code: 
  
  
  
  
  """simple docstring"""
  
  
  
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import ScoreSdeVeScheduler
  
  
  
  
  class 			UpperCAmelCase_						(		unittest.TestCase							):
      lowercase__		      =   (ScoreSdeVeScheduler,)
      lowercase__		      =   ()
  
  
  
  
      @property
      def 							__magic_name__							(					self		: str   )   ->							List[Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       4
       A__ =       3
       A__ =       8
       A__ =       8
  
       A__ =       torch.rand((batch_size, num_channels, height, width)   )
  
       return sample
  
  
  
  
      @property
      def 							__magic_name__							(					self		: Union[str, Any]   )   ->							List[str]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       4
       A__ =       3
       A__ =       8
       A__ =       8
  
       A__ =       batch_size * num_channels * height * width
       A__ =       torch.arange(_lowerCamelCase   )
       A__ =       sample.reshape(_lowerCamelCase			,				_lowerCamelCase			,				_lowerCamelCase			,				_lowerCamelCase   )
       A__ =       sample / num_elems
       A__ =       sample.permute(3			,				0			,				1			,				2   )
  
       return sample
  
  
  
  
      def 							__magic_name__							(					self		: List[str]   )   ->							int:
  
  
  
  
       '''simple docstring'''
  
       def model(snake_case_		: List[str]			,				snake_case_		: str			,				*snake_case_		: Tuple   ):
        return sample * t / (t + 1)
  
       return model
  
  
  
  
      def 							__magic_name__							(					self		: Tuple			,				**snake_case_		: Optional[Any]   )   ->							str:
  
  
  
  
       '''simple docstring'''
  
       A__ =       {
           """num_train_timesteps""": 2_000,
           """snr""": 0.15,
           """sigma_min""": 0.01,
           """sigma_max""": 1_348,
           """sampling_eps""": 1e-5,
       }
  
       config.update(**_lowerCamelCase   )
       return config
  
  
  
  
      def 							__magic_name__							(					self		: Optional[int]			,				snake_case_		: Dict=0			,				**snake_case_		: List[str]   )   ->							int:
  
  
  
  
       '''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(**_lowerCamelCase   )
        A__ =       scheduler_class(**_lowerCamelCase   )
  
        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(_lowerCamelCase   )
         A__ =       scheduler_class.from_pretrained(_lowerCamelCase   )
  
        A__ =       scheduler.step_pred(
            _lowerCamelCase			,				_lowerCamelCase			,				_lowerCamelCase			,				generator=torch.manual_seed(0   )			,				**_lowerCamelCase   ).prev_sample
        A__ =       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"
  
        A__ =       scheduler.step_correct(_lowerCamelCase			,				_lowerCamelCase			,				generator=torch.manual_seed(0   )			,				**_lowerCamelCase   ).prev_sample
        A__ =       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 							__magic_name__							(					self		: Union[str, Any]			,				snake_case_		: Dict=0			,				**snake_case_		: Optional[int]   )   ->							Tuple:
  
  
  
  
       '''simple docstring'''
  
       A__ =       dict(self.forward_default_kwargs   )
       kwargs.update(_lowerCamelCase   )
  
       for scheduler_class in self.scheduler_classes:
        A__ =       self.dummy_sample
        A__ =       0.1 * sample
  
        A__ =       self.get_scheduler_config()
        A__ =       scheduler_class(**_lowerCamelCase   )
  
        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(_lowerCamelCase   )
         A__ =       scheduler_class.from_pretrained(_lowerCamelCase   )
  
        A__ =       scheduler.step_pred(
            _lowerCamelCase			,				_lowerCamelCase			,				_lowerCamelCase			,				generator=torch.manual_seed(0   )			,				**_lowerCamelCase   ).prev_sample
        A__ =       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"
  
        A__ =       scheduler.step_correct(_lowerCamelCase			,				_lowerCamelCase			,				generator=torch.manual_seed(0   )			,				**_lowerCamelCase   ).prev_sample
        A__ =       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 							__magic_name__							(					self		: List[str]   )   ->							List[str]:
  
  
  
  
       '''simple docstring'''
  
       for timesteps in [10, 100, 1_000]:
        self.check_over_configs(num_train_timesteps=_lowerCamelCase   )
  
  
  
  
      def 							__magic_name__							(					self		: Tuple   )   ->							Optional[int]:
  
  
  
  
       '''simple docstring'''
  
       for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]			,				[1, 100, 1_000]   ):
        self.check_over_configs(sigma_min=_lowerCamelCase			,				sigma_max=_lowerCamelCase   )
  
  
  
  
      def 							__magic_name__							(					self		: Any   )   ->							List[Any]:
  
  
  
  
       '''simple docstring'''
  
       for t in [0.1, 0.5, 0.75]:
        self.check_over_forward(time_step=_lowerCamelCase   )
  
  
  
  
      def 							__magic_name__							(					self		: List[str]   )   ->							Tuple:
  
  
  
  
       '''simple docstring'''
  
       A__ =       dict(self.forward_default_kwargs   )
  
       A__ =       self.scheduler_classes[0]
       A__ =       self.get_scheduler_config()
       A__ =       scheduler_class(**_lowerCamelCase   )
  
       A__ =       3
  
       A__ =       self.dummy_model()
       A__ =       self.dummy_sample_deter
  
       scheduler.set_sigmas(_lowerCamelCase   )
       scheduler.set_timesteps(_lowerCamelCase   )
       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(_lowerCamelCase			,				_lowerCamelCase   )
         A__ =       scheduler.step_correct(_lowerCamelCase			,				_lowerCamelCase			,				generator=_lowerCamelCase			,				**_lowerCamelCase   ).prev_sample
  
        with torch.no_grad():
         A__ =       model(_lowerCamelCase			,				_lowerCamelCase   )
  
        A__ =       scheduler.step_pred(_lowerCamelCase			,				_lowerCamelCase			,				_lowerCamelCase			,				generator=_lowerCamelCase			,				**_lowerCamelCase   )
        A__ =       output.prev_sample, output.prev_sample_mean
  
       A__ =       torch.sum(torch.abs(_lowerCamelCase   )   )
       A__ =       torch.mean(torch.abs(_lowerCamelCase   )   )
  
       assert np.isclose(result_sum.item()			,				14_372_758_528.0   )
       assert np.isclose(result_mean.item()			,				18_714_530.0   )
  
  
  
  
      def 							__magic_name__							(					self		: List[Any]   )   ->							Dict:
  
  
  
  
       '''simple docstring'''
  
       A__ =       dict(self.forward_default_kwargs   )
  
       A__ =       kwargs.pop("num_inference_steps"			,				_lowerCamelCase   )
  
       for scheduler_class in self.scheduler_classes:
        A__ =       self.get_scheduler_config()
        A__ =       scheduler_class(**_lowerCamelCase   )
  
        A__ =       self.dummy_sample
        A__ =       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"   ):
         A__ =       num_inference_steps
  
        A__ =       scheduler.step_pred(_lowerCamelCase			,				0			,				_lowerCamelCase			,				generator=torch.manual_seed(0   )			,				**_lowerCamelCase   ).prev_sample
        A__ =       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   )
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  from transformers import PretrainedConfig
  
  
  
  
  class 			UpperCAmelCase_						(		A_							):
      lowercase__		      =   '''custom'''
  
  
  
  
      def __init__(					self		: Any			,				snake_case_		: List[Any]=1			,				**snake_case_		: Any   )   ->							str:
  
  
  
  
       '''simple docstring'''
  
       A__ =       attribute
       super().__init__(**snake_case_   )
  
  
  
  
  
  
  class 			UpperCAmelCase_						(		A_							):
      lowercase__		      =   '''custom'''
  
  
  
  
      def __init__(					self		: Any			,				snake_case_		: Tuple=1			,				**snake_case_		: Dict   )   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       A__ =       attribute
  
  
  
  
  
  