IЦi:SSKJr SSKJr SSKJrJr SSKrSSKJ s J r SSK J r Sr/SQrSrS rS rSS jrS rSS jr"SS5r"SS\\5rSS\R2S\S\R24SjjrSS\R2S\S\R24Sjjrg))update_wrapper)Number)AnyDictNis_tensor_likegox?) broadcast_alllogits_to_probs clamp_probsprobs_to_logits lazy_propertytril_matrix_to_vecvec_to_tril_matrixc[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$s snf)a Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules: - `torch.*Tensor` instances are broadcasted as per :ref:`_broadcasting-semantics`. - numbers.Number instances (scalars) are upcast to tensors having the same size and type as the first tensor passed to `values`. If all the values are scalars, then they are upcasted to scalar Tensors. Args: values (list of `numbers.Number`, `torch.*Tensor` or objects implementing __torch_function__) Raises: ValueError: if any of the values is not a `numbers.Number` instance, a `torch.*Tensor` instance, or an instance implementing __torch_function__ c3f# UH'n[U5=(d [U[5v M) g7fN)r isinstancer.0vs X/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/torch/distributions/utils.py broadcast_all..(s$J6a~a 9Jq&$996s/1zqInput arguments must all be instances of numbers.Number, torch.Tensor or objects implementing __torch_function__.c38# UHn[U5v M g7frrrs rrr-s1&Q~a  &s)dtyperdevice) all ValueErrordicttorchget_default_dtyperTensorrrrtensorbroadcast_tensors)valuesoptionsvaluer new_valuess rr r s J6J J J G   1&1 1 1"&U-D-D-F"GE%..U[[F MS LRq""A Q(B'(B BF  && 33  " "F ++  s"3C=c [RR5(a=[R"[R"XUS9[R "XUS95$[R "XUS9R5$)Nr)r!_C_get_tracing_statenormalzerosonesemptynormal_)shaperrs r_standard_normalr3:s^ xx""$$|| KK6 : JJu& 9   ;;u& 9 A A CCcxUS:XaU$URSU*S-nURU5RS5$)z Sum out ``dim`` many rightmost dimensions of a given tensor. Args: value (Tensor): A tensor of ``.dim()`` at least ``dim``. dim (int): The number of rightmost dims to sum out. rN)r6)r2reshapesum)r(dimrequired_shapes r_sum_rightmostr;DsA ax [[3$'%/N == ( , ,R 00r4cfU(a[R"U5$[R"USS9$)z Converts a tensor of logits into probabilities. Note that for the binary case, each value denotes log odds, whereas for the multi-dimensional case, the values along the last dimension denote the log probabilities (possibly unnormalized) of the events. r6)r9)r!sigmoidFsoftmax)logits is_binarys rr r Rs'}}V$$ 99V $$r4c|[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!finforepsclamp)probsrGs rr r ^s3, ++ekk " & &C ;;3AG; ,,r4c[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. )r r!loglog1p)rIrA ps_clampeds rr r xs?U#Jyy$u{{J;'??? 99Z  r4c(\rSrSrSrSrSSjrSrg)r z Used as a decorator for lazy loading of class attributes. This uses a non-data descriptor that calls the wrapped method to compute the property on first call; thereafter replacing the wrapped method into an instance attribute. c&Xl[X5 gr)wrappedrselfrQs r__init__lazy_property.__init__s t%r4NcUc[UR5$[R"5 URU5nSSS5 [ XRR W5 U$!,(df  N0=fr)_lazy_property_and_propertyrQr! enable_gradsetattr__name__)rSinstanceobj_typer(s r__get__lazy_property.__get__sX  .t||< <    LL*E!,,//7 ! s A** A8)rQr)rZ __module__ __qualname____firstlineno____doc__rTr]__static_attributes__r4rr r s&r4r c\rSrSrSrSrSrg)rWzWe want lazy properties to look like multiple things. * property when Sphinx autodoc looks * lazy_property when Distribution validate_args looks c.[RX5 gr)propertyrTrRs rrT$_lazy_property_and_property.__init__s$(r4rdN)rZr_r`rarbrTrcrdr4rrWrWs  )r4rWmatdiagreturnc 0URSn[RR5(d$X*:dX:a[ SUSU*SUS- S35e[R "X R S9nX3RSS5US--:nUSU4nU$) z Convert a `D x D` matrix or a batch of matrices into a (batched) vector which comprises of lower triangular elements from the matrix in row order. r6zdiag (z) provided is outside [z, rCz].r.)r2r!r+r,rarangerview)rjrknro tril_maskvecs rrrs " A 88 & & ( (dRi496$'>rd"QqSELMM \\!JJ /FR+tax88I c9n C Jr4rscSSU--*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$) z Convert a vector or a batch of vectors into a batched `D x D` lower triangular matrix containing elements from the vector in row order. rCr6g?zThe size of last dimension is z which cannot be expressed as z3the lower triangular part of a square D x D matrix.Nrn.)r2absr!rFrrGr+r,roundrrr#item new_zerosSizerorrp)rsrkrqrGrjrorrs rrrsT a$h, DLQ SYYr]!2 2QT]dQh5O OTW W X  A ++cii $ $C 88 & & ( (eAhlS.@,SYYr]O;Y ZC D  &a66affhE!HA -- #2QF);; torch.overridesreuler_constant__all__r r3r;r r r r rhrWr#intrrrdr4rrs$ *( ,DD 1 %-4 !*)-) ELL  ELL ELLELLr4