





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class 					_a		(lowercase_       ):


					'''simple docstring'''






					UpperCAmelCase__:     List[str]													= (DEISMultistepScheduler,)
					UpperCAmelCase__:     Tuple													= (('''num_inference_steps''', 25),)






					def 						__A    (				self					, **A__    ):
						A__							:					Dict       =	{
						    """num_train_timesteps""": 1000,
						    """beta_start""": 0.0_0_0_1,
						    """beta_end""": 0.0_2,
						    """beta_schedule""": """linear""",
						    """solver_order""": 2,
						}

						config.update(**lowerCamelCase_    )
						return config






					def 						__A    (				self					, A__=0					, **A__    ):
						A__							:					Any       =	dict(self.forward_default_kwargs    )
						A__							:					List[str]       =	kwargs.pop("""num_inference_steps"""					, lowerCamelCase_    )
						A__							:					Dict       =	self.dummy_sample
						A__							:					Any       =	0.1 * sample
						A__							:					str       =	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
							A__							:					int       =	self.get_scheduler_config(**lowerCamelCase_    )
							A__							:					Optional[int]       =	scheduler_class(**lowerCamelCase_    )
							scheduler.set_timesteps(lowerCamelCase_    )
							# copy over dummy past residuals
							A__							:					Optional[Any]       =	dummy_past_residuals[: scheduler.config.solver_order]

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

							A__							:					str       =	sample, sample
							for t in range(lowerCamelCase_					, time_step + scheduler.config.solver_order + 1    ):
								A__							:					Tuple       =	scheduler.step(lowerCamelCase_					, lowerCamelCase_					, lowerCamelCase_					, **lowerCamelCase_    ).prev_sample
								A__							:					Optional[int]       =	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    ):
						pass






					def 						__A    (				self					, A__=0					, **A__    ):
						A__							:					Tuple       =	dict(self.forward_default_kwargs    )
						A__							:					Optional[Any]       =	kwargs.pop("""num_inference_steps"""					, lowerCamelCase_    )
						A__							:					Optional[int]       =	self.dummy_sample
						A__							:					Optional[Any]       =	0.1 * sample
						A__							:					Optional[Any]       =	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
							A__							:					str       =	self.get_scheduler_config()
							A__							:					Any       =	scheduler_class(**lowerCamelCase_    )
							scheduler.set_timesteps(lowerCamelCase_    )

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

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(lowerCamelCase_    )
								A__							:					List[str]       =	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__							:					Dict       =	dummy_past_residuals[: new_scheduler.config.solver_order]

							A__							:					List[Any]       =	scheduler.step(lowerCamelCase_					, lowerCamelCase_					, lowerCamelCase_					, **lowerCamelCase_    ).prev_sample
							A__							:					Tuple       =	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					, A__=None					, **A__    ):



						if scheduler is None:
							A__							:					Optional[int]       =	self.scheduler_classes[0]
							A__							:					Optional[Any]       =	self.get_scheduler_config(**lowerCamelCase_    )
							A__							:					int       =	scheduler_class(**lowerCamelCase_    )

						A__							:					List[Any]       =	self.scheduler_classes[0]
						A__							:					str       =	self.get_scheduler_config(**lowerCamelCase_    )
						A__							:					str       =	scheduler_class(**lowerCamelCase_    )

						A__							:					Tuple       =	10
						A__							:					Any       =	self.dummy_model()
						A__							:					Union[str, Any]       =	self.dummy_sample_deter
						scheduler.set_timesteps(lowerCamelCase_    )

						for i, t in enumerate(scheduler.timesteps    ):
							A__							:					Dict       =	model(lowerCamelCase_					, lowerCamelCase_    )
							A__							:					Optional[int]       =	scheduler.step(lowerCamelCase_					, lowerCamelCase_					, lowerCamelCase_    ).prev_sample

						return sample






					def 						__A    (				self    ):
						A__							:					Tuple       =	dict(self.forward_default_kwargs    )

						A__							:					int       =	kwargs.pop("""num_inference_steps"""					, lowerCamelCase_    )

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

							A__							:					Optional[int]       =	self.dummy_sample
							A__							:					Any       =	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__							:					str       =	num_inference_steps

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

							A__							:					int       =	scheduler.timesteps[5]
							A__							:					int       =	scheduler.timesteps[6]

							A__							:					List[Any]       =	scheduler.step(lowerCamelCase_					, lowerCamelCase_					, lowerCamelCase_					, **lowerCamelCase_    ).prev_sample
							A__							:					Tuple       =	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    ):
						A__							:					str       =	DEISMultistepScheduler(**self.get_scheduler_config()    )
						A__							:					Union[str, Any]       =	self.full_loop(scheduler=lowerCamelCase_    )
						A__							:					List[Any]       =	torch.mean(torch.abs(lowerCamelCase_    )    )

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

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

						A__							:					Optional[Any]       =	self.full_loop(scheduler=lowerCamelCase_    )
						A__							:					List[str]       =	torch.mean(torch.abs(lowerCamelCase_    )    )

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






					def 						__A    (				self    ):



						for timesteps in [25, 50, 100, 999, 1000]:
							self.check_over_configs(num_train_timesteps=lowerCamelCase_    )






					def 						__A    (				self    ):
						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 						__A    (				self    ):



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






					def 						__A    (				self    ):



						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_					, )
										A__							:					Dict       =	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    ):
						self.check_over_configs(lower_order_final=lowerCamelCase_    )
						self.check_over_configs(lower_order_final=lowerCamelCase_    )






					def 						__A    (				self    ):



						for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
							self.check_over_forward(num_inference_steps=lowerCamelCase_					, time_step=0    )






					def 						__A    (				self    ):
						A__							:					List[Any]       =	self.full_loop()
						A__							:					List[Any]       =	torch.mean(torch.abs(lowerCamelCase_    )    )

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






					def 						__A    (				self    ):
						A__							:					List[str]       =	self.full_loop(prediction_type="""v_prediction"""    )
						A__							:					Any       =	torch.mean(torch.abs(lowerCamelCase_    )    )

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






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	self.scheduler_classes[0]
						A__							:					List[str]       =	self.get_scheduler_config(thresholding=lowerCamelCase_					, dynamic_thresholding_ratio=0    )
						A__							:					Dict       =	scheduler_class(**lowerCamelCase_    )

						A__							:					List[str]       =	10
						A__							:					Optional[int]       =	self.dummy_model()
						A__							:					Dict       =	self.dummy_sample_deter.half()
						scheduler.set_timesteps(lowerCamelCase_    )

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

						assert sample.dtype == torch.floataa






def 		UpperCamelCase      (lowercase_:				int					,   lowercase_:				int      )  ->   int:
	while second != 0:
		A__							:					int       =	first & second
		first ^= second
		A__							:					int       =	c << 1
	return first


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	A_ :				Optional[Any]						 =    int(input('Enter the first number: ').strip())
	A_ :				List[str]						 =    int(input('Enter the second number: ').strip())
	print(f'''{add(first, second) = }''')



