ȅi6zSSKrSSKrSSKrSSKrSSKJr SSKrSSKJs J r SSKJ r SSK J r SSKJr SSKJrJrJrJrJr SSKJrJr SSKJr /S Qr"S S 5r"S S \5r"SS\5r\"/5r"SS\5r"SS\5r "SS\5r!"SS\5r"Sr#"SS\5r$"SS\5r%"SS\5r&"SS \5r'"S!S"\5r("S#S$\5r)"S%S&\5r*"S'S(\5r+"S)S*\5r,"S+S,\5r-"S-S.\5r."S/S0\5r/"S1S2\5r0g)3N)Sequence)Tensor) constraints) Distribution)_sum_rightmost broadcast_all lazy_propertytril_matrix_to_vecvec_to_tril_matrix)padsoftplus)_Number) AbsTransformAffineTransform CatTransformComposeTransformCorrCholeskyTransformCumulativeDistributionTransform ExpTransformIndependentTransformLowerCholeskyTransformPositiveDefiniteTransformPowerTransformReshapeTransformSigmoidTransformSoftplusTransform TanhTransformSoftmaxTransformStackTransformStickBreakingTransform Transformidentity_transformc^\rSrSr%SrSr\R\S'\R\S'SS\ SS4U4S jjjr S r \ S\ 4S j5r \ SS j5r\ S\ 4S j5rSSjrSrSrSrSrSrSrSrSrSrSrSrU=r$)r!0ac Abstract class for invertable transformations with computable log det jacobians. They are primarily used in :class:`torch.distributions.TransformedDistribution`. Caching is useful for transforms whose inverses are either expensive or numerically unstable. Note that care must be taken with memoized values since the autograd graph may be reversed. For example while the following works with or without caching:: y = t(x) t.log_abs_det_jacobian(x, y).backward() # x will receive gradients. However the following will error when caching due to dependency reversal:: y = t(x) z = t.inv(y) grad(z.sum(), [y]) # error because z is x Derived classes should implement one or both of :meth:`_call` or :meth:`_inverse`. Derived classes that set `bijective=True` should also implement :meth:`log_abs_det_jacobian`. Args: cache_size (int): Size of cache. If zero, no caching is done. If one, the latest single value is cached. Only 0 and 1 are supported. Attributes: domain (:class:`~torch.distributions.constraints.Constraint`): The constraint representing valid inputs to this transform. codomain (:class:`~torch.distributions.constraints.Constraint`): The constraint representing valid outputs to this transform which are inputs to the inverse transform. bijective (bool): Whether this transform is bijective. A transform ``t`` is bijective iff ``t.inv(t(x)) == x`` and ``t(t.inv(y)) == y`` for every ``x`` in the domain and ``y`` in the codomain. Transforms that are not bijective should at least maintain the weaker pseudoinverse properties ``t(t.inv(t(x)) == t(x)`` and ``t.inv(t(t.inv(y))) == t.inv(y)``. sign (int or Tensor): For bijective univariate transforms, this should be +1 or -1 depending on whether transform is monotone increasing or decreasing. Fdomaincodomain cache_sizereturnNc|>XlSUlUS:XaOUS:XaSUlO [S5e[TU]5 g)Nr)NNzcache_size must be 0 or 1) _cache_size_inv _cached_x_y ValueErrorsuper__init__)selfr' __class__s X/home/james-whalen/.local/lib/python3.13/site-packages/torch/distributions/transforms.pyr0Transform.__init__as@%=A ?  1_)D 89 9 cDURR5nSUS'U$)Nr,)__dict__copy)r1states r3 __getstate__Transform.__getstate__ls" ""$f  r5cURRURR:XaURR$[S5e)Nz:Please use either .domain.event_dim or .codomain.event_dim)r% event_dimr&r.r1s r3r=Transform.event_dimqs: ;; DMM$;$; ;;;(( (UVVr5cSnURbUR5nUc&[U5n[R"U5UlU$)zc Returns the inverse :class:`Transform` of this transform. This should satisfy ``t.inv.inv is t``. N)r,_InverseTransformweakrefref)r1invs r3rD Transform.invwsB  99 ))+C ;#D)C C(DI r5c[e)z Returns the sign of the determinant of the Jacobian, if applicable. In general this only makes sense for bijective transforms. NotImplementedErrorr>s r3signTransform.signs "!r5cURU:XaU$[U5R[RLa[U5"US9$[ [U5S35e)Nr'z.with_cache is not implemented)r+typer0r!rHr1r's r3 with_cacheTransform.with_cachesS   z )K :  )"4"4 4:4 4!T$ZL0N"OPPr5cXL$Nr1others r3__eq__Transform.__eq__s }r5c.URU5(+$rR)rVrTs r3__ne__Transform.__ne__s;;u%%%r5cURS:XaURU5$URup#XLaU$URU5nX4UlU$)z" Computes the transform `x => y`. r)r+_callr-)r1xx_oldy_oldys r3__call__Transform.__call__sS   q ::a= ''  :L JJqM4r5cURS:XaURU5$URup#XLaU$URU5nXA4UlU$)z! Inverts the transform `y => x`. r)r+_inverser-)r1r`r^r_r]s r3 _inv_callTransform._inv_callsU   q ==# #''  :L MM! 4r5c[e)z4 Abstract method to compute forward transformation. rGr1r]s r3r\Transform._call "!r5c[e)z4 Abstract method to compute inverse transformation. rGr1r`s r3rdTransform._inverserjr5c[e)zE Computes the log det jacobian `log |dy/dx|` given input and output. rGr1r]r`s r3log_abs_det_jacobianTransform.log_abs_det_jacobianrjr5c4URRS-$)Nz())r2__name__r>s r3__repr__Transform.__repr__s~~&&--r5cU$)zc Infers the shape of the forward computation, given the input shape. Defaults to preserving shape. rSr1shapes r3 forward_shapeTransform.forward_shape  r5cU$)ze Infers the shapes of the inverse computation, given the output shape. Defaults to preserving shape. rSrws r3 inverse_shapeTransform.inverse_shaper{r5)r+r-r,r)r(r!r*)rs __module__ __qualname____firstlineno____doc__ bijectiver Constraint__annotations__intr0r:propertyr=rDrIrOrVrYrarer\rdrprtryr}__static_attributes__ __classcell__r2s@r3r!r!0s*XI  " ""$$$ 3 t   W3WW   "c""Q&  " " " .r5r!c^\rSrSrSrS\SS4U4Sjjr\R"SS9S 5r \R"SS9S 5r \ S\ 4S j5r \ S\4S j5r\ S\4S j5rSSjrSrSrSrSrSrSrSrU=r$)rAzp Inverts a single :class:`Transform`. This class is private; please instead use the ``Transform.inv`` property. transformr(Nc@>[TU]URS9 XlgNrL)r/r0r+r,)r1rr2s r3r0_InverseTransform.__init__s I$9$9:( r5F is_discretec^URc [S5eURR$N_inv must not be None)r,AssertionErrorr&r>s r3r%_InverseTransform.domains* 99  !89 9yy!!!r5c^URc [S5eURR$r)r,rr%r>s r3r&_InverseTransform.codomains* 99  !89 9yyr5c^URc [S5eURR$r)r,rrr>s r3r_InverseTransform.bijectives( 99  !89 9yy"""r5c^URc [S5eURR$r)r,rrIr>s r3rI_InverseTransform.signs& 99  !89 9yy~~r5cUR$rRr,r>s r3rD_InverseTransform.invs yyr5c|URc [S5eURRU5R$r)r,rrDrOrNs r3rO_InverseTransform.with_caches3 99  !89 9xx"":.222r5c[U[5(dgURc [S5eURUR:H$)NFr) isinstancerAr,rrTs r3rV_InverseTransform.__eq__s<%!233 99  !89 9yyEJJ&&r5c`URRS[UR5S3$)N())r2rsreprr,r>s r3rt_InverseTransform.__repr__ s)..))*!DO+>r5chURc [S5eURRU5$r)r,rrerhs r3ra_InverseTransform.__call__s- 99  !89 9yy""1%%r5cjURc [S5eURRX!5*$r)r,rrpros r3rp&_InverseTransform.log_abs_det_jacobians0 99  !89 9 ..q444r5c8URRU5$rR)r,r}rws r3ry_InverseTransform.forward_shapeyy&&u--r5c8URRU5$rR)r,ryrws r3r}_InverseTransform.inverse_shaperr5rr)rsrrrrr!r0rdependent_propertyr%r&rboolrrrIrDrOrVrtrarpryr}rrrs@r3rArAs ))))##6"7" ##6 7 #4## c Y3 '?& 5 ...r5rAc^\rSrSrSrSS\\S\SS4U4SjjjrSr \ R"S S 9S 5r \ R"S S 9S 5r \S\4S j5r\S\4Sj5r\S\4Sj5rSSjrSrSrSrSrSrSrU=r$)ri!aF Composes multiple transforms in a chain. The transforms being composed are responsible for caching. Args: parts (list of :class:`Transform`): A list of transforms to compose. cache_size (int): Size of cache. If zero, no caching is done. If one, the latest single value is cached. Only 0 and 1 are supported. partsr'r(Nc>U(a UVs/sHo3RU5PM nn[TU] US9 Xlgs snfr)rOr/r0r)r1rr'partr2s r3r0ComposeTransform.__init__,s< =BCUT__Z0UEC J/ Ds=c`[U[5(dgURUR:H$NF)rrrrTs r3rVComposeTransform.__eq__2s&%!122zzU[[((r5FrcLUR(d[R$URSRnURSRR n[ UR5HQnX#RR URR - - n[X#RR 5nMS X!R :a[SUSUR 35eX!R :a#[R"XUR - 5nU$)Nr event_dim z must be >= domain.event_dim ) rrrealr%r&r=reversedmaxr independent)r1r%r=rs r3r%ComposeTransform.domain7szz## #A%%JJrN++55 TZZ(D ..1H1HH HII{{'<'<=I) '' ' YK'DVEUEUDVW  '' ' ,,VAQAQ5QRF r5c:UR(d[R$URSRnURSRR nURHQnX#RR URR - - n[ X#RR 5nMS X!R :a[SUSUR 35eX!R :a#[R"XUR - 5nU$)Nrrrz must be >= codomain.event_dim ) rrrr&r%r=rrr)r1r&r=rs r3r&ComposeTransform.codomainJszz## #::b>**JJqM((22 JJD 004;;3H3HH HII}}'>'>?I )) ) YK'FxGYGYFZ[  )) )"..xXEWEW9WXHr5c:[SUR55$)Nc38# UHoRv M g7frRr).0ps r3 -ComposeTransform.bijective.._s3 1;; )allrr>s r3rComposeTransform.bijective]s3 333r5cLSnURHnXR-nM U$Nr*)rrI)r1rIrs r3rIComposeTransform.signas%A&&=D r5c0SnURbUR5nUcn[[UR5Vs/sHo"RPM sn5n[ R "U5Ul[ R "U5UlU$s snfrR)r,rrrrDrBrC)r1rDrs r3rDComposeTransform.invhsr 99 ))+C ;"8DJJ3G#H3GaEE3G#HIC C(DI{{4(CH $IsBcNURU:XaU$[URUS9$r)r+rrrNs r3rOComposeTransform.with_cachess&   z )K zBBr5c<URH nU"U5nM U$rR)r)r1r]rs r3raComposeTransform.__call__xsJJDQAr5c ~UR(d[R"U5$U/nURSSHnURU"US55 M URU5 /nURR n[ URUSSUSS5HuupAnUR[URX5XdRR - 55 XdRR URR - - nMw [R"[RU5$)Nrr*)rtorch zeros_likeappendr%r=ziprrpr& functoolsreduceoperatoradd)r1r]r`xsrtermsr=s r3rp%ComposeTransform.log_abs_det_jacobian}szz##A& &SJJsOD IId2b6l #$ ! KK)) djj"Sb'2ab6:JDQ LL--a3YAVAV5V  004;;3H3HH HI ; e44r5cNURHnURU5nM U$rR)rryr1rxrs r3ryComposeTransform.forward_shapes%JJD&&u-E r5c`[UR5HnURU5nM U$rR)rrr}rs r3r}ComposeTransform.inverse_shapes*TZZ(D&&u-E) r5cURRS-nUSRURVs/sHo"R 5PM sn5- nUS- nU$s snf)Nz( z, z ))r2rsjoinrrt)r1 fmt_stringrs r3rtComposeTransform.__repr__sV^^,,y8 innDJJ%GJqjjlJ%GHH e &HsA )r,rrr)rsrrrrlistr!rr0rVrrr%r&r rrrIrrDrOrarpryr}rtrrrs@r3rr!sd9o3t ) ##67"##67"4444c YC  5*  r5rc ^\rSrSrSrSS\S\S\SS4U4SjjjrSS jr\ R"S S 9S 5r \ R"S S 9S 5r \ S\4Sj5r\ S\4Sj5rSrSrSrSrSrSrSrU=r$)ria Wrapper around another transform to treat ``reinterpreted_batch_ndims``-many extra of the right most dimensions as dependent. This has no effect on the forward or backward transforms, but does sum out ``reinterpreted_batch_ndims``-many of the rightmost dimensions in :meth:`log_abs_det_jacobian`. Args: base_transform (:class:`Transform`): A base transform. reinterpreted_batch_ndims (int): The number of extra rightmost dimensions to treat as dependent. base_transformreinterpreted_batch_ndimsr'r(NcX>[TU]US9 URU5UlX lgr)r/r0rOrr)r1rrr'r2s r3r0IndependentTransform.__init__s. J/,77 C)B&r5cdURU:XaU$[URURUS9$r)r+rrrrNs r3rOIndependentTransform.with_caches5   z )K#   !?!?J  r5Frcl[R"URRUR5$rR)rrrr%rr>s r3r%IndependentTransform.domains.&&    & &(F(F  r5cl[R"URRUR5$rR)rrrr&rr>s r3r&IndependentTransform.codomains.&&    ( ($*H*H  r5c.URR$rR)rrr>s r3rIndependentTransform.bijectives"",,,r5c.URR$rR)rrIr>s r3rIIndependentTransform.signs""'''r5cUR5URR:a [S5eUR U5$NToo few dimensions on input)dimr%r=r.rrhs r3r\IndependentTransform._calls7 557T[[** *:; ;""1%%r5cUR5URR:a [S5eURR U5$r)rr&r=r.rrDrls r3rdIndependentTransform._inverses= 557T]],, ,:; ;""&&q))r5cfURRX5n[X0R5nU$rR)rrprr)r1r]r`results r3rp)IndependentTransform.log_abs_det_jacobians-$$99!?(F(FG r5czURRS[UR5SURS3$)Nrz, r)r2rsrrrr>s r3rtIndependentTransform.__repr__s:..))*!D1D1D,E+FbIgIgHhhijjr5c8URRU5$rR)rryrws r3ry"IndependentTransform.forward_shape""0077r5c8URRU5$rR)rr}rws r3r}"IndependentTransform.inverse_shaperr5)rrrr)rsrrrrr!rr0rOrrr%r&rrrrIr\rdrprtryr}rrrs@r3rrs " C!C$'C C  CC ##6 7 ##6 7 -4--(c((& *  k888r5rc ^\rSrSrSrSrSS\RS\RS\SS4U4S jjjr \ RS 5r \ RS 5r SS jrS rSrSrSrSrSrU=r$)ria Unit Jacobian transform to reshape the rightmost part of a tensor. Note that ``in_shape`` and ``out_shape`` must have the same number of elements, just as for :meth:`torch.Tensor.reshape`. Arguments: in_shape (torch.Size): The input event shape. out_shape (torch.Size): The output event shape. cache_size (int): Size of cache. If zero, no caching is done. If one, the latest single value is cached. Only 0 and 1 are supported. (Default 0.) Tin_shape out_shaper'r(Nc>[R"U5Ul[R"U5UlURR 5URR 5:wa [ S5e[ TU]US9 g)Nz6in_shape, out_shape have different numbers of elementsrL)rSizerrnumelr.r/r0)r1rrr'r2s r3r0ReshapeTransform.__init__sc  8, I. ==   DNN$8$8$: :UV V J/r5cr[R"[R[UR55$rR)rrrlenrr>s r3r%ReshapeTransform.domains&&&{'7'7T]]9KLLr5cr[R"[R[UR55$rR)rrrrrr>s r3r&ReshapeTransform.codomains&&&{'7'7T^^9LMMr5cdURU:XaU$[URURUS9$r)r+rrrrNs r3rOReshapeTransform.with_caches,   z )K t~~*UUr5cURSUR5[UR5- nUR X R -5$rR)rxrrrreshaper)r1r] batch_shapes r3r\ReshapeTransform._calls=gg<#dmm*< <= yy~~566r5cURSUR5[UR5- nUR X R -5$rR)rxrrrr"r)r1r`r#s r3rdReshapeTransform._inverse#s=gg=#dnn*= => yy}}455r5cURSUR5[UR5- nUR U5$rR)rxrrr new_zeros)r1r]r`r#s r3rp%ReshapeTransform.log_abs_det_jacobian's6gg<#dmm*< <= {{;''r5c [U5[UR5:a [S5e[U5[UR5- nXSUR:wa[SXSSUR35eUSUUR-$NrzShape mismatch: expected z but got )rrr.rr1rxcuts r3ryReshapeTransform.forward_shape+s u:DMM* *:; ;%j3t}}-- ;$-- '+E$K= $--Q Tc{T^^++r5c [U5[UR5:a [S5e[U5[UR5- nXSUR:wa[SXSSUR35eUSUUR-$r+)rrr.rr,s r3r}ReshapeTransform.inverse_shape5s u:DNN+ +:; ;%j3t~~.. ;$.. (+E$K= $..AQR Tc{T]]**r5)rrrr)rsrrrrrrrrr0rrr%r&rOr\rdrpryr}rrrs@r3rrs I  0** 0:: 0 0  0 0##M$M##N$NV 76(,++r5rch\rSrSrSr\R r\Rr Sr Sr Sr Sr SrSrS rg ) ri@z0 Transform via the mapping :math:`y = \exp(x)`. Tr*c"[U[5$rR)rrrTs r3rVExpTransform.__eq__J%..r5c"UR5$rR)exprhs r3r\ExpTransform._callM uuwr5c"UR5$rRlogrls r3rdExpTransform._inversePr8r5cU$rRrSros r3rp!ExpTransform.log_abs_det_jacobianSr5rSNrsrrrrrrr%positiver&rrIrVr\rdrprrSr5r3rr@s=  F##HI D/r5rc^\rSrSrSr\R r\R rSr SS\ S\ SS4U4Sjjjr SS jr \S\ 4S j5rS rS rS rSrSrSrSrU=r$)riWz< Transform via the mapping :math:`y = x^{\text{exponent}}`. Texponentr'r(NcD>[TU]US9 [U5uUlgr)r/r0rrC)r1rCr'r2s r3r0PowerTransform.__init__`s" J/(2r5cNURU:XaU$[URUS9$r)r+rrCrNs r3rOPowerTransform.with_cacheds&   z )Kdmm CCr5c6URR5$rR)rCrIr>s r3rIPowerTransform.signis}}!!##r5c[U[5(dgURRUR5R 5R 5$r)rrrCeqritemrTs r3rVPowerTransform.__eq__ms=%00}}/335::<URSUR- 5$rrOrls r3rdPowerTransform._inverseusuuQ&''r5c^URU-U- R5R5$rR)rCabsr;ros r3rp#PowerTransform.log_abs_det_jacobianxs( !A%**,0022r5cZ[R"U[URSS55$NrxrSrbroadcast_shapesgetattrrCrws r3ryPowerTransform.forward_shape{"%%eWT]]GR-PQQr5cZ[R"U[URSS55$rXrYrws r3r}PowerTransform.inverse_shape~r]r5)rCrr)rsrrrrrrAr%r&rrrr0rOr rIrVr\rdrpryr}rrrs@r3rrWs ! !F##HI33S333D $c$$= $(3RRRr5rc[R"UR5n[R"[R"U5UR SUR - S9$N?minr)rfinfodtypeclampsigmoidtinyeps)r]res r3_clipped_sigmoidrks< KK E ;;u}}Q'UZZS599_ MMr5ch\rSrSrSr\R r\Rr Sr Sr Sr Sr SrSrS rg ) riz_ Transform via the mapping :math:`y = \frac{1}{1 + \exp(-x)}` and :math:`x = \text{logit}(y)`. Tr*c"[U[5$rR)rrrTs r3rVSigmoidTransform.__eq__%!122r5c[U5$rR)rkrhs r3r\SigmoidTransform._calls ""r5c[R"UR5nURURSUR - S9nUR 5U*R5- $ra)rrerfrgrirjr;log1p)r1r`res r3rdSigmoidTransform._inversesK AGG$ GG eiiG 8uuw1"%%r5c`[R"U*5*[R"U5- $rR)Fr ros r3rp%SigmoidTransform.log_abs_det_jacobians! A2A..r5rSN)rsrrrrrrr% unit_intervalr&rrIrVr\rdrprrSr5r3rrs=  F((HI D3#& /r5rch\rSrSrSr\R r\Rr Sr Sr Sr Sr SrSrS rg ) riz Transform via the mapping :math:`\text{Softplus}(x) = \log(1 + \exp(x))`. The implementation reverts to the linear function when :math:`x > 20`. Tr*c"[U[5$rR)rrrTs r3rVSoftplusTransform.__eq__s%!233r5c[U5$rRr rhs r3r\SoftplusTransform._calls {r5cbU*R5R5R5U-$rR)expm1negr;rls r3rdSoftplusTransform._inverses'zz|!%%'!++r5c[U*5*$rRr}ros r3rp&SoftplusTransform.log_abs_det_jacobians! }r5rSNr@rSr5r3rrs=   F##HI D4,r5rcv\rSrSrSr\R r\R"SS5r Sr Sr Sr Sr S rS rS rg ) ria Transform via the mapping :math:`y = \tanh(x)`. It is equivalent to .. code-block:: python ComposeTransform( [ AffineTransform(0.0, 2.0), SigmoidTransform(), AffineTransform(-1.0, 2.0), ] ) However this might not be numerically stable, thus it is recommended to use `TanhTransform` instead. Note that one should use `cache_size=1` when it comes to `NaN/Inf` values. grbTr*c"[U[5$rR)rrrTs r3rVTanhTransform.__eq__s%//r5c"UR5$rR)tanhrhs r3r\TanhTransform._calls vvxr5c.[R"U5$rR)ratanhrls r3rdTanhTransform._inverses{{1~r5cXS[R"S5U- [SU-5- -$)N@g)mathr;r ros r3rp"TanhTransform.log_abs_det_jacobians*dhhsma'(4!8*<<==r5rSN)rsrrrrrrr%intervalr&rrIrVr\rdrprrSr5r3rrsD,  F##D#.HI D0 >r5rcZ\rSrSrSr\R r\Rr Sr Sr Sr Sr g)riz*Transform via the mapping :math:`y = |x|`.c"[U[5$rR)rrrTs r3rVAbsTransform.__eq__r4r5c"UR5$rR)rUrhs r3r\AbsTransform._callr8r5cU$rRrSrls r3rdAbsTransform._inverser?r5rSN)rsrrrrrrr%rAr&rVr\rdrrSr5r3rrs*5   F##H/r5rc ^\rSrSrSrSrSS\\-S\\-S\S\SS 4 U4S jjjr \ S\4S j5r \ R"S S 9S5r\ R"S S 9S5rSSjrSr\ S\\-4Sj5rSrSrSrSrSrSrU=r$)ria Transform via the pointwise affine mapping :math:`y = \text{loc} + \text{scale} \times x`. Args: loc (Tensor or float): Location parameter. scale (Tensor or float): Scale parameter. event_dim (int): Optional size of `event_shape`. This should be zero for univariate random variables, 1 for distributions over vectors, 2 for distributions over matrices, etc. Tlocscaler=r'r(NcD>[TU]US9 XlX lX0lgr)r/r0rr _event_dim)r1rrr=r'r2s r3r0AffineTransform.__init__s$ J/ #r5cUR$rR)rr>s r3r=AffineTransform.event_dims r5FrcURS:Xa[R$[R"[RUR5$Nrr=rrrr>s r3r%AffineTransform.domain9 >>Q ## #&&{'7'7HHr5cURS:Xa[R$[R"[RUR5$rrr>s r3r&AffineTransform.codomainrr5czURU:XaU$[URURURUS9$r)r+rrrr=rNs r3rOAffineTransform.with_cache!s7   z )K HHdjj$..Z  r5c[U[5(dg[UR[5(a;[UR[5(aURUR:wagO;URUR:HR 5R 5(dg[UR [5(a<[UR [5(aUR UR :waggUR UR :HR 5R 5(dgg)NFT)rrrrrrLrrTs r3rVAffineTransform.__eq__(s%11 dhh ( (Z 7-K-Kxx599$%HH )..05577 djj' * *z%++w/O/OzzU[[() JJ%++-22499;;r5c[UR[5(a8[UR5S:aS$[UR5S:aS$S$URR 5$)Nrr*r)rrrfloatrIr>s r3rIAffineTransform.sign<sU djj' * *djj)A-1 Utzz9JQ9N2 UTU Uzz  r5c:URURU--$rRrrrhs r3r\AffineTransform._callBsxx$**q.((r5c8XR- UR- $rRrrls r3rdAffineTransform._inverseEsHH  **r5cURnURn[U[5(a5[R "U[ R"[U555nO$[R"U5R5nUR(aQUR5SUR*S-nURU5RS5nUSUR*nURU5$)N)rr)rxrrrr full_likerr;rUr=sizeviewsumexpand)r1r]r`rxrr  result_sizes r3rp$AffineTransform.log_abs_det_jacobianHs  eW % %__QU(<=FYYu%))+F >> ++-(94>>/:UBK[[-11"5F+T^^O,E}}U##r5c [R"U[URSS5[URSS55$rXrrZr[rrrws r3ryAffineTransform.forward_shapeU7%% 7488Wb174::wPR3S  r5c [R"U[URSS5[URSS55$rXrrws r3r}AffineTransform.inverse_shapeZrr5)rrrrrr)rsrrrrrrrrr0rr=rrr%r&rOrVrIr\rdrpryr}rrrs@r3rrs I  $ e^ $~ $ $  $  $ $3##6I7I ##6I7I  (!fsl!! )+ $   r5rcn\rSrSrSr\R r\Rr Sr Sr Sr S Sjr SrS rS rg) ri`a| Transforms an unconstrained real vector :math:`x` with length :math:`D*(D-1)/2` into the Cholesky factor of a D-dimension correlation matrix. This Cholesky factor is a lower triangular matrix with positive diagonals and unit Euclidean norm for each row. The transform is processed as follows: 1. First we convert x into a lower triangular matrix in row order. 2. For each row :math:`X_i` of the lower triangular part, we apply a *signed* version of class :class:`StickBreakingTransform` to transform :math:`X_i` into a unit Euclidean length vector using the following steps: - Scales into the interval :math:`(-1, 1)` domain: :math:`r_i = \tanh(X_i)`. - Transforms into an unsigned domain: :math:`z_i = r_i^2`. - Applies :math:`s_i = StickBreakingTransform(z_i)`. - Transforms back into signed domain: :math:`y_i = sign(r_i) * \sqrt{s_i}`. Tc[R"U5n[R"UR5RnUR SU-SU- S9n[ USS9nUS-nSU- R5RS5nU[R"URSURURS9-nU[USSS24SS/SS 9-nU$) Nrr*rcdiag)rfdevice.rvalue) rrrerfrjrgr sqrtcumprodeyerxrr )r1r]rjrzz1m_cumprod_sqrtr`s r3r\CorrCholeskyTransform._callus JJqMkk!''"&& GGSa#gG . qr * qDE<<>11"5  !''"+QWWQXXF F $S#2#X.Aa@ @r5cS[R"X-SS9- n[USSS24SS/SS9n[USS9n[USS9nXER 5- nUR 5UR 5R 5- S- nU$) Nr*rr.rrrr)rcumsumr r rrsr)r1r`y_cumsumy_cumsum_shiftedy_vec y_cumsum_vectr]s r3rdCorrCholeskyTransform._inversesu||AEr22xSbS1Aq6C"12.)*:D '') ) WWY (A -r5NcSX"-RSS9- n[USS9nSUR5RS5-nSU[ SU-5-[ R"S5- RSS9-nXg-$)Nr*rrr?r)rr r;rr r)r1r]r` intermediates y1m_cumsumy1m_cumsum_trilstick_breaking_logdet tanh_logdets r3rp*CorrCholeskyTransform.log_abs_det_jacobians !%B// -ZbA #&;&;&=&A&A"&E EAa 00488C=@EE"EMM $22r5c[U5S:a [S5eUSn[SSU--S-S-5nX3S- -S-U:wa [S5eUSSX34-$)Nr*rrg?rrz.Input is not a flattened lower-diagonal number)rr.round)r1rxNDs r3ry#CorrCholeskyTransform.forward_shapesp u:>:; ; "I 4!a%:; ; 9b !23 3 "I QK1 SbzQD  r5rSrR)rsrrrrr real_vectorr% corr_choleskyr&rr\rdrpryr}rrSr5r3rr`s=  $ $F((HI  3#!r5rcf\rSrSrSr\R r\Rr Sr Sr Sr Sr SrSrg ) ria$ Transform from unconstrained space to the simplex via :math:`y = \exp(x)` then normalizing. This is not bijective and cannot be used for HMC. However this acts mostly coordinate-wise (except for the final normalization), and thus is appropriate for coordinate-wise optimization algorithms. c"[U[5$rR)rrrTs r3rVSoftmaxTransform.__eq__ror5cxUnX"RSS5S- R5nX3RSS5- $)NrTr)rr6r)r1r]logprobsprobss r3r\SoftmaxTransform._calls<LLT2155::<yyT***r5c&UnUR5$rRr:)r1r`rs r3rdSoftmaxTransform._inversesyy{r5c:[U5S:a [S5eU$Nr*rrrws r3rySoftmaxTransform.forward_shape u:>:; ; r5c:[U5S:a [S5eU$rrrws r3r}SoftmaxTransform.inverse_shaperr5rSN)rsrrrrrrr%simplexr&rVr\rdryr}rrSr5r3rrs8 $ $F""H3+  r5rcp\rSrSrSr\R r\Rr Sr Sr Sr Sr SrSrS rS rg ) r ia Transform from unconstrained space to the simplex of one additional dimension via a stick-breaking process. This transform arises as an iterated sigmoid transform in a stick-breaking construction of the `Dirichlet` distribution: the first logit is transformed via sigmoid to the first probability and the probability of everything else, and then the process recurses. This is bijective and appropriate for use in HMC; however it mixes coordinates together and is less appropriate for optimization. Tc"[U[5$rR)rr rTs r3rVStickBreakingTransform.__eq__%!788r5cURSS-URURS5RS5- n[XR 5- 5nSU- R S5n[ USS/SS9[ USS/SS9-nU$)Nrr*rr)rxnew_onesrrkr;rr )r1r]offsetr z_cumprodr`s r3r\StickBreakingTransform._callsq1::aggbk#:#A#A"#EE Q- .UOOB' Aq6 #c)aV1&E Er5cUSSS24nURSURURS5RS5- nSURS5- n[R"U[R "UR 5RS9nUR5UR5- UR5-nU$)N.rr*)rd) rxrrrrgrerfrir;)r1r`y_croprsfr]s r3rdStickBreakingTransform._inverses38qzz&,,r*:;BB2FF r" "[[QWW!5!:!: ; JJL2668 #fjjl 2r5c,URSS-URURS5RS5- nXR5- nU*[R "U5-USSS24R5-R S5nU$)Nrr*.)rxrrr;rv logsigmoidr)r1r]r`rdetJs r3rp+StickBreakingTransform.log_abs_det_jacobians~q1::aggbk#:#A#A"#EE Q\\!_$qcrc{'88==bA r5cT[U5S:a [S5eUSSUSS-4-$Nr*rrrrws r3ry$StickBreakingTransform.forward_shape5 u:>:; ;SbzU2Y],,,r5cT[U5S:a [S5eUSSUSS- 4-$rrrws r3r}$StickBreakingTransform.inverse_shape rr5rSN)rsrrrrrrr%rr&rrVr\rdrpryr}rrSr5r3r r sB  $ $F""HI9- -r5r c|\rSrSrSr\R "\RS5r\Rr Sr Sr Sr Srg) riz Transform from unconstrained matrices to lower-triangular matrices with nonnegative diagonal entries. This is useful for parameterizing positive definite matrices in terms of their Cholesky factorization. rc"[U[5$rR)rrrTs r3rVLowerCholeskyTransform.__eq__rr5c~URS5URSSS9R5R5-$Nrr)dim1dim2)trildiagonalr6 diag_embedrhs r3r\LowerCholeskyTransform._call4vvbzAJJBRJ8<<>IIKKKr5c~URS5URSSS9R5R5-$r)rrr;rrls r3rdLowerCholeskyTransform._inverse"r!r5rSN)rsrrrrrrrr%lower_choleskyr&rVr\rdrrSr5r3rrs= $ $[%5%5q 9F))H9LLr5rc|\rSrSrSr\R "\RS5r\Rr Sr Sr Sr Srg) ri&zF Transform from unconstrained matrices to positive-definite matrices. rc"[U[5$rR)rrrTs r3rV PositiveDefiniteTransform.__eq__.s%!:;;r5c>[5"U5nXR-$rR)rmTrhs r3r\PositiveDefiniteTransform._call1s " $Q '44xr5cr[RRU5n[5R U5$rR)rlinalgcholeskyrrDrls r3rd"PositiveDefiniteTransform._inverse5s* LL ! !! $%'++A..r5rSN)rsrrrrrrrr%positive_definiter&rVr\rdrrSr5r3rr&s; $ $[%5%5q 9F,,H</r5rc ^\rSrSr%Sr\\\S'SS\\S\ S\\ S-S\ S S4 U4S jjjr \ S \ 4S j5r \ S \ 4S j5r SS jrSrSrSr\S \4Sj5r\R,S5r\R,S5rSrU=r$)ri:a Transform functor that applies a sequence of transforms `tseq` component-wise to each submatrix at `dim`, of length `lengths[dim]`, in a way compatible with :func:`torch.cat`. Example:: x0 = torch.cat([torch.range(1, 10), torch.range(1, 10)], dim=0) x = torch.cat([x0, x0], dim=0) t0 = CatTransform([ExpTransform(), identity_transform], dim=0, lengths=[10, 10]) t = CatTransform([t0, t0], dim=0, lengths=[20, 20]) y = t(x) transformsNtseqrlengthsr'r(c>[SU55(d [S5eU(a UVs/sHoURU5PM nn[TU]US9 [ U5UlUcS/[UR 5-n[ U5Ul[UR5[UR 5:wa8[S[UR5S[UR 5S35eX l gs snf)Nc3B# UHn[U[5v M g7frRrr!rrs r3r(CatTransform.__init__..R:T:a++T0All elements of tseq must be Transform instancesrLr*z lengths (z) must match transforms (r) rrrOr/r0rr1rr3r)r1r2rr3r'rr2s r3r0CatTransform.__init__Ks:T::: !ST T 6:;dLL,dD; J/t* ?cC00GG} t|| DOO 4 4 C -..GDOOH\G]]^_ .c81;;r)rr1r>s r3r=CatTransform.event_dima8888r5c,[UR5$rR)rr3r>s r3lengthCatTransform.lengthes4<<  r5c~URU:XaU$[URURURU5$rR)r+rr1rr3rNs r3rOCatTransform.with_cacheis2   z )KDOOTXXt||ZPPr5cUR5*URs=::aUR5:d,O [SURSUR5S35eURUR5UR:waA[SURSURUR5SUR35e/nSn[ UR UR 5H<upEURURX55nURU"U55 X5-nM> [R"X RS9$) Ndim  out of range for tensor with dimensionsx.size() =  must equal length rr) rrrrDrr1r3narrowrrcat)r1r]yslicesstarttransrDxslices r3r\CatTransform._callnsDHH.quuw. txxj >quuwi{S  66$(( t{{ * $((4txx(8'99LT[[MZ  $,,?MEXXdhh6F NN5= )NE@yyhh//r5cUR5*URs=::aUR5:d,O [SURSUR5S35eURUR5UR:waA[SURSURUR5SUR35e/nSn[ UR UR 5HEupEURURX55nURURU55 X5-nMG [R"X RS9$) NrIrJrKy.size(rMrNrr) rrrrDrr1r3rOrrDrrP)r1r`xslicesrRrSrDyslices r3rdCatTransform._inverses DHH.quuw. txxj >quuwi{S  66$(( t{{ * $((4txx(8'99LT[[MZ  $,,?MEXXdhh6F NN599V, -NE@yyhh//r5cfUR5*URs=::aUR5:d,O [SURSUR5S35eURUR5UR:waA[SURSURUR5SUR35eUR5*URs=::aUR5:d,O [SURSUR5S35eURUR5UR:waA[SURSURUR5SUR35e/nS n[ UR UR 5HupVURURXF5nURURXF5nURXx5n URUR:a"[XRUR- 5n URU 5 XF-nM URn U S :aXR5- n XR-n U S :a[R"X:S 9$[U5$) NrI out of range for x with rKrLrMrN out of range for y with rWrr)rrrrDrr1r3rOrpr=rrrrPr) r1r]r` logdetjacsrRrSrDrTrY logdetjacrs r3rp!CatTransform.log_abs_det_jacobians'DHH.quuw. txxj 9!%%'+N  66$(( t{{ * $((4txx(8'99LT[[MZ DHH.quuw. txxj 9!%%'+N  66$(( t{{ * $((4txx(8'99LT[[MZ   $,,?MEXXdhh6FXXdhh6F226BI/*9nnu6VW   i (NE@hh !8-CNN" 799Z1 1z? "r5c:[SUR55$)Nc38# UHoRv M g7frRrr7s r3r)CatTransform.bijective..r@rrr1r>s r3rCatTransform.bijectiverBr5c[R"URVs/sHoRPM snURUR 5$s snfrR)rrPr1r%rr3r1rs r3r%CatTransform.domains<# /!XX /4<<  /Ac[R"URVs/sHoRPM snURUR 5$s snfrR)rrPr1r&rr3rgs r3r&CatTransform.codomains<!% 1AZZ 1488T\\  1ri)rr3r1)rNrr)rsrrrrrr!rrrr0r r=rDrOr\rdrprrrrrr%r&rrrs@r3rr:s Y (, y!#%     ,9399!!!Q 0"0"##J9499## $ ## $ r5rc ^\rSrSr%Sr\\\S'SS\\S\ S\ SS4U4S jjjr SS jr S r S r S rSr\S\4Sj5r\R(S5r\R(S5rSrU=r$)ria7 Transform functor that applies a sequence of transforms `tseq` component-wise to each submatrix at `dim` in a way compatible with :func:`torch.stack`. Example:: x = torch.stack([torch.range(1, 10), torch.range(1, 10)], dim=1) t = StackTransform([ExpTransform(), identity_transform], dim=1) y = t(x) r1r2rr'r(Nc>[SU55(d [S5eU(a UVs/sHoDRU5PM nn[TU]US9 [ U5UlX lgs snf)Nc3B# UHn[U[5v M g7frRr6r7s r3r*StackTransform.__init__..r9r:r;rL)rrrOr/r0rr1r)r1r2rr'rr2s r3r0StackTransform.__init__se:T::: !ST T 6:;dLL,dD; J/t*quuwi{S  66$(( s4??3 3 $((4txx(8'99UVYZ^ZiZiVjUkl  QAMF NN5= )B{{711r5c UR5*URs=::aUR5:d,O [SURSUR5S35eURUR5[UR5:waJ[SURSURUR5S[UR535e/n[ UR U5UR5H%up4URURU55 M' [R"X RS9$)NrIrJrKrWrMrzr) rrrrr1rrwrrDrr{)r1r`rXrYrSs r3rdStackTransform._inversesDHH.quuw. txxj >quuwi{S  66$(( s4??3 3 $((4txx(8'99UVYZ^ZiZiVjUkl  QAMF NN599V, -B{{711r5c vUR5*URs=::aUR5:d,O [SURSUR5S35eURUR5[UR5:waJ[SURSURUR5S[UR535eUR5*URs=::aUR5:d,O [SURSUR5S35eURUR5[UR5:waJ[SURSURUR5S[UR535e/nUR U5nUR U5n[ XTUR5H&upgnURURXg55 M( [R"X0RS 9$) NrIr\rKrLrMrzr]rWr) rrrrr1rwrrrprr{) r1r]r`r^rQrXrTrYrSs r3rp#StackTransform.log_abs_det_jacobiansDHH.quuw. txxj 9!%%'+N  66$(( s4??3 3 $((4txx(8'99UVYZ^ZiZiVjUkl DHH.quuw. txxj 9!%%'+N  66$(( s4??3 3 $((4txx(8'99UVYZ^ZiZiVjUkl  ++a.++a.%(4??%K !FE   e88H I&L{{:8844r5c:[SUR55$)Nc38# UHoRv M g7frRrr7s r3r+StackTransform.bijective.. r@rrdr>s r3rStackTransform.bijectiverBr5c[R"URVs/sHoRPM snUR5$s snfrR)rr{r1r%rrgs r3r%StackTransform.domain"s3  DOO!DOq((O!DdhhOO!DAc[R"URVs/sHoRPM snUR5$s snfrR)rr{r1r&rrgs r3r&StackTransform.codomain's3  doo!Fo**o!FQQ!Fr)rr1rr)rsrrrrrr!rrrr0rOrwr\rdrprrrrrr%r&rrrs@r3rrs YJK Y' .1 CF    E H 2 2509499##P$P##R$Rr5rc^\rSrSrSrSr\RrSr SS\ S\ SS4U4S jjjr \ S\RS-4S j5rS rS rS rSSjrSrU=r$)ri-a  Transform via the cumulative distribution function of a probability distribution. Args: distribution (Distribution): Distribution whose cumulative distribution function to use for the transformation. Example:: # Construct a Gaussian copula from a multivariate normal. base_dist = MultivariateNormal( loc=torch.zeros(2), scale_tril=LKJCholesky(2).sample(), ) transform = CumulativeDistributionTransform(Normal(0, 1)) copula = TransformedDistribution(base_dist, [transform]) Tr* distributionr'r(Nc,>[TU]US9 Xlgr)r/r0r)r1rr'r2s r3r0(CumulativeDistributionTransform.__init__Ds J/(r5c.URR$rR)rsupportr>s r3r%&CumulativeDistributionTransform.domainHs  (((r5c8URRU5$rR)rcdfrhs r3r\%CumulativeDistributionTransform._callLs  $$Q''r5c8URRU5$rR)ricdfrls r3rd(CumulativeDistributionTransform._inverseOs  %%a((r5c8URRU5$rR)rlog_probros r3rp4CumulativeDistributionTransform.log_abs_det_jacobianRs  ))!,,r5cNURU:XaU$[URUS9$r)r+rrrNs r3rO*CumulativeDistributionTransform.with_cacheUs(   z )K.t/@/@ZXXr5)rrr)rsrrrrrrrxr&rIrrr0rrr%r\rdrprOrrrs@r3rr-s$I((H D)\)s)4))) ..5))()-YYr5r)1rrrrBcollections.abcrrtorch.nn.functionalnn functionalrvrtorch.distributionsr torch.distributions.distributionrtorch.distributions.utilsrrr r r r r torch.typesr__all__r!rArr"rrrrrkrrrrrrrr rrrrrrSr5r3rsg $ +9. 0ffRE. E.PyD&b)K89K8\I+yI+X9.(RY(RVN /y/2 0*>I*>Z 9  h ih VQ!IQ!h!y!H5-Y5-pLYL,/ /(K 9K \bRYbRJ+Yi+Yr5