


# 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__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		42
    __magic_name__        =		42






class   A__ (   snake_case__	, snake_case__     ):







    """simple docstring"""

    __magic_name__        =		1
    @register_to_config
    def __init__(	self      , __snake_case = 2_0_0_0      , __snake_case = 0.15      , __snake_case = 0.01      , __snake_case = 1348.0      , __snake_case = 1E-5      , __snake_case = 1      , ):
         # standard deviation of the initial noise distribution
         snake_case					=    sigma_max

         # setable values
         snake_case					=    None

         self.set_sigmas(__snake_case      , __snake_case      , __snake_case      , __snake_case   )
    def 		a_      (	self      , __snake_case      , __snake_case = None   ):
         return sample
    def 		a_      (	self      , __snake_case      , __snake_case = None      , __snake_case = None   ):
         snake_case					=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

         snake_case					=    torch.linspace(1      , __snake_case      , __snake_case      , device=__snake_case   )
    def 		a_      (	self      , __snake_case      , __snake_case = None      , __snake_case = None      , __snake_case = None   ):
         snake_case					=    sigma_min if sigma_min is not None else self.config.sigma_min
         snake_case					=    sigma_max if sigma_max is not None else self.config.sigma_max
         snake_case					=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
         if self.timesteps is None:
              self.set_timesteps(__snake_case      , __snake_case   )

         snake_case					=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
         snake_case					=    torch.exp(torch.linspace(math.log(__snake_case   )      , math.log(__snake_case   )      , __snake_case   )   )
         snake_case					=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]   )
    def 		a_      (	self      , __snake_case      , __snake_case   ):
         return torch.where(
             timesteps == 0      , torch.zeros_like(t.to(timesteps.device   )   )      , self.discrete_sigmas[timesteps - 1].to(timesteps.device   )      , )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case = None      , __snake_case = True      , ):
         if self.timesteps is None:
              raise ValueError(
                  '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''   )

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

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

         #  equation 6: sample noise for the diffusion term of
         snake_case					=    randn_tensor(
             sample.shape      , layout=sample.layout      , generator=__snake_case      , device=sample.device      , dtype=sample.dtype   )
         snake_case					=    sample - drift  # subtract because `dt` is a small negative timestep
         # TODO is the variable diffusion the correct scaling term for the noise?
         snake_case					=    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=__snake_case      , prev_sample_mean=__snake_case   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case = None      , __snake_case = 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
         snake_case					=    randn_tensor(sample.shape      , layout=sample.layout      , generator=__snake_case   ).to(sample.device   )

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

         if not return_dict:
              return (prev_sample,)

         return SchedulerOutput(prev_sample=__snake_case   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , ):
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         snake_case					=    timesteps.to(original_samples.device   )
         snake_case					=    self.discrete_sigmas.to(original_samples.device   )[timesteps]
         snake_case					=    (
             noise * sigmas[:, None, None, None]
             if noise is not None
             else torch.randn_like(__snake_case   ) * sigmas[:, None, None, None]
         )
         snake_case					=    noise + original_samples
         return noisy_samples




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








from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_SCREAMING_SNAKE_CASE      :			Dict      				=   {"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE      :			Dict      				=   [
     "VAN_PRETRAINED_MODEL_ARCHIVE_LIST",
     "VanForImageClassification",
     "VanModel",
     "VanPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_van import (
      VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
      VanForImageClassification,
      VanModel,
      VanPreTrainedModel,
  )

else:
 import sys

 _SCREAMING_SNAKE_CASE      :			str      				=   _LazyModule(__name__, globals()["__file__"], _import_structure)




