





import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       (IPNDMScheduler,)
							lowerCamelCase_							     =       (("num_inference_steps", 50),)


							def   _snake_case	(				self     :int				,					**__A     :Any		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   {"""num_train_timesteps""": 1000}
									config.update(**__A		)
									return config


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :int=0				,					**__A     :str		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   dict(self.forward_default_kwargs		)
									SCREAMING_SNAKE_CASE__								=   kwargs.pop("""num_inference_steps"""				,					__A		)
									SCREAMING_SNAKE_CASE__								=   self.dummy_sample
									SCREAMING_SNAKE_CASE__								=   0.1 * sample
									SCREAMING_SNAKE_CASE__								=   [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

									for scheduler_class in self.scheduler_classes:
											SCREAMING_SNAKE_CASE__								=   self.get_scheduler_config(**__A		)
											SCREAMING_SNAKE_CASE__								=   scheduler_class(**__A		)
											scheduler.set_timesteps(__A		)
											# copy over dummy past residuals
											SCREAMING_SNAKE_CASE__								=   dummy_past_residuals[:]

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

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

											SCREAMING_SNAKE_CASE__								=   scheduler.step(__A				,					__A				,					__A				,					**__A		).prev_sample
											SCREAMING_SNAKE_CASE__								=   new_scheduler.step(__A				,					__A				,					__A				,					**__A		).prev_sample

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

											SCREAMING_SNAKE_CASE__								=   scheduler.step(__A				,					__A				,					__A				,					**__A		).prev_sample
											SCREAMING_SNAKE_CASE__								=   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     :List[str]		) -> Union[str, Any]:



									"""simple docstring"""



									pass


							def   _snake_case	(				self     :Optional[Any]				,					__A     :Union[str, Any]=0				,					**__A     :Optional[int]		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   dict(self.forward_default_kwargs		)
									SCREAMING_SNAKE_CASE__								=   kwargs.pop("""num_inference_steps"""				,					__A		)
									SCREAMING_SNAKE_CASE__								=   self.dummy_sample
									SCREAMING_SNAKE_CASE__								=   0.1 * sample
									SCREAMING_SNAKE_CASE__								=   [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

									for scheduler_class in self.scheduler_classes:
											SCREAMING_SNAKE_CASE__								=   self.get_scheduler_config()
											SCREAMING_SNAKE_CASE__								=   scheduler_class(**__A		)
											scheduler.set_timesteps(__A		)

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

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

											with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(__A		)
													SCREAMING_SNAKE_CASE__								=   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)
													SCREAMING_SNAKE_CASE__								=   dummy_past_residuals[:]

											SCREAMING_SNAKE_CASE__								=   scheduler.step(__A				,					__A				,					__A				,					**__A		).prev_sample
											SCREAMING_SNAKE_CASE__								=   new_scheduler.step(__A				,					__A				,					__A				,					**__A		).prev_sample

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

											SCREAMING_SNAKE_CASE__								=   scheduler.step(__A				,					__A				,					__A				,					**__A		).prev_sample
											SCREAMING_SNAKE_CASE__								=   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     :List[Any]				,					**__A     :int		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__								=   self.get_scheduler_config(**__A		)
									SCREAMING_SNAKE_CASE__								=   scheduler_class(**__A		)

									SCREAMING_SNAKE_CASE__								=   10
									SCREAMING_SNAKE_CASE__								=   self.dummy_model()
									SCREAMING_SNAKE_CASE__								=   self.dummy_sample_deter
									scheduler.set_timesteps(__A		)

									for i, t in enumerate(scheduler.timesteps		):
											SCREAMING_SNAKE_CASE__								=   model(__A				,					__A		)
											SCREAMING_SNAKE_CASE__								=   scheduler.step(__A				,					__A				,					__A		).prev_sample

									for i, t in enumerate(scheduler.timesteps		):
											SCREAMING_SNAKE_CASE__								=   model(__A				,					__A		)
											SCREAMING_SNAKE_CASE__								=   scheduler.step(__A				,					__A				,					__A		).prev_sample

									return sample


							def   _snake_case	(				self     :Optional[Any]		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   dict(self.forward_default_kwargs		)

									SCREAMING_SNAKE_CASE__								=   kwargs.pop("""num_inference_steps"""				,					__A		)

									for scheduler_class in self.scheduler_classes:
											SCREAMING_SNAKE_CASE__								=   self.get_scheduler_config()
											SCREAMING_SNAKE_CASE__								=   scheduler_class(**__A		)

											SCREAMING_SNAKE_CASE__								=   self.dummy_sample
											SCREAMING_SNAKE_CASE__								=   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"""		):
													SCREAMING_SNAKE_CASE__								=   num_inference_steps

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

											SCREAMING_SNAKE_CASE__								=   scheduler.timesteps[5]
											SCREAMING_SNAKE_CASE__								=   scheduler.timesteps[6]

											SCREAMING_SNAKE_CASE__								=   scheduler.step(__A				,					__A				,					__A				,					**__A		).prev_sample
											SCREAMING_SNAKE_CASE__								=   scheduler.step(__A				,					__A				,					__A				,					**__A		).prev_sample

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

											SCREAMING_SNAKE_CASE__								=   scheduler.step(__A				,					__A				,					__A				,					**__A		).prev_sample
											SCREAMING_SNAKE_CASE__								=   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     :List[str]		) -> Optional[int]:



									"""simple docstring"""



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


							def   _snake_case	(				self     :Optional[Any]		) -> Tuple:



									"""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     :Optional[Any]		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.full_loop()
									SCREAMING_SNAKE_CASE__								=   torch.mean(torch.abs(__A		)		)

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






from torch import nn






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str     ):
		if act_fn in ["swish", "silu"]:
				return nn.SiLU()
		elif act_fn == "mish":
				return nn.Mish()
		elif act_fn == "gelu":
				return nn.GELU()
		else:
				raise ValueError(f'''Unsupported activation function: {act_fn}'''     )