
"""simple docstring"""





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class __lowerCAmelCase				(					__SCREAMING_SNAKE_CASE      ):




			"""simple docstring"""







			snake_case	      =				(CMStochasticIterativeScheduler,)
			snake_case	      =				10




			def      lowerCamelCase__     (				self						:					Dict       ,   **_snake_case						:					int	)      -> Any:







								"""simple docstring"""
								A_				 =						{
								    """num_train_timesteps""": 201,
								    """sigma_min""": 0.0_0_2,
								    """sigma_max""": 8_0.0,
								}

								config.update(**_a	)
								return config




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







								"""simple docstring"""
								A_				 =						10

								A_				 =						self.get_scheduler_config()
								A_				 =						self.scheduler_classes[0](**_a	)

								scheduler.set_timesteps(_a	)

								A_				 =						scheduler.timesteps[0]
								A_				 =						scheduler.timesteps[1]

								A_				 =						self.dummy_sample
								A_				 =						0.1 * sample

								A_				 =						scheduler.step(_a       ,   _a       ,   _a	).prev_sample
								A_				 =						scheduler.step(_a       ,   _a       ,   _a	).prev_sample

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




			def      lowerCamelCase__     (				self						:					Any	)      -> List[str]:







								"""simple docstring"""




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




			def      lowerCamelCase__     (				self						:					int	)      -> List[Any]:







								"""simple docstring"""




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




			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.scheduler_classes[0]
								A_				 =						self.get_scheduler_config()
								A_				 =						scheduler_class(**_a	)

								A_				 =						1
								scheduler.set_timesteps(_a	)
								A_				 =						scheduler.timesteps

								A_				 =						torch.manual_seed(0	)

								A_				 =						self.dummy_model()
								A_				 =						self.dummy_sample_deter * scheduler.init_noise_sigma

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

													# 2. predict noise residual
													A_				 =						model(_a       ,   _a	)

													# 3. predict previous sample x_t-1
													A_				 =						scheduler.step(_a       ,   _a       ,   _a       ,   generator=_a	).prev_sample

													A_				 =						pred_prev_sample

								A_				 =						torch.sum(torch.abs(_a	)	)
								A_				 =						torch.mean(torch.abs(_a	)	)

								assert abs(result_sum.item() - 1_9_2.7_6_1_4	) < 1e-2
								assert abs(result_mean.item() - 0.2_5_1_0	) < 1e-3




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







								"""simple docstring"""
								A_				 =						self.scheduler_classes[0]
								A_				 =						self.get_scheduler_config()
								A_				 =						scheduler_class(**_a	)

								A_				 =						[106, 0]
								scheduler.set_timesteps(timesteps=_a	)
								A_				 =						scheduler.timesteps

								A_				 =						torch.manual_seed(0	)

								A_				 =						self.dummy_model()
								A_				 =						self.dummy_sample_deter * scheduler.init_noise_sigma

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

													# 2. predict noise residual
													A_				 =						model(_a       ,   _a	)

													# 3. predict previous sample x_t-1
													A_				 =						scheduler.step(_a       ,   _a       ,   _a       ,   generator=_a	).prev_sample

													A_				 =						pred_prev_sample

								A_				 =						torch.sum(torch.abs(_a	)	)
								A_				 =						torch.mean(torch.abs(_a	)	)

								assert abs(result_sum.item() - 3_4_7.6_3_5_7	) < 1e-2
								assert abs(result_mean.item() - 0.4_5_2_7	) < 1e-3




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







								"""simple docstring"""
								A_				 =						self.scheduler_classes[0]
								A_				 =						self.get_scheduler_config()
								A_				 =						scheduler_class(**_a	)

								A_				 =						[39, 30, 12, 15, 0]

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




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







								"""simple docstring"""
								A_				 =						self.scheduler_classes[0]
								A_				 =						self.get_scheduler_config()
								A_				 =						scheduler_class(**_a	)

								A_				 =						[39, 30, 12, 1, 0]
								A_				 =						len(_a	)

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






			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.scheduler_classes[0]
								A_				 =						self.get_scheduler_config()
								A_				 =						scheduler_class(**_a	)

								A_				 =						[scheduler.config.num_train_timesteps]

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






"""simple docstring"""





import baseaa
def 							A_     (__a				):

					'''simple docstring'''





					return baseaa.aaaencode(string.encode("utf-8"				)				)
def 							A_     (__a				):

					'''simple docstring'''





					return baseaa.aaadecode(__a				).decode("utf-8"				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




