h[>UddlmZddlmZddlmZmZmZmZm Z m Z m Z m Z ddl Z ddlmcmZddl mZmZddlmZddlmZmZmZmZdZeeed <gd Zd e eefd eed ffdZ dee e!efde ede ed efdZ"dede!d efdZ#d+dede$d efdZ%ded efdZ&d+dede$d efdZ'e ddZ(e d d!Z)Gd"d#ee(e)fZ*Gd$d%e*e(e)fe+Z,d,d&ed'e!d efd(Z-d,d)ed'e!d efd*Z.dS)-)Sequence)update_wrapper)AnyCallableFinalGenericOptionaloverloadTypeVarUnionN)SymIntTensoris_tensor_like)_dtype_NumberDeviceNumbergox?euler_constant) broadcast_alllogits_to_probs clamp_probsprobs_to_logits lazy_propertytril_matrix_to_vecvec_to_tril_matrixvaluesreturn.ctd|Dstdtd|Dsyttj|D]9}t |tjrt|j|jn:fd|D}tj |Stj |S)a Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules: - `torch.*Tensor` instances are broadcasted as per :ref:`_broadcasting-semantics`. - Number instances (scalars) are upcast to tensors having the same size and type as the first tensor passed to `values`. If all the values are scalars, then they are upcasted to scalar Tensors. Args: values (list of `Number`, `torch.*Tensor` or objects implementing __torch_function__) Raises: ValueError: if any of the values is not a `Number` instance, a `torch.*Tensor` instance, or an instance implementing __torch_function__ c3^K|](}t|pt|tV)dSN)r isinstancer.0vs a/var/www/html/services/rag-service/venv/lib/python3.11/site-packages/torch/distributions/utils.py z broadcast_all..+s9KKq~a  :Jq'$:$:KKKKKKziInput arguments must all be instances of Number, torch.Tensor or objects implementing __torch_function__.c34K|]}t|VdSr!rr#s r&r'z broadcast_all..0s*11Q~a  111111r()dtyper*devicecVg|]%}t|r|ntj|fi&S)rtorchtensor)r$r%optionss r& z!broadcast_all..6sI   GH"" BAA Q(B(B'(B(B   r() all ValueErrordictr/get_default_dtyper"rr*r,broadcast_tensors)rvalue new_valuesr1s @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_)r:r*r,s r&_standard_normalrC=s{  x""$$ | KU6 : : : JuE& 9 9 9    ;uE& 9 9 9 A A C CCr(r8dimc|dkr|S|jd| dz}||dS)z Sum out ``dim`` many rightmost dimensions of a given tensor. Args: value (Tensor): A tensor of ``.dim()`` at least ``dim``. dim (int): The number of rightmost dims to sum out. rN)rF)r:reshapesum)r8rDrequired_shapes r&_sum_rightmostrJKsI axx [3$'%/N == ( ( , ,R 0 00r(Flogits is_binarycZ|rtj|Stj|dS)a Converts a tensor of logits into probabilities. Note that for the binary case, each value denotes log odds, whereas for the multi-dimensional case, the values along the last dimension denote the log probabilities (possibly unnormalized) of the events. rF)rD)r/sigmoidFsoftmax)rKrLs r&rrYs1%}V$$$ 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)rQrWs 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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