






'''simple docstring'''




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest







class       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  (IPNDMScheduler,)
	_lowerCamelCase              =  (('''num_inference_steps''', 50),)

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					**UpperCAmelCase_   :						Union[str, Any]):
			UpperCamelCase__				:					str					=					{'num_train_timesteps': 1_000}
			config.update(**UpperCAmelCase_)
			return config

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					UpperCAmelCase_   :						Union[str, Any]=0  ,					**UpperCAmelCase_   :						str):
			UpperCamelCase__				:					Dict					=					dict(self.forward_default_kwargs)
			UpperCamelCase__				:					Optional[int]					=					kwargs.pop('num_inference_steps'  ,					UpperCAmelCase_)
			UpperCamelCase__				:					Optional[int]					=					self.dummy_sample
			UpperCamelCase__				:					Union[str, Any]					=					0.1 * sample
			UpperCamelCase__				:					Dict					=					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

			for scheduler_class in self.scheduler_classes:
					UpperCamelCase__				:					Tuple					=					self.get_scheduler_config(**UpperCAmelCase_)
					UpperCamelCase__				:					Optional[int]					=					scheduler_class(**UpperCAmelCase_)
					scheduler.set_timesteps(UpperCAmelCase_)
					# copy over dummy past residuals
					UpperCamelCase__				:					List[Any]					=					dummy_past_residuals[:]

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

					with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(UpperCAmelCase_)
							UpperCamelCase__				:					Optional[int]					=					scheduler_class.from_pretrained(UpperCAmelCase_)
							new_scheduler.set_timesteps(UpperCAmelCase_)
							# copy over dummy past residuals
							UpperCamelCase__				:					Optional[int]					=					dummy_past_residuals[:]

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

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

					UpperCamelCase__				:					Optional[int]					=					scheduler.step(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_  ,					**UpperCAmelCase_).prev_sample
					UpperCamelCase__				:					Tuple					=					new_scheduler.step(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_  ,					**UpperCAmelCase_).prev_sample

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

	def      __UpperCamelCase							(      self   :						List[str]):
			pass

	def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						List[str]=0  ,					**UpperCAmelCase_   :						Union[str, Any]):
			UpperCamelCase__				:					Optional[Any]					=					dict(self.forward_default_kwargs)
			UpperCamelCase__				:					List[str]					=					kwargs.pop('num_inference_steps'  ,					UpperCAmelCase_)
			UpperCamelCase__				:					Union[str, Any]					=					self.dummy_sample
			UpperCamelCase__				:					str					=					0.1 * sample
			UpperCamelCase__				:					List[Any]					=					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

			for scheduler_class in self.scheduler_classes:
					UpperCamelCase__				:					List[str]					=					self.get_scheduler_config()
					UpperCamelCase__				:					Tuple					=					scheduler_class(**UpperCAmelCase_)
					scheduler.set_timesteps(UpperCAmelCase_)

					# copy over dummy past residuals (must be after setting timesteps)
					UpperCamelCase__				:					int					=					dummy_past_residuals[:]

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

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

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

					UpperCamelCase__				:					Optional[Any]					=					scheduler.step(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_  ,					**UpperCAmelCase_).prev_sample
					UpperCamelCase__				:					Dict					=					new_scheduler.step(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_  ,					**UpperCAmelCase_).prev_sample

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

					UpperCamelCase__				:					int					=					scheduler.step(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_  ,					**UpperCAmelCase_).prev_sample
					UpperCamelCase__				:					List[str]					=					new_scheduler.step(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_  ,					**UpperCAmelCase_).prev_sample

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

	def      __UpperCamelCase							(      self   :						List[Any]  ,					**UpperCAmelCase_   :						Optional[Any]):
			UpperCamelCase__				:					int					=					self.scheduler_classes[0]
			UpperCamelCase__				:					Tuple					=					self.get_scheduler_config(**UpperCAmelCase_)
			UpperCamelCase__				:					Optional[int]					=					scheduler_class(**UpperCAmelCase_)

			UpperCamelCase__				:					List[str]					=					10
			UpperCamelCase__				:					List[Any]					=					self.dummy_model()
			UpperCamelCase__				:					Optional[int]					=					self.dummy_sample_deter
			scheduler.set_timesteps(UpperCAmelCase_)

			for i, t in enumerate(scheduler.timesteps):
					UpperCamelCase__				:					str					=					model(UpperCAmelCase_  ,					UpperCAmelCase_)
					UpperCamelCase__				:					int					=					scheduler.step(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_).prev_sample

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

			return sample

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			UpperCamelCase__				:					Optional[int]					=					dict(self.forward_default_kwargs)

			UpperCamelCase__				:					List[Any]					=					kwargs.pop('num_inference_steps'  ,					UpperCAmelCase_)

			for scheduler_class in self.scheduler_classes:
					UpperCamelCase__				:					Any					=					self.get_scheduler_config()
					UpperCamelCase__				:					Optional[Any]					=					scheduler_class(**UpperCAmelCase_)

					UpperCamelCase__				:					Optional[Any]					=					self.dummy_sample
					UpperCamelCase__				:					List[Any]					=					0.1 * sample

					if num_inference_steps is not None and hasattr(UpperCAmelCase_  ,					'set_timesteps'):
							scheduler.set_timesteps(UpperCAmelCase_)
					elif num_inference_steps is not None and not hasattr(UpperCAmelCase_  ,					'set_timesteps'):
							UpperCamelCase__				:					Optional[Any]					=					num_inference_steps

					# copy over dummy past residuals (must be done after set_timesteps)
					UpperCamelCase__				:					List[str]					=					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
					UpperCamelCase__				:					Union[str, Any]					=					dummy_past_residuals[:]

					UpperCamelCase__				:					Dict					=					scheduler.timesteps[5]
					UpperCamelCase__				:					List[str]					=					scheduler.timesteps[6]

					UpperCamelCase__				:					Optional[Any]					=					scheduler.step(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_  ,					**UpperCAmelCase_).prev_sample
					UpperCamelCase__				:					Optional[Any]					=					scheduler.step(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_  ,					**UpperCAmelCase_).prev_sample

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

					UpperCamelCase__				:					int					=					scheduler.step(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_  ,					**UpperCAmelCase_).prev_sample
					UpperCamelCase__				:					List[Any]					=					scheduler.step(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_  ,					**UpperCAmelCase_).prev_sample

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

	def      __UpperCamelCase							(      self   :						str):
			for timesteps in [100, 1_000]:
					self.check_over_configs(num_train_timesteps=UpperCAmelCase_  ,					time_step=UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						List[str]):
			for t, num_inference_steps in zip([1, 5, 10]  ,					[10, 50, 100]):
					self.check_over_forward(num_inference_steps=UpperCAmelCase_  ,					time_step=UpperCAmelCase_)





	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			UpperCamelCase__				:					int					=					self.full_loop()
			UpperCamelCase__				:					Optional[Any]					=					torch.mean(torch.abs(UpperCAmelCase_))

			assert abs(result_mean.item() - 2_540_529) < 10












'''simple docstring'''




from ..utils import DummyObject, requires_backends







class       __lowercase					(metaclass=__lowerCamelCase    ):
	_lowerCamelCase              =  ['''torch''', '''scipy''']

	def __init__(      self   :						List[Any]  ,					*UpperCAmelCase_   :						Any  ,					**UpperCAmelCase_   :						int):
			requires_backends(self  ,					['torch', 'scipy'])

	@classmethod
	def      __UpperCamelCase							(      cls   :						Union[str, Any]  ,					*UpperCAmelCase_   :						Any  ,					**UpperCAmelCase_   :						List[Any]):
			requires_backends(cls  ,					['torch', 'scipy'])





	@classmethod
	def      __UpperCamelCase							(      cls   :						Union[str, Any]  ,					*UpperCAmelCase_   :						Optional[Any]  ,					**UpperCAmelCase_   :						Any):
			requires_backends(cls  ,					['torch', 'scipy'])




