






"""simple docstring"""

import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				(EulerDiscreteScheduler,)
				UpperCAmelCase__        =				1_0







				def     _snake_case					(							self							:						List[str]						,    **__lowerCamelCase							:						Optional[Any]	)				->					Dict:
										__magic_name__            =     {
										    "num_train_timesteps": 1_1_0_0,
										    "beta_start": 0.0001,
										    "beta_end": 0.02,
										    "beta_schedule": "linear",
										}

										config.update(**__lowerCamelCase	)
										return config







				def     _snake_case					(							self							:						Union[str, Any]	)				->					List[Any]:
										for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																self.check_over_configs(num_train_timesteps=__lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[int]	)				->					List[str]:
										for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]						,    [0.0002, 0.002, 0.02]	):
																self.check_over_configs(beta_start=__lowerCamelCase						,    beta_end=__lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Union[str, Any]:
										for schedule in ["linear", "scaled_linear"]:
																self.check_over_configs(beta_schedule=__lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Optional[int]:
										for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=__lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[int]	)				->					Optional[int]:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config()
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										scheduler.set_timesteps(self.num_inference_steps	)

										__magic_name__            =     torch.manual_seed(0	)

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

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

																__magic_name__            =     model(__lowerCamelCase						,    __lowerCamelCase	)

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

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

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







				def     _snake_case					(							self							:						Optional[Any]	)				->					List[str]:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config(prediction_type="v_prediction"	)
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										scheduler.set_timesteps(self.num_inference_steps	)

										__magic_name__            =     torch.manual_seed(0	)

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

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

																__magic_name__            =     model(__lowerCamelCase						,    __lowerCamelCase	)

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

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

										assert abs(result_sum.item() - 0.0002	) < 1e-2
										assert abs(result_mean.item() - 2.26_76e-06	) < 1e-3







				def     _snake_case					(							self							:						Tuple	)				->					Tuple:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config()
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

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

										__magic_name__            =     torch.manual_seed(0	)

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

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

																__magic_name__            =     model(__lowerCamelCase						,    __lowerCamelCase	)

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

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

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







				def     _snake_case					(							self							:						Optional[int]	)				->					str:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config()
										__magic_name__            =     scheduler_class(**__lowerCamelCase						,    use_karras_sigmas=__lowerCamelCase	)

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

										__magic_name__            =     torch.manual_seed(0	)

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

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

																__magic_name__            =     model(__lowerCamelCase						,    __lowerCamelCase	)

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

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

										assert abs(result_sum.item() - 124.52_2994_9951_1719	) < 1e-2
										assert abs(result_mean.item() - 0.1_6213_9326_3339_9963	) < 1e-3











"""simple docstring"""

from __future__ import annotations

from random import choice






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






						return choice(__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:list[int] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     random_pivot(__lowerCamelCase   )

						# partition based on pivot
						# linear time
						__magic_name__            =     [e for e in lst if e < pivot]
						__magic_name__            =     [e for e in lst if e > pivot]

						# if we get lucky, pivot might be the element we want.
						# we can easily see this:
						# small (elements smaller than k)
						# + pivot (kth element)
						# + big (elements larger than k)
						if len(__lowerCamelCase   ) == k - 1:
												return pivot
						# pivot is in elements bigger than k
						elif len(__lowerCamelCase   ) < k - 1:
												return kth_number(__lowerCamelCase ,						k - len(__lowerCamelCase   ) - 1   )
						# pivot is in elements smaller than k
						else:
												return kth_number(__lowerCamelCase ,						__lowerCamelCase   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



