

'''simple docstring'''







import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 					__a   (		lowercase__  ):
						__SCREAMING_SNAKE_CASE     :					Tuple  =      (KDPMaAncestralDiscreteScheduler,)
						__SCREAMING_SNAKE_CASE     :					str  =      1_0

						def 	_UpperCAmelCase							(		self	:						int    ,			**lowercase__	:						Union[str, Any])						->List[Any]:

							"""simple docstring"""



							_lowercase							=     {
							    "num_train_timesteps": 11_00,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**lowercase_)
							return config

						def 	_UpperCAmelCase							(		self	:						Tuple)						->List[Any]:

							"""simple docstring"""



							for timesteps in [10, 50, 1_00, 10_00]:
								self.check_over_configs(num_train_timesteps=lowercase_)

						def 	_UpperCAmelCase							(		self	:						List[str])						->List[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=lowercase_    ,			beta_end=lowercase_)

						def 	_UpperCAmelCase							(		self	:						str)						->Union[str, Any]:

							"""simple docstring"""



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

						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->Optional[int]:

							"""simple docstring"""



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

							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(lowercase_)

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

								_lowercase							=     model(lowercase_    ,			lowercase_)

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

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

							assert abs(result_sum.item() - 1_38_49.38_77) < 1e-2
							assert abs(result_mean.item() - 18.0331) < 5e-3

						def 	_UpperCAmelCase							(		self	:						List[str])						->Optional[int]:

							"""simple docstring"""



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

						def 	_UpperCAmelCase							(		self	:						Dict)						->Tuple:

							"""simple docstring"""



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

							scheduler.set_timesteps(self.num_inference_steps)

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

							_lowercase							=     torch.manual_seed(0)

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

								_lowercase							=     model(lowercase_    ,			lowercase_)

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

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

							assert abs(result_sum.item() - 3_28.99_70) < 1e-2
							assert abs(result_mean.item() - 0.4284) < 1e-3


						def 	_UpperCAmelCase							(		self	:						Union[str, Any])						->Optional[int]:

							"""simple docstring"""



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

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

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

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

								_lowercase							=     model(lowercase_    ,			lowercase_)

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

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

							assert abs(result_sum.item() - 1_38_49.38_18) < 1e-1
							assert abs(result_mean.item() - 18.0331) < 1e-3






"""simple docstring"""





from math import exp







def       __a	(						__lowerCamelCase    ):
   return 8 * x - 2 * exp(-x    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Any     	=       lower_bound
   UpperCAmelCase_   :   int     	=       upper_bound
   for _ in range(0,						__lowerCamelCase    ):
      UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[Any]     	=       xa, xa - (f(__lowerCamelCase    ) * (xa - xa)) / (f(__lowerCamelCase    ) - f(__lowerCamelCase    ))
   return xa


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


