


'''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"""






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



class 			a       (	_a			,						_a ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Union[str, Any]							     =     2

     @register_to_config
     def __init__(       self  :	Any							,				snake_case  :	float = 0.02							,				snake_case  :	float = 100							,				snake_case  :	float = 1.007							,				snake_case  :	float = 80							,				snake_case  :	float = 0.05							,				snake_case  :	float = 50							,				)		->							str:
          # standard deviation of the initial noise distribution
          __UpperCAmelCase			:				str													=    sigma_max

          # setable values
          __UpperCAmelCase			:				int													=    None
          __UpperCAmelCase			:				np.IntTensor													=    None
          __UpperCAmelCase			:				torch.FloatTensor													=    None  # sigma(t_i)

     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	torch.FloatTensor							,				snake_case  :	Optional[int] = None    )		->							torch.FloatTensor:
          return sample

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	int							,				snake_case  :	Union[str, torch.device] = None    )		->							List[str]:
          __UpperCAmelCase			:				str													=    num_inference_steps
          __UpperCAmelCase			:				Any													=    np.arange(0							,				self.num_inference_steps    )[::-1].copy()
          __UpperCAmelCase			:				int													=    torch.from_numpy(snake_case    ).to(snake_case    )
          __UpperCAmelCase			:				str													=    [
              (
                  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
          ]
          __UpperCAmelCase			:				int													=    torch.tensor(snake_case							,				dtype=torch.floataa							,				device=snake_case    )

     def 				lowerCamelCase__				(       self  :	List[Any]							,				snake_case  :	torch.FloatTensor							,				snake_case  :	float							,				snake_case  :	Optional[torch.Generator] = None    )		->							Tuple[torch.FloatTensor, float]:
          if self.config.s_min <= sigma <= self.config.s_max:
               __UpperCAmelCase			:				int													=    min(self.config.s_churn / self.num_inference_steps							,				2**0.5 - 1    )
          else:
               __UpperCAmelCase			:				int													=    0

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

          return sample_hat, sigma_hat

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	torch.FloatTensor							,				snake_case  :	float							,				snake_case  :	float							,				snake_case  :	torch.FloatTensor							,				snake_case  :	bool = True							,				)		->							Union[KarrasVeOutput, Tuple]:
          __UpperCAmelCase			:				Optional[int]													=    sample_hat + sigma_hat * model_output
          __UpperCAmelCase			:				List[str]													=    (sample_hat - pred_original_sample) / sigma_hat
          __UpperCAmelCase			:				List[Any]													=    sample_hat + (sigma_prev - sigma_hat) * derivative

          if not return_dict:
               return (sample_prev, derivative)

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

     def 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	torch.FloatTensor							,				snake_case  :	float							,				snake_case  :	float							,				snake_case  :	torch.FloatTensor							,				snake_case  :	torch.FloatTensor							,				snake_case  :	torch.FloatTensor							,				snake_case  :	bool = True							,				)		->							Union[KarrasVeOutput, Tuple]:
          __UpperCAmelCase			:				Union[str, Any]													=    sample_prev + sigma_prev * model_output
          __UpperCAmelCase			:				str													=    (sample_prev - pred_original_sample) / sigma_prev
          __UpperCAmelCase			:				List[str]													=    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=snake_case							,				derivative=snake_case							,				pred_original_sample=snake_case    )






     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	List[str]							,				snake_case  :	Optional[Any]							,				snake_case  :	List[str]    )		->							str:
          raise NotImplementedError()



'''simple docstring'''



def 			_a			(		_lowercase      :			int = 4000000						):





     '''simple docstring'''






     __UpperCAmelCase			:				Tuple													=    []
     __UpperCAmelCase							,     __UpperCAmelCase			:				int													=    0, 1
     while b <= n:
          if b % 2 == 0:
               even_fibs.append(_lowercase						)
          __UpperCAmelCase							,     __UpperCAmelCase			:				Dict													=    b, a + b
     return sum(_lowercase						)


if __name__ == "__main__":
      print(f"""{solution() = }""")