o ,h[@sUddlmZddlmZddlmZmZmZmZm Z m Z m Z m Z ddl Z ddlmmZddl mZmZddlmZddlmZmZmZmZdZeeed <gd Zd e eefd eed ffddZ dee e!efde ede ed efddZ"dede!d efddZ#d3dede$d efddZ%ded efdd Z&d3dede$d efd!d"Z'e d#d$d%Z(e d&d$d'Z)Gd(d)d)ee(e)fZ*Gd*d+d+e*e(e)fe+Z,d4d,ed-e!d efd.d/Z-d4d0ed-e!d efd1d2Z.dS)5)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.cstdd|Ds tdtdd|Ds@ttd|D]}t|tjr1t|j|jdnqfdd|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__ css"|] }t|p t|tVqdSN)r isinstancer.0vr#T/var/www/html/scripts/venv/lib/python3.10/site-packages/torch/distributions/utils.py +s z broadcast_all..ziInput arguments must all be instances of Number, torch.Tensor or objects implementing __torch_function__.css|]}t|VqdSrrr r#r#r$r%0s)dtyper&devicecs*g|]}t|r |ntj|fiqSr#)rtorchtensorr optionsr#r$ 6sz!broadcast_all..) all ValueErrordictr)get_default_dtyperr r&r(broadcast_tensors)rvalue new_valuesr#r+r$rs     rshaper&r(cCsBtjrttj|||dtj|||dStj|||dS)Nr')r)_C_get_tracing_statenormalzerosonesemptynormal_)r5r&r(r#r#r$_standard_normal=s r=r3dimcCs0|dkr|S|jd| d}||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)r?)r5reshapesum)r3r>required_shaper#r#r$_sum_rightmostKsrCFlogits is_binarycCs|rt|Stj|ddS)a Converts a tensor of logits into probabilities. Note that for the binary case, each value denotes log odds, whereas for the multi-dimensional case, the values along the last dimension denote the log probabilities (possibly unnormalized) of the events. r?)r>)r)sigmoidFsoftmax)rDrEr#r#r$rYs rprobscCs t|jj}|j|d|dS)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)rIrNr#r#r$resrcCs,t|}|rt|t| St|S)a$ 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)rIrE ps_clampedr#r#r$rs rTT) contravariantR) covariantc@seZdZdZdeegefddfddZe dddde dd fd d Z eddede defd d Z dde edfde dd fdd Z dS)rz Used as a decorator for lazy loading of class attributes. 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