




def 		_A(       UpperCamelCase__							:			int				)    -> int:
							'''simple docstring'''


							assert (
							    isinstance(UpperCamelCase__				,       UpperCamelCase__				) and number_of_steps > 0
							), F'number_of_steps needs to be positive integer, your input {number_of_steps}'
							if number_of_steps == 1:
														return 1
							__lowercase     ,     __lowercase      =    1, 1
							for _ in range(number_of_steps - 1				):
														__lowercase     ,     __lowercase      =    current + previous, current
							return current


if __name__ == "__main__":
			import doctest

			doctest.testmod()












import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       List[Any]       	=		(DEISMultistepScheduler,)
						UpperCamelCase_	:       Any       	=		(('num_inference_steps', 25),)






						def     UpperCAmelCase_      ( self :     List[str]				,							**lowerCamelCase__ :     Union[str, Any]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    {
													    '''num_train_timesteps''': 1_000,
													    '''beta_start''': 0.0_0_0_1,
													    '''beta_end''': 0.0_2,
													    '''beta_schedule''': '''linear''',
													    '''solver_order''': 2,
													}

													config.update(**lowerCamelCase__				)
													return config






						def     UpperCAmelCase_      ( self :     List[Any]				,							lowerCamelCase__ :     Union[str, Any]=0				,							**lowerCamelCase__ :     Dict				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    dict(self.forward_default_kwargs				)
													__lowercase      =    kwargs.pop('''num_inference_steps'''				,							lowerCamelCase__				)
													__lowercase      =    self.dummy_sample
													__lowercase      =    0.1 * sample
													__lowercase      =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

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






						def     UpperCAmelCase_      ( self :     Any				)    ->						Tuple:
													"""simple docstring"""


													pass






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     Any=0				,							**lowerCamelCase__ :     Any				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    dict(self.forward_default_kwargs				)
													__lowercase      =    kwargs.pop('''num_inference_steps'''				,							lowerCamelCase__				)
													__lowercase      =    self.dummy_sample
													__lowercase      =    0.1 * sample
													__lowercase      =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

													for scheduler_class in self.scheduler_classes:
																				__lowercase      =    self.get_scheduler_config()
																				__lowercase      =    scheduler_class(**lowerCamelCase__				)
																				scheduler.set_timesteps(lowerCamelCase__				)

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

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

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

																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample
																				__lowercase      =    new_scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample

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






						def     UpperCAmelCase_      ( self :     Union[str, Any]				,							lowerCamelCase__ :     Optional[int]=None				,							**lowerCamelCase__ :     Dict				)    ->						Optional[Any]:
													"""simple docstring"""


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

													__lowercase      =    self.scheduler_classes[0]
													__lowercase      =    self.get_scheduler_config(**lowerCamelCase__				)
													__lowercase      =    scheduler_class(**lowerCamelCase__				)

													__lowercase      =    10
													__lowercase      =    self.dummy_model()
													__lowercase      =    self.dummy_sample_deter
													scheduler.set_timesteps(lowerCamelCase__				)

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

													return sample






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    dict(self.forward_default_kwargs				)

													__lowercase      =    kwargs.pop('''num_inference_steps'''				,							lowerCamelCase__				)

													for scheduler_class in self.scheduler_classes:
																				__lowercase      =    self.get_scheduler_config()
																				__lowercase      =    scheduler_class(**lowerCamelCase__				)

																				__lowercase      =    self.dummy_sample
																				__lowercase      =    0.1 * sample

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

																				# copy over dummy past residuals (must be done after set_timesteps)
																				__lowercase      =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
																				__lowercase      =    dummy_past_residuals[: scheduler.config.solver_order]

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

																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample
																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				).prev_sample

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






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


													__lowercase      =    DEISMultistepScheduler(**self.get_scheduler_config()				)
													__lowercase      =    self.full_loop(scheduler=lowerCamelCase__				)
													__lowercase      =    torch.mean(torch.abs(lowerCamelCase__				)				)

													assert abs(result_mean.item() - 0.2_3_9_1_6				) < 1e-3

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

													__lowercase      =    self.full_loop(scheduler=lowerCamelCase__				)
													__lowercase      =    torch.mean(torch.abs(lowerCamelCase__				)				)

													assert abs(result_mean.item() - 0.2_3_9_1_6				) < 1e-3






						def     UpperCAmelCase_      ( self :     int				)    ->						List[Any]:
													"""simple docstring"""


													for timesteps in [25, 50, 100, 999, 1_000]:
																				self.check_over_configs(num_train_timesteps=lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Any:
													"""simple docstring"""


													self.check_over_configs(thresholding=lowerCamelCase__				)
													for order in [1, 2, 3]:
																				for solver_type in ["logrho"]:
																											for threshold in [0.5, 1.0, 2.0]:
																																		for prediction_type in ["epsilon", "sample"]:
																																									self.check_over_configs(
																																									    thresholding=lowerCamelCase__				,							prediction_type=lowerCamelCase__				,							sample_max_value=lowerCamelCase__				,							algorithm_type='''deis'''				,							solver_order=lowerCamelCase__				,							solver_type=lowerCamelCase__				,							)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Optional[Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						int:
													"""simple docstring"""


													for algorithm_type in ["deis"]:
																				for solver_type in ["logrho"]:
																											for order in [1, 2, 3]:
																																		for prediction_type in ["epsilon", "sample"]:
																																									self.check_over_configs(
																																									    solver_order=lowerCamelCase__				,							solver_type=lowerCamelCase__				,							prediction_type=lowerCamelCase__				,							algorithm_type=lowerCamelCase__				,							)
																																									__lowercase      =    self.full_loop(
																																									    solver_order=lowerCamelCase__				,							solver_type=lowerCamelCase__				,							prediction_type=lowerCamelCase__				,							algorithm_type=lowerCamelCase__				,							)
																																									assert not torch.isnan(lowerCamelCase__				).any(), "Samples have nan numbers"






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


													self.check_over_configs(lower_order_final=lowerCamelCase__				)
													self.check_over_configs(lower_order_final=lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Dict:
													"""simple docstring"""


													for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																				self.check_over_forward(num_inference_steps=lowerCamelCase__				,							time_step=0				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.full_loop()
													__lowercase      =    torch.mean(torch.abs(lowerCamelCase__				)				)

													assert abs(result_mean.item() - 0.2_3_9_1_6				) < 1e-3






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.full_loop(prediction_type='''v_prediction'''				)
													__lowercase      =    torch.mean(torch.abs(lowerCamelCase__				)				)

													assert abs(result_mean.item() - 0.0_9_1				) < 1e-3






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.scheduler_classes[0]
													__lowercase      =    self.get_scheduler_config(thresholding=lowerCamelCase__				,							dynamic_thresholding_ratio=0				)
													__lowercase      =    scheduler_class(**lowerCamelCase__				)

													__lowercase      =    10
													__lowercase      =    self.dummy_model()
													__lowercase      =    self.dummy_sample_deter.half()
													scheduler.set_timesteps(lowerCamelCase__				)

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

													assert sample.dtype == torch.floataa






