier_std
									lowerCAmelCase_			:     Optional[Any]									= use_auxiliary_loss
									lowerCAmelCase_			:     List[Any]									= feature_strides
									lowerCAmelCase_			:     int									= output_auxiliary_logits
									lowerCAmelCase_			:     Optional[Any]									= decoder_layers

									super().__init__(**A_)




							@classmethod
							def    UpperCAmelCase__			(       cls     :       List[str]				,				A_     :       PretrainedConfig				,				**A_     :       List[Any]):
									return cls(
									    backbone_config=A_				,				**A_				,				)







							def    UpperCAmelCase__			(       self     :       List[Any]):
									lowerCAmelCase_			:     str									= copy.deepcopy(self.__dict__)
									lowerCAmelCase_			:     Dict									= self.backbone_config.to_dict()
									lowerCAmelCase_			:     Optional[int]									= self.__class__.model_type
									return output





