o zi@sXddlZddlmZddlmZddlZddlmZmZddl m Z GdddeZ dS) N)SDE)Tensor)Normal Independent)DEVICEc seZdZ  ddedededeffdd Zd ed efd d Zd efddZddZ d eded efddZ d eded efddZ Z S)VESDE? sigma_min sigma_maxTepsilonc st||||_||_dS)a Variance Exploding stochastic differential equation Args: sigma_min (float): The minimum value of the standard deviation of the noise term. sigma_max (float): The maximum value of the standard deviation of the noise term. T (float, optional): The time horizon for the VESDE. Defaults to 1.0. device (str, optional): The device to use for computation. Defaults to DEVICE. N)super__init__r r )selfr r r rkwargs __class__J/home/ubuntu/.local/lib/python3.10/site-packages/score_models/sde/vesde.pyr s zVESDE.__init__treturncCs|j|j|j||jSN)r r r rrrrrsigmasz VESDE.sigmaNcCsB|dur t||}n|j|ksJtt||jddt|S)z Technically, VESDE does not change the mean of the 0 temperature distribution, so I give the option to provide for more accuracy. In practice, sigma_max is chosen large enough to make this choice irrelevant NF)locscale validate_args)torchzerostoshaperrr len)rr"mudevicerrrprior"sz VESDE.priorcCst|||fSr)r ones_likerrrrrmarginal_prob_scalars.szVESDE.marginal_prob_scalarsxcCsN|j^}}||jdgdgt|Rtdt|jt|jS)Nr) r"rviewr#npsqrtlogr r )rrr)_Drrr diffusion1s DzVESDE.diffusioncCs t|Sr)r zeros_like)rrr)rrrdrift5s z VESDE.drift)r r ) __name__ __module__ __qualname__floatrrrrr&r(r2r4 __classcell__rrrrr s" r) rsderrnumpyr-torch.distributionsrrscore_models.utilsrrrrrrs