+ j*aR"t)0t^RIHtHt^RIHt^RIHtHtH t H t H t ^RI t ^RI HuHt^RI HtHt^RIHt^RIHtHtHtHtRt]^k.R#OtR R ltR R ltRRltR$RRlltRRlt R$RRllt!] !RRR7t"] !RRR7t#!RR ] ]"]#3,4t$!RR]$]"]#3,]%4t&R%RRllt'R%R R!llt(R#)&)CallableSequence)update_wrapper)AnyFinalGenericoverloadTypeVarN)SymIntTensoris_tensor_like)_dtype_NumberDeviceNumbergox? lazy_propertyc`V^8dQhR\\,R\\R3,/#)valuesreturn.)r rtuple)formats"Z/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/distributions/utils.py __annotate__rs' , ,6F? ,uVS[/A ,c\;QJdRV4F 'dK RM RM !RV44'g \R4h\;QJdRV4F 'dK RM RM !RV44'g\\P!4R7pVFFp\ V\P 4'gK%\VPVPR7pM VUu.uF-p\V4'dTM\P!V3/VBNK/ pp\P!V!#\P!V!#uupi)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__ c3h"TF(p\V4;'g\V\4xK* R#5iN)r isinstancer.0vs& r broadcast_all..,s'KFq~a ::Jq'$::Fs22FTziInput arguments must all be instances of Number, torch.Tensor or objects implementing __torch_function__.c38"TFp\V4xK R#5irr r!s& rr$r%1s1&Q~a  &s)dtyper'device) all ValueErrordicttorchget_default_dtyper r r'r)rtensorbroadcast_tensors)roptionsvaluer# new_valuess* r broadcast_allr4s" 3KFK333KFK K K G   31&13331&1 1 1"&U-D-D-F"GE%..U[[F MS LRq""A Q(B'(B BF  && 33  " "F ++  s/3E cV^8dQhR\\\,,R\R,R\R,R\ /#)rshaper'Nr)r)rintr rrr )rs"rrr>sG D D C&L ! D D= D TM D Drc \PP4'dD\P!\P!WVR7\P !WVR74#\P !WVR7P4#)r()r-_C_get_tracing_statenormalzerosonesemptynormal_)r6r'r)s&&&r_standard_normalr@>s`  xx""$$|| KK6 : JJu& 9   ;;u& 9 A A CCrc<V^8dQhR\R\R\/#)rr2dimrr r7)rs"rrrLs! 1 1& 1s 1v 1rcV^8XdV#VPRV)R,pVPV4PR4#)z Sum out ``dim`` many rightmost dimensions of a given tensor. Args: value (Tensor): A tensor of ``.dim()`` at least ``dim``. dim (int): The number of rightmost dims to sum out. N)rE)r6reshapesum)r2rBrequired_shapes&& r_sum_rightmostrILsA ax [[3$'%/N == ( , ,R 00rc<V^8dQhR\R\R\/#)rlogits is_binaryrr bool)rs"rrrZs! % %F %t % %rcnV'd\P!V4#\P!VRR7#)z Converts a tensor of logits into probabilities. Note that for the binary case, each value denotes log odds, whereas for the multi-dimensional case, the values along the last dimension denote the log probabilities (possibly unnormalized) of the events. )rBrE)r-sigmoidFsoftmax)rKrLs&&rlogits_to_probsrSZs'}}V$$ 99V $$rc0V^8dQhR\R\/#)rprobsr)r )rs"rrrfs--v-&-rc\P!VP4PpVP V^V, R7#)ahClamps 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)rUrZs& r clamp_probsr\fs3, ++ekk " & &C ;;3AG; ,,rc<V^8dQhR\R\R\/#)rrUrLrrM)rs"rrrs! ! !6 !d !v !rc\V4pV'd3\P!V4\P!V)4, #\P!V4#)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)rUrL ps_clampeds&& rprobs_to_logitsrbs?U#Jyy$u{{J;'??? 99Z  rTT) contravariantR) covariantca]tRt^toRtV3RlRlt]R V3RlRll4t]R V3RlRll4tR V3R lR lltR tVt R#) rz Used as a decorator for lazy loading of class attributes. This uses a non-data descriptor that calls the wrapped method to compute the property on first call; thereafter replacing the wrapped method into an instance attribute. c<<V^8dQhRS[S[.S[3,RR/#rwrappedrNrrcre)r __classdict__s"rrlazy_property.__annotate__s&&&!a 0&T&rc(Wn\W4R#r)rjrselfrjs&&r__init__lazy_property.__init__s)0 t%rNc(<V^8dQhRRRS[RR/#)rinstanceNobj_typerz!_lazy_property_and_property[T, R])r)rrls"rrrms%222(+2 ,2rcR#rrprtrus&&&r__get__lazy_property.__get__s/2rc,<V^8dQhRS[RS[RS[/#)rrtrur)rcrre)rrls"rrrmsBBBSBABrcR#rrwrxs&&&rryrzs?Brc8<V^8dQhRS[R,RS[RR/#)rrtNrurz%R | _lazy_property_and_property[T, R])rcr)rrls"rrrms)D,/ 0rc Vf\VP4#\P!4;_uu_4VPV4pRRR4\ WPP X4V# +'giL2;ir)_lazy_property_and_propertyrjr- enable_gradsetattr__name__)rprtrur2s&&& rryrzs]  .t||< <    LL*E!,,//7 ! s A33 B )rjr) r __module__ __qualname____firstlineno____doc__rqr ry__static_attributes____classdictcell__rls@rrrsO&&222BBBrc6a]tRt^toRtV3RlRltRtVtR#)rzWe want lazy properties to look like multiple things. * property when Sphinx autodoc looks * lazy_property when Distribution validate_args looks c<<V^8dQhRS[S[.S[3,RR/#rirk)rrls"rr(_lazy_property_and_property.__annotate__s&))!a 0)T)rc0\PW4R#r)propertyrqros&&rrq$_lazy_property_and_property.__init__s$(rrwN)rrrrrrqrrrs@rrrs ))rrc<V^8dQhR\R\R\/#)rmatdiagrrC)rs"rrrs!  F # f rc dVPR,p\PP4'g+W)8gW8d\ RV RV) RV^, R24h\P !W P R7pW3PR^4V^,,8pVRV3,pV#)z Convert a `D x D` matrix or a batch of matrices into a (batched) vector which comprises of lower triangular elements from the matrix in row order. zdiag (z) provided is outside [z, z].r).rE)r6r-r9r:r+aranger)view)rrnr tril_maskvecs&& rtril_matrix_to_vecrs " A 88 & & ( (dRi496$'>rd"QUG2NOO \\!JJ /FR+tax88I c9n C Jrc<V^8dQhR\R\R\/#)rrrrrC)rs"rrrs!F#frc^^V,,)^^V,,^,^VPR,,,^\V4,V^,,,R,,^, p\P!VP4P p\P P4'g?\V4V, V8d(\RVPR, R2R,4h\V\P4'd\VP44M \V4pVPVPRR\P!W"34,4p\P!W P R7pWUP#R^4V^,,8pWRV3&V#) 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. g?zThe size of last dimension is z which cannot be expressed as z3the lower triangular part of a square D x D matrix.Nr.rE)r6absr-rYr'rZr9r:roundr+r r item new_zerosSizerr)r)rrrrZrrrs&& rvec_to_tril_matrixrsT a$h, DLQ SYYr]!2 2QT]dQh5O OTW W X  A ++cii $ $C 88 & & ( (eAhlS.@,SYYr]O;Y ZC D  &a66affhE!HA -- #2QF);; rs..$99  *77 65  ,F D 1 %-4 ! Ct$ C4 GAqDM>)-1"5x) r