Vh@ddlmZddlmZmZmZmZmZddlm Z ddl Z ddl m cm Zddl mZddlmZddlmZdZgd Zd Zd Zd Zdd ZdZddZe ddZe ddZGddeeefZGddeeefeZ d dede!defdZ"d dede!defdZ#y)!)update_wrapper)AnyCallableGenericoverloadUnion)TypeVarN)Tensoris_tensor_like)_Numbergox?) broadcast_alllogits_to_probs clamp_probsprobs_to_logits lazy_propertytril_matrix_to_vecvec_to_tril_matrixctd|Ds tdtd|Dsttj}|D]?}t |tj st|j|j}n|Dcgc]%}t|r|ntj|fi|'}}tj|Stj|Scc}w)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__ c3VK|]!}t|xst|t#ywN)r isinstancer .0vs I/home/dcms/DCMS/lib/python3.12/site-packages/torch/distributions/utils.py z broadcast_all..*s$Kq~a :Jq'$::Ks')ziInput arguments must all be instances of Number, torch.Tensor or objects implementing __torch_function__.c32K|]}t|ywrr rs rrz broadcast_all../s1Q~a 1s)dtyperdevice) all ValueErrordicttorchget_default_dtyperr rr!r tensorbroadcast_tensors)valuesoptionsvaluer new_valuess rrrs KFK K G   1&1 1"&U-D-D-F"G E%.U[[F  MS GH"A Q(B'(B B  && 33  " "F ++  s*C&ctjjrBtjtj|||tj |||Stj |||jS)Nr )r%_C_get_tracing_statenormalzerosonesemptynormal_)shaperr!s r_standard_normalr6<sa xx""$|| KKU6 : JJuE& 9   ;;uE& 9 A A CCcv|dk(r|S|jd| dz}|j|jdS)z Sum out ``dim`` many rightmost dimensions of a given tensor. Args: value (Tensor): A tensor of ``.dim()`` at least ``dim``. dim (int): The number of rightmost dims to sum out. rN)r9)r5reshapesum)r+dimrequired_shapes r_sum_rightmostr>FsA ax [[3$'%/N == ( , ,R 00r7c^|rtj|Stj|dS)a 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. r9)r<)r%sigmoidFsoftmax)logits is_binarys rrrTs'}}V$$ 99V $$r7ctj|jj}|j |d|z S)aClamps 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)probsrJs rrr`s3, ++ekk " & &C ;;3AG; ,,r7ct|}|r,tj|tj| z Stj|S)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)rLrD ps_clampeds rrrzs?U#Jyy$u{{J;'??? 99Z  r7TT) contravariantR) covariantceZdZdZdeegefddfdZe d ddde ddfd Z ed dede defd Z d de edfde dd fd Z y)rz Used as a decorator for lazy loading of class attributes. This uses a non-data descriptor that calls the wrapped method to compute the property on first call; thereafter replacing the wrapped method into an instance attribute. wrappedreturnNc*||_t||yr)rVrselfrVs r__init__zlazy_property.__init__s)0 tW%r7instanceobj_typez!_lazy_property_and_property[T, R]cyrrZr\r]s r__get__zlazy_property.__get__s/2r7cyrr_r`s rrazlazy_property.__get__s?Br7z%R | _lazy_property_and_property[T, R]c|t|jStj5|j|}dddt ||jj |S#1swY,xYwr)_lazy_property_and_propertyrVr% enable_gradsetattr__name__)rZr\r]r+s rrazlazy_property.__get__sc  .t||< <     +LL*E +$,,//7  + +s A))A2r) rg __module__ __qualname____doc__rrQrSr[rrrarr_r7rrrs&!a 0&T&.222(+2 ,22BBSBABB9=ag25 0r7rc,eZdZdZdeegefddfdZy)rdzWe want lazy properties to look like multiple things. * property when Sphinx autodoc looks * lazy_property when Distribution validate_args looks rVrWNc0tj||yr)propertyr[rYs rr[z$_lazy_property_and_property.__init__s$(r7)rgrhrirjrrQrSr[r_r7rrdrds% )!a 0)T)r7rdmatdiagrWc .|jd}tjjs$|| ks||k\rt d|d| d|dz dtj ||j }||jdd|dzzk}|d|f}|S) z Convert a `D x D` matrix or a batch of matrices into a (batched) vector which comprises of lower triangular elements from the matrix in row order. r9zdiag (z) provided is outside [z, rFz].r!.)r5r%r.r/r#aranger!view)rnronrr tril_maskvecs rrrs " A 88 & & (daRi4196$'>rd"QUG2NOO \\!CJJ /FR+tax88I c9n C Jr7rvcdd|zz dd|zzdzd|jdzzdt|z|dzzzdzzdz }tj|jj }tj js0t||z |kDrtd|jddd zt|tjrt|jn t|}|j|jd dtj||fz}tj||j }||j#dd|dzzk}||d |f<|S) 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. rFr9g?zThe size of last dimension is z which cannot be expressed as z3the lower triangular part of a square D x D matrix.Nrq.)r5absr%rIrrJr.r/roundr#rr item new_zerosSizerrr!rs)rvrortrJrnrrrus rrrsV a$h, DLQ SYYr]!2 2QT]dQh5O OTW W X  A ++cii $ $C 88 & & (eAhlS.@,SYYr]O;Y ZC D  &a6affhE!HA -- #2QF);; rrrrQrSrrmrdintrrr_r7rrs$::% *( ,DD 1 %-4 ! 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