





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class        _a(    __A    ):
		lowerCamelCase__     :List[str]   				=		(CMStochasticIterativeScheduler,)
		lowerCamelCase__     :str   				=		10







		def        lowercase       (    self ,						**__snake_case				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   {
							    "num_train_timesteps": 2_0_1,
							    "sigma_min": 0.0_02,
							    "sigma_max": 80.0,
							}

							config.update(**__snake_case				)
							return config







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


							'''simple docstring'''

							_snake_case       :					Tuple				       =   1_0

							_snake_case       :					Optional[int]				       =   self.get_scheduler_config()
							_snake_case       :					int				       =   self.scheduler_classes[0](**__snake_case				)

							scheduler.set_timesteps(__snake_case				)

							_snake_case       :					Optional[int]				       =   scheduler.timesteps[0]
							_snake_case       :					Optional[int]				       =   scheduler.timesteps[1]

							_snake_case       :					Tuple				       =   self.dummy_sample
							_snake_case       :					Optional[Any]				       =   0.1 * sample

							_snake_case       :					Dict				       =   scheduler.step(__snake_case ,						__snake_case ,						__snake_case				).prev_sample
							_snake_case       :					Any				       =   scheduler.step(__snake_case ,						__snake_case ,						__snake_case				).prev_sample

							self.assertEqual(output_a.shape ,						sample.shape				)
							self.assertEqual(output_a.shape ,						output_a.shape				)







		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
												self.check_over_configs(num_train_timesteps=__snake_case				)







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							for clip_denoised in [True, False]:
												self.check_over_configs(clip_denoised=__snake_case				)







		def        lowercase       (    self				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   self.scheduler_classes[0]
							_snake_case       :					Union[str, Any]				       =   self.get_scheduler_config()
							_snake_case       :					Optional[Any]				       =   scheduler_class(**__snake_case				)

							_snake_case       :					Union[str, Any]				       =   1
							scheduler.set_timesteps(__snake_case				)
							_snake_case       :					Dict				       =   scheduler.timesteps

							_snake_case       :					Union[str, Any]				       =   torch.manual_seed(0				)

							_snake_case       :					str				       =   self.dummy_model()
							_snake_case       :					str				       =   self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(__snake_case				):
												# 1. scale model input
												_snake_case       :					List[str]				       =   scheduler.scale_model_input(__snake_case ,						__snake_case				)

												# 2. predict noise residual
												_snake_case       :					Optional[int]				       =   model(__snake_case ,						__snake_case				)

												# 3. predict previous sample x_t-1
												_snake_case       :					List[Any]				       =   scheduler.step(__snake_case ,						__snake_case ,						__snake_case ,						generator=__snake_case				).prev_sample

												_snake_case       :					Any				       =   pred_prev_sample

							_snake_case       :					List[Any]				       =   torch.sum(torch.abs(__snake_case				)				)
							_snake_case       :					Dict				       =   torch.mean(torch.abs(__snake_case				)				)

							assert abs(result_sum.item() - 1_92.76_14				) < 1E-2
							assert abs(result_mean.item() - 0.25_10				) < 1E-3







		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   self.scheduler_classes[0]
							_snake_case       :					Any				       =   self.get_scheduler_config()
							_snake_case       :					List[Any]				       =   scheduler_class(**__snake_case				)

							_snake_case       :					int				       =   [1_0_6, 0]
							scheduler.set_timesteps(timesteps=__snake_case				)
							_snake_case       :					Tuple				       =   scheduler.timesteps

							_snake_case       :					Optional[Any]				       =   torch.manual_seed(0				)

							_snake_case       :					Tuple				       =   self.dummy_model()
							_snake_case       :					List[str]				       =   self.dummy_sample_deter * scheduler.init_noise_sigma

							for t in timesteps:
												# 1. scale model input
												_snake_case       :					List[str]				       =   scheduler.scale_model_input(__snake_case ,						__snake_case				)

												# 2. predict noise residual
												_snake_case       :					Optional[int]				       =   model(__snake_case ,						__snake_case				)

												# 3. predict previous sample x_t-1
												_snake_case       :					str				       =   scheduler.step(__snake_case ,						__snake_case ,						__snake_case ,						generator=__snake_case				).prev_sample

												_snake_case       :					Optional[int]				       =   pred_prev_sample

							_snake_case       :					List[Any]				       =   torch.sum(torch.abs(__snake_case				)				)
							_snake_case       :					int				       =   torch.mean(torch.abs(__snake_case				)				)

							assert abs(result_sum.item() - 3_47.63_57				) < 1E-2
							assert abs(result_mean.item() - 0.45_27				) < 1E-3







		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   self.scheduler_classes[0]
							_snake_case       :					Dict				       =   self.get_scheduler_config()
							_snake_case       :					str				       =   scheduler_class(**__snake_case				)

							_snake_case       :					List[Any]				       =   [3_9, 3_0, 1_2, 1_5, 0]

							with self.assertRaises(__snake_case ,						msg="`timesteps` must be in descending order."				):
												scheduler.set_timesteps(timesteps=__snake_case				)







		def        lowercase       (    self				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Any				       =   self.scheduler_classes[0]
							_snake_case       :					Optional[Any]				       =   self.get_scheduler_config()
							_snake_case       :					Tuple				       =   scheduler_class(**__snake_case				)

							_snake_case       :					List[Any]				       =   [3_9, 3_0, 1_2, 1, 0]
							_snake_case       :					Dict				       =   len(__snake_case				)

							with self.assertRaises(__snake_case ,						msg="Can only pass one of `num_inference_steps` or `timesteps`."				):
												scheduler.set_timesteps(num_inference_steps=__snake_case ,						timesteps=__snake_case				)







		def        lowercase       (    self				)      ->      str:


							'''simple docstring'''

							_snake_case       :					int				       =   self.scheduler_classes[0]
							_snake_case       :					List[str]				       =   self.get_scheduler_config()
							_snake_case       :					List[str]				       =   scheduler_class(**__snake_case				)

							_snake_case       :					Union[str, Any]				       =   [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    __snake_case ,						msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}" ,						):
												scheduler.set_timesteps(timesteps=__snake_case				)






import math

from numpy import inf
from scipy.integrate import quad







def    A				(							UpperCAmelCase				):
					if num <= 0:
										raise ValueError("math domain error"				)

					return quad(UpperCAmelCase  ,  0  ,  UpperCAmelCase  ,  args=(UpperCAmelCase)				)[0]







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return math.pow(UpperCAmelCase  ,  z - 1				) * math.exp(-x				)


if __name__ == "__main__":
							from doctest import testmod

							testmod()