






'''simple docstring'''




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler






class 						_UpperCamelCase  (							unittest.TestCase   ):


			'''simple docstring'''

			_A				:		Tuple         =		(ScoreSdeVeScheduler,)
			_A				:		Optional[int]         =		()

			@property
			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






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

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

					return sample

			@property
			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






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

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

					return sample

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					def model(lowerCAmelCase__					:			int							,					lowerCAmelCase__					:			int							,					*lowerCAmelCase__					:			Optional[Any]						):
							return sample * t / (t + 1)

					return model

			def UpperCamelCase__ (							self					:			List[str]							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		{
					    "num_train_timesteps": 2_0_0_0,
					    "snr": 0.15,
					    "sigma_min": 0.01,
					    "sigma_max": 1_3_4_8,
					    "sampling_eps": 1E-5,
					}

					config.update(**lowerCAmelCase__						)
					return config

			def UpperCamelCase__ (							self					:			List[Any]							,					lowerCAmelCase__					:			Optional[Any]=0							,					**lowerCAmelCase__					:			Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		dict(self.forward_default_kwargs						)

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

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

							with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(lowerCAmelCase__						)
									__SCREAMING_SNAKE_CASE	:		int   	=		scheduler_class.from_pretrained(lowerCAmelCase__						)

							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		scheduler.step_pred(
							    lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					generator=torch.manual_seed(0						)							,					**lowerCAmelCase__						).prev_sample
							__SCREAMING_SNAKE_CASE	:		Any   	=		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"

							__SCREAMING_SNAKE_CASE	:		Dict   	=		scheduler.step_correct(lowerCAmelCase__							,					lowerCAmelCase__							,					generator=torch.manual_seed(0						)							,					**lowerCAmelCase__						).prev_sample
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		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 UpperCamelCase__ (							self					:			int							,					lowerCAmelCase__					:			Optional[int]=0							,					**lowerCAmelCase__					:			str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		dict(self.forward_default_kwargs						)
					kwargs.update(lowerCAmelCase__						)

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

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

							with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(lowerCAmelCase__						)
									__SCREAMING_SNAKE_CASE	:		Any   	=		scheduler_class.from_pretrained(lowerCAmelCase__						)

							__SCREAMING_SNAKE_CASE	:		Tuple   	=		scheduler.step_pred(
							    lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					generator=torch.manual_seed(0						)							,					**lowerCAmelCase__						).prev_sample
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		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"

							__SCREAMING_SNAKE_CASE	:		Any   	=		scheduler.step_correct(lowerCAmelCase__							,					lowerCAmelCase__							,					generator=torch.manual_seed(0						)							,					**lowerCAmelCase__						).prev_sample
							__SCREAMING_SNAKE_CASE	:		int   	=		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 UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					for timesteps in [1_0, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]							,					[1, 1_0_0, 1_0_0_0]						):
							self.check_over_configs(sigma_min=lowerCAmelCase__							,					sigma_max=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






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

			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		dict(self.forward_default_kwargs						)

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

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		3

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

					scheduler.set_sigmas(lowerCAmelCase__						)
					scheduler.set_timesteps(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		torch.manual_seed(0						)

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

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

							with torch.no_grad():
									__SCREAMING_SNAKE_CASE	:		Any   	=		model(lowerCAmelCase__							,					lowerCAmelCase__						)

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

					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		torch.sum(torch.abs(lowerCAmelCase__						)						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		torch.mean(torch.abs(lowerCAmelCase__						)						)

					assert np.isclose(result_sum.item()							,					1_4_3_7_2_7_5_8_5_2_8.0						)
					assert np.isclose(result_mean.item()							,					1_8_7_1_4_5_3_0.0						)



			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE	:		str   	=		kwargs.pop("""num_inference_steps"""							,					lowerCAmelCase__						)

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

							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.dummy_sample
							__SCREAMING_SNAKE_CASE	:		str   	=		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"""						):
									__SCREAMING_SNAKE_CASE	:		List[str]   	=		num_inference_steps

							__SCREAMING_SNAKE_CASE	:		str   	=		scheduler.step_pred(lowerCAmelCase__							,					0							,					lowerCAmelCase__							,					generator=torch.manual_seed(0						)							,					**lowerCAmelCase__						).prev_sample
							__SCREAMING_SNAKE_CASE	:		str   	=		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						)




'''simple docstring'''




from __future__ import annotations


def     lowerCAmelCase_       (     _lowerCamelCase:       list[int | float] ):
		if len(_lowerCamelCase ) == 0:
				raise ValueError("""find_min() arg is an empty sequence""" )
		__SCREAMING_SNAKE_CASE	:		List[str]   	=		nums[0]
		for num in nums:
				__SCREAMING_SNAKE_CASE	:		int   	=		min(_lowerCamelCase	,				_lowerCamelCase )
		return min_num


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)