"""simple docstring"""


# 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        a				(		lowercase__   ):
    _snake_case     :  torch.FloatTensor
    _snake_case     :  torch.FloatTensor



class        a				(		lowercase__	,							lowercase__   ):
    _snake_case     :  Optional[int]     							=							1


    @register_to_config
    def __init__(					self	:  List[str]   ,				__lowerCAmelCase	:  Optional[int] = 2000   ,				__lowerCAmelCase	:  List[Any] = 0.15   ,				__lowerCAmelCase	:  int = 0.01   ,				__lowerCAmelCase	:  Union[str, Any] = 1_348.0   ,				__lowerCAmelCase	:  Tuple = 1e-5   ,				__lowerCAmelCase	:  List[str] = 1   ,				):
      # standard deviation of the initial noise distribution
      _UpperCAmelCase						 =      sigma_max

      # setable values
      _UpperCAmelCase						 =      None

      self.set_sigmas(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Tuple   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  Dict = None					):
      return sample


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  List[Any] = None   ,				__lowerCAmelCase	:  str = None					):
      _UpperCAmelCase						 =      sampling_eps if sampling_eps is not None else self.config.sampling_eps

      _UpperCAmelCase						 =      torch.linspace(1   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				device=__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Any   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Any = None   ,				__lowerCAmelCase	:  Dict = None   ,				__lowerCAmelCase	:  int = None					):
      _UpperCAmelCase						 =      sigma_min if sigma_min is not None else self.config.sigma_min
      _UpperCAmelCase						 =      sigma_max if sigma_max is not None else self.config.sigma_max
      _UpperCAmelCase						 =      sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if self.timesteps is None:
        self.set_timesteps(__lowerCAmelCase   ,				__lowerCAmelCase					)

      _UpperCAmelCase						 =      sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
      _UpperCAmelCase						 =      torch.exp(torch.linspace(math.log(__lowerCAmelCase					)   ,				math.log(__lowerCAmelCase					)   ,				__lowerCAmelCase					)					)
      _UpperCAmelCase						 =      torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]					)


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Any					):
      return torch.where(
          timesteps == 0   ,				torch.zeros_like(t.to(timesteps.device					)					)   ,				self.discrete_sigmas[timesteps - 1].to(timesteps.device					)   ,				)


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  List[Any] = None   ,				__lowerCAmelCase	:  Dict = True   ,				):
      if self.timesteps is None:
        raise ValueError(
            """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""					)

      _UpperCAmelCase						 =      timestep * torch.ones(
          sample.shape[0]   ,				device=sample.device					)  # torch.repeat_interleave(timestep, sample.shape[0])
      _UpperCAmelCase						 =      (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
      _UpperCAmelCase						 =      timesteps.to(self.discrete_sigmas.device					)

      _UpperCAmelCase						 =      self.discrete_sigmas[timesteps].to(sample.device					)
      _UpperCAmelCase						 =      self.get_adjacent_sigma(__lowerCAmelCase   ,				__lowerCAmelCase					).to(sample.device					)
      _UpperCAmelCase						 =      torch.zeros_like(__lowerCAmelCase					)
      _UpperCAmelCase						 =      (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
      _UpperCAmelCase						 =      diffusion.flatten()
      while len(diffusion.shape					) < len(sample.shape					):
        _UpperCAmelCase						 =      diffusion.unsqueeze(-1					)
      _UpperCAmelCase						 =      drift - diffusion**2 * model_output

      #  equation 6: sample noise for the diffusion term of
      _UpperCAmelCase						 =      randn_tensor(
          sample.shape   ,				layout=sample.layout   ,				generator=__lowerCAmelCase   ,				device=sample.device   ,				dtype=sample.dtype					)
      _UpperCAmelCase						 =      sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      _UpperCAmelCase						 =      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=__lowerCAmelCase   ,				prev_sample_mean=__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  List[str] = None   ,				__lowerCAmelCase	:  Dict = True   ,				):
      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
      _UpperCAmelCase						 =      randn_tensor(sample.shape   ,				layout=sample.layout   ,				generator=__lowerCAmelCase					).to(sample.device					)

      # compute step size from the model_output, the noise, and the snr
      _UpperCAmelCase						 =      torch.norm(model_output.reshape(model_output.shape[0]   ,				-1					)   ,				dim=-1					).mean()
      _UpperCAmelCase						 =      torch.norm(noise.reshape(noise.shape[0]   ,				-1					)   ,				dim=-1					).mean()
      _UpperCAmelCase						 =      (self.config.snr * noise_norm / grad_norm) ** 2 * 2
      _UpperCAmelCase						 =      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
      _UpperCAmelCase						 =      step_size.flatten()
      while len(step_size.shape					) < len(sample.shape					):
        _UpperCAmelCase						 =      step_size.unsqueeze(-1					)
      _UpperCAmelCase						 =      sample + step_size * model_output
      _UpperCAmelCase						 =      prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
        return (prev_sample,)

      return SchedulerOutput(prev_sample=__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  List[str]   ,				):
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      _UpperCAmelCase						 =      timesteps.to(original_samples.device					)
      _UpperCAmelCase						 =      self.discrete_sigmas.to(original_samples.device					)[timesteps]
      _UpperCAmelCase						 =      (
          noise * sigmas[:, None, None, None]
          if noise is not None
          else torch.randn_like(__lowerCAmelCase					) * sigmas[:, None, None, None]
      )
      _UpperCAmelCase						 =      noise + original_samples
      return noisy_samples


    def __len__(					self	:  Optional[int]					):
      return self.config.num_train_timesteps

"""simple docstring"""


from abc import ABC, abstractmethod
from argparse import ArgumentParser


class        a				(		lowerCAmelCase_   ):


    @staticmethod
    @abstractmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  ArgumentParser					):
      raise NotImplementedError()


    @abstractmethod
    def      lowerCAmelCase_  (					self	:  int					):
      raise NotImplementedError()
