


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 				SCREAMING_SNAKE_CASE__						(       a__    ):
						snake_case__       :  torch.FloatTensor
						snake_case__       :  torch.FloatTensor
						snake_case__       :  Optional[torch.FloatTensor]       =		None
class 				SCREAMING_SNAKE_CASE__						(       a__		,					a__    ):
						snake_case__       :  Tuple       =		2
						@register_to_config
						def __init__( self       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Dict = 0.02  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any] = 1_0_0  ,							SCREAMING_SNAKE_CASE__       : str = 1.007  ,							SCREAMING_SNAKE_CASE__       : Optional[int] = 8_0  ,							SCREAMING_SNAKE_CASE__       : Dict = 0.05  ,							SCREAMING_SNAKE_CASE__       : str = 5_0  ,							)	->    Union[str, Any]:
													# standard deviation of the initial noise distribution
													a_      :	Tuple							=						sigma_max

													# setable values
													a_      :	int							=						None
													a_      :	np.IntTensor							=						None
													a_      :	torch.FloatTensor							=						None  # sigma(t_i)
						def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Optional[Any] = None  )	->    torch.FloatTensor:
													return sample
						def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : str = None  )	->    List[str]:
													a_      :	List[Any]							=						num_inference_steps
													a_      :	List[str]							=						np.arange(0  ,							self.num_inference_steps  )[::-1].copy()
													a_      :	int							=						torch.from_numpy(lowerCAmelCase__  ).to(lowerCAmelCase__  )
													a_      :	List[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
													]
													a_      :	Any							=						torch.tensor(lowerCAmelCase__  ,							dtype=torch.floataa  ,							device=lowerCAmelCase__  )
						def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Tuple = None  )	->    Tuple[torch.FloatTensor, float]:
													if self.config.s_min <= sigma <= self.config.s_max:
																				a_      :	str							=						min(self.config.s_churn / self.num_inference_steps  ,							2**0.5 - 1  )
													else:
																				a_      :	Dict							=						0

													# sample eps ~ N(0, S_noise^2 * I)
													a_      :	Union[str, Any]							=						self.config.s_noise * randn_tensor(sample.shape  ,							generator=lowerCAmelCase__  ).to(sample.device  )
													a_      :	Any							=						sigma + gamma * sigma
													a_      :	Tuple							=						sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

													return sample_hat, sigma_hat
						def  SCREAMING_SNAKE_CASE							( self       : str  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Tuple = True  ,							)	->    Union[KarrasVeOutput, Tuple]:
													a_      :	Union[str, Any]							=						sample_hat + sigma_hat * model_output
													a_      :	Tuple							=						(sample_hat - pred_original_sample) / sigma_hat
													a_      :	List[Any]							=						sample_hat + (sigma_prev - sigma_hat) * derivative

													if not return_dict:
																				return (sample_prev, derivative)

													return KarrasVeOutput(
													    prev_sample=lowerCAmelCase__  ,							derivative=lowerCAmelCase__  ,							pred_original_sample=lowerCAmelCase__  )
						def  SCREAMING_SNAKE_CASE							( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : Any = True  ,							)	->    Union[KarrasVeOutput, Tuple]:
													a_      :	Optional[int]							=						sample_prev + sigma_prev * model_output
													a_      :	str							=						(sample_prev - pred_original_sample) / sigma_prev
													a_      :	Dict							=						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=lowerCAmelCase__  ,							derivative=lowerCAmelCase__  ,							pred_original_sample=lowerCAmelCase__  )

						def  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : str  )	->    str:
													raise NotImplementedError()






def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    int						,					__A				:    int       )			->	float:

       """simple docstring"""

       a_      :	Optional[int]							=						(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
       # formula for sum of series
       return total







def  SCREAMING_SNAKE_CASE_		(    )			->	Optional[Any]:

       """simple docstring"""

       print(sum_of_series(1						,					1						,					10       )       )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


