DŽg.ddlmZddlmZddlmZmZddlZddlm cm Z ddl m Z dZgdZdZd Zd Zdd Zd Zdd ZGddZGddeeZddej2dedej2fdZddej2dedej2fdZy))update_wrapper)Number)AnyDictNis_tensor_likegox?) broadcast_alllogits_to_probs clamp_probsprobs_to_logits lazy_propertytril_matrix_to_vecvec_to_tril_matrixctd|Ds tdtd|Dsttj}|D]?}t |tj st|j|j}n|Dcgc]%}t|r|ntj|fi|'}}tj|Stj|Scc}w)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`. - numbers.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 `numbers.Number`, `torch.*Tensor` or objects implementing __torch_function__) Raises: ValueError: if any of the values is not a `numbers.Number` instance, a `torch.*Tensor` instance, or an instance implementing __torch_function__ c3VK|]!}t|xst|t#ywN)r isinstancer.0vs a/home/mcse/projects/flask_80/flask-venv/lib/python3.12/site-packages/torch/distributions/utils.py z broadcast_all..(s$Ja~a 9Jq&$99Js')zqInput arguments must all be instances of numbers.Number, torch.Tensor or objects implementing __torch_function__.c32K|]}t|ywrrrs rrz broadcast_all..-s1Q~a 1s)dtyperdevice) all ValueErrordicttorchget_default_dtyperTensorrrrtensorbroadcast_tensors)valuesoptionsvaluer new_valuess rr r s J6J J G   1&1 1"&U-D-D-F"G E%.U[[F  MS GH"A Q(B'(B B  && 33  " "F ++  s*C&ctjjrBtjtj|||tj |||Stj |||jS)Nr)r _C_get_tracing_statenormalzerosonesemptynormal_)shaperrs r_standard_normalr2:sa xx""$|| KKU6 : JJuE& 9   ;;uE& 9 A A CCcv|dk(r|S|jd| dz}|j|jdS)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)r1reshapesum)r'dimrequired_shapes r_sum_rightmostr:DsA ax [[3$'%/N == ( , ,R 00r3c^|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. r5)r8)r sigmoidFsoftmax)logits is_binarys rr r Rs'}}V$$ 99V $$r3ctj|jj}|j |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 finforepsclamp)probsrFs rr r ^s3, ++ekk " & &C ;;3AG; ,,r3ct|}|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`. For the multi-dimensional case, the values along the last dimension denote the probabilities of occurrence of each of the events. )r r loglog1p)rHr@ ps_clampeds rr r xs?U#Jyy$u{{J;'??? 99Z  r3ceZdZdZdZddZy)r z Used as a decorator for lazy loading of class attributes. This uses a non-data descriptor that calls the wrapped method to compute the property on first call; thereafter replacing the wrapped method into an instance attribute. c*||_t||yr)wrappedrselfrOs r__init__zlazy_property.__init__s tW%r3Nc|t|jStj5|j|}dddt ||jj |S#1swY,xYwr)_lazy_property_and_propertyrOr enable_gradsetattr__name__)rQinstanceobj_typer's r__get__zlazy_property.__get__sa  .t||< <     +LL*E +$,,//7  + +s A))A2r)rW __module__ __qualname____doc__rRrZr3rr r s&r3r ceZdZdZdZy)rTzWe want lazy properties to look like multiple things. * property when Sphinx autodoc looks * lazy_property when Distribution validate_args looks c0tj||yr)propertyrRrPs rrRz$_lazy_property_and_property.__init__s$(r3N)rWr[r\r]rRr^r3rrTrTs  )r3rTmatdiagreturnc .|jd}tjjs$|| ks||k\rt d|d| d|dz dtj ||j }||jdd|dzzk}|d|f}|S) z Convert a `D x D` matrix or a batch of matrices into a (batched) vector which comprises of lower triangular elements from the matrix in row order. r5zdiag (z) provided is outside [z, rBz].r.)r1r r*r+rarangerview)rbrcnrg tril_maskvecs rrrs " A 88 & & (daRi4196$'>rd"QqSELMM \\!CJJ /FR+tax88I c9n C Jr3rkcdd|zz dd|zzdzd|jdzzdt|z|dzzzdzzdz }tj|jj }tj js0t||z |kDrtd|jddd zt|tjrt|jn t|}|j|jd dtj||fz}tj||j }||j#dd|dzzk}||d |f<|S) z Convert a vector or a batch of vectors into a batched `D x D` lower triangular matrix containing elements from the vector in row order. rBr5g?zThe size of last dimension is z which cannot be expressed as z3the lower triangular part of a square D x D matrix.Nrf.)r1absr rErrFr*r+roundrrr"item new_zerosSizergrrh)rkrcrirFrbrgrjs rrrsV a$h, DLQ SYYr]!2 2QT]dQh5O OTW W X  A ++cii $ $C 88 & & (eAhlS.@,SYYr]O;Y ZC D  &a6affhE!HA -- #2QF);; rs$ *( ,DD 1 %-4 !*)-) ELL  ELL ELLELLr3