o h@s0ddlmZddlmZmZmZmZmZddlm Z ddl Z ddl m m Zddl mZddlmZddlmZdZgd Zd d Zd d ZddZd(ddZddZd(ddZe dddZe dddZGdddeeefZGdddeeefeZ d)d ed!e!d"efd#d$Z"d)d%ed!e!d"efd&d'Z#dS)*)update_wrapper)AnyCallableGenericoverloadUnion)TypeVarN)Tensoris_tensor_like)_Numbergox?) broadcast_alllogits_to_probs clamp_probsprobs_to_logits lazy_propertytril_matrix_to_vecvec_to_tril_matrixcstdd|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 .0vrm/var/www/html/construction_image-detection-poc/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 rrrrr/s)dtyperdevicecs*g|]}t|r |ntj|fiqSr)r torchtensorroptionsrr 5sz!broadcast_all..) all ValueErrordictrget_default_dtyperr rrbroadcast_tensors)valuesvalue new_valuesrr!rr s     r cCsBtjrttj|||dtj|||dStj|||dS)Nr)r_C_get_tracing_statenormalzerosonesemptynormal_)shaperrrrr_standard_normal<s r4cCs0|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)r5)r3reshapesum)r*dimrequired_shaperrr_sum_rightmostFsr:FcCs|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. r5)r8)rsigmoidFsoftmax)logits is_binaryrrrrTs rcCs 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)rfinforepsclamp)probsrDrrrr`srcCs,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. )rrloglog1p)rFr? ps_clampedrrrrzs 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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