

'''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 					_snake_case		(__SCREAMING_SNAKE_CASE):
			__A  :	torch.FloatTensor
			__A  :	torch.FloatTensor
			__A  :	Optional[torch.FloatTensor]        =None




class 					_snake_case		(__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE):
			__A  :	List[Any]        =2






			@register_to_config
			def __init__(  self			,_snake_case = 0.02			,_snake_case = 1_00			,_snake_case = 1.007			,_snake_case = 80			,_snake_case = 0.05			,_snake_case = 50			,):
				# standard deviation of the initial noise distribution
				UpperCAmelCase_     :      List[str]						=  sigma_max

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






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case = None     ):
				return sample






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case = None     ):
				UpperCAmelCase_     :      Optional[Any]						=  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_     :      List[Any]						=  torch.tensor(_snake_case			,dtype=torch.floataa			,device=_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case = None     ):
				if self.config.s_min <= sigma <= self.config.s_max:
					UpperCAmelCase_     :      List[Any]						=  min(self.config.s_churn / self.num_inference_steps			,2**0.5 - 1     )
				else:
					UpperCAmelCase_     :      Any						=  0

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

				return sample_hat, sigma_hat






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case = True			,):
				UpperCAmelCase_     :      Optional[int]						=  sample_hat + sigma_hat * model_output
				UpperCAmelCase_     :      Dict						=  (sample_hat - pred_original_sample) / sigma_hat
				UpperCAmelCase_     :      Union[str, 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 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case = True			,):
				UpperCAmelCase_     :      List[Any]						=  sample_prev + sigma_prev * model_output
				UpperCAmelCase_     :      Union[str, Any]						=  (sample_prev - pred_original_sample) / sigma_prev
				UpperCAmelCase_     :      Optional[int]						=  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 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case     ):
				raise NotImplementedError()











def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : int			)   ->							Optional[int]:




		'''simple docstring'''
		if b == 0:
				return 1
		if (b % 2) == 0:
				return actual_power(_lowercase   ,    int(b / 2			)			) * actual_power(_lowercase   ,    int(b / 2			)			)
		else:
				return a * actual_power(_lowercase   ,    int(b / 2			)			) * actual_power(_lowercase   ,    int(b / 2			)			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : int			)   ->							float:




		'''simple docstring'''
		if b < 0:
				return 1 / actual_power(_lowercase   ,    _lowercase			)
		return actual_power(_lowercase   ,    _lowercase			)


if __name__ == "__main__":
			print(power(-2, -3))


