



'''simple docstring'''






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class     SCREAMING_SNAKE_CASE(							A__      ):







	"""simple docstring"""






	lowerCamelCase__      			=	(DDIMScheduler,)
	lowerCamelCase__      			=	(("""eta""", 0.0), ("""num_inference_steps""", 50))







	def 					A     (						self      :					Union[str, Any]		,      **__snake_case      :					Optional[Any]		)      ->		Union[str, Any]:
								UpperCAmelCase			:	Any										=		{
								    '''num_train_timesteps''': 1000,
								    '''beta_start''': 0.00_01,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''clip_sample''': True,
								}

								config.update(**__snake_case		)
								return config







	def 					A     (						self      :					str		,      **__snake_case      :					Any		)      ->		List[str]:
								UpperCAmelCase			:	int										=		self.scheduler_classes[0]
								UpperCAmelCase			:	int										=		self.get_scheduler_config(**__snake_case		)
								UpperCAmelCase			:	Optional[Any]										=		scheduler_class(**__snake_case		)

								UpperCAmelCase							,		UpperCAmelCase			:	Optional[Any]										=		10, 0.0

								UpperCAmelCase			:	Union[str, Any]										=		self.dummy_model()
								UpperCAmelCase			:	Optional[Any]										=		self.dummy_sample_deter

								scheduler.set_timesteps(__snake_case		)

								for t in scheduler.timesteps:
															UpperCAmelCase			:	Tuple										=		model(__snake_case		,      __snake_case		)
															UpperCAmelCase			:	Optional[Any]										=		scheduler.step(__snake_case		,      __snake_case		,      __snake_case		,      __snake_case		).prev_sample

								return sample







	def 					A     (						self      :					Tuple		)      ->		Optional[Any]:
								for timesteps in [100, 500, 1000]:
															self.check_over_configs(num_train_timesteps=__snake_case		)







	def 					A     (						self      :					Tuple		)      ->		List[str]:
								for steps_offset in [0, 1]:
															self.check_over_configs(steps_offset=__snake_case		)

								UpperCAmelCase			:	Union[str, Any]										=		self.scheduler_classes[0]
								UpperCAmelCase			:	Dict										=		self.get_scheduler_config(steps_offset=1		)
								UpperCAmelCase			:	Any										=		scheduler_class(**__snake_case		)
								scheduler.set_timesteps(5		)
								assert torch.equal(scheduler.timesteps		,      torch.LongTensor([801, 601, 401, 201, 1]		)		)







	def 					A     (						self      :					Union[str, Any]		)      ->		Any:
								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=__snake_case		,      beta_end=__snake_case		)







	def 					A     (						self      :					Any		)      ->		str:
								for schedule in ["linear", "squaredcos_cap_v2"]:
															self.check_over_configs(beta_schedule=__snake_case		)







	def 					A     (						self      :					Tuple		)      ->		Optional[int]:
								for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=__snake_case		)







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







	def 					A     (						self      :					Optional[int]		)      ->		Dict:
								for timestep_spacing in ["trailing", "leading"]:
															self.check_over_configs(timestep_spacing=__snake_case		)







	def 					A     (						self      :					List[str]		)      ->		Any:
								for rescale_betas_zero_snr in [True, False]:
															self.check_over_configs(rescale_betas_zero_snr=__snake_case		)







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







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







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







	def 					A     (						self      :					List[Any]		)      ->		Dict:
								for t, eta in zip([1, 10, 49]		,      [0.0, 0.5, 1.0]		):
															self.check_over_forward(time_step=__snake_case		,      eta=__snake_case		)







	def 					A     (						self      :					str		)      ->		Optional[int]:
								UpperCAmelCase			:	Union[str, Any]										=		self.scheduler_classes[0]
								UpperCAmelCase			:	Optional[int]										=		self.get_scheduler_config()
								UpperCAmelCase			:	str										=		scheduler_class(**__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 					A     (						self      :					Any		)      ->		Optional[int]:
								UpperCAmelCase			:	Union[str, Any]										=		self.full_loop()

								UpperCAmelCase			:	Any										=		torch.sum(torch.abs(__snake_case		)		)
								UpperCAmelCase			:	Optional[Any]										=		torch.mean(torch.abs(__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 					A     (						self      :					List[str]		)      ->		Optional[int]:
								UpperCAmelCase			:	int										=		self.full_loop(prediction_type='''v_prediction'''		)

								UpperCAmelCase			:	List[Any]										=		torch.sum(torch.abs(__snake_case		)		)
								UpperCAmelCase			:	str										=		torch.mean(torch.abs(__snake_case		)		)

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







	def 					A     (						self      :					Union[str, Any]		)      ->		Optional[Any]:
								# We specify different beta, so that the first alpha is 0.99
								UpperCAmelCase			:	List[Any]										=		self.full_loop(set_alpha_to_one=__snake_case		,      beta_start=0.01		)
								UpperCAmelCase			:	Optional[Any]										=		torch.sum(torch.abs(__snake_case		)		)
								UpperCAmelCase			:	List[str]										=		torch.mean(torch.abs(__snake_case		)		)

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







	def 					A     (						self      :					Union[str, Any]		)      ->		Any:
								# We specify different beta, so that the first alpha is 0.99
								UpperCAmelCase			:	Optional[Any]										=		self.full_loop(set_alpha_to_one=__snake_case		,      beta_start=0.01		)
								UpperCAmelCase			:	Dict										=		torch.sum(torch.abs(__snake_case		)		)
								UpperCAmelCase			:	Dict										=		torch.mean(torch.abs(__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'''






UpperCamelCase__:       str							     =      [0.485, 0.456, 0.406]
UpperCamelCase__:       List[str]							     =      [0.229, 0.224, 0.225]
UpperCamelCase__:       List[str]							     =      [0.5, 0.5, 0.5]
UpperCamelCase__:       Optional[Any]							     =      [0.5, 0.5, 0.5]
UpperCamelCase__:       List[str]							     =      [0.48145466, 0.4578275, 0.40821073]
UpperCamelCase__:       List[str]							     =      [0.26862954, 0.26130258, 0.27577711]





