



'''simple docstring'''





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

import numpy as np
import torch

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






@dataclass
class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: torch.FloatTensor
 __lowerCamelCase					: torch.FloatTensor
 __lowerCamelCase					: Optional[torch.FloatTensor]    = None






class __A    (						A     ,     A		):



 '''simple docstring'''







 __lowerCamelCase					: Tuple    = 2



 @register_to_config
 def __init__(self				,     A = 0.02				,     A = 100				,     A = 1.007				,     A = 80				,     A = 0.05				,     A = 50				,     )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						sigma_max

      # setable values
      _a											=						None
      _a											=						None
      _a											=						None  # sigma(t_i)



 def       a__						(self				,     A				,     A = None )					->  torch.FloatTensor:


      """simple docstring"""



      return sample



 def       a__						(self				,     A				,     A = None )					->  Any:


      """simple docstring"""



      _a											=						num_inference_steps
      _a											=						np.arange(0				,     self.num_inference_steps )[::-1].copy()
      _a											=						torch.from_numpy(A ).to(A )
      _a											=						[
          (
              self.config.sigma_max**2
              * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
          )
          for i in self.timesteps
      ]
      _a											=						torch.tensor(A				,     dtype=torch.floataa				,     device=A )



 def       a__						(self				,     A				,     A				,     A = None )					->  Tuple[torch.FloatTensor, float]:


      """simple docstring"""



      if self.config.s_min <= sigma <= self.config.s_max:
           _a											=						min(self.config.s_churn / self.num_inference_steps				,     2**0.5 - 1 )
      else:
           _a											=						0

      # sample eps ~ N(0, S_noise^2 * I)
      _a											=						self.config.s_noise * randn_tensor(sample.shape				,     generator=A ).to(sample.device )
      _a											=						sigma + gamma * sigma
      _a											=						sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

      return sample_hat, sigma_hat



 def       a__						(self				,     A				,     A				,     A				,     A				,     A = True				,     )					->  Union[KarrasVeOutput, Tuple]:


      """simple docstring"""



      _a											=						sample_hat + sigma_hat * model_output
      _a											=						(sample_hat - pred_original_sample) / sigma_hat
      _a											=						sample_hat + (sigma_prev - sigma_hat) * derivative

      if not return_dict:
           return (sample_prev, derivative)

      return KarrasVeOutput(
          prev_sample=A				,     derivative=A				,     pred_original_sample=A )



 def       a__						(self				,     A				,     A				,     A				,     A				,     A				,     A				,     A = True				,     )					->  Union[KarrasVeOutput, Tuple]:


      """simple docstring"""



      _a											=						sample_prev + sigma_prev * model_output
      _a											=						(sample_prev - pred_original_sample) / sigma_prev
      _a											=						sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

      if not return_dict:
           return (sample_prev, derivative)

      return KarrasVeOutput(
          prev_sample=A				,     derivative=A				,     pred_original_sample=A )



 def       a__						(self				,     A				,     A				,     A )					->  int:


      """simple docstring"""



      raise NotImplementedError()








'''simple docstring'''





# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


lowercase_	 =						open  # noqa: we just need to have a builtin inside this module to test it properly



