'''simple docstring'''




import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest





class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = (CMStochasticIterativeScheduler,)
			UpperCamelCase__       = 10







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]       ,    **lowercase_       :						List[str]					):
										lowercase_      :    List[str]       		= {
										    """num_train_timesteps""": 201,
										    """sigma_min""": 0.0_02,
										    """sigma_max""": 80.0,
										}

										config.update(**lowercase_					)
										return config







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										lowercase_      :    Optional[int]       		= 10

										lowercase_      :    Any       		= self.get_scheduler_config()
										lowercase_      :    Dict       		= self.scheduler_classes[0](**lowercase_					)

										scheduler.set_timesteps(lowercase_					)

										lowercase_      :    Dict       		= scheduler.timesteps[0]
										lowercase_      :    Optional[int]       		= scheduler.timesteps[1]

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

										lowercase_      :    List[Any]       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_					).prev_sample
										lowercase_      :    Optional[int]       		= scheduler.step(lowercase_       ,    lowercase_       ,    lowercase_					).prev_sample

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







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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										for clip_denoised in [True, False]:
																	self.check_over_configs(clip_denoised=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_      :    List[Any]       		= self.scheduler_classes[0]
										lowercase_      :    Any       		= self.get_scheduler_config()
										lowercase_      :    Dict       		= scheduler_class(**lowercase_					)

										lowercase_      :    Optional[int]       		= 1
										scheduler.set_timesteps(lowercase_					)
										lowercase_      :    Any       		= scheduler.timesteps

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

										lowercase_      :    Optional[int]       		= self.dummy_model()
										lowercase_      :    int       		= self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(lowercase_					):
																	# 1. scale model input
																	lowercase_      :    int       		= scheduler.scale_model_input(lowercase_       ,    lowercase_					)

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

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

																	lowercase_      :    str       		= pred_prev_sample

										lowercase_      :    List[Any]       		= torch.sum(torch.abs(lowercase_					)					)
										lowercase_      :    List[Any]       		= torch.mean(torch.abs(lowercase_					)					)

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										lowercase_      :    int       		= self.scheduler_classes[0]
										lowercase_      :    List[Any]       		= self.get_scheduler_config()
										lowercase_      :    List[str]       		= scheduler_class(**lowercase_					)

										lowercase_      :    List[Any]       		= [106, 0]
										scheduler.set_timesteps(timesteps=lowercase_					)
										lowercase_      :    str       		= scheduler.timesteps

										lowercase_      :    Any       		= torch.manual_seed(0					)

										lowercase_      :    List[Any]       		= self.dummy_model()
										lowercase_      :    Optional[Any]       		= self.dummy_sample_deter * scheduler.init_noise_sigma

										for t in timesteps:
																	# 1. scale model input
																	lowercase_      :    Any       		= scheduler.scale_model_input(lowercase_       ,    lowercase_					)

																	# 2. predict noise residual
																	lowercase_      :    List[Any]       		= model(lowercase_       ,    lowercase_					)

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

																	lowercase_      :    Dict       		= pred_prev_sample

										lowercase_      :    List[str]       		= torch.sum(torch.abs(lowercase_					)					)
										lowercase_      :    str       		= torch.mean(torch.abs(lowercase_					)					)

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										lowercase_      :    Union[str, Any]       		= self.scheduler_classes[0]
										lowercase_      :    List[Any]       		= self.get_scheduler_config()
										lowercase_      :    Optional[Any]       		= scheduler_class(**lowercase_					)

										lowercase_      :    List[Any]       		= [39, 30, 12, 15, 0]

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										lowercase_      :    List[Any]       		= self.scheduler_classes[0]
										lowercase_      :    Optional[Any]       		= self.get_scheduler_config()
										lowercase_      :    Optional[Any]       		= scheduler_class(**lowercase_					)

										lowercase_      :    Union[str, Any]       		= [39, 30, 12, 1, 0]
										lowercase_      :    Optional[Any]       		= len(lowercase_					)

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										lowercase_      :    List[str]       		= self.scheduler_classes[0]
										lowercase_      :    str       		= self.get_scheduler_config()
										lowercase_      :    Optional[int]       		= scheduler_class(**lowercase_					)

										lowercase_      :    Dict       		= [scheduler.config.num_train_timesteps]

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






'''simple docstring'''




from __future__ import annotations




def 							lowerCamelCase			(   UpperCAmelCase__ :      list   ,   UpperCAmelCase__ :      int | None = None   ,   UpperCAmelCase__ :      int | None = None				)    ->   None:
							if start is None:
														lowercase_      :    Any       		= 0

							if end is None:
														lowercase_      :    List[Any]       		= len(UpperCAmelCase__				) - 1

							if start >= end:
														return

							lowercase_      :    Optional[int]       		= (start + end) // 2

							slowsort(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__				)
							slowsort(UpperCAmelCase__   ,   mid + 1   ,   UpperCAmelCase__				)

							if sequence[end] < sequence[mid]:
														lowercase_		,		lowercase_      :    Dict       		= sequence[mid], sequence[end]

							slowsort(UpperCAmelCase__   ,   UpperCAmelCase__   ,   end - 1				)


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

						testmod()





