





'''simple docstring'''






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    (UniPCMultistepScheduler,)
					_SCREAMING_SNAKE_CASE							   =    (("num_inference_steps", 25),)


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ,     **__snake_case	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		{
											    'num_train_timesteps': 10_00,
											    'beta_start': 0.0_001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											    'solver_order': 2,
											    'solver_type': 'bh2',
											}

											config.update(**__snake_case       )
											return config


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ,     __snake_case	:  Union[str, Any]=0       ,     **__snake_case	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		dict(self.forward_default_kwargs       )
											_snake_case:   Any  					=		kwargs.pop('num_inference_steps'       ,     __snake_case       )
											_snake_case:   Dict  					=		self.dummy_sample
											_snake_case:   Tuple  					=		0.1 * sample
											_snake_case:   Union[str, Any]  					=		[residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																	_snake_case:   Tuple  					=		self.get_scheduler_config(**__snake_case       )
																	_snake_case:   Tuple  					=		scheduler_class(**__snake_case       )
																	scheduler.set_timesteps(__snake_case       )
																	# copy over dummy past residuals
																	_snake_case:   List[str]  					=		dummy_past_residuals[: scheduler.config.solver_order]

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

																	_snake_case					,						_snake_case:   Any  					=		sample, sample
																	for t in range(__snake_case       ,     time_step + scheduler.config.solver_order + 1       ):
																							_snake_case:   Dict  					=		scheduler.step(__snake_case       ,     __snake_case       ,     __snake_case       ,     **__snake_case       ).prev_sample
																							_snake_case:   Dict  					=		new_scheduler.step(__snake_case       ,     __snake_case       ,     __snake_case       ,     **__snake_case       ).prev_sample

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ,     __snake_case	:  List[str]=0       ,     **__snake_case	:  Any       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		dict(self.forward_default_kwargs       )
											_snake_case:   Optional[Any]  					=		kwargs.pop('num_inference_steps'       ,     __snake_case       )
											_snake_case:   Optional[int]  					=		self.dummy_sample
											_snake_case:   Any  					=		0.1 * sample
											_snake_case:   Optional[int]  					=		[residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																	_snake_case:   Union[str, Any]  					=		self.get_scheduler_config()
																	_snake_case:   str  					=		scheduler_class(**__snake_case       )
																	scheduler.set_timesteps(__snake_case       )

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

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

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

																	_snake_case:   List[Any]  					=		scheduler.step(__snake_case       ,     __snake_case       ,     __snake_case       ,     **__snake_case       ).prev_sample
																	_snake_case:   Optional[Any]  					=		new_scheduler.step(__snake_case       ,     __snake_case       ,     __snake_case       ,     **__snake_case       ).prev_sample

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ,     __snake_case	:  Optional[int]=None       ,     **__snake_case	:  List[str]       ):





											'''simple docstring'''





											if scheduler is None:
																	_snake_case:   Any  					=		self.scheduler_classes[0]
																	_snake_case:   Dict  					=		self.get_scheduler_config(**__snake_case       )
																	_snake_case:   int  					=		scheduler_class(**__snake_case       )

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

											_snake_case:   List[Any]  					=		10
											_snake_case:   List[Any]  					=		self.dummy_model()
											_snake_case:   Dict  					=		self.dummy_sample_deter
											scheduler.set_timesteps(__snake_case       )

											for i, t in enumerate(scheduler.timesteps       ):
																	_snake_case:   Optional[int]  					=		model(__snake_case       ,     __snake_case       )
																	_snake_case:   Optional[Any]  					=		scheduler.step(__snake_case       ,     __snake_case       ,     __snake_case       ).prev_sample

											return sample


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		dict(self.forward_default_kwargs       )

											_snake_case:   Optional[int]  					=		kwargs.pop('num_inference_steps'       ,     __snake_case       )

											for scheduler_class in self.scheduler_classes:
																	_snake_case:   int  					=		self.get_scheduler_config()
																	_snake_case:   int  					=		scheduler_class(**__snake_case       )

																	_snake_case:   Union[str, Any]  					=		self.dummy_sample
																	_snake_case:   str  					=		0.1 * sample

																	if num_inference_steps is not None and hasattr(__snake_case       ,     'set_timesteps'       ):
																							scheduler.set_timesteps(__snake_case       )
																	elif num_inference_steps is not None and not hasattr(__snake_case       ,     'set_timesteps'       ):
																							_snake_case:   Dict  					=		num_inference_steps

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

																	_snake_case:   Optional[Any]  					=		scheduler.timesteps[5]
																	_snake_case:   Any  					=		scheduler.timesteps[6]

																	_snake_case:   List[Any]  					=		scheduler.step(__snake_case       ,     __snake_case       ,     __snake_case       ,     **__snake_case       ).prev_sample
																	_snake_case:   Union[str, Any]  					=		scheduler.step(__snake_case       ,     __snake_case       ,     __snake_case       ,     **__snake_case       ).prev_sample

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		UniPCMultistepScheduler(**self.get_scheduler_config()       )
											_snake_case:   str  					=		self.full_loop(scheduler=__snake_case       )
											_snake_case:   Any  					=		torch.mean(torch.abs(__snake_case       )       )

											assert abs(result_mean.item() - 0.2_464       ) < 1e-3

											_snake_case:   int  					=		DPMSolverSinglestepScheduler.from_config(scheduler.config       )
											_snake_case:   List[str]  					=		DEISMultistepScheduler.from_config(scheduler.config       )
											_snake_case:   int  					=		DPMSolverMultistepScheduler.from_config(scheduler.config       )
											_snake_case:   Tuple  					=		UniPCMultistepScheduler.from_config(scheduler.config       )

											_snake_case:   Union[str, Any]  					=		self.full_loop(scheduler=__snake_case       )
											_snake_case:   int  					=		torch.mean(torch.abs(__snake_case       )       )

											assert abs(result_mean.item() - 0.2_464       ) < 1e-3


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											self.check_over_configs(thresholding=__snake_case       )
											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=__snake_case       ,     prediction_type=__snake_case       ,     sample_max_value=__snake_case       ,     solver_order=__snake_case       ,     solver_type=__snake_case       ,     )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[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=__snake_case       ,     solver_type=__snake_case       ,     prediction_type=__snake_case       ,     )
																													_snake_case:   List[Any]  					=		self.full_loop(
																													    solver_order=__snake_case       ,     solver_type=__snake_case       ,     prediction_type=__snake_case       ,     )
																													assert not torch.isnan(__snake_case       ).any(), "Samples have nan numbers"


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											self.check_over_configs(lower_order_final=__snake_case       )
											self.check_over_configs(lower_order_final=__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  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=__snake_case       ,     time_step=0       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   Any  					=		self.full_loop()
											_snake_case:   List[Any]  					=		torch.mean(torch.abs(__snake_case       )       )

											assert abs(result_mean.item() - 0.2_464       ) < 1e-3


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





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		self.full_loop(prediction_type='v_prediction'       )
											_snake_case:   List[str]  					=		torch.mean(torch.abs(__snake_case       )       )

											assert abs(result_mean.item() - 0.1_014       ) < 1e-3


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		self.scheduler_classes[0]
											_snake_case:   Tuple  					=		self.get_scheduler_config(thresholding=__snake_case       ,     dynamic_thresholding_ratio=0       )
											_snake_case:   List[str]  					=		scheduler_class(**__snake_case       )

											_snake_case:   Optional[int]  					=		10
											_snake_case:   List[Any]  					=		self.dummy_model()
											_snake_case:   List[Any]  					=		self.dummy_sample_deter.half()
											scheduler.set_timesteps(__snake_case       )

											for i, t in enumerate(scheduler.timesteps       ):
																	_snake_case:   Optional[int]  					=		model(__snake_case       ,     __snake_case       )
																	_snake_case:   List[str]  					=		scheduler.step(__snake_case       ,     __snake_case       ,     __snake_case       ).prev_sample

											assert sample.dtype == torch.floataa




					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





											for scheduler_class in self.scheduler_classes:
																	_snake_case:   List[Any]  					=		self.get_scheduler_config(**__snake_case       )
																	_snake_case:   Optional[Any]  					=		scheduler_class(**__snake_case       )

																	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 			lowerCamelCase					(						metaclass=__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    ["torch", "scipy"]


					def __init__(					self	:  Tuple       ,     *__snake_case	:  List[Any]       ,     **__snake_case	:  List[Any]       ):





											'''simple docstring'''





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


					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  Optional[int]       ,     *__snake_case	:  Optional[Any]       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





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




					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  List[Any]       ,     *__snake_case	:  Optional[Any]       ,     **__snake_case	:  str       ):





											'''simple docstring'''





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





