




'''simple docstring'''


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class     A					(	_a					):
					lowercase_           =       (UniPCMultistepScheduler,)
					lowercase_           =       (('num_inference_steps', 25),)







					def      __lowerCAmelCase    (						self :						Dict							,    **lowerCAmelCase_ :						int      )    ->							List[str]:


												"""simple docstring"""


												_a			  =       {
												    '''num_train_timesteps''': 10_00,
												    '''beta_start''': 0.0_0_0_1,
												    '''beta_end''': 0.0_2,
												    '''beta_schedule''': '''linear''',
												    '''solver_order''': 2,
												    '''solver_type''': '''bh2''',
												}

												config.update(**lowerCAmelCase_      )
												return config







					def      __lowerCAmelCase    (						self :						Union[str, Any]							,    lowerCAmelCase_ :						int=0							,    **lowerCAmelCase_ :						Optional[int]      )    ->							Dict:


												"""simple docstring"""


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

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

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

																			_a  ,							_a			  =       sample, sample
																			for t in range(lowerCAmelCase_							,    time_step + scheduler.config.solver_order + 1      ):
																										_a			  =       scheduler.step(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    **lowerCAmelCase_      ).prev_sample
																										_a			  =       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      __lowerCAmelCase    (						self :						Any							,    lowerCAmelCase_ :						Any=0							,    **lowerCAmelCase_ :						Tuple      )    ->							Dict:


												"""simple docstring"""


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

												for scheduler_class in self.scheduler_classes:
																			_a			  =       self.get_scheduler_config()
																			_a			  =       scheduler_class(**lowerCAmelCase_      )
																			scheduler.set_timesteps(lowerCAmelCase_      )

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

																			with tempfile.TemporaryDirectory() as tmpdirname:
																										scheduler.save_config(lowerCAmelCase_      )
																										_a			  =       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)
																										_a			  =       dummy_past_residuals[: new_scheduler.config.solver_order]

																			_a			  =       scheduler.step(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    **lowerCAmelCase_      ).prev_sample
																			_a			  =       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      __lowerCAmelCase    (						self :						Dict							,    lowerCAmelCase_ :						List[str]=None							,    **lowerCAmelCase_ :						Any      )    ->							List[str]:


												"""simple docstring"""


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

												_a			  =       self.scheduler_classes[0]
												_a			  =       self.get_scheduler_config(**lowerCAmelCase_      )
												_a			  =       scheduler_class(**lowerCAmelCase_      )

												_a			  =       10
												_a			  =       self.dummy_model()
												_a			  =       self.dummy_sample_deter
												scheduler.set_timesteps(lowerCAmelCase_      )

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

												return sample







					def      __lowerCAmelCase    (						self :						Tuple      )    ->							Optional[Any]:


												"""simple docstring"""


												_a			  =       dict(self.forward_default_kwargs      )

												_a			  =       kwargs.pop('''num_inference_steps'''							,    lowerCAmelCase_      )

												for scheduler_class in self.scheduler_classes:
																			_a			  =       self.get_scheduler_config()
																			_a			  =       scheduler_class(**lowerCAmelCase_      )

																			_a			  =       self.dummy_sample
																			_a			  =       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'''      ):
																										_a			  =       num_inference_steps

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

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

																			_a			  =       scheduler.step(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    **lowerCAmelCase_      ).prev_sample
																			_a			  =       scheduler.step(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    **lowerCAmelCase_      ).prev_sample

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







					def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							Dict:


												"""simple docstring"""


												_a			  =       UniPCMultistepScheduler(**self.get_scheduler_config()      )
												_a			  =       self.full_loop(scheduler=lowerCAmelCase_      )
												_a			  =       torch.mean(torch.abs(lowerCAmelCase_      )      )

												assert abs(result_mean.item() - 0.2_4_6_4      ) < 1e-3

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

												_a			  =       self.full_loop(scheduler=lowerCAmelCase_      )
												_a			  =       torch.mean(torch.abs(lowerCAmelCase_      )      )

												assert abs(result_mean.item() - 0.2_4_6_4      ) < 1e-3







					def      __lowerCAmelCase    (						self :						List[str]      )    ->							Union[str, Any]:


												"""simple docstring"""


												for timesteps in [25, 50, 1_00, 9_99, 10_00]:
																			self.check_over_configs(num_train_timesteps=lowerCAmelCase_      )







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


												"""simple docstring"""


												self.check_over_configs(thresholding=lowerCAmelCase_      )
												for order in [1, 2, 3]:
																			for solver_type in ["bh1", "bh2"]:
																										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_							,    solver_order=lowerCAmelCase_							,    solver_type=lowerCAmelCase_							,    )







					def      __lowerCAmelCase    (						self :						Any      )    ->							int:


												"""simple docstring"""


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







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


												"""simple docstring"""


												for solver_type in ["bh1", "bh2"]:
																			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_							,    )
																																	_a			  =       self.full_loop(
																																	    solver_order=lowerCAmelCase_							,    solver_type=lowerCAmelCase_							,    prediction_type=lowerCAmelCase_							,    )
																																	assert not torch.isnan(lowerCAmelCase_      ).any(), "Samples have nan numbers"







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


												"""simple docstring"""


												self.check_over_configs(lower_order_final=lowerCAmelCase_      )
												self.check_over_configs(lower_order_final=lowerCAmelCase_      )







					def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Tuple:


												"""simple docstring"""


												for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
																			self.check_over_forward(num_inference_steps=lowerCAmelCase_							,    time_step=0      )







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


												"""simple docstring"""


												_a			  =       self.full_loop()
												_a			  =       torch.mean(torch.abs(lowerCAmelCase_      )      )

												assert abs(result_mean.item() - 0.2_4_6_4      ) < 1e-3







					def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Dict:


												"""simple docstring"""


												_a			  =       self.full_loop(prediction_type='''v_prediction'''      )
												_a			  =       torch.mean(torch.abs(lowerCAmelCase_      )      )

												assert abs(result_mean.item() - 0.1_0_1_4      ) < 1e-3







					def      __lowerCAmelCase    (						self :						Dict      )    ->							int:


												"""simple docstring"""


												_a			  =       self.scheduler_classes[0]
												_a			  =       self.get_scheduler_config(thresholding=lowerCAmelCase_							,    dynamic_thresholding_ratio=0      )
												_a			  =       scheduler_class(**lowerCAmelCase_      )

												_a			  =       10
												_a			  =       self.dummy_model()
												_a			  =       self.dummy_sample_deter.half()
												scheduler.set_timesteps(lowerCAmelCase_      )

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

												assert sample.dtype == torch.floataa







					def      __lowerCAmelCase    (						self :						List[str]							,    **lowerCAmelCase_ :						int      )    ->							Dict:


												"""simple docstring"""


												for scheduler_class in self.scheduler_classes:
																			_a			  =       self.get_scheduler_config(**lowerCAmelCase_      )
																			_a			  =       scheduler_class(**lowerCAmelCase_      )

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







'''simple docstring'''


from ..utils import DummyObject, requires_backends

class     A					(	metaclass=_a					):
							lowercase_           =       ['torch', 'torchsde']







							def __init__(						self :						Optional[Any]							,    *lowerCAmelCase_ :						Any							,    **lowerCAmelCase_ :						Tuple      )    ->							Dict:


														"""simple docstring"""


														requires_backends(self							,    ['''torch''', '''torchsde''']      )







							@classmethod
							def      __lowerCAmelCase    (						cls :						Dict							,    *lowerCAmelCase_ :						Optional[int]							,    **lowerCAmelCase_ :						Optional[Any]      )    ->							int:


														"""simple docstring"""


														requires_backends(cls							,    ['''torch''', '''torchsde''']      )







							@classmethod
							def      __lowerCAmelCase    (						cls :						List[str]							,    *lowerCAmelCase_ :						str							,    **lowerCAmelCase_ :						Optional[Any]      )    ->							Any:


														"""simple docstring"""


														requires_backends(cls							,    ['''torch''', '''torchsde''']      )

