



import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 	__snake_case (				unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__			: Any             =   (ScoreSdeVeScheduler,)
							UpperCamelCase__			: int             =   ()


							@property
							def _a (							self						):
											a__			 =						4
											a__			 =						3
											a__			 =						8
											a__			 =						8

											a__			 =						torch.rand((batch_size, num_channels, height, width)						)

											return sample


							@property
							def _a (							self						):
											a__			 =						4
											a__			 =						3
											a__			 =						8
											a__			 =						8

											a__			 =						batch_size * num_channels * height * width
											a__			 =						torch.arange(a_						)
											a__			 =						sample.reshape(a_					,					a_					,					a_					,					a_						)
											a__			 =						sample / num_elems
											a__			 =						sample.permute(3					,					0					,					1					,					2						)

											return sample


							def _a (							self						):
											def model(a_					,					a_					,					*a_						):
															return sample * t / (t + 1)

											return model


							def _a (							self					,					**a_						):
											a__			 =						{
											    """num_train_timesteps""": 2_000,
											    """snr""": 0.15,
											    """sigma_min""": 0.01,
											    """sigma_max""": 1_348,
											    """sampling_eps""": 1E-5,
											}

											config.update(**a_						)
											return config


							def _a (							self					,					a_=0					,					**a_						):
											a__			 =						dict(self.forward_default_kwargs						)

											for scheduler_class in self.scheduler_classes:
															a__			 =						self.dummy_sample
															a__			 =						0.1 * sample

															a__			 =						self.get_scheduler_config(**a_						)
															a__			 =						scheduler_class(**a_						)

															with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(a_						)
																			a__			 =						scheduler_class.from_pretrained(a_						)

															a__			 =						scheduler.step_pred(
															    a_					,					a_					,					a_					,					generator=torch.manual_seed(0						)					,					**a_						).prev_sample
															a__			 =						new_scheduler.step_pred(
															    a_					,					a_					,					a_					,					generator=torch.manual_seed(0						)					,					**a_						).prev_sample

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

															a__			 =						scheduler.step_correct(a_					,					a_					,					generator=torch.manual_seed(0						)					,					**a_						).prev_sample
															a__			 =						new_scheduler.step_correct(
															    a_					,					a_					,					generator=torch.manual_seed(0						)					,					**a_						).prev_sample

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


							def _a (							self					,					a_=0					,					**a_						):
											a__			 =						dict(self.forward_default_kwargs						)
											kwargs.update(a_						)

											for scheduler_class in self.scheduler_classes:
															a__			 =						self.dummy_sample
															a__			 =						0.1 * sample

															a__			 =						self.get_scheduler_config()
															a__			 =						scheduler_class(**a_						)

															with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(a_						)
																			a__			 =						scheduler_class.from_pretrained(a_						)

															a__			 =						scheduler.step_pred(
															    a_					,					a_					,					a_					,					generator=torch.manual_seed(0						)					,					**a_						).prev_sample
															a__			 =						new_scheduler.step_pred(
															    a_					,					a_					,					a_					,					generator=torch.manual_seed(0						)					,					**a_						).prev_sample

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

															a__			 =						scheduler.step_correct(a_					,					a_					,					generator=torch.manual_seed(0						)					,					**a_						).prev_sample
															a__			 =						new_scheduler.step_correct(
															    a_					,					a_					,					generator=torch.manual_seed(0						)					,					**a_						).prev_sample

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


							def _a (							self						):
											for timesteps in [10, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=a_						)


							def _a (							self						):
											for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]					,					[1, 100, 1_000]						):
															self.check_over_configs(sigma_min=a_					,					sigma_max=a_						)


							def _a (							self						):
											for t in [0.1, 0.5, 0.75]:
															self.check_over_forward(time_step=a_						)


							def _a (							self						):
											a__			 =						dict(self.forward_default_kwargs						)

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

											a__			 =						3

											a__			 =						self.dummy_model()
											a__			 =						self.dummy_sample_deter

											scheduler.set_sigmas(a_						)
											scheduler.set_timesteps(a_						)
											a__			 =						torch.manual_seed(0						)

											for i, t in enumerate(scheduler.timesteps						):
															a__			 =						scheduler.sigmas[i]

															for _ in range(scheduler.config.correct_steps						):
																			with torch.no_grad():
																							a__			 =						model(a_					,					a_						)
																			a__			 =						scheduler.step_correct(a_					,					a_					,					generator=a_					,					**a_						).prev_sample

															with torch.no_grad():
																			a__			 =						model(a_					,					a_						)

															a__			 =						scheduler.step_pred(a_					,					a_					,					a_					,					generator=a_					,					**a_						)
															a__ ,     a__			 =						output.prev_sample, output.prev_sample_mean

											a__			 =						torch.sum(torch.abs(a_						)						)
											a__			 =						torch.mean(torch.abs(a_						)						)

											assert np.isclose(result_sum.item()					,					14_372_758_528.0						)
											assert np.isclose(result_mean.item()					,					18_714_530.0						)







							def _a (							self						):
											a__			 =						dict(self.forward_default_kwargs						)

											a__			 =						kwargs.pop("""num_inference_steps"""					,					a_						)

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

															a__			 =						self.dummy_sample
															a__			 =						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__			 =						num_inference_steps

															a__			 =						scheduler.step_pred(a_					,					0					,					a_					,					generator=torch.manual_seed(0						)					,					**a_						).prev_sample
															a__			 =						scheduler.step_pred(a_					,					1					,					a_					,					generator=torch.manual_seed(0						)					,					**a_						).prev_sample

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






def      A_ (		__a       :			int = 1_000						):

				"""simple docstring"""
				return sum(i for i in range(__a						) if i % 3 == 0 or i % 5 == 0						)


if __name__ == "__main__":
	print(f"""{solution() = }""")

