code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import ScoreSdeVeScheduler
  
  
  class 							__snake_case    (				unittest.TestCase       ):
  	# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
  	__lowerCAmelCase						=     (ScoreSdeVeScheduler,)
  	__lowerCAmelCase						=     ()
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		Optional[int]:
  		snake_case__  					=      4
  		snake_case__  					=      3
  		snake_case__  					=      8
  		snake_case__  					=      8
  
  		snake_case__  					=      torch.rand((batch_size, num_channels, height, width)		)
  
  		return sample
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		Optional[int]:
  		snake_case__  					=      4
  		snake_case__  					=      3
  		snake_case__  					=      8
  		snake_case__  					=      8
  
  		snake_case__  					=      batch_size * num_channels * height * width
  		snake_case__  					=      torch.arange(UpperCamelCase_		)
  		snake_case__  					=      sample.reshape(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)
  		snake_case__  					=      sample / num_elems
  		snake_case__  					=      sample.permute(3			,					0			,					1			,					2		)
  
  		return sample
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		def model(UpperCamelCase_			,					UpperCamelCase_			,					*UpperCamelCase_		):
  			return sample * t / (t + 1)
  
  		return model
  
  
  
  
  
  	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		str:
  		snake_case__  					=      {
  		    'num_train_timesteps': 2000,
  		    'snr': 0.1_5,
  		    'sigma_min': 0.0_1,
  		    'sigma_max': 1348,
  		    'sampling_eps': 1E-5,
  		}
  
  		config.update(**UpperCamelCase_		)
  		return config
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_=0			,					**UpperCamelCase_		)			->		List[Any]:
  		snake_case__  					=      dict(self.forward_default_kwargs		)
  
  		for scheduler_class in self.scheduler_classes:
  			snake_case__  					=      self.dummy_sample
  			snake_case__  					=      0.1 * sample
  
  			snake_case__  					=      self.get_scheduler_config(**UpperCamelCase_		)
  			snake_case__  					=      scheduler_class(**UpperCamelCase_		)
  
  			with tempfile.TemporaryDirectory() as tmpdirname:
  				scheduler.save_config(UpperCamelCase_		)
  				snake_case__  					=      scheduler_class.from_pretrained(UpperCamelCase_		)
  
  			snake_case__  					=      scheduler.step_pred(
  			    UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					generator=torch.manual_seed(0		)			,					**UpperCamelCase_		).prev_sample
  			snake_case__  					=      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"
  
  			snake_case__  					=      scheduler.step_correct(UpperCamelCase_			,					UpperCamelCase_			,					generator=torch.manual_seed(0		)			,					**UpperCamelCase_		).prev_sample
  			snake_case__  					=      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      _snake_case      (		self			,					UpperCamelCase_=0			,					**UpperCamelCase_		)			->		Optional[int]:
  		snake_case__  					=      dict(self.forward_default_kwargs		)
  		kwargs.update(UpperCamelCase_		)
  
  		for scheduler_class in self.scheduler_classes:
  			snake_case__  					=      self.dummy_sample
  			snake_case__  					=      0.1 * sample
  
  			snake_case__  					=      self.get_scheduler_config()
  			snake_case__  					=      scheduler_class(**UpperCamelCase_		)
  
  			with tempfile.TemporaryDirectory() as tmpdirname:
  				scheduler.save_config(UpperCamelCase_		)
  				snake_case__  					=      scheduler_class.from_pretrained(UpperCamelCase_		)
  
  			snake_case__  					=      scheduler.step_pred(
  			    UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					generator=torch.manual_seed(0		)			,					**UpperCamelCase_		).prev_sample
  			snake_case__  					=      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"
  
  			snake_case__  					=      scheduler.step_correct(UpperCamelCase_			,					UpperCamelCase_			,					generator=torch.manual_seed(0		)			,					**UpperCamelCase_		).prev_sample
  			snake_case__  					=      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      _snake_case      (		self		)			->		Union[str, Any]:
  		for timesteps in [10, 100, 1000]:
  			self.check_over_configs(num_train_timesteps=UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Tuple:
  		for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]			,					[1, 100, 1000]		):
  			self.check_over_configs(sigma_min=UpperCamelCase_			,					sigma_max=UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		for t in [0.1, 0.5, 0.7_5]:
  			self.check_over_forward(time_step=UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		str:
  		snake_case__  					=      dict(self.forward_default_kwargs		)
  
  		snake_case__  					=      self.scheduler_classes[0]
  		snake_case__  					=      self.get_scheduler_config()
  		snake_case__  					=      scheduler_class(**UpperCamelCase_		)
  
  		snake_case__  					=      3
  
  		snake_case__  					=      self.dummy_model()
  		snake_case__  					=      self.dummy_sample_deter
  
  		scheduler.set_sigmas(UpperCamelCase_		)
  		scheduler.set_timesteps(UpperCamelCase_		)
  		snake_case__  					=      torch.manual_seed(0		)
  
  		for i, t in enumerate(scheduler.timesteps		):
  			snake_case__  					=      scheduler.sigmas[i]
  
  			for _ in range(scheduler.config.correct_steps		):
  				with torch.no_grad():
  					snake_case__  					=      model(UpperCamelCase_			,					UpperCamelCase_		)
  				snake_case__  					=      scheduler.step_correct(UpperCamelCase_			,					UpperCamelCase_			,					generator=UpperCamelCase_			,					**UpperCamelCase_		).prev_sample
  
  			with torch.no_grad():
  				snake_case__  					=      model(UpperCamelCase_			,					UpperCamelCase_		)
  
  			snake_case__  					=      scheduler.step_pred(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					generator=UpperCamelCase_			,					**UpperCamelCase_		)
  			snake_case__					,     snake_case__  					=      output.prev_sample, output.prev_sample_mean
  
  		snake_case__  					=      torch.sum(torch.abs(UpperCamelCase_		)		)
  		snake_case__  					=      torch.mean(torch.abs(UpperCamelCase_		)		)
  
  		assert np.isclose(result_sum.item()			,					143_7275_8528.0		)
  		assert np.isclose(result_mean.item()			,					1871_4530.0		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[Any]:
  		snake_case__  					=      dict(self.forward_default_kwargs		)
  
  		snake_case__  					=      kwargs.pop('num_inference_steps'			,					UpperCamelCase_		)
  
  		for scheduler_class in self.scheduler_classes:
  			snake_case__  					=      self.get_scheduler_config()
  			snake_case__  					=      scheduler_class(**UpperCamelCase_		)
  
  			snake_case__  					=      self.dummy_sample
  			snake_case__  					=      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'		):
  				snake_case__  					=      num_inference_steps
  
  			snake_case__  					=      scheduler.step_pred(UpperCamelCase_			,					0			,					UpperCamelCase_			,					generator=torch.manual_seed(0		)			,					**UpperCamelCase_		).prev_sample
  			snake_case__  					=      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: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->int:
  	return 1 if input_a == 0 else 0
  
  
  
  
  def 	__lowerCamelCase (  )      ->None:
  	assert not_gate(0       ) == 1
  	assert not_gate(1       ) == 0
  
  
  if __name__ == "__main__":
  			print(not_gate(0))
  			print(not_gate(1))
  
  
  
  