+ QV-j8Rt^RIHt^RIt^RIHt^RIHtHtHtH t H t H t H t !RR] 4t !RR]P4t!R R ]P4t!R R 4t!R R]4t!RR]4t!RR]4tR#)z: Time series distributional output classes and utilities. )CallableN)nn)AffineTransform Distribution IndependentNegativeBinomialNormalStudentTTransformedDistributioncraa]tRt^"toRV3RlV3Rlllt]R4t]R4t]R4tRt Vt V;t #)AffineTransformedc <V^8dQhRS[/#)base_distributionr)format __classdict__s"o/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/transformers/time_series_utils.py __annotate__AffineTransformed.__annotate__#stt,tc <VfRMTVnVfRMTVn\SV` V\ VPVPVR7.4R#)Ng?locscale event_dim)rrsuper__init__r)selfrrrr __class__s&&&&&rrAffineTransformed.__init__#sE!MSu +33 *_QUQ[Q[gp-q,rsrc rVPPVP,VP,#)z' Returns the mean of the distribution. ) base_distmeanrrrs&rr$AffineTransformed.mean)s& ~~""TZZ/$((::rc ^VPPVP^,,#)z+ Returns the variance of the distribution. )r#variancerr%s&rr(AffineTransformed.variance0s! ~~&&Q66rc 6VPP4#)z5 Returns the standard deviation of the distribution. )r(sqrtr%s&rstddevAffineTransformed.stddev7s }}!!##r)rr)NN) __name__ __module__ __qualname____firstlineno__rpropertyr$r(r,__static_attributes____classdictcell__ __classcell__r rs@@rr r "sRtt ;; 77 $$$rr cPaa]tRt^?toV3RlV3RlltV3RlRltRtVtV;t#)ParameterProjectionc ~<V^8dQhRS[RS[S[S[3,RS[RS[S[P ,3,RR/#)r in_featuresargs_dim domain_map.returnN)intdictstrrtupletorchTensor)rrs"rr ParameterProjection.__annotate__@sL%%%*.sCx.%FNsTYZ_ZfZfTgOgFh% %rc <\SV`!R/VBW n\P!VP 4Uu.uFp\P !W4NK up4VnW0nR#uupi)N) rrr<r ModuleListvaluesLinearprojr=)rr;r<r=kwargsdimr s&&&&, rrParameterProjection.__init__@sV "6" MM(//J["\J[3299[#>J["\] $#]sA-c^<V^8dQhRS[PRS[S[P,/#rxr>)rCrDrB)rrs"rrrEHs(222% *=2rclVPUu.uF q"!V4NK ppVP!V!#uupiN)rKr=)rrQrKparams_unboundeds&& rforwardParameterProjection.forwardHs404 : DG : 011;s1)r<r=rK r/r0r1r2rrUr4r5r6r7s@@rr9r9?s%%222rr9c8aa]tRt^NtoV3RltRtRtVtV;t#) LambdaLayerc0<\SV`4WnR#rS)rrfunction)rr[r s&&rrLambdaLayer.__init__Os  rc*VP!V.VO5!#rSr[)rrQargss&&*rrULambdaLayer.forwardSs}}Q&&&rr^rWr7s@@rrYrYNs!''rrYca]tRt^WtoRV3RlRlltRtRV3RlRllt]V3RlRl4t]V3R lR l4t ]V3R lR l4t V3R lRlt V3RlRlt ] V3RlRl4tV3RltRtVtR#)DistributionOutputc$<V^8dQhRS[RR/#)rrMr>Nr?)rrs"rrDistributionOutput.__annotate__\sKKCKKrcWnVPUu/uFq"WPV,,bK upVnR#uupirS)rMr<)rrMks&& rrDistributionOutput.__init__\s3<@MMJMqC--"222MJ Js"AcxVP^8XdVP!V!#\VP!V!^4#rMdistribution_classr)r distr_argss&&r_base_distribution%DistributionOutput._base_distribution`s6 88q=**J7 7t66 CQG GrNcp<V^8dQhRS[PR,RS[PR,RS[/#rrNrr>rCrDr)rrs"rrrefsB \ \\\D  \||d" \  \rcfVPV4pVfVfV#\WBW0PR7#)Nr)ror r)rrnrrdistrs&&&& r distributionDistributionOutput.distributionfs3 '' 3 ;5=L$U5NN[ [rc <V^8dQhRS[/#rr>)rB)rrs"rrress44U4rc BVP^8XdR#VP3#)z_ Shape of each individual event contemplated by the distributions that this object constructs. rG)rMr%s&r event_shapeDistributionOutput.event_shapers XX]r3 3rc <V^8dQhRS[/#ryrd)rrs"rrrezs%%3%rc ,\VP4#)zx Number of event dimensions, i.e., length of the `event_shape` tuple, of the distributions that this object constructs. )lenr{r%s&rrDistributionOutput.event_dimys 4##$$rc <V^8dQhRS[/#ry)float)rrs"rrres%rc R#)z A float that will have a valid numeric value when computing the log-loss of the corresponding distribution. By default 0.0. This value will be used when padding data series. rrGr%s&rvalue_in_support#DistributionOutput.value_in_supports rc:<V^8dQhRS[RS[P/#)rr;r>)r?rModule)rrs"rrres  C BII rc X\VVP\VP4R7#)zn Return the parameter projection layer that maps the input to the appropriate parameters of the distribution. )r;r<r=)r9r<rYr=)rr;s&&rget_parameter_projection+DistributionOutput.get_parameter_projections'##]]"4??3  rc4<V^8dQhRS[P/#)rr_rCrD)rrs"rrres$$ $rc \4h)z Converts arguments to the right shape and domain. The domain depends on the type of distribution, while the correct shape is obtained by reshaping the trailing axis in such a way that the returned tensors define a distribution of the right event_shape. )NotImplementedError)rr_s&*rr=DistributionOutput.domain_maps "##rcN<V^8dQhRS[PRS[P/#rPr)rrs"rrres#==ell=u||=rc V\P!\P!V4R,4,R, #)z Helper to map inputs to the positive orthant by applying the square-plus operation. Reference: https://twitter.com/jon_barron/status/1387167648669048833 g@@)rCr+square)rQs&r squareplusDistributionOutput.squarepluss* EJJu||A455<>"% %zz"~s{{2 b0AAArcH<V^8dQh/S[S[S[3,;R&S[;R&#rr<rmr@rAr?r)rrs"rrrs' 38n>  ' rrGN) r/r0r1r2__doc__r<r rm classmethodr=rr4r5rs@rrrs?!%a7A>H'BBrrc\a]tRt^toRtR^R^/t]t]V3RlRl4t V3Rlt Rt Vt R#) NormalOutputz# Normal distribution output class. rrcN<V^8dQhRS[PRS[P/#)rrrr)rrs"rrNormalOutput.__annotate__s#22U\\2%,,2rcVPV4P\P!VP4P 4pVP R4VP R43#rkrr)rrrs&&&rr=NormalOutput.domain_mapsJu%// EKK0H0L0LM{{2 b 111rcH<V^8dQh/S[S[S[3,;R&S[;R&#rr)rrs"rrrs' 38n5  % rrGN) r/r0r1r2rr<rrmrr=rr4r5rs@rrrs9!&q'15H%22rrca]tRt^toRtR^R^/t]t]V3RlRl4t V3RlRlt R V3R lR llt V3R lt R t VtR#)NegativeBinomialOutputz. Negative Binomial distribution output class. total_countlogitscN<V^8dQhRS[PRS[P/#)rrrr)rrs"rr#NegativeBinomialOutput.__annotate__s#;;U\\;5<<;rchVPV4pVPR4VPR43#r)rr)rrrs&&&rr=!NegativeBinomialOutput.domain_maps/nn[1 ""2&r(:::rc <V^8dQhRS[/#ryr)rrs"rrrscc crcVwr#VP^8XdVPW#R7#\VPW#R7^4#)rk)rrrl)rrnrrs&& rro)NegativeBinomialOutput._base_distributionsE( 88q=**{*R Rt66;6^`ab brNcp<V^8dQhRS[PR,RS[PR,RS[/#rrrs)rrs"rrrs8 > >$||d2 >BG,,QUBU >  >rcbVwrEVeWSP4, pVPWE34#rS)logro)rrnrrrrs&&&& rrv#NegativeBinomialOutput.distributions4)   iik !F&& '<==rcH<V^8dQh/S[S[S[3,;R&S[;R&#rr)rrs"rrrs' 38n>  / rrGr)r/r0r1r2rr<rrmrr=rorvrr4r5rs@rrrsO!.q(A>H/;;cc > >/rr)rcollections.abcrrCrtorch.distributionsrrrrrr r r rr9rYrbrrrrGrrrs% $/$: 2")) 2'"))'J=J=Z B' B 2% 2 >/ >r