

"""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__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						42
			SCREAMING_SNAKE_CASE__     				=						42
			SCREAMING_SNAKE_CASE__     				=						None







class       snake_case__  ( lowerCAmelCase_					,  lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						2







			@register_to_config
			def __init__(			self			: Dict			,  lowercase			: float = 0.0_2			,  lowercase			: float = 1_00			,  lowercase			: float = 1.0_0_7			,  lowercase			: float = 80			,  lowercase			: float = 0.0_5			,  lowercase			: float = 50			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				sigma_max

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







			def 			__lowerCAmelCase				(			self			: str			,  lowercase			: torch.FloatTensor			,  lowercase			: Optional[int] = None  ):





								'''simple docstring'''



								return sample







			def 			__lowerCAmelCase				(			self			: Union[str, Any]			,  lowercase			: int			,  lowercase			: Union[str, torch.device] = None  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				num_inference_steps
								UpperCAmelCase			:   List[str]      =				np.arange(0			,  self.num_inference_steps  )[::-1].copy()
								UpperCAmelCase			:   Union[str, Any]      =				torch.from_numpy(lowercase  ).to(lowercase  )
								UpperCAmelCase			:   Union[str, Any]      =				[
								    (
								        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			:   Any      =				torch.tensor(lowercase			,  dtype=torch.floataa			,  device=lowercase  )







			def 			__lowerCAmelCase				(			self			: Dict			,  lowercase			: torch.FloatTensor			,  lowercase			: float			,  lowercase			: Optional[torch.Generator] = None  ):





								'''simple docstring'''



								if self.config.s_min <= sigma <= self.config.s_max:
													UpperCAmelCase			:   str      =				min(self.config.s_churn / self.num_inference_steps			,  2**0.5 - 1  )
								else:
													UpperCAmelCase			:   List[Any]      =				0

								# sample eps ~ N(0, S_noise^2 * I)
								UpperCAmelCase			:   List[Any]      =				self.config.s_noise * randn_tensor(sample.shape			,  generator=lowercase  ).to(sample.device  )
								UpperCAmelCase			:   int      =				sigma + gamma * sigma
								UpperCAmelCase			:   List[Any]      =				sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat







			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: torch.FloatTensor			,  lowercase			: float			,  lowercase			: float			,  lowercase			: torch.FloatTensor			,  lowercase			: bool = True			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				sample_hat + sigma_hat * model_output
								UpperCAmelCase			:   Dict      =				(sample_hat - pred_original_sample) / sigma_hat
								UpperCAmelCase			:   Optional[int]      =				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 			__lowerCAmelCase				(			self			: str			,  lowercase			: torch.FloatTensor			,  lowercase			: float			,  lowercase			: float			,  lowercase			: torch.FloatTensor			,  lowercase			: torch.FloatTensor			,  lowercase			: torch.FloatTensor			,  lowercase			: bool = True			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				sample_prev + sigma_prev * model_output
								UpperCAmelCase			:   Dict      =				(sample_prev - pred_original_sample) / sigma_prev
								UpperCAmelCase			:   Optional[Any]      =				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 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: List[str]			,  lowercase			: str			,  lowercase			: List[Any]  ):





								'''simple docstring'''



								raise NotImplementedError()





"""simple docstring"""






def   lowercase_	(    _lowercase       :		list							,      _lowercase       :		int							,      _lowercase       :		int = 0							,      _lowercase       :		int = 0	):




					'''simple docstring'''






					UpperCAmelCase			:   Tuple      =				right or len(_lowercase	) - 1
					if left > right:
										return -1
					elif list_data[left] == key:
										return left
					elif list_data[right] == key:
										return right
					else:
										return search(_lowercase							,      _lowercase							,      left + 1							,      right - 1	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


