






"""simple docstring"""
def 							snake_case (  A__      ,A__      ,A__      ,A__		):
			global f  # a global dp table for knapsack
			if f[i][j] < 0:
						if j < wt[i - 1]:
									UpperCAmelCase_	:					Optional[int]     	=   mf_knapsack(i - 1      ,A__      ,A__      ,A__		)
						else:
									UpperCAmelCase_	:					str     	=   max(
									    mf_knapsack(i - 1      ,A__      ,A__      ,A__		)      ,mf_knapsack(i - 1      ,A__      ,A__      ,j - wt[i - 1]		) + val[i - 1]      ,)
						UpperCAmelCase_	:					int     	=   val
			return f[i][j]

def 							snake_case (  A__      ,A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Optional[Any]     	=   [[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_	:					Tuple     	=   max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]      ,dp[i - 1][w_]		)
									else:
												UpperCAmelCase_	:					Optional[int]     	=   dp[i - 1][w_]

			return dp[n][w_], dp

def 							snake_case (  A__      ,A__      ,A__		):
			if not (isinstance(A__      ,(list, tuple)		) and isinstance(A__      ,(list, tuple)		)):
						raise ValueError(
						    "Both the weights and values vectors must be either lists or tuples"		)

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

			UpperCAmelCase_	,      UpperCAmelCase_	:					Tuple     	=   knapsack(A__      ,A__      ,A__      ,A__		)
			UpperCAmelCase_	:					set     	=   set()
			_construct_solution(A__      ,A__      ,A__      ,A__      ,A__		)

			return optimal_val, example_optional_set

def 							snake_case (  A__      ,A__      ,A__      ,A__      ,A__		):
			# 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(A__      ,A__      ,i - 1      ,A__      ,A__		)
						else:
									optimal_set.add(A__		)
									_construct_solution(A__      ,A__      ,i - 1      ,j - wt[i - 1]      ,A__		)


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

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	(DEISMultistepScheduler,)
     SCREAMING_SNAKE_CASE_		 =	(('''num_inference_steps''', 25),)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					{
               '''num_train_timesteps''': 1_0_0_0,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''solver_order''': 2,
           }

           config.update(**__SCREAMING_SNAKE_CASE	)
           return config






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=0    ,    **__SCREAMING_SNAKE_CASE	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					dict(self.forward_default_kwargs	)
           UpperCamelCase__     : Dict											=					kwargs.pop('''num_inference_steps'''    ,    __SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Tuple											=					self.dummy_sample
           UpperCamelCase__     : Optional[int]											=					0.1 * sample
           UpperCamelCase__     : Tuple											=					[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__     : Union[str, Any]											=					self.get_scheduler_config(**__SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : Optional[Any]											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)
                 scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)
                 # copy over dummy past residuals
                 UpperCamelCase__     : Optional[Any]											=					dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__SCREAMING_SNAKE_CASE	)
                       UpperCamelCase__     : List[str]											=					scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE	)
                       new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)
                       # copy over dummy past residuals
                       UpperCamelCase__     : int											=					dummy_past_residuals[: new_scheduler.config.solver_order]

                 UpperCamelCase__					,UpperCamelCase__     : Any											=					sample, sample
                 for t in range(__SCREAMING_SNAKE_CASE    ,    time_step + scheduler.config.solver_order + 1	):
                       UpperCamelCase__     : Union[str, Any]											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample
                       UpperCamelCase__     : int											=					new_scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample

                       assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           pass






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=0    ,    **__SCREAMING_SNAKE_CASE	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					dict(self.forward_default_kwargs	)
           UpperCamelCase__     : Tuple											=					kwargs.pop('''num_inference_steps'''    ,    __SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Union[str, Any]											=					self.dummy_sample
           UpperCamelCase__     : List[Any]											=					0.1 * sample
           UpperCamelCase__     : Dict											=					[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__     : Optional[int]											=					self.get_scheduler_config()
                 UpperCamelCase__     : Union[str, Any]											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)
                 scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

                 # copy over dummy past residuals (must be after setting timesteps)
                 UpperCamelCase__     : List[Any]											=					dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__SCREAMING_SNAKE_CASE	)
                       UpperCamelCase__     : str											=					scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE	)
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

                       # copy over dummy past residual (must be after setting timesteps)
                       UpperCamelCase__     : Optional[int]											=					dummy_past_residuals[: new_scheduler.config.solver_order]

                 UpperCamelCase__     : Optional[Any]											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample
                 UpperCamelCase__     : Dict											=					new_scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample

                 assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=None    ,    **__SCREAMING_SNAKE_CASE	)	-> int:




           """simple docstring"""







           if scheduler is None:
                 UpperCamelCase__     : List[str]											=					self.scheduler_classes[0]
                 UpperCamelCase__     : str											=					self.get_scheduler_config(**__SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : Dict											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[str]											=					self.scheduler_classes[0]
           UpperCamelCase__     : Tuple											=					self.get_scheduler_config(**__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Any											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					1_0
           UpperCamelCase__     : Tuple											=					self.dummy_model()
           UpperCamelCase__     : Dict											=					self.dummy_sample_deter
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

           for i, t in enumerate(scheduler.timesteps	):
                 UpperCamelCase__     : List[Any]											=					model(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : Optional[Any]											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	).prev_sample

           return sample






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					dict(self.forward_default_kwargs	)

           UpperCamelCase__     : List[Any]											=					kwargs.pop('''num_inference_steps'''    ,    __SCREAMING_SNAKE_CASE	)

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__     : Optional[int]											=					self.get_scheduler_config()
                 UpperCamelCase__     : Any											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)

                 UpperCamelCase__     : int											=					self.dummy_sample
                 UpperCamelCase__     : int											=					0.1 * sample

                 if num_inference_steps is not None and hasattr(__SCREAMING_SNAKE_CASE    ,    '''set_timesteps'''	):
                       scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)
                 elif num_inference_steps is not None and not hasattr(__SCREAMING_SNAKE_CASE    ,    '''set_timesteps'''	):
                       UpperCamelCase__     : str											=					num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 UpperCamelCase__     : Tuple											=					[residual + 0.2, residual + 0.15, residual + 0.10]
                 UpperCamelCase__     : Dict											=					dummy_past_residuals[: scheduler.config.solver_order]

                 UpperCamelCase__     : int											=					scheduler.timesteps[5]
                 UpperCamelCase__     : Optional[int]											=					scheduler.timesteps[6]

                 UpperCamelCase__     : Any											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample
                 UpperCamelCase__     : Any											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample

                 self.assertEqual(output_a.shape    ,    sample.shape	)
                 self.assertEqual(output_a.shape    ,    output_a.shape	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					DEISMultistepScheduler(**self.get_scheduler_config()	)
           UpperCamelCase__     : Tuple											=					self.full_loop(scheduler=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[Any]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.23916	) < 1e-3

           UpperCamelCase__     : List[Any]											=					DPMSolverSinglestepScheduler.from_config(scheduler.config	)
           UpperCamelCase__     : Union[str, Any]											=					DPMSolverMultistepScheduler.from_config(scheduler.config	)
           UpperCamelCase__     : int											=					UniPCMultistepScheduler.from_config(scheduler.config	)
           UpperCamelCase__     : List[str]											=					DEISMultistepScheduler.from_config(scheduler.config	)

           UpperCamelCase__     : Dict											=					self.full_loop(scheduler=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[Any]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.23916	) < 1e-3






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




           """simple docstring"""







           for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE	)
           for order in [1, 2, 3]:
                 for solver_type in ["logrho"]:
                       for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       thresholding=__SCREAMING_SNAKE_CASE    ,    prediction_type=__SCREAMING_SNAKE_CASE    ,    sample_max_value=__SCREAMING_SNAKE_CASE    ,    algorithm_type='''deis'''    ,    solver_order=__SCREAMING_SNAKE_CASE    ,    solver_type=__SCREAMING_SNAKE_CASE    ,    )






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           for algorithm_type in ["deis"]:
                 for solver_type in ["logrho"]:
                       for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       solver_order=__SCREAMING_SNAKE_CASE    ,    solver_type=__SCREAMING_SNAKE_CASE    ,    prediction_type=__SCREAMING_SNAKE_CASE    ,    algorithm_type=__SCREAMING_SNAKE_CASE    ,    )
                                   UpperCamelCase__     : Any											=					self.full_loop(
                                       solver_order=__SCREAMING_SNAKE_CASE    ,    solver_type=__SCREAMING_SNAKE_CASE    ,    prediction_type=__SCREAMING_SNAKE_CASE    ,    algorithm_type=__SCREAMING_SNAKE_CASE    ,    )
                                   assert not torch.isnan(__SCREAMING_SNAKE_CASE	).any(), "Samples have nan numbers"






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE	)
           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE    ,    time_step=0	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : str											=					self.full_loop()
           UpperCamelCase__     : Optional[int]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.23916	) < 1e-3






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Any											=					self.full_loop(prediction_type='''v_prediction'''	)
           UpperCamelCase__     : List[Any]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.091	) < 1e-3






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.scheduler_classes[0]
           UpperCamelCase__     : Optional[Any]											=					self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE    ,    dynamic_thresholding_ratio=0	)
           UpperCamelCase__     : Tuple											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					1_0
           UpperCamelCase__     : Dict											=					self.dummy_model()
           UpperCamelCase__     : Tuple											=					self.dummy_sample_deter.half()
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

           for i, t in enumerate(scheduler.timesteps	):
                 UpperCamelCase__     : Any											=					model(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : Tuple											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	).prev_sample

           assert sample.dtype == torch.floataa


