




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler
class      lowerCamelCase__      (					unittest.TestCase     ):
			# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
			a__	: Tuple        =       (ScoreSdeVeScheduler,)
			a__	: Optional[int]        =       ()


			@property
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  str        =			4
					snake_case						:  List[str]        =			3
					snake_case						:  Union[str, Any]        =			8
					snake_case						:  Any        =			8

					snake_case						:  str        =			torch.rand((batch_size, num_channels, height, width)							)

					return sample


			@property
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[Any]        =			4
					snake_case						:  Dict        =			3
					snake_case						:  Any        =			8
					snake_case						:  Any        =			8

					snake_case						:  Optional[int]        =			batch_size * num_channels * height * width
					snake_case						:  Union[str, Any]        =			torch.arange(SCREAMING_SNAKE_CASE							)
					snake_case						:  str        =			sample.reshape(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)
					snake_case						:  Any        =			sample / num_elems
					snake_case						:  List[str]        =			sample.permute(3			,	0			,	1			,	2							)

					return sample


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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

					return model


			def 							lowerCamelCase_			(       self			,	**SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  List[str]        =			{
					    "num_train_timesteps": 2_000,
					    "snr": 0.15,
					    "sigma_min": 0.01,
					    "sigma_max": 1_348,
					    "sampling_eps": 1E-5,
					}

					config.update(**SCREAMING_SNAKE_CASE							)
					return config


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE=0			,	**SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Any        =			dict(self.forward_default_kwargs							)

					for scheduler_class in self.scheduler_classes:
							snake_case						:  List[Any]        =			self.dummy_sample
							snake_case						:  Any        =			0.1 * sample

							snake_case						:  Optional[Any]        =			self.get_scheduler_config(**SCREAMING_SNAKE_CASE							)
							snake_case						:  Union[str, Any]        =			scheduler_class(**SCREAMING_SNAKE_CASE							)

							with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(SCREAMING_SNAKE_CASE							)
									snake_case						:  int        =			scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE							)

							snake_case						:  Union[str, Any]        =			scheduler.step_pred(
							    SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=torch.manual_seed(0							)			,	**SCREAMING_SNAKE_CASE							).prev_sample
							snake_case						:  Any        =			new_scheduler.step_pred(
							    SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=torch.manual_seed(0							)			,	**SCREAMING_SNAKE_CASE							).prev_sample

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

							snake_case						:  Dict        =			scheduler.step_correct(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=torch.manual_seed(0							)			,	**SCREAMING_SNAKE_CASE							).prev_sample
							snake_case						:  List[Any]        =			new_scheduler.step_correct(
							    SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=torch.manual_seed(0							)			,	**SCREAMING_SNAKE_CASE							).prev_sample

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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE=0			,	**SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Any        =			dict(self.forward_default_kwargs							)
					kwargs.update(SCREAMING_SNAKE_CASE							)

					for scheduler_class in self.scheduler_classes:
							snake_case						:  Tuple        =			self.dummy_sample
							snake_case						:  Dict        =			0.1 * sample

							snake_case						:  Union[str, Any]        =			self.get_scheduler_config()
							snake_case						:  Optional[int]        =			scheduler_class(**SCREAMING_SNAKE_CASE							)

							with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(SCREAMING_SNAKE_CASE							)
									snake_case						:  Any        =			scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE							)

							snake_case						:  Tuple        =			scheduler.step_pred(
							    SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=torch.manual_seed(0							)			,	**SCREAMING_SNAKE_CASE							).prev_sample
							snake_case						:  Optional[Any]        =			new_scheduler.step_pred(
							    SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=torch.manual_seed(0							)			,	**SCREAMING_SNAKE_CASE							).prev_sample

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

							snake_case						:  Any        =			scheduler.step_correct(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=torch.manual_seed(0							)			,	**SCREAMING_SNAKE_CASE							).prev_sample
							snake_case						:  int        =			new_scheduler.step_correct(
							    SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=torch.manual_seed(0							)			,	**SCREAMING_SNAKE_CASE							).prev_sample

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


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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


			def 							lowerCamelCase_			(       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=SCREAMING_SNAKE_CASE			,	sigma_max=SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Tuple        =			dict(self.forward_default_kwargs							)

					snake_case						:  str        =			self.scheduler_classes[0]
					snake_case						:  Any        =			self.get_scheduler_config()
					snake_case						:  Optional[int]        =			scheduler_class(**SCREAMING_SNAKE_CASE							)

					snake_case						:  Optional[Any]        =			3

					snake_case						:  Optional[int]        =			self.dummy_model()
					snake_case						:  Tuple        =			self.dummy_sample_deter

					scheduler.set_sigmas(SCREAMING_SNAKE_CASE							)
					scheduler.set_timesteps(SCREAMING_SNAKE_CASE							)
					snake_case						:  List[str]        =			torch.manual_seed(0							)

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

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

							with torch.no_grad():
									snake_case						:  Any        =			model(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

							snake_case						:  List[Any]        =			scheduler.step_pred(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE			,	**SCREAMING_SNAKE_CASE							)
							snake_case						:  Tuple        =			output.prev_sample, output.prev_sample_mean

					snake_case						:  Optional[int]        =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE							)							)
					snake_case						:  List[str]        =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE							)							)

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



			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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

					snake_case						:  str        =			kwargs.pop("num_inference_steps"			,	SCREAMING_SNAKE_CASE							)

					for scheduler_class in self.scheduler_classes:
							snake_case						:  Dict        =			self.get_scheduler_config()
							snake_case						:  List[Any]        =			scheduler_class(**SCREAMING_SNAKE_CASE							)

							snake_case						:  Optional[Any]        =			self.dummy_sample
							snake_case						:  str        =			0.1 * sample

							if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE			,	"set_timesteps"							):
									scheduler.set_timesteps(SCREAMING_SNAKE_CASE							)
							elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE			,	"set_timesteps"							):
									snake_case						:  List[str]        =			num_inference_steps

							snake_case						:  str        =			scheduler.step_pred(SCREAMING_SNAKE_CASE			,	0			,	SCREAMING_SNAKE_CASE			,	generator=torch.manual_seed(0							)			,	**SCREAMING_SNAKE_CASE							).prev_sample
							snake_case						:  str        =			scheduler.step_pred(SCREAMING_SNAKE_CASE			,	1			,	SCREAMING_SNAKE_CASE			,	generator=torch.manual_seed(0							)			,	**SCREAMING_SNAKE_CASE							).prev_sample

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







"""simple docstring"""

def 					UpperCamelCase__       (						lowercase__					:       int				):
		if not isinstance(lowercase__    ,       lowercase__				):
				raise ValueError("double_factorial() only accepts integral values"				)
		if n < 0:
				raise ValueError("double_factorial() not defined for negative values"				)
		return 1 if n <= 1 else n * double_factorial(n - 2				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

