


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 	lowerCamelCase_       (				__a				):



					'''simple docstring'''







					__UpperCAmelCase						     =				(IPNDMScheduler,)
					__UpperCAmelCase						     =				(("num_inference_steps", 50),)






					def A      (				self ,       **snake_case_						)   ->			Optional[Any]:







								'''simple docstring'''

								__lowercase 					=   {"""num_train_timesteps""": 1_0_0_0}
								config.update(**a_						)
								return config






					def A      (				self ,       snake_case_=0 ,       **snake_case_						)   ->			Optional[Any]:







								'''simple docstring'''

								__lowercase 					=   dict(self.forward_default_kwargs						)
								__lowercase 					=   kwargs.pop('''num_inference_steps''' ,       a_						)
								__lowercase 					=   self.dummy_sample
								__lowercase 					=   0.1 * sample
								__lowercase 					=   [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

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

											__lowercase 					=   scheduler.step(a_ ,       a_ ,       a_ ,       **a_						).prev_sample
											__lowercase 					=   new_scheduler.step(a_ ,       a_ ,       a_ ,       **a_						).prev_sample

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

											__lowercase 					=   scheduler.step(a_ ,       a_ ,       a_ ,       **a_						).prev_sample
											__lowercase 					=   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						)   ->			Any:







								'''simple docstring'''

								pass






					def A      (				self ,       snake_case_=0 ,       **snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   dict(self.forward_default_kwargs						)
								__lowercase 					=   kwargs.pop('''num_inference_steps''' ,       a_						)
								__lowercase 					=   self.dummy_sample
								__lowercase 					=   0.1 * sample
								__lowercase 					=   [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

								for scheduler_class in self.scheduler_classes:
											__lowercase 					=   self.get_scheduler_config()
											__lowercase 					=   scheduler_class(**a_						)
											scheduler.set_timesteps(a_						)

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

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

											with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(a_						)
														__lowercase 					=   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)
														__lowercase 					=   dummy_past_residuals[:]

											__lowercase 					=   scheduler.step(a_ ,       a_ ,       a_ ,       **a_						).prev_sample
											__lowercase 					=   new_scheduler.step(a_ ,       a_ ,       a_ ,       **a_						).prev_sample

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

											__lowercase 					=   scheduler.step(a_ ,       a_ ,       a_ ,       **a_						).prev_sample
											__lowercase 					=   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 ,       **snake_case_						)   ->			Optional[int]:







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config(**a_						)
								__lowercase 					=   scheduler_class(**a_						)

								__lowercase 					=   1_0
								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter
								scheduler.set_timesteps(a_						)

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

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

								return sample






					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   dict(self.forward_default_kwargs						)

								__lowercase 					=   kwargs.pop('''num_inference_steps''' ,       a_						)

								for scheduler_class in self.scheduler_classes:
											__lowercase 					=   self.get_scheduler_config()
											__lowercase 					=   scheduler_class(**a_						)

											__lowercase 					=   self.dummy_sample
											__lowercase 					=   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'''						):
														__lowercase 					=   num_inference_steps

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

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

											__lowercase 					=   scheduler.step(a_ ,       a_ ,       a_ ,       **a_						).prev_sample
											__lowercase 					=   scheduler.step(a_ ,       a_ ,       a_ ,       **a_						).prev_sample

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

											__lowercase 					=   scheduler.step(a_ ,       a_ ,       a_ ,       **a_						).prev_sample
											__lowercase 					=   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						)   ->			Tuple:







								'''simple docstring'''

								for timesteps in [1_0_0, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=a_ ,       time_step=a_						)






					def A      (				self						)   ->			Tuple:







								'''simple docstring'''

								for t, num_inference_steps in zip([1, 5, 1_0] ,       [1_0, 5_0, 1_0_0]						):
											self.check_over_forward(num_inference_steps=a_ ,       time_step=a_						)






					def A      (				self						)   ->			Optional[Any]:







								'''simple docstring'''

								__lowercase 					=   self.full_loop()
								__lowercase 					=   torch.mean(torch.abs(a_						)						)

								assert abs(result_mean.item() - 2_5_4_0_5_2_9						) < 1_0






from ..utils import DummyObject, requires_backends
class 	lowerCamelCase_       (				metaclass=lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				["onnx"]






					def __init__(				self ,       *snake_case_ ,       **snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								requires_backends(self ,       ['''onnx''']						)






					@classmethod
					def A      (				cls ,       *snake_case_ ,       **snake_case_						)   ->			int:







								'''simple docstring'''

								requires_backends(cls ,       ['''onnx''']						)






					@classmethod
					def A      (				cls ,       *snake_case_ ,       **snake_case_						)   ->			List[str]:







								'''simple docstring'''

								requires_backends(cls ,       ['''onnx''']						)


