9i[%SSKJr SSKJr SSKJrJrJrJrJ r J r J r J r SSK r SSKJs Jr SSK JrJr SSKJr SSKJrJrJrJr Sr\\\S '/S QrS \ \\4S \\S 44Sjr S\\ \!\4S\ \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),)Sequence)update_wrapper)AnyCallableFinalGenericOptionaloverloadTypeVarUnionN)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$s snf)a Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules: - `torch.*Tensor` instances are broadcasted as per :ref:`_broadcasting-semantics`. - Number instances (scalars) are upcast to tensors having the same size and type as the first tensor passed to `values`. If all the values are scalars, then they are upcasted to scalar Tensors. Args: values (list of `Number`, `torch.*Tensor` or objects implementing __torch_function__) Raises: ValueError: if any of the values is not a `Number` instance, a `torch.*Tensor` instance, or an instance implementing __torch_function__ c3f# UH'n[U5=(d [U[5v M) g7fN)r isinstancer.0vs Y/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/torch/distributions/utils.py broadcast_all..+s$KFq~a :Jq'$::Fs/1ziInput arguments must all be instances of Number, torch.Tensor or objects implementing __torch_function__.c38# UHn[U5v M g7fr!rr#s r&r'r(0s1&Q~a  &s)dtyper*device) all ValueErrordicttorchget_default_dtyper"rr*r,rtensorbroadcast_tensors)roptionsvaluer% new_valuess r&rrs KFK K K G   1&1 1 1"&U-D-D-F"GE%..U[[F MS LRq""A Q(B'(B BF  && 33  " "F ++  s"3C=shaper*r,c [RR5(a=[R"[R"XUS9[R "XUS95$[R "XUS9R5$)Nr+)r0_C_get_tracing_statenormalzerosonesemptynormal_)r7r*r,s r&_standard_normalr@=s`  xx""$$|| KK6 : JJu& 9   ;;u& 9 A A CCr5dimcxUS:XaU$URSU*S-nURU5RS5$)z Sum out ``dim`` many rightmost dimensions of a given tensor. Args: value (Tensor): A tensor of ``.dim()`` at least ``dim``. dim (int): The number of rightmost dims to sum out. rN)rD)r7reshapesum)r5rBrequired_shapes r&_sum_rightmostrHKsA ax [[3$'%/N == ( , ,R 00rAlogits is_binarycfU(a[R"U5$[R"USS9$)z Converts a tensor of logits into probabilities. Note that for the binary case, each value denotes log odds, whereas for the multi-dimensional case, the values along the last dimension denote the log probabilities (possibly unnormalized) of the events. rD)rB)r0sigmoidFsoftmax)rIrJs r&rrYs'}}V$$ 99V $$rAprobsc|[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)r0finfor*epsclamp)rOrUs r&rres3, ++ekk " & &C ;;3AG; ,,rAc[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. )rr0loglog1p)rOrJ ps_clampeds r&rrs?U#Jyy$u{{J;'??? 99Z  rATT) contravariantR) covariantc\rSrSrSrS\\/\4SS4Sjr\ SSSS\ SS 4S jj5r \ SS\S\ S\4S jj5r SS\ \S4S\ 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!)rarselfras r&__init__lazy_property.__init__s)0 t%rAinstanceobj_typez!_lazy_property_and_property[T, R]cgr!rdrgrhs r&__get__lazy_property.__get__s/2rAcgr!rjrks r&rlrms?BrAz%R | _lazy_property_and_property[T, R]cUc[UR5$[R"5 URU5nSSS5 [ XRR W5 U$!,(df  N0=fr!)_lazy_property_and_propertyrar0 enable_gradsetattr__name__)rdrgrhr5s r&rlrmsZ  .t||< <    LL*E!,,//7 ! s A** A8)rar!)rs __module__ __qualname____firstlineno____doc__rr[r]rer rrlr __static_attributes__rjrAr&rrs&!a 0&T&.222(+2 ,22BBSBABB9=ag25 0rArc6\rSrSrSrS\\/\4SS4SjrSr g)rpzWe want lazy properties to look like multiple things. * property when Sphinx autodoc looks * lazy_property when Distribution validate_args looks rarNc.[RX5 gr!)propertyrercs r&re$_lazy_property_and_property.__init__s$(rArj) rsrtrurvrwrr[r]rerxrjrAr&rprps% )!a 0)T)rArpmatdiagc 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. rDzdiag (z) provided is outside [z, rQz].r,.)r7r0r9r:r.aranger,view)r~rnr tril_maskvecs r&rrs " A 88 & & ( (dRi496$'>rd"QUG2NOO \\!JJ /FR+tax88I c9n C JrArcSSU--*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. rQrDg?zThe size of last dimension is z which cannot be expressed as z3the lower triangular part of a square D x D matrix.Nr.)r7absr0rTr*rUr9r:roundr.r"ritem new_zerosSizerr,r)rrrrUr~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);; rs$$TTT  *77 6e 5 ,50,U63;5G,D D E#v+& ' D F  D V  D D 1& 1s 1v 1 %F %t % %-v-&-4 !6 !d !v ! 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