"""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							(				UpperCAmelCase__	):
     _UpperCamelCase							:    Optional[int]	      =	42
     _UpperCamelCase							:    str	      =	42





class 		__A							(				UpperCAmelCase__					,			UpperCAmelCase__	):
     _UpperCamelCase							:    str	      =	1






     @register_to_config
     def __init__(	self       ,  a__ = 2000       ,  a__ = 0.1_5       ,  a__ = 0.0_1       ,  a__ = 1348.0       ,  a__ = 1e-5       ,  a__ = 1       ,  ):
       _lowerCAmelCase		: List[Any]						    =  sigma_max

       # setable values
       _lowerCAmelCase		: List[Any]						    =  None

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






     def 					__A						(	self       ,  a__       ,  a__ = None						):
       return sample






     def 					__A						(	self       ,  a__       ,  a__ = None       ,  a__ = None						):
       _lowerCAmelCase		: List[str]						    =  sampling_eps if sampling_eps is not None else self.config.sampling_eps

       _lowerCAmelCase		: Optional[int]						    =  torch.linspace(1       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  device=__lowerCAmelCase						)






     def 					__A						(	self       ,  a__       ,  a__ = None       ,  a__ = None       ,  a__ = None						):
       _lowerCAmelCase		: List[str]						    =  sigma_min if sigma_min is not None else self.config.sigma_min
       _lowerCAmelCase		: List[str]						    =  sigma_max if sigma_max is not None else self.config.sigma_max
       _lowerCAmelCase		: Union[str, Any]						    =  sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if self.timesteps is None:
         self.set_timesteps(__lowerCAmelCase       ,  __lowerCAmelCase						)

       _lowerCAmelCase		: List[str]						    =  sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
       _lowerCAmelCase		: Tuple						    =  torch.exp(torch.linspace(math.log(__lowerCAmelCase						)       ,  math.log(__lowerCAmelCase						)       ,  __lowerCAmelCase						)						)
       _lowerCAmelCase		: Union[str, Any]						    =  torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)






     def 					__A						(	self       ,  a__       ,  a__						):
       return torch.where(
           timesteps == 0       ,  torch.zeros_like(t.to(timesteps.device						)						)       ,  self.discrete_sigmas[timesteps - 1].to(timesteps.device						)       ,  )






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__ = None       ,  a__ = True       ,  ):






       if self.timesteps is None:
         raise ValueError(
             """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""						)

       _lowerCAmelCase		: Optional[Any]						    =  timestep * torch.ones(
           sample.shape[0]       ,  device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
       _lowerCAmelCase		: List[str]						    =  (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		: Optional[int]						    =  timesteps.to(self.discrete_sigmas.device						)

       _lowerCAmelCase		: Dict						    =  self.discrete_sigmas[timesteps].to(sample.device						)
       _lowerCAmelCase		: str						    =  self.get_adjacent_sigma(__lowerCAmelCase       ,  __lowerCAmelCase						).to(sample.device						)
       _lowerCAmelCase		: Optional[int]						    =  torch.zeros_like(__lowerCAmelCase						)
       _lowerCAmelCase		: Any						    =  (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		: Any						    =  diffusion.flatten()
       while len(diffusion.shape						) < len(sample.shape						):
         _lowerCAmelCase		: Union[str, Any]						    =  diffusion.unsqueeze(-1						)
       _lowerCAmelCase		: int						    =  drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       _lowerCAmelCase		: Optional[int]						    =  randn_tensor(
           sample.shape       ,  layout=sample.layout       ,  generator=__lowerCAmelCase       ,  device=sample.device       ,  dtype=sample.dtype						)
       _lowerCAmelCase		: Tuple						    =  sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       _lowerCAmelCase		: List[str]						    =  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 					__A						(	self       ,  a__       ,  a__       ,  a__ = None       ,  a__ = 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
       _lowerCAmelCase		: Optional[int]						    =  randn_tensor(sample.shape       ,  layout=sample.layout       ,  generator=__lowerCAmelCase						).to(sample.device						)

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

       if not return_dict:
         return (prev_sample,)

       return SchedulerOutput(prev_sample=__lowerCAmelCase						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  ):
       _lowerCAmelCase		: Optional[Any]						    =  timesteps.to(original_samples.device						)
       _lowerCAmelCase		: int						    =  self.discrete_sigmas.to(original_samples.device						)[timesteps]
       _lowerCAmelCase		: List[Any]						    =  (
           noise * sigmas[:, None, None, None]
           if noise is not None
           else torch.randn_like(__lowerCAmelCase						) * sigmas[:, None, None, None]
       )
       _lowerCAmelCase		: str						    =  noise + original_samples
       return noisy_samples






     def __len__(	self						):
       return self.config.num_train_timesteps





"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class 		__A							(				unittest.TestCase	):






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  torch.nn.Linear(10       ,  10						)
       _lowerCAmelCase		: Optional[Any]						    =  torch.optim.SGD(model.parameters()       ,  0.1						)
       _lowerCAmelCase		: Optional[Any]						    =  Accelerator()
       _lowerCAmelCase		: Tuple						    =  accelerator.prepare(a__						)
       try:
         pickle.loads(pickle.dumps(a__						)						)
       except Exception as e:
         self.fail(F"Accelerated optimizer pickling failed with {e}"						)
       AcceleratorState._reset_state()




