




import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 					__lowerCAmelCase				( UpperCAmelCase__				):
			snake_case_   :				Dict				  =  (KDPMaAncestralDiscreteScheduler,)
			snake_case_   :				Optional[Any]				  =  10







			def UpperCamelCase    ( self					:    List[Any]					,					**snake_case__					:    Union[str, Any]   ):




									"""simple docstring"""


									_UpperCAmelCase									=  {
									    "num_train_timesteps": 1_100,
									    "beta_start": 0.0_001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									}

									config.update(**snake_case__   )
									return config







			def UpperCamelCase    ( self					:    Dict   ):




									"""simple docstring"""


									for timesteps in [10, 50, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=snake_case__   )







			def UpperCamelCase    ( self					:    Optional[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=snake_case__					,					beta_end=snake_case__   )







			def UpperCamelCase    ( self					:    Union[str, Any]   ):




									"""simple docstring"""


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







			def UpperCamelCase    ( self					:    Optional[int]   ):




									"""simple docstring"""


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

									scheduler.set_timesteps(self.num_inference_steps   )

									_UpperCAmelCase									=  torch.manual_seed(0   )

									_UpperCAmelCase									=  self.dummy_model()
									_UpperCAmelCase									=  self.dummy_sample_deter * scheduler.init_noise_sigma
									_UpperCAmelCase									=  sample.to(snake_case__   )

									for i, t in enumerate(scheduler.timesteps   ):
															_UpperCAmelCase									=  scheduler.scale_model_input(snake_case__					,					snake_case__   )

															_UpperCAmelCase									=  model(snake_case__					,					snake_case__   )

															_UpperCAmelCase									=  scheduler.step(snake_case__					,					snake_case__					,					snake_case__					,					generator=snake_case__   )
															_UpperCAmelCase									=  output.prev_sample

									_UpperCAmelCase									=  torch.sum(torch.abs(snake_case__   )   )
									_UpperCAmelCase									=  torch.mean(torch.abs(snake_case__   )   )

									assert abs(result_sum.item() - 13_849.3_877   ) < 1e-2
									assert abs(result_mean.item() - 18.0_331   ) < 5e-3







			def UpperCamelCase    ( self					:    str   ):




									"""simple docstring"""


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







			def UpperCamelCase    ( self					:    Optional[Any]   ):




									"""simple docstring"""


									if torch_device == "mps":
															return
									_UpperCAmelCase									=  self.scheduler_classes[0]
									_UpperCAmelCase									=  self.get_scheduler_config(prediction_type="v_prediction"   )
									_UpperCAmelCase									=  scheduler_class(**snake_case__   )

									scheduler.set_timesteps(self.num_inference_steps   )

									_UpperCAmelCase									=  self.dummy_model()
									_UpperCAmelCase									=  self.dummy_sample_deter * scheduler.init_noise_sigma
									_UpperCAmelCase									=  sample.to(snake_case__   )

									_UpperCAmelCase									=  torch.manual_seed(0   )

									for i, t in enumerate(scheduler.timesteps   ):
															_UpperCAmelCase									=  scheduler.scale_model_input(snake_case__					,					snake_case__   )

															_UpperCAmelCase									=  model(snake_case__					,					snake_case__   )

															_UpperCAmelCase									=  scheduler.step(snake_case__					,					snake_case__					,					snake_case__					,					generator=snake_case__   )
															_UpperCAmelCase									=  output.prev_sample

									_UpperCAmelCase									=  torch.sum(torch.abs(snake_case__   )   )
									_UpperCAmelCase									=  torch.mean(torch.abs(snake_case__   )   )

									assert abs(result_sum.item() - 328.9_970   ) < 1e-2
									assert abs(result_mean.item() - 0.4_284   ) < 1e-3







			def UpperCamelCase    ( self					:    Union[str, Any]   ):




									"""simple docstring"""


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

									scheduler.set_timesteps(self.num_inference_steps					,					device=snake_case__   )
									_UpperCAmelCase									=  torch.manual_seed(0   )

									_UpperCAmelCase									=  self.dummy_model()
									_UpperCAmelCase									=  self.dummy_sample_deter.to(snake_case__   ) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
															_UpperCAmelCase									=  scheduler.scale_model_input(snake_case__					,					snake_case__   )

															_UpperCAmelCase									=  model(snake_case__					,					snake_case__   )

															_UpperCAmelCase									=  scheduler.step(snake_case__					,					snake_case__					,					snake_case__					,					generator=snake_case__   )
															_UpperCAmelCase									=  output.prev_sample

									_UpperCAmelCase									=  torch.sum(torch.abs(snake_case__   )   )
									_UpperCAmelCase									=  torch.mean(torch.abs(snake_case__   )   )

									assert abs(result_sum.item() - 13_849.3_818   ) < 1e-1
									assert abs(result_mean.item() - 18.0_331   ) < 1e-3










from math import exp


def 							__SCREAMING_SNAKE_CASE						(    snake_case_		):

						'''simple docstring'''


						return 8 * x - 2 * exp(-x		)


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_			,					snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase									=  lower_bound
						_UpperCAmelCase									=  upper_bound
						for _ in range(0			,					snake_case_		):
												_UpperCAmelCase							,				_UpperCAmelCase									=  xa, xa - (f(snake_case_		) * (xa - xa)) / (f(snake_case_		) - f(snake_case_		))
						return xa


if __name__ == "__main__":
				print(f"""Example: {secant_method(1, 3, 2)}""")




