




"""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(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						42
 UpperCamelCase						       =						42
 UpperCamelCase						       =						None







class   A(						UpperCamelCase							,				UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						2

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

       """simple docstring"""






       lowerCamelCase_							=   sigma_max

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

 def     a__			(    self :  List[Any]						,							A_ :  torch.FloatTensor						,							A_ :  Optional[int] = None  )       ->     torch.FloatTensor:

       """simple docstring"""






       return sample

 def     a__			(    self :  List[str]						,							A_ :  int						,							A_ :  Union[str, torch.device] = None  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   num_inference_steps
       lowerCamelCase_							=   np.arange(0						,							self.num_inference_steps  )[::-1].copy()
       lowerCamelCase_							=   torch.from_numpy(A_  ).to(A_  )
       lowerCamelCase_							=   [
           (
               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
       ]
       lowerCamelCase_							=   torch.tensor(A_						,							dtype=torch.floataa						,							device=A_  )

 def     a__			(    self :  str						,							A_ :  torch.FloatTensor						,							A_ :  float						,							A_ :  Optional[torch.Generator] = None  )       ->     Tuple[torch.FloatTensor, float]:

       """simple docstring"""






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

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

       return sample_hat, sigma_hat

 def     a__			(    self :  Dict						,							A_ :  torch.FloatTensor						,							A_ :  float						,							A_ :  float						,							A_ :  torch.FloatTensor						,							A_ :  bool = True						,							)       ->     Union[KarrasVeOutput, Tuple]:

       """simple docstring"""






       lowerCamelCase_							=   sample_hat + sigma_hat * model_output
       lowerCamelCase_							=   (sample_hat - pred_original_sample) / sigma_hat
       lowerCamelCase_							=   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 :  Union[str, Any]						,							A_ :  torch.FloatTensor						,							A_ :  float						,							A_ :  float						,							A_ :  torch.FloatTensor						,							A_ :  torch.FloatTensor						,							A_ :  torch.FloatTensor						,							A_ :  bool = True						,							)       ->     Union[KarrasVeOutput, Tuple]:

       """simple docstring"""






       lowerCamelCase_							=   sample_prev + sigma_prev * model_output
       lowerCamelCase_							=   (sample_prev - pred_original_sample) / sigma_prev
       lowerCamelCase_							=   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 :  Optional[Any]						,							A_ :  Any						,							A_ :  Tuple						,							A_ :  Tuple  )       ->     List[Any]:

       """simple docstring"""






       raise NotImplementedError()









import math

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float     ,							lowercase     :	float					):


      '''simple docstring'''



      return math.pow(lowercase     ,							2					) - a

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float					):


      '''simple docstring'''



      return 2 * x

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float					):


      '''simple docstring'''



      lowerCamelCase_							=   2.0

      while start <= a:
            lowerCamelCase_							=   math.pow(lowercase     ,							2					)

      return start

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float     ,							lowercase     :	int = 99_99     ,							lowercase     :	float = 0.00_0000_0000_0001					):


      '''simple docstring'''



      if a < 0:
            raise ValueError('math domain error'					)

      lowerCamelCase_							=   get_initial_point(lowercase					)

      for _ in range(lowercase					):
            lowerCamelCase_							=   value
            lowerCamelCase_							=   value - fx(lowercase     ,							lowercase					) / fx_derivative(lowercase					)
            if abs(prev_value - value					) < tolerance:
                  return value

      return value


if __name__ == "__main__":
 from doctest import testmod

 testmod()



