
"""simple docstring"""






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest



class  lowercase				(   _a						):
			lowercase			     =    (DDIMScheduler,)
			lowercase			     =    (('eta', 0.0), ('num_inference_steps', 50))






			def 				UpperCAmelCase       (self     :  Dict			,**SCREAMING_SNAKE_CASE_     :  Optional[int] )					->  Optional[int]:





				"""simple docstring"""


				lowerCAmelCase				       =     {
				    '''num_train_timesteps''': 1_000,
				    '''beta_start''': 0.00_01,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				    '''clip_sample''': True,
				}

				config.update(**SCREAMING_SNAKE_CASE_ )
				return config






			def 				UpperCAmelCase       (self     :  Tuple			,**SCREAMING_SNAKE_CASE_     :  List[str] )					->  List[str]:





				"""simple docstring"""


				lowerCAmelCase				       =     self.scheduler_classes[0]
				lowerCAmelCase				       =     self.get_scheduler_config(**SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     scheduler_class(**SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase       ,					lowerCAmelCase				       =     10, 0.0

				lowerCAmelCase				       =     self.dummy_model()
				lowerCAmelCase				       =     self.dummy_sample_deter

				scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )

				for t in scheduler.timesteps:
					lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     scheduler.step(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ ).prev_sample

				return sample






			def 				UpperCAmelCase       (self     :  List[str] )					->  List[str]:





				"""simple docstring"""


				for timesteps in [100, 500, 1_000]:
					self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_ )






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





				"""simple docstring"""


				for steps_offset in [0, 1]:
					self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE_ )

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






			def 				UpperCAmelCase       (self     :  Tuple )					->  str:





				"""simple docstring"""


				for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]			,[0.0_02, 0.02, 0.2, 2] ):
					self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_			,beta_end=SCREAMING_SNAKE_CASE_ )






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





				"""simple docstring"""


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






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





				"""simple docstring"""


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






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





				"""simple docstring"""


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






			def 				UpperCAmelCase       (self     :  Tuple )					->  Optional[int]:





				"""simple docstring"""


				for timestep_spacing in ["trailing", "leading"]:
					self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE_ )






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





				"""simple docstring"""


				for rescale_betas_zero_snr in [True, False]:
					self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  str )					->  List[str]:





				"""simple docstring"""


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






			def 				UpperCAmelCase       (self     :  str )					->  Tuple:





				"""simple docstring"""


				for t in [1, 10, 49]:
					self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_ )






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





				"""simple docstring"""


				for t, num_inference_steps in zip([1, 10, 50]			,[10, 50, 500] ):
					self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_			,num_inference_steps=SCREAMING_SNAKE_CASE_ )






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





				"""simple docstring"""


				for t, eta in zip([1, 10, 49]			,[0.0, 0.5, 1.0] ):
					self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_			,eta=SCREAMING_SNAKE_CASE_ )






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





				"""simple docstring"""


				lowerCAmelCase				       =     self.scheduler_classes[0]
				lowerCAmelCase				       =     self.get_scheduler_config()
				lowerCAmelCase				       =     scheduler_class(**SCREAMING_SNAKE_CASE_ )

				assert torch.sum(torch.abs(scheduler._get_variance(0			,0 ) - 0.0 ) ) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(420			,400 ) - 0.1_47_71 ) ) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(980			,960 ) - 0.3_24_60 ) ) < 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 ) - 0.0_09_79 ) ) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(999			,998 ) - 0.02 ) ) < 1e-5






			def 				UpperCAmelCase       (self     :  Tuple )					->  str:





				"""simple docstring"""


				lowerCAmelCase				       =     self.full_loop()

				lowerCAmelCase				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
				lowerCAmelCase				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

				assert abs(result_sum.item() - 1_72.00_67 ) < 1e-2
				assert abs(result_mean.item() - 0.22_39_67 ) < 1e-3






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





				"""simple docstring"""


				lowerCAmelCase				       =     self.full_loop(prediction_type='''v_prediction''' )

				lowerCAmelCase				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
				lowerCAmelCase				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

				assert abs(result_sum.item() - 52.53_02 ) < 1e-2
				assert abs(result_mean.item() - 0.06_84 ) < 1e-3






			def 				UpperCAmelCase       (self     :  Tuple )					->  Dict:





				"""simple docstring"""


				lowerCAmelCase				       =     self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_			,beta_start=0.01 )
				lowerCAmelCase				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
				lowerCAmelCase				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

				assert abs(result_sum.item() - 1_49.82_95 ) < 1e-2
				assert abs(result_mean.item() - 0.19_51 ) < 1e-3






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





				"""simple docstring"""


				lowerCAmelCase				       =     self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_			,beta_start=0.01 )
				lowerCAmelCase				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
				lowerCAmelCase				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

				assert abs(result_sum.item() - 1_49.07_84 ) < 1e-2
				assert abs(result_mean.item() - 0.19_41 ) < 1e-3




"""simple docstring"""






def __magic_name__  (							_lowerCamelCase:		int,				_lowerCamelCase:		int )				->	int:

	'''simple docstring'''




	lowerCAmelCase				       =     1.0
	for i in range(1,				r + 1 ):
		ret *= (n + 1 - i) / i
	return round(_lowerCamelCase )




def __magic_name__  (							_lowerCamelCase:		int )				->	int:

	'''simple docstring'''




	return choose(8 + n,				_lowerCamelCase ) + choose(9 + n,				_lowerCamelCase ) - 10




def __magic_name__  (							_lowerCamelCase:		int )				->	int:

	'''simple docstring'''




	return sum(non_bouncy_exact(_lowerCamelCase ) for i in range(1,				n + 1 ) )




def __magic_name__  (							_lowerCamelCase:		int = 100 )				->	int:

	'''simple docstring'''




	return non_bouncy_upto(_lowerCamelCase )


if __name__ == "__main__":
					print(f"""{solution() = }""")


