

import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     str													= (IPNDMScheduler,)
					UpperCAmelCase__:     Union[str, Any]													= (('''num_inference_steps''', 50),)






					def 						__A    (				self					, **A__    ):
						A__							:					str       =	{"""num_train_timesteps""": 1000}
						config.update(**A__    )
						return config






					def 						__A    (				self					, A__=0					, **A__    ):
						A__							:					Optional[Any]       =	dict(self.forward_default_kwargs    )
						A__							:					str       =	kwargs.pop("""num_inference_steps"""					, A__    )
						A__							:					Optional[int]       =	self.dummy_sample
						A__							:					List[str]       =	0.1 * sample
						A__							:					Tuple       =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

						for scheduler_class in self.scheduler_classes:
							A__							:					Union[str, Any]       =	self.get_scheduler_config(**A__    )
							A__							:					Optional[Any]       =	scheduler_class(**A__    )
							scheduler.set_timesteps(A__    )
							# copy over dummy past residuals
							A__							:					Dict       =	dummy_past_residuals[:]

							if time_step is None:
								A__							:					Dict       =	scheduler.timesteps[len(scheduler.timesteps    ) // 2]

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

							A__							:					Dict       =	scheduler.step(A__					, A__					, A__					, **A__    ).prev_sample
							A__							:					int       =	new_scheduler.step(A__					, A__					, A__					, **A__    ).prev_sample

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

							A__							:					Optional[Any]       =	scheduler.step(A__					, A__					, A__					, **A__    ).prev_sample
							A__							:					Tuple       =	new_scheduler.step(A__					, A__					, A__					, **A__    ).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__							:					Union[str, Any]       =	dict(self.forward_default_kwargs    )
						A__							:					int       =	kwargs.pop("""num_inference_steps"""					, A__    )
						A__							:					Optional[int]       =	self.dummy_sample
						A__							:					str       =	0.1 * sample
						A__							:					Optional[Any]       =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

							# copy over dummy past residuals (must be after setting timesteps)
							A__							:					Union[str, Any]       =	dummy_past_residuals[:]

							if time_step is None:
								A__							:					Any       =	scheduler.timesteps[len(scheduler.timesteps    ) // 2]

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

								# copy over dummy past residual (must be after setting timesteps)
								A__							:					Any       =	dummy_past_residuals[:]

							A__							:					Dict       =	scheduler.step(A__					, A__					, A__					, **A__    ).prev_sample
							A__							:					List[str]       =	new_scheduler.step(A__					, A__					, A__					, **A__    ).prev_sample

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

							A__							:					Dict       =	scheduler.step(A__					, A__					, A__					, **A__    ).prev_sample
							A__							:					Union[str, Any]       =	new_scheduler.step(A__					, A__					, A__					, **A__    ).prev_sample

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






					def 						__A    (				self					, **A__    ):
						A__							:					Any       =	self.scheduler_classes[0]
						A__							:					List[Any]       =	self.get_scheduler_config(**A__    )
						A__							:					Optional[Any]       =	scheduler_class(**A__    )

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

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

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

						return sample






					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	dict(self.forward_default_kwargs    )

						A__							:					Any       =	kwargs.pop("""num_inference_steps"""					, A__    )

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

							A__							:					str       =	self.dummy_sample
							A__							:					int       =	0.1 * sample

							if num_inference_steps is not None and hasattr(A__					, """set_timesteps"""    ):
								scheduler.set_timesteps(A__    )
							elif num_inference_steps is not None and not hasattr(A__					, """set_timesteps"""    ):
								A__							:					int       =	num_inference_steps

							# copy over dummy past residuals (must be done after set_timesteps)
							A__							:					int       =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
							A__							:					int       =	dummy_past_residuals[:]

							A__							:					str       =	scheduler.timesteps[5]
							A__							:					List[str]       =	scheduler.timesteps[6]

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

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

							A__							:					str       =	scheduler.step(A__					, A__					, A__					, **A__    ).prev_sample
							A__							:					Dict       =	scheduler.step(A__					, A__					, A__					, **A__    ).prev_sample

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






					def 						__A    (				self    ):
						for timesteps in [100, 1000]:
							self.check_over_configs(num_train_timesteps=A__					, time_step=A__    )






					def 						__A    (				self    ):
						for t, num_inference_steps in zip([1, 5, 10]					, [10, 50, 100]    ):
							self.check_over_forward(num_inference_steps=A__					, time_step=A__    )






					def 						__A    (				self    ):
						A__							:					Any       =	self.full_loop()
						A__							:					int       =	torch.mean(torch.abs(A__    )    )

						assert abs(result_mean.item() - 254_0529    ) < 10






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) = }''')



