j2UddlmZmZddlmZddlmZmZmZm Z m Z ddl Z ddl m cmZddl mZmZddlmZddlmZmZmZmZdZeeed <gd Zd eezd eed ffdZdeeezdedzdedzd efdZ deded efdZ!d+dede"d efdZ#ded efdZ$d+dede"d efdZ%e ddZ&e d d!Z'Gd"d#ee&e'fZ(Gd$d%e(e&e'fe)Z*d,d&ed'ed efd(Z+d,d)ed'ed efd*Z,dS)-)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.ctd|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`. - 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__ c3^K|](}t|pt|tV)dSN)r isinstancer.0vs c/lsinfo/ai/hellotax_ai/base_platform/venv/lib/python3.11/site-packages/torch/distributions/utils.py z broadcast_all..+s9KKq~a  :Jq'$:$:KKKKKKziInput arguments must all be instances of Number, torch.Tensor or objects implementing __torch_function__.c34K|]}t|VdSrr r!s r$r%z broadcast_all..0s*11Q~a  111111r&)dtyper(devicecVg|]%}t|r|ntj|fi&S)rtorchtensor)r"r#optionss r$ z!broadcast_all..6sI   GH"" BAA Q(B(B'(B(B   r&) all ValueErrordictr-get_default_dtyper r r(r*broadcast_tensors)rvalue new_valuesr/s @r$rrs KKFKKK K K  G    11&111 1 1 4"&U-D-F-F"G"G"G  E%.. U[FFF     LR   & 33  "F ++r&shaper(r*ctjr?tjtj|||tj|||Stj|||S)Nr))r-_C_get_tracing_statenormalzerosonesemptynormal_)r8r(r*s r$_standard_normalrA=s{  x""$$ | KU6 : : : JuE& 9 9 9    ;uE& 9 9 9 A A C CCr&r6dimc|dkr|S|jd| dz}||dS)z Sum out ``dim`` many rightmost dimensions of a given tensor. Args: value (Tensor): A tensor of ``.dim()`` at least ``dim``. dim (int): The number of rightmost dims to sum out. rN)rD)r8reshapesum)r6rBrequired_shapes r$_sum_rightmostrHKsI axx [3$'%/N == ( ( , ,R 0 00r&Flogits is_binarycZ|rtj|Stj|dS)a Converts a tensor of logits into probabilities. Note that for the binary case, each value denotes log odds, whereas for the multi-dimensional case, the values along the last dimension denote the log probabilities (possibly unnormalized) of the events. rD)rB)r-sigmoidFsoftmax)rIrJs r$rrYs1%}V$$$ 9V $ $ $$r&probscrtj|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-finfor(epsclamp)rOrUs r$rres2, +ek " " &C ;;3AG; , ,,r&ct|}|r*tj|tj| z Stj|S)a$ Converts a tensor of probabilities into logits. For the binary case, this denotes the probability of occurrence of the event indexed by `1`. 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