



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     =(DDPMParallelScheduler,)



		def 		lowerCAmelCase__				(self		,**__lowerCamelCase			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					{
						    '''num_train_timesteps''': 10_00,
						    '''beta_start''': 0.0001,
						    '''beta_end''': 0.02,
						    '''beta_schedule''': '''linear''',
						    '''variance_type''': '''fixed_small''',
						    '''clip_sample''': True,
						}

						config.update(**__lowerCamelCase			)
						return config



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						for timesteps in [1, 5, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						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=__lowerCamelCase		,beta_end=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						for schedule in ["linear", "squaredcos_cap_v2"]:
										self.check_over_configs(beta_schedule=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      List[Any]:

						"""simple docstring"""






						for variance in ["fixed_small", "fixed_large", "other"]:
										self.check_over_configs(variance_type=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						self.check_over_configs(thresholding=__lowerCamelCase			)
						for threshold in [0.5, 1.0, 2.0]:
										for prediction_type in ["epsilon", "sample", "v_prediction"]:
														self.check_over_configs(
														    thresholding=__lowerCamelCase		,prediction_type=__lowerCamelCase		,sample_max_value=__lowerCamelCase		,)



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						for prediction_type in ["epsilon", "sample", "v_prediction"]:
										self.check_over_configs(prediction_type=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						for t in [0, 5_00, 9_99]:
										self.check_over_forward(time_step=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self.scheduler_classes[0]
						lowerCAmelCase__					:    Union[str, Any]												=					self.get_scheduler_config()
						lowerCAmelCase__					:    List[str]												=					scheduler_class(**__lowerCamelCase			)

						assert torch.sum(torch.abs(scheduler._get_variance(0			) - 0.0			)			) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(4_87			) - 0.0_0979			)			) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(9_99			) - 0.02			)			) < 1e-5



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self.scheduler_classes[0]
						lowerCAmelCase__					:    Optional[Any]												=					self.get_scheduler_config()
						lowerCAmelCase__					:    List[str]												=					scheduler_class(**__lowerCamelCase			)

						lowerCAmelCase__					:    Tuple												=					len(__lowerCamelCase			)

						lowerCAmelCase__					:    Tuple												=					self.dummy_model()
						lowerCAmelCase__					:    str												=					self.dummy_sample_deter
						lowerCAmelCase__					:    List[Any]												=					self.dummy_sample_deter + 0.1
						lowerCAmelCase__					:    Optional[Any]												=					self.dummy_sample_deter - 0.1

						lowerCAmelCase__					:    Tuple												=					samplea.shape[0]
						lowerCAmelCase__					:    int												=					torch.stack([samplea, samplea, samplea]		,dim=0			)
						lowerCAmelCase__					:    Dict												=					torch.arange(__lowerCamelCase			)[0:3, None].repeat(1		,__lowerCamelCase			)

						lowerCAmelCase__					:    Tuple												=					model(samples.flatten(0		,1			)		,timesteps.flatten(0		,1			)			)
						lowerCAmelCase__					:    Any												=					scheduler.batch_step_no_noise(__lowerCamelCase		,timesteps.flatten(0		,1			)		,samples.flatten(0		,1			)			)

						lowerCAmelCase__					:    List[str]												=					torch.sum(torch.abs(__lowerCamelCase			)			)
						lowerCAmelCase__					:    Dict												=					torch.mean(torch.abs(__lowerCamelCase			)			)

						assert abs(result_sum.item() - 1153.1833			) < 1e-2
						assert abs(result_mean.item() - 0.5005			) < 1e-3



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

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					self.scheduler_classes[0]
						lowerCAmelCase__					:    str												=					self.get_scheduler_config()
						lowerCAmelCase__					:    str												=					scheduler_class(**__lowerCamelCase			)

						lowerCAmelCase__					:    List[str]												=					len(__lowerCamelCase			)

						lowerCAmelCase__					:    Dict												=					self.dummy_model()
						lowerCAmelCase__					:    Any												=					self.dummy_sample_deter
						lowerCAmelCase__					:    str												=					torch.manual_seed(0			)

						for t in reversed(range(__lowerCamelCase			)			):
										# 1. predict noise residual
										lowerCAmelCase__					:    Dict												=					model(__lowerCamelCase		,__lowerCamelCase			)

										# 2. predict previous mean of sample x_t-1
										lowerCAmelCase__					:    int												=					scheduler.step(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,generator=__lowerCamelCase			).prev_sample

										lowerCAmelCase__					:    Dict												=					pred_prev_sample

						lowerCAmelCase__					:    Dict												=					torch.sum(torch.abs(__lowerCamelCase			)			)
						lowerCAmelCase__					:    Dict												=					torch.mean(torch.abs(__lowerCamelCase			)			)

						assert abs(result_sum.item() - 258.9606			) < 1e-2
						assert abs(result_mean.item() - 0.3372			) < 1e-3



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					self.scheduler_classes[0]
						lowerCAmelCase__					:    Optional[Any]												=					self.get_scheduler_config(prediction_type='''v_prediction'''			)
						lowerCAmelCase__					:    List[str]												=					scheduler_class(**__lowerCamelCase			)

						lowerCAmelCase__					:    Dict												=					len(__lowerCamelCase			)

						lowerCAmelCase__					:    Optional[int]												=					self.dummy_model()
						lowerCAmelCase__					:    Tuple												=					self.dummy_sample_deter
						lowerCAmelCase__					:    str												=					torch.manual_seed(0			)

						for t in reversed(range(__lowerCamelCase			)			):
										# 1. predict noise residual
										lowerCAmelCase__					:    Optional[int]												=					model(__lowerCamelCase		,__lowerCamelCase			)

										# 2. predict previous mean of sample x_t-1
										lowerCAmelCase__					:    Optional[Any]												=					scheduler.step(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,generator=__lowerCamelCase			).prev_sample

										lowerCAmelCase__					:    str												=					pred_prev_sample

						lowerCAmelCase__					:    Optional[Any]												=					torch.sum(torch.abs(__lowerCamelCase			)			)
						lowerCAmelCase__					:    Optional[Any]												=					torch.mean(torch.abs(__lowerCamelCase			)			)

						assert abs(result_sum.item() - 202.0296			) < 1e-2
						assert abs(result_mean.item() - 0.2631			) < 1e-3



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

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					self.scheduler_classes[0]
						lowerCAmelCase__					:    Dict												=					self.get_scheduler_config()
						lowerCAmelCase__					:    int												=					scheduler_class(**__lowerCamelCase			)

						lowerCAmelCase__					:    Optional[int]												=					[1_00, 87, 50, 1, 0]

						scheduler.set_timesteps(timesteps=__lowerCamelCase			)

						lowerCAmelCase__					:    Tuple												=					scheduler.timesteps

						for i, timestep in enumerate(__lowerCamelCase			):
										if i == len(__lowerCamelCase			) - 1:
														lowerCAmelCase__					:    List[str]												=					-1
										else:
														lowerCAmelCase__					:    int												=					timesteps[i + 1]

										lowerCAmelCase__					:    Tuple												=					scheduler.previous_timestep(__lowerCamelCase			)
										lowerCAmelCase__					:    int												=					prev_t.item()

										self.assertEqual(__lowerCamelCase		,__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self.scheduler_classes[0]
						lowerCAmelCase__					:    Union[str, Any]												=					self.get_scheduler_config()
						lowerCAmelCase__					:    Optional[Any]												=					scheduler_class(**__lowerCamelCase			)

						lowerCAmelCase__					:    str												=					[1_00, 87, 50, 51, 0]

						with self.assertRaises(__lowerCamelCase		,msg='''`custom_timesteps` must be in descending order.'''			):
										scheduler.set_timesteps(timesteps=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					self.scheduler_classes[0]
						lowerCAmelCase__					:    int												=					self.get_scheduler_config()
						lowerCAmelCase__					:    Optional[Any]												=					scheduler_class(**__lowerCamelCase			)

						lowerCAmelCase__					:    str												=					[1_00, 87, 50, 1, 0]
						lowerCAmelCase__					:    Tuple												=					len(__lowerCamelCase			)

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



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

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					self.scheduler_classes[0]
						lowerCAmelCase__					:    Dict												=					self.get_scheduler_config()
						lowerCAmelCase__					:    List[Any]												=					scheduler_class(**__lowerCamelCase			)

						lowerCAmelCase__					:    Dict												=					[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			)






def      lowerCAmelCase__		(   lowerCamelCase_		:		int = 1000):





				'''simple docstring'''

				lowerCAmelCase__						,		lowerCAmelCase__					:    int												=					1, 1
				lowerCAmelCase__					:    Any												=					2
				while True:
								lowerCAmelCase__					:    Optional[Any]												=					0
								lowerCAmelCase__					:    Any												=					fa + fa
								lowerCAmelCase__						,		lowerCAmelCase__					:    Optional[Any]												=					fa, f
								index += 1
								for _ in str(lowerCamelCase_):
												i += 1
								if i == n:
												break
				return index


if __name__ == "__main__":
							print(solution(int(str(input()).strip())))

