






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				str				    =						(UnCLIPScheduler,)


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {
						    'num_train_timesteps': 1000,
						    'variance_type': 'fixed_small_log',
						    'clip_sample': True,
						    'clip_sample_range': 1.0,
						    'prediction_type': 'epsilon',
						}

						config.update(**UpperCamelCase__       )
						return config


		def   __snake_case			(						self       ):
						for timesteps in [1, 5, 100, 1000]:
										self.check_over_configs(num_train_timesteps=UpperCamelCase__       )


		def   __snake_case			(						self       ):
						for variance in ["fixed_small_log", "learned_range"]:
										self.check_over_configs(variance_type=UpperCamelCase__       )


		def   __snake_case			(						self       ):
						for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=UpperCamelCase__       )


		def   __snake_case			(						self       ):
						for clip_sample_range in [1, 5, 10, 20]:
										self.check_over_configs(clip_sample_range=UpperCamelCase__       )


		def   __snake_case			(						self       ):
						for prediction_type in ["epsilon", "sample"]:
										self.check_over_configs(prediction_type=UpperCamelCase__       )


		def   __snake_case			(						self       ):
						for time_step in [0, 500, 999]:
										for prev_timestep in [None, 5, 100, 250, 500, 750]:
														if prev_timestep is not None and prev_timestep >= time_step:
																		continue

														self.check_over_forward(time_step=UpperCamelCase__ ,    prev_timestep=UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_scheduler_config(variance_type='fixed_small_log'       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   scheduler_class(**UpperCamelCase__       )

						assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.0_000E-10       )       ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.0_54_96_25       )       ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.9_99_49_87       )       ) < 1E-5


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE_   :	str													=   self.get_scheduler_config(variance_type='learned_range'       )
						SCREAMING_SNAKE_CASE_   :	int													=   scheduler_class(**UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   0.5

						assert scheduler._get_variance(1 ,    predicted_variance=UpperCamelCase__       ) - -10.1_71_27_90 < 1E-5
						assert scheduler._get_variance(487 ,    predicted_variance=UpperCamelCase__       ) - -5.7_99_80_52 < 1E-5
						assert scheduler._get_variance(999 ,    predicted_variance=UpperCamelCase__       ) - -0.0_01_00_11 < 1E-5


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

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   scheduler.timesteps

						SCREAMING_SNAKE_CASE_   :	Any													=   self.dummy_model()
						SCREAMING_SNAKE_CASE_   :	str													=   self.dummy_sample_deter
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.manual_seed(0       )

						for i, t in enumerate(UpperCamelCase__       ):
										# 1. predict noise residual
										SCREAMING_SNAKE_CASE_   :	Tuple													=   model(UpperCamelCase__ ,    UpperCamelCase__       )

										# 2. predict previous mean of sample x_t-1
										SCREAMING_SNAKE_CASE_   :	Any													=   scheduler.step(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    generator=UpperCamelCase__       ).prev_sample

										SCREAMING_SNAKE_CASE_   :	str													=   pred_prev_sample

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   torch.sum(torch.abs(UpperCamelCase__       )       )
						SCREAMING_SNAKE_CASE_   :	int													=   torch.mean(torch.abs(UpperCamelCase__       )       )

						assert abs(result_sum.item() - 2_52.2_68_24_95       ) < 1E-2
						assert abs(result_mean.item() - 0.3_28_47_43       ) < 1E-3


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_scheduler_config()
						SCREAMING_SNAKE_CASE_   :	int													=   scheduler_class(**UpperCamelCase__       )

						scheduler.set_timesteps(25       )

						SCREAMING_SNAKE_CASE_   :	str													=   scheduler.timesteps

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.dummy_model()
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.dummy_sample_deter
						SCREAMING_SNAKE_CASE_   :	Dict													=   torch.manual_seed(0       )

						for i, t in enumerate(UpperCamelCase__       ):
										# 1. predict noise residual
										SCREAMING_SNAKE_CASE_   :	Any													=   model(UpperCamelCase__ ,    UpperCamelCase__       )

										if i + 1 == timesteps.shape[0]:
														SCREAMING_SNAKE_CASE_   :	Dict													=   None
										else:
														SCREAMING_SNAKE_CASE_   :	List[Any]													=   timesteps[i + 1]

										# 2. predict previous mean of sample x_t-1
										SCREAMING_SNAKE_CASE_   :	Any													=   scheduler.step(
										    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    prev_timestep=UpperCamelCase__ ,    generator=UpperCamelCase__       ).prev_sample

										SCREAMING_SNAKE_CASE_   :	List[str]													=   pred_prev_sample

						SCREAMING_SNAKE_CASE_   :	str													=   torch.sum(torch.abs(UpperCamelCase__       )       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   torch.mean(torch.abs(UpperCamelCase__       )       )

						assert abs(result_sum.item() - 2_58.2_04_49_83       ) < 1E-2
						assert abs(result_mean.item() - 0.3_36_20_38       ) < 1E-3


		def   __snake_case			(						self       ):
						pass






		def   __snake_case			(						self       ):
						pass







def 					_lowerCamelCase(			):





				'''simple docstring'''







				return 1



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else two_pence(x - 2      ) + one_pence()



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else five_pence(x - 5      ) + two_pence(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else ten_pence(x - 10      ) + five_pence(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else twenty_pence(x - 20      ) + ten_pence(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else fifty_pence(x - 50      ) + twenty_pence(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else one_pound(x - 100      ) + fifty_pence(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else two_pound(x - 200      ) + one_pound(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int = 200      ):





				'''simple docstring'''







				return two_pound(lowerCAmelCase__      )


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