




"""simple docstring"""

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 							lowercase   (     __UpperCAmelCase):
				__lowerCAmelCase     :     Optional[Any]								= (DPMSolverMultistepScheduler,)
				__lowerCAmelCase     :     Optional[int]								= (("""num_inference_steps""", 25),)







				def        a_	(				self :       Optional[int]   ,  **_lowerCamelCase :       Any							):



					"""simple docstring"""

					A_	:					Union[str, Any]						 =		{
					    '''num_train_timesteps''': 10_00,
					    '''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(**_lowerCamelCase							)
					return config







				def        a_	(				self :       int   ,  _lowerCamelCase :       List[str]=0   ,  **_lowerCamelCase :       Union[str, Any]							):



					"""simple docstring"""

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

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

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

						A_ ,			A_	:					Optional[Any]						 =		sample, sample
						for t in range(_lowerCamelCase   ,  time_step + scheduler.config.solver_order + 1							):
							A_	:					Dict						 =		scheduler.step(_lowerCamelCase   ,  _lowerCamelCase   ,  _lowerCamelCase   ,  **_lowerCamelCase							).prev_sample
							A_	:					List[Any]						 =		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        a_	(				self :       Union[str, Any]							):



					"""simple docstring"""

					pass







				def        a_	(				self :       int   ,  _lowerCamelCase :       Any=0   ,  **_lowerCamelCase :       List[str]							):



					"""simple docstring"""

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

					for scheduler_class in self.scheduler_classes:
						A_	:					List[Any]						 =		self.get_scheduler_config()
						A_	:					str						 =		scheduler_class(**_lowerCamelCase							)
						scheduler.set_timesteps(_lowerCamelCase							)

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

						with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(_lowerCamelCase							)
							A_	:					Union[str, Any]						 =		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_	:					List[Any]						 =		dummy_past_residuals[: new_scheduler.config.solver_order]

						A_	:					Optional[Any]						 =		scheduler.step(_lowerCamelCase   ,  _lowerCamelCase   ,  _lowerCamelCase   ,  **_lowerCamelCase							).prev_sample
						A_	:					Any						 =		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        a_	(				self :       Any   ,  _lowerCamelCase :       Optional[int]=None   ,  **_lowerCamelCase :       Optional[int]							):



					"""simple docstring"""

					if scheduler is None:
						A_	:					List[str]						 =		self.scheduler_classes[0]
						A_	:					int						 =		self.get_scheduler_config(**_lowerCamelCase							)
						A_	:					List[str]						 =		scheduler_class(**_lowerCamelCase							)

					A_	:					List[Any]						 =		10
					A_	:					List[Any]						 =		self.dummy_model()
					A_	:					Optional[Any]						 =		self.dummy_sample_deter
					scheduler.set_timesteps(_lowerCamelCase							)

					for i, t in enumerate(scheduler.timesteps							):
						A_	:					Tuple						 =		model(_lowerCamelCase   ,  _lowerCamelCase							)
						A_	:					List[Any]						 =		scheduler.step(_lowerCamelCase   ,  _lowerCamelCase   ,  _lowerCamelCase							).prev_sample

					return sample







				def        a_	(				self :       Dict							):



					"""simple docstring"""

					A_	:					Dict						 =		dict(self.forward_default_kwargs							)

					A_	:					Optional[Any]						 =		kwargs.pop('''num_inference_steps'''   ,  _lowerCamelCase							)

					for scheduler_class in self.scheduler_classes:
						A_	:					Union[str, Any]						 =		self.get_scheduler_config()
						A_	:					Any						 =		scheduler_class(**_lowerCamelCase							)

						A_	:					List[str]						 =		self.dummy_sample
						A_	:					str						 =		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_	:					Union[str, Any]						 =		num_inference_steps

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

						A_	:					Optional[Any]						 =		scheduler.timesteps[5]
						A_	:					Dict						 =		scheduler.timesteps[6]

						A_	:					List[Any]						 =		scheduler.step(_lowerCamelCase   ,  _lowerCamelCase   ,  _lowerCamelCase   ,  **_lowerCamelCase							).prev_sample
						A_	:					int						 =		scheduler.step(_lowerCamelCase   ,  _lowerCamelCase   ,  _lowerCamelCase   ,  **_lowerCamelCase							).prev_sample

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







				def        a_	(				self :       Optional[Any]							):



					"""simple docstring"""

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







				def        a_	(				self :       str							):



					"""simple docstring"""

					self.check_over_configs(thresholding=_lowerCamelCase							)
					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=_lowerCamelCase   ,  prediction_type=_lowerCamelCase   ,  sample_max_value=_lowerCamelCase   ,  algorithm_type='''dpmsolver++'''   ,  solver_order=_lowerCamelCase   ,  solver_type=_lowerCamelCase   ,  )







				def        a_	(				self :       Optional[Any]							):



					"""simple docstring"""

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







				def        a_	(				self :       Optional[Any]							):



					"""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=_lowerCamelCase   ,  solver_type=_lowerCamelCase   ,  prediction_type=_lowerCamelCase   ,  algorithm_type=_lowerCamelCase   ,  )
									A_	:					List[str]						 =		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        a_	(				self :       Union[str, Any]							):



					"""simple docstring"""

					self.check_over_configs(lower_order_final=_lowerCamelCase							)
					self.check_over_configs(lower_order_final=_lowerCamelCase							)







				def        a_	(				self :       Tuple							):



					"""simple docstring"""

					self.check_over_configs(lambda_min_clipped=-float('''inf'''							)							)
					self.check_over_configs(lambda_min_clipped=-5.1							)







				def        a_	(				self :       Optional[Any]							):



					"""simple docstring"""

					self.check_over_configs(variance_type=_lowerCamelCase							)
					self.check_over_configs(variance_type='''learned_range'''							)







				def        a_	(				self :       Optional[int]							):



					"""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        a_	(				self :       int							):



					"""simple docstring"""

					A_	:					int						 =		self.full_loop()
					A_	:					Dict						 =		torch.mean(torch.abs(_lowerCamelCase							)							)

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







				def        a_	(				self :       Tuple							):



					"""simple docstring"""

					A_	:					Tuple						 =		self.full_loop(thresholding=_lowerCamelCase   ,  dynamic_thresholding_ratio=0.87   ,  sample_max_value=0.5							)
					A_	:					Dict						 =		torch.mean(torch.abs(_lowerCamelCase							)							)

					assert abs(result_mean.item() - 1.1364							) < 1E-3







				def        a_	(				self :       Optional[int]							):



					"""simple docstring"""

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

					assert abs(result_mean.item() - 0.2251							) < 1E-3







				def        a_	(				self :       Tuple							):



					"""simple docstring"""

					A_	:					Tuple						 =		self.full_loop(prediction_type='''v_prediction'''   ,  use_karras_sigmas=_lowerCamelCase							)
					A_	:					List[Any]						 =		torch.mean(torch.abs(_lowerCamelCase							)							)

					assert abs(result_mean.item() - 0.2096							) < 1E-3







				def        a_	(				self :       Any							):



					"""simple docstring"""

					A_	:					str						 =		DPMSolverMultistepScheduler(**self.get_scheduler_config()							)
					A_	:					Any						 =		self.full_loop(scheduler=_lowerCamelCase							)
					A_	:					Union[str, Any]						 =		torch.mean(torch.abs(_lowerCamelCase							)							)

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

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

					A_	:					Tuple						 =		self.full_loop(scheduler=_lowerCamelCase							)
					A_	:					int						 =		torch.mean(torch.abs(_lowerCamelCase							)							)

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







				def        a_	(				self :       List[str]							):



					"""simple docstring"""

					A_	:					List[Any]						 =		self.scheduler_classes[0]
					A_	:					Optional[int]						 =		self.get_scheduler_config(thresholding=_lowerCamelCase   ,  dynamic_thresholding_ratio=0							)
					A_	:					Optional[int]						 =		scheduler_class(**_lowerCamelCase							)

					A_	:					str						 =		10
					A_	:					Dict						 =		self.dummy_model()
					A_	:					Dict						 =		self.dummy_sample_deter.half()
					scheduler.set_timesteps(_lowerCamelCase							)

					for i, t in enumerate(scheduler.timesteps							):
						A_	:					Dict						 =		model(_lowerCamelCase   ,  _lowerCamelCase							)
						A_	:					List[str]						 =		scheduler.step(_lowerCamelCase   ,  _lowerCamelCase   ,  _lowerCamelCase							).prev_sample

					assert sample.dtype == torch.floataa







				def        a_	(				self :       Optional[int]   ,  **_lowerCamelCase :       Union[str, Any]							):



					"""simple docstring"""

					for scheduler_class in self.scheduler_classes:
						A_	:					str						 =		self.get_scheduler_config(**_lowerCamelCase							)
						A_	:					Optional[Any]						 =		scheduler_class(**_lowerCamelCase							)

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











"""simple docstring"""

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	return a + b


if __name__ == "__main__":
		_lowerCamelCase	:      Tuple   =							5
		_lowerCamelCase	:      Union[str, Any]   =							6
		print(f'The sum of {a} + {b} is {add(a, b)}')





