jvddlmZddlmZddlmZmZddlZddlm cm Z ddl m Z dZgdZdZd Zd Zdd Zd ZddZGddZGddeeZddejdedejfdZddejdedejfdZdS))update_wrapper)Number)AnyDictNis_tensor_likegox?) broadcast_alllogits_to_probs clamp_probsprobs_to_logits lazy_propertytril_matrix_to_vecvec_to_tril_matrixctd|Dstdtd|Dsyttj|D]9}t |tjrt|j|jn:fd|D}tj |Stj |S)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__ c3^K|](}t|pt|tV)dSN)r isinstancer.0vs JC:\Projects\Historical\venv\Lib\site-packages\torch/distributions/utils.py z broadcast_all..(s9JJa~a  9Jq&$9$9JJJJJJzqInput arguments must all be instances of numbers.Number, torch.Tensor or objects implementing __torch_function__.c34K|]}t|VdSrrrs rrz broadcast_all..-s*11Q~a  111111r)dtyperdevicecVg|]%}t|r|ntj|fi&S)rtorchtensor)rroptionss r z!broadcast_all..3sI   GH"" BAA Q(B(B'(B(B   r) all ValueErrordictr get_default_dtyperTensorrrbroadcast_tensors)valuesvalue new_valuesr"s @rr r s JJ6JJJ J J  G    11&111 1 1 4"&U-D-F-F"G"G"G  E%.. U[FFF     LR   & 33  "F ++rctjr?tjtj|||tj|||Stj|||S)Nr)r _C_get_tracing_statenormalzerosonesemptynormal_)shaperrs r_standard_normalr6:sy x""$$ | KU6 : : : JuE& 9 9 9    ;uE& 9 9 9 A A C CCrc|dkr|S|jd| dz}||dS)z Sum out ``dim`` many rightmost dimensions of a given tensor. Args: value (Tensor): A tensor of ``.dim()`` at least ``dim``. dim (int): The number of rightmost dims to sum out. rN)r8)r5reshapesum)r+dimrequired_shapes r_sum_rightmostr=DsI axx [3$'%/N == ( ( , ,R 0 00rFcZ|rtj|Stj|dS)a Converts a tensor of logits into probabilities. Note that for the binary case, each value denotes log odds, whereas for the multi-dimensional case, the values along the last dimension denote the log probabilities (possibly unnormalized) of the events. r8)r;)r sigmoidFsoftmax)logits is_binarys rr r Rs1%}V$$$ 9V $ $ $$rcrtj|jj}||d|z S)aClamps the probabilities to be in the open interval `(0, 1)`. The probabilities would be clamped between `eps` and `1 - eps`, and `eps` would be the smallest representable positive number for the input data type. Args: probs (Tensor): A tensor of probabilities. Returns: Tensor: The clamped probabilities. Examples: >>> probs = torch.tensor([0.0, 0.5, 1.0]) >>> clamp_probs(probs) tensor([1.1921e-07, 5.0000e-01, 1.0000e+00]) >>> probs = torch.tensor([0.0, 0.5, 1.0], dtype=torch.float64) >>> clamp_probs(probs) tensor([2.2204e-16, 5.0000e-01, 1.0000e+00], dtype=torch.float64) )minmax)r finforepsclamp)probsrIs rr r ^s2, +ek " " &C ;;3AG; , ,,rct|}|r*tj|tj| z Stj|S)a$ Converts a tensor of probabilities into logits. For the binary case, this denotes the probability of occurrence of the event indexed by `1`. For the multi-dimensional case, the values along the last dimension denote the probabilities of occurrence of each of the events. )r r loglog1p)rKrC ps_clampeds rr r xsKU##J@y$$u{J;'?'??? 9Z  rc eZdZdZdZddZdS)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. c4||_t||dSr)wrappedrselfrRs r__init__zlazy_property.__init__s tW%%%%%rNc|t|jStj5||}dddn #1swxYwYt ||jj||Sr)_lazy_property_and_propertyrRr enable_gradsetattr__name__)rTinstanceobj_typer+s r__get__zlazy_property.__get__s  .t|<< <    + +LL**E + + + + + + + + + + + + + + +$,/777 sA  AAr)rZ __module__ __qualname____doc__rUr]rrrr r sA&&&rr ceZdZdZdZdS)rWzWe want lazy properties to look like multiple things. * property when Sphinx autodoc looks * lazy_property when Distribution validate_args looks c<t||dSr)propertyrUrSs rrUz$_lazy_property_and_property.__init__s$(((((rN)rZr^r_r`rUrrrrWrWs- )))))rrWmatdiagreturnc :|jd}tjs*|| ks||krt d|d| d|dz dtj||j}||dd|dzzk}|d|f}|S) z Convert a `D x D` matrix or a batch of matrices into a (batched) vector which comprises of lower triangular elements from the matrix in row order. r8zdiag (z) provided is outside [z, rEz].r.)r5r r.r/r%arangerview)rdrenri tril_maskvecs rrrs " A 8 & & ( (NdaRii4199L$LLrLLQqSLLLMMM \!CJ / / /FR++tax88I c9n C Jrrmcdd|zz dd|zzdzd|jdzzdt|z|dzzzdzzdz }tj|jj}tjs7t||z |kr!td|jddd zt|tj r!t| nt|}| |jd dtj||fz}tj||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. rEr8g?zThe size of last dimension is z which cannot be expressed as z3the lower triangular part of a square D x D matrix.Nrh.)r5absr rHrrIr.r/roundr%rr(item new_zerosSizerirrj)rmrerkrIrdrirls rrrsv a$h, DLQ SYr]!2 2QT]dQh5O OTW W X  A +ci $C 8 & & ( ( eAhhlS.@.@ ZSYr] Z Z ZC D   &a66DaffhhE!HHA -- #2#QF););; < rs$$$$$$ ******(   ,,,DDDD 1 1 1 % % % %---4 ! ! ! !*)))))-)))  EL  EL    ELELr