o ei2@sUddlmZmZddlmZddlmZmZmZm Z m Z ddl Z ddl m mZddl mZmZddlmZddlmZmZmZmZdZeeed <gd Zd eeBd eed ffddZdeeeBdedBdedBd efddZ deded efddZ!d3dede"d efddZ#ded efdd Z$d3dede"d efd!d"Z%e d#d$d%Z&e d&d$d'Z'Gd(d)d)ee&e'fZ(Gd*d+d+e(e&e'fe)Z*d4d,ed-ed efd.d/Z+d4d0ed-ed efd1d2Z,dS)5)CallableSequence)update_wrapper)AnyFinalGenericoverloadTypeVarN)SymIntTensoris_tensor_like)_dtype_NumberDeviceNumbergox?euler_constant) broadcast_alllogits_to_probs clamp_probsprobs_to_logits lazy_propertytril_matrix_to_vecvec_to_tril_matrixvaluesreturn.cstdd|Ds tdtdd|Ds@ttd|D]}t|tjr1t|j|jdnqfdd|D}tj |Stj |S) a Given a list of values (possibly containing numbers), returns a list where each value is broadcasted based on the following rules: - `torch.*Tensor` instances are broadcasted as per :ref:`_broadcasting-semantics`. - Number instances (scalars) are upcast to tensors having the same size and type as the first tensor passed to `values`. If all the values are scalars, then they are upcasted to scalar Tensors. Args: values (list of `Number`, `torch.*Tensor` or objects implementing __torch_function__) Raises: ValueError: if any of the values is not a `Number` instance, a `torch.*Tensor` instance, or an instance implementing __torch_function__ css"|] }t|p t|tVqdSN)r isinstancer.0vr!c/var/www/addictedbytheproject.nl/epg/venv/lib/python3.10/site-packages/torch/distributions/utils.py +s z broadcast_all..ziInput arguments must all be instances of Number, torch.Tensor or objects implementing __torch_function__.css|]}t|VqdSrr rr!r!r"r#0s)dtyper$devicecs*g|]}t|r |ntj|fiqSr!)r torchtensorroptionsr!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_)r3r$r&r!r!r"_standard_normal=s r;r1dimcCs0|dkr|S|jd| d}||dS)z Sum out ``dim`` many rightmost dimensions of a given tensor. Args: value (Tensor): A tensor of ``.dim()`` at least ``dim``. dim (int): The number of rightmost dims to sum out. rN)r=)r3reshapesum)r1r<required_shaper!r!r"_sum_rightmostKsrAFlogits is_binarycCs|rt|Stj|ddS)a Converts a tensor of logits into probabilities. Note that for the binary case, each value denotes log odds, whereas for the multi-dimensional case, the values along the last dimension denote the log probabilities (possibly unnormalized) of the events. r=)r<)r'sigmoidFsoftmax)rBrCr!r!r"rYs rprobscCs t|jj}|j|d|dS)aClamps the probabilities to be in the open interval `(0, 1)`. The probabilities would be clamped between `eps` and `1 - eps`, and `eps` would be the smallest representable positive number for the input data type. Args: probs (Tensor): A tensor of probabilities. Returns: Tensor: The clamped probabilities. Examples: >>> probs = torch.tensor([0.0, 0.5, 1.0]) >>> clamp_probs(probs) tensor([1.1921e-07, 5.0000e-01, 1.0000e+00]) >>> probs = torch.tensor([0.0, 0.5, 1.0], dtype=torch.float64) >>> clamp_probs(probs) tensor([2.2204e-16, 5.0000e-01, 1.0000e+00], dtype=torch.float64) )minmax)r'finfor$epsclamp)rGrLr!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)rGrC ps_clampedr!r!r"rs rTT) contravariantR) covariantc@seZdZdZdeegefddfddZe dddde dd fd d Z eddede defd d Z ddedBde dd fdd Z dS)rz Used as a decorator for lazy loading of class attributes. 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