






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__ (							__lowercase				):
	lowercase__						:					torch.FloatTensor
	lowercase__						:					torch.FloatTensor
	lowercase__						:					Optional[torch.FloatTensor]											=   None





class 	snake_case__ (							__lowercase						,  __lowercase				):
	lowercase__						:					str											=   2
	@register_to_config
	def __init__(    self      ,       lowerCAmelCase__ = 0.0_2      ,       lowerCAmelCase__ = 1_00      ,       lowerCAmelCase__ = 1.0_0_7      ,       lowerCAmelCase__ = 80      ,       lowerCAmelCase__ = 0.0_5      ,       lowerCAmelCase__ = 50      ,       )							->				Dict:
							# standard deviation of the initial noise distribution
							__magic_name__      :       int       						= sigma_max

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

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

							return sample_hat, sigma_hat
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = True      ,       )							->				Union[KarrasVeOutput, Tuple]:
							__magic_name__      :       List[str]       						= sample_hat + sigma_hat * model_output
							__magic_name__      :       Any       						= (sample_hat - pred_original_sample) / sigma_hat
							__magic_name__      :       List[str]       						= sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=_a      ,       derivative=_a      ,       pred_original_sample=_a			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = True      ,       )							->				Union[KarrasVeOutput, Tuple]:
							__magic_name__      :       str       						= sample_prev + sigma_prev * model_output
							__magic_name__      :       Union[str, Any]       						= (sample_prev - pred_original_sample) / sigma_prev
							__magic_name__      :       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=_a      ,       derivative=_a      ,       pred_original_sample=_a			)







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Tuple:
							raise NotImplementedError()








def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						if not isinstance(_A,   _A			) or number < 0:
												raise ValueError("""Input must be a non-negative integer"""			)

						__magic_name__      :       str       						= 0
						while number:
												# This way we arrive at next set bit (next 1) instead of looping
												# through each bit and checking for 1s hence the
												# loop won't run 32 times it will only run the number of `1` times
												number &= number - 1
												count += 1
						return count


if __name__ == "__main__":
		import doctest

		doctest.testmod()
