content: from typing import Optional, Tuple
  
  import numpy as np
  import torch
  from diffusers.utils.torch_utils import randn_tensor
  
  
  class DiagonalGaussianDistribution(object):
      def __init__(
          self,
          parameters: torch.Tensor,
          deterministic: bool = False,
          feature_dim: int = 1,
      ):
          self.parameters = parameters
          self.feature_dim = feature_dim
          self.mean, self.logvar = torch.chunk(parameters, 2, dim=feature_dim)
          self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
          self.deterministic = deterministic
          self.std = torch.exp(0.5 * self.logvar)
          self.var = torch.exp(self.logvar)
          if self.deterministic:
              self.var = self.std = torch.zeros_like(
                  self.mean, device=self.parameters.device, dtype=self.parameters.dtype
              )
  
      def sample(self, generator: Optional[torch.Generator] = None) -> torch.Tensor:
          # make sure sample is on the same device as the parameters and has same dtype
          sample = randn_tensor(
              self.mean.shape,
              generator=generator,
              device=self.parameters.device,
              dtype=self.parameters.dtype,
          )
          x = self.mean + self.std * sample
          return x
  
      def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor:
          if self.deterministic:
              return torch.Tensor([0.0])
          else:
              if other is None:
                  return 0.5 * torch.sum(
                      torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
                      dim=[1, 2, 3],
                  )
              else:
                  return 0.5 * torch.sum(
                      torch.pow(self.mean - other.mean, 2) / other.var
                      + self.var / other.var
                      - 1.0
                      - self.logvar
                      + other.logvar,
                      dim=[1, 2, 3],
                  )
  
      def nll(
          self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3]
      ) -> torch.Tensor:
          if self.deterministic:
              return torch.Tensor([0.0])
          logtwopi = np.log(2.0 * np.pi)
          return 0.5 * torch.sum(
              logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
              dim=dims,
          )
  
      def mode(self) -> torch.Tensor:
          return self.mean
  