




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler


class        __SCREAMING_SNAKE_CASE							(	unittest.TestCase						):
					_lowerCamelCase							=				(ScoreSdeVeScheduler,)
					_lowerCamelCase							=				()

					@property
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Dict							     =							4
						lowerCamelCase_							:	List[Any]							     =							3
						lowerCamelCase_							:	Optional[Any]							     =							8
						lowerCamelCase_							:	Optional[int]							     =							8

						lowerCamelCase_							:	List[str]							     =							torch.rand((batch_size, num_channels, height, width)      )

						return sample

					@property
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Optional[int]							     =							4
						lowerCamelCase_							:	List[str]							     =							3
						lowerCamelCase_							:	Tuple							     =							8
						lowerCamelCase_							:	List[Any]							     =							8

						lowerCamelCase_							:	str							     =							batch_size * num_channels * height * width
						lowerCamelCase_							:	str							     =							torch.arange(__A      )
						lowerCamelCase_							:	Union[str, Any]							     =							sample.reshape(__A				, __A				, __A				, __A      )
						lowerCamelCase_							:	Dict							     =							sample / num_elems
						lowerCamelCase_							:	Union[str, Any]							     =							sample.permute(3				, 0				, 1				, 2      )

						return sample

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						def model(__lowercase				, __lowercase				, *__lowercase      ):
							return sample * t / (t + 1)

						return model

					def 			lowercase_							(       self				, **__lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	Any							     =							{
						    "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 			lowercase_							(       self				, __lowercase=0				, **__lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	str							     =							dict(self.forward_default_kwargs      )

						for scheduler_class in self.scheduler_classes:
							lowerCamelCase_							:	int							     =							self.dummy_sample
							lowerCamelCase_							:	str							     =							0.1 * sample

							lowerCamelCase_							:	str							     =							self.get_scheduler_config(**__A      )
							lowerCamelCase_							:	int							     =							scheduler_class(**__A      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(__A      )
								lowerCamelCase_							:	Optional[int]							     =							scheduler_class.from_pretrained(__A      )

							lowerCamelCase_							:	Any							     =							scheduler.step_pred(
							    __A				, __A				, __A				, generator=torch.manual_seed(0      )				, **__A      ).prev_sample
							lowerCamelCase_							:	Dict							     =							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"

							lowerCamelCase_							:	Optional[Any]							     =							scheduler.step_correct(__A				, __A				, generator=torch.manual_seed(0      )				, **__A      ).prev_sample
							lowerCamelCase_							:	Any							     =							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 			lowercase_							(       self				, __lowercase=0				, **__lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	Any							     =							dict(self.forward_default_kwargs      )
						kwargs.update(__A      )

						for scheduler_class in self.scheduler_classes:
							lowerCamelCase_							:	Tuple							     =							self.dummy_sample
							lowerCamelCase_							:	Union[str, Any]							     =							0.1 * sample

							lowerCamelCase_							:	List[str]							     =							self.get_scheduler_config()
							lowerCamelCase_							:	Optional[Any]							     =							scheduler_class(**__A      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(__A      )
								lowerCamelCase_							:	Tuple							     =							scheduler_class.from_pretrained(__A      )

							lowerCamelCase_							:	List[str]							     =							scheduler.step_pred(
							    __A				, __A				, __A				, generator=torch.manual_seed(0      )				, **__A      ).prev_sample
							lowerCamelCase_							:	Optional[int]							     =							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"

							lowerCamelCase_							:	int							     =							scheduler.step_correct(__A				, __A				, generator=torch.manual_seed(0      )				, **__A      ).prev_sample
							lowerCamelCase_							:	int							     =							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 			lowercase_							(       self      ):







						'''simple docstring'''




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

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]				, [1, 100, 1_000]      ):
							self.check_over_configs(sigma_min=__A				, sigma_max=__A      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




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

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[str]							     =							dict(self.forward_default_kwargs      )

						lowerCamelCase_							:	Union[str, Any]							     =							self.scheduler_classes[0]
						lowerCamelCase_							:	Any							     =							self.get_scheduler_config()
						lowerCamelCase_							:	int							     =							scheduler_class(**__A      )

						lowerCamelCase_							:	Tuple							     =							3

						lowerCamelCase_							:	List[Any]							     =							self.dummy_model()
						lowerCamelCase_							:	Dict							     =							self.dummy_sample_deter

						scheduler.set_sigmas(__A      )
						scheduler.set_timesteps(__A      )
						lowerCamelCase_							:	List[str]							     =							torch.manual_seed(0      )

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

							for _ in range(scheduler.config.correct_steps      ):
								with torch.no_grad():
									lowerCamelCase_							:	Any							     =							model(__A				, __A      )
								lowerCamelCase_							:	Optional[Any]							     =							scheduler.step_correct(__A				, __A				, generator=__A				, **__A      ).prev_sample

							with torch.no_grad():
								lowerCamelCase_							:	Union[str, Any]							     =							model(__A				, __A      )

							lowerCamelCase_							:	Tuple							     =							scheduler.step_pred(__A				, __A				, __A				, generator=__A				, **__A      )
							lowerCamelCase_							:	int							     =							output.prev_sample, output.prev_sample_mean

						lowerCamelCase_							:	List[str]							     =							torch.sum(torch.abs(__A      )      )
						lowerCamelCase_							:	Tuple							     =							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 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[str]							     =							dict(self.forward_default_kwargs      )

						lowerCamelCase_							:	str							     =							kwargs.pop('num_inference_steps'				, __A      )

						for scheduler_class in self.scheduler_classes:
							lowerCamelCase_							:	Optional[Any]							     =							self.get_scheduler_config()
							lowerCamelCase_							:	Optional[int]							     =							scheduler_class(**__A      )

							lowerCamelCase_							:	int							     =							self.dummy_sample
							lowerCamelCase_							:	Union[str, 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

							lowerCamelCase_							:	Tuple							     =							scheduler.step_pred(__A				, 0				, __A				, generator=torch.manual_seed(0      )				, **__A      ).prev_sample
							lowerCamelCase_							:	Optional[Any]							     =							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       snake_case      (							A = 998001    )							->							int:




	'''simple docstring'''


	for number in range(n - 1		,     9999		,     -1    ):
		lowerCamelCase_							:	Tuple							     =							str(A    )

		# checks whether 'str_number' is a palindrome.
		if str_number == str_number[::-1]:
			lowerCamelCase_							:	Tuple							     =							999

			# if 'number' is a product of two 3-digit numbers
			# then number is the answer otherwise fetch next number.
			while divisor != 99:
				if (number % divisor == 0) and (len(str(number // divisor    )    ) == 3.0):
					return number
				divisor -= 1
	raise ValueError('That number is larger than our acceptable range.'    )


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