




import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    (EulerDiscreteScheduler,)
							UpperCamelCase__   	=    10
							def  lowerCamelCase__				(		self		:int						, **__magic_name__		:Dict							):







								'''simple docstring'''







								a									=				{
								    """num_train_timesteps""": 1100,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								}

								config.update(**__magic_name__							)
								return config
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								for timesteps in [10, 50, 100, 1000]:
									self.check_over_configs(num_train_timesteps=__magic_name__							)
							def  lowerCamelCase__				(		self		:Any							):







								'''simple docstring'''







								for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]						, [0.0002, 0.002, 0.02]							):
									self.check_over_configs(beta_start=__magic_name__						, beta_end=__magic_name__							)
							def  lowerCamelCase__				(		self		:Any							):







								'''simple docstring'''







								for schedule in ["linear", "scaled_linear"]:
									self.check_over_configs(beta_schedule=__magic_name__							)
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=__magic_name__							)
							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								a									=				self.scheduler_classes[0]
								a									=				self.get_scheduler_config()
								a									=				scheduler_class(**__magic_name__							)

								scheduler.set_timesteps(self.num_inference_steps							)

								a									=				torch.manual_seed(0							)

								a									=				self.dummy_model()
								a									=				self.dummy_sample_deter * scheduler.init_noise_sigma
								a									=				sample.to(__magic_name__							)

								for i, t in enumerate(scheduler.timesteps							):
									a									=				scheduler.scale_model_input(__magic_name__						, __magic_name__							)

									a									=				model(__magic_name__						, __magic_name__							)

									a									=				scheduler.step(__magic_name__						, __magic_name__						, __magic_name__						, generator=__magic_name__							)
									a									=				output.prev_sample

								a									=				torch.sum(torch.abs(__magic_name__							)							)
								a									=				torch.mean(torch.abs(__magic_name__							)							)

								assert abs(result_sum.item() - 10.0807							) < 1E-2
								assert abs(result_mean.item() - 0.0131							) < 1E-3
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								a									=				self.scheduler_classes[0]
								a									=				self.get_scheduler_config(prediction_type="""v_prediction"""							)
								a									=				scheduler_class(**__magic_name__							)

								scheduler.set_timesteps(self.num_inference_steps							)

								a									=				torch.manual_seed(0							)

								a									=				self.dummy_model()
								a									=				self.dummy_sample_deter * scheduler.init_noise_sigma
								a									=				sample.to(__magic_name__							)

								for i, t in enumerate(scheduler.timesteps							):
									a									=				scheduler.scale_model_input(__magic_name__						, __magic_name__							)

									a									=				model(__magic_name__						, __magic_name__							)

									a									=				scheduler.step(__magic_name__						, __magic_name__						, __magic_name__						, generator=__magic_name__							)
									a									=				output.prev_sample

								a									=				torch.sum(torch.abs(__magic_name__							)							)
								a									=				torch.mean(torch.abs(__magic_name__							)							)

								assert abs(result_sum.item() - 0.0002							) < 1E-2
								assert abs(result_mean.item() - 2.2_6_7_6E-0_6							) < 1E-3
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								a									=				self.scheduler_classes[0]
								a									=				self.get_scheduler_config()
								a									=				scheduler_class(**__magic_name__							)

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

								a									=				torch.manual_seed(0							)

								a									=				self.dummy_model()
								a									=				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								a									=				sample.to(__magic_name__							)

								for t in scheduler.timesteps:
									a									=				scheduler.scale_model_input(__magic_name__						, __magic_name__							)

									a									=				model(__magic_name__						, __magic_name__							)

									a									=				scheduler.step(__magic_name__						, __magic_name__						, __magic_name__						, generator=__magic_name__							)
									a									=				output.prev_sample

								a									=				torch.sum(torch.abs(__magic_name__							)							)
								a									=				torch.mean(torch.abs(__magic_name__							)							)

								assert abs(result_sum.item() - 10.0807							) < 1E-2
								assert abs(result_mean.item() - 0.0131							) < 1E-3





							def  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







								a									=				self.scheduler_classes[0]
								a									=				self.get_scheduler_config()
								a									=				scheduler_class(**__magic_name__						, use_karras_sigmas=__magic_name__							)

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

								a									=				torch.manual_seed(0							)

								a									=				self.dummy_model()
								a									=				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								a									=				sample.to(__magic_name__							)

								for t in scheduler.timesteps:
									a									=				scheduler.scale_model_input(__magic_name__						, __magic_name__							)

									a									=				model(__magic_name__						, __magic_name__							)

									a									=				scheduler.step(__magic_name__						, __magic_name__						, __magic_name__						, generator=__magic_name__							)
									a									=				output.prev_sample

								a									=				torch.sum(torch.abs(__magic_name__							)							)
								a									=				torch.mean(torch.abs(__magic_name__							)							)

								assert abs(result_sum.item() - 124.52299499511719							) < 1E-2
								assert abs(result_mean.item() - 0.16213932633399963							) < 1E-3







from bisect import bisect
from itertools import accumulate





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				sorted(zip(__lowerCamelCase				,     __lowerCamelCase       )				,     key=lambda __lowerCamelCase       : x[0] / x[1]				,     reverse=__lowerCamelCase       )
	a							,					a									=				[i[0] for i in r], [i[1] for i in r]
	a									=				list(accumulate(__lowerCamelCase       )       )
	a									=				bisect(__lowerCamelCase				,     __lowerCamelCase       )
	return (
	    0
	    if k == 0
	    else sum(vl[:k]       ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
	    if k != n
	    else sum(vl[:k]       )
	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

