code: 
  
  
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import ScoreSdeVeScheduler
  
  
  
  
  
  class        lowercase_				( unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		(ScoreSdeVeScheduler,)
  							snake_case_        =		()
  
  
  							@property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		4
  													a_					=		3
  													a_					=		8
  													a_					=		8
  
  													a_					=		torch.rand((batch_size, num_channels, height, width)		)
  
  													return sample
  
  
  							@property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		4
  													a_					=		3
  													a_					=		8
  													a_					=		8
  
  													a_					=		batch_size * num_channels * height * width
  													a_					=		torch.arange(_UpperCAmelCase		)
  													a_					=		sample.reshape(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  													a_					=		sample / num_elems
  													a_					=		sample.permute(3			,    0			,    1			,    2		)
  
  													return sample
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													def model(_UpperCAmelCase			,    _UpperCAmelCase			,    *_UpperCAmelCase		):
  																			return sample * t / (t + 1)
  
  													return model
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		{
  													    """num_train_timesteps""": 2_000,
  													    """snr""": 0.1_5,
  													    """sigma_min""": 0.0_1,
  													    """sigma_max""": 1_348,
  													    """sampling_eps""": 1e-5,
  													}
  
  													config.update(**_UpperCAmelCase		)
  													return config
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=0			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													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(**_UpperCAmelCase		)
  																			a_					=		scheduler_class(**_UpperCAmelCase		)
  
  																			with tempfile.TemporaryDirectory() as tmpdirname:
  																									scheduler.save_config(_UpperCAmelCase		)
  																									a_					=		scheduler_class.from_pretrained(_UpperCAmelCase		)
  
  																			a_					=		scheduler.step_pred(
  																			    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  																			a_					=		new_scheduler.step_pred(
  																			    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  
  																			assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"
  
  																			a_					=		scheduler.step_correct(_UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  																			a_					=		new_scheduler.step_correct(
  																			    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  
  																			assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=0			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		dict(self.forward_default_kwargs		)
  													kwargs.update(_UpperCAmelCase		)
  
  													for scheduler_class in self.scheduler_classes:
  																			a_					=		self.dummy_sample
  																			a_					=		0.1 * sample
  
  																			a_					=		self.get_scheduler_config()
  																			a_					=		scheduler_class(**_UpperCAmelCase		)
  
  																			with tempfile.TemporaryDirectory() as tmpdirname:
  																									scheduler.save_config(_UpperCAmelCase		)
  																									a_					=		scheduler_class.from_pretrained(_UpperCAmelCase		)
  
  																			a_					=		scheduler.step_pred(
  																			    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  																			a_					=		new_scheduler.step_pred(
  																			    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  
  																			assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"
  
  																			a_					=		scheduler.step_correct(_UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  																			a_					=		new_scheduler.step_correct(
  																			    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).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=_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]			,    [1, 100, 1_000]		):
  																			self.check_over_configs(sigma_min=_UpperCAmelCase			,    sigma_max=_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for t in [0.1, 0.5, 0.7_5]:
  																			self.check_over_forward(time_step=_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		dict(self.forward_default_kwargs		)
  
  													a_					=		self.scheduler_classes[0]
  													a_					=		self.get_scheduler_config()
  													a_					=		scheduler_class(**_UpperCAmelCase		)
  
  													a_					=		3
  
  													a_					=		self.dummy_model()
  													a_					=		self.dummy_sample_deter
  
  													scheduler.set_sigmas(_UpperCAmelCase		)
  													scheduler.set_timesteps(_UpperCAmelCase		)
  													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(_UpperCAmelCase			,    _UpperCAmelCase		)
  																									a_					=		scheduler.step_correct(_UpperCAmelCase			,    _UpperCAmelCase			,    generator=_UpperCAmelCase			,    **_UpperCAmelCase		).prev_sample
  
  																			with torch.no_grad():
  																									a_					=		model(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  																			a_					=		scheduler.step_pred(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=_UpperCAmelCase			,    **_UpperCAmelCase		)
  																			a_			,   a_					=		output.prev_sample, output.prev_sample_mean
  
  													a_					=		torch.sum(torch.abs(_UpperCAmelCase		)		)
  													a_					=		torch.mean(torch.abs(_UpperCAmelCase		)		)
  
  													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 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		dict(self.forward_default_kwargs		)
  
  													a_					=		kwargs.pop("""num_inference_steps"""			,    _UpperCAmelCase		)
  
  													for scheduler_class in self.scheduler_classes:
  																			a_					=		self.get_scheduler_config()
  																			a_					=		scheduler_class(**_UpperCAmelCase		)
  
  																			a_					=		self.dummy_sample
  																			a_					=		0.1 * sample
  
  																			if num_inference_steps is not None and hasattr(_UpperCAmelCase			,    """set_timesteps"""		):
  																									scheduler.set_timesteps(_UpperCAmelCase		)
  																			elif num_inference_steps is not None and not hasattr(_UpperCAmelCase			,    """set_timesteps"""		):
  																									a_					=		num_inference_steps
  
  																			a_					=		scheduler.step_pred(_UpperCAmelCase			,    0			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  																			a_					=		scheduler.step_pred(_UpperCAmelCase			,    1			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  
  																			self.assertEqual(output_a.shape			,    sample.shape		)
  																			self.assertEqual(output_a.shape			,    output_a.shape		)
style_context: 
  
  
  import abc
  import copy
  import dataclasses
  from dataclasses import dataclass
  from typing import ClassVar, Dict, Type, TypeVar
  
  from ..features import Features
  
  
  A_    :					Tuple             =TypeVar("""T""", bound="""TaskTemplate""")
  
  
  
  
  
  @dataclass(frozen=UpperCamelCase__)
  class        lowercase_				( abc.ABC):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		42
  							snake_case_        =		42
  							snake_case_        =		42
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return copy.deepcopy(self		)
  
  
  							@property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return Features(**self.input_schema			,    **self.label_schema		)
  
  
  							@property
  							@abc.abstractmethod
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													raise NotImplementedError
  
  
  
  
  
  
  							@classmethod
  							def 						lowercase__     (   cls			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		{f.name for f in dataclasses.fields(cls		)}
  													return cls(**{k: v for k, v in template_dict.items() if k in field_names}		)