import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					(EulerAncestralDiscreteScheduler,)
	__SCREAMING_SNAKE_CASE              =					10


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		_lowercase   ={
		    "num_train_timesteps": 1100,
		    "beta_start": 0.0_0_0_1,
		    "beta_end": 0.0_2,
		    "beta_schedule": "linear",
		}

		config.update(**lowerCAmelCase_       )
		return config


	def __lowerCAmelCase      (   self       ):
		for timesteps in [10, 50, 100, 1000]:
			self.check_over_configs(num_train_timesteps=lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]       ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]       ):
			self.check_over_configs(beta_start=lowerCAmelCase_       ,  beta_end=lowerCAmelCase_       )


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


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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.scheduler_classes[0]
		_lowercase   =self.get_scheduler_config()
		_lowercase   =scheduler_class(**lowerCAmelCase_       )

		scheduler.set_timesteps(self.num_inference_steps       )

		_lowercase   =torch.manual_seed(0       )

		_lowercase   =self.dummy_model()
		_lowercase   =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
		_lowercase   =sample.to(lowerCAmelCase_       )

		for i, t in enumerate(scheduler.timesteps       ):
			_lowercase   =scheduler.scale_model_input(lowerCAmelCase_       ,  lowerCAmelCase_       )

			_lowercase   =model(lowerCAmelCase_       ,  lowerCAmelCase_       )

			_lowercase   =scheduler.step(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  generator=lowerCAmelCase_       )
			_lowercase   =output.prev_sample

		_lowercase   =torch.sum(torch.abs(lowerCAmelCase_       )       )
		_lowercase   =torch.mean(torch.abs(lowerCAmelCase_       )       )

		assert abs(result_sum.item() - 1_5_2.3_1_9_2       ) < 1e-2
		assert abs(result_mean.item() - 0.1_9_8_3       ) < 1e-3


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.scheduler_classes[0]
		_lowercase   =self.get_scheduler_config(prediction_type="v_prediction"       )
		_lowercase   =scheduler_class(**lowerCAmelCase_       )

		scheduler.set_timesteps(self.num_inference_steps       )

		_lowercase   =torch.manual_seed(0       )

		_lowercase   =self.dummy_model()
		_lowercase   =self.dummy_sample_deter * scheduler.init_noise_sigma
		_lowercase   =sample.to(lowerCAmelCase_       )

		for i, t in enumerate(scheduler.timesteps       ):
			_lowercase   =scheduler.scale_model_input(lowerCAmelCase_       ,  lowerCAmelCase_       )

			_lowercase   =model(lowerCAmelCase_       ,  lowerCAmelCase_       )

			_lowercase   =scheduler.step(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  generator=lowerCAmelCase_       )
			_lowercase   =output.prev_sample

		_lowercase   =torch.sum(torch.abs(lowerCAmelCase_       )       )
		_lowercase   =torch.mean(torch.abs(lowerCAmelCase_       )       )

		assert abs(result_sum.item() - 1_0_8.4_4_3_9       ) < 1e-2
		assert abs(result_mean.item() - 0.1_4_1_2       ) < 1e-3


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.scheduler_classes[0]
		_lowercase   =self.get_scheduler_config()
		_lowercase   =scheduler_class(**lowerCAmelCase_       )

		scheduler.set_timesteps(self.num_inference_steps       ,  device=lowerCAmelCase_       )
		_lowercase   =torch.manual_seed(0       )

		_lowercase   =self.dummy_model()
		_lowercase   =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
		_lowercase   =sample.to(lowerCAmelCase_       )

		for t in scheduler.timesteps:
			_lowercase   =scheduler.scale_model_input(lowerCAmelCase_       ,  lowerCAmelCase_       )

			_lowercase   =model(lowerCAmelCase_       ,  lowerCAmelCase_       )

			_lowercase   =scheduler.step(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  generator=lowerCAmelCase_       )
			_lowercase   =output.prev_sample

		_lowercase   =torch.sum(torch.abs(lowerCAmelCase_       )       )
		_lowercase   =torch.mean(torch.abs(lowerCAmelCase_       )       )

		assert abs(result_sum.item() - 1_5_2.3_1_9_2       ) < 1e-2
		assert abs(result_mean.item() - 0.1_9_8_3       ) < 1e-3











'''simple docstring'''

from __future__ import annotations




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_						):





      """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(lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   counter - 1						)
      else:
            _snake_case		:						List[str]         =  capacity - weights[counter - 1]
            _snake_case		:						Tuple         =  values[counter - 1] + knapsack(
                lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   counter - 1						)
            _snake_case		:						Any         =  knapsack(lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   counter - 1						)
            return max(lowerCAmelCase_      ,   lowerCAmelCase_						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()


