





from __future__ import annotations
def 			__UpperCamelCase				(			lowercase__	:       int		,    lowercase__	:       list[int]		,    lowercase__	:       list[int]		,    lowercase__	:       int		)	->      int:
    '''simple docstring'''





    if counter == 0 or capacity == 0:
        return 0

    # If weight of the nth item is more than Knapsack of capacity,
    #   then this item cannot be included in the optimal solution,
    # else return the maximum of two cases:
    #   (1) nth item included
    #   (2) not included
    if weights[counter - 1] > capacity:
        return knapsack(lowercase__		,    lowercase__		,    lowercase__		,    counter - 1		)
    else:
        lowerCAmelCase_					:	int						=   capacity - weights[counter - 1]
        lowerCAmelCase_					:	str						=   values[counter - 1] + knapsack(
            lowercase__		,    lowercase__		,    lowercase__		,    counter - 1		)
        lowerCAmelCase_					:	Dict						=   knapsack(lowercase__		,    lowercase__		,    lowercase__		,    counter - 1		)
        return max(lowercase__		,    lowercase__		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()









import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 		__a							(	__UpperCamelCase      ):
       __snake_case						:   Optional[Any]      =		(VQDiffusionScheduler,)



       def  A							(     self	:   Optional[int]					,			**UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Any						=   {
               """num_vec_classes""": 40_97,
               """num_train_timesteps""": 1_00,
           }

           config.update(**UpperCAmelCase			)
           return config



       def  A							(     self	:   str					,			UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Union[str, Any]						=   4
           lowerCAmelCase_					:	str						=   8
           lowerCAmelCase_					:	Optional[Any]						=   8

           lowerCAmelCase_					:	str						=   torch.randint(0					,			UpperCAmelCase					,			(batch_size, height * width)			)

           return sample



       @property
       def  A							(     self	:   Optional[int]			):
           assert False



       def  A							(     self	:   int					,			UpperCAmelCase	:   Dict			):
           def model(UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   List[str]					,			*UpperCAmelCase	:   List[Any]			):
               lowerCAmelCase_		,		lowerCAmelCase_					:	Tuple						=   sample.shape
               lowerCAmelCase_					:	int						=   torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)			)
               lowerCAmelCase_					:	str						=   F.log_softmax(logits.double()					,			dim=1			).float()
               return return_value

           return model



       def  A							(     self	:   Union[str, Any]			):
           for timesteps in [2, 5, 1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=UpperCAmelCase			)



       def  A							(     self	:   str			):
           for num_vec_classes in [5, 1_00, 10_00, 40_00]:
               self.check_over_configs(num_vec_classes=UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]			):
           for t in [0, 50, 99]:
               self.check_over_forward(time_step=UpperCAmelCase			)



       def  A							(     self	:   int			):
           pass


