





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			Dict       =       (DDPMScheduler,)






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   **_A      :      Optional[Any]	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			List[str]											=	{
							    'num_train_timesteps': 1000,
							    'beta_start': 0.0_0_0_1,
							    'beta_end': 0.0_2,
							    'beta_schedule': 'linear',
							    'variance_type': 'fixed_small',
							    'clip_sample': True,
							}

							config.update(**_A	)
							return config






						def UpperCAmelCase_				(      self      :      Dict	)							->    Any:







							"""simple docstring"""


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






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







							"""simple docstring"""


							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=_A		,   beta_end=_A	)






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







							"""simple docstring"""


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






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







							"""simple docstring"""


							for variance in ["fixed_small", "fixed_large", "other"]:
								self.check_over_configs(variance_type=_A	)






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







							"""simple docstring"""


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






						def UpperCAmelCase_				(      self      :      Any	)							->    List[Any]:







							"""simple docstring"""


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






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







							"""simple docstring"""


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






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Any:







							"""simple docstring"""


							for t in [0, 500, 999]:
								self.check_over_forward(time_step=_A	)






						def UpperCAmelCase_				(      self      :      str	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	self.scheduler_classes[0]
							snake_case_		:			Tuple											=	self.get_scheduler_config()
							snake_case_		:			Dict											=	scheduler_class(**_A	)

							assert torch.sum(torch.abs(scheduler._get_variance(0	) - 0.0	)	) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(487	) - 0.0_0_9_7_9	)	) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(999	) - 0.0_2	)	) < 1E-5






						def UpperCAmelCase_				(      self      :      Any	)							->    str:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	self.scheduler_classes[0]
							snake_case_		:			List[Any]											=	self.get_scheduler_config()
							snake_case_		:			Any											=	scheduler_class(**_A	)

							snake_case_		:			int											=	len(_A	)

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

							for t in reversed(range(_A	)	):
								# 1. predict noise residual
								snake_case_		:			Optional[int]											=	model(_A		,   _A	)

								# 2. predict previous mean of sample x_t-1
								snake_case_		:			Tuple											=	scheduler.step(_A		,   _A		,   _A		,   generator=_A	).prev_sample

								# if t > 0:
								#     noise = self.dummy_sample_deter
								#     variance = scheduler.get_variance(t) ** (0.5) * noise
								#
								# sample = pred_prev_sample + variance
								snake_case_		:			List[str]											=	pred_prev_sample

							snake_case_		:			int											=	torch.sum(torch.abs(_A	)	)
							snake_case_		:			Tuple											=	torch.mean(torch.abs(_A	)	)

							assert abs(result_sum.item() - 2_5_8.9_6_0_6	) < 1E-2
							assert abs(result_mean.item() - 0.3_3_7_2	) < 1E-3






						def UpperCAmelCase_				(      self      :      int	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			str											=	self.scheduler_classes[0]
							snake_case_		:			int											=	self.get_scheduler_config(prediction_type='v_prediction'	)
							snake_case_		:			Tuple											=	scheduler_class(**_A	)

							snake_case_		:			Dict											=	len(_A	)

							snake_case_		:			Dict											=	self.dummy_model()
							snake_case_		:			Dict											=	self.dummy_sample_deter
							snake_case_		:			List[str]											=	torch.manual_seed(0	)

							for t in reversed(range(_A	)	):
								# 1. predict noise residual
								snake_case_		:			List[str]											=	model(_A		,   _A	)

								# 2. predict previous mean of sample x_t-1
								snake_case_		:			List[Any]											=	scheduler.step(_A		,   _A		,   _A		,   generator=_A	).prev_sample

								# if t > 0:
								#     noise = self.dummy_sample_deter
								#     variance = scheduler.get_variance(t) ** (0.5) * noise
								#
								# sample = pred_prev_sample + variance
								snake_case_		:			Optional[Any]											=	pred_prev_sample

							snake_case_		:			Any											=	torch.sum(torch.abs(_A	)	)
							snake_case_		:			List[Any]											=	torch.mean(torch.abs(_A	)	)

							assert abs(result_sum.item() - 2_0_2.0_2_9_6	) < 1E-2
							assert abs(result_mean.item() - 0.2_6_3_1	) < 1E-3






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







							"""simple docstring"""


							snake_case_		:			str											=	self.scheduler_classes[0]
							snake_case_		:			Union[str, Any]											=	self.get_scheduler_config()
							snake_case_		:			Any											=	scheduler_class(**_A	)

							snake_case_		:			str											=	[100, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=_A	)

							snake_case_		:			Tuple											=	scheduler.timesteps

							for i, timestep in enumerate(_A	):
								if i == len(_A	) - 1:
									snake_case_		:			Tuple											=	-1
								else:
									snake_case_		:			Union[str, Any]											=	timesteps[i + 1]

								snake_case_		:			str											=	scheduler.previous_timestep(_A	)
								snake_case_		:			Optional[int]											=	prev_t.item()

								self.assertEqual(_A		,   _A	)






						def UpperCAmelCase_				(      self      :      Tuple	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	self.scheduler_classes[0]
							snake_case_		:			Union[str, Any]											=	self.get_scheduler_config()
							snake_case_		:			List[str]											=	scheduler_class(**_A	)

							snake_case_		:			Tuple											=	[100, 87, 50, 51, 0]

							with self.assertRaises(_A		,   msg='`custom_timesteps` must be in descending order.'	):
								scheduler.set_timesteps(timesteps=_A	)






						def UpperCAmelCase_				(      self      :      Dict	)							->    int:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	self.scheduler_classes[0]
							snake_case_		:			int											=	self.get_scheduler_config()
							snake_case_		:			List[str]											=	scheduler_class(**_A	)

							snake_case_		:			int											=	[100, 87, 50, 1, 0]
							snake_case_		:			Any											=	len(_A	)

							with self.assertRaises(_A		,   msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'	):
								scheduler.set_timesteps(num_inference_steps=_A		,   timesteps=_A	)






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







							"""simple docstring"""


							snake_case_		:			str											=	self.scheduler_classes[0]
							snake_case_		:			List[str]											=	self.get_scheduler_config()
							snake_case_		:			Optional[Any]											=	scheduler_class(**_A	)

							snake_case_		:			Union[str, Any]											=	[scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    _A		,   msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'		,   ):
								scheduler.set_timesteps(timesteps=_A	)








import numpy as np


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	return (2 / (1 + np.exp(-2 * vector   ))) - 1


if __name__ == "__main__":
						import doctest

						doctest.testmod()

