
'''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     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






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






class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Optional[int]										=  2




						@register_to_config
						def __init__(   self       ,  _lowercase = 0.02       ,  _lowercase = 100       ,  _lowercase = 1.007       ,  _lowercase = 80       ,  _lowercase = 0.05       ,  _lowercase = 50       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					sigma_max

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




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = None ):

												"""simple docstring"""



												return sample




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = None ):

												"""simple docstring"""



												_lowerCAmelCase    		=					num_inference_steps
												_lowerCAmelCase    		=					np.arange(0       ,  self.num_inference_steps )[::-1].copy()
												_lowerCAmelCase    		=					torch.from_numpy(_lowercase ).to(_lowercase )
												_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(_lowercase       ,  dtype=torch.floataa       ,  device=_lowercase )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase = None ):

												"""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=_lowercase ).to(sample.device )
												_lowerCAmelCase    		=					sigma + gamma * sigma
												_lowerCAmelCase    		=					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

												return sample_hat, sigma_hat




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase = True       ,  ):

												"""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=_lowercase       ,  derivative=_lowercase       ,  pred_original_sample=_lowercase )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase = True       ,  ):

												"""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=_lowercase       ,  derivative=_lowercase       ,  pred_original_sample=_lowercase )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												raise NotImplementedError()








import numpy as np






def      _snake_case     (__lowercase):
   return 1 / (1 + np.exp(-vector))






def      _snake_case     (__lowercase):
   return vector * sigmoid(__lowercase)


if __name__ == "__main__":
 import doctest

 doctest.testmod()





