




"""simple docstring"""




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class        UpperCAmelCase_  (	__A						):
		UpperCamelCase									=(DDIMScheduler,)
		UpperCamelCase									=(("eta", 0.0), ("num_inference_steps", 50))
		def 					_lowerCamelCase    (		self							,		**UpperCamelCase_   )		->   Optional[int]:
					__lowercase	:       Dict				=   {
					    "num_train_timesteps": 10_00,
					    "beta_start": 0.0_0_0_1,
					    "beta_end": 0.0_2,
					    "beta_schedule": "linear",
					    "clip_sample": True,
					}

					config.update(**UpperCamelCase_   )
					return config
		def 					_lowerCamelCase    (		self							,		**UpperCamelCase_   )		->   Tuple:
					__lowercase	:       int				=   self.scheduler_classes[0]
					__lowercase	:       Optional[Any]				=   self.get_scheduler_config(**UpperCamelCase_   )
					__lowercase	:       Optional[Any]				=   scheduler_class(**UpperCamelCase_   )

					__lowercase	:       Optional[Any]				=   10, 0.0

					__lowercase	:       str				=   self.dummy_model()
					__lowercase	:       List[Any]				=   self.dummy_sample_deter

					scheduler.set_timesteps(UpperCamelCase_   )

					for t in scheduler.timesteps:
								__lowercase	:       List[str]				=   model(UpperCamelCase_							,		UpperCamelCase_   )
								__lowercase	:       List[str]				=   scheduler.step(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   ).prev_sample

					return sample
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:

					for timesteps in [1_00, 5_00, 10_00]:
								self.check_over_configs(num_train_timesteps=UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:

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

					__lowercase	:       Tuple				=   self.scheduler_classes[0]
					__lowercase	:       List[Any]				=   self.get_scheduler_config(steps_offset=1   )
					__lowercase	:       List[Any]				=   scheduler_class(**UpperCamelCase_   )
					scheduler.set_timesteps(5   )
					assert torch.equal(scheduler.timesteps							,		torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]   )   )
		def 					_lowerCamelCase    (		self   )		->   int:

					for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]							,		[0.0_0_2, 0.0_2, 0.2, 2]   ):
								self.check_over_configs(beta_start=UpperCamelCase_							,		beta_end=UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   List[Any]:

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

					for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(prediction_type=UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Tuple:

					for clip_sample in [True, False]:
								self.check_over_configs(clip_sample=UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Any:

					for timestep_spacing in ["trailing", "leading"]:
								self.check_over_configs(timestep_spacing=UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   List[str]:

					for rescale_betas_zero_snr in [True, False]:
								self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   List[str]:
					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 					_lowerCamelCase    (		self   )		->   Tuple:

					for t in [1, 10, 49]:
								self.check_over_forward(time_step=UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Any:

					for t, num_inference_steps in zip([1, 10, 50]							,		[10, 50, 5_00]   ):
								self.check_over_forward(time_step=UpperCamelCase_							,		num_inference_steps=UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Any:

					for t, eta in zip([1, 10, 49]							,		[0.0, 0.5, 1.0]   ):
								self.check_over_forward(time_step=UpperCamelCase_							,		eta=UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   List[str]:
					__lowercase	:       Optional[int]				=   self.scheduler_classes[0]
					__lowercase	:       int				=   self.get_scheduler_config()
					__lowercase	:       int				=   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(4_20							,		4_00   ) - 0.1_4_7_7_1   )   ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_80							,		9_60   ) - 0.3_2_4_6_0   )   ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(0							,		0   ) - 0.0   )   ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_87							,		4_86   ) - 0.0_0_9_7_9   )   ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_99							,		9_98   ) - 0.0_2   )   ) < 1E-5
		def 					_lowerCamelCase    (		self   )		->   Any:
					__lowercase	:       Any				=   self.full_loop()

					__lowercase	:       Dict				=   torch.sum(torch.abs(UpperCamelCase_   )   )
					__lowercase	:       List[Any]				=   torch.mean(torch.abs(UpperCamelCase_   )   )

					assert abs(result_sum.item() - 1_7_2.0_0_6_7   ) < 1E-2
					assert abs(result_mean.item() - 0.2_2_3_9_6_7   ) < 1E-3
		def 					_lowerCamelCase    (		self   )		->   int:
					__lowercase	:       Optional[Any]				=   self.full_loop(prediction_type='''v_prediction'''   )

					__lowercase	:       Union[str, Any]				=   torch.sum(torch.abs(UpperCamelCase_   )   )
					__lowercase	:       Dict				=   torch.mean(torch.abs(UpperCamelCase_   )   )

					assert abs(result_sum.item() - 5_2.5_3_0_2   ) < 1E-2
					assert abs(result_mean.item() - 0.0_6_8_4   ) < 1E-3
		def 					_lowerCamelCase    (		self   )		->   Tuple:
					__lowercase	:       str				=   self.full_loop(set_alpha_to_one=UpperCamelCase_							,		beta_start=0.0_1   )
					__lowercase	:       List[str]				=   torch.sum(torch.abs(UpperCamelCase_   )   )
					__lowercase	:       Union[str, Any]				=   torch.mean(torch.abs(UpperCamelCase_   )   )

					assert abs(result_sum.item() - 1_4_9.8_2_9_5   ) < 1E-2
					assert abs(result_mean.item() - 0.1_9_5_1   ) < 1E-3
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					__lowercase	:       Union[str, Any]				=   self.full_loop(set_alpha_to_one=UpperCamelCase_							,		beta_start=0.0_1   )
					__lowercase	:       int				=   torch.sum(torch.abs(UpperCamelCase_   )   )
					__lowercase	:       List[Any]				=   torch.mean(torch.abs(UpperCamelCase_   )   )

					assert abs(result_sum.item() - 1_4_9.0_7_8_4   ) < 1E-2
					assert abs(result_mean.item() - 0.1_9_4_1   ) < 1E-3






"""simple docstring"""




def       __UpperCAmelCase      (				__UpperCamelCase					):
			if len(__UpperCamelCase					) <= 1:
						return data
			else:
						return [
						    *quick_sort([e for e in data[1:] if e <= data[0]]					),
						    data[0],
						    *quick_sort([e for e in data[1:] if e > data[0]]					),
						]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



