





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput


@dataclass
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  torch.FloatTensor
       _UpperCAmelCase				:  torch.FloatTensor


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,lowerCAmelCase_ ):
       _UpperCAmelCase				:  Union[str, Any]			   =   1







       @register_to_config
       def __init__(				self					:     int		, A					:     int = 2_0_0_0		, A					:     float = 0.15		, A					:     float = 0.01		, A					:     float = 13_48.0		, A					:     float = 1e-5		, A					:     int = 1		, )       ->str:
          # standard deviation of the initial noise distribution
          lowerCamelCase__       :						Optional[Any]        =							sigma_max

          # setable values
          lowerCamelCase__       :						List[Any]        =							None

          self.set_sigmas(A		, A		, A		, A      )







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     torch.FloatTensor		, A					:     Optional[int] = None      )       ->torch.FloatTensor:
          return sample







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     int		, A					:     float = None		, A					:     Union[str, torch.device] = None      )       ->List[str]:
          lowerCamelCase__       :						List[Any]        =							sampling_eps if sampling_eps is not None else self.config.sampling_eps

          lowerCamelCase__       :						Optional[int]        =							torch.linspace(1		, A		, A		, device=A      )







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     int		, A					:     float = None		, A					:     float = None		, A					:     float = None      )       ->Optional[int]:
          lowerCamelCase__       :						int        =							sigma_min if sigma_min is not None else self.config.sigma_min
          lowerCamelCase__       :						int        =							sigma_max if sigma_max is not None else self.config.sigma_max
          lowerCamelCase__       :						Any        =							sampling_eps if sampling_eps is not None else self.config.sampling_eps
          if self.timesteps is None:
             self.set_timesteps(A		, A      )

          lowerCamelCase__       :						int        =							sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
          lowerCamelCase__       :						Optional[int]        =							torch.exp(torch.linspace(math.log(A      )		, math.log(A      )		, A      )      )
          lowerCamelCase__       :						Tuple        =							torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     str		, A					:     Tuple      )       ->int:
          return torch.where(
              timesteps == 0		, torch.zeros_like(t.to(timesteps.device      )      )		, self.discrete_sigmas[timesteps - 1].to(timesteps.device      )		, )







       def 						__lowerCamelCase   (				self					:     int		, A					:     torch.FloatTensor		, A					:     int		, A					:     torch.FloatTensor		, A					:     Optional[torch.Generator] = None		, A					:     bool = True		, )       ->Union[SdeVeOutput, Tuple]:
          if self.timesteps is None:
             raise ValueError(
                 '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''      )

          lowerCamelCase__       :						Dict        =							timestep * torch.ones(
              sample.shape[0]		, device=sample.device      )  # torch.repeat_interleave(timestep, sample.shape[0])
          lowerCamelCase__       :						Optional[Any]        =							(timestep * (len(self.timesteps      ) - 1)).long()

          # mps requires indices to be in the same device, so we use cpu as is the default with cuda
          lowerCamelCase__       :						Dict        =							timesteps.to(self.discrete_sigmas.device      )

          lowerCamelCase__       :						Optional[int]        =							self.discrete_sigmas[timesteps].to(sample.device      )
          lowerCamelCase__       :						Union[str, Any]        =							self.get_adjacent_sigma(A		, A      ).to(sample.device      )
          lowerCamelCase__       :						List[str]        =							torch.zeros_like(A      )
          lowerCamelCase__       :						str        =							(sigma**2 - adjacent_sigma**2) ** 0.5

          # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
          # also equation 47 shows the analog from SDE models to ancestral sampling methods
          lowerCamelCase__       :						List[Any]        =							diffusion.flatten()
          while len(diffusion.shape      ) < len(sample.shape      ):
             lowerCamelCase__       :						int        =							diffusion.unsqueeze(-1      )
          lowerCamelCase__       :						List[Any]        =							drift - diffusion**2 * model_output

          #  equation 6: sample noise for the diffusion term of
          lowerCamelCase__       :						List[Any]        =							randn_tensor(
              sample.shape		, layout=sample.layout		, generator=A		, device=sample.device		, dtype=sample.dtype      )
          lowerCamelCase__       :						Optional[Any]        =							sample - drift  # subtract because `dt` is a small negative timestep
          # TODO is the variable diffusion the correct scaling term for the noise?
          lowerCamelCase__       :						Dict        =							prev_sample_mean + diffusion * noise  # add impact of diffusion field g

          if not return_dict:
             return (prev_sample, prev_sample_mean)

          return SdeVeOutput(prev_sample=A		, prev_sample_mean=A      )







       def 						__lowerCamelCase   (				self					:     Optional[int]		, A					:     torch.FloatTensor		, A					:     torch.FloatTensor		, A					:     Optional[torch.Generator] = None		, A					:     bool = True		, )       ->Union[SchedulerOutput, Tuple]:
          if self.timesteps is None:
             raise ValueError(
                 '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''      )

          # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
          # sample noise for correction
          lowerCamelCase__       :						Any        =							randn_tensor(sample.shape		, layout=sample.layout		, generator=A      ).to(sample.device      )

          # compute step size from the model_output, the noise, and the snr
          lowerCamelCase__       :						List[str]        =							torch.norm(model_output.reshape(model_output.shape[0]		, -1      )		, dim=-1      ).mean()
          lowerCamelCase__       :						Optional[int]        =							torch.norm(noise.reshape(noise.shape[0]		, -1      )		, dim=-1      ).mean()
          lowerCamelCase__       :						Any        =							(self.config.snr * noise_norm / grad_norm) ** 2 * 2
          lowerCamelCase__       :						str        =							step_size * torch.ones(sample.shape[0]      ).to(sample.device      )
          # self.repeat_scalar(step_size, sample.shape[0])

          # compute corrected sample: model_output term and noise term
          lowerCamelCase__       :						Tuple        =							step_size.flatten()
          while len(step_size.shape      ) < len(sample.shape      ):
             lowerCamelCase__       :						Dict        =							step_size.unsqueeze(-1      )
          lowerCamelCase__       :						int        =							sample + step_size * model_output
          lowerCamelCase__       :						int        =							prev_sample_mean + ((step_size * 2) ** 0.5) * noise

          if not return_dict:
             return (prev_sample,)

          return SchedulerOutput(prev_sample=A      )







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     torch.FloatTensor		, A					:     torch.FloatTensor		, A					:     torch.FloatTensor		, )       ->torch.FloatTensor:
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          lowerCamelCase__       :						Dict        =							timesteps.to(original_samples.device      )
          lowerCamelCase__       :						Optional[int]        =							self.discrete_sigmas.to(original_samples.device      )[timesteps]
          lowerCamelCase__       :						Any        =							(
              noise * sigmas[:, None, None, None]
              if noise is not None
              else torch.randn_like(A      ) * sigmas[:, None, None, None]
          )
          lowerCamelCase__       :						int        =							noise + original_samples
          return noisy_samples







       def __len__(				self					:     Dict      )       ->Optional[Any]:
          return self.config.num_train_timesteps










def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""
   if not isinstance(UpperCAmelCase      ,  UpperCAmelCase					) or number < 0:
      raise ValueError('''Input must be a non-negative integer'''					)

   lowerCamelCase__       :						List[str]        =							0
   while number:
      # This way we arrive at next set bit (next 1) instead of looping
      # through each bit and checking for 1s hence the
      # loop won't run 32 times it will only run the number of `1` times
      number &= number - 1
      count += 1
   return count


if __name__ == "__main__":
       import doctest

       doctest.testmod()



