


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class 	__a   (       lowercase_    ):
		_a			:						Any    			=			(DPMSolverMultistepScheduler,)
		_a			:						List[str]    			=			(("""num_inference_steps""", 25),)
		def 			UpperCAmelCase__       (    self    ,					**_SCREAMING_SNAKE_CASE		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    {
									    'num_train_timesteps': 1000,
									    'beta_start': 0.0001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									    'solver_order': 2,
									    'prediction_type': 'epsilon',
									    'thresholding': False,
									    'sample_max_value': 1.0,
									    'algorithm_type': 'dpmsolver++',
									    'solver_type': 'midpoint',
									    'lower_order_final': False,
									    'lambda_min_clipped': -float('inf'		),
									    'variance_type': None,
									}

									config.update(**_SCREAMING_SNAKE_CASE		)
									return config
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE=0    ,					**_SCREAMING_SNAKE_CASE		)				-> int:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)
									_UpperCAmelCase					=    kwargs.pop('num_inference_steps'    ,					_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    self.dummy_sample
									_UpperCAmelCase					=    0.1 * sample
									_UpperCAmelCase					=    [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
																_UpperCAmelCase					=    self.get_scheduler_config(**_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)
																scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
																# copy over dummy past residuals
																_UpperCAmelCase					=    dummy_past_residuals[: scheduler.config.solver_order]

																with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase					=    scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE		)
																							new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
																							# copy over dummy past residuals
																							_UpperCAmelCase					=    dummy_past_residuals[: new_scheduler.config.solver_order]

																_UpperCAmelCase	, _UpperCAmelCase					=    sample, sample
																for t in range(_SCREAMING_SNAKE_CASE    ,					time_step + scheduler.config.solver_order + 1		):
																							_UpperCAmelCase					=    scheduler.step(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																							_UpperCAmelCase					=    new_scheduler.step(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																							assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									pass
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE=0    ,					**_SCREAMING_SNAKE_CASE		)				-> Any:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)
									_UpperCAmelCase					=    kwargs.pop('num_inference_steps'    ,					_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    self.dummy_sample
									_UpperCAmelCase					=    0.1 * sample
									_UpperCAmelCase					=    [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
																_UpperCAmelCase					=    self.get_scheduler_config()
																_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)
																scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

																# copy over dummy past residuals (must be after setting timesteps)
																_UpperCAmelCase					=    dummy_past_residuals[: scheduler.config.solver_order]

																with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase					=    scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE		)
																							# copy over dummy past residuals
																							new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

																							# copy over dummy past residual (must be after setting timesteps)
																							_UpperCAmelCase					=    dummy_past_residuals[: new_scheduler.config.solver_order]

																_UpperCAmelCase					=    scheduler.step(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    new_scheduler.step(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE=None    ,					**_SCREAMING_SNAKE_CASE		)				-> Optional[Any]:




									"""simple docstring"""





									if scheduler is None:
																_UpperCAmelCase					=    self.scheduler_classes[0]
																_UpperCAmelCase					=    self.get_scheduler_config(**_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    10
									_UpperCAmelCase					=    self.dummy_model()
									_UpperCAmelCase					=    self.dummy_sample_deter
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

									for i, t in enumerate(scheduler.timesteps		):
																_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    scheduler.step(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		).prev_sample

									return sample
		def 			UpperCAmelCase__       (    self		)				-> Dict:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)

									_UpperCAmelCase					=    kwargs.pop('num_inference_steps'    ,					_SCREAMING_SNAKE_CASE		)

									for scheduler_class in self.scheduler_classes:
																_UpperCAmelCase					=    self.get_scheduler_config()
																_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    self.dummy_sample
																_UpperCAmelCase					=    0.1 * sample

																if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE    ,					'set_timesteps'		):
																							scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
																elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE    ,					'set_timesteps'		):
																							_UpperCAmelCase					=    num_inference_steps

																# copy over dummy past residuals (must be done after set_timesteps)
																_UpperCAmelCase					=    [residual + 0.2, residual + 0.15, residual + 0.10]
																_UpperCAmelCase					=    dummy_past_residuals[: scheduler.config.solver_order]

																_UpperCAmelCase					=    scheduler.timesteps[5]
																_UpperCAmelCase					=    scheduler.timesteps[6]

																_UpperCAmelCase					=    scheduler.step(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    scheduler.step(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																self.assertEqual(output_a.shape    ,					sample.shape		)
																self.assertEqual(output_a.shape    ,					output_a.shape		)
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									for timesteps in [25, 50, 100, 999, 1000]:
																self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE		)
									for order in [1, 2, 3]:
																for solver_type in ["midpoint", "heun"]:
																							for threshold in [0.5, 1.0, 2.0]:
																														for prediction_type in ["epsilon", "sample"]:
																																					self.check_over_configs(
																																					    thresholding=_SCREAMING_SNAKE_CASE    ,					prediction_type=_SCREAMING_SNAKE_CASE    ,					sample_max_value=_SCREAMING_SNAKE_CASE    ,					algorithm_type='dpmsolver++'    ,					solver_order=_SCREAMING_SNAKE_CASE    ,					solver_type=_SCREAMING_SNAKE_CASE    ,					)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





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




									"""simple docstring"""





									for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
																for solver_type in ["midpoint", "heun"]:
																							for order in [1, 2, 3]:
																														for prediction_type in ["epsilon", "sample"]:
																																					if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																																												if order == 3:
																																																			continue
																																					else:
																																												self.check_over_configs(
																																												    solver_order=_SCREAMING_SNAKE_CASE    ,					solver_type=_SCREAMING_SNAKE_CASE    ,					prediction_type=_SCREAMING_SNAKE_CASE    ,					algorithm_type=_SCREAMING_SNAKE_CASE    ,					)
																																					_UpperCAmelCase					=    self.full_loop(
																																					    solver_order=_SCREAMING_SNAKE_CASE    ,					solver_type=_SCREAMING_SNAKE_CASE    ,					prediction_type=_SCREAMING_SNAKE_CASE    ,					algorithm_type=_SCREAMING_SNAKE_CASE    ,					)
																																					assert not torch.isnan(_SCREAMING_SNAKE_CASE		).any(), "Samples have nan numbers"
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE		)
									self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									self.check_over_configs(lambda_min_clipped=-float('inf'		)		)
									self.check_over_configs(lambda_min_clipped=-5.1		)
		def 			UpperCAmelCase__       (    self		)				-> Optional[Any]:




									"""simple docstring"""





									self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE		)
									self.check_over_configs(variance_type='learned_range'		)
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
																self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE    ,					time_step=0		)
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=    self.full_loop()
									_UpperCAmelCase					=    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

									assert abs(result_mean.item() - 0.3301		) < 1e-3
		def 			UpperCAmelCase__       (    self		)				-> Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=    self.full_loop(thresholding=_SCREAMING_SNAKE_CASE    ,					dynamic_thresholding_ratio=0.87    ,					sample_max_value=0.5		)
									_UpperCAmelCase					=    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

									assert abs(result_mean.item() - 1.1364		) < 1e-3
		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									_UpperCAmelCase					=    self.full_loop(prediction_type='v_prediction'		)
									_UpperCAmelCase					=    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

									assert abs(result_mean.item() - 0.2251		) < 1e-3
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									_UpperCAmelCase					=    self.full_loop(prediction_type='v_prediction'    ,					use_karras_sigmas=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

									assert abs(result_mean.item() - 0.2096		) < 1e-3
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									_UpperCAmelCase					=    DPMSolverMultistepScheduler(**self.get_scheduler_config()		)
									_UpperCAmelCase					=    self.full_loop(scheduler=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

									assert abs(result_mean.item() - 0.3301		) < 1e-3

									_UpperCAmelCase					=    DPMSolverSinglestepScheduler.from_config(scheduler.config		)
									_UpperCAmelCase					=    UniPCMultistepScheduler.from_config(scheduler.config		)
									_UpperCAmelCase					=    DEISMultistepScheduler.from_config(scheduler.config		)
									_UpperCAmelCase					=    DPMSolverMultistepScheduler.from_config(scheduler.config		)

									_UpperCAmelCase					=    self.full_loop(scheduler=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

									assert abs(result_mean.item() - 0.3301		) < 1e-3
		def 			UpperCAmelCase__       (    self		)				-> Dict:




									"""simple docstring"""





									_UpperCAmelCase					=    self.scheduler_classes[0]
									_UpperCAmelCase					=    self.get_scheduler_config(thresholding=_SCREAMING_SNAKE_CASE    ,					dynamic_thresholding_ratio=0		)
									_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    10
									_UpperCAmelCase					=    self.dummy_model()
									_UpperCAmelCase					=    self.dummy_sample_deter.half()
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

									for i, t in enumerate(scheduler.timesteps		):
																_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    scheduler.step(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		).prev_sample

									assert sample.dtype == torch.floataa






		def 			UpperCAmelCase__       (    self    ,					**_SCREAMING_SNAKE_CASE		)				-> Optional[Any]:




									"""simple docstring"""





									for scheduler_class in self.scheduler_classes:
																_UpperCAmelCase					=    self.get_scheduler_config(**_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

																scheduler.set_timesteps(scheduler.config.num_train_timesteps		)
																assert len(scheduler.timesteps.unique()		) == scheduler.num_inference_steps




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 	__a   (       unittest.TestCase    ):
		# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
		_a			:						List[Any]    			=			(ScoreSdeVeScheduler,)
		_a			:						Any    			=			()
		@property
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=    4
									_UpperCAmelCase					=    3
									_UpperCAmelCase					=    8
									_UpperCAmelCase					=    8

									_UpperCAmelCase					=    torch.rand((batch_size, num_channels, height, width)		)

									return sample
		@property
		def 			UpperCAmelCase__       (    self		)				-> Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    4
									_UpperCAmelCase					=    3
									_UpperCAmelCase					=    8
									_UpperCAmelCase					=    8

									_UpperCAmelCase					=    batch_size * num_channels * height * width
									_UpperCAmelCase					=    torch.arange(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    sample.reshape(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    sample / num_elems
									_UpperCAmelCase					=    sample.permute(3    ,					0    ,					1    ,					2		)

									return sample
		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									def model(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					*_SCREAMING_SNAKE_CASE		):
																return sample * t / (t + 1)

									return model
		def 			UpperCAmelCase__       (    self    ,					**_SCREAMING_SNAKE_CASE		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    {
									    'num_train_timesteps': 2000,
									    'snr': 0.15,
									    'sigma_min': 0.01,
									    'sigma_max': 1348,
									    'sampling_eps': 1e-5,
									}

									config.update(**_SCREAMING_SNAKE_CASE		)
									return config
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE=0    ,					**_SCREAMING_SNAKE_CASE		)				-> List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)

									for scheduler_class in self.scheduler_classes:
																_UpperCAmelCase					=    self.dummy_sample
																_UpperCAmelCase					=    0.1 * sample

																_UpperCAmelCase					=    self.get_scheduler_config(**_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

																with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase					=    scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    scheduler.step_pred(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    new_scheduler.step_pred(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

																_UpperCAmelCase					=    scheduler.step_correct(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    new_scheduler.step_correct(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE=0    ,					**_SCREAMING_SNAKE_CASE		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)
									kwargs.update(_SCREAMING_SNAKE_CASE		)

									for scheduler_class in self.scheduler_classes:
																_UpperCAmelCase					=    self.dummy_sample
																_UpperCAmelCase					=    0.1 * sample

																_UpperCAmelCase					=    self.get_scheduler_config()
																_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

																with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase					=    scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    scheduler.step_pred(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    new_scheduler.step_pred(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

																_UpperCAmelCase					=    scheduler.step_correct(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    new_scheduler.step_correct(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									for timesteps in [10, 100, 1000]:
																self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]    ,					[1, 100, 1000]		):
																self.check_over_configs(sigma_min=_SCREAMING_SNAKE_CASE    ,					sigma_max=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									for t in [0.1, 0.5, 0.75]:
																self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)

									_UpperCAmelCase					=    self.scheduler_classes[0]
									_UpperCAmelCase					=    self.get_scheduler_config()
									_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    3

									_UpperCAmelCase					=    self.dummy_model()
									_UpperCAmelCase					=    self.dummy_sample_deter

									scheduler.set_sigmas(_SCREAMING_SNAKE_CASE		)
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    torch.manual_seed(0		)

									for i, t in enumerate(scheduler.timesteps		):
																_UpperCAmelCase					=    scheduler.sigmas[i]

																for _ in range(scheduler.config.correct_steps		):
																							with torch.no_grad():
																														_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase					=    scheduler.step_correct(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																with torch.no_grad():
																							_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    scheduler.step_pred(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase	, _UpperCAmelCase					=    output.prev_sample, output.prev_sample_mean

									_UpperCAmelCase					=    torch.sum(torch.abs(_SCREAMING_SNAKE_CASE		)		)
									_UpperCAmelCase					=    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

									assert np.isclose(result_sum.item()    ,					14372758528.0		)
									assert np.isclose(result_mean.item()    ,					18714530.0		)






		def 			UpperCAmelCase__       (    self		)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)

									_UpperCAmelCase					=    kwargs.pop('num_inference_steps'    ,					_SCREAMING_SNAKE_CASE		)

									for scheduler_class in self.scheduler_classes:
																_UpperCAmelCase					=    self.get_scheduler_config()
																_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    self.dummy_sample
																_UpperCAmelCase					=    0.1 * sample

																if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE    ,					'set_timesteps'		):
																							scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
																elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE    ,					'set_timesteps'		):
																							_UpperCAmelCase					=    num_inference_steps

																_UpperCAmelCase					=    scheduler.step_pred(_SCREAMING_SNAKE_CASE    ,					0    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    scheduler.step_pred(_SCREAMING_SNAKE_CASE    ,					1    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																self.assertEqual(output_a.shape    ,					sample.shape		)
																self.assertEqual(output_a.shape    ,					output_a.shape		)
