



import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	(IPNDMScheduler,)
							lowerCamelCase__         =	(('num_inference_steps', 50),)







							def      snake_case__   (     self,    **__a):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					{"num_train_timesteps": 1000}
														config.update(**__a)
														return config







							def      snake_case__   (     self,    __a=0,    **__a):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					dict(self.forward_default_kwargs)
														_lowerCAmelCase  :							str				     =					kwargs.pop("num_inference_steps",    __a)
														_lowerCAmelCase  :							Any				     =					self.dummy_sample
														_lowerCAmelCase  :							Optional[int]				     =					0.1 * sample
														_lowerCAmelCase  :							Optional[Any]				     =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

														for scheduler_class in self.scheduler_classes:
																					_lowerCAmelCase  :							Any				     =					self.get_scheduler_config(**__a)
																					_lowerCAmelCase  :							List[str]				     =					scheduler_class(**__a)
																					scheduler.set_timesteps(__a)
																					# copy over dummy past residuals
																					_lowerCAmelCase  :							Any				     =					dummy_past_residuals[:]

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

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

																					_lowerCAmelCase  :							Any				     =					scheduler.step(__a,    __a,    __a,    **__a).prev_sample
																					_lowerCAmelCase  :							Dict				     =					new_scheduler.step(__a,    __a,    __a,    **__a).prev_sample

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

																					_lowerCAmelCase  :							List[Any]				     =					scheduler.step(__a,    __a,    __a,    **__a).prev_sample
																					_lowerCAmelCase  :							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"







							def      snake_case__   (     self):

														'''simple docstring'''

														pass







							def      snake_case__   (     self,    __a=0,    **__a):

														'''simple docstring'''

														_lowerCAmelCase  :							str				     =					dict(self.forward_default_kwargs)
														_lowerCAmelCase  :							str				     =					kwargs.pop("num_inference_steps",    __a)
														_lowerCAmelCase  :							Tuple				     =					self.dummy_sample
														_lowerCAmelCase  :							str				     =					0.1 * sample
														_lowerCAmelCase  :							Optional[int]				     =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(__a)
																												_lowerCAmelCase  :							List[Any]				     =					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)
																												_lowerCAmelCase  :							str				     =					dummy_past_residuals[:]

																					_lowerCAmelCase  :							Dict				     =					scheduler.step(__a,    __a,    __a,    **__a).prev_sample
																					_lowerCAmelCase  :							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"

																					_lowerCAmelCase  :							Optional[Any]				     =					scheduler.step(__a,    __a,    __a,    **__a).prev_sample
																					_lowerCAmelCase  :							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      snake_case__   (     self,    **__a):

														'''simple docstring'''

														_lowerCAmelCase  :							str				     =					self.scheduler_classes[0]
														_lowerCAmelCase  :							Any				     =					self.get_scheduler_config(**__a)
														_lowerCAmelCase  :							Dict				     =					scheduler_class(**__a)

														_lowerCAmelCase  :							Dict				     =					10
														_lowerCAmelCase  :							Optional[int]				     =					self.dummy_model()
														_lowerCAmelCase  :							Union[str, Any]				     =					self.dummy_sample_deter
														scheduler.set_timesteps(__a)

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

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

														return sample







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[Any]				     =					dict(self.forward_default_kwargs)

														_lowerCAmelCase  :							str				     =					kwargs.pop("num_inference_steps",    __a)

														for scheduler_class in self.scheduler_classes:
																					_lowerCAmelCase  :							str				     =					self.get_scheduler_config()
																					_lowerCAmelCase  :							Dict				     =					scheduler_class(**__a)

																					_lowerCAmelCase  :							Optional[Any]				     =					self.dummy_sample
																					_lowerCAmelCase  :							Optional[Any]				     =					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"):
																												_lowerCAmelCase  :							Union[str, Any]				     =					num_inference_steps

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

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

																					_lowerCAmelCase  :							Union[str, Any]				     =					scheduler.step(__a,    __a,    __a,    **__a).prev_sample
																					_lowerCAmelCase  :							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)

																					_lowerCAmelCase  :							Union[str, Any]				     =					scheduler.step(__a,    __a,    __a,    **__a).prev_sample
																					_lowerCAmelCase  :							Optional[Any]				     =					scheduler.step(__a,    __a,    __a,    **__a).prev_sample

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







							def      snake_case__   (     self):

														'''simple docstring'''

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







							def      snake_case__   (     self):

														'''simple docstring'''

														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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Dict				     =					self.full_loop()
														_lowerCAmelCase  :							Any				     =					torch.mean(torch.abs(__a))

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





from __future__ import annotations






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							if not nums:
														raise ValueError("List is empty"						)
							return sum(_lowerCamelCase						) / len(_lowerCamelCase						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
