code: 
  
  """simple docstring"""
  import math
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging, randn_tensor
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
  
  
  _A						=     logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  @dataclass
  # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
  class 			lowerCamelCase  (					lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     42
    SCREAMING_SNAKE_CASE							=     None
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase=0.999 ,  lowerCAmelCase="cosine" ,  )  ->   Union[str, Any]:
        if alpha_transform_type == "cosine":
  
              def alpha_bar_fn(lowerCAmelCase						):
                    return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2
  
        elif alpha_transform_type == "exp":
  
              def alpha_bar_fn(lowerCAmelCase						):
                    return math.exp(t * -12.0						)
  
        else:
              raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""						)
  
        UpperCAmelCase__ :       Any						=						[]
        for i in range(lowerCAmelCase						):
              UpperCAmelCase__ :       Any						=						i / num_diffusion_timesteps
              UpperCAmelCase__ :       Optional[Any]						=						(i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(lowerCAmelCase						) / alpha_bar_fn(lowerCAmelCase						) ,  lowerCAmelCase						)						)
        return torch.tensor(lowerCAmelCase ,  dtype=torch.floataa						)
  
  
  
  
  
  class 			lowerCamelCase  (					lowerCAmelCase__				,				lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     [e.name for e in KarrasDiffusionSchedulers]
    SCREAMING_SNAKE_CASE							=     1
  
  
  
    @register_to_config
    def __init__(self       ,					_lowerCamelCase = 1000       ,					_lowerCamelCase = 0.0_001       ,					_lowerCamelCase = 0.02       ,					_lowerCamelCase = "linear"       ,					_lowerCamelCase = None       ,					_lowerCamelCase = "epsilon"       ,					_lowerCamelCase = "linspace"       ,					_lowerCamelCase = 0       ,					):
  
  
          """simple docstring"""
  
          if trained_betas is not None:
                UpperCAmelCase__ :       Union[str, Any]						=						torch.tensor(_lowerCamelCase       ,					dtype=torch.floataa  )
          elif beta_schedule == "linear":
                UpperCAmelCase__ :       Dict						=						torch.linspace(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					dtype=torch.floataa  )
          elif beta_schedule == "scaled_linear":
                # this schedule is very specific to the latent diffusion model.
                UpperCAmelCase__ :       Optional[int]						=						(
                    torch.linspace(beta_start**0.5       ,					beta_end**0.5       ,					_lowerCamelCase       ,					dtype=torch.floataa  ) ** 2
                )
          elif beta_schedule == "squaredcos_cap_v2":
                # Glide cosine schedule
                UpperCAmelCase__ :       Any						=						betas_for_alpha_bar(_lowerCamelCase  )
          else:
                raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""  )
  
          UpperCAmelCase__ :       List[str]						=						1.0 - self.betas
          UpperCAmelCase__ :       Any						=						torch.cumprod(self.alphas       ,					dim=0  )
  
          UpperCAmelCase__ :       Tuple						=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
          UpperCAmelCase__ :       Optional[int]						=						np.concatenate([sigmas[::-1], [0.0]]  ).astype(np.floataa  )
          UpperCAmelCase__ :       Tuple						=						torch.from_numpy(_lowerCamelCase  )
  
          # setable values
          UpperCAmelCase__ :       int						=						None
          UpperCAmelCase__ :       Any						=						np.linspace(0       ,					num_train_timesteps - 1       ,					_lowerCamelCase       ,					dtype=_lowerCamelCase  )[::-1].copy()
          UpperCAmelCase__ :       int						=						torch.from_numpy(_lowerCamelCase  )
          UpperCAmelCase__ :       Optional[Any]						=						False
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          if self.config.timestep_spacing in ["linspace", "trailing"]:
                return self.sigmas.max()
  
          return (self.sigmas.max() ** 2 + 1) ** 0.5
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          if isinstance(_lowerCamelCase       ,					torch.Tensor  ):
                UpperCAmelCase__ :       Optional[int]						=						timestep.to(self.timesteps.device  )
          UpperCAmelCase__ :       List[str]						=						(self.timesteps == timestep).nonzero().item()
          UpperCAmelCase__ :       int						=						self.sigmas[step_index]
          UpperCAmelCase__ :       List[Any]						=						sample / ((sigma**2 + 1) ** 0.5)
          UpperCAmelCase__ :       int						=						True
          return sample
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase = None  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						num_inference_steps
  
          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
                UpperCAmelCase__ :       Union[str, Any]						=						np.linspace(0       ,					self.config.num_train_timesteps - 1       ,					_lowerCamelCase       ,					dtype=_lowerCamelCase  )[
                    ::-1
                ].copy()
          elif self.config.timestep_spacing == "leading":
                UpperCAmelCase__ :       Any						=						self.config.num_train_timesteps // self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                UpperCAmelCase__ :       Tuple						=						(np.arange(0       ,					_lowerCamelCase  ) * step_ratio).round()[::-1].copy().astype(_lowerCamelCase  )
                timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
                UpperCAmelCase__ :       int						=						self.config.num_train_timesteps / self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                UpperCAmelCase__ :       int						=						(np.arange(self.config.num_train_timesteps       ,					0       ,					-step_ratio  )).round().copy().astype(_lowerCamelCase  )
                timesteps -= 1
          else:
                raise ValueError(
                    F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""  )
  
          UpperCAmelCase__ :       Any						=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
          UpperCAmelCase__ :       Dict						=						np.interp(_lowerCamelCase       ,					np.arange(0       ,					len(_lowerCamelCase  )  )       ,					_lowerCamelCase  )
          UpperCAmelCase__ :       Union[str, Any]						=						np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
          UpperCAmelCase__ :       Tuple						=						torch.from_numpy(_lowerCamelCase  ).to(device=_lowerCamelCase  )
          if str(_lowerCamelCase  ).startswith("""mps"""  ):
                # mps does not support float64
                UpperCAmelCase__ :       List[Any]						=						torch.from_numpy(_lowerCamelCase  ).to(_lowerCamelCase       ,					dtype=torch.floataa  )
          else:
                UpperCAmelCase__ :       List[Any]						=						torch.from_numpy(_lowerCamelCase  ).to(device=_lowerCamelCase  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase = None       ,					_lowerCamelCase = True       ,					):
  
  
          """simple docstring"""
  
          if (
              isinstance(_lowerCamelCase       ,					_lowerCamelCase  )
              or isinstance(_lowerCamelCase       ,					torch.IntTensor  )
              or isinstance(_lowerCamelCase       ,					torch.LongTensor  )
          ):
                raise ValueError(
                    (
                        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
                        """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
                        """ one of the `scheduler.timesteps` as a timestep."""
                    )       ,					)
  
          if not self.is_scale_input_called:
                logger.warning(
                    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
                    """See `StableDiffusionPipeline` for a usage example."""  )
  
          if isinstance(_lowerCamelCase       ,					torch.Tensor  ):
                UpperCAmelCase__ :       Tuple						=						timestep.to(self.timesteps.device  )
  
          UpperCAmelCase__ :       Dict						=						(self.timesteps == timestep).nonzero().item()
          UpperCAmelCase__ :       Optional[Any]						=						self.sigmas[step_index]
  
          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
                UpperCAmelCase__ :       Tuple						=						sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
                # * c_out + input * c_skip
                UpperCAmelCase__ :       Tuple						=						model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          elif self.config.prediction_type == "sample":
                raise NotImplementedError("""prediction_type not implemented yet: sample"""  )
          else:
                raise ValueError(
                    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""  )
  
          UpperCAmelCase__ :       Dict						=						self.sigmas[step_index]
          UpperCAmelCase__ :       Optional[int]						=						self.sigmas[step_index + 1]
          UpperCAmelCase__ :       List[Any]						=						(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
          UpperCAmelCase__ :       List[Any]						=						(sigma_to**2 - sigma_up**2) ** 0.5
  
          # 2. Convert to an ODE derivative
          UpperCAmelCase__ :       Union[str, Any]						=						(sample - pred_original_sample) / sigma
  
          UpperCAmelCase__ :       Optional[Any]						=						sigma_down - sigma
  
          UpperCAmelCase__ :       Tuple						=						sample + derivative * dt
  
          UpperCAmelCase__ :       Dict						=						model_output.device
          UpperCAmelCase__ :       Optional[Any]						=						randn_tensor(model_output.shape       ,					dtype=model_output.dtype       ,					device=_lowerCamelCase       ,					generator=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Optional[Any]						=						prev_sample + noise * sigma_up
  
          if not return_dict:
                return (prev_sample,)
  
          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=_lowerCamelCase       ,					pred_original_sample=_lowerCamelCase  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						self.sigmas.to(device=original_samples.device       ,					dtype=original_samples.dtype  )
          if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCamelCase  ):
                # mps does not support float64
                UpperCAmelCase__ :       Any						=						self.timesteps.to(original_samples.device       ,					dtype=torch.floataa  )
                UpperCAmelCase__ :       str						=						timesteps.to(original_samples.device       ,					dtype=torch.floataa  )
          else:
                UpperCAmelCase__ :       List[str]						=						self.timesteps.to(original_samples.device  )
                UpperCAmelCase__ :       Dict						=						timesteps.to(original_samples.device  )
  
          UpperCAmelCase__ :       Optional[int]						=						[(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
          UpperCAmelCase__ :       Optional[int]						=						sigmas[step_indices].flatten()
          while len(sigma.shape  ) < len(original_samples.shape  ):
                UpperCAmelCase__ :       List[Any]						=						sigma.unsqueeze(-1  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						original_samples + noise * sigma
          return noisy_samples
  
  
  
  
    def __len__(self  ):
  
  
          """simple docstring"""
  
          return self.config.num_train_timesteps
  
  
style_context: 
  
  """simple docstring"""
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import ScoreSdeVeScheduler
  
  class 			lowerCamelCase  (					unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     (ScoreSdeVeScheduler,)
    SCREAMING_SNAKE_CASE							=     ()
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						4
          UpperCAmelCase__ :       Tuple						=						3
          UpperCAmelCase__ :       Optional[Any]						=						8
          UpperCAmelCase__ :       Dict						=						8
  
          UpperCAmelCase__ :       List[Any]						=						torch.rand((batch_size, num_channels, height, width)  )
  
          return sample
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						4
          UpperCAmelCase__ :       Optional[Any]						=						3
          UpperCAmelCase__ :       List[str]						=						8
          UpperCAmelCase__ :       Dict						=						8
  
          UpperCAmelCase__ :       List[str]						=						batch_size * num_channels * height * width
          UpperCAmelCase__ :       Any						=						torch.arange(_lowerCamelCase  )
          UpperCAmelCase__ :       Any						=						sample.reshape(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
          UpperCAmelCase__ :       Dict						=						sample / num_elems
          UpperCAmelCase__ :       Optional[int]						=						sample.permute(3       ,					0       ,					1       ,					2  )
  
          return sample
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          def model(_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
                return sample * t / (t + 1)
  
          return model
  
  
  
    def    _a			(self       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						{
              """num_train_timesteps""": 2000,
              """snr""": 0.15,
              """sigma_min""": 0.01,
              """sigma_max""": 1348,
              """sampling_eps""": 1e-5,
          }
  
          config.update(**_lowerCamelCase  )
          return config
  
  
  
    def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						dict(self.forward_default_kwargs  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       int						=						self.dummy_sample
                UpperCAmelCase__ :       Tuple						=						0.1 * sample
  
                UpperCAmelCase__ :       int						=						self.get_scheduler_config(**_lowerCamelCase  )
                UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )
  
                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(_lowerCamelCase  )
                      UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )
  
                UpperCAmelCase__ :       Tuple						=						scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Optional[int]						=						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"
  
                UpperCAmelCase__ :       Optional[int]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Union[str, 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    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )
          kwargs.update(_lowerCamelCase  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       List[str]						=						self.dummy_sample
                UpperCAmelCase__ :       List[str]						=						0.1 * sample
  
                UpperCAmelCase__ :       Optional[Any]						=						self.get_scheduler_config()
                UpperCAmelCase__ :       Optional[Any]						=						scheduler_class(**_lowerCamelCase  )
  
                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(_lowerCamelCase  )
                      UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )
  
                UpperCAmelCase__ :       Optional[int]						=						scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       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"
  
                UpperCAmelCase__ :       Optional[Any]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Dict						=						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    _a			(self  ):
  
  
          """simple docstring"""
  
          for timesteps in [10, 100, 1000]:
                self.check_over_configs(num_train_timesteps=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]       ,					[1, 100, 1000]  ):
                self.check_over_configs(sigma_min=_lowerCamelCase       ,					sigma_max=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for t in [0.1, 0.5, 0.75]:
                self.check_over_forward(time_step=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						dict(self.forward_default_kwargs  )
  
          UpperCAmelCase__ :       int						=						self.scheduler_classes[0]
          UpperCAmelCase__ :       int						=						self.get_scheduler_config()
          UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[str]						=						3
  
          UpperCAmelCase__ :       List[Any]						=						self.dummy_model()
          UpperCAmelCase__ :       int						=						self.dummy_sample_deter
  
          scheduler.set_sigmas(_lowerCamelCase  )
          scheduler.set_timesteps(_lowerCamelCase  )
          UpperCAmelCase__ :       Tuple						=						torch.manual_seed(0  )
  
          for i, t in enumerate(scheduler.timesteps  ):
                UpperCAmelCase__ :       Tuple						=						scheduler.sigmas[i]
  
                for _ in range(scheduler.config.correct_steps  ):
                      with torch.no_grad():
                            UpperCAmelCase__ :       Optional[int]						=						model(_lowerCamelCase       ,					_lowerCamelCase  )
                      UpperCAmelCase__ :       List[str]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  ).prev_sample
  
                with torch.no_grad():
                      UpperCAmelCase__ :       Tuple						=						model(_lowerCamelCase       ,					_lowerCamelCase  )
  
                UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  )
                UpperCAmelCase__				,		UpperCAmelCase__ :       List[str]						=						output.prev_sample, output.prev_sample_mean
  
          UpperCAmelCase__ :       int						=						torch.sum(torch.abs(_lowerCamelCase  )  )
          UpperCAmelCase__ :       str						=						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    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						kwargs.pop("""num_inference_steps"""       ,					_lowerCamelCase  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       Dict						=						self.get_scheduler_config()
                UpperCAmelCase__ :       List[Any]						=						scheduler_class(**_lowerCamelCase  )
  
                UpperCAmelCase__ :       str						=						self.dummy_sample
                UpperCAmelCase__ :       Tuple						=						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"""  ):
                      UpperCAmelCase__ :       Dict						=						num_inference_steps
  
                UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					0       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       int						=						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  )
  
  