




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class 				UpperCAmelCase(					unittest.TestCase					):



	"""simple docstring"""






	a     : str	  =					(ScoreSdeVeScheduler,)
	a     : int	  =					()

	@property
	def    __a     (					self    ) ->      str:





								"""simple docstring"""


								lowercase__        =							4
								lowercase__        =							3
								lowercase__        =							8
								lowercase__        =							8

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

								return sample

	@property
	def    __a     (					self    ) ->      Tuple:





								"""simple docstring"""


								lowercase__        =							4
								lowercase__        =							3
								lowercase__        =							8
								lowercase__        =							8

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

								return sample

	def    __a     (					self    ) ->      Dict:





								"""simple docstring"""


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

								return model

	def    __a     (					self	,				**lowerCamelCase    ) ->      Union[str, Any]:





								"""simple docstring"""


								lowercase__        =							{
								    "num_train_timesteps": 2000,
								    "snr": 0.15,
								    "sigma_min": 0.01,
								    "sigma_max": 1348,
								    "sampling_eps": 1E-5,
								}

								config.update(**lowerCamelCase    )
								return config

	def    __a     (					self	,				lowerCamelCase=0	,				**lowerCamelCase    ) ->      int:





								"""simple docstring"""


								lowercase__        =							dict(self.forward_default_kwargs    )

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

															lowercase__        =							self.get_scheduler_config(**lowerCamelCase    )
															lowercase__        =							scheduler_class(**lowerCamelCase    )

															with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(lowerCamelCase    )
																						lowercase__        =							scheduler_class.from_pretrained(lowerCamelCase    )

															lowercase__        =							scheduler.step_pred(
															    lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				generator=torch.manual_seed(0    )	,				**lowerCamelCase    ).prev_sample
															lowercase__        =							new_scheduler.step_pred(
															    lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				generator=torch.manual_seed(0    )	,				**lowerCamelCase    ).prev_sample

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

															lowercase__        =							scheduler.step_correct(lowerCamelCase	,				lowerCamelCase	,				generator=torch.manual_seed(0    )	,				**lowerCamelCase    ).prev_sample
															lowercase__        =							new_scheduler.step_correct(
															    lowerCamelCase	,				lowerCamelCase	,				generator=torch.manual_seed(0    )	,				**lowerCamelCase    ).prev_sample

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

	def    __a     (					self	,				lowerCamelCase=0	,				**lowerCamelCase    ) ->      str:





								"""simple docstring"""


								lowercase__        =							dict(self.forward_default_kwargs    )
								kwargs.update(lowerCamelCase    )

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

															lowercase__        =							self.get_scheduler_config()
															lowercase__        =							scheduler_class(**lowerCamelCase    )

															with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(lowerCamelCase    )
																						lowercase__        =							scheduler_class.from_pretrained(lowerCamelCase    )

															lowercase__        =							scheduler.step_pred(
															    lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				generator=torch.manual_seed(0    )	,				**lowerCamelCase    ).prev_sample
															lowercase__        =							new_scheduler.step_pred(
															    lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				generator=torch.manual_seed(0    )	,				**lowerCamelCase    ).prev_sample

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

															lowercase__        =							scheduler.step_correct(lowerCamelCase	,				lowerCamelCase	,				generator=torch.manual_seed(0    )	,				**lowerCamelCase    ).prev_sample
															lowercase__        =							new_scheduler.step_correct(
															    lowerCamelCase	,				lowerCamelCase	,				generator=torch.manual_seed(0    )	,				**lowerCamelCase    ).prev_sample

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

	def    __a     (					self    ) ->      Tuple:





								"""simple docstring"""


								for timesteps in [10, 100, 1000]:
															self.check_over_configs(num_train_timesteps=lowerCamelCase    )

	def    __a     (					self    ) ->      Any:





								"""simple docstring"""


								for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]	,				[1, 100, 1000]    ):
															self.check_over_configs(sigma_min=lowerCamelCase	,				sigma_max=lowerCamelCase    )

	def    __a     (					self    ) ->      Optional[int]:





								"""simple docstring"""


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

	def    __a     (					self    ) ->      int:





								"""simple docstring"""


								lowercase__        =							dict(self.forward_default_kwargs    )

								lowercase__        =							self.scheduler_classes[0]
								lowercase__        =							self.get_scheduler_config()
								lowercase__        =							scheduler_class(**lowerCamelCase    )

								lowercase__        =							3

								lowercase__        =							self.dummy_model()
								lowercase__        =							self.dummy_sample_deter

								scheduler.set_sigmas(lowerCamelCase    )
								scheduler.set_timesteps(lowerCamelCase    )
								lowercase__        =							torch.manual_seed(0    )

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

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

															with torch.no_grad():
																						lowercase__        =							model(lowerCamelCase	,				lowerCamelCase    )

															lowercase__        =							scheduler.step_pred(lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				generator=lowerCamelCase	,				**lowerCamelCase    )
															lowercase__        =							output.prev_sample, output.prev_sample_mean

								lowercase__        =							torch.sum(torch.abs(lowerCamelCase    )    )
								lowercase__        =							torch.mean(torch.abs(lowerCamelCase    )    )

								assert np.isclose(result_sum.item()	,				14372758528.0    )
								assert np.isclose(result_mean.item()	,				18714530.0    )







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





								"""simple docstring"""


								lowercase__        =							dict(self.forward_default_kwargs    )

								lowercase__        =							kwargs.pop("num_inference_steps"	,				lowerCamelCase    )

								for scheduler_class in self.scheduler_classes:
															lowercase__        =							self.get_scheduler_config()
															lowercase__        =							scheduler_class(**lowerCamelCase    )

															lowercase__        =							self.dummy_sample
															lowercase__        =							0.1 * sample

															if num_inference_steps is not None and hasattr(lowerCamelCase	,				"set_timesteps"    ):
																						scheduler.set_timesteps(lowerCamelCase    )
															elif num_inference_steps is not None and not hasattr(lowerCamelCase	,				"set_timesteps"    ):
																						lowercase__        =							num_inference_steps

															lowercase__        =							scheduler.step_pred(lowerCamelCase	,				0	,				lowerCamelCase	,				generator=torch.manual_seed(0    )	,				**lowerCamelCase    ).prev_sample
															lowercase__        =							scheduler.step_pred(lowerCamelCase	,				1	,				lowerCamelCase	,				generator=torch.manual_seed(0    )	,				**lowerCamelCase    ).prev_sample

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





def  snake_case_					(		)      ->			int:

							for a in range(3_00    ):
														for b in range(a + 1    ,4_00    ):
																					for c in range(b + 1    ,5_00    ):
																												if (a + b + c) == 10_00 and (a**2) + (b**2) == (c**2):
																																			return a * b * c

							return -1
def  snake_case_					(		)      ->			int:

							for a in range(3_00    ):
														for b in range(4_00    ):
																					lowercase__  :	Dict        =							10_00 - a - b
																					if a < b < c and (a**2) + (b**2) == (c**2):
																												return a * b * c

							return -1
def  snake_case_					(		)      ->			None:
							import timeit

							print(
							    timeit.timeit("solution()"    ,setup="from __main__ import solution"    ,number=10_00    )    )
							print(
							    timeit.timeit(
							        "solution_fast()"    ,setup="from __main__ import solution_fast"    ,number=10_00    )    )


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