


'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest


class 		_snake_case					(     a__	):
					lowerCAmelCase      :int   =					(CMStochasticIterativeScheduler,)
					lowerCAmelCase      :int   =					10




					def 				snake_case__						(      self							,							**_lowerCamelCase):
								UpperCAmelCase__  :					Dict        =     {
								    """num_train_timesteps""": 201,
								    """sigma_min""": 0.002,
								    """sigma_max""": 80.0,
								}

								config.update(**_lowerCamelCase)
								return config




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[Any]        =     10

								UpperCAmelCase__  :					str        =     self.get_scheduler_config()
								UpperCAmelCase__  :					Optional[Any]        =     self.scheduler_classes[0](**_lowerCamelCase)

								scheduler.set_timesteps(_lowerCamelCase)

								UpperCAmelCase__  :					Any        =     scheduler.timesteps[0]
								UpperCAmelCase__  :					Optional[Any]        =     scheduler.timesteps[1]

								UpperCAmelCase__  :					Optional[int]        =     self.dummy_sample
								UpperCAmelCase__  :					Optional[int]        =     0.1 * sample

								UpperCAmelCase__  :					List[Any]        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase).prev_sample
								UpperCAmelCase__  :					str        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase).prev_sample

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




					def 				snake_case__						(      self):
								for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=_lowerCamelCase)




					def 				snake_case__						(      self):
								for clip_denoised in [True, False]:
											self.check_over_configs(clip_denoised=_lowerCamelCase)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					str        =     self.scheduler_classes[0]
								UpperCAmelCase__  :					Optional[Any]        =     self.get_scheduler_config()
								UpperCAmelCase__  :					Optional[int]        =     scheduler_class(**_lowerCamelCase)

								UpperCAmelCase__  :					List[Any]        =     1
								scheduler.set_timesteps(_lowerCamelCase)
								UpperCAmelCase__  :					Dict        =     scheduler.timesteps

								UpperCAmelCase__  :					str        =     torch.manual_seed(0)

								UpperCAmelCase__  :					Dict        =     self.dummy_model()
								UpperCAmelCase__  :					Union[str, Any]        =     self.dummy_sample_deter * scheduler.init_noise_sigma

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

											# 2. predict noise residual
											UpperCAmelCase__  :					Tuple        =     model(_lowerCamelCase							,							_lowerCamelCase)

											# 3. predict previous sample x_t-1
											UpperCAmelCase__  :					Dict        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							generator=_lowerCamelCase).prev_sample

											UpperCAmelCase__  :					Tuple        =     pred_prev_sample

								UpperCAmelCase__  :					Tuple        =     torch.sum(torch.abs(_lowerCamelCase))
								UpperCAmelCase__  :					int        =     torch.mean(torch.abs(_lowerCamelCase))

								assert abs(result_sum.item() - 192.7614) < 1e-2
								assert abs(result_mean.item() - 0.2510) < 1e-3




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Any        =     self.scheduler_classes[0]
								UpperCAmelCase__  :					int        =     self.get_scheduler_config()
								UpperCAmelCase__  :					Any        =     scheduler_class(**_lowerCamelCase)

								UpperCAmelCase__  :					Union[str, Any]        =     [106, 0]
								scheduler.set_timesteps(timesteps=_lowerCamelCase)
								UpperCAmelCase__  :					Optional[Any]        =     scheduler.timesteps

								UpperCAmelCase__  :					Optional[int]        =     torch.manual_seed(0)

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

								for t in timesteps:
											# 1. scale model input
											UpperCAmelCase__  :					Dict        =     scheduler.scale_model_input(_lowerCamelCase							,							_lowerCamelCase)

											# 2. predict noise residual
											UpperCAmelCase__  :					Optional[Any]        =     model(_lowerCamelCase							,							_lowerCamelCase)

											# 3. predict previous sample x_t-1
											UpperCAmelCase__  :					Tuple        =     scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							generator=_lowerCamelCase).prev_sample

											UpperCAmelCase__  :					List[str]        =     pred_prev_sample

								UpperCAmelCase__  :					Tuple        =     torch.sum(torch.abs(_lowerCamelCase))
								UpperCAmelCase__  :					str        =     torch.mean(torch.abs(_lowerCamelCase))

								assert abs(result_sum.item() - 347.6357) < 1e-2
								assert abs(result_mean.item() - 0.4527) < 1e-3




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[int]        =     self.scheduler_classes[0]
								UpperCAmelCase__  :					Optional[int]        =     self.get_scheduler_config()
								UpperCAmelCase__  :					Optional[int]        =     scheduler_class(**_lowerCamelCase)

								UpperCAmelCase__  :					Optional[Any]        =     [39, 30, 12, 15, 0]

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




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[Any]        =     self.scheduler_classes[0]
								UpperCAmelCase__  :					List[str]        =     self.get_scheduler_config()
								UpperCAmelCase__  :					Any        =     scheduler_class(**_lowerCamelCase)

								UpperCAmelCase__  :					int        =     [39, 30, 12, 1, 0]
								UpperCAmelCase__  :					List[Any]        =     len(_lowerCamelCase)

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




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[str]        =     self.scheduler_classes[0]
								UpperCAmelCase__  :					Tuple        =     self.get_scheduler_config()
								UpperCAmelCase__  :					Optional[Any]        =     scheduler_class(**_lowerCamelCase)

								UpperCAmelCase__  :					str        =     [scheduler.config.num_train_timesteps]

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



'''simple docstring'''



def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			if height >= 1:
						move_tower(height - 1 ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
						move_disk(UpperCamelCase__ ,      UpperCamelCase__					)
						move_tower(height - 1 ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			print("""moving disk from""" ,      UpperCamelCase__ ,      """to""" ,      UpperCamelCase__					)







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					List[str]        =     int(input("""Height of hanoi: """					).strip()					)
			move_tower(UpperCamelCase__ ,      """A""" ,      """B""" ,      """C"""					)


if __name__ == "__main__":
		main()