
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					=(DDIMParallelScheduler,)
				_lowercase					=(('''eta''', 0.0), ('''num_inference_steps''', 50))




				def      __a						(					self				,				**_UpperCamelCase     )				->      List[str]:
							lowerCAmelCase_						=	{
							    "num_train_timesteps": 1_000,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "clip_sample": True,
							}

							config.update(**_UpperCamelCase     )
							return config




				def      __a						(					self				,				**_UpperCamelCase     )				->      int:
							lowerCAmelCase_						=	self.scheduler_classes[0]
							lowerCAmelCase_						=	self.get_scheduler_config(**_UpperCamelCase     )
							lowerCAmelCase_						=	scheduler_class(**_UpperCamelCase     )

							lowerCAmelCase_	,  lowerCAmelCase_						=	10, 0.0

							lowerCAmelCase_						=	self.dummy_model()
							lowerCAmelCase_						=	self.dummy_sample_deter

							scheduler.set_timesteps(_UpperCamelCase     )

							for t in scheduler.timesteps:
										lowerCAmelCase_						=	model(_UpperCamelCase				,				_UpperCamelCase     )
										lowerCAmelCase_						=	scheduler.step(_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     ).prev_sample

							return sample




				def      __a						(					self     )				->      List[Any]:
							for timesteps in [100, 500, 1_000]:
										self.check_over_configs(num_train_timesteps=_UpperCamelCase     )




				def      __a						(					self     )				->      Tuple:
							for steps_offset in [0, 1]:
										self.check_over_configs(steps_offset=_UpperCamelCase     )

							lowerCAmelCase_						=	self.scheduler_classes[0]
							lowerCAmelCase_						=	self.get_scheduler_config(steps_offset=1     )
							lowerCAmelCase_						=	scheduler_class(**_UpperCamelCase     )
							scheduler.set_timesteps(5     )
							assert torch.equal(scheduler.timesteps				,				torch.LongTensor([801, 601, 401, 201, 1]     )     )




				def      __a						(					self     )				->      Union[str, Any]:
							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]				,				[0.002, 0.02, 0.2, 2]     ):
										self.check_over_configs(beta_start=_UpperCamelCase				,				beta_end=_UpperCamelCase     )




				def      __a						(					self     )				->      Optional[int]:
							for schedule in ["linear", "squaredcos_cap_v2"]:
										self.check_over_configs(beta_schedule=_UpperCamelCase     )




				def      __a						(					self     )				->      int:
							for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=_UpperCamelCase     )




				def      __a						(					self     )				->      Optional[Any]:
							for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=_UpperCamelCase     )




				def      __a						(					self     )				->      List[Any]:
							for timestep_spacing in ["trailing", "leading"]:
										self.check_over_configs(timestep_spacing=_UpperCamelCase     )




				def      __a						(					self     )				->      Optional[Any]:
							for rescale_betas_zero_snr in [True, False]:
										self.check_over_configs(rescale_betas_zero_snr=_UpperCamelCase     )




				def      __a						(					self     )				->      List[Any]:
							self.check_over_configs(thresholding=_UpperCamelCase     )
							for threshold in [0.5, 1.0, 2.0]:
										for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(
													    thresholding=_UpperCamelCase				,				prediction_type=_UpperCamelCase				,				sample_max_value=_UpperCamelCase				,				)




				def      __a						(					self     )				->      Optional[int]:
							for t in [1, 10, 49]:
										self.check_over_forward(time_step=_UpperCamelCase     )




				def      __a						(					self     )				->      Any:
							for t, num_inference_steps in zip([1, 10, 50]				,				[10, 50, 500]     ):
										self.check_over_forward(time_step=_UpperCamelCase				,				num_inference_steps=_UpperCamelCase     )




				def      __a						(					self     )				->      Optional[int]:
							for t, eta in zip([1, 10, 49]				,				[0.0, 0.5, 1.0]     ):
										self.check_over_forward(time_step=_UpperCamelCase				,				eta=_UpperCamelCase     )




				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	self.scheduler_classes[0]
							lowerCAmelCase_						=	self.get_scheduler_config()
							lowerCAmelCase_						=	scheduler_class(**_UpperCamelCase     )

							assert torch.sum(torch.abs(scheduler._get_variance(0				,				0     ) - 0.0     )     ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(420				,				400     ) - 0.14771     )     ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(980				,				960     ) - 0.32460     )     ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0				,				0     ) - 0.0     )     ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487				,				486     ) - 0.00979     )     ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999				,				998     ) - 0.02     )     ) < 1e-5




				def      __a						(					self     )				->      Union[str, Any]:
							lowerCAmelCase_						=	self.scheduler_classes[0]
							lowerCAmelCase_						=	self.get_scheduler_config()
							lowerCAmelCase_						=	scheduler_class(**_UpperCamelCase     )

							lowerCAmelCase_	,  lowerCAmelCase_						=	10, 0.0
							scheduler.set_timesteps(_UpperCamelCase     )

							lowerCAmelCase_						=	self.dummy_model()
							lowerCAmelCase_						=	self.dummy_sample_deter
							lowerCAmelCase_						=	self.dummy_sample_deter + 0.1
							lowerCAmelCase_						=	self.dummy_sample_deter - 0.1

							lowerCAmelCase_						=	samplea.shape[0]
							lowerCAmelCase_						=	torch.stack([samplea, samplea, samplea]				,				dim=0     )
							lowerCAmelCase_						=	torch.arange(_UpperCamelCase     )[0:3, None].repeat(1				,				_UpperCamelCase     )

							lowerCAmelCase_						=	model(samples.flatten(0				,				1     )				,				timesteps.flatten(0				,				1     )     )
							lowerCAmelCase_						=	scheduler.batch_step_no_noise(_UpperCamelCase				,				timesteps.flatten(0				,				1     )				,				samples.flatten(0				,				1     )				,				_UpperCamelCase     )

							lowerCAmelCase_						=	torch.sum(torch.abs(_UpperCamelCase     )     )
							lowerCAmelCase_						=	torch.mean(torch.abs(_UpperCamelCase     )     )

							assert abs(result_sum.item() - 1147.7904     ) < 1e-2
							assert abs(result_mean.item() - 0.4982     ) < 1e-3




				def      __a						(					self     )				->      Union[str, Any]:
							lowerCAmelCase_						=	self.full_loop()

							lowerCAmelCase_						=	torch.sum(torch.abs(_UpperCamelCase     )     )
							lowerCAmelCase_						=	torch.mean(torch.abs(_UpperCamelCase     )     )

							assert abs(result_sum.item() - 172.0067     ) < 1e-2
							assert abs(result_mean.item() - 0.223967     ) < 1e-3




				def      __a						(					self     )				->      Union[str, Any]:
							lowerCAmelCase_						=	self.full_loop(prediction_type="v_prediction"     )

							lowerCAmelCase_						=	torch.sum(torch.abs(_UpperCamelCase     )     )
							lowerCAmelCase_						=	torch.mean(torch.abs(_UpperCamelCase     )     )

							assert abs(result_sum.item() - 52.5302     ) < 1e-2
							assert abs(result_mean.item() - 0.0684     ) < 1e-3




				def      __a						(					self     )				->      Optional[int]:
							# We specify different beta, so that the first alpha is 0.99
							lowerCAmelCase_						=	self.full_loop(set_alpha_to_one=_UpperCamelCase				,				beta_start=0.01     )
							lowerCAmelCase_						=	torch.sum(torch.abs(_UpperCamelCase     )     )
							lowerCAmelCase_						=	torch.mean(torch.abs(_UpperCamelCase     )     )

							assert abs(result_sum.item() - 149.8295     ) < 1e-2
							assert abs(result_mean.item() - 0.1951     ) < 1e-3




				def      __a						(					self     )				->      Optional[Any]:
							# We specify different beta, so that the first alpha is 0.99
							lowerCAmelCase_						=	self.full_loop(set_alpha_to_one=_UpperCamelCase				,				beta_start=0.01     )
							lowerCAmelCase_						=	torch.sum(torch.abs(_UpperCamelCase     )     )
							lowerCAmelCase_						=	torch.mean(torch.abs(_UpperCamelCase     )     )

							assert abs(result_sum.item() - 149.0784     ) < 1e-2
							assert abs(result_mean.item() - 0.1941     ) < 1e-3





import math






def    lowerCamelCase__				(				__lowerCAmelCase  :  int				):




			"""simple docstring"""


			lowerCAmelCase_						=	0
			lowerCAmelCase_						=	0
			while num > 0:
						lowerCAmelCase_						=	num % 8
						lowerCAmelCase_						=	octal + (remainder * math.floor(math.pow(10    ,							__lowerCAmelCase				)				))
						counter += 1
						lowerCAmelCase_						=	math.floor(num / 8				)  # basically /= 8 without remainder if any
						# This formatting removes trailing '.0' from `octal`.
			return F"""0o{int(__lowerCAmelCase				)}"""






def    lowerCamelCase__				(				):




			"""simple docstring"""


			print("\n2 in octal is:"				)
			print(decimal_to_octal(2				)				)  # = 2
			print("\n8 in octal is:"				)
			print(decimal_to_octal(8				)				)  # = 10
			print("\n65 in octal is:"				)
			print(decimal_to_octal(65				)				)  # = 101
			print("\n216 in octal is:"				)
			print(decimal_to_octal(216				)				)  # = 330
			print("\n512 in octal is:"				)
			print(decimal_to_octal(512				)				)  # = 1000
			print("\n"				)


if __name__ == "__main__":
			main()



