



"""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__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	42
	SCREAMING_SNAKE_CASE          =	42
	SCREAMING_SNAKE_CASE          =	None






class 			A__   (  __SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	2

	@register_to_config
	def __init__(     self:		str       ,			_SCREAMING_SNAKE_CASE:		float = 0.02       ,			_SCREAMING_SNAKE_CASE:		float = 100       ,			_SCREAMING_SNAKE_CASE:		float = 1.007       ,			_SCREAMING_SNAKE_CASE:		float = 80       ,			_SCREAMING_SNAKE_CASE:		float = 0.05       ,			_SCREAMING_SNAKE_CASE:		float = 50       ,			)				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       sigma_max

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		int       ,			_SCREAMING_SNAKE_CASE:		torch.FloatTensor       ,			_SCREAMING_SNAKE_CASE:		Optional[int] = None)				->       torch.FloatTensor:

				"""simple docstring"""

				return sample

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		int       ,			_SCREAMING_SNAKE_CASE:		Union[str, torch.device] = None)				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       num_inference_steps
				__lowerCAmelCase      :  Tuple												=       np.arange(0       ,			self.num_inference_steps)[::-1].copy()
				__lowerCAmelCase      :  Union[str, Any]												=       torch.from_numpy(_SCREAMING_SNAKE_CASE).to(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Optional[int]												=       [
				    (
				        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      :  Dict												=       torch.tensor(_SCREAMING_SNAKE_CASE       ,			dtype=torch.floataa       ,			device=_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		int       ,			_SCREAMING_SNAKE_CASE:		torch.FloatTensor       ,			_SCREAMING_SNAKE_CASE:		float       ,			_SCREAMING_SNAKE_CASE:		Optional[torch.Generator] = None)				->       Tuple[torch.FloatTensor, float]:

				"""simple docstring"""

				if self.config.s_min <= sigma <= self.config.s_max:
							__lowerCAmelCase      :  Dict												=       min(self.config.s_churn / self.num_inference_steps       ,			2**0.5 - 1)
				else:
							__lowerCAmelCase      :  int												=       0

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

				return sample_hat, sigma_hat

	def 							_SCREAMING_SNAKE_CASE   (     self:		str       ,			_SCREAMING_SNAKE_CASE:		torch.FloatTensor       ,			_SCREAMING_SNAKE_CASE:		float       ,			_SCREAMING_SNAKE_CASE:		float       ,			_SCREAMING_SNAKE_CASE:		torch.FloatTensor       ,			_SCREAMING_SNAKE_CASE:		bool = True       ,			)				->       Union[KarrasVeOutput, Tuple]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       sample_hat + sigma_hat * model_output
				__lowerCAmelCase      :  Any												=       (sample_hat - pred_original_sample) / sigma_hat
				__lowerCAmelCase      :  Dict												=       sample_hat + (sigma_prev - sigma_hat) * derivative

				if not return_dict:
							return (sample_prev, derivative)

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str]       ,			_SCREAMING_SNAKE_CASE:		torch.FloatTensor       ,			_SCREAMING_SNAKE_CASE:		float       ,			_SCREAMING_SNAKE_CASE:		float       ,			_SCREAMING_SNAKE_CASE:		torch.FloatTensor       ,			_SCREAMING_SNAKE_CASE:		torch.FloatTensor       ,			_SCREAMING_SNAKE_CASE:		torch.FloatTensor       ,			_SCREAMING_SNAKE_CASE:		bool = True       ,			)				->       Union[KarrasVeOutput, Tuple]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       sample_prev + sigma_prev * model_output
				__lowerCAmelCase      :  Any												=       (sample_prev - pred_original_sample) / sigma_prev
				__lowerCAmelCase      :  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=_SCREAMING_SNAKE_CASE       ,			derivative=_SCREAMING_SNAKE_CASE       ,			pred_original_sample=_SCREAMING_SNAKE_CASE)







	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		int       ,			_SCREAMING_SNAKE_CASE:		Any)				->       List[str]:

				"""simple docstring"""

				raise NotImplementedError()




"""simple docstring"""






from ..utils import DummyObject, requires_backends



class 			A__   (  metaclass=__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	['torch', 'torchsde']

	def __init__(     self:		int       ,			*_SCREAMING_SNAKE_CASE:		Optional[Any]       ,			**_SCREAMING_SNAKE_CASE:		str)				->       Dict:

				"""simple docstring"""

				requires_backends(self       ,			["torch", "torchsde"])

	@classmethod
	def 							_SCREAMING_SNAKE_CASE   (     cls:		Optional[Any]       ,			*_SCREAMING_SNAKE_CASE:		Optional[int]       ,			**_SCREAMING_SNAKE_CASE:		Optional[int])				->       Optional[Any]:

				"""simple docstring"""

				requires_backends(cls       ,			["torch", "torchsde"])







	@classmethod
	def 							_SCREAMING_SNAKE_CASE   (     cls:		Dict       ,			*_SCREAMING_SNAKE_CASE:		List[Any]       ,			**_SCREAMING_SNAKE_CASE:		Any)				->       Optional[int]:

				"""simple docstring"""

				requires_backends(cls       ,			["torch", "torchsde"])