ȅi2%SSKJrJr SSKJr SSKJrJrJrJ r J r SSK r SSK J s Jr SSK JrJr SSKJr SSKJrJrJrJr Sr\\\S '/S QrS \\-S \\S 44SjrS\\\-S\S-S\S-S \4Sjr S\S\S \4Sjr!S*S\S\"S \4Sjjr#S\S \4Sjr$S*S\S\"S \4Sjjr%\ "SSS9r&\ "SSS 9r'"S!S"\\&\'45r("S#S$\(\&\'4\)5r*S+S%\S&\S \4S'jjr+S+S(\S&\S \4S)jjr,g),)CallableSequence)update_wrapper)AnyFinalGenericoverloadTypeVarN)SymIntTensoris_tensor_like)_dtype_NumberDeviceNumbergox?euler_constant) broadcast_alllogits_to_probs clamp_probsprobs_to_logits lazy_propertytril_matrix_to_vecvec_to_tril_matrixvaluesreturn.c[SU55(d [S5e[SU55(d[[R"5S9nUHBn[ U[R 5(dM$[URURS9n O UVs/sH,n[U5(aUO[R"U40UD6PM. nn[R"U6$[R"U6$s snf)a Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules: - `torch.*Tensor` instances are broadcasted as per :ref:`_broadcasting-semantics`. - Number instances (scalars) are upcast to tensors having the same size and type as the first tensor passed to `values`. If all the values are scalars, then they are upcasted to scalar Tensors. Args: values (list of `Number`, `torch.*Tensor` or objects implementing __torch_function__) Raises: ValueError: if any of the values is not a `Number` instance, a `torch.*Tensor` instance, or an instance implementing __torch_function__ c3f# UH'n[U5=(d [U[5v M) g7fN)r isinstancer.0vs S/home/james-whalen/.local/lib/python3.13/site-packages/torch/distributions/utils.py broadcast_all..+s$KFq~a :Jq'$::Fs/1ziInput arguments must all be instances of Number, torch.Tensor or objects implementing __torch_function__.c38# UHn[U5v M g7frr r!s r$r%r&0s1&Q~a  &s)dtyper(device) all ValueErrordicttorchget_default_dtyper r r(r*rtensorbroadcast_tensors)roptionsvaluer# new_valuess r$rrs KFK K K G   1&1 1 1"&U-D-D-F"GE%..U[[F MS LRq""A Q(B'(B BF  && 33  " "F ++  s"3C=shaper(r*c [RR5(a=[R"[R"XUS9[R "XUS95$[R "XUS9R5$)Nr))r._C_get_tracing_statenormalzerosonesemptynormal_)r5r(r*s r$_standard_normalr>=s`  xx""$$|| KK6 : JJu& 9   ;;u& 9 A A CCr3dimcxUS:XaU$URSU*S-nURU5RS5$)z Sum out ``dim`` many rightmost dimensions of a given tensor. Args: value (Tensor): A tensor of ``.dim()`` at least ``dim``. dim (int): The number of rightmost dims to sum out. rN)rB)r5reshapesum)r3r@required_shapes r$_sum_rightmostrFKsA ax [[3$'%/N == ( , ,R 00r?logits is_binarycfU(a[R"U5$[R"USS9$)z Converts a tensor of logits into probabilities. Note that for the binary case, each value denotes log odds, whereas for the multi-dimensional case, the values along the last dimension denote the log probabilities (possibly unnormalized) of the events. rB)r@)r.sigmoidFsoftmax)rGrHs r$rrYs'}}V$$ 99V $$r?probsc|[R"UR5RnUR USU- S9$)ahClamps the probabilities to be in the open interval `(0, 1)`. The probabilities would be clamped between `eps` and `1 - eps`, and `eps` would be the smallest representable positive number for the input data type. Args: probs (Tensor): A tensor of probabilities. Returns: Tensor: The clamped probabilities. Examples: >>> probs = torch.tensor([0.0, 0.5, 1.0]) >>> clamp_probs(probs) tensor([1.1921e-07, 5.0000e-01, 1.0000e+00]) >>> probs = torch.tensor([0.0, 0.5, 1.0], dtype=torch.float64) >>> clamp_probs(probs) tensor([2.2204e-16, 5.0000e-01, 1.0000e+00], dtype=torch.float64) )minmax)r.finfor(epsclamp)rMrSs r$rres3, ++ekk " & &C ;;3AG; ,,r?c[U5nU(a.[R"U5[R"U*5- $[R"U5$)a Converts a tensor of probabilities into logits. For the binary case, this denotes the probability of occurrence of the event indexed by `1`. For the multi-dimensional case, the values along the last dimension denote the probabilities of occurrence of each of the events. )rr.loglog1p)rMrH ps_clampeds r$rrs?U#Jyy$u{{J;'??? 99Z  r?TT) contravariantR) covariantc\rSrSrSrS\\/\4SS4Sjr\ SSSS\ SS 4S jj5r \ SS\S\ S\4S jj5r SS\S-S\ SS 4S jjr Sr g)rz Used as a decorator for lazy loading of class attributes. This uses a non-data descriptor that calls the wrapped method to compute the property on first call; thereafter replacing the wrapped method into an instance attribute. wrappedrNc&Xl[X5 gr)r_rselfr_s r$__init__lazy_property.__init__s)0 t%r?instanceobj_typez!_lazy_property_and_property[T, R]cgrrbrerfs r$__get__lazy_property.__get__s/2r?cgrrhris r$rjrks?Br?z%R | _lazy_property_and_property[T, R]cUc[UR5$[R"5 URU5nSSS5 [ XRR W5 U$!,(df  N0=fr)_lazy_property_and_propertyr_r. enable_gradsetattr__name__)rbrerfr3s r$rjrksZ  .t||< <    LL*E!,,//7 ! s A** A8)r_r) rq __module__ __qualname____firstlineno____doc__rrYr[rcr rrj__static_attributes__rhr?r$rrs&!a 0&T&.222(+2 ,22BBSBABB37D,/ 0r?rc6\rSrSrSrS\\/\4SS4SjrSr g)rnzWe want lazy properties to look like multiple things. * property when Sphinx autodoc looks * lazy_property when Distribution validate_args looks r_rNc.[RX5 gr)propertyrcras r$rc$_lazy_property_and_property.__init__s$(r?rh) rqrrrsrtrurrYr[rcrvrhr?r$rnrns% )!a 0)T)r?rnmatdiagc 0URSn[RR5(d$X*:dX:a[ SUSU*SUS- S35e[R "X R S9nX3RSS5US--:nUSU4nU$) z Convert a `D x D` matrix or a batch of matrices into a (batched) vector which comprises of lower triangular elements from the matrix in row order. rBzdiag (z) provided is outside [z, rOz].r*.)r5r.r7r8r,aranger*view)r|r}nr tril_maskvecs r$rrs " A 88 & & ( (dRi496$'>rd"QUG2NOO \\!JJ /FR+tax88I c9n C Jr?rcSSU--*SSU--S-SURS--S[U5-US---S--S- n[R"UR5R n[R R5(d1[U5U- U:a[SURSS3S -5e[U[R5(a[UR55O [U5nURURS S[R"X"45-5n[R"X R S 9nXUR#SS5US--:nXS U4'U$) z Convert a vector or a batch of vectors into a batched `D x D` lower triangular matrix containing elements from the vector in row order. rOrBg?zThe size of last dimension is z which cannot be expressed as z3the lower triangular part of a square D x D matrix.Nr.)r5absr.rRr(rSr7r8roundr,r r item new_zerosSizerr*r)rr}rrSr|rrs r$rrsT a$h, DLQ SYYr]!2 2QT]dQh5O OTW W X  A ++cii $ $C 88 & & ( (eAhlS.@,SYYr]O;Y ZC D  &a66affhE!HA -- #2QF);; rFboolrrrrYr[rrzrnrrrhr?r$rsz..$99  *77 6e 5 ,6F?,uVS[/A,D D C&L ! D D= D TM D D 1& 1s 1v 1 %F %t % %-v-&-4 !6 !d !v ! Ct$ C4 GAqDM>)-1"5x) F # f F#fr?