

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							(KDPMaDiscreteScheduler,)
    lowerCamelCase			=							10


    def 						snake_case__     (      self :       Dict,**lowercase_ :       Union[str, Any]		)->    Any:





        '''simple docstring'''







        A__          =   {
            'num_train_timesteps': 1_1_0_0,
            'beta_start': 0.0_001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
        }

        config.update(**lowercase_		)
        return config


    def 						snake_case__     (      self :       List[str]		)->    Union[str, Any]:





        '''simple docstring'''







        for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=lowercase_		)


    def 						snake_case__     (      self :       Any		)->    List[Any]:





        '''simple docstring'''







        for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001],[0.0_002, 0.002, 0.02]		):
            self.check_over_configs(beta_start=lowercase_,beta_end=lowercase_		)


    def 						snake_case__     (      self :       Union[str, Any]		)->    Optional[int]:





        '''simple docstring'''







        for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=lowercase_		)


    def 						snake_case__     (      self :       Union[str, Any]		)->    Tuple:





        '''simple docstring'''







        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=lowercase_		)


    def 						snake_case__     (      self :       Union[str, Any]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.scheduler_classes[0]
        A__          =   self.get_scheduler_config(prediction_type='v_prediction'		)
        A__          =   scheduler_class(**lowercase_		)

        scheduler.set_timesteps(self.num_inference_steps		)

        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter * scheduler.init_noise_sigma
        A__          =   sample.to(lowercase_		)

        for i, t in enumerate(scheduler.timesteps		):
            A__          =   scheduler.scale_model_input(lowercase_,lowercase_		)

            A__          =   model(lowercase_,lowercase_		)

            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_		)
            A__          =   output.prev_sample

        A__          =   torch.sum(torch.abs(lowercase_		)		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 4.6_934E-07		) < 1E-2
            assert abs(result_mean.item() - 6.1_112E-10		) < 1E-3
        else:
            # CUDA
            assert abs(result_sum.item() - 4.693_428_650_170_972E-07		) < 1E-2
            assert abs(result_mean.item() - 0.0_002		) < 1E-3


    def 						snake_case__     (      self :       Tuple		)->    Tuple:





        '''simple docstring'''







        if torch_device == "mps":
            return
        A__          =   self.scheduler_classes[0]
        A__          =   self.get_scheduler_config()
        A__          =   scheduler_class(**lowercase_		)

        scheduler.set_timesteps(self.num_inference_steps		)

        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter * scheduler.init_noise_sigma
        A__          =   sample.to(lowercase_		)

        for i, t in enumerate(scheduler.timesteps		):
            A__          =   scheduler.scale_model_input(lowercase_,lowercase_		)

            A__          =   model(lowercase_,lowercase_		)

            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_		)
            A__          =   output.prev_sample

        A__          =   torch.sum(torch.abs(lowercase_		)		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 20.4_125		) < 1E-2
            assert abs(result_mean.item() - 0.0_266		) < 1E-3
        else:
            # CUDA
            assert abs(result_sum.item() - 20.4_125		) < 1E-2
            assert abs(result_mean.item() - 0.0_266		) < 1E-3


    def 						snake_case__     (      self :       Optional[Any]		)->    Any:





        '''simple docstring'''







        if torch_device == "mps":
            return
        A__          =   self.scheduler_classes[0]
        A__          =   self.get_scheduler_config()
        A__          =   scheduler_class(**lowercase_		)

        scheduler.set_timesteps(self.num_inference_steps,device=lowercase_		)

        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter.to(lowercase_		) * scheduler.init_noise_sigma

        for t in scheduler.timesteps:
            A__          =   scheduler.scale_model_input(lowercase_,lowercase_		)

            A__          =   model(lowercase_,lowercase_		)

            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_		)
            A__          =   output.prev_sample

        A__          =   torch.sum(torch.abs(lowercase_		)		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        if str(lowercase_		).startswith('cpu'		):
            # The following sum varies between 148 and 156 on mps. Why?
            assert abs(result_sum.item() - 20.4_125		) < 1E-2
            assert abs(result_mean.item() - 0.0_266		) < 1E-3
        else:
            # CUDA
            assert abs(result_sum.item() - 20.4_125		) < 1E-2
            assert abs(result_mean.item() - 0.0_266		) < 1E-3




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Any      )							->      Optional[int]:





    '''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    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        else:
            A__          =   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]    ,			)
        A__          =   val
    return f[i][j]
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      Tuple:





    '''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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	list      )							->      Union[str, Any]:





    '''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'      )

    A__          =   len(SCREAMING_SNAKE_CASE__      )
    if num_items != len(SCREAMING_SNAKE_CASE__      ):
        A__          =   (
            '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__      ):
            A__          =   (
                'All weights must be integers but got weight of '
                f'type {type(wt[i]      )} at index {i}'
            )
            raise TypeError(SCREAMING_SNAKE_CASE__      )

    A__      ,						A__          =   knapsack(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__          =   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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	set      )							->      Optional[int]:





    '''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__":
     lowercase_       		=  [3, 2, 4, 4]
     lowercase_       		=  [4, 3, 2, 3]
     lowercase_       		=  4
     lowercase_       		=  6
     lowercase_       		=  [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
     lowercase_		,			lowercase_       		=  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
     lowercase_		,			lowercase_       		=  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)

