





'''simple docstring'''




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest




class       UpperCamelCase__			(			lowercase_	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					(DDIMScheduler,)
					SCREAMING_SNAKE_CASE__						=					(('''eta''', 0.0), ('''num_inference_steps''', 50))


					def lowerCamelCase_	(		self							:	Optional[int]     ,		**lowerCamelCase_							:	Optional[int]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Dict						=	{
							    """num_train_timesteps""": 10_00,
							    """beta_start""": 0.0_001,
							    """beta_end""": 0.02,
							    """beta_schedule""": """linear""",
							    """clip_sample""": True,
							}

							config.update(**lowerCamelCase_		)
							return config


					def lowerCamelCase_	(		self							:	List[Any]     ,		**lowerCamelCase_							:	int		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							str						=	self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	self.get_scheduler_config(**lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	scheduler_class(**lowerCamelCase_		)

							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Optional[int]						=	10, 0.0

							SCREAMING_SNAKE_CASE				:							List[Any]						=	self.dummy_model()
							SCREAMING_SNAKE_CASE				:							int						=	self.dummy_sample_deter

							scheduler.set_timesteps(lowerCamelCase_		)

							for t in scheduler.timesteps:
									SCREAMING_SNAKE_CASE				:							Any						=	model(lowerCamelCase_     ,		lowerCamelCase_		)
									SCREAMING_SNAKE_CASE				:							Tuple						=	scheduler.step(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		).prev_sample

							return sample


					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



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


					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



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

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE				:							Tuple						=	self.get_scheduler_config(steps_offset=1		)
							SCREAMING_SNAKE_CASE				:							Tuple						=	scheduler_class(**lowerCamelCase_		)
							scheduler.set_timesteps(5		)
							assert torch.equal(scheduler.timesteps     ,		torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]		)		)


					def lowerCamelCase_	(		self							:	str		):

							'''simple docstring'''



							for beta_start, beta_end in zip([0.0_001, 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							:	int		):

							'''simple docstring'''



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


					def lowerCamelCase_	(		self							:	Union[str, Any]		):

							'''simple docstring'''



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


					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



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


					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



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


					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



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


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



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


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



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


					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							for t, num_inference_steps in zip([1, 10, 50]     ,		[10, 50, 5_00]		):
									self.check_over_forward(time_step=lowerCamelCase_     ,		num_inference_steps=lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



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


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Any						=	self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE				:							List[str]						=	self.get_scheduler_config()
							SCREAMING_SNAKE_CASE				:							int						=	scheduler_class(**lowerCamelCase_		)

							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		) - 0.14_771		)		) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_80     ,		9_60		) - 0.32_460		)		) < 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		) - 0.00_979		)		) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99     ,		9_98		) - 0.02		)		) < 1e-5


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	self.full_loop()

							SCREAMING_SNAKE_CASE				:							Any						=	torch.sum(torch.abs(lowerCamelCase_		)		)
							SCREAMING_SNAKE_CASE				:							int						=	torch.mean(torch.abs(lowerCamelCase_		)		)

							assert abs(result_sum.item() - 172.0_067		) < 1e-2
							assert abs(result_mean.item() - 0.223_967		) < 1e-3


					def lowerCamelCase_	(		self							:	List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	self.full_loop(prediction_type="""v_prediction"""		)

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	torch.sum(torch.abs(lowerCamelCase_		)		)
							SCREAMING_SNAKE_CASE				:							Any						=	torch.mean(torch.abs(lowerCamelCase_		)		)

							assert abs(result_sum.item() - 52.5_302		) < 1e-2
							assert abs(result_mean.item() - 0.0_684		) < 1e-3


					def lowerCamelCase_	(		self							:	List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[int]						=	self.full_loop(set_alpha_to_one=lowerCamelCase_     ,		beta_start=0.01		)
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	torch.sum(torch.abs(lowerCamelCase_		)		)
							SCREAMING_SNAKE_CASE				:							List[Any]						=	torch.mean(torch.abs(lowerCamelCase_		)		)

							assert abs(result_sum.item() - 149.8_295		) < 1e-2
							assert abs(result_mean.item() - 0.1_951		) < 1e-3



					def lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self.full_loop(set_alpha_to_one=lowerCamelCase_     ,		beta_start=0.01		)
							SCREAMING_SNAKE_CASE				:							str						=	torch.sum(torch.abs(lowerCamelCase_		)		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	torch.mean(torch.abs(lowerCamelCase_		)		)

							assert abs(result_sum.item() - 149.0_784		) < 1e-2
							assert abs(result_mean.item() - 0.1_941		) < 1e-3










'''simple docstring'''




from __future__ import annotations



def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		if len(lowerCamelCase_					) == 0:
				raise ValueError("""find_min() arg is an empty sequence"""					)
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	nums[0]
		for num in nums:
				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	min(lowerCamelCase_	,	lowerCamelCase_					)
		return min_num


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)



