ambiser
/
human
mirror of https://github.com/vladmandic/human
9
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
human/dist/human.esm.js

4950 lines
1.3 MiB
Raw Blame History

/*
Human library
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
var F8=Object.create,vh=Object.defineProperty,M8=Object.getPrototypeOf,O8=Object.prototype.hasOwnProperty,$8=Object.getOwnPropertyNames,D8=Object.getOwnPropertyDescriptor;var eg=e=>vh(e,"__esModule",{value:!0});var ot=(e,t)=>()=>(t||(t={exports:{}},e(t.exports,t)),t.exports),tg=(e,t)=>{eg(e);for(var n in t)vh(e,n,{get:t[n],enumerable:!0})},z8=(e,t,n)=>{if(eg(e),t&&typeof t=="object"||typeof t=="function")for(let r of $8(t))!O8.call(e,r)&&r!=="default"&&vh(e,r,{get:()=>t[r],enumerable:!(n=D8(t,r))||n.enumerable});return e},Pe=e=>e&&e.__esModule?e:z8(vh(e!=null?F8(M8(e)):{},"default",{value:e,enumerable:!0}),e);var Vv=ot(m0=>{var Pv=6;function mae(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let r=0;r<t.strides.length;r++){let a=t.strides[r],s=Math.floor((e+a-1)/a),i=Math.floor((e+a-1)/a),o=t.anchors[r];for(let l=0;l<s;l++){let c=a*(l+.5);for(let u=0;u<i;u++){let h=a*(u+.5);for(let p=0;p<o;p++)n.push([h,c])}}}return n}var Lv=e=>{e.startEndTensor.dispose(),e.startPoint.dispose(),e.endPoint.dispose()},Wv=e=>({startEndTensor:e,startPoint:Te(e,[0,0],[-1,2]),endPoint:Te(e,[0,2],[-1,2])}),Bv=(e,t)=>{let n=L(e.startPoint,t),r=L(e.endPoint,t),a=ii([n,r],1);return Wv(a)};function Aae(e,t,n){let r=Te(e,[0,1],[-1,2]),a=se(r,t),s=Te(e,[0,3],[-1,2]),i=be(s,n),o=be(a,n),l=be(i,2),c=Ae(o,l),u=se(o,l),h=L(c,n),p=L(u,n);return ii([h,p],1)}function yae(e,t){return W(()=>{let n=e.box?e.box:e;return Bv(n,t).startEndTensor.squeeze()})}var s2=class{constructor(t,n){this.blazeFaceModel=t,this.width=n.face.detector.inputSize,this.height=n.face.detector.inputSize,this.anchorsData=mae(n.face.detector.inputSize),this.anchors=yn(this.anchorsData),this.inputSize=Bt([this.width,this.height]),this.config=n,this.scaleFaces=.8}async getBoundingBoxes(t){if(!t||t.isDisposedInternal||t.shape.length!==4||t.shape[1]<1||t.shape[2]<1)return null;let[n,r,a]=W(()=>{let h=t.resizeBilinear([this.width,this.height]),p=Ae(h.div(127.5),1),d=this.blazeFaceModel.predict(p),f;if(Array.isArray(d)){let g=d.sort((b,N)=>b.size-N.size),_=rt([g[0],g[2]],2),x=rt([g[1],g[3]],2);f=rt([x,_],1).squeeze(0)}else f=d.squeeze();let m=Aae(f,this.anchors,this.inputSize),A=Te(f,[0,0],[-1,1]),y=In(A).squeeze();return[f,m,y]}),s=await at.nonMaxSuppressionAsync(r,a,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),i=s.arraySync();s.dispose();let l=i.map(h=>Te(r,[h,0],[1,-1])).map(h=>{let p=h.arraySync();return h.dispose(),p}),c=a.dataSync(),u=[];for(let h=0;h<l.length;h++){let p=i[h],d=c[p];if(d>this.config.face.detector.minConfidence){let f=Wv(l[h]),m=this.anchorsData[p],A=W(()=>Te(n,[p,Pv-1],[1,-1]).squeeze().reshape([Pv,-1]));u.push({box:f,landmarks:A,anchor:m,confidence:d})}}return n.dispose(),r.dispose(),a.dispose(),n.dispose(),{boxes:u,scaleFactor:[t.shape[2]/this.width,t.shape[1]/this.height]}}async estimateFaces(t){let{boxes:n,scaleFactor:r}=await this.getBoundingBoxes(t),a=[];for(let s of n){let i=s.landmarks.arraySync(),o=yae(s,r),l=Bv.arraySync(),c=s.probability.arraySync(),u=s.anchor,[h,p]=r,d=i.map(m=>[(m[0]+u[0])*h,(m[1]+u[1])*p]),f={topLeft:l.slice(0,2),bottomRight:l.slice(2),landmarks:d,probability:c};Lv(s.box),s.landmarks.dispose(),s.probability.dispose(),o.dispose(),a.push(f)}return a}};async function gae(e){let t=await Ht(e.face.detector.modelPath,{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new s2(t,e);return Fe(`load model: ${e.face.detector.modelPath.match(/\/(.*)\./)[1]}`),n}m0.load=gae;m0.BlazeFaceModel=s2;m0.disposeBox=Lv});var Uv=ot(Ci=>{function xae(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:r}}Ci.scaleBoxCoordinates=xae;function i2(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}Ci.getBoxSize=i2;function o2(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}Ci.getBoxCenter=o2;function wae(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return at.cropAndResize(t,s,[0],n)}Ci.cutBoxFromImageAndResize=wae;function _ae(e,t=1.5){let n=o2(e),r=i2(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}Ci.enlargeBox=_ae;function bae(e){let t=o2(e),n=i2(e),a=Math.max(...n)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}Ci.squarifyBox=bae});var Xv=ot(kr=>{kr.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function jv(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}kr.normalizeRadians=jv;function vae(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return jv(n)}kr.computeRotation=vae;function kae(e){return e*180/Math.PI}kr.radToDegrees=kae;function Hv(e,t){return[[1,0,e],[0,1,t],[0,0,1]]}function Hl(e,t){let n=0;for(let r=0;r<e.length;r++)n+=e[r]*t[r];return n}kr.dot=Hl;function Gv(e,t){let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n}kr.getColumnFrom2DArr=Gv;function qv(e,t){let n=[],r=e.length;for(let a=0;a<r;a++){n.push([]);for(let s=0;s<r;s++)n[a].push(Hl(e[a],Gv(t,s)))}return n}function Iae(e,t){let n=Math.cos(e),r=Math.sin(e),a=[[n,-r,0],[r,n,0],[0,0,1]],s=Hv(t[0],t[1]),i=qv(s,a),o=Hv(-t[0],-t[1]);return qv(i,o)}kr.buildRotationMatrix=Iae;function Nae(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],r=[-Hl(t[0],n),-Hl(t[1],n)];return[t[0].concat(r[0]),t[1].concat(r[1]),[0,0,1]]}kr.invertTransformMatrix=Nae;function Sae(e,t){return[Hl(e,t[0]),Hl(e,t[1])]}kr.rotatePoint=Sae;function Tae(e,t){return Math.sqrt((e[0]-t[0])**2+(e[1]-t[1])**2)}kr.xyDistanceBetweenPoints=Tae});var l2=ot(Ir=>{var Eae={silhouette:[10,338,297,332,284,251,389,356,454,323,361,288,397,365,379,378,400,377,152,148,176,149,150,136,172,58,132,93,234,127,162,21,54,103,67,109],lipsUpperOuter:[61,185,40,39,37,0,267,269,270,409,291],lipsLowerOuter:[146,91,181,84,17,314,405,321,375,291],lipsUpperInner:[78,191,80,81,82,13,312,311,310,415,308],lipsLowerInner:[78,95,88,178,87,14,317,402,318,324,308],rightEyeUpper0:[246,161,160,159,158,157,173],rightEyeLower0:[33,7,163,144,145,153,154,155,133],rightEyeUpper1:[247,30,29,27,28,56,190],rightEyeLower1:[130,25,110,24,23,22,26,112,243],rightEyeUpper2:[113,225,224,223,222,221,189],rightEyeLower2:[226,31,228,229,230,231,232,233,244],rightEyeLower3:[143,111,117,118,119,120,121,128,245],rightEyebrowUpper:[156,70,63,105,66,107,55,193],rightEyebrowLower:[35,124,46,53,52,65],rightEyeIris:[473,474,475,476,477],leftEyeUpper0:[466,388,387,386,385,384,398],leftEyeLower0:[263,249,390,373,374,380,381,382,362],leftEyeUpper1:[467,260,259,257,258,286,414],leftEyeLower1:[359,255,339,254,253,252,256,341,463],leftEyeUpper2:[342,445,444,443,442,441,413],leftEyeLower2:[446,261,448,449,450,451,452,453,464],leftEyeLower3:[372,340,346,347,348,349,350,357,465],leftEyebrowUpper:[383,300,293,334,296,336,285,417],leftEyebrowLower:[265,353,276,283,282,295],leftEyeIris:[468,469,470,471,472],midwayBetweenEyes:[168],noseTip:[1],noseBottom:[2],noseRightCorner:[98],noseLeftCorner:[327],rightCheek:[205],leftCheek:[425]},Cae=[{key:"EyeUpper0",indices:[9,10,11,12,13,14,15]},{key:"EyeUpper1",indices:[25,26,27,28,29,30,31]},{key:"EyeUpper2",indices:[41,42,43,44,45,46,47]},{key:"EyeLower0",indices:[0,1,2,3,4,5,6,7,8]},{key:"EyeLower1",indices:[16,17,18,19,20,21,22,23,24]},{key:"EyeLower2",indices:[32,33,34,35,36,37,38,39,40]},{key:"EyeLower3",indices:[54,55,56,57,58,59,60,61,62]}],A0=[[.499976992607117,.652534008026123],[.500025987625122,.547487020492554],[.499974012374878,.602371990680695],[.482113003730774,.471979022026062],[.500150978565216,.527155995368958],[.499909996986389,.498252987861633],[.499523013830185,.40106201171875],[.289712011814117,.380764007568359],[.499954998493195,.312398016452789],[.499987006187439,.269918978214264],[.500023007392883,.107050001621246],[.500023007392883,.666234016418457],[.5000159740448,.679224014282227],[.500023007392883,.692348003387451],[.499976992607117,.695277988910675],[.499976992607117,.70593398809433],[.499976992607117,.719385027885437],[.499976992607117,.737019002437592],[.499967992305756,.781370997428894],[.499816000461578,.562981009483337],[.473773002624512,.573909997940063],[.104906998574734,.254140973091125],[.365929991006851,.409575998783112],[.338757991790771,.41302502155304],[.311120003461838,.409460008144379],[.274657994508743,.389131009578705],[.393361985683441,.403706014156342],[.345234006643295,.344011008739471],[.370094001293182,.346076011657715],[.319321990013123,.347265005111694],[.297903001308441,.353591024875641],[.24779200553894,.410809993743896],[.396889001131058,.842755019664764],[.280097991228104,.375599980354309],[.106310002505779,.399955987930298],[.2099249958992,.391353011131287],[.355807989835739,.534406006336212],[.471751004457474,.65040397644043],[.474155008792877,.680191993713379],[.439785003662109,.657229006290436],[.414617002010345,.66654098033905],[.450374007225037,.680860996246338],[.428770989179611,.682690978050232],[.374971002340317,.727805018424988],[.486716985702515,.547628998756409],[.485300987958908,.527395009994507],[.257764995098114,.314490020275116],[.401223003864288,.455172002315521],[.429818987846375,.548614978790283],[.421351999044418,.533740997314453],[.276895999908447,.532056987285614],[.483370006084442,.499586999416351],[.33721199631691,.282882988452911],[.296391993761063,.293242990970612],[.169294998049736,.193813979625702],[.447580009698868,.302609980106354],[.392390012741089,.353887975215912],[.354490011930466,.696784019470215],[.067304998636246,.730105042457581],[.442739009857178,.572826027870178],[.457098007202148,.584792017936707],[.381974011659622,.694710969924927],[.392388999462128,.694203019142151],[.277076005935669,.271932005882263],[.422551989555359,.563233017921448],[.385919004678726,.281364023685455],[.383103013038635,.255840003490448],[.331431001424789,.119714021682739],[.229923993349075,.232002973556519],[.364500999450684,.189113974571228],[.229622006416321,.299540996551514],[.173287004232407,.278747975826263],[.472878992557526,.666198015213013],[.446828007698059,.668527007102966],[.422762006521225,.673889994621277],[.445307999849319,.580065965652466],[.388103008270264,.693961024284363],[.403039008378983,.706539988517761],[.403629004955292,.693953037261963],[.460041999816895,.557139039039612],[.431158006191254,.692366003990173],[.452181994915009,.692366003990173],[.475387006998062,.692366003990173],[.465828001499176,.779190003871918],[.472328990697861,.736225962638855],[.473087012767792,.717857003211975],[.473122000694275,.704625964164734],[.473033010959625,.695277988910675],[.427942007780075,.695277988910675],[.426479011774063,.703539967536926],[.423162013292313,.711845993995667],[.4183090031147,.720062971115112],[.390094995498657,.639572978019714],[.013953999616206,.560034036636353],[.499913990497589,.58014702796936],[.413199990987778,.69539999961853],[.409626007080078,.701822996139526],[.468080013990402,.601534962654114],[.422728985548019,.585985004901886],[.463079988956451,.593783974647522],[.37211999297142,.47341400384903],[.334562003612518,.496073007583618],[.411671012639999,.546965003013611],[.242175996303558,.14767599105835],[.290776997804642,.201445996761322],[.327338010072708,.256527006626129],[.399509996175766,.748921036720276],[.441727995872498,.261676013469696],[.429764986038208,.187834024429321],[.412198007106781,.108901023864746],[.288955003023148,.398952007293701],[.218936994671822,.435410976409912],[.41278201341629,.398970007896423],[.257135003805161,.355440020561218],[.427684992551804,.437960982322693],[.448339998722076,.536936044692993],[.178560003638268,.45755398273468],[.247308000922203,.457193970680237],[.286267012357712,.467674970626831],[.332827985286713,.460712015628815],[.368755996227264,.447206974029541],[.398963987827301,.432654976844788],[.476410001516342,.405806005001068],[.189241006970406,.523923993110657],[.228962004184723,.348950982093811],[.490725994110107,.562400996685028],[.404670000076294,.485132992267609],[.019469000399113,.401564002037048],[.426243007183075,.420431017875671],[.396993011236191,.548797011375427],[.266469985246658,.376977026462555],[.439121007919312,.51895797252655],[.032313998788595,.644356966018677],[.419054001569748,.387154996395111],[.462783008813858,.505746960639954],[.238978996872902,.779744982719421],[.198220998048782,.831938028335571],[.107550002634525,.540755033493042],[.183610007166862,.740257024765015],[.134409993886948,.333683013916016],[.385764002799988,.883153975009918],[.490967005491257,.579378008842468],[.382384985685349,.508572995662689],[.174399003386497,.397670984268188],[.318785011768341,.39623498916626],[.343364000320435,.400596976280212],[.396100014448166,.710216999053955],[.187885001301765,.588537991046906],[.430987000465393,.944064974784851],[.318993002176285,.898285031318665],[.266247987747192,.869701027870178],[.500023007392883,.190576016902924],[.499976992607117,.954452991485596],[.366169989109039,.398822009563446],[.393207013607025,.39553701877594],[.410373002290726,.391080021858215],[.194993004202843,.342101991176605],[.388664990663528,.362284004688263],[.365961998701096,.355970978736877],[.343364000320435,.355356991291046],[.318785011768341,.35834002494812],[.301414996385574,.363156020641327],[.058132998645306,.319076001644135],[.301414996385574,.387449026107788],[.499987989664078,.618434011936188],[.415838003158569,.624195992946625],[.445681989192963,.566076993942261],[.465844005346298,.620640993118286],[.49992299079895,.351523995399475],[.288718998432159,.819945991039276],[.335278987884521,.852819979190826],[.440512001514435,.902418971061707],[.128294005990028,.791940987110138],[.408771991729736,.373893976211548],[.455606997013092,.451801002025604],[.499877005815506,.908990025520325],[.375436991453171,.924192011356354],[.11421000212431,.615022003650665],[.448662012815475,.695277988910675],[.4480200111866,.704632043838501],[.447111994028091,.715808033943176],[.444831997156143,.730794012546539],[.430011987686157,.766808986663818],[.406787008047104,.685672998428345],[.400738000869751,.681069016456604],[.392399996519089,.677703022956848],[.367855995893478,.663918972015381],[.247923001646996,.601333022117615],[.452769994735718,.420849978923798],[.43639200925827,.359887003898621],[.416164010763168,.368713974952698],[.413385987281799,.692366003990173],[.228018000721931,.683571994304657],[.468268007040024,.352671027183533],[.411361992359161,.804327011108398],[.499989002943039,.469825029373169],[.479153990745544,.442654013633728],[.499974012374878,.439637005329132],[.432112008333206,.493588984012604],[.499886006116867,.866917014122009],[.49991300702095,.821729004383087],[.456548988819122,.819200992584229],[.344549000263214,.745438992977142],[.37890899181366,.574010014533997],[.374292999505997,.780184984207153],[.319687992334366,.570737957954407],[.357154995203018,.604269981384277],[.295284003019333,.621580958366394],[.447750002145767,.862477004528046],[.410986006259918,.508723020553589],[.31395098567009,.775308012962341],[.354128003120422,.812552988529205],[.324548006057739,.703992962837219],[.189096003770828,.646299958229065],[.279776990413666,.71465802192688],[.1338230073452,.682700991630554],[.336768001317978,.644733011722565],[.429883986711502,.466521978378296],[.455527991056442,.548622965812683],[.437114000320435,.558896005153656],[.467287987470627,.529924988746643],[.414712011814117,.335219979286194],[.37704598903656,.322777986526489],[.344107985496521,.320150971412659],[.312875986099243,.32233202457428],[.283526003360748,.333190023899078],[.241245999932289,.382785975933075],[.102986000478268,.468762993812561],[.267612010240555,.424560010433197],[.297879010438919,.433175981044769],[.333433985710144,.433878004550934],[.366427004337311,.426115989685059],[.396012008190155,.416696012020111],[.420121014118195,.41022801399231],[.007561000064015,.480777025222778],[.432949006557465,.569517970085144],[.458638995885849,.479089021682739],[.473466008901596,.545744001865387],[.476087987422943,.563830018043518],[.468472003936768,.555056989192963],[.433990985155106,.582361996173859],[.483518004417419,.562983989715576],[.482482999563217,.57784903049469],[.42645001411438,.389798998832703],[.438998997211456,.39649498462677],[.450067013502121,.400434017181396],[.289712011814117,.368252992630005],[.276670008897781,.363372981548309],[.517862021923065,.471948027610779],[.710287988185883,.380764007568359],[.526226997375488,.573909997940063],[.895093023777008,.254140973091125],[.634069979190826,.409575998783112],[.661242008209229,.41302502155304],[.688880026340485,.409460008144379],[.725341975688934,.389131009578705],[.606630027294159,.40370500087738],[.654766023159027,.344011008739471],[.629905998706818,.346076011657715],[.680678009986877,.347265005111694],[.702096998691559,.353591024875641],[.75221198797226,.410804986953735],[.602918028831482,.842862963676453],[.719901978969574,.375599980354309],[.893692970275879,.399959981441498],[.790081977844238,.391354024410248],[.643998026847839,.534487962722778],[.528249025344849,.65040397644043],[.525849997997284,.680191040039062],[.560214996337891,.657229006290436],[.585384011268616,.66654098033905],[.549625992774963,.680860996246338],[.57122802734375,.682691991329193],[.624852001667023,.72809898853302],[.513050019741058,.547281980514526],[.51509702205658,.527251958847046],[.742246985435486,.314507007598877],[.598631024360657,.454979002475739],[.570338010787964,.548575043678284],[.578631997108459,.533622980117798],[.723087012767792,.532054007053375],[.516445994377136,.499638974666595],[.662801027297974,.282917976379395],[.70362401008606,.293271005153656],[.830704987049103,.193813979625702],[.552385985851288,.302568018436432],[.607609987258911,.353887975215912],[.645429015159607,.696707010269165],[.932694971561432,.730105042457581],[.557260990142822,.572826027870178],[.542901992797852,.584792017936707],[.6180260181427,.694710969924927],[.607590973377228,.694203019142151],[.722943007946014,.271963000297546],[.577413976192474,.563166975975037],[.614082992076874,.281386971473694],[.616907000541687,.255886018276215],[.668509006500244,.119913995265961],[.770092010498047,.232020974159241],[.635536015033722,.189248979091644],[.77039098739624,.299556016921997],[.826722025871277,.278755009174347],[.527121007442474,.666198015213013],[.553171992301941,.668527007102966],[.577238023281097,.673889994621277],[.554691970348358,.580065965652466],[.611896991729736,.693961024284363],[.59696102142334,.706539988517761],[.596370995044708,.693953037261963],[.539958000183105,.557139039039612],[.568841993808746,.692366003990173],[.547818005084991,.692366003990173],[.52461302280426,.692366003990173],[.534089982509613,.779141008853912],[.527670979499817,.736225962638855],[.526912987232208,.717857003211975],[.526877999305725,.704625964164734],[.526966989040375,.695277988910675],[.572058022022247,.695277988910675],[.573521018028259,.703539967536926],[.57683801651001,.711845993995667],[.581691026687622,.720062971115112],[.609944999217987,.639909982681274],[.986046016216278,.560034036636353],[.5867999792099,.69539999961853],[.590372025966644,.701822996139526],[.531915009021759,.601536989212036],[.577268004417419,.585934996604919],[.536915004253387,.593786001205444],[.627542972564697,.473352015018463],[.665585994720459,.495950996875763],[.588353991508484,.546862006187439],[.757824003696442,.14767599105835],[.709249973297119,.201507985591888],[.672684013843536,.256581008434296],[.600408971309662,.74900496006012],[.55826598405838,.261672019958496],[.570303976535797,.187870979309082],[.588165998458862,.109044015407562],[.711045026779175,.398952007293701],[.781069993972778,.435405015945435],[.587247014045715,.398931980133057],[.742869973182678,.355445981025696],[.572156012058258,.437651991844177],[.55186802148819,.536570012569427],[.821442008018494,.457556009292603],[.752701997756958,.457181990146637],[.71375697851181,.467626988887787],[.66711300611496,.460672974586487],[.631101012229919,.447153985500336],[.6008620262146,.432473003864288],[.523481011390686,.405627012252808],[.810747981071472,.523926019668579],[.771045982837677,.348959028720856],[.509127020835876,.562718033790588],[.595292985439301,.485023975372314],[.980530977249146,.401564002037048],[.573499977588654,.420000016689301],[.602994978427887,.548687994480133],[.733529984951019,.376977026462555],[.560611009597778,.519016981124878],[.967685997486115,.644356966018677],[.580985009670258,.387160003185272],[.537728011608124,.505385041236877],[.760966002941132,.779752969741821],[.801778972148895,.831938028335571],[.892440974712372,.54076099395752],[.816350996494293,.740260004997253],[.865594983100891,.333687007427216],[.614073991775513,.883246004581451],[.508952975273132,.579437971115112],[.617941975593567,.508316040039062],[.825608015060425,.397674977779388],[.681214988231659,.39623498916626],[.656635999679565,.400596976280212],[.603900015354156,.710216999053955],[.81208598613739,.588539004325867],[.56801301240921,.944564998149872],[.681007981300354,.898285031318665],[.733752012252808,.869701027870178],[.633830010890961,.398822009563446],[.606792986392975,.39553701877594],[.589659988880157,.391062021255493],[.805015981197357,.342108011245728],[.611334979534149,.362284004688263],[.634037971496582,.355970978736877],[.656635999679565,.355356991291046],[.681214988231659,.35834002494812],[.698584973812103,.363156020641327],[.941866993904114,.319076001644135],[.698584973812103,.387449026107788],[.584177017211914,.624107003211975],[.554318010807037,.566076993942261],[.534153997898102,.62064003944397],[.711217999458313,.819975018501282],[.664629995822906,.852871000766754],[.559099972248077,.902631998062134],[.871706008911133,.791940987110138],[.591234028339386,.373893976211548],[.544341027736664,.451583981513977],[.624562978744507,.924192011356354],[.88577002286911,.615028977394104],[.551338016986847,.695277988910675],[.551980018615723,.704632043838501],[.552887976169586,.715808033943176],[.555167973041534,.730794012546539],[.569944024085999,.767035007476807],[.593203008174896,.685675978660583],[.599261999130249,.681069016456604],[.607599973678589,.677703022956848],[.631937980651855,.663500010967255],[.752032995223999,.601315021514893],[.547226011753082,.420395016670227],[.563543975353241,.359827995300293],[.583841025829315,.368713974952698],[.586614012718201,.692366003990173],[.771915018558502,.683578014373779],[.531597018241882,.352482974529266],[.588370978832245,.804440975189209],[.52079701423645,.442565023899078],[.567984998226166,.493479013442993],[.543282985687256,.819254994392395],[.655317008495331,.745514988899231],[.621008992195129,.574018001556396],[.625559985637665,.78031200170517],[.680198013782501,.570719003677368],[.64276397228241,.604337990283966],[.704662978649139,.621529996395111],[.552012026309967,.862591981887817],[.589071989059448,.508637011051178],[.685944974422455,.775357007980347],[.645735025405884,.812640011310577],[.675342977046967,.703978002071381],[.810858011245728,.646304965019226],[.72012197971344,.714666962623596],[.866151988506317,.682704985141754],[.663187026977539,.644596993923187],[.570082008838654,.466325998306274],[.544561982154846,.548375964164734],[.562758982181549,.558784961700439],[.531987011432648,.530140042304993],[.585271000862122,.335177004337311],[.622952997684479,.32277899980545],[.655896008014679,.320163011550903],[.687132000923157,.322345972061157],[.716481983661652,.333200991153717],[.758756995201111,.382786989212036],[.897013008594513,.468769013881683],[.732392013072968,.424547016620636],[.70211398601532,.433162987232208],[.66652500629425,.433866024017334],[.633504986763,.426087975502014],[.603875994682312,.416586995124817],[.579657971858978,.409945011138916],[.992439985275269,.480777025222778],[.567192018032074,.569419980049133],[.54136598110199,.478899002075195],[.526564002037048,.546118021011353],[.523913025856018,.563830018043518],[.531529009342194,.555056989192963],[.566035985946655,.582329034805298],[.51631098985672,.563053965568542],[.5174720287323,.577877044677734],[.573594987392426,.389806985855103],[.560697972774506,.395331978797913],[.549755990505219,.399751007556915],[.710287988185883,.368252992630005],[.723330020904541,.363372981548309]],Rae=[127,34,139,11,0,37,232,231,120,72,37,39,128,121,47,232,121,128,104,69,67,175,171,148,157,154,155,118,50,101,73,39,40,9,151,108,48,115,131,194,204,211,74,40,185,80,42,183,40,92,186,230,229,118,202,212,214,83,18,17,76,61,146,160,29,30,56,157,173,106,204,194,135,214,192,203,165,98,21,71,68,51,45,4,144,24,23,77,146,91,205,50,187,201,200,18,91,106,182,90,91,181,85,84,17,206,203,36,148,171,140,92,40,39,193,189,244,159,158,28,247,246,161,236,3,196,54,68,104,193,168,8,117,228,31,189,193,55,98,97,99,126,47,100,166,79,218,155,154,26,209,49,131,135,136,150,47,126,217,223,52,53,45,51,134,211,170,140,67,69,108,43,106,91,230,119,120,226,130,247,63,53,52,238,20,242,46,70,156,78,62,96,46,53,63,143,34,227,173,155,133,123,117,111,44,125,19,236,134,51,216,206,205,154,153,22,39,37,167,200,201,208,36,142,100,57,212,202,20,60,99,28,158,157,35,226,113,160,159,27,204,202,210,113,225,46,43,202,204,62,76,77,137,123,116,41,38,72,203,129,142,64,98,240,49,102,64,41,73,74,212,216,207,42,74,184,169,170,211,170,149,176,105,66,69,122,6,168,123,147,187,96,77,90,65,55,107,89,90,180,101,100,120,63,105,104,93,137,227,15,86,85,129,102,49,14,87,86,55,8,9,100,47,121,145,23,22,88,89,179,6,122,196,88,95,96,138,172,136,215,58,172,115,48,219,42,80,81,195,3,51,43,146,61,171,175,199,81,82,38,53,46,225,144,163,110,246,33,7,52,65,66,229,228,117,34,127,234,107,108,69,109,108,151,48,64,235,62,78,191,129,209,126,111,35,143,163,161,246,117,123,50,222,65,52,19,125,141,221,55,65,3,195,197,25,7,33,220,237,44,70,71,139,122,193,245,247,130,33,71,21,162,153,158,159,170,169,150,188,174,196,216,186,92,144,160,161,2,97,167,141,125,241,164,167,37,72,38,12,145,159,160,38,82,13,63,68,71,226,35,111,158,153,154,101,50,205,206,92,165,209,198,217,165,167,97,220,115,218,133,112,243,239,238,241,214,135,169,190,173,133,171,208,32,125,44,237,86,87,178,85,86,179,84,85,180,83,84,181,201,83,182,137,93,132,76,62,183,61,76,184,57,61,185,212,57,186,214,207,187,34,143,156,79,239,237,123,137,177,44,1,4,201,194,32,64,102,129,213,215,138,59,166,219,242,99,97,2,94,141,75,59,235,24,110,228,25,130,226,23,24,229,22,23,230,26,22,231,112,26,232,189,190,243,221,56,190,28,56,221,27,28,222,29,27,223,30,29,224,247,30,225,238,79,20,166,59,75,60,75,240,147,177,215,20,79,166,187,147,213,112,233,244,233,128,245,128,114,188,114,217,174,131,115,220,217,198,236,198,131,134,177,132,58,143,35,124,110,163,7,228,110,25,356,389,368,11,302,267,452,350,349,302,303,269,357,343,277,452,453,357,333,332,297,175,152,377,384,398,382,347,348,330,303,304,270,9,336,337,278,279,360,418,262,431,304,408,409,310,415,407,270,409,410,450,348,347,422,430,434,313,314,17,306,307,375,387,388,260,286,414,398,335,406,418,364,367,416,423,358,327,251,284,298,281,5,4,373,374,253,307,320,321,425,427,411,421,313,18,321,405,406,320,404,405,315,16,17,426,425,266,377,400,369,322,391,269,417,465,464,386,257,258,466,260,388,456,399,419,284,332,333,417,285,8,346,340,261,413,441,285,327,460,328,355,371,329,392,439,438,382,341,256,429,420,360,364,394,379,277,343,437,443,444,283,275,440,363,431,262,369,297,338,337,273,375,321,450,451,349,446,342,467,293,334,282,458,461,462,276,353,383,308,324,325,276,300,293,372,345,447,382,398,362,352,345,340,274,1,19,456,248,281,436,427,425,381,256,252,269,391,393,200,199,428,266,330,329,287,273,422,250,462,328,258,286,384,265,353,342,387,259,257,424,431,430,342,353,276,273,335,424,292,325,307,366,447,345,271,303,302,423,266,371,294,455,460,279,278,294,271,272,304,432,434,427,272,407,408,394,430,431,395,369,400,334,333,299,351,417,168,352,280,411,325,319,320,295,296,336,319,403,404,330,348,349,293,298,333,323,454,447,15,16,315,358,429,279,14,15,316,285,336,9,329,349,350,374,380,252,318,402,403,6,197,419,318,319,325,367,364,365,435,367,397,344,438,439,272,271,311,195,5,281,273,287,291,396,428,199,311,271,268,283,444,445,373,254,339,263,466,249,282,334,296,449,347,346,264,447,454,336,296,299,338,10,151,278,439,455,292,407,415,358,371,355,340,345,372,390,249,466,346,347,280,442,443,282,19,94,370,441,442,295,248,419,197,263,255,359,440,275,274,300,383,368,351,412,465,263,467,466,301,368,389,380,374,386,395,378,379,412,351,419,436,426,322,373,390,388,2,164,393,370,462,461,164,0,267,302,11,12,374,373,387,268,12,13,293,300,301,446,261,340,385,384,381,330,266,425,426,423,391,429,355,437,391,327,326,440,457,438,341,382,362,459,457,461,434,430,394,414,463,362,396,369,262,354,461,457,316,403,402,315,404,403,314,405,404,313,406,405,421,418,406,366,401,361,306,408,407,291,409,408,287,410,409,432,436,410,434,416,411,264,368,383,309,438,457,352,376,401,274,275,4,421,428,262,294,327,358,433,416,367,289,455,439,462,370,326,2,326,370,305,460,455,254,449,448,255,261,446,253,450,449,252,451,450,256,452,451,341,453,452,413,464,463,441,413,414,258,442,441,257,443,442,259,444,443,260,445,444,467,342,445,459,458,250,289,392,290,290,328,460,376,433,435,250,290,392,411,416,433,341,463,464,453,464,465,357,465,412,343,412,399,360,363,440,437,399,456,420,456,363,401,435,288,372,383,353,339,255,249,448,261,255,133,243,190,133,155,112,33,246,247,33,130,25,398,384,286,362,398,414,362,463,341,263,359,467,263,249,255,466,467,260,75,60,166,238,239,79,162,127,139,72,11,37,121,232,120,73,72,39,114,128,47,233,232,128,103,104,67,152,175,148,173,157,155,119,118,101,74,73,40,107,9,108,49,48,131,32,194,211,184,74,185,191,80,183,185,40,186,119,230,118,210,202,214,84,83,17,77,76,146,161,160,30,190,56,173,182,106,194,138,135,192,129,203,98,54,21,68,5,51,4,145,144,23,90,77,91,207,205,187,83,201,18,181,91,182,180,90,181,16,85,17,205,206,36,176,148,140,165,92,39,245,193,244,27,159,28,30,247,161,174,236,196,103,54,104,55,193,8,111,117,31,221,189,55,240,98,99,142,126,100,219,166,218,112,155,26,198,209,131,169,135,150,114,47,217,224,223,53,220,45,134,32,211,140,109,67,108,146,43,91,231,230,120,113,226,247,105,63,52,241,238,242,124,46,156,95,78,96,70,46,63,116,143,227,116,123,111,1,44,19,3,236,51,207,216,205,26,154,22,165,39,167,199,200,208,101,36,100,43,57,202,242,20,99,56,28,157,124,35,113,29,160,27,211,204,210,124,113,46,106,43,204,96,62,77,227,137,116,73,41,72,36,203,142,235,64,240,48,49,64,42,41,74,214,212,207,183,42,184,210,169,211,140,170,176,104,105,69,193,122,168,50,123,187,89,96,90,66,65,107,179,89,180,119,101,120,68,63,104,234,93,227,16,15,85,209,129,49,15,14,86,107,55,9,120,100,121,153,145,22,178,88,179,197,6,196,89,88,96,135,138,136,138,215,172,218,115,219,41,42,81,5,195,51,57,43,61,208,171,199,41,81,38,224,53,225,24,144,110,105,52,66,118,229,117,227,34,234,66,107,69,10,109,151,219,48,235,183,62,191,142,129,126,116,111,143,7,163,246,118,117,50,223,222,52,94,19,141,222,221,65,196,3,197,45,220,44,156,70,139,188,122,245,139,71,162,145,153,159,149,170,150,122,188,196,206,216,92,163,144,161,164,2,167,242,141,241,0,164,37,11,72,12,144,145,160,12,38,13,70,63,71,31,226,111,157,158,154,36,101,205,203,206,165,126,209,217,98,165,97,237,220,218,237,239,241,210,214,169,140,171,32,241,125,237,179,86,178,180,85,179,181,84,180,182,83,181,194,201,182,177,137,132,184,76,183,185,61,184,186,57,185,216,212,186,192,214,187,139,34,156,218,79,237,147,123,177,45,44,4,208,201,32,98,64,129,192,213,138,235,59,219,141,242,97,97,2,141,240,75,235,229,24,228,31,25,226,230,23,229,231,22,230,232,26,231,233,112,232,244,189,243,189,221,190,222,28,221,223,27,222,224,29,223,225,30,224,113,247,225,99,60,240,213,147,215,60,20,166,192,187,213,243,112,244,244,233,245,245,128,188,188,114,174,134,131,220,174,217,236,236,198,134,215,177,58,156,143,124,25,110,7,31,228,25,264,356,368,0,11,267,451,452,349,267,302,269,350,357,277,350,452,357,299,333,297,396,175,377,381,384,382,280,347,330,269,303,270,151,9,337,344,278,360,424,418,431,270,304,409,272,310,407,322,270,410,449,450,347,432,422,434,18,313,17,291,306,375,259,387,260,424,335,418,434,364,416,391,423,327,301,251,298,275,281,4,254,373,253,375,307,321,280,425,411,200,421,18,335,321,406,321,320,405,314,315,17,423,426,266,396,377,369,270,322,269,413,417,464,385,386,258,248,456,419,298,284,333,168,417,8,448,346,261,417,413,285,326,327,328,277,355,329,309,392,438,381,382,256,279,429,360,365,364,379,355,277,437,282,443,283,281,275,363,395,431,369,299,297,337,335,273,321,348,450,349,359,446,467,283,293,282,250,458,462,300,276,383,292,308,325,283,276,293,264,372,447,346,352,340,354,274,19,363,456,281,426,436,425,380,381,252,267,269,393,421,200,428,371,266,329,432,287,422,290,250,328,385,258,384,446,265,342,386,387,257,422,424,430,445,342,276,422,273,424,306,292,307,352,366,345,268,271,302,358,423,371,327,294,460,331,279,294,303,271,304,436,432,427,304,272,408,395,394,431,378,395,400,296,334,299,6,351,168,376,352,411,307,325,320,285,295,336,320,319,404,329,330,349,334,293,333,366,323,447,316,15,315,331,358,279,317,14,316,8,285,9,277,329,350,253,374,252,319,318,403,351,6,419,324,318,325,397,367,365,288,435,397,278,344,439,310,272,311,248,195,281,375,273,291,175,396,199,312,311,268,276,283,445,390,373,339,295,282,296,448,449,346,356,264,454,337,336,299,337,338,151,294,278,455,308,292,415,429,358,355,265,340,372,388,390,466,352,346,280,295,442,282,354,19,370,285,441,295,195,248,197,457,440,274,301,300,368,417,351,465,251,301,389,385,380,386,394,395,379,399,412,419,410,436,322,387,373,388,326,2,393,354,370,461,393,164,267,268,302,12,386,374,387,312,268,13,298,293,301,265,446,340,380,385,381,280,330,425,322,426,391,420,429,437,393,391,326,344,440,438,458,459,461,364,434,394,428,396,262,274,354,457,317,316,402,316,315,403,315,314,404,314,313,405,313,421,406,323,366,361,292,306,407,306,291,408,291,287,409,287,432,410,427,434,411,372,264,383,459,309,457,366,352,401,1,274,4,418,421,262,331,294,358,435,433,367,392,289,439,328,462,326,94,2,370,289,305,455,339,254,448,359,255,446,254,253,449,253,252,450,252,256,451,256,341,452,414,413,463,286,441,414,286,258,441,258,257,442,257,259,443,259,260,444,260,467,445,309,459,250,305,289,290,305,290,460,401,376,435,309,250,392,376,411,433,453,341,464,357,453,465,343,357,412,437,343,399,344,360,440,420,437,456,360,420,363,361,401,288,265,372,353,390,339,249,339,448,255],Fae=[0,1,36,0,36,17,1,2,41,1,41,36,2,3,31,2,31,41,3,4,48,3,48,31,4,5,48,5,6,48,6,7,59,6,59,48,7,8,58,7,58,59,8,9,56,8,56,57,8,57,58,9,10,55,9,55,56,10,11,54,10,54,55,11,12,54,12,13,54,13,14,35,13,35,54,14,15,46,14,46,35,15,16,45,15,45,46,16,26,45,17,36,18,18,37,19,18,36,37,19,38,20,19,37,38,20,39,21,20,38,39,21,39,27,22,42,23,22,27,42,23,43,24,23,42,43,24,44,25,24,43,44,25,45,26,25,44,45,27,39,28,27,28,42,28,39,29,28,29,42,29,31,30,29,30,35,29,40,31,29,35,47,29,39,40,29,47,42,30,31,32,30,32,33,30,33,34,30,34,35,31,50,32,31,40,41,31,48,49,31,49,50,32,51,33,32,50,51,33,51,34,34,52,35,34,51,52,35,46,47,35,52,53,35,53,54,36,41,37,37,40,38,37,41,40,38,40,39,42,47,43,43,47,44,44,46,45,44,47,46,48,60,49,48,59,60,49,61,50,49,60,61,50,62,51,50,61,62,51,62,52,52,63,53,52,62,63,53,64,54,53,63,64,54,64,55,55,65,56,55,64,65,56,66,57,56,65,66,57,66,58,58,67,59,58,66,67,59,67,60,60,67,61,61,66,62,61,67,66,62,66,63,63,65,64,63,66,65,21,27,22],Mae=[0,8,7,7,8,1,2,10,9,9,10,3,17,0,18,18,0,7,18,7,19,19,7,1,19,1,11,19,11,20,21,3,22,21,9,3,20,9,21,20,2,9,20,11,2,23,17,18,25,21,22,24,19,20,24,18,19,24,20,21,24,23,18,24,21,25,11,12,4,11,4,13,1,12,11,11,13,2,12,14,4,4,14,13,14,5,15,14,15,6,12,5,14,14,6,13,8,12,1,2,13,10,8,26,12,10,13,27,26,5,12,13,6,27,0,26,8,10,27,3,5,32,16,16,32,6,5,30,32,6,32,31,26,30,5,27,6,31,0,28,26,3,27,29,17,28,0,3,29,22,23,28,17,22,29,25,28,30,26,27,31,29],Oae=[0,4,1,2,4,3,4,5,6],$ae=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Dae=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],zae=[33,133,362,263,1,78,308];Ir.MESH_ANNOTATIONS=Eae;Ir.MESH_TO_IRIS_INDICES_MAP=Cae;Ir.TRI468=Rae;Ir.TRI68=Fae;Ir.TRI33=Mae;Ir.TRI7=Oae;Ir.UV468=A0;Ir.UV68=$ae.map(e=>A0[e]);Ir.UV33=Dae.map(e=>A0[e]);Ir.UV7=zae.map(e=>A0[e])});var Yv=ot(Kv=>{var qt=Pe(Uv()),dn=Pe(Xv()),Gr=Pe(l2()),Pae=468,Lae=13,Wae=[Lae,Gr.MESH_ANNOTATIONS.midwayBetweenEyes[0]],Bae=3,Vae=2,Uae=[Bae,Vae],u2=Gr.MESH_ANNOTATIONS.leftEyeLower0,c2=[u2[0],u2[u2.length-1]],h2=Gr.MESH_ANNOTATIONS.rightEyeLower0,d2=[h2[0],h2[h2.length-1]],jae=3,Hae=4,Gae=71,p2=76;function y0(e,t,n,r){for(let a=0;a<Gr.MESH_TO_IRIS_INDICES_MAP.length;a++){let{key:s,indices:i}=Gr.MESH_TO_IRIS_INDICES_MAP[a],o=Gr.MESH_ANNOTATIONS[`${n}${s}`];if(r==null||r.includes(s))for(let c=0;c<i.length;c++){let u=i[c];e[o[c]]=[t[u][0],t[u][1],(t[u][2]+e[o[c]][2])/2]}}}var Zv=class{constructor(t,n,r,a){this.storedBoxes=[],this.runsWithoutFaceDetector=0,this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.meshWidth=a.face.mesh.inputSize,this.meshHeight=a.face.mesh.inputSize,this.irisSize=a.face.iris.inputSize,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,a){let s=qt.getBoxSize({startPoint:n.startPoint,endPoint:n.endPoint}),i=[s[0]/this.meshWidth,s[1]/this.meshHeight],o=t.map(p=>[i[0]*(p[0]-this.meshWidth/2),i[1]*(p[1]-this.meshHeight/2),p[2]]),l=r!==0?dn.buildRotationMatrix(r,[0,0]):dn.IDENTITY_MATRIX,c=r!==0?o.map(p=>[...dn.rotatePoint(p,l),p[2]]):o,u=r!==0?dn.invertTransformMatrix(a):dn.IDENTITY_MATRIX,h=[...qt.getBoxCenter({startPoint:n.startPoint,endPoint:n.endPoint}),1];return c.map(p=>[p[0]+dn.dot(h,u[0]),p[1]+dn.dot(h,u[1]),p[2]])}getLeftToRightEyeDepthDifference(t){let n=t[c2[0]][2],r=t[d2[0]][2];return n-r}getEyeBox(t,n,r,a,s=!1){let i=qt.squarifyBox(qt.enlargeBox(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=qt.getBoxSize(i),l=at.cropAndResize(n,[[i.startPoint[1]/this.meshHeight,i.startPoint[0]/this.meshWidth,i.endPoint[1]/this.meshHeight,i.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return s&&(l=at.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i<p2;i++){let o=t[i*3],l=t[i*3+1],c=t[i*3+2];s.push([(a?1-o/this.irisSize:o/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],c])}return{rawCoords:s,iris:s.slice(Gae)}}getAdjustedIrisCoords(t,n,r){let a=t[Gr.MESH_ANNOTATIONS[`${r}EyeUpper0`][jae]][2],s=t[Gr.MESH_ANNOTATIONS[`${r}EyeLower0`][Hae]][2],i=(a+s)/2;return n.map((o,l)=>{let c=i;return l===2?c=a:l===4&&(c=s),[o[0],o[1],c]})}async predict(t,n){let r=!1,a;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(a=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,a&&a.boxes&&(!n.face.mesh.enabled||a.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let i of a.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks,confidence:i.confidence});this.storedBoxes.length>0&&(r=!0)}if(r){if(!a||!a.boxes||a.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=qt.scaleBoxCoordinates({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},a.scaleFactor),l=qt.enlargeBox(o),c=qt.squarifyBox(l),u=this.storedBoxes[i].landmarks.arraySync(),h=this.storedBoxes[i].confidence;this.storedBoxes[i]={...c,confidence:h,landmarks:u}}this.runsWithoutFaceDetector=0}a&&a.boxes&&a.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=W(()=>this.storedBoxes.map((i,o)=>{let l,c=0,u;if(n.face.detector.rotation){let[w,b]=i.landmarks.length>=Pae?Wae:Uae;c=dn.computeRotation(i.landmarks[w],i.landmarks[b]);let N=qt.getBoxCenter({startPoint:i.startPoint,endPoint:i.endPoint}),T=[N[0]/t.shape[2],N[1]/t.shape[1]],E=at.rotateWithOffset(t,c,0,T);u=dn.buildRotationMatrix(-c,N),l=qt.cutBoxFromImageAndResize({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshHeight,this.meshWidth]).div(255)}else{u=dn.IDENTITY_MATRIX;let w=t.clone();l=qt.cutBoxFromImageAndResize({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.meshHeight,this.meshWidth]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,confidence:i.confidence,image:l};let[,h,p]=this.meshDetector.predict(l),d=h.dataSync()[0];if(d<n.face.detector.minConfidence)return null;let m=q(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:w,boxSize:b,crop:N}=this.getEyeBox(m,l,c2[0],c2[1],!0),{box:T,boxSize:E,crop:M}=this.getEyeBox(m,l,d2[0],d2[1]),P=this.irisModel.predict(rt([N,M])).dataSync(),V=P.slice(0,p2*3),{rawCoords:G,iris:U}=this.getEyeCoords(V,w,b,!0),K=P.slice(p2*3),{rawCoords:X,iris:ee}=this.getEyeCoords(K,T,E),Z=this.getLeftToRightEyeDepthDifference(m);Math.abs(Z)<30?(y0(m,G,"left"),y0(m,X,"right")):Z<1?y0(m,G,"left",["EyeUpper0","EyeLower0"]):y0(m,X,"right",["EyeUpper0","EyeLower0"]);let ae=this.getAdjustedIrisCoords(m,U,"left"),J=this.getAdjustedIrisCoords(m,ee,"right");m=m.concat(ae).concat(J)}let A=this.transformRawCoords(m,i,c,u),y=qt.enlargeBox(this.calculateLandmarksBoundingBox(A)),g=qt.squarifyBox(y),_=yn(A),x={coords:_,box:y,faceConfidence:d,confidence:i.confidence,image:l};return n.face.mesh.returnRawData&&(x.rawCoords=m),this.storedBoxes[o]={...g,landmarks:_.arraySync(),confidence:i.confidence,faceConfidence:d},x}));return s=s.filter(i=>i!==null),this.detectedFaces=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s,landmarks:t}}};Kv.Pipeline=Zv});var e6=ot(g0=>{var Jv=Pe(Vv()),Qv=Pe(Yv()),Kc=Pe(l2()),f2=class{constructor(t,n,r,a){this.facePipeline=new Qv.Pipeline(t,n,r,a),this.config=a}async estimateFaces(t,n){let r=await this.facePipeline.predict(t,n),a=[];for(let s of r||[]){if(s.isDisposedInternal)continue;let i=s.coords?s.coords.arraySync():null,o=s.rawCoords,l={};if(i&&i.length>0)for(let h of Object.keys(Kc.MESH_ANNOTATIONS))l[h]=Kc.MESH_ANNOTATIONS[h].map(p=>i[p]);let c=n.face.mesh.returnRawData&&s.box?{topLeft:s.box.startPoint,bottomRight:s.box.endPoint}:null,u=s.box?[Math.max(0,s.box.startPoint[0]),Math.max(0,s.box.startPoint[1]),Math.min(t.shape[2],s.box.endPoint[0])-s.box.startPoint[0],Math.min(t.shape[1],s.box.endPoint[1])-s.box.startPoint[1]]:0;a.push({confidence:s.confidence||0,box:u,mesh:i,boxRaw:c,meshRaw:o,annotations:l,image:s.image?tr(s.image):null}),s.coords&&s.coords.dispose(),s.image&&s.image.dispose()}return a}},Ri=[null,null,null];async function qae(e){Ri=await Promise.all([!Ri[0]&&e.face.enabled?Jv.load(e):null,!Ri[1]&&e.face.mesh.enabled?Ht(e.face.mesh.modelPath,{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Ri[2]&&e.face.iris.enabled?Ht(e.face.iris.modelPath,{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]);let t=new f2(Ri[0],Ri[1],Ri[2],e);return e.face.mesh.enabled&&Fe(`load model: ${e.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),e.face.iris.enabled&&Fe(`load model: ${e.face.iris.modelPath.match(/\/(.*)\./)[1]}`),t}g0.load=qae;g0.MediaPipeFaceMesh=f2;g0.triangulation=Kc.TRI468});var Gl=ot(t6=>{var Xae={};function Kae(e,t){if(!t||!t.kernels)return;let n=5,r=t.kernels.filter(o=>o.kernelTimeMs>0).reduce((o,l)=>o+=l.kernelTimeMs,0),a=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.kernelTimeMs>0).sort((o,l)=>l.kernelTimeMs-o.kernelTimeMs),s=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.totalBytesSnapshot>0).sort((o,l)=>l.totalBytesSnapshot-o.totalBytesSnapshot);a.length>n&&(a.length=n),s.length>n&&(s.length=n);let i={newBytes:t.newBytes,newTensors:t.newTensors,peakBytes:t.peakBytes,numKernelOps:t.kernels.length,timeKernelOps:r,slowestKernelOps:a,largestKernelOps:s};Xae[e]=i,Fe("Human profiler",e,i)}t6.run=Kae});var r6=ot(m2=>{var n6=Pe(Gl()),ql={},x0={age:0},w0=Number.MAX_SAFE_INTEGER;async function Zae(e){return ql.age||(ql.age=await Ht(e.face.age.modelPath),Fe(`load model: ${e.face.age.modelPath.match(/\/(.*)\./)[1]}`)),ql.age}async function Yae(e,t){return ql.age?w0<t.face.age.skipFrames&&t.videoOptimized&&x0.age&&x0.age>0?(w0++,x0):(t.videoOptimized?w0=0:w0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=at.resizeBilinear(e,[t.face.age.inputSize,t.face.age.inputSize],!1),a=L(r,[255]);Se(r);let s,i={};if(!t.profile)t.face.age.enabled&&(s=await ql.age.predict(a));else{let o=t.face.age.enabled?await ta(()=>ql.age.predict(a)):{};s=o.result.clone(),o.result.dispose(),n6.run("age",o)}if(a.dispose(),s){let o=s.dataSync();i.age=Math.trunc(10*o[0])/10}s.dispose(),x0=i,n(i)})):null}m2.predict=Yae;m2.load=Zae});var s6=ot(A2=>{var a6=Pe(Gl()),Fi={},y2={gender:""},_0=Number.MAX_SAFE_INTEGER,g2=!1,x2=[.2989,.587,.114];async function Jae(e){return Fi.gender||(Fi.gender=await Ht(e.face.gender.modelPath),g2=Fi.gender.inputs[0].shape[3]===1,Fe(`load model: ${e.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),Fi.gender}async function Qae(e,t){return Fi.gender?_0<t.face.gender.skipFrames&&t.videoOptimized&&y2.gender!==""?(_0++,y2):(t.videoOptimized?_0=0:_0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=at.resizeBilinear(e,[t.face.gender.inputSize,t.face.gender.inputSize],!1),a;g2?a=W(()=>{let[o,l,c]=Kt(r,3,3),u=L(o,x2[0]),h=L(l,x2[1]),p=L(c,x2[2]);return ll([u,h,p]).sub(.5).mul(2)}):a=L(r,[255]),Se(r);let s,i={};if(!t.profile)t.face.gender.enabled&&(s=await Fi.gender.predict(a));else{let o=t.face.gender.enabled?await ta(()=>Fi.gender.predict(a)):{};s=o.result.clone(),o.result.dispose(),a6.run("gender",o)}if(a.dispose(),s){let o=s.dataSync();if(g2){let l=Math.trunc(100*Math.abs(o[0]-o[1]))/100;l>t.face.gender.minConfidence&&(i.gender=o[0]>o[1]?"female":"male",i.confidence=l)}else{let l=Math.trunc(200*Math.abs(o[0]-.5))/100;l>t.face.gender.minConfidence&&(i.gender=o[0]<=.5?"female":"male",i.confidence=Math.min(.99,l))}}s.dispose(),y2=i,n(i)})):null}A2.predict=Qae;A2.load=Jae});var l6=ot(w2=>{var i6=Pe(Gl()),ese=["angry","disgust","fear","happy","sad","surprise","neutral"],Xl={},_2=[],b0=Number.MAX_SAFE_INTEGER,b2=[.2989,.587,.114],o6=1;async function tse(e){return Xl.emotion||(Xl.emotion=await Ht(e.face.emotion.modelPath),Fe(`load model: ${e.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),Xl.emotion}async function nse(e,t){return Xl.emotion?b0<t.face.emotion.skipFrames&&t.videoOptimized&&_2.length>0?(b0++,_2):(t.videoOptimized?b0=0:b0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=at.resizeBilinear(e,[t.face.emotion.inputSize,t.face.emotion.inputSize],!1),[a,s,i]=Kt(r,3,3);r.dispose();let o=L(a,b2[0]),l=L(s,b2[1]),c=L(i,b2[2]);a.dispose(),s.dispose(),i.dispose();let u=ll([o,l,c]);o.dispose(),l.dispose(),c.dispose();let h=W(()=>u.sub(.5).mul(2));u.dispose();let p=[];if(t.face.emotion.enabled){let d;if(t.profile){let f=await ta(()=>Xl.emotion.predict(h));d=f.result.dataSync(),f.result.dispose(),i6.run("emotion",f)}else{let f=await Xl.emotion.predict(h);d=f.dataSync(),Se(f)}for(let f=0;f<d.length;f++)o6*d[f]>t.face.emotion.minConfidence&&p.push({score:Math.min(.99,Math.trunc(100*o6*d[f])/100),emotion:ese[f]});p.sort((f,m)=>m.score-f.score)}h.dispose(),_2=p,n(p)})):null}w2.predict=nse;w2.load=tse});var c6=ot(v0=>{var u6=Pe(Gl()),Kl={};async function rse(e){return Kl.embedding||(Kl.embedding=await Ht(e.face.embedding.modelPath),Fe(`load model: ${e.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),Kl.embedding}function ase(e,t){if((e==null?void 0:e.length)!==(t==null?void 0:t.length))return 0;let n=2,r=10*e.map((a,s)=>a-t[s]).reduce((a,s)=>a+s**n,0)**(1/n);return Math.trunc(1e3*(1-r))/1e3}async function sse(e,t){return Kl.embedding?new Promise(async n=>{let r=at.resizeBilinear(e,[t.face.embedding.inputSize,t.face.embedding.inputSize],!1),a=[];if(t.face.embedding.enabled)if(t.profile){let s=await ta(()=>Kl.embedding.predict({img_inputs:r}));a=[...s.result.dataSync()],s.result.dispose(),u6.run("emotion",s)}else{let s=await Kl.embedding.predict({img_inputs:r});a=[...s.dataSync()],Se(s)}r.dispose(),n(a)}):null}v0.predict=sse;v0.simmilarity=ase;v0.load=rse});var p6=ot(h6=>{var ise=[-123.15,-115.9,-103.06];function ose(e){let[t,n,r,a]=e;return{offsets:t,heatmap:n,displacementFwd:r,displacementBwd:a}}function lse(e){let[t,n,r,a]=e;return{offsets:r,heatmap:a,displacementFwd:t,displacementBwd:n}}var d6=class{constructor(t){this.model=t}predict(t,n){return W(()=>{let a=(n.body.modelType==="ResNet"?t.toFloat().add(ise):t.toFloat().div(127.5).sub(1)).expandDims(0),i=this.model.predict(a).map(l=>l.squeeze([0])),o=n.body.modelType==="ResNet"?lse(i):ose(i);return{heatmapScores:o.heatmap.sigmoid(),offsets:o.offsets,displacementFwd:o.displacementFwd,displacementBwd:o.displacementBwd}})}dispose(){this.model.dispose()}};h6.BaseModel=d6});var A6=ot(f6=>{function v2(e){return Math.floor(e/2)}var m6=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(v2(t),t);)this.exchange(t,v2(t)),t=v2(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let r=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=r}};f6.MaxHeap=m6});var x6=ot(y6=>{var g6=Pe(A6());function use(e,t,n,r,a,s){let[i,o]=s.shape,l=!0,c=Math.max(n-a,0),u=Math.min(n+a+1,i);for(let h=c;h<u;++h){let p=Math.max(r-a,0),d=Math.min(r+a+1,o);for(let f=p;f<d;++f)if(s.get(h,f,e)>t){l=!1;break}if(!l)break}return l}function cse(e,t,n){let[r,a,s]=n.shape,i=new g6.MaxHeap(r*a*s,({score:o})=>o);for(let o=0;o<r;++o)for(let l=0;l<a;++l)for(let c=0;c<s;++c){let u=n.get(o,l,c);u<e||use(c,u,o,l,t,n)&&i.enqueue({score:u,part:{heatmapY:o,heatmapX:l,id:c}})}return i}y6.buildPartWithScoreQueue=cse});var Zl=ot(Nr=>{Nr.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];Nr.NUM_KEYPOINTS=Nr.partNames.length;Nr.partIds=Nr.partNames.reduce((e,t,n)=>(e[t]=n,e),{});var hse=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]];Nr.connectedPartIndices=hse.map(([e,t])=>[Nr.partIds[e],Nr.partIds[t]]);Nr.poseChain=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];Nr.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]});var I2=ot(Ha=>{var w6=Pe(Zl());function _6(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+w6.NUM_KEYPOINTS)}}Ha.getOffsetPoint=_6;function dse(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=_6(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}Ha.getImageCoords=dse;function pse(e,t){let n=new Array(t);for(let r=0;r<t;r++)n[r]=e;return n}Ha.fillArray=pse;function k2(e,t,n){return e<t?t:e>n?n:e}Ha.clamp=k2;function fse(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}Ha.squaredDistance=fse;function mse(e,t){return{x:e.x+t.x,y:e.y+t.y}}Ha.addVectors=mse;function Ase(e,t,n){return{y:k2(e.y,t,n),x:k2(e.x,t,n)}}Ha.clampVector=Ase});var v6=ot(Zc=>{var k0=Pe(Zl());function yse(e,t){let n=t.shape[0],r=new Float32Array(n);for(let a=0;a<n;a++){let s=t.get(a,0),i=t.get(a,1);r[a]=e.get(s,i,a)}return r}Zc.getPointsConfidence=yse;function gse(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+k0.NUM_KEYPOINTS)}}function b6(e,t){let n=[];for(let r=0;r<k0.NUM_KEYPOINTS;r++){let a=e.get(r,0).valueOf(),s=e.get(r,1).valueOf(),{x:i,y:o}=gse(a,s,r,t);n.push(o),n.push(i)}return yn(n,[k0.NUM_KEYPOINTS,2])}Zc.getOffsetVectors=b6;function xse(e,t,n){return W(()=>e.toTensor().mul(ke(t,"int32")).toFloat().add(b6(e,n)))}Zc.getOffsetPoints=xse;function wse(e,t){return W(()=>{let n=e.div(ke(t,"int32"));return e.sub(n.mul(ke(t,"int32")))})}function _se(e){let[t,n,r]=e.shape;return W(()=>{let s=e.reshape([t*n,r]).argMax(0),i=s.div(ke(n,"int32")).expandDims(1),o=wse(s,n).expandDims(1);return rt([i,o],1)})}Zc.argmax2d=_se});var E2=ot(N2=>{var pa=Pe(Zl()),qr=Pe(I2()),Yl=Pe(v6()),k6=pa.poseChain.map(([e,t])=>[pa.partIds[e],pa.partIds[t]]),S2=k6.map(([,e])=>e),I6=k6.map(([e])=>e);function bse(e,t,n){let r=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,r+e)}}function T2(e,t,n,r){return{y:qr.clamp(Math.round(e.y/t),0,n-1),x:qr.clamp(Math.round(e.x/t),0,r-1)}}function N6(e,t,n,r,a,s,i,o=2){let[l,c]=r.shape,u=T2(t.position,s,l,c),h=bse(e,u,i),d=qr.addVectors(t.position,h);for(let A=0;A<o;A++){let y=T2(d,s,l,c),g=qr.getOffsetPoint(y.y,y.x,n,a);d=qr.addVectors({x:y.x*s,y:y.y*s},{x:g.x,y:g.y})}let f=T2(d,s,l,c),m=r.get(f.y,f.x,n);return{position:d,part:pa.partNames[n],score:m}}function vse(e,t,n,r,a,s){let i=t.shape[2],o=S2.length,l=new Array(i),{part:c,score:u}=e,h=qr.getImageCoords(c,r,n);l[c.id]={score:u,part:pa.partNames[c.id],position:h};for(let p=o-1;p>=0;--p){let d=S2[p],f=I6[p];l[d]&&!l[f]&&(l[f]=N6(p,l[d],f,t,n,r,s))}for(let p=0;p<o;++p){let d=I6[p],f=S2[p];l[d]&&!l[f]&&(l[f]=N6(p,l[d],f,t,n,r,a))}return l}N2.decodePose=vse;async function kse(e,t,n){let r=0,a=Yl.argmax2d(e),s=await Promise.all([e.buffer(),t.buffer(),a.buffer()]),i=s[0],o=s[1],l=s[2],c=Yl.getOffsetPoints(l,n.body.outputStride,o),u=await c.buffer(),p=Array.from(Yl.getPointsConfidence(i,l)).map((f,m)=>(r+=f,{position:{y:u.get(m,0),x:u.get(m,1)},part:pa.partNames[m],score:f})),d=p.filter(f=>f.score>n.body.scoreThreshold);return a.dispose(),c.dispose(),{keypoints:d,score:r/p.length}}N2.decodeSinglePose=kse});var R6=ot(S6=>{var T6=Pe(x6()),E6=Pe(E2()),I0=Pe(I2()),Ise=1;function C6(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return I0.squaredDistance(r,n,i.y,i.x)<=t})}function Nse(e,t,n){return n.reduce((a,{position:s,score:i},o)=>(C6(e,t,s,o)||(a+=i),a),0)/n.length}function Sse(e,t,n,r,a){let s=[],i=T6.buildPartWithScoreQueue(a.body.scoreThreshold,Ise,e),o=a.body.nmsRadius^2;for(;s.length<a.body.maxDetections&&!i.empty();){let l=i.dequeue(),c=I0.getImageCoords(l.part,a.body.outputStride,t);if(C6(s,o,c,l.part.id))continue;let u=E6.decodePose(l,e,t,a.body.outputStride,n,r),h=Nse(s,o,u);h>a.body.scoreThreshold&&s.push({keypoints:u,score:h})}return s}S6.decodeMultiplePoses=Sse});var C2=ot(Ga=>{var F6=Pe(Zl());function Tse(e,t,n){return e<n||t<n}function Ese(e,t){return F6.connectedPartIndices.reduce((n,[r,a])=>(Tse(e[r].score,e[a].score,t)||n.push([e[r],e[a]]),n),[])}Ga.getAdjacentKeyPoints=Ese;var{NEGATIVE_INFINITY:M6,POSITIVE_INFINITY:O6}=Number;function $6(e){return e.reduce(({maxX:t,maxY:n,minX:r,minY:a},{position:{x:s,y:i}})=>({maxX:Math.max(t,s),maxY:Math.max(n,i),minX:Math.min(r,s),minY:Math.min(a,i)}),{maxX:M6,maxY:M6,minX:O6,minY:O6})}Ga.getBoundingBox=$6;function Cse(e){let{minX:t,minY:n,maxX:r,maxY:a}=$6(e);return[{x:t,y:n},{x:r,y:n},{x:r,y:a},{x:t,y:a}]}Ga.getBoundingBoxPoints=Cse;async function Rse(e){return Promise.all(e.map(t=>t.buffer()))}Ga.toTensorBuffers3D=Rse;function D6(e,t,n){return{score:e.score,keypoints:e.keypoints.map(({score:r,part:a,position:s})=>({score:r,part:a,position:{x:s.x*n,y:s.y*t}}))}}Ga.scalePose=D6;function Fse(e,[t,n]){let r=e.squeeze(0),a=r.resizeBilinear([t,n]);return r.dispose(),a}Ga.resizeTo=Fse;function Mse(e,[t,n],[r,a]){return e.map(i=>D6(i,t/r,n/a))}Ga.scaleAndFlipPoses=Mse});var W6=ot(R2=>{var z6=Pe(p6()),P6=Pe(R6()),L6=Pe(E2()),Mi=Pe(C2());async function Ose(e,t,n){return new Promise(async r=>{let a=e.shape[1],s=e.shape[2],i=await Mi.toTensorBuffers3D([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),o=i[0],l=i[1],c=i[2],u=i[3],h=await P6.decodeMultiplePoses(o,l,c,u,n),p=Mi.scaleAndFlipPoses(h,[a,s],[n.body.inputSize,n.body.inputSize]);r(p)})}async function $se(e,t,n){return new Promise(async r=>{let a=e.shape[1],s=e.shape[2],o=[await L6.decodeSinglePose(t.heatmapScores,t.offsets,n)],l=Mi.scaleAndFlipPoses(o,[a,s],[n.body.inputSize,n.body.inputSize]);r(l)})}var F2=class{constructor(t){this.baseModel=t}async estimatePoses(t,n){let r=Mi.resizeTo(t,[n.body.inputSize,n.body.inputSize]),a=this.baseModel.predict(r,n),s=n.body.maxDetections<2?await $se(t,a,n):await Ose(t,a,n);return a.heatmapScores.dispose(),a.offsets.dispose(),a.displacementFwd.dispose(),a.displacementBwd.dispose(),r.dispose(),s}dispose(){this.baseModel.dispose()}};R2.PoseNet=F2;async function Dse(e){let t=await Ht(e.body.modelPath),n=new z6.BaseModel(t);return Fe(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`),new F2(n)}R2.load=Dse});var B6=ot(cr=>{var N0=Pe(W6()),qa=Pe(Zl()),Xr=Pe(C2());cr.load=N0.load;cr.PoseNet=N0.PoseNet;cr.partChannels=qa.partChannels;cr.partIds=qa.partIds;cr.partNames=qa.partNames;cr.poseChain=qa.poseChain;cr.getAdjacentKeyPoints=Xr.getAdjacentKeyPoints;cr.getBoundingBox=Xr.getBoundingBox;cr.getBoundingBoxPoints=Xr.getBoundingBoxPoints;cr.scaleAndFlipPoses=Xr.scaleAndFlipPoses;cr.scalePose=Xr.scalePose});var G6=ot(j6=>{var H6=class{constructor(t,n,r){this.model=t,this.anchors=r.map(a=>[a.x_center,a.y_center]),this.anchorsTensor=yn(this.anchors),this.inputSizeTensor=Bt([n,n]),this.doubleInputSizeTensor=Bt([n*2,n*2])}normalizeBoxes(t){return W(()=>{let n=Te(t,[0,0],[-1,2]),r=Te(t,[0,2],[-1,2]),a=se(be(n,this.inputSizeTensor),this.anchorsTensor),s=be(r,this.doubleInputSizeTensor),i=L(Ae(a,s),this.inputSizeTensor),o=L(se(a,s),this.inputSizeTensor);return ii([i,o],1)})}normalizeLandmarks(t,n){return W(()=>{let r=se(be(t.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[n]);return L(r,this.inputSizeTensor)})}async getBoxes(t,n){let r=this.model.predict(t),a=r.squeeze();r.dispose();let s=W(()=>In(Te(a,[0,0],[-1,1])).squeeze()),i=s.dataSync(),o=Te(a,[0,1],[-1,4]),l=this.normalizeBoxes(o);o.dispose();let c=await at.nonMaxSuppressionAsync(l,i,n.hand.maxHands,n.hand.iouThreshold,n.hand.scoreThreshold),u=c.arraySync();s.dispose(),c.dispose();let h=[];for(let p of u)if(i[p]>=n.hand.minConfidence){let d=Te(l,[p,0],[1,-1]),f=Te(a,[p,5],[1,14]),m=W(()=>this.normalizeLandmarks(f,p).reshape([-1,2]));f.dispose(),h.push({box:d,palmLandmarks:m,confidence:i[p]})}return a.dispose(),l.dispose(),h}async estimateHandBounds(t,n){let r=t.shape[1],a=t.shape[2],s=W(()=>t.resizeBilinear([n.hand.inputSize,n.hand.inputSize]).div(127.5).sub(1)),i=await this.getBoxes(s,n);s.dispose();let o=[];if(!i||i.length===0)return o;for(let l of i){let c=l.box.dataSync(),u=c.slice(0,2),h=c.slice(2,4),p=l.palmLandmarks.arraySync();l.box.dispose(),l.palmLandmarks.dispose(),o.push(U6({startPoint:u,endPoint:h,palmLandmarks:p,confidence:l.confidence},[a/n.hand.inputSize,r/n.hand.inputSize]))}return o}};j6.HandDetector=H6});var t4=ot(Y6=>{var Lse=5,J6=1.65,Q6=[0,5,9,13,17,1,2],Wse=0,Bse=2,e4=class{constructor(t,n,r){this.handDetector=t,this.landmarkDetector=n,this.inputSize=r,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(t,n){let r=t.map(s=>O2([...s,1],n)),a=this.calculateLandmarksBoundingBox(r);return T0(E0(a),Lse)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),r=T0(E0(n),J6);r.palmLandmarks=[];for(let a=0;a<Q6.length;a++)r.palmLandmarks.push(t[Q6[a]].slice(0,2));return r}transformRawCoords(t,n,r,a){let s=S0(n),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(d=>[i[0]*(d[0]-this.inputSize/2),i[1]*(d[1]-this.inputSize/2),i[2]*d[2]]),l=M2(r,[0,0]),c=o.map(d=>[...O2(d,l),d[2]]),u=Z6(a),h=[...Yc(n),1],p=[Xa(h,u[0]),Xa(h,u[1])];return c.map(d=>[d[0]+p[0],d[1]+p[1],d[2]])}async estimateHands(t,n){let r=!1,a;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.videoOptimized)&&(a=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.videoOptimized&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==n.hand.maxHands||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(r=!0));let s=[];for(let i=0;i<this.storedBoxes.length;i++){let o=this.storedBoxes[i];if(!!o)if(n.hand.landmarks){let l=n.hand.rotation?q6(o.palmLandmarks[Wse],o.palmLandmarks[Bse]):0,c=Yc(o),u=[c[0]/t.shape[2],c[1]/t.shape[1]],h=n.hand.rotation?at.rotateWithOffset(t,l,0,u):t.clone(),p=M2(-l,c),d=r?this.getBoxForPalmLandmarks(o.palmLandmarks,p):o,f=V6(d,h,[this.inputSize,this.inputSize]),m=f.div(255);f.dispose(),h.dispose();let[A,y]=await this.landmarkDetector.predict(m);m.dispose();let g=A.dataSync()[0];if(A.dispose(),g>=n.hand.minConfidence){let _=q(y,[-1,3]),x=_.arraySync();y.dispose(),_.dispose();let w=this.transformRawCoords(x,d,l,p),b=this.getBoxForHandLandmarks(w);this.storedBoxes[i]=b;let N={landmarks:w,confidence:g,box:{topLeft:b.startPoint,bottomRight:b.endPoint}};s.push(N)}else this.storedBoxes[i]=null;y.dispose()}else{let l=T0(E0(o),J6),c={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(c)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};Y6.HandPipeline=e4});var r4=ot(n4=>{n4.anchors=[{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.671875,y_center:.046875},{w:1,h:1,x_center:.671875,y_center:.046875},{w:1,h:1,x_center:.703125,y_center:.046875},{w:1,h:1,x_center:.703125,y_center:.046875},{w:1,h:1,x_center:.734375,y_center:.046875},{w:1,h:1,x_center:.734375,y_center:.046875},{w:1,h:1,x_center:.765625,y_center:.046875},{w:1,h:1,x_center:.765625,y_center:.046875},{w:1,h:1,x_center:.796875,y_center:.046875},{w:1,h:1,x_center:.796875,y_center:.046875},{w:1,h:1,x_center:.828125,y_center:.046875},{w:1,h:1,x_center:.828125,y_center:.046875},{w:1,h:1,x_center:.859375,y_center:.046875},{w:1,h:1,x_center:.859375,y_center:.046875},{w:1,h:1,x_center:.890625,y_center:.046875},{w:1,h:1,x_center:.890625,y_center:.046875},{w:1,h:1,x_center:.921875,y_center:.046875},{w:1,h:1,x_center:.921875,y_center:.046875},{w:1,h:1,x_center:.953125,y_center:.046875},{w:1,h:1,x_center:.953125,y_center:.046875},{w:1,h:1,x_center:.984375,y_center:.046875},{w:1,h:1,x_center:.984375,y_center:.046875},{w:1,h:1,x_center:.015625,y_center:.078125},{w:1,h:1,x_center:.015625,y_center:.078125},{w:1,h:1,x_center:.046875,y_center:.078125},{w:1,h:1,x_center:.046875,y_center:.078125},{w:1,h:1,x_center:.078125,y_center:.078125},{w:1,h:1,x_center:.078125,y_center:.078125},{w:1,h:1,x_center:.109375,y_center:.078125},{w:1,h:1,x_center:.109375,y_center:.078125},{w:1,h:1,x_center:.140625,y_center:.078125},{w:1,h:1,x_center:.140625,y_center:.078125},{w:1,h:1,x_center:.171875,y_center:.078125},{w:1,h:1,x_center:.171875,y_center:.078125},{w:1,h:1,x_center:.203125,y_center:.078125},{w:1,h:1,x_center:.203125,y_center:.078125},{w:1,h:1,x_center:.234375,y_center:.078125},{w:1,h:1,x_center:.234375,y_center:.078125},{w:1,h:1,x_center:.265625,y_center:.078125},{w:1,h:1,x_center:.265625,y_center:.078125},{w:1,h:1,x_center:.296875,y_center:.078125},{w:1,h:1,x_center:.296875,y_center:.078125},{w:1,h:1,x_center:.328125,y_center:.078125},{w:1,h:1,x_center:.328125,y_center:.078125},{w:1,h:1,x_center:.359375,y_center:.078125},{w:1,h:1,x_center:.359375,y_center:.078125},{w:1,h:1,x_center:.390625,y_center:.078125},{w:1,h:1,x_center:.390625,y_center:.078125},{w:1,h:1,x_center:.421875,y_center:.078125},{w:1,h:1,x_center:.421875,y_center:.078125},{w:1,h:1,x_center:.453125,y_center:.078125},{w:1,h:1,x_center:.453125,y_center:.078125},{w:1,h:1,x_center:.484375,y_center:.078125},{w:1,h:1,x_center:.484375,y_center:.078125},{w:1,h:1,x_center:.515625,y_center:.078125},{w:1,h:1,x_center:.515625,y_center:.078125},{w:1,h:1,x_center:.546875,y_center:.078125},{w:1,h:1,x_center:.546875,y_center:.078125},{w:1,h:1,x_center:.578125,y_center:.078125},{w:1,h:1,x_center:.578125,y_center:.078125},{w:1,h:1,x_center:.609375,y_center:.078125},{w:1,h:1,x_center:.609375,y_center:.078125},{w:1,h:1,x_center:.640625,y_center:.078125},{w:1,h:1,x_center:.640625,y_center:.078125},{w:1,h:1,x_center:.671875,y_center:.078125},{w:1,h:1,x_center:.671875,y_center:.078125},{w:1,h:1,x_center:.703125,y_center:.078125},{w:1,h:1,x_center:.703125,y_center:.078125},{w:1,h:1,x_center:.734375,y_center:.078125},{w:1,h:1,x_center:.734375,y_center:.078125},{w:1,h:1,x_center:.765625,y_center:.078125},{w:1,h:1,x_center:.765625,y_center:.078125},{w:1,h:1,x_center:.796875,y_center:.078125},{w:1,h:1,x_center:.796875,y_center:.078125},{w:1,h:1,x_center:.828125,y_center:.078125},{w:1,h:1,x_center:.828125,y_center:.078125},{w:1,h:1,x_center:.859375,y_center:.078125},{w:1,h:1,x_center:.859375,y_center:.078125},{w:1,h:1,x_center:.890625,y_center:.078125},{w:1,h:1,x_center:.890625,y_center:.078125},{w:1,h:1,x_center:.921875,y_center:.078125},{w:1,h:1,x_center:.921875,y_center:.078125},{w:1,h:1,x_center:.953125,y_center:.078125},{w:1,h:1,x_center:.953125,y_center:.078125},{w:1,h:1,x_center:.984375,y_center:.078125},{w:1,h:1,x_center:.984375,y_center:.078125},{w:1,h:1,x_center:.015625,y_center:.109375},{w:1,h:1,x_center:.015625,y_center:.109375},{w:1,h:1,x_center:.046875,y_center:.109375},{w:1,h:1,x_center:.046875,y_center:.109375},{w:1,h:1,x_center:.078125,y_center:.109375},{w:1,h:1,x_center:.078125,y_center:.109375},{w:1,h:1,x_center:.109375,y_center:.109375},{w:1,h:1,x_center:.109375,y_center:.109375},{w:1,h:1,x_center:.140625,y_center:.109375},{w:1,h:1,x_center:.140625,y_center:.109375},{w:1,h:1,x_center:.171875,y_center:.109375},{w:1,h:1,x_center:.171875,y_center:.109375},{w:1,h:1,x_center:.203125,y_center:.109375},{w:1,h:1,x_center:.203125,y_center:.109375},{w:1,h:1,x_center:.234375,y_center:.109375},{w:1,h:1,x_center:.234375,y_center:.109375},{w:1,h:1,x_center:.265625,y_center:.109375},{w:1,h:1,x_center:.265625,y_center:.109375},{w:1,h:1,x_center:.296875,y_center:.109375},{w:1,h:1,x_center:.296875,y_center:.109375},{w:1,h:1,x_center:.328125,y_center:.109375},{w:1,h:1,x_center:.328125,y_center:.109375},{w:1,h:1,x_center:.359375,y_center:.109375},{w:1,h:1,x_center:.359375,y_center:.109375},{w:1,h:1,x_center:.390625,y_center:.109375},{w:1,h:1,x_center:.390625,y_center:.109375},{w:1,h:1,x_center:.421875,y_center:.109375},{w:1,h:1,x_center:.421875,y_center:.109375},{w:1,h:1,x_center:.453125,y_center:.109375},{w:1,h:1,x_center:.453125,y_center:.109375},{w:1,h:1,x_center:.484375,y_center:.109375},{w:1,h:1,x_center:.484375,y_center:.109375},{w:1,h:1,x_center:.515625,y_center:.109375},{w:1,h:1,x_center:.515625,y_center:.109375},{w:1,h:1,x_center:.546875,y_center:.109375},{w:1,h:1,x_center:.546875,y_center:.109375},{w:1,h:1,x_center:.578125,y_center:.109375},{w:1,h:1,x_center:.578125,y_center:.109375},{w:1,h:1,x_center:.609375,y_center:.109375},{w:1,h:1,x_center:.609375,y_center:.109375},{w:1,h:1,x_center:.640625,y_center:.109375},{w:1,h:1,x_center:.640625,y_center:.109375},{w:1,h:1,x_center:.671875,y_center:.109375},{w:1,h:1,x_center:.671875,y_center:.109375},{w:1,h:1,x_center:.703125,y_center:.109375},{w:1,h:1,x_center:.703125,y_center:.109375},{w:1,h:1,x_center:.734375,y_center:.109375},{w:1,h:1,x_center:.734375,y_center:.109375},{w:1,h:1,x_center:.765625,y_center:.109375},{w:1,h:1,x_center:.765625,y_center:.109375},{w:1,h:1,x_center:.796875,y_center:.109375},{w:1,h:1,x_center:.796875,y_center:.109375},{w:1,h:1,x_center:.828125,y_center:.109375},{w:1,h:1,x_center:.828125,y_center:.109375},{w:1,h:1,x_center:.859375,y_center:.109375},{w:1,h:1,x_center:.859375,y_center:.109375},{w:1,h:1,x_center:.890625,y_center:.109375},{w:1,h:1,x_center:.890625,y_center:.109375},{w:1,h:1,x_center:.921875,y_center:.109375},{w:1,h:1,x_center:.921875,y_center:.109375},{w:1,h:1,x_center:.953125,y_center:.109375},{w:1,h:1,x_center:.953125,y_center:.109375},{w:1,h:1,x_center:.984375,y_center:.109375},{w:1,h:1,x_center:.984375,y_center:.109375},{w:1,h:1,x_center:.015625,y_center:.140625},{w:1,h:1,x_center:.015625,y_center:.140625},{w:1,h:1,x_center:.046875,y_center:.140625},{w:1,h:1,x_center:.046875,y_center:.140625},{w:1,h:1,x_center:.078125,y_center:.140625},{w:1,h:1,x_center:.078125,y_center:.140625},{w:1,h:1,x_center:.109375,y_center:.140625},{w:1,h:1,x_center:.109375,y_center:.140625},{w:1,h:1,x_center:.140625,y_center:.140625},{w:1,h:1,x_center:.140625,y_center:.140625},{w:1,h:1,x_center:.171875,y_center:.140625},{w:1,h:1,x_center:.171875,y_center:.140625},{w:1,h:1,x_center:.203125,y_center:.140625},{w:1,h:1,x_center:.203125,y_center:.140625},{w:1,h:1,x_center:.234375,y_center:.140625},{w:1,h:1,x_center:.234375,y_center:.140625},{w:1,h:1,x_center:.265625,y_center:.140625},{w:1,h:1,x_center:.265625,y_center:.140625},{w:1,h:1,x_center:.296875,y_center:.140625},{w:1,h:1,x_center:.296875,y_center:.140625},{w:1,h:1,x_center:.328125,y_center:.140625},{w:1,h:1,x_center:.328125,y_center:.140625},{w:1,h:1,x_center:.359375,y_center:.140625},{w:1,h:1,x_center:.359375,y_center:.140625},{w:1,h:1,x_center:.390625,y_center:.140625},{w:1,h:1,x_center:.390625,y_center:.140625},{w:1,h:1,x_center:.421875,y_center:.140625},{w:1,h:1,x_center:.421875,y_center:.140625},{w:1,h:1,x_center:.453125,y_center:.140625},{w:1,h:1,x_center:.453125,y_center:.140625},{w:1,h:1,x_center:.484375,y_center:.140625},{w:1,h:1,x_center:.484375,y_center:.140625},{w:1,h:1,x_center:.515625,y_center:.140625},{w:1,h:1,x_center:.515625,y_center:.140625},{w:1,h:1,x_center:.546875,y_center:.140625},{w:1,h:1,x_center:.546875,y_center:.140625},{w:1,h:1,x_center:.578125,y_center:.140625},{w:1,h:1,x_center:.578125,y_center:.140625},{w:1,h:1,x_center:.609375,y_center:.140625},{w:1,h:1,x_center:.609375,y_center:.140625},{w:1,h:1,x_center:.640625,y_center:.140625},{w:1,h:1,x_center:.640625,y_center:.140625},{w:1,h:1,x_center:.671875,y_center:.140625},{w:1,h:1,x_center:.671875,y_center:.140625},{w:1,h:1,x_center:.703125,y_center:.140625},{w:1,h:1,x_center:.703125,y_center:.140625},{w:1,h:1,x_center:.734375,y_center:.140625},{w:1,h:1,x_center:.734375,y_center:.140625},{w:1,h:1,x_center:.765625,y_center:.140625},{w:1,h:1,x_center:.765625,y_center:.140625},{w:1,h:1,x_center:.796875,y_center:.140625},{w:1,h:1,x_center:.796875,y_center:.140625},{w:1,h:1,x_center:.828125,y_center:.140625},{w:1,h:1,x_center:.828125,y_center:.140625},{w:1,h:1,x_center:.859375,y_center:.140625},{w:1,h:1,x_center:.859375,y_center:.140625},{w:1,h:1,x_center:.890625,y_center:.140625},{w:1,h:1,x_center:.890625,y_center:.140625},{w:1,h:1,x_center:.921875,y_center:.140625},{w:1,h:1,x_center:.921875,y_center:.140625},{w:1,h:1,x_center:.953125,y_center:.140625},{w:1,h:1,x_center:.953125,y_center:.140625},{w:1,h:1,x_center:.984375,y_center:.140625},{w:1,h:1,x_center:.984375,y_center:.140625},{w:1,h:1,x_center:.015625,y_center:.171875},{w:1,h:1,x_center:.015625,y_center:.171875},{w:1,h:1,x_center:.046875,y_center:.171875},{w:1,h:1,x_center:.046875,y_center:.171875},{w:1,h:1,x_center:.078125,y_center:.171875},{w:1,h:1,x_center:.078125,y_center:.171875},{w:1,h:1,x_center:.109375,y_center:.171875},{w:1,h:1,x_center:.109375,y_center:.171875},{w:1,h:1,x_center:.140625,y_center:.171875},{w:1,h:1,x_center:.140625,y_center:.171875},{w:1,h:1,x_center:.171875,y_center:.171875},{w:1,h:1,x_center:.171875,y_center:.171875},{w:1,h:1,x_center:.203125,y_center:.171875},{w:1,h:1,x_center:.203125,y_center:.171875},{w:1,h:1,x_center:.234375,y_center:.171875},{w:1,h:1,x_center:.234375,y_center:.171875},{w:1,h:1,x_center:.265625,y_center:.171875},{w:1,h:1,x_center:.265625,y_center:.171875},{w:1,h:1,x_center:.296875,y_center:.171875},{w:1,h:1,x_center:.296875,y_center:.171875},{w:1,h:1,x_center:.328125,y_center:.171875},{w:1,h:1,x_center:.328125,y_center:.171875},{w:1,h:1,x_center:.359375,y_center:.171875},{w:1,h:1,x_center:.359375,y_center:.171875},{w:1,h:1,x_center:.390625,y_center:.171875},{w:1,h:1,x_center:.390625,y_center:.171875},{w:1,h:1,x_center:.421875,y_center:.171875},{w:1,h:1,x_center:.421875,y_center:.171875},{w:1,h:1,x_center:.453125,y_center:.171875},{w:1,h:1,x_center:.453125,y_center:.171875},{w:1,h:1,x_center:.484375,y_center:.171875},{w:1,h:1,x_center:.484375,y_center:.171875},{w:1,h:1,x_center:.515625,y_center:.171875},{w:1,h:1,x_center:.515625,y_center:.171875},{w:1,h:1,x_center:.546875,y_center:.171875},{w:1,h:1,x_center:.546875,y_center:.171875},{w:1,h:1,x_center:.578125,y_center:.171875},{w:1,h:1,x_center:.578125,y_center:.171875},{w:1,h:1,x_center:.609375,y_center:.171875},{w:1,h:1,x_center:.609375,y_center:.171875},{w:1,h:1,x_center:.640625,y_center:.171875},{w:1,h:1,x_center:.640625,y_center:.171875},{w:1,h:1,x_center:.671875,y_center:.171875},{w:1,h:1,x_center:.671875,y_center:.171875},{w:1,h:1,x_center:.703125,y_center:.171875},{w:1,h:1,x_center:.703125,y_center:.171875},{w:1,h:1,x_center:.734375,y_center:.171875},{w:1,h:1,x_center:.734375,y_center:.171875},{w:1,h:1,x_center:.765625,y_center:.171875},{w:1,h:1,x_center:.765625,y_center:.171875},{w:1,h:1,x_center:.796875,y_center:.171875},{w:1,h:1,x_center:.796875,y_center:.171875},{w:1,h:1,x_center:.828125,y_center:.171875},{w:1,h:1,x_center:.828125,y_center:.171875},{w:1,h:1,x_center:.859375,y_center:.171875},{w:1,h:1,x_center:.859375,y_center:.171875},{w:1,h:1,x_center:.890625,y_center:.171875},{w:1,h:1,x_center:.890625,y_center:.171875},{w:1,h:1,x_center:.921875,y_center:.171875},{w:1,h:1,x_center:.921875,y_center:.171875},{w:1,h:1,x_center:.953125,y_center:.171875},{w:1,h:1,x_center:.953125,y_center:.171875},{w:1,h:1,x_center:.984375,y_center:.171875},{w:1,h:1,x_center:.984375,y_center:.171875},{w:1,h:1,x_center:.015625,y_center:.203125},{w:1,h:1,x_center:.015625,y_center:.203125},{w:1,h:1,x_center:.046875,y_center:.203125},{w:1,h:1,x_center:.046875,y_center:.203125},{w:1,h:1,x_center:.078125,y_center:.203125},{w:1,h:1,x_center:.078125,y_center:.203125},{w:1,h:1,x_center:.109375,y_center:.203125},{w:1,h:1,x_center:.109375,y_center:.203125},{w:1,h:1,x_center:.140625,y_center:.203125},{w:1,h:1,x_center:.140625,y_center:.203125},{w:1,h:1,x_center:.171875,y_center:.203125},{w:1,h:1,x_center:.171875,y_center:.203125},{w:1,h:1,x_center:.203125,y_center:.203125},{w:1,h:1,x_center:.203125,y_center:.203125},{w:1,h:1,x_center:.234375,y_center:.203125},{w:1,h:1,x_center:.234375,y_center:.203125},{w:1,h:1,x_center:.265625,y_center:.203125},{w:1,h:1,x_center:.265625,y_center:.203125},{w:1,h:1,x_center:.296875,y_center:.203125},{w:1,h:1,x_center:.296875,y_center:.203125},{w:1,h:1,x_center:.328125,y_center:.203125},{w:1,h:1,x_center:.328125,y_center:.203125},{w:1,h:1,x_center:.359375,y_center:.203125},{w:1,h:1,x_center:.359375,y_center:.203125},{w:1,h:1,x_center:.390625,y_center:.203125},{w:1,h:1,x_center:.390625,y_center:.203125},{w:1,h:1,x_center:.421875,y_center:.203125},{w:1,h:1,x_center:.421875,y_center:.203125},{w:1,h:1,x_center:.453125,y_center:.203125},{w:1,h:1,x_center:.453125,y_center:.203125},{w:1,h:1,x_center:.484375,y_center:.203125},{w:1,h:1,x_center:.484375,y_center:.203125},{w:1,h:1,x_center:.515625,y_center:.203125},{w:1,h:1,x_center:.515625,y_center:.203125},{w:1,h:1,x_center:.546875,y_center:.203125},{w:1,h:1,x_center:.546875,y_center:.203125},{w:1,h:1,x_center:.578125,y_center:.203125},{w:1,h:1,x_center:.578125,y_center:.203125},{w:1,h:1,x_center:.609375,y_center:.203125},{w:1,h:1,x_center:.609375,y_center:.203125},{w:1,h:1,x_center:.640625,y_center:.203125},{w:1,h:1,x_center:.640625,y_center:.203125},{w:1,h:1,x_center:.671875,y_center:.203125},{w:1,h:1,x_center:.671875,y_center:.203125},{w:1,h:1,x_center:.703125,y_center:.203125},{w:1,h:1,x_center:.703125,y_center:.203125},{w:1,h:1,x_center:.734375,y_center:.203125},{w:1,h:1,x_center:.734375,y_center:.203125},{w:1,h:1,x_center:.765625,y_center:.203125},{w:1,h:1,x_center:.765625,y_center:.203125},{w:1,h:1,x_center:.796875,y_center:.203125},{w:1,h:1,x_center:.796875,y_center:.203125},{w:1,h:1,x_center:.828125,y_center:.203125},{w:1,h:1,x_center:.828125,y_center:.203125},{w:1,h:1,x_center:.859375,y_center:.203125},{w:1,h:1,x_center:.859375,y_center:.203125},{w:1,h:1,x_center:.890625,y_center:.203125},{w:1,h:1,x_center:.890625,y_center:.203125},{w:1,h:1,x_center:.921875,y_center:.203125},{w:1,h:1,x_center:.921875,y_center:.203125},{w:1,h:1,x_center:.953125,y_center:.203125},{w:1,h:1,x_center:.953125,y_center:.203125},{w:1,h:1,x_center:.984375,y_center:.203125},{w:1,h:1,x_center:.984375,y_center:.203125},{w:1,h:1,x_center:.015625,y_center:.234375},{w:1,h:1,x_center:.015625,y_center:.234375},{w:1,h:1,x_center:.046875,y_center:.234375},{w:1,h:1,x_center:.046875,y_center:.234375},{w:1,h:1,x_center:.078125,y_center:.234375},{w:1,h:1,x_center:.078125,y_center:.234375},{w:1,h:1,x_center:.109375,y_center:.234375},{w:1,h:1,x_center:.109375,y_center:.234375},{w:1,h:1,x_center:.140625,y_center:.234375},{w:1,h:1,x_center:.140625,y_center:.234375},{w:1,h:1,x_center:.171875,y_center:.234375},{w:1,h:1,x_center:.171875,y_center:.234375},{w:1,h:1,x_center:.203125,y_center:.234375},{w:1,h:1,x_center:.203125,y_center:.234375},{w:1,h:1,x_center:.234375,y_center:.234375},{w:1,h:1,x_center:.234375,y_center:.234375},{w:1,h:1,x_center:.265625,y_center:.234375},{w:1,h:1,x_center:.265625,y_center:.234375},{w:1,h:1,x_center:.296875,y_center:.234375},{w:1,h:1,x_center:.296875,y_center:.234375},{w:1,h:1,x_center:.328125,y_center:.234375},{w:1,h:1,x_center:.328125,y_center:.234375},{w:1,h:1,x_center:.359375,y_center:.234375},{w:1,h:1,x_center:.359375,y_center:.234375},{w:1,h:1,x_center:.390625,y_center:.234375},{w:1,h:1,x_center:.390625,y_center:.234375},{w:1,h:1,x_center:.421875,y_center:.234375},{w:1,h:1,x_center:.421875,y_center:.234375},{w:1,h:1,x_center:.453125,y_center:.234375},{w:1,h:1,x_center:.453125,y_center:.234375},{w:1,h:1,x_center:.484375,y_center:.234375},{w:1,h:1,x_center:.484375,y_center:.234375},{w:1,h:1,x_center:.515625,y_center:.234375},{w:1,h:1,x_center:.515625,y_center:.234375},{w:1,h:1,x_center:.546875,y_center:.234375},{w:1,h:1,x_center:.546875,y_center:.234375},{w:1,h:1,x_center:.578125,y_center:.234375},{w:1,h:1,x_center:.578125,y_center:.234375},{w:1,h:1,x_center:.609375,y_center:.234375},{w:1,h:1,x_center:.609375,y_center:.234375},{w:1,h:1,x_center:.640625,y_center:.234375},{w:1,h:1,x_center:.640625,y_center:.234375},{w:1,h:1,x_center:.671875,y_center:.234375},{w:1,h:1,x_center:.671875,y_center:.234375},{w:1,h:1,x_center:.703125,y_center:.234375},{w:1,h:1,x_center:.703125,y_center:.234375},{w:1,h:1,x_center:.734375,y_center:.234375},{w:1,h:1,x_center:.734375,y_center:.234375},{w:1,h:1,x_center:.765625,y_center:.234375},{w:1,h:1,x_center:.765625,y_center:.234375},{w:1,h:1,x_center:.796875,y_center:.234375},{w:1,h:1,x_center:.796875,y_center:.234375},{w:1,h:1,x_center:.828125,y_center:.234375},{w:1,h:1,x_center:.828125,y_center:.234375},{w:1,h:1,x_center:.859375,y_center:.234375},{w:1,h:1,x_center:.859375,y_center:.234375},{w:1,h:1,x_center:.890625,y_center:.234375},{w:1,h:1,x_center:.890625,y_center:.234375},{w:1,h:1,x_center:.921875,y_center:.234375},{w:1,h:1,x_center:.921875,y_center:.234375},{w:1,h:1,x_center:.953125,y_center:.234375},{w:1,h:1,x_center:.953125,y_center:.234375},{w:1,h:1,x_center:.984375,y_center:.234375},{w:1,h:1,x_center:.984375,y_center:.234375},{w:1,h:1,x_center:.015625,y_center:.265625},{w:1,h:1,x_center:.015625,y_center:.265625},{w:1,h:1,x_center:.046875,y_center:.265625},{w:1,h:1,x_center:.046875,y_center:.265625},{w:1,h:1,x_center:.078125,y_center:.265625},{w:1,h:1,x_center:.078125,y_center:.265625},{w:1,h:1,x_center:.109375,y_center:.265625},{w:1,h:1,x_center:.109375,y_center:.265625},{w:1,h:1,x_center:.140625,y_center:.265625},{w:1,h:1,x_center:.140625,y_center:.265625},{w:1,h:1,x_center:.171875,y_center:.265625},{w:1,h:1,x_center:.171875,y_center:.265625},{w:1,h:1,x_center:.203125,y_center:.265625},{w:1,h:1,x_center:.203125,y_center:.265625},{w:1,h:1,x_center:.234375,y_center:.265625},{w:1,h:1,x_center:.234375,y_center:.265625},{w:1,h:1,x_center:.265625,y_center:.265625},{w:1,h:1,x_center:.265625,y_center:.265625},{w:1,h:1,x_center:.296875,y_center:.265625},{w:1,h:1,x_center:.296875,y_center:.265625},{w:1,h:1,x_center:.328125,y_center:.265625},{w:1,h:1,x_center:.328125,y_center:.265625},{w:1,h:1,x_center:.359375,y_center:.265625},{w:1,h:1,x_center:.359375,y_center:.265625},{w:1,h:1,x_center:.390625,y_center:.265625},{w:1,h:1,x_center:.390625,y_center:.265625},{w:1,h:1,x_center:.421875,y_center:.265625},{w:1,h:1,x_center:.421875,y_center:.265625},{w:1,h:1,x_center:.453125,y_center:.265625},{w:1,h:1,x_center:.453125,y_center:.265625},{w:1,h:1,x_center:.484375,y_center:.265625},{w:1,h:1,x_center:.484375,y_center:.265625},{w:1,h:1,x_center:.515625,y_center:.265625},{w:1,h:1,x_center:.515625,y_center:.265625},{w:1,h:1,x_center:.546875,y_center:.265625},{w:1,h:1,x_center:.546875,y_center:.265625},{w:1,h:1,x_center:.578125,y_center:.265625},{w:1,h:1,x_center:.578125,y_center:.265625},{w:1,h:1,x_center:.609375,y_center:.265625},{w:1,h:1,x_center:.609375,y_center:.265625},{w:1,h:1,x_center:.640625,y_center:.265625},{w:1,h:1,x_center:.640625,y_center:.265625},{w:1,h:1,x_center:.671875,y_center:.265625},{w:1,h:1,x_center:.671875,y_center:.265625},{w:1,h:1,x_center:.703125,y_center:.265625},{w:1,h:1,x_center:.703125,y_center:.265625},{w:1,h:1,x_center:.734375,y_center:.265625},{w:1,h:1,x_center:.734375,y_center:.265625},{w:1,h:1,x_center:.765625,y_center:.265625},{w:1,h:1,x_center:.765625,y_center:.265625},{w:1,h:1,x_center:.796875,y_center:.265625},{w:1,h:1,x_center:.796875,y_center:.265625},{w:1,h:1,x_center:.828125,y_center:.265625},{w:1,h:1,x_center:.828125,y_center:.265625},{w:1,h:1,x_center:.859375,y_center:.265625},{w:1,h:1,x_center:.859375,y_center:.265625},{w:1,h:1,x_center:.890625,y_center:.265625},{w:1,h:1,x_center:.890625,y_center:.265625},{w:1,h:1,x_center:.921875,y_center:.265625},{w:1,h:1,x_center:.921875,y_center:.265625},{w:1,h:1,x_center:.953125,y_center:.265625},{w:1,h:1,x_center:.953125,y_center:.265625},{w:1,h:1,x_center:.984375,y_center:.265625},{w:1,h:1,x_center:.984375,y_center:.265625},{w:1,h:1,x_center:.015625,y_center:.296875},{w:1,h:1,x_center:.015625,y_center:.296875},{w:1,h:1,x_center:.046875,y_center:.296875},{w:1,h:1,x_center:.046875,y_center:.296875},{w:1,h:1,x_center:.078125,y_center:.296875},{w:1,h:1,x_center:.078125,y_center:.296875},{w:1,h:1,x_center:.109375,y_center:.296875},{w:1,h:1,x_center:.109375,y_center:.296875},{w:1,h:1,x_center:.140625,y_center:.296875},{w:1,h:1,x_center:.140625,y_center:.296875},{w:1,h:1,x_center:.171875,y_center:.296875},{w:1,h:1,x_center:.171875,y_center:.296875},{w:1,h:1,x_center:.203125,y_center:.296875},{w:1,h:1,x_center:.203125,y_center:.296875},{w:1,h:1,x_center:.234375,y_center:.296875},{w:1,h:1,x_center:.234375,y_center:.296875},{w:1,h:1,x_center:.265625,y_center:.296875},{w:1,h:1,x_center:.265625,y_center:.296875},{w:1,h:1,x_center:.296875,y_center:.296875},{w:1,h:1,x_center:.296875,y_center:.296875},{w:1,h:1,x_center:.328125,y_center:.296875},{w:1,h:1,x_center:.328125,y_center:.296875},{w:1,h:1,x_center:.359375,y_center:.296875},{w:1,h:1,x_center:.359375,y_center:.296875},{w:1,h:1,x_center:.390625,y_center:.296875},{w:1,h:1,x_center:.390625,y_center:.296875},{w:1,h:1,x_center:.421875,y_center:.296875},{w:1,h:1,x_center:.421875,y_center:.296875},{w:1,h:1,x_center:.453125,y_center:.296875},{w:1,h:1,x_center:.453125,y_center:.296875},{w:1,h:1,x_center:.484375,y_center:.296875},{w:1,h:1,x_center:.484375,y_center:.296875},{w:1,h:1,x_center:.515625,y_center:.296875},{w:1,h:1,x_center:.515625,y_center:.296875},{w:1,h:1,x_center:.546875,y_center:.296875},{w:1,h:1,x_center:.546875,y_center:.296875},{w:1,h:1,x_center:.578125,y_center:.296875},{w:1,h:1,x_center:.578125,y_center:.296875},{w:1,h:1,x_center:.609375,y_center:.296875},{w:1,h:1,x_center:.609375,y_center:.296875},{w:1,h:1,x_center:.640625,y_center:.296875},{w:1,h:1,x_center:.640625,y_center:.296875},{w:1,h:1,x_center:.671875,y_center:.296875},{w:1,h:1,x_center:.671875,y_center:.296875},{w:1,h:1,x_center:.703125,y_center:.296875},{w:1,h:1,x_center:.703125,y_center:.296875},{w:1,h:1,x_center:.734375,y_center:.296875},{w:1,h:1,x_center:.734375,y_center:.296875},{w:1,h:1,x_center:.765625,y_center:.296875},{w:1,h:1,x_center:.765625,y_center:.296875},{w:1,h:1,x_center:.796875,y_center:.296875},{w:1,h:1,x_center:.796875,y_center:.296875},{w:1,h:1,x_center:.828125,y_center:.296875},{w:1,h:1,x_center:.828125,y_center:.296875},{w:1,h:1,x_center:.859375,y_center:.296875},{w:1,h:1,x_center:.859375,y_center:.296875},{w:1,h:1,x_center:.890625,y_center:.296875},{w:1,h:1,x_center:.890625,y_center:.296875},{w:1,h:1,x_center:.921875,y_center:.296875},{w:1,h:1,x_center:.921875,y_center:.296875},{w:1,h:1,x_center:.953125,y_center:.296875},{w:1,h:1,x_center:.953125,y_center:.296875},{w:1,h:1,x_center:.984375,y_center:.296875},{w:1,h:1,x_center:.984375,y_center:.296875},{w:1,h:1,x_center:.015625,y_center:.328125},{w:1,h:1,x_center:.015625,y_center:.328125},{w:1,h:1,x_center:.046875,y_center:.328125},{w:1,h:1,x_center:.046875,y_center:.328125},{w:1,h:1,x_center:.078125,y_center:.328125},{w:1,h:1,x_center:.078125,y_center:.328125},{w:1,h:1,x_center:.109375,y_center:.328125},{w:1,h:1,x_center:.109375,y_center:.328125},{w:1,h:1,x_center:.140625,y_center:.328125},{w:1,h:1,x_center:.140625,y_center:.328125},{w:1,h:1,x_center:.171875,y_center:.328125},{w:1,h:1,x_center:.171875,y_center:.328125},{w:1,h:1,x_center:.203125,y_center:.328125},{w:1,h:1,x_center:.203125,y_center:.328125},{w:1,h:1,x_center:.234375,y_center:.328125},{w:1,h:1,x_center:.234375,y_center:.328125},{w:1,h:1,x_center:.265625,y_center:.328125},{w:1,h:1,x_center:.265625,y_center:.328125},{w:1,h:1,x_center:.296875,y_center:.328125},{w:1,h:1,x_center:.296875,y_center:.328125},{w:1,h:1,x_center:.328125,y_center:.328125},{w:1,h:1,x_center:.328125,y_center:.328125},{w:1,h:1,x_center:.359375,y_center:.328125},{w:1,h:1,x_center:.359375,y_center:.328125},{w:1,h:1,x_center:.390625,y_center:.328125},{w:1,h:1,x_center:.390625,y_center:.328125},{w:1,h:1,x_center:.421875,y_center:.328125},{w:1,h:1,x_center:.421875,y_center:.328125},{w:1,h:1,x_center:.453125,y_center:.328125},{w:1,h:1,x_center:.453125,y_center:.328125},{w:1,h:1,x_center:.484375,y_center:.328125},{w:1,h:1,x_center:.484375,y_center:.328125},{w:1,h:1,x_center:.515625,y_center:.328125},{w:1,h:1,x_center:.515625,y_center:.328125},{w:1,h:1,x_center:.546875,y_center:.328125},{w:1,h:1,x_center:.546875,y_center:.328125},{w:1,h:1,x_center:.578125,y_center:.328125},{w:1,h:1,x_center:.578125,y_center:.328125},{w:1,h:1,x_center:.609375,y_center:.328125},{w:1,h:1,x_center:.609375,y_center:.328125},{w:1,h:1,x_center:.640625,y_center:.328125},{w:1,h:1,x_center:.640625,y_center:.328125},{w:1,h:1,x_center:.671875,y_center:.328125},{w:1,h:1,x_center:.671875,y_center:.328125},{w:1,h:1,x_center:.703125,y_center:.328125},{w:1,h:1,x_center:.703125,y_center:.328125},{w:1,h:1,x_center:.734375,y_center:.328125},{w:1,h:1,x_center:.734375,y_center:.328125},{w:1,h:1,x_center:.765625,y_center:.328125},{w:1,h:1,x_center:.765625,y_center:.328125},{w:1,h:1,x_center:.796875,y_center:.328125},{w:1,h:1,x_center:.796875,y_center:.328125},{w:1,h:1,x_center:.828125,y_center:.328125},{w:1,h:1,x_center:.828125,y_center:.328125},{w:1,h:1,x_center:.859375,y_center:.328125},{w:1,h:1,x_center:.859375,y_center:.328125},{w:1,h:1,x_center:.890625,y_center:.328125},{w:1,h:1,x_center:.890625,y_center:.328125},{w:1,h:1,x_center:.921875,y_center:.328125},{w:1,h:1,x_center:.921875,y_center:.328125},{w:1,h:1,x_center:.953125,y_center:.328125},{w:1,h:1,x_center:.953125,y_center:.328125},{w:1,h:1,x_center:.984375,y_center:.328125},{w:1,h:1,x_center:.984375,y_center:.328125},{w:1,h:1,x_center:.015625,y_center:.359375},{w:1,h:1,x_center:.015625,y_center:.359375},{w:1,h:1,x_center:.046875,y_center:.359375},{w:1,h:1,x_center:.046875,y_center:.359375},{w:1,h:1,x_center:.078125,y_center:.359375},{w:1,h:1,x_center:.078125,y_center:.359375},{w:1,h:1,x_center:.109375,y_center:.359375},{w:1,h:1,x_center:.109375,y_center:.359375},{w:1,h:1,x_center:.140625,y_center:.359375},{w:1,h:1,x_center:.140625,y_center:.359375},{w:1,h:1,x_center:.171875,y_center:.359375},{w:1,h:1,x_center:.171875,y_center:.359375},{w:1,h:1,x_center:.203125,y_center:.359375},{w:1,h:1,x_center:.203125,y_center:.359375},{w:1,h:1,x_center:.234375,y_center:.359375},{w:1,h:1,x_center:.234375,y_center:.359375},{w:1,h:1,x_center:.265625,y_center:.359375},{w:1,h:1,x_center:.265625,y_center:.359375},{w:1,h:1,x_center:.296875,y_center:.359375},{w:1,h:1,x_center:.296875,y_center:.359375},{w:1,h:1,x_center:.328125,y_center:.359375},{w:1,h:1,x_center:.328125,y_center:.359375},{w:1,h:1,x_center:.359375,y_center:.359375},{w:1,h:1,x_center:.359375,y_center:.359375},{w:1,h:1,x_center:.390625,y_center:.359375},{w:1,h:1,x_center:.390625,y_center:.359375},{w:1,h:1,x_center:.421875,y_center:.359375},{w:1,h:1,x_center:.421875,y_center:.359375},{w:1,h:1,x_center:.453125,y_center:.359375},{w:1,h:1,x_center:.453125,y_center:.359375},{w:1,h:1,x_center:.484375,y_center:.359375},{w:1,h:1,x_center:.484375,y_center:.359375},{w:1,h:1,x_center:.515625,y_center:.359375},{w:1,h:1,x_center:.515625,y_center:.359375},{w:1,h:1,x_center:.546875,y_center:.359375},{w:1,h:1,x_center:.546875,y_center:.359375},{w:1,h:1,x_center:.578125,y_center:.359375},{w:1,h:1,x_center:.578125,y_center:.359375},{w:1,h:1,x_center:.609375,y_center:.359375},{w:1,h:1,x_center:.609375,y_center:.359375},{w:1,h:1,x_center:.640625,y_center:.359375},{w:1,h:1,x_center:.640625,y_center:.359375},{w:1,h:1,x_center:.671875,y_center:.359375},{w:1,h:1,x_center:.671875,y_center:.359375},{w:1,h:1,x_center:.703125,y_center:.359375},{w:1,h:1,x_center:.703125,y_center:.359375},{w:1,h:1,x_center:.734375,y_center:.359375},{w:1,h:1,x_center:.734375,y_center:.359375},{w:1,h:1,x_center:.765625,y_center:.359375},{w:1,h:1,x_center:.765625,y_center:.359375},{w:1,h:1,x_center:.796875,y_center:.359375},{w:1,h:1,x_center:.796875,y_center:.359375},{w:1,h:1,x_center:.828125,y_center:.359375},{w:1,h:1,x_center:.828125,y_center:.359375},{w:1,h:1,x_center:.859375,y_center:.359375},{w:1,h:1,x_center:.859375,y_center:.359375},{w:1,h:1,x_center:.890625,y_center:.359375},{w:1,h:1,x_center:.890625,y_center:.359375},{w:1,h:1,x_center:.921875,y_center:.359375},{w:1,h:1,x_center:.921875,y_center:.359375},{w:1,h:1,x_center:.953125,y_center:.359375},{w:1,h:1,x_center:.953125,y_center:.359375},{w:1,h:1,x_center:.984375,y_center:.359375},{w:1,h:1,x_center:.984375,y_center:.359375},{w:1,h:1,x_center:.015625,y_center:.390625},{w:1,h:1,x_center:.015625,y_center:.390625},{w:1,h:1,x_center:.046875,y_center:.390625},{w:1,h:1,x_center:.046875,y_center:.390625},{w:1,h:1,x_center:.078125,y_center:.390625},{w:1,h:1,x_center:.078125,y_center:.390625},{w:1,h:1,x_center:.109375,y_center:.390625},{w:1,h:1,x_center:.109375,y_center:.390625},{w:1,h:1,x_center:.140625,y_center:.390625},{w:1,h:1,x_center:.140625,y_center:.390625},{w:1,h:1,x_center:.171875,y_center:.390625},{w:1,h:1,x_center:.171875,y_center:.390625},{w:1,h:1,x_center:.203125,y_center:.390625},{w:1,h:1,x_center:.203125,y_center:.390625},{w:1,h:1,x_center:.234375,y_center:.390625},{w:1,h:1,x_center:.234375,y_center:.390625},{w:1,h:1,x_center:.265625,y_center:.390625},{w:1,h:1,x_center:.265625,y_center:.390625},{w:1,h:1,x_center:.296875,y_center:.390625},{w:1,h:1,x_center:.296875,y_center:.390625},{w:1,h:1,x_center:.328125,y_center:.390625},{w:1,h:1,x_center:.328125,y_center:.390625},{w:1,h:1,x_center:.359375,y_center:.390625},{w:1,h:1,x_center:.359375,y_center:.390625},{w:1,h:1,x_center:.390625,y_center:.390625},{w:1,h:1,x_center:.390625,y_center:.390625},{w:1,h:1,x_center:.421875,y_center:.390625},{w:1,h:1,x_center:.421875,y_center:.390625},{w:1,h:1,x_center:.453125,y_center:.390625},{w:1,h:1,x_center:.453125,y_center:.390625},{w:1,h:1,x_center:.484375,y_center:.390625},{w:1,h:1,x_center:.484375,y_center:.390625},{w:1,h:1,x_center:.515625,y_center:.390625},{w:1,h:1,x_center:.515625,y_center:.390625},{w:1,h:1,x_center:.546875,y_center:.390625},{w:1,h:1,x_center:.546875,y_center:.390625},{w:1,h:1,x_center:.578125,y_center:.390625},{w:1,h:1,x_center:.578125,y_center:.390625},{w:1,h:1,x_center:.609375,y_center:.390625},{w:1,h:1,x_center:.609375,y_center:.390625},{w:1,h:1,x_center:.640625,y_center:.390625},{w:1,h:1,x_center:.640625,y_center:.390625},{w:1,h:1,x_center:.671875,y_center:.390625},{w:1,h:1,x_center:.671875,y_center:.390625},{w:1,h:1,x_center:.703125,y_center:.390625},{w:1,h:1,x_center:.703125,y_center:.390625},{w:1,h:1,x_center:.734375,y_center:.390625},{w:1,h:1,x_center:.734375,y_center:.390625},{w:1,h:1,x_center:.765625,y_center:.390625},{w:1,h:1,x_center:.765625,y_center:.390625},{w:1,h:1,x_center:.796875,y_center:.390625},{w:1,h:1,x_center:.796875,y_center:.390625},{w:1,h:1,x_center:.828125,y_center:.390625},{w:1,h:1,x_center:.828125,y_center:.390625},{w:1,h:1,x_center:.859375,y_center:.390625},{w:1,h:1,x_center:.859375,y_center:.390625},{w:1,h:1,x_center:.890625,y_center:.390625},{w:1,h:1,x_center:.890625,y_center:.390625},{w:1,h:1,x_center:.921875,y_center:.390625},{w:1,h:1,x_center:.921875,y_center:.390625},{w:1,h:1,x_center:.953125,y_center:.390625},{w:1,h:1,x_center:.953125,y_center:.390625},{w:1,h:1,x_center:.984375,y_center:.390625},{w:1,h:1,x_center:.984375,y_center:.390625},{w:1,h:1,x_center:.015625,y_center:.421875},{w:1,h:1,x_center:.015625,y_center:.421875},{w:1,h:1,x_center:.046875,y_center:.421875},{w:1,h:1,x_center:.046875,y_center:.421875},{w:1,h:1,x_center:.078125,y_center:.421875},{w:1,h:1,x_center:.078125,y_center:.421875},{w:1,h:1,x_center:.109375,y_center:.421875},{w:1,h:1,x_center:.109375,y_center:.421875},{w:1,h:1,x_center:.140625,y_center:.421875},{w:1,h:1,x_center:.140625,y_center:.421875},{w:1,h:1,x_center:.171875,y_center:.421875},{w:1,h:1,x_center:.171875,y_center:.421875},{w:1,h:1,x_center:.203125,y_center:.421875},{w:1,h:1,x_center:.203125,y_center:.421875},{w:1,h:1,x_center:.234375,y_center:.421875},{w:1,h:1,x_center:.234375,y_center:.421875},{w:1,h:1,x_center:.265625,y_center:.421875},{w:1,h:1,x_center:.265625,y_center:.421875},{w:1,h:1,x_center:.296875,y_center:.421875},{w:1,h:1,x_center:.296875,y_center:.421875},{w:1,h:1,x_center:.328125,y_center:.421875},{w:1,h:1,x_center:.328125,y_center:.421875},{w:1,h:1,x_center:.359375,y_center:.421875},{w:1,h:1,x_center:.359375,y_center:.421875},{w:1,h:1,x_center:.390625,y_center:.421875},{w:1,h:1,x_center:.390625,y_center:.421875},{w:1,h:1,x_center:.421875,y_center:.421875},{w:1,h:1,x_center:.421875,y_center:.421875},{w:1,h:1,x_center:.453125,y_center:.421875},{w:1,h:1,x_center:.453125,y_center:.421875},{w:1,h:1,x_center:.484375,y_center:.421875},{w:1,h:1,x_center:.484375,y_center:.421875},{w:1,h:1,x_center:.515625,y_center:.421875},{w:1,h:1,x_center:.515625,y_center:.421875},{w:1,h:1,x_center:.546875,y_center:.421875},{w:1,h:1,x_center:.546875,y_center:.421875},{w:1,h:1,x_center:.578125,y_center:.421875},{w:1,h:1,x_center:.578125,y_center:.421875},{w:1,h:1,x_center:.609375,y_center:.421875},{w:1,h:1,x_center:.609375,y_center:.421875},{w:1,h:1,x_center:.640625,y_center:.421875},{w:1,h:1,x_center:.640625,y_center:.421875},{w:1,h:1,x_center:.671875,y_center:.421875},{w:1,h:1,x_center:.671875,y_center:.421875},{w:1,h:1,x_center:.703125,y_center:.421875},{w:1,h:1,x_center:.703125,y_center:.421875},{w:1,h:1,x_center:.734375,y_center:.421875},{w:1,h:1,x_center:.734375,y_center:.421875},{w:1,h:1,x_center:.765625,y_center:.421875},{w:1,h:1,x_center:.765625,y_center:.421875},{w:1,h:1,x_center:.796875,y_center:.421875},{w:1,h:1,x_center:.796875,y_center:.421875},{w:1,h:1,x_center:.828125,y_center:.421875},{w:1,h:1,x_center:.828125,y_center:.421875},{w:1,h:1,x_center:.859375,y_center:.421875},{w:1,h:1,x_center:.859375,y_center:.421875},{w:1,h:1,x_center:.890625,y_center:.421875},{w:1,h:1,x_center:.890625,y_center:.421875},{w:1,h:1,x_center:.921875,y_center:.421875},{w:1,h:1,x_center:.921875,y_center:.421875},{w:1,h:1,x_center:.953125,y_center:.421875},{w:1,h:1,x_center:.953125,y_center:.421875},{w:1,h:1,x_center:.984375,y_center:.421875},{w:1,h:1,x_center:.984375,y_center:.421875},{w:1,h:1,x_center:.015625,y_center:.453125},{w:1,h:1,x_center:.015625,y_center:.453125},{w:1,h:1,x_center:.046875,y_center:.453125},{w:1,h:1,x_center:.046875,y_center:.453125},{w:1,h:1,x_center:.078125,y_center:.453125},{w:1,h:1,x_center:.078125,y_center:.453125},{w:1,h:1,x_center:.109375,y_center:.453125},{w:1,h:1,x_center:.109375,y_center:.453125},{w:1,h:1,x_center:.140625,y_center:.453125},{w:1,h:1,x_center:.140625,y_center:.453125},{w:1,h:1,x_center:.171875,y_center:.453125},{w:1,h:1,x_center:.171875,y_center:.453125},{w:1,h:1,x_center:.203125,y_center:.453125},{w:1,h:1,x_center:.203125,y_center:.453125},{w:1,h:1,x_center:.234375,y_center:.453125},{w:1,h:1,x_center:.234375,y_center:.453125},{w:1,h:1,x_center:.265625,y_center:.453125},{w:1,h:1,x_center:.265625,y_center:.453125},{w:1,h:1,x_center:.296875,y_center:.453125},{w:1,h:1,x_center:.296875,y_center:.453125},{w:1,h:1,x_center:.328125,y_center:.453125},{w:1,h:1,x_center:.328125,y_center:.453125},{w:1,h:1,x_center:.359375,y_center:.453125},{w:1,h:1,x_center:.359375,y_center:.453125},{w:1,h:1,x_center:.390625,y_center:.453125},{w:1,h:1,x_center:.390625,y_center:.453125},{w:1,h:1,x_center:.421875,y_center:.453125},{w:1,h:1,x_center:.421875,y_center:.453125},{w:1,h:1,x_center:.453125,y_center:.453125},{w:1,h:1,x_center:.453125,y_center:.453125},{w:1,h:1,x_center:.484375,y_center:.453125},{w:1,h:1,x_center:.484375,y_center:.453125},{w:1,h:1,x_center:.515625,y_center:.453125},{w:1,h:1,x_center:.515625,y_center:.453125},{w:1,h:1,x_center:.546875,y_center:.453125},{w:1,h:1,x_center:.546875,y_center:.453125},{w:1,h:1,x_center:.578125,y_center:.453125},{w:1,h:1,x_center:.578125,y_center:.453125},{w:1,h:1,x_center:.609375,y_center:.453125},{w:1,h:1,x_center:.609375,y_center:.453125},{w:1,h:1,x_center:.640625,y_center:.453125},{w:1,h:1,x_center:.640625,y_center:.453125},{w:1,h:1,x_center:.671875,y_center:.453125},{w:1,h:1,x_center:.671875,y_center:.453125},{w:1,h:1,x_center:.703125,y_center:.453125},{w:1,h:1,x_center:.703125,y_center:.453125},{w:1,h:1,x_center:.734375,y_center:.453125},{w:1,h:1,x_center:.734375,y_center:.453125},{w:1,h:1,x_center:.765625,y_center:.453125},{w:1,h:1,x_center:.765625,y_center:.453125},{w:1,h:1,x_center:.796875,y_center:.453125},{w:1,h:1,x_center:.796875,y_center:.453125},{w:1,h:1,x_center:.828125,y_center:.453125},{w:1,h:1,x_center:.828125,y_center:.453125},{w:1,h:1,x_center:.859375,y_center:.453125},{w:1,h:1,x_center:.859375,y_center:.453125},{w:1,h:1,x_center:.890625,y_center:.453125},{w:1,h:1,x_center:.890625,y_center:.453125},{w:1,h:1,x_center:.921875,y_center:.453125},{w:1,h:1,x_center:.921875,y_center:.453125},{w:1,h:1,x_center:.953125,y_center:.453125},{w:1,h:1,x_center:.953125,y_center:.453125},{w:1,h:1,x_center:.984375,y_center:.453125},{w:1,h:1,x_center:.984375,y_center:.453125},{w:1,h:1,x_center:.015625,y_center:.484375},{w:1,h:1,x_center:.015625,y_center:.484375},{w:1,h:1,x_center:.046875,y_center:.484375},{w:1,h:1,x_center:.046875,y_center:.484375},{w:1,h:1,x_center:.078125,y_center:.484375},{w:1,h:1,x_center:.078125,y_center:.484375},{w:1,h:1,x_center:.109375,y_center:.484375},{w:1,h:1,x_center:.109375,y_center:.484375},{w:1,h:1,x_center:.140625,y_center:.484375},{w:1,h:1,x_center:.140625,y_center:.484375},{w:1,h:1,x_center:.171875,y_center:.484375},{w:1,h:1,x_center:.171875,y_center:.484375},{w:1,h:1,x_center:.203125,y_center:.484375},{w:1,h:1,x_center:.203125,y_center:.484375},{w:1,h:1,x_center:.234375,y_center:.484375},{w:1,h:1,x_center:.234375,y_center:.484375},{w:1,h:1,x_center:.265625,y_center:.484375},{w:1,h:1,x_center:.265625,y_center:.484375},{w:1,h:1,x_center:.296875,y_center:.484375},{w:1,h:1,x_center:.296875,y_center:.484375},{w:1,h:1,x_center:.328125,y_center:.484375},{w:1,h:1,x_center:.328125,y_center:.484375},{w:1,h:1,x_center:.359375,y_center:.484375},{w:1,h:1,x_center:.359375,y_center:.484375},{w:1,h:1,x_center:.390625,y_center:.484375},{w:1,h:1,x_center:.390625,y_center:.484375},{w:1,h:1,x_center:.421875,y_center:.484375},{w:1,h:1,x_center:.421875,y_center:.484375},{w:1,h:1,x_center:.453125,y_center:.484375},{w:1,h:1,x_center:.453125,y_center:.484375},{w:1,h:1,x_center:.484375,y_center:.484375},{w:1,h:1,x_center:.484375,y_center:.484375},{w:1,h:1,x_center:.515625,y_center:.484375},{w:1,h:1,x_center:.515625,y_center:.484375},{w:1,h:1,x_center:.546875,y_center:.484375},{w:1,h:1,x_center:.546875,y_center:.484375},{w:1,h:1,x_center:.578125,y_center:.484375},{w:1,h:1,x_center:.578125,y_center:.484375},{w:1,h:1,x_center:.609375,y_center:.484375},{w:1,h:1,x_center:.609375,y_center:.484375},{w:1,h:1,x_center:.640625,y_center:.484375},{w:1,h:1,x_center:.640625,y_center:.484375},{w:1,h:1,x_center:.671875,y_center:.484375},{w:1,h:1,x_center:.671875,y_center:.484375},{w:1,h:1,x_center:.703125,y_center:.484375},{w:1,h:1,x_center:.703125,y_center:.484375},{w:1,h:1,x_center:.734375,y_center:.484375},{w:1,h:1,x_center:.734375,y_center:.484375},{w:1,h:1,x_center:.765625,y_center:.484375},{w:1,h:1,x_center:.765625,y_center:.484375},{w:1,h:1,x_center:.796875,y_center:.484375},{w:1,h:1,x_center:.796875,y_center:.484375},{w:1,h:1,x_center:.828125,y_center:.484375},{w:1,h:1,x_center:.828125,y_center:.484375},{w:1,h:1,x_center:.859375,y_center:.484375},{w:1,h:1,x_center:.859375,y_center:.484375},{w:1,h:1,x_center:.890625,y_center:.484375},{w:1,h:1,x_center:.890625,y_center:.484375},{w:1,h:1,x_center:.921875,y_center:.484375},{w:1,h:1,x_center:.921875,y_center:.484375},{w:1,h:1,x_center:.953125,y_center:.484375},{w:1,h:1,x_center:.953125,y_center:.484375},{w:1,h:1,x_center:.984375,y_center:.484375},{w:1,h:1,x_center:.984375,y_center:.484375},{w:1,h:1,x_center:.015625,y_center:.515625},{w:1,h:1,x_center:.015625,y_center:.515625},{w:1,h:1,x_center:.046875,y_center:.515625},{w:1,h:1,x_center:.046875,y_center:.515625},{w:1,h:1,x_center:.078125,y_center:.515625},{w:1,h:1,x_center:.078125,y_center:.515625},{w:1,h:1,x_center:.109375,y_center:.515625},{w:1,h:1,x_center:.109375,y_center:.515625},{w:1,h:1,x_center:.140625,y_center:.515625},{w:1,h:1,x_center:.140625,y_center:.515625},{w:1,h:1,x_center:.171875,y_center:.515625},{w:1,h:1,x_center:.171875,y_center:.515625},{w:1,h:1,x_center:.203125,y_center:.515625},{w:1,h:1,x_center:.203125,y_center:.515625},{w:1,h:1,x_center:.234375,y_center:.515625},{w:1,h:1,x_center:.234375,y_center:.515625},{w:1,h:1,x_center:.265625,y_center:.515625},{w:1,h:1,x_center:.265625,y_center:.515625},{w:1,h:1,x_center:.296875,y_center:.515625},{w:1,h:1,x_center:.296875,y_center:.515625},{w:1,h:1,x_center:.328125,y_center:.515625},{w:1,h:1,x_center:.328125,y_center:.515625},{w:1,h:1,x_center:.359375,y_center:.515625},{w:1,h:1,x_center:.359375,y_center:.515625},{w:1,h:1,x_center:.390625,y_center:.515625},{w:1,h:1,x_center:.390625,y_center:.515625},{w:1,h:1,x_center:.421875,y_center:.515625},{w:1,h:1,x_center:.421875,y_center:.515625},{w:1,h:1,x_center:.453125,y_center:.515625},{w:1,h:1,x_center:.453125,y_center:.515625},{w:1,h:1,x_center:.484375,y_center:.515625},{w:1,h:1,x_center:.484375,y_center:.515625},{w:1,h:1,x_center:.515625,y_center:.515625},{w:1,h:1,x_center:.515625,y_center:.515625},{w:1,h:1,x_center:.546875,y_center:.515625},{w:1,h:1,x_center:.546875,y_center:.515625},{w:1,h:1,x_center:.578125,y_center:.515625},{w:1,h:1,x_center:.578125,y_center:.515625},{w:1,h:1,x_center:.609375,y_center:.515625},{w:1,h:1,x_center:.609375,y_center:.515625},{w:1,h:1,x_center:.640625,y_center:.515625},{w:1,h:1,x_center:.640625,y_center:.515625},{w:1,h:1,x_center:.671875,y_center:.515625},{w:1,h:1,x_center:.671875,y_center:.515625},{w:1,h:1,x_center:.703125,y_center:.515625},{w:1,h:1,x_center:.703125,y_center:.515625},{w:1,h:1,x_center:.734375,y_center:.515625},{w:1,h:1,x_center:.734375,y_center:.515625},{w:1,h:1,x_center:.765625,y_center:.515625},{w:1,h:1,x_center:.765625,y_center:.515625},{w:1,h:1,x_center:.796875,y_center:.515625},{w:1,h:1,x_center:.796875,y_center:.515625},{w:1,h:1,x_center:.828125,y_center:.515625},{w:1,h:1,x_center:.828125,y_center:.515625},{w:1,h:1,x_center:.859375,y_center:.515625},{w:1,h:1,x_center:.859375,y_center:.515625},{w:1,h:1,x_center:.890625,y_center:.515625},{w:1,h:1,x_center:.890625,y_center:.515625},{w:1,h:1,x_center:.921875,y_center:.515625},{w:1,h:1,x_center:.921875,y_center:.515625},{w:1,h:1,x_center:.953125,y_center:.515625},{w:1,h:1,x_center:.953125,y_center:.515625},{w:1,h:1,x_center:.984375,y_center:.515625},{w:1,h:1,x_center:.984375,y_center:.515625},{w:1,h:1,x_center:.015625,y_center:.546875},{w:1,h:1,x_center:.015625,y_center:.546875},{w:1,h:1,x_center:.046875,y_center:.546875},{w:1,h:1,x_center:.046875,y_center:.546875},{w:1,h:1,x_center:.078125,y_center:.546875},{w:1,h:1,x_center:.078125,y_center:.546875},{w:1,h:1,x_center:.109375,y_center:.546875},{w:1,h:1,x_center:.109375,y_center:.546875},{w:1,h:1,x_center:.140625,y_center:.546875},{w:1,h:1,x_center:.140625,y_center:.546875},{w:1,h:1,x_center:.171875,y_center:.546875},{w:1,h:1,x_center:.171875,y_center:.546875},{w:1,h:1,x_center:.203125,y_center:.546875},{w:1,h:1,x_center:.203125,y_center:.546875},{w:1,h:1,x_center:.234375,y_center:.546875},{w:1,h:1,x_center:.234375,y_center:.546875},{w:1,h:1,x_center:.265625,y_center:.546875},{w:1,h:1,x_center:.265625,y_center:.546875},{w:1,h:1,x_center:.296875,y_center:.546875},{w:1,h:1,x_center:.296875,y_center:.546875},{w:1,h:1,x_center:.328125,y_center:.546875},{w:1,h:1,x_center:.328125,y_center:.546875},{w:1,h:1,x_center:.359375,y_center:.546875},{w:1,h:1,x_center:.359375,y_center:.546875},{w:1,h:1,x_center:.390625,y_center:.546875},{w:1,h:1,x_center:.390625,y_center:.546875},{w:1,h:1,x_center:.421875,y_center:.546875},{w:1,h:1,x_center:.421875,y_center:.546875},{w:1,h:1,x_center:.453125,y_center:.546875},{w:1,h:1,x_center:.453125,y_center:.546875},{w:1,h:1,x_center:.484375,y_center:.546875},{w:1,h:1,x_center:.484375,y_center:.546875},{w:1,h:1,x_center:.515625,y_center:.546875},{w:1,h:1,x_center:.515625,y_center:.546875},{w:1,h:1,x_center:.546875,y_center:.546875},{w:1,h:1,x_center:.546875,y_center:.546875},{w:1,h:1,x_center:.578125,y_center:.546875},{w:1,h:1,x_center:.578125,y_center:.546875},{w:1,h:1,x_center:.609375,y_center:.546875},{w:1,h:1,x_center:.609375,y_center:.546875},{w:1,h:1,x_center:.640625,y_center:.546875},{w:1,h:1,x_center:.640625,y_center:.546875},{w:1,h:1,x_center:.671875,y_center:.546875},{w:1,h:1,x_center:.671875,y_center:.546875},{w:1,h:1,x_center:.703125,y_center:.546875},{w:1,h:1,x_center:.703125,y_center:.546875},{w:1,h:1,x_center:.734375,y_center:.546875},{w:1,h:1,x_center:.734375,y_center:.546875},{w:1,h:1,x_center:.765625,y_center:.546875},{w:1,h:1,x_center:.765625,y_center:.546875},{w:1,h:1,x_center:.796875,y_center:.546875},{w:1,h:1,x_center:.796875,y_center:.546875},{w:1,h:1,x_center:.828125,y_center:.546875},{w:1,h:1,x_center:.828125,y_center:.546875},{w:1,h:1,x_center:.859375,y_center:.546875},{w:1,h:1,x_center:.859375,y_center:.546875},{w:1,h:1,x_center:.890625,y_center:.546875},{w:1,h:1,x_center:.890625,y_center:.546875},{w:1,h:1,x_center:.921875,y_center:.546875},{w:1,h:1,x_center:.921875,y_center:.546875},{w:1,h:1,x_center:.953125,y_center:.546875},{w:1,h:1,x_center:.953125,y_center:.546875},{w:1,h:1,x_center:.984375,y_center:.546875},{w:1,h:1,x_center:.984375,y_center:.546875},{w:1,h:1,x_center:.015625,y_center:.578125},{w:1,h:1,x_center:.015625,y_center:.578125},{w:1,h:1,x_center:.046875,y_center:.578125},{w:1,h:1,x_center:.046875,y_center:.578125},{w:1,h:1,x_center:.078125,y_center:.578125},{w:1,h:1,x_center:.078125,y_center:.578125},{w:1,h:1,x_center:.109375,y_center:.578125},{w:1,h:1,x_center:.109375,y_center:.578125},{w:1,h:1,x_center:.140625,y_center:.578125},{w:1,h:1,x_center:.140625,y_center:.578125},{w:1,h:1,x_center:.171875,y_center:.578125},{w:1,h:1,x_center:.171875,y_center:.578125},{w:1,h:1,x_center:.203125,y_center:.578125},{w:1,h:1,x_center:.203125,y_center:.578125},{w:1,h:1,x_center:.234375,y_center:.578125},{w:1,h:1,x_center:.234375,y_center:.578125},{w:1,h:1,x_center:.265625,y_center:.578125},{w:1,h:1,x_center:.265625,y_center:.578125},{w:1,h:1,x_center:.296875,y_center:.578125},{w:1,h:1,x_center:.296875,y_center:.578125},{w:1,h:1,x_center:.328125,y_center:.578125},{w:1,h:1,x_center:.328125,y_center:.578125},{w:1,h:1,x_center:.359375,y_center:.578125},{w:1,h:1,x_center:.359375,y_center:.578125},{w:1,h:1,x_center:.390625,y_center:.578125},{w:1,h:1,x_center:.390625,y_center:.578125},{w:1,h:1,x_center:.421875,y_center:.578125},{w:1,h:1,x_center:.421875,y_center:.578125},{w:1,h:1,x_center:.453125,y_center:.578125},{w:1,h:1,x_center:.453125,y_center:.578125},{w:1,h:1,x_center:.484375,y_center:.578125},{w:1,h:1,x_center:.484375,y_center:.578125},{w:1,h:1,x_center:.515625,y_center:.578125},{w:1,h:1,x_center:.515625,y_center:.578125},{w:1,h:1,x_center:.546875,y_center:.578125},{w:1,h:1,x_center:.546875,y_center:.578125},{w:1,h:1,x_center:.578125,y_center:.578125},{w:1,h:1,x_center:.578125,y_center:.578125},{w:1,h:1,x_center:.609375,y_center:.578125},{w:1,h:1,x_center:.609375,y_center:.578125},{w:1,h:1,x_center:.640625,y_center:.578125},{w:1,h:1,x_center:.640625,y_center:.578125},{w:1,h:1,x_center:.671875,y_center:.578125},{w:1,h:1,x_center:.671875,y_center:.578125},{w:1,h:1,x_center:.703125,y_center:.578125},{w:1,h:1,x_center:.703125,y_center:.578125},{w:1,h:1,x_center:.734375,y_center:.578125},{w:1,h:1,x_center:.734375,y_center:.578125},{w:1,h:1,x_center:.765625,y_center:.578125},{w:1,h:1,x_center:.765625,y_center:.578125},{w:1,h:1,x_center:.796875,y_center:.578125},{w:1,h:1,x_center:.796875,y_center:.578125},{w:1,h:1,x_center:.828125,y_center:.578125},{w:1,h:1,x_center:.828125,y_center:.578125},{w:1,h:1,x_center:.859375,y_center:.578125},{w:1,h:1,x_center:.859375,y_center:.578125},{w:1,h:1,x_center:.890625,y_center:.578125},{w:1,h:1,x_center:.890625,y_center:.578125},{w:1,h:1,x_center:.921875,y_center:.578125},{w:1,h:1,x_center:.921875,y_center:.578125},{w:1,h:1,x_center:.953125,y_center:.578125},{w:1,h:1,x_center:.953125,y_center:.578125},{w:1,h:1,x_center:.984375,y_center:.578125},{w:1,h:1,x_center:.984375,y_center:.578125},{w:1,h:1,x_center:.015625,y_center:.609375},{w:1,h:1,x_center:.015625,y_center:.609375},{w:1,h:1,x_center:.046875,y_center:.609375},{w:1,h:1,x_center:.046875,y_center:.609375},{w:1,h:1,x_center:.078125,y_center:.609375},{w:1,h:1,x_center:.078125,y_center:.609375},{w:1,h:1,x_center:.109375,y_center:.609375},{w:1,h:1,x_center:.109375,y_center:.609375},{w:1,h:1,x_center:.140625,y_center:.609375},{w:1,h:1,x_center:.140625,y_center:.609375},{w:1,h:1,x_center:.171875,y_center:.609375},{w:1,h:1,x_center:.171875,y_center:.609375},{w:1,h:1,x_center:.203125,y_center:.609375},{w:1,h:1,x_center:.203125,y_center:.609375},{w:1,h:1,x_center:.234375,y_center:.609375},{w:1,h:1,x_center:.234375,y_center:.609375},{w:1,h:1,x_center:.265625,y_center:.609375},{w:1,h:1,x_center:.265625,y_center:.609375},{w:1,h:1,x_center:.296875,y_center:.609375},{w:1,h:1,x_center:.296875,y_center:.609375},{w:1,h:1,x_center:.328125,y_center:.609375},{w:1,h:1,x_center:.328125,y_center:.609375},{w:1,h:1,x_center:.359375,y_center:.609375},{w:1,h:1,x_center:.359375,y_center:.609375},{w:1,h:1,x_center:.390625,y_center:.609375},{w:1,h:1,x_center:.390625,y_center:.609375},{w:1,h:1,x_center:.421875,y_center:.609375},{w:1,h:1,x_center:.421875,y_center:.609375},{w:1,h:1,x_center:.453125,y_center:.609375},{w:1,h:1,x_center:.453125,y_center:.609375},{w:1,h:1,x_center:.484375,y_center:.609375},{w:1,h:1,x_center:.484375,y_center:.609375},{w:1,h:1,x_center:.515625,y_center:.609375},{w:1,h:1,x_center:.515625,y_center:.609375},{w:1,h:1,x_center:.546875,y_center:.609375},{w:1,h:1,x_center:.546875,y_center:.609375},{w:1,h:1,x_center:.578125,y_center:.609375},{w:1,h:1,x_center:.578125,y_center:.609375},{w:1,h:1,x_center:.609375,y_center:.609375},{w:1,h:1,x_center:.609375,y_center:.609375},{w:1,h:1,x_center:.640625,y_center:.609375},{w:1,h:1,x_center:.640625,y_center:.609375},{w:1,h:1,x_center:.671875,y_center:.609375},{w:1,h:1,x_center:.671875,y_center:.609375},{w:1,h:1,x_center:.703125,y_center:.609375},{w:1,h:1,x_center:.703125,y_center:.609375},{w:1,h:1,x_center:.734375,y_center:.609375},{w:1,h:1,x_center:.734375,y_center:.609375},{w:1,h:1,x_center:.765625,y_center:.609375},{w:1,h:1,x_center:.765625,y_center:.609375},{w:1,h:1,x_center:.796875,y_center:.609375},{w:1,h:1,x_center:.796875,y_center:.609375},{w:1,h:1,x_center:.828125,y_center:.609375},{w:1,h:1,x_center:.828125,y_center:.609375},{w:1,h:1,x_center:.859375,y_center:.609375},{w:1,h:1,x_center:.859375,y_center:.609375},{w:1,h:1,x_center:.890625,y_center:.609375},{w:1,h:1,x_center:.890625,y_center:.609375},{w:1,h:1,x_center:.921875,y_center:.609375},{w:1,h:1,x_center:.921875,y_center:.609375},{w:1,h:1,x_center:.953125,y_center:.609375},{w:1,h:1,x_center:.953125,y_center:.609375},{w:1,h:1,x_center:.984375,y_center:.609375},{w:1,h:1,x_center:.984375,y_center:.609375},{w:1,h:1,x_center:.015625,y_center:.640625},{w:1,h:1,x_center:.015625,y_center:.640625},{w:1,h:1,x_center:.046875,y_center:.640625},{w:1,h:1,x_center:.046875,y_center:.640625},{w:1,h:1,x_center:.078125,y_center:.640625},{w:1,h:1,x_center:.078125,y_center:.640625},{w:1,h:1,x_center:.109375,y_center:.640625},{w:1,h:1,x_center:.109375,y_center:.640625},{w:1,h:1,x_center:.140625,y_center:.640625},{w:1,h:1,x_center:.140625,y_center:.640625},{w:1,h:1,x_center:.171875,y_center:.640625},{w:1,h:1,x_center:.171875,y_center:.640625},{w:1,h:1,x_center:.203125,y_center:.640625},{w:1,h:1,x_center:.203125,y_center:.640625},{w:1,h:1,x_center:.234375,y_center:.640625},{w:1,h:1,x_center:.234375,y_center:.640625},{w:1,h:1,x_center:.265625,y_center:.640625},{w:1,h:1,x_center:.265625,y_center:.640625},{w:1,h:1,x_center:.296875,y_center:.640625},{w:1,h:1,x_center:.296875,y_center:.640625},{w:1,h:1,x_center:.328125,y_center:.640625},{w:1,h:1,x_center:.328125,y_center:.640625},{w:1,h:1,x_center:.359375,y_center:.640625},{w:1,h:1,x_center:.359375,y_center:.640625},{w:1,h:1,x_center:.390625,y_center:.640625},{w:1,h:1,x_center:.390625,y_center:.640625},{w:1,h:1,x_center:.421875,y_center:.640625},{w:1,h:1,x_center:.421875,y_center:.640625},{w:1,h:1,x_center:.453125,y_center:.640625},{w:1,h:1,x_center:.453125,y_center:.640625},{w:1,h:1,x_center:.484375,y_center:.640625},{w:1,h:1,x_center:.484375,y_center:.640625},{w:1,h:1,x_center:.515625,y_center:.640625},{w:1,h:1,x_center:.515625,y_center:.640625},{w:1,h:1,x_center:.546875,y_center:.640625},{w:1,h:1,x_center:.546875,y_center:.640625},{w:1,h:1,x_center:.578125,y_center:.640625},{w:1,h:1,x_center:.578125,y_center:.640625},{w:1,h:1,x_center:.609375,y_center:.640625},{w:1,h:1,x_center:.609375,y_center:.640625},{w:1,h:1,x_center:.640625,y_center:.640625},{w:1,h:1,x_center:.640625,y_center:.640625},{w:1,h:1,x_center:.671875,y_center:.640625},{w:1,h:1,x_center:.671875,y_center:.640625},{w:1,h:1,x_center:.703125,y_center:.640625},{w:1,h:1,x_center:.703125,y_center:.640625},{w:1,h:1,x_center:.734375,y_center:.640625},{w:1,h:1,x_center:.734375,y_center:.640625},{w:1,h:1,x_center:.765625,y_center:.640625},{w:1,h:1,x_center:.765625,y_center:.640625},{w:1,h:1,x_center:.796875,y_center:.640625},{w:1,h:1,x_center:.796875,y_center:.640625},{w:1,h:1,x_center:.828125,y_center:.640625},{w:1,h:1,x_center:.828125,y_center:.640625},{w:1,h:1,x_center:.859375,y_center:.640625},{w:1,h:1,x_center:.859375,y_center:.640625},{w:1,h:1,x_center:.890625,y_center:.640625},{w:1,h:1,x_center:.890625,y_center:.640625},{w:1,h:1,x_center:.921875,y_center:.640625},{w:1,h:1,x_center:.921875,y_center:.640625},{w:1,h:1,x_center:.953125,y_center:.640625},{w:1,h:1,x_center:.953125,y_center:.640625},{w:1,h:1,x_center:.984375,y_center:.640625},{w:1,h:1,x_center:.984375,y_center:.640625},{w:1,h:1,x_center:.015625,y_center:.671875},{w:1,h:1,x_center:.015625,y_center:.671875},{w:1,h:1,x_center:.046875,y_center:.671875},{w:1,h:1,x_center:.046875,y_center:.671875},{w:1,h:1,x_center:.078125,y_center:.671875},{w:1,h:1,x_center:.078125,y_center:.671875},{w:1,h:1,x_center:.109375,y_center:.671875},{w:1,h:1,x_center:.109375,y_center:.671875},{w:1,h:1,x_center:.140625,y_center:.671875},{w:1,h:1,x_center:.140625,y_center:.671875},{w:1,h:1,x_center:.171875,y_center:.671875},{w:1,h:1,x_center:.171875,y_center:.671875},{w:1,h:1,x_center:.203125,y_center:.671875},{w:1,h:1,x_center:.203125,y_center:.671875},{w:1,h:1,x_center:.234375,y_center:.671875},{w:1,h:1,x_center:.234375,y_center:.671875},{w:1,h:1,x_center:.265625,y_center:.671875},{w:1,h:1,x_center:.265625,y_center:.671875},{w:1,h:1,x_center:.296875,y_center:.671875},{w:1,h:1,x_center:.296875,y_center:.671875},{w:1,h:1,x_center:.328125,y_center:.671875},{w:1,h:1,x_center:.328125,y_center:.671875},{w:1,h:1,x_center:.359375,y_center:.671875},{w:1,h:1,x_center:.359375,y_center:.671875},{w:1,h:1,x_center:.390625,y_center:.671875},{w:1,h:1,x_center:.390625,y_center:.671875},{w:1,h:1,x_center:.421875,y_center:.671875},{w:1,h:1,x_center:.421875,y_center:.671875},{w:1,h:1,x_center:.453125,y_center:.671875},{w:1,h:1,x_center:.453125,y_center:.671875},{w:1,h:1,x_center:.484375,y_center:.671875},{w:1,h:1,x_center:.484375,y_center:.671875},{w:1,h:1,x_center:.515625,y_center:.671875},{w:1,h:1,x_center:.515625,y_center:.671875},{w:1,h:1,x_center:.546875,y_center:.671875},{w:1,h:1,x_center:.546875,y_center:.671875},{w:1,h:1,x_center:.578125,y_center:.671875},{w:1,h:1,x_center:.578125,y_center:.671875},{w:1,h:1,x_center:.609375,y_center:.671875},{w:1,h:1,x_center:.609375,y_center:.671875},{w:1,h:1,x_center:.640625,y_center:.671875},{w:1,h:1,x_center:.640625,y_center:.671875},{w:1,h:1,x_center:.671875,y_center:.671875},{w:1,h:1,x_center:.671875,y_center:.671875},{w:1,h:1,x_center:.703125,y_center:.671875},{w:1,h:1,x_center:.703125,y_center:.671875},{w:1,h:1,x_center:.734375,y_center:.671875},{w:1,h:1,x_center:.734375,y_center:.671875},{w:1,h:1,x_center:.765625,y_center:.671875},{w:1,h:1,x_center:.765625,y_center:.671875},{w:1,h:1,x_center:.796875,y_center:.671875},{w:1,h:1,x_center:.796875,y_center:.671875},{w:1,h:1,x_center:.828125,y_center:.671875},{w:1,h:1,x_center:.828125,y_center:.671875},{w:1,h:1,x_center:.859375,y_center:.671875},{w:1,h:1,x_center:.859375,y_center:.671875},{w:1,h:1,x_center:.890625,y_center:.671875},{w:1,h:1,x_center:.890625,y_center:.671875},{w:1,h:1,x_center:.921875,y_center:.671875},{w:1,h:1,x_center:.921875,y_center:.671875},{w:1,h:1,x_center:.953125,y_center:.671875},{w:1,h:1,x_center:.953125,y_center:.671875},{w:1,h:1,x_center:.984375,y_center:.671875},{w:1,h:1,x_center:.984375,y_center:.671875},{w:1,h:1,x_center:.015625,y_center:.703125},{w:1,h:1,x_center:.015625,y_center:.703125},{w:1,h:1,x_center:.046875,y_center:.703125},{w:1,h:1,x_center:.046875,y_center:.703125},{w:1,h:1,x_center:.078125,y_center:.703125},{w:1,h:1,x_center:.078125,y_center:.703125},{w:1,h:1,x_center:.109375,y_center:.703125},{w:1,h:1,x_center:.109375,y_center:.703125},{w:1,h:1,x_center:.140625,y_center:.703125},{w:1,h:1,x_center:.140625,y_center:.703125},{w:1,h:1,x_center:.171875,y_center:.703125},{w:1,h:1,x_center:.171875,y_center:.703125},{w:1,h:1,x_center:.203125,y_center:.703125},{w:1,h:1,x_center:.203125,y_center:.703125},{w:1,h:1,x_center:.234375,y_center:.703125},{w:1,h:1,x_center:.234375,y_center:.703125},{w:1,h:1,x_center:.265625,y_center:.703125},{w:1,h:1,x_center:.265625,y_center:.703125},{w:1,h:1,x_center:.296875,y_center:.703125},{w:1,h:1,x_center:.296875,y_center:.703125},{w:1,h:1,x_center:.328125,y_center:.703125},{w:1,h:1,x_center:.328125,y_center:.703125},{w:1,h:1,x_center:.359375,y_center:.703125},{w:1,h:1,x_center:.359375,y_center:.703125},{w:1,h:1,x_center:.390625,y_center:.703125},{w:1,h:1,x_center:.390625,y_center:.703125},{w:1,h:1,x_center:.421875,y_center:.703125},{w:1,h:1,x_center:.421875,y_center:.703125},{w:1,h:1,x_center:.453125,y_center:.703125},{w:1,h:1,x_center:.453125,y_center:.703125},{w:1,h:1,x_center:.484375,y_center:.703125},{w:1,h:1,x_center:.484375,y_center:.703125},{w:1,h:1,x_center:.515625,y_center:.703125},{w:1,h:1,x_center:.515625,y_center:.703125},{w:1,h:1,x_center:.546875,y_center:.703125},{w:1,h:1,x_center:.546875,y_center:.703125},{w:1,h:1,x_center:.578125,y_center:.703125},{w:1,h:1,x_center:.578125,y_center:.703125},{w:1,h:1,x_center:.609375,y_center:.703125},{w:1,h:1,x_center:.609375,y_center:.703125},{w:1,h:1,x_center:.640625,y_center:.703125},{w:1,h:1,x_center:.640625,y_center:.703125},{w:1,h:1,x_center:.671875,y_center:.703125},{w:1,h:1,x_center:.671875,y_center:.703125},{w:1,h:1,x_center:.703125,y_center:.703125},{w:1,h:1,x_center:.703125,y_center:.703125},{w:1,h:1,x_center:.734375,y_center:.703125},{w:1,h:1,x_center:.734375,y_center:.703125},{w:1,h:1,x_center:.765625,y_center:.703125},{w:1,h:1,x_center:.765625,y_center:.703125},{w:1,h:1,x_center:.796875,y_center:.703125},{w:1,h:1,x_center:.796875,y_center:.703125},{w:1,h:1,x_center:.828125,y_center:.703125},{w:1,h:1,x_center:.828125,y_center:.703125},{w:1,h:1,x_center:.859375,y_center:.703125},{w:1,h:1,x_center:.859375,y_center:.703125},{w:1,h:1,x_center:.890625,y_center:.703125},{w:1,h:1,x_center:.890625,y_center:.703125},{w:1,h:1,x_center:.921875,y_center:.703125},{w:1,h:1,x_center:.921875,y_center:.703125},{w:1,h:1,x_center:.953125,y_center:.703125},{w:1,h:1,x_center:.953125,y_center:.703125},{w:1,h:1,x_center:.984375,y_center:.703125},{w:1,h:1,x_center:.984375,y_center:.703125},{w:1,h:1,x_center:.015625,y_center:.734375},{w:1,h:1,x_center:.015625,y_center:.734375},{w:1,h:1,x_center:.046875,y_center:.734375},{w:1,h:1,x_center:.046875,y_center:.734375},{w:1,h:1,x_center:.078125,y_center:.734375},{w:1,h:1,x_center:.078125,y_center:.734375},{w:1,h:1,x_center:.109375,y_center:.734375},{w:1,h:1,x_center:.109375,y_center:.734375},{w:1,h:1,x_center:.140625,y_center:.734375},{w:1,h:1,x_center:.140625,y_center:.734375},{w:1,h:1,x_center:.171875,y_center:.734375},{w:1,h:1,x_center:.171875,y_center:.734375},{w:1,h:1,x_center:.203125,y_center:.734375},{w:1,h:1,x_center:.203125,y_center:.734375},{w:1,h:1,x_center:.234375,y_center:.734375},{w:1,h:1,x_center:.234375,y_center:.734375},{w:1,h:1,x_center:.265625,y_center:.734375},{w:1,h:1,x_center:.265625,y_center:.734375},{w:1,h:1,x_center:.296875,y_center:.734375},{w:1,h:1,x_center:.296875,y_center:.734375},{w:1,h:1,x_center:.328125,y_center:.734375},{w:1,h:1,x_center:.328125,y_center:.734375},{w:1,h:1,x_center:.359375,y_center:.734375},{w:1,h:1,x_center:.359375,y_center:.734375},{w:1,h:1,x_center:.390625,y_center:.734375},{w:1,h:1,x_center:.390625,y_center:.734375},{w:1,h:1,x_center:.421875,y_center:.734375},{w:1,h:1,x_center:.421875,y_center:.734375},{w:1,h:1,x_center:.453125,y_center:.734375},{w:1,h:1,x_center:.453125,y_center:.734375},{w:1,h:1,x_center:.484375,y_center:.734375},{w:1,h:1,x_center:.484375,y_center:.734375},{w:1,h:1,x_center:.515625,y_center:.734375},{w:1,h:1,x_center:.515625,y_center:.734375},{w:1,h:1,x_center:.546875,y_center:.734375},{w:1,h:1,x_center:.546875,y_center:.734375},{w:1,h:1,x_center:.578125,y_center:.734375},{w:1,h:1,x_center:.578125,y_center:.734375},{w:1,h:1,x_center:.609375,y_center:.734375},{w:1,h:1,x_center:.609375,y_center:.734375},{w:1,h:1,x_center:.640625,y_center:.734375},{w:1,h:1,x_center:.640625,y_center:.734375},{w:1,h:1,x_center:.671875,y_center:.734375},{w:1,h:1,x_center:.671875,y_center:.734375},{w:1,h:1,x_center:.703125,y_center:.734375},{w:1,h:1,x_center:.703125,y_center:.734375},{w:1,h:1,x_center:.734375,y_center:.734375},{w:1,h:1,x_center:.734375,y_center:.734375},{w:1,h:1,x_center:.765625,y_center:.734375},{w:1,h:1,x_center:.765625,y_center:.734375},{w:1,h:1,x_center:.796875,y_center:.734375},{w:1,h:1,x_center:.796875,y_center:.734375},{w:1,h:1,x_center:.828125,y_center:.734375},{w:1,h:1,x_center:.828125,y_center:.734375},{w:1,h:1,x_center:.859375,y_center:.734375},{w:1,h:1,x_center:.859375,y_center:.734375},{w:1,h:1,x_center:.890625,y_center:.734375},{w:1,h:1,x_center:.890625,y_center:.734375},{w:1,h:1,x_center:.921875,y_center:.734375},{w:1,h:1,x_center:.921875,y_center:.734375},{w:1,h:1,x_center:.953125,y_center:.734375},{w:1,h:1,x_center:.953125,y_center:.734375},{w:1,h:1,x_center:.984375,y_center:.734375},{w:1,h:1,x_center:.984375,y_center:.734375},{w:1,h:1,x_center:.015625,y_center:.765625},{w:1,h:1,x_center:.015625,y_center:.765625},{w:1,h:1,x_center:.046875,y_center:.765625},{w:1,h:1,x_center:.046875,y_center:.765625},{w:1,h:1,x_center:.078125,y_center:.765625},{w:1,h:1,x_center:.078125,y_center:.765625},{w:1,h:1,x_center:.109375,y_center:.765625},{w:1,h:1,x_center:.109375,y_center:.765625},{w:1,h:1,x_center:.140625,y_center:.765625},{w:1,h:1,x_center:.140625,y_center:.765625},{w:1,h:1,x_center:.171875,y_center:.765625},{w:1,h:1,x_center:.171875,y_center:.765625},{w:1,h:1,x_center:.203125,y_center:.765625},{w:1,h:1,x_center:.203125,y_center:.765625},{w:1,h:1,x_center:.234375,y_center:.765625},{w:1,h:1,x_center:.234375,y_center:.765625},{w:1,h:1,x_center:.265625,y_center:.765625},{w:1,h:1,x_center:.265625,y_center:.765625},{w:1,h:1,x_center:.296875,y_center:.765625},{w:1,h:1,x_center:.296875,y_center:.765625},{w:1,h:1,x_center:.328125,y_center:.765625},{w:1,h:1,x_center:.328125,y_center:.765625},{w:1,h:1,x_center:.359375,y_center:.765625},{w:1,h:1,x_center:.359375,y_center:.765625},{w:1,h:1,x_center:.390625,y_center:.765625},{w:1,h:1,x_center:.390625,y_center:.765625},{w:1,h:1,x_center:.421875,y_center:.765625},{w:1,h:1,x_center:.421875,y_center:.765625},{w:1,h:1,x_center:.453125,y_center:.765625},{w:1,h:1,x_center:.453125,y_center:.765625},{w:1,h:1,x_center:.484375,y_center:.765625},{w:1,h:1,x_center:.484375,y_center:.765625},{w:1,h:1,x_center:.515625,y_center:.765625},{w:1,h:1,x_center:.515625,y_center:.765625},{w:1,h:1,x_center:.546875,y_center:.765625},{w:1,h:1,x_center:.546875,y_center:.765625},{w:1,h:1,x_center:.578125,y_center:.765625},{w:1,h:1,x_center:.578125,y_center:.765625},{w:1,h:1,x_center:.609375,y_center:.765625},{w:1,h:1,x_center:.609375,y_center:.765625},{w:1,h:1,x_center:.640625,y_center:.765625},{w:1,h:1,x_center:.640625,y_center:.765625},{w:1,h:1,x_center:.671875,y_center:.765625},{w:1,h:1,x_center:.671875,y_center:.765625},{w:1,h:1,x_center:.703125,y_center:.765625},{w:1,h:1,x_center:.703125,y_center:.765625},{w:1,h:1,x_center:.734375,y_center:.765625},{w:1,h:1,x_center:.734375,y_center:.765625},{w:1,h:1,x_center:.765625,y_center:.765625},{w:1,h:1,x_center:.765625,y_center:.765625},{w:1,h:1,x_center:.796875,y_center:.765625},{w:1,h:1,x_center:.796875,y_center:.765625},{w:1,h:1,x_center:.828125,y_center:.765625},{w:1,h:1,x_center:.828125,y_center:.765625},{w:1,h:1,x_center:.859375,y_center:.765625},{w:1,h:1,x_center:.859375,y_center:.765625},{w:1,h:1,x_center:.890625,y_center:.765625},{w:1,h:1,x_center:.890625,y_center:.765625},{w:1,h:1,x_center:.921875,y_center:.765625},{w:1,h:1,x_center:.921875,y_center:.765625},{w:1,h:1,x_center:.953125,y_center:.765625},{w:1,h:1,x_center:.953125,y_center:.765625},{w:1,h:1,x_center:.984375,y_center:.765625},{w:1,h:1,x_center:.984375,y_center:.765625},{w:1,h:1,x_center:.015625,y_center:.796875},{w:1,h:1,x_center:.015625,y_center:.796875},{w:1,h:1,x_center:.046875,y_center:.796875},{w:1,h:1,x_center:.046875,y_center:.796875},{w:1,h:1,x_center:.078125,y_center:.796875},{w:1,h:1,x_center:.078125,y_center:.796875},{w:1,h:1,x_center:.109375,y_center:.796875},{w:1,h:1,x_center:.109375,y_center:.796875},{w:1,h:1,x_center:.140625,y_center:.796875},{w:1,h:1,x_center:.140625,y_center:.796875},{w:1,h:1,x_center:.171875,y_center:.796875},{w:1,h:1,x_center:.171875,y_center:.796875},{w:1,h:1,x_center:.203125,y_center:.796875},{w:1,h:1,x_center:.203125,y_center:.796875},{w:1,h:1,x_center:.234375,y_center:.796875},{w:1,h:1,x_center:.234375,y_center:.796875},{w:1,h:1,x_center:.265625,y_center:.796875},{w:1,h:1,x_center:.265625,y_center:.796875},{w:1,h:1,x_center:.296875,y_center:.796875},{w:1,h:1,x_center:.296875,y_center:.796875},{w:1,h:1,x_center:.328125,y_center:.796875},{w:1,h:1,x_center:.328125,y_center:.796875},{w:1,h:1,x_center:.359375,y_center:.796875},{w:1,h:1,x_center:.359375,y_center:.796875},{w:1,h:1,x_center:.390625,y_center:.796875},{w:1,h:1,x_center:.390625,y_center:.796875},{w:1,h:1,x_center:.421875,y_center:.796875},{w:1,h:1,x_center:.421875,y_center:.796875},{w:1,h:1,x_center:.453125,y_center:.796875},{w:1,h:1,x_center:.453125,y_center:.796875},{w:1,h:1,x_center:.484375,y_center:.796875},{w:1,h:1,x_center:.484375,y_center:.796875},{w:1,h:1,x_center:.515625,y_center:.796875},{w:1,h:1,x_center:.515625,y_center:.796875},{w:1,h:1,x_center:.546875,y_center:.796875},{w:1,h:1,x_center:.546875,y_center:.796875},{w:1,h:1,x_center:.578125,y_center:.796875},{w:1,h:1,x_center:.578125,y_center:.796875},{w:1,h:1,x_center:.609375,y_center:.796875},{w:1,h:1,x_center:.609375,y_center:.796875},{w:1,h:1,x_center:.640625,y_center:.796875},{w:1,h:1,x_center:.640625,y_center:.796875},{w:1,h:1,x_center:.671875,y_center:.796875},{w:1,h:1,x_center:.671875,y_center:.796875},{w:1,h:1,x_center:.703125,y_center:.796875},{w:1,h:1,x_center:.703125,y_center:.796875},{w:1,h:1,x_center:.734375,y_center:.796875},{w:1,h:1,x_center:.734375,y_center:.796875},{w:1,h:1,x_center:.765625,y_center:.796875},{w:1,h:1,x_center:.765625,y_center:.796875},{w:1,h:1,x_center:.796875,y_center:.796875},{w:1,h:1,x_center:.796875,y_center:.796875},{w:1,h:1,x_center:.828125,y_center:.796875},{w:1,h:1,x_center:.828125,y_center:.796875},{w:1,h:1,x_center:.859375,y_center:.796875},{w:1,h:1,x_center:.859375,y_center:.796875},{w:1,h:1,x_center:.890625,y_center:.796875},{w:1,h:1,x_center:.890625,y_center:.796875},{w:1,h:1,x_center:.921875,y_center:.796875},{w:1,h:1,x_center:.921875,y_center:.796875},{w:1,h:1,x_center:.953125,y_center:.796875},{w:1,h:1,x_center:.953125,y_center:.796875},{w:1,h:1,x_center:.984375,y_center:.796875},{w:1,h:1,x_center:.984375,y_center:.796875},{w:1,h:1,x_center:.015625,y_center:.828125},{w:1,h:1,x_center:.015625,y_center:.828125},{w:1,h:1,x_center:.046875,y_center:.828125},{w:1,h:1,x_center:.046875,y_center:.828125},{w:1,h:1,x_center:.078125,y_center:.828125},{w:1,h:1,x_center:.078125,y_center:.828125},{w:1,h:1,x_center:.109375,y_center:.828125},{w:1,h:1,x_center:.109375,y_center:.828125},{w:1,h:1,x_center:.140625,y_center:.828125},{w:1,h:1,x_center:.140625,y_center:.828125},{w:1,h:1,x_center:.171875,y_center:.828125},{w:1,h:1,x_center:.171875,y_center:.828125},{w:1,h:1,x_center:.203125,y_center:.828125},{w:1,h:1,x_center:.203125,y_center:.828125},{w:1,h:1,x_center:.234375,y_center:.828125},{w:1,h:1,x_center:.234375,y_center:.828125},{w:1,h:1,x_center:.265625,y_center:.828125},{w:1,h:1,x_center:.265625,y_center:.828125},{w:1,h:1,x_center:.296875,y_center:.828125},{w:1,h:1,x_center:.296875,y_center:.828125},{w:1,h:1,x_center:.328125,y_center:.828125},{w:1,h:1,x_center:.328125,y_center:.828125},{w:1,h:1,x_center:.359375,y_center:.828125},{w:1,h:1,x_center:.359375,y_center:.828125},{w:1,h:1,x_center:.390625,y_center:.828125},{w:1,h:1,x_center:.390625,y_center:.828125},{w:1,h:1,x_center:.421875,y_center:.828125},{w:1,h:1,x_center:.421875,y_center:.828125},{w:1,h:1,x_center:.453125,y_center:.828125},{w:1,h:1,x_center:.453125,y_center:.828125},{w:1,h:1,x_center:.484375,y_center:.828125},{w:1,h:1,x_center:.484375,y_center:.828125},{w:1,h:1,x_center:.515625,y_center:.828125},{w:1,h:1,x_center:.515625,y_center:.828125},{w:1,h:1,x_center:.546875,y_center:.828125},{w:1,h:1,x_center:.546875,y_center:.828125},{w:1,h:1,x_center:.578125,y_center:.828125},{w:1,h:1,x_center:.578125,y_center:.828125},{w:1,h:1,x_center:.609375,y_center:.828125},{w:1,h:1,x_center:.609375,y_center:.828125},{w:1,h:1,x_center:.640625,y_center:.828125},{w:1,h:1,x_center:.640625,y_center:.828125},{w:1,h:1,x_center:.671875,y_center:.828125},{w:1,h:1,x_center:.671875,y_center:.828125},{w:1,h:1,x_center:.703125,y_center:.828125},{w:1,h:1,x_center:.703125,y_center:.828125},{w:1,h:1,x_center:.734375,y_center:.828125},{w:1,h:1,x_center:.734375,y_center:.828125},{w:1,h:1,x_center:.765625,y_center:.828125},{w:1,h:1,x_center:.765625,y_center:.828125},{w:1,h:1,x_center:.796875,y_center:.828125},{w:1,h:1,x_center:.796875,y_center:.828125},{w:1,h:1,x_center:.828125,y_center:.828125},{w:1,h:1,x_center:.828125,y_center:.828125},{w:1,h:1,x_center:.859375,y_center:.828125},{w:1,h:1,x_center:.859375,y_center:.828125},{w:1,h:1,x_center:.890625,y_center:.828125},{w:1,h:1,x_center:.890625,y_center:.828125},{w:1,h:1,x_center:.921875,y_center:.828125},{w:1,h:1,x_center:.921875,y_center:.828125},{w:1,h:1,x_center:.953125,y_center:.828125},{w:1,h:1,x_center:.953125,y_center:.828125},{w:1,h:1,x_center:.984375,y_center:.828125},{w:1,h:1,x_center:.984375,y_center:.828125},{w:1,h:1,x_center:.015625,y_center:.859375},{w:1,h:1,x_center:.015625,y_center:.859375},{w:1,h:1,x_center:.046875,y_center:.859375},{w:1,h:1,x_center:.046875,y_center:.859375},{w:1,h:1,x_center:.078125,y_center:.859375},{w:1,h:1,x_center:.078125,y_center:.859375},{w:1,h:1,x_center:.109375,y_center:.859375},{w:1,h:1,x_center:.109375,y_center:.859375},{w:1,h:1,x_center:.140625,y_center:.859375},{w:1,h:1,x_center:.140625,y_center:.859375},{w:1,h:1,x_center:.171875,y_center:.859375},{w:1,h:1,x_center:.171875,y_center:.859375},{w:1,h:1,x_center:.203125,y_center:.859375},{w:1,h:1,x_center:.203125,y_center:.859375},{w:1,h:1,x_center:.234375,y_center:.859375},{w:1,h:1,x_center:.234375,y_center:.859375},{w:1,h:1,x_center:.265625,y_center:.859375},{w:1,h:1,x_center:.265625,y_center:.859375},{w:1,h:1,x_center:.296875,y_center:.859375},{w:1,h:1,x_center:.296875,y_center:.859375},{w:1,h:1,x_center:.328125,y_center:.859375},{w:1,h:1,x_center:.328125,y_center:.859375},{w:1,h:1,x_center:.359375,y_center:.859375},{w:1,h:1,x_center:.359375,y_center:.859375},{w:1,h:1,x_center:.390625,y_center:.859375},{w:1,h:1,x_center:.390625,y_center:.859375},{w:1,h:1,x_center:.421875,y_center:.859375},{w:1,h:1,x_center:.421875,y_center:.859375},{w:1,h:1,x_center:.453125,y_center:.859375},{w:1,h:1,x_center:.453125,y_center:.859375},{w:1,h:1,x_center:.484375,y_center:.859375},{w:1,h:1,x_center:.484375,y_center:.859375},{w:1,h:1,x_center:.515625,y_center:.859375},{w:1,h:1,x_center:.515625,y_center:.859375},{w:1,h:1,x_center:.546875,y_center:.859375},{w:1,h:1,x_center:.546875,y_center:.859375},{w:1,h:1,x_center:.578125,y_center:.859375},{w:1,h:1,x_center:.578125,y_center:.859375},{w:1,h:1,x_center:.609375,y_center:.859375},{w:1,h:1,x_center:.609375,y_center:.859375},{w:1,h:1,x_center:.640625,y_center:.859375},{w:1,h:1,x_center:.640625,y_center:.859375},{w:1,h:1,x_center:.671875,y_center:.859375},{w:1,h:1,x_center:.671875,y_center:.859375},{w:1,h:1,x_center:.703125,y_center:.859375},{w:1,h:1,x_center:.703125,y_center:.859375},{w:1,h:1,x_center:.734375,y_center:.859375},{w:1,h:1,x_center:.734375,y_center:.859375},{w:1,h:1,x_center:.765625,y_center:.859375},{w:1,h:1,x_center:.765625,y_center:.859375},{w:1,h:1,x_center:.796875,y_center:.859375},{w:1,h:1,x_center:.796875,y_center:.859375},{w:1,h:1,x_center:.828125,y_center:.859375},{w:1,h:1,x_center:.828125,y_center:.859375},{w:1,h:1,x_center:.859375,y_center:.859375},{w:1,h:1,x_center:.859375,y_center:.859375},{w:1,h:1,x_center:.890625,y_center:.859375},{w:1,h:1,x_center:.890625,y_center:.859375},{w:1,h:1,x_center:.921875,y_center:.859375},{w:1,h:1,x_center:.921875,y_center:.859375},{w:1,h:1,x_center:.953125,y_center:.859375},{w:1,h:1,x_center:.953125,y_center:.859375},{w:1,h:1,x_center:.984375,y_center:.859375},{w:1,h:1,x_center:.984375,y_center:.859375},{w:1,h:1,x_center:.015625,y_center:.890625},{w:1,h:1,x_center:.015625,y_center:.890625},{w:1,h:1,x_center:.046875,y_center:.890625},{w:1,h:1,x_center:.046875,y_center:.890625},{w:1,h:1,x_center:.078125,y_center:.890625},{w:1,h:1,x_center:.078125,y_center:.890625},{w:1,h:1,x_center:.109375,y_center:.890625},{w:1,h:1,x_center:.109375,y_center:.890625},{w:1,h:1,x_center:.140625,y_center:.890625},{w:1,h:1,x_center:.140625,y_center:.890625},{w:1,h:1,x_center:.171875,y_center:.890625},{w:1,h:1,x_center:.171875,y_center:.890625},{w:1,h:1,x_center:.203125,y_center:.890625},{w:1,h:1,x_center:.203125,y_center:.890625},{w:1,h:1,x_center:.234375,y_center:.890625},{w:1,h:1,x_center:.234375,y_center:.890625},{w:1,h:1,x_center:.265625,y_center:.890625},{w:1,h:1,x_center:.265625,y_center:.890625},{w:1,h:1,x_center:.296875,y_center:.890625},{w:1,h:1,x_center:.296875,y_center:.890625},{w:1,h:1,x_center:.328125,y_center:.890625},{w:1,h:1,x_center:.328125,y_center:.890625},{w:1,h:1,x_center:.359375,y_center:.890625},{w:1,h:1,x_center:.359375,y_center:.890625},{w:1,h:1,x_center:.390625,y_center:.890625},{w:1,h:1,x_center:.390625,y_center:.890625},{w:1,h:1,x_center:.421875,y_center:.890625},{w:1,h:1,x_center:.421875,y_center:.890625},{w:1,h:1,x_center:.453125,y_center:.890625},{w:1,h:1,x_center:.453125,y_center:.890625},{w:1,h:1,x_center:.484375,y_center:.890625},{w:1,h:1,x_center:.484375,y_center:.890625},{w:1,h:1,x_center:.515625,y_center:.890625},{w:1,h:1,x_center:.515625,y_center:.890625},{w:1,h:1,x_center:.546875,y_center:.890625},{w:1,h:1,x_center:.546875,y_center:.890625},{w:1,h:1,x_center:.578125,y_center:.890625},{w:1,h:1,x_center:.578125,y_center:.890625},{w:1,h:1,x_center:.609375,y_center:.890625},{w:1,h:1,x_center:.609375,y_center:.890625},{w:1,h:1,x_center:.640625,y_center:.890625},{w:1,h:1,x_center:.640625,y_center:.890625},{w:1,h:1,x_center:.671875,y_center:.890625},{w:1,h:1,x_center:.671875,y_center:.890625},{w:1,h:1,x_center:.703125,y_center:.890625},{w:1,h:1,x_center:.703125,y_center:.890625},{w:1,h:1,x_center:.734375,y_center:.890625},{w:1,h:1,x_center:.734375,y_center:.890625},{w:1,h:1,x_center:.765625,y_center:.890625},{w:1,h:1,x_center:.765625,y_center:.890625},{w:1,h:1,x_center:.796875,y_center:.890625},{w:1,h:1,x_center:.796875,y_center:.890625},{w:1,h:1,x_center:.828125,y_center:.890625},{w:1,h:1,x_center:.828125,y_center:.890625},{w:1,h:1,x_center:.859375,y_center:.890625},{w:1,h:1,x_center:.859375,y_center:.890625},{w:1,h:1,x_center:.890625,y_center:.890625},{w:1,h:1,x_center:.890625,y_center:.890625},{w:1,h:1,x_center:.921875,y_center:.890625},{w:1,h:1,x_center:.921875,y_center:.890625},{w:1,h:1,x_center:.953125,y_center:.890625},{w:1,h:1,x_center:.953125,y_center:.890625},{w:1,h:1,x_center:.984375,y_center:.890625},{w:1,h:1,x_center:.984375,y_center:.890625},{w:1,h:1,x_center:.015625,y_center:.921875},{w:1,h:1,x_center:.015625,y_center:.921875},{w:1,h:1,x_center:.046875,y_center:.921875},{w:1,h:1,x_center:.046875,y_center:.921875},{w:1,h:1,x_center:.078125,y_center:.921875},{w:1,h:1,x_center:.078125,y_center:.921875},{w:1,h:1,x_center:.109375,y_center:.921875},{w:1,h:1,x_center:.109375,y_center:.921875},{w:1,h:1,x_center:.140625,y_center:.921875},{w:1,h:1,x_center:.140625,y_center:.921875},{w:1,h:1,x_center:.171875,y_center:.921875},{w:1,h:1,x_center:.171875,y_center:.921875},{w:1,h:1,x_center:.203125,y_center:.921875},{w:1,h:1,x_center:.203125,y_center:.921875},{w:1,h:1,x_center:.234375,y_center:.921875},{w:1,h:1,x_center:.234375,y_center:.921875},{w:1,h:1,x_center:.265625,y_center:.921875},{w:1,h:1,x_center:.265625,y_center:.921875},{w:1,h:1,x_center:.296875,y_center:.921875},{w:1,h:1,x_center:.296875,y_center:.921875},{w:1,h:1,x_center:.328125,y_center:.921875},{w:1,h:1,x_center:.328125,y_center:.921875},{w:1,h:1,x_center:.359375,y_center:.921875},{w:1,h:1,x_center:.359375,y_center:.921875},{w:1,h:1,x_center:.390625,y_center:.921875},{w:1,h:1,x_center:.390625,y_center:.921875},{w:1,h:1,x_center:.421875,y_center:.921875},{w:1,h:1,x_center:.421875,y_center:.921875},{w:1,h:1,x_center:.453125,y_center:.921875},{w:1,h:1,x_center:.453125,y_center:.921875},{w:1,h:1,x_center:.484375,y_center:.921875},{w:1,h:1,x_center:.484375,y_center:.921875},{w:1,h:1,x_center:.515625,y_center:.921875},{w:1,h:1,x_center:.515625,y_center:.921875},{w:1,h:1,x_center:.546875,y_center:.921875},{w:1,h:1,x_center:.546875,y_center:.921875},{w:1,h:1,x_center:.578125,y_center:.921875},{w:1,h:1,x_center:.578125,y_center:.921875},{w:1,h:1,x_center:.609375,y_center:.921875},{w:1,h:1,x_center:.609375,y_center:.921875},{w:1,h:1,x_center:.640625,y_center:.921875},{w:1,h:1,x_center:.640625,y_center:.921875},{w:1,h:1,x_center:.671875,y_center:.921875},{w:1,h:1,x_center:.671875,y_center:.921875},{w:1,h:1,x_center:.703125,y_center:.921875},{w:1,h:1,x_center:.703125,y_center:.921875},{w:1,h:1,x_center:.734375,y_center:.921875},{w:1,h:1,x_center:.734375,y_center:.921875},{w:1,h:1,x_center:.765625,y_center:.921875},{w:1,h:1,x_center:.765625,y_center:.921875},{w:1,h:1,x_center:.796875,y_center:.921875},{w:1,h:1,x_center:.796875,y_center:.921875},{w:1,h:1,x_center:.828125,y_center:.921875},{w:1,h:1,x_center:.828125,y_center:.921875},{w:1,h:1,x_center:.859375,y_center:.921875},{w:1,h:1,x_center:.859375,y_center:.921875},{w:1,h:1,x_center:.890625,y_center:.921875},{w:1,h:1,x_center:.890625,y_center:.921875},{w:1,h:1,x_center:.921875,y_center:.921875},{w:1,h:1,x_center:.921875,y_center:.921875},{w:1,h:1,x_center:.953125,y_center:.921875},{w:1,h:1,x_center:.953125,y_center:.921875},{w:1,h:1,x_center:.984375,y_center:.921875},{w:1,h:1,x_center:.984375,y_center:.921875},{w:1,h:1,x_center:.015625,y_center:.953125},{w:1,h:1,x_center:.015625,y_center:.953125},{w:1,h:1,x_center:.046875,y_center:.953125},{w:1,h:1,x_center:.046875,y_center:.953125},{w:1,h:1,x_center:.078125,y_center:.953125},{w:1,h:1,x_center:.078125,y_center:.953125},{w:1,h:1,x_center:.109375,y_center:.953125},{w:1,h:1,x_center:.109375,y_center:.953125},{w:1,h:1,x_center:.140625,y_center:.953125},{w:1,h:1,x_center:.140625,y_center:.953125},{w:1,h:1,x_center:.171875,y_center:.953125},{w:1,h:1,x_center:.171875,y_center:.953125},{w:1,h:1,x_center:.203125,y_center:.953125},{w:1,h:1,x_center:.203125,y_center:.953125},{w:1,h:1,x_center:.234375,y_center:.953125},{w:1,h:1,x_center:.234375,y_center:.953125},{w:1,h:1,x_center:.265625,y_center:.953125},{w:1,h:1,x_center:.265625,y_center:.953125},{w:1,h:1,x_center:.296875,y_center:.953125},{w:1,h:1,x_center:.296875,y_center:.953125},{w:1,h:1,x_center:.328125,y_center:.953125},{w:1,h:1,x_center:.328125,y_center:.953125},{w:1,h:1,x_center:.359375,y_center:.953125},{w:1,h:1,x_center:.359375,y_center:.953125},{w:1,h:1,x_center:.390625,y_center:.953125},{w:1,h:1,x_center:.390625,y_center:.953125},{w:1,h:1,x_center:.421875,y_center:.953125},{w:1,h:1,x_center:.421875,y_center:.953125},{w:1,h:1,x_center:.453125,y_center:.953125},{w:1,h:1,x_center:.453125,y_center:.953125},{w:1,h:1,x_center:.484375,y_center:.953125},{w:1,h:1,x_center:.484375,y_center:.953125},{w:1,h:1,x_center:.515625,y_center:.953125},{w:1,h:1,x_center:.515625,y_center:.953125},{w:1,h:1,x_center:.546875,y_center:.953125},{w:1,h:1,x_center:.546875,y_center:.953125},{w:1,h:1,x_center:.578125,y_center:.953125},{w:1,h:1,x_center:.578125,y_center:.953125},{w:1,h:1,x_center:.609375,y_center:.953125},{w:1,h:1,x_center:.609375,y_center:.953125},{w:1,h:1,x_center:.640625,y_center:.953125},{w:1,h:1,x_center:.640625,y_center:.953125},{w:1,h:1,x_center:.671875,y_center:.953125},{w:1,h:1,x_center:.671875,y_center:.953125},{w:1,h:1,x_center:.703125,y_center:.953125},{w:1,h:1,x_center:.703125,y_center:.953125},{w:1,h:1,x_center:.734375,y_center:.953125},{w:1,h:1,x_center:.734375,y_center:.953125},{w:1,h:1,x_center:.765625,y_center:.953125},{w:1,h:1,x_center:.765625,y_center:.953125},{w:1,h:1,x_center:.796875,y_center:.953125},{w:1,h:1,x_center:.796875,y_center:.953125},{w:1,h:1,x_center:.828125,y_center:.953125},{w:1,h:1,x_center:.828125,y_center:.953125},{w:1,h:1,x_center:.859375,y_center:.953125},{w:1,h:1,x_center:.859375,y_center:.953125},{w:1,h:1,x_center:.890625,y_center:.953125},{w:1,h:1,x_center:.890625,y_center:.953125},{w:1,h:1,x_center:.921875,y_center:.953125},{w:1,h:1,x_center:.921875,y_center:.953125},{w:1,h:1,x_center:.953125,y_center:.953125},{w:1,h:1,x_center:.953125,y_center:.953125},{w:1,h:1,x_center:.984375,y_center:.953125},{w:1,h:1,x_center:.984375,y_center:.953125},{w:1,h:1,x_center:.015625,y_center:.984375},{w:1,h:1,x_center:.015625,y_center:.984375},{w:1,h:1,x_center:.046875,y_center:.984375},{w:1,h:1,x_center:.046875,y_center:.984375},{w:1,h:1,x_center:.078125,y_center:.984375},{w:1,h:1,x_center:.078125,y_center:.984375},{w:1,h:1,x_center:.109375,y_center:.984375},{w:1,h:1,x_center:.109375,y_center:.984375},{w:1,h:1,x_center:.140625,y_center:.984375},{w:1,h:1,x_center:.140625,y_center:.984375},{w:1,h:1,x_center:.171875,y_center:.984375},{w:1,h:1,x_center:.171875,y_center:.984375},{w:1,h:1,x_center:.203125,y_center:.984375},{w:1,h:1,x_center:.203125,y_center:.984375},{w:1,h:1,x_center:.234375,y_center:.984375},{w:1,h:1,x_center:.234375,y_center:.984375},{w:1,h:1,x_center:.265625,y_center:.984375},{w:1,h:1,x_center:.265625,y_center:.984375},{w:1,h:1,x_center:.296875,y_center:.984375},{w:1,h:1,x_center:.296875,y_center:.984375},{w:1,h:1,x_center:.328125,y_center:.984375},{w:1,h:1,x_center:.328125,y_center:.984375},{w:1,h:1,x_center:.359375,y_center:.984375},{w:1,h:1,x_center:.359375,y_center:.984375},{w:1,h:1,x_center:.390625,y_center:.984375},{w:1,h:1,x_center:.390625,y_center:.984375},{w:1,h:1,x_center:.421875,y_center:.984375},{w:1,h:1,x_center:.421875,y_center:.984375},{w:1,h:1,x_center:.453125,y_center:.984375},{w:1,h:1,x_center:.453125,y_center:.984375},{w:1,h:1,x_center:.484375,y_center:.984375},{w:1,h:1,x_center:.484375,y_center:.984375},{w:1,h:1,x_center:.515625,y_center:.984375},{w:1,h:1,x_center:.515625,y_center:.984375},{w:1,h:1,x_center:.546875,y_center:.984375},{w:1,h:1,x_center:.546875,y_center:.984375},{w:1,h:1,x_center:.578125,y_center:.984375},{w:1,h:1,x_center:.578125,y_center:.984375},{w:1,h:1,x_center:.609375,y_center:.984375},{w:1,h:1,x_center:.609375,y_center:.984375},{w:1,h:1,x_center:.640625,y_center:.984375},{w:1,h:1,x_center:.640625,y_center:.984375},{w:1,h:1,x_center:.671875,y_center:.984375},{w:1,h:1,x_center:.671875,y_center:.984375},{w:1,h:1,x_center:.703125,y_center:.984375},{w:1,h:1,x_center:.703125,y_center:.984375},{w:1,h:1,x_center:.734375,y_center:.984375},{w:1,h:1,x_center:.734375,y_center:.984375},{w:1,h:1,x_center:.765625,y_center:.984375},{w:1,h:1,x_center:.765625,y_center:.984375},{w:1,h:1,x_center:.796875,y_center:.984375},{w:1,h:1,x_center:.796875,y_center:.984375},{w:1,h:1,x_center:.828125,y_center:.984375},{w:1,h:1,x_center:.828125,y_center:.984375},{w:1,h:1,x_center:.859375,y_center:.984375},{w:1,h:1,x_center:.859375,y_center:.984375},{w:1,h:1,x_center:.890625,y_center:.984375},{w:1,h:1,x_center:.890625,y_center:.984375},{w:1,h:1,x_center:.921875,y_center:.984375},{w:1,h:1,x_center:.921875,y_center:.984375},{w:1,h:1,x_center:.953125,y_center:.984375},{w:1,h:1,x_center:.953125,y_center:.984375},{w:1,h:1,x_center:.984375,y_center:.984375},{w:1,h:1,x_center:.984375,y_center:.984375},{w:1,h:1,x_center:.03125,y_center:.03125},{w:1,h:1,x_center:.03125,y_center:.03125},{w:1,h:1,x_center:.09375,y_center:.03125},{w:1,h:1,x_center:.09375,y_center:.03125},{w:1,h:1,x_center:.15625,y_center:.03125},{w:1,h:1,x_center:.15625,y_center:.03125},{w:1,h:1,x_center:.21875,y_center:.03125},{w:1,h:1,x_center:.21875,y_center:.03125},{w:1,h:1,x_center:.28125,y_center:.03125},{w:1,h:1,x_center:.28125,y_center:.03125},{w:1,h:1,x_center:.34375,y_center:.03125},{w:1,h:1,x_center:.34375,y_center:.03125},{w:1,h:1,x_center:.40625,y_center:.03125},{w:1,h:1,x_center:.40625,y_center:.03125},{w:1,h:1,x_center:.46875,y_center:.03125},{w:1,h:1,x_center:.46875,y_center:.03125},{w:1,h:1,x_center:.53125,y_center:.03125},{w:1,h:1,x_center:.53125,y_center:.03125},{w:1,h:1,x_center:.59375,y_center:.03125},{w:1,h:1,x_center:.59375,y_center:.03125},{w:1,h:1,x_center:.65625,y_center:.03125},{w:1,h:1,x_center:.65625,y_center:.03125},{w:1,h:1,x_center:.71875,y_center:.03125},{w:1,h:1,x_center:.71875,y_center:.03125},{w:1,h:1,x_center:.78125,y_center:.03125},{w:1,h:1,x_center:.78125,y_center:.03125},{w:1,h:1,x_center:.84375,y_center:.03125},{w:1,h:1,x_center:.84375,y_center:.03125},{w:1,h:1,x_center:.90625,y_center:.03125},{w:1,h:1,x_center:.90625,y_center:.03125},{w:1,h:1,x_center:.96875,y_center:.03125},{w:1,h:1,x_center:.96875,y_center:.03125},{w:1,h:1,x_center:.03125,y_center:.09375},{w:1,h:1,x_center:.03125,y_center:.09375},{w:1,h:1,x_center:.09375,y_center:.09375},{w:1,h:1,x_center:.09375,y_center:.09375},{w:1,h:1,x_center:.15625,y_center:.09375},{w:1,h:1,x_center:.15625,y_center:.09375},{w:1,h:1,x_center:.21875,y_center:.09375},{w:1,h:1,x_center:.21875,y_center:.09375},{w:1,h:1,x_center:.28125,y_center:.09375},{w:1,h:1,x_center:.28125,y_center:.09375},{w:1,h:1,x_center:.34375,y_center:.09375},{w:1,h:1,x_center:.34375,y_center:.09375},{w:1,h:1,x_center:.40625,y_center:.09375},{w:1,h:1,x_center:.40625,y_center:.09375},{w:1,h:1,x_center:.46875,y_center:.09375},{w:1,h:1,x_center:.46875,y_center:.09375},{w:1,h:1,x_center:.53125,y_center:.09375},{w:1,h:1,x_center:.53125,y_center:.09375},{w:1,h:1,x_center:.59375,y_center:.09375},{w:1,h:1,x_center:.59375,y_center:.09375},{w:1,h:1,x_center:.65625,y_center:.09375},{w:1,h:1,x_center:.65625,y_center:.09375},{w:1,h:1,x_center:.71875,y_center:.09375},{w:1,h:1,x_center:.71875,y_center:.09375},{w:1,h:1,x_center:.78125,y_center:.09375},{w:1,h:1,x_center:.78125,y_center:.09375},{w:1,h:1,x_center:.84375,y_center:.09375},{w:1,h:1,x_center:.84375,y_center:.09375},{w:1,h:1,x_center:.90625,y_center:.09375},{w:1,h:1,x_center:.90625,y_center:.09375},{w:1,h:1,x_center:.96875,y_center:.09375},{w:1,h:1,x_center:.96875,y_center:.09375},{w:1,h:1,x_center:.03125,y_center:.15625},{w:1,h:1,x_center:.03125,y_center:.15625},{w:1,h:1,x_center:.09375,y_center:.15625},{w:1,h:1,x_center:.09375,y_center:.15625},{w:1,h:1,x_center:.15625,y_center:.15625},{w:1,h:1,x_center:.15625,y_center:.15625},{w:1,h:1,x_center:.21875,y_center:.15625},{w:1,h:1,x_center:.21875,y_center:.15625},{w:1,h:1,x_center:.28125,y_center:.15625},{w:1,h:1,x_center:.28125,y_center:.15625},{w:1,h:1,x_center:.34375,y_center:.15625},{w:1,h:1,x_center:.34375,y_center:.15625},{w:1,h:1,x_center:.40625,y_center:.15625},{w:1,h:1,x_center:.40625,y_center:.15625},{w:1,h:1,x_center:.46875,y_center:.15625},{w:1,h:1,x_center:.46875,y_center:.15625},{w:1,h:1,x_center:.53125,y_center:.15625},{w:1,h:1,x_center:.53125,y_center:.15625},{w:1,h:1,x_center:.59375,y_center:.15625},{w:1,h:1,x_center:.59375,y_center:.15625},{w:1,h:1,x_center:.65625,y_center:.15625},{w:1,h:1,x_center:.65625,y_center:.15625},{w:1,h:1,x_center:.71875,y_center:.15625},{w:1,h:1,x_center:.71875,y_center:.15625},{w:1,h:1,x_center:.78125,y_center:.15625},{w:1,h:1,x_center:.78125,y_center:.15625},{w:1,h:1,x_center:.84375,y_center:.15625},{w:1,h:1,x_center:.84375,y_center:.15625},{w:1,h:1,x_center:.90625,y_center:.15625},{w:1,h:1,x_center:.90625,y_center:.15625},{w:1,h:1,x_center:.96875,y_center:.15625},{w:1,h:1,x_center:.96875,y_center:.15625},{w:1,h:1,x_center:.03125,y_center:.21875},{w:1,h:1,x_center:.03125,y_center:.21875},{w:1,h:1,x_center:.09375,y_center:.21875},{w:1,h:1,x_center:.09375,y_center:.21875},{w:1,h:1,x_center:.15625,y_center:.21875},{w:1,h:1,x_center:.15625,y_center:.21875},{w:1,h:1,x_center:.21875,y_center:.21875},{w:1,h:1,x_center:.21875,y_center:.21875},{w:1,h:1,x_center:.28125,y_center:.21875},{w:1,h:1,x_center:.28125,y_center:.21875},{w:1,h:1,x_center:.34375,y_center:.21875},{w:1,h:1,x_center:.34375,y_center:.21875},{w:1,h:1,x_center:.40625,y_center:.21875},{w:1,h:1,x_center:.40625,y_center:.21875},{w:1,h:1,x_center:.46875,y_center:.21875},{w:1,h:1,x_center:.46875,y_center:.21875},{w:1,h:1,x_center:.53125,y_center:.21875},{w:1,h:1,x_center:.53125,y_center:.21875},{w:1,h:1,x_center:.59375,y_center:.21875},{w:1,h:1,x_center:.59375,y_center:.21875},{w:1,h:1,x_center:.65625,y_center:.21875},{w:1,h:1,x_center:.65625,y_center:.21875},{w:1,h:1,x_center:.71875,y_center:.21875},{w:1,h:1,x_center:.71875,y_center:.21875},{w:1,h:1,x_center:.78125,y_center:.21875},{w:1,h:1,x_center:.78125,y_center:.21875},{w:1,h:1,x_center:.84375,y_center:.21875},{w:1,h:1,x_center:.84375,y_center:.21875},{w:1,h:1,x_center:.90625,y_center:.21875},{w:1,h:1,x_center:.90625,y_center:.21875},{w:1,h:1,x_center:.96875,y_center:.21875},{w:1,h:1,x_center:.96875,y_center:.21875},{w:1,h:1,x_center:.03125,y_center:.28125},{w:1,h:1,x_center:.03125,y_center:.28125},{w:1,h:1,x_center:.09375,y_center:.28125},{w:1,h:1,x_center:.09375,y_center:.28125},{w:1,h:1,x_center:.15625,y_center:.28125},{w:1,h:1,x_center:.15625,y_center:.28125},{w:1,h:1,x_center:.21875,y_center:.28125},{w:1,h:1,x_center:.21875,y_center:.28125},{w:1,h:1,x_center:.28125,y_center:.28125},{w:1,h:1,x_center:.28125,y_center:.28125},{w:1,h:1,x_center:.34375,y_center:.28125},{w:1,h:1,x_center:.34375,y_center:.28125},{w:1,h:1,x_center:.40625,y_center:.28125},{w:1,h:1,x_center:.40625,y_center:.28125},{w:1,h:1,x_center:.46875,y_center:.28125},{w:1,h:1,x_center:.46875,y_center:.28125},{w:1,h:1,x_center:.53125,y_center:.28125},{w:1,h:1,x_center:.53125,y_center:.28125},{w:1,h:1,x_center:.59375,y_center:.28125},{w:1,h:1,x_center:.59375,y_center:.28125},{w:1,h:1,x_center:.65625,y_center:.28125},{w:1,h:1,x_center:.65625,y_center:.28125},{w:1,h:1,x_center:.71875,y_center:.28125},{w:1,h:1,x_center:.71875,y_center:.28125},{w:1,h:1,x_center:.78125,y_center:.28125},{w:1,h:1,x_center:.78125,y_center:.28125},{w:1,h:1,x_center:.84375,y_center:.28125},{w:1,h:1,x_center:.84375,y_center:.28125},{w:1,h:1,x_center:.90625,y_center:.28125},{w:1,h:1,x_center:.90625,y_center:.28125},{w:1,h:1,x_center:.96875,y_center:.28125},{w:1,h:1,x_center:.96875,y_center:.28125},{w:1,h:1,x_center:.03125,y_center:.34375},{w:1,h:1,x_center:.03125,y_center:.34375},{w:1,h:1,x_center:.09375,y_center:.34375},{w:1,h:1,x_center:.09375,y_center:.34375},{w:1,h:1,x_center:.15625,y_center:.34375},{w:1,h:1,x_center:.15625,y_center:.34375},{w:1,h:1,x_center:.21875,y_center:.34375},{w:1,h:1,x_center:.21875,y_center:.34375},{w:1,h:1,x_center:.28125,y_center:.34375},{w:1,h:1,x_center:.28125,y_center:.34375},{w:1,h:1,x_center:.34375,y_center:.34375},{w:1,h:1,x_center:.34375,y_center:.34375},{w:1,h:1,x_center:.40625,y_center:.34375},{w:1,h:1,x_center:.40625,y_center:.34375},{w:1,h:1,x_center:.46875,y_center:.34375},{w:1,h:1,x_center:.46875,y_center:.34375},{w:1,h:1,x_center:.53125,y_center:.34375},{w:1,h:1,x_center:.53125,y_center:.34375},{w:1,h:1,x_center:.59375,y_center:.34375},{w:1,h:1,x_center:.59375,y_center:.34375},{w:1,h:1,x_center:.65625,y_center:.34375},{w:1,h:1,x_center:.65625,y_center:.34375},{w:1,h:1,x_center:.71875,y_center:.34375},{w:1,h:1,x_center:.71875,y_center:.34375},{w:1,h:1,x_center:.78125,y_center:.34375},{w:1,h:1,x_center:.78125,y_center:.34375},{w:1,h:1,x_center:.84375,y_center:.34375},{w:1,h:1,x_center:.84375,y_center:.34375},{w:1,h:1,x_center:.90625,y_center:.34375},{w:1,h:1,x_center:.90625,y_center:.34375},{w:1,h:1,x_center:.96875,y_center:.34375},{w:1,h:1,x_center:.96875,y_center:.34375},{w:1,h:1,x_center:.03125,y_center:.40625},{w:1,h:1,x_center:.03125,y_center:.40625},{w:1,h:1,x_center:.09375,y_center:.40625},{w:1,h:1,x_center:.09375,y_center:.40625},{w:1,h:1,x_center:.15625,y_center:.40625},{w:1,h:1,x_center:.15625,y_center:.40625},{w:1,h:1,x_center:.21875,y_center:.40625},{w:1,h:1,x_center:.21875,y_center:.40625},{w:1,h:1,x_center:.28125,y_center:.40625},{w:1,h:1,x_center:.28125,y_center:.40625},{w:1,h:1,x_center:.34375,y_center:.40625},{w:1,h:1,x_center:.34375,y_center:.40625},{w:1,h:1,x_center:.40625,y_center:.40625},{w:1,h:1,x_center:.40625,y_center:.40625},{w:1,h:1,x_center:.46875,y_center:.40625},{w:1,h:1,x_center:.46875,y_center:.40625},{w:1,h:1,x_center:.53125,y_center:.40625},{w:1,h:1,x_center:.53125,y_center:.40625},{w:1,h:1,x_center:.59375,y_center:.40625},{w:1,h:1,x_center:.59375,y_center:.40625},{w:1,h:1,x_center:.65625,y_center:.40625},{w:1,h:1,x_center:.65625,y_center:.40625},{w:1,h:1,x_center:.71875,y_center:.40625},{w:1,h:1,x_center:.71875,y_center:.40625},{w:1,h:1,x_center:.78125,y_center:.40625},{w:1,h:1,x_center:.78125,y_center:.40625},{w:1,h:1,x_center:.84375,y_center:.40625},{w:1,h:1,x_center:.84375,y_center:.40625},{w:1,h:1,x_center:.90625,y_center:.40625},{w:1,h:1,x_center:.90625,y_center:.40625},{w:1,h:1,x_center:.96875,y_center:.40625},{w:1,h:1,x_center:.96875,y_center:.40625},{w:1,h:1,x_center:.03125,y_center:.46875},{w:1,h:1,x_center:.03125,y_center:.46875},{w:1,h:1,x_center:.09375,y_center:.46875},{w:1,h:1,x_center:.09375,y_center:.46875},{w:1,h:1,x_center:.15625,y_center:.46875},{w:1,h:1,x_center:.15625,y_center:.46875},{w:1,h:1,x_center:.21875,y_center:.46875},{w:1,h:1,x_center:.21875,y_center:.46875},{w:1,h:1,x_center:.28125,y_center:.46875},{w:1,h:1,x_center:.28125,y_center:.46875},{w:1,h:1,x_center:.34375,y_center:.46875},{w:1,h:1,x_center:.34375,y_center:.46875},{w:1,h:1,x_center:.40625,y_center:.46875},{w:1,h:1,x_center:.40625,y_center:.46875},{w:1,h:1,x_center:.46875,y_center:.46875},{w:1,h:1,x_center:.46875,y_center:.46875},{w:1,h:1,x_center:.53125,y_center:.46875},{w:1,h:1,x_center:.53125,y_center:.46875},{w:1,h:1,x_center:.59375,y_center:.46875},{w:1,h:1,x_center:.59375,y_center:.46875},{w:1,h:1,x_center:.65625,y_center:.46875},{w:1,h:1,x_center:.65625,y_center:.46875},{w:1,h:1,x_center:.71875,y_center:.46875},{w:1,h:1,x_center:.71875,y_center:.46875},{w:1,h:1,x_center:.78125,y_center:.46875},{w:1,h:1,x_center:.78125,y_center:.46875},{w:1,h:1,x_center:.84375,y_center:.46875},{w:1,h:1,x_center:.84375,y_center:.46875},{w:1,h:1,x_center:.90625,y_center:.46875},{w:1,h:1,x_center:.90625,y_center:.46875},{w:1,h:1,x_center:.96875,y_center:.46875},{w:1,h:1,x_center:.96875,y_center:.46875},{w:1,h:1,x_center:.03125,y_center:.53125},{w:1,h:1,x_center:.03125,y_center:.53125},{w:1,h:1,x_center:.09375,y_center:.53125},{w:1,h:1,x_center:.09375,y_center:.53125},{w:1,h:1,x_center:.15625,y_center:.53125},{w:1,h:1,x_center:.15625,y_center:.53125},{w:1,h:1,x_center:.21875,y_center:.53125},{w:1,h:1,x_center:.21875,y_center:.53125},{w:1,h:1,x_center:.28125,y_center:.53125},{w:1,h:1,x_center:.28125,y_center:.53125},{w:1,h:1,x_center:.34375,y_center:.53125},{w:1,h:1,x_center:.34375,y_center:.53125},{w:1,h:1,x_center:.40625,y_center:.53125},{w:1,h:1,x_center:.40625,y_center:.53125},{w:1,h:1,x_center:.46875,y_center:.53125},{w:1,h:1,x_center:.46875,y_center:.53125},{w:1,h:1,x_center:.53125,y_center:.53125},{w:1,h:1,x_center:.53125,y_center:.53125},{w:1,h:1,x_center:.59375,y_center:.53125},{w:1,h:1,x_center:.59375,y_center:.53125},{w:1,h:1,x_center:.65625,y_center:.53125},{w:1,h:1,x_center:.65625,y_center:.53125},{w:1,h:1,x_center:.71875,y_center:.53125},{w:1,h:1,x_center:.71875,y_center:.53125},{w:1,h:1,x_center:.78125,y_center:.53125},{w:1,h:1,x_center:.78125,y_center:.53125},{w:1,h:1,x_center:.84375,y_center:.53125},{w:1,h:1,x_center:.84375,y_center:.53125},{w:1,h:1,x_center:.90625,y_center:.53125},{w:1,h:1,x_center:.90625,y_center:.53125},{w:1,h:1,x_center:.96875,y_center:.53125},{w:1,h:1,x_center:.96875,y_center:.53125},{w:1,h:1,x_center:.03125,y_center:.59375},{w:1,h:1,x_center:.03125,y_center:.59375},{w:1,h:1,x_center:.09375,y_center:.59375},{w:1,h:1,x_center:.09375,y_center:.59375},{w:1,h:1,x_center:.15625,y_center:.59375},{w:1,h:1,x_center:.15625,y_center:.59375},{w:1,h:1,x_center:.21875,y_center:.59375},{w:1,h:1,x_center:.21875,y_center:.59375},{w:1,h:1,x_center:.28125,y_center:.59375},{w:1,h:1,x_center:.28125,y_center:.59375},{w:1,h:1,x_center:.34375,y_center:.59375},{w:1,h:1,x_center:.34375,y_center:.59375},{w:1,h:1,x_center:.40625,y_center:.59375},{w:1,h:1,x_center:.40625,y_center:.59375},{w:1,h:1,x_center:.46875,y_center:.59375},{w:1,h:1,x_center:.46875,y_center:.59375},{w:1,h:1,x_center:.53125,y_center:.59375},{w:1,h:1,x_center:.53125,y_center:.59375},{w:1,h:1,x_center:.59375,y_center:.59375},{w:1,h:1,x_center:.59375,y_center:.59375},{w:1,h:1,x_center:.65625,y_center:.59375},{w:1,h:1,x_center:.65625,y_center:.59375},{w:1,h:1,x_center:.71875,y_center:.59375},{w:1,h:1,x_center:.71875,y_center:.59375},{w:1,h:1,x_center:.78125,y_center:.59375},{w:1,h:1,x_center:.78125,y_center:.59375},{w:1,h:1,x_center:.84375,y_center:.59375},{w:1,h:1,x_center:.84375,y_center:.59375},{w:1,h:1,x_center:.90625,y_center:.59375},{w:1,h:1,x_center:.90625,y_center:.59375},{w:1,h:1,x_center:.96875,y_center:.59375},{w:1,h:1,x_center:.96875,y_center:.59375},{w:1,h:1,x_center:.03125,y_center:.65625},{w:1,h:1,x_center:.03125,y_center:.65625},{w:1,h:1,x_center:.09375,y_center:.65625},{w:1,h:1,x_center:.09375,y_center:.65625},{w:1,h:1,x_center:.15625,y_center:.65625},{w:1,h:1,x_center:.15625,y_center:.65625},{w:1,h:1,x_center:.21875,y_center:.65625},{w:1,h:1,x_center:.21875,y_center:.65625},{w:1,h:1,x_center:.28125,y_center:.65625},{w:1,h:1,x_center:.28125,y_center:.65625},{w:1,h:1,x_center:.34375,y_center:.65625},{w:1,h:1,x_center:.34375,y_center:.65625},{w:1,h:1,x_center:.40625,y_center:.65625},{w:1,h:1,x_center:.40625,y_center:.65625},{w:1,h:1,x_center:.46875,y_center:.65625},{w:1,h:1,x_center:.46875,y_center:.65625},{w:1,h:1,x_center:.53125,y_center:.65625},{w:1,h:1,x_center:.53125,y_center:.65625},{w:1,h:1,x_center:.59375,y_center:.65625},{w:1,h:1,x_center:.59375,y_center:.65625},{w:1,h:1,x_center:.65625,y_center:.65625},{w:1,h:1,x_center:.65625,y_center:.65625},{w:1,h:1,x_center:.71875,y_center:.65625},{w:1,h:1,x_center:.71875,y_center:.65625},{w:1,h:1,x_center:.78125,y_center:.65625},{w:1,h:1,x_center:.78125,y_center:.65625},{w:1,h:1,x_center:.84375,y_center:.65625},{w:1,h:1,x_center:.84375,y_center:.65625},{w:1,h:1,x_center:.90625,y_center:.65625},{w:1,h:1,x_center:.90625,y_center:.65625},{w:1,h:1,x_center:.96875,y_center:.65625},{w:1,h:1,x_center:.96875,y_center:.65625},{w:1,h:1,x_center:.03125,y_center:.71875},{w:1,h:1,x_center:.03125,y_center:.71875},{w:1,h:1,x_center:.09375,y_center:.71875},{w:1,h:1,x_center:.09375,y_center:.71875},{w:1,h:1,x_center:.15625,y_center:.71875},{w:1,h:1,x_center:.15625,y_center:.71875},{w:1,h:1,x_center:.21875,y_center:.71875},{w:1,h:1,x_center:.21875,y_center:.71875},{w:1,h:1,x_center:.28125,y_center:.71875},{w:1,h:1,x_center:.28125,y_center:.71875},{w:1,h:1,x_center:.34375,y_center:.71875},{w:1,h:1,x_center:.34375,y_center:.71875},{w:1,h:1,x_center:.40625,y_center:.71875},{w:1,h:1,x_center:.40625,y_center:.71875},{w:1,h:1,x_center:.46875,y_center:.71875},{w:1,h:1,x_center:.46875,y_center:.71875},{w:1,h:1,x_center:.53125,y_center:.71875},{w:1,h:1,x_center:.53125,y_center:.71875},{w:1,h:1,x_center:.59375,y_center:.71875},{w:1,h:1,x_center:.59375,y_center:.71875},{w:1,h:1,x_center:.65625,y_center:.71875},{w:1,h:1,x_center:.65625,y_center:.71875},{w:1,h:1,x_center:.71875,y_center:.71875},{w:1,h:1,x_center:.71875,y_center:.71875},{w:1,h:1,x_center:.78125,y_center:.71875},{w:1,h:1,x_center:.78125,y_center:.71875},{w:1,h:1,x_center:.84375,y_center:.71875},{w:1,h:1,x_center:.84375,y_center:.71875},{w:1,h:1,x_center:.90625,y_center:.71875},{w:1,h:1,x_center:.90625,y_center:.71875},{w:1,h:1,x_center:.96875,y_center:.71875},{w:1,h:1,x_center:.96875,y_center:.71875},{w:1,h:1,x_center:.03125,y_center:.78125},{w:1,h:1,x_center:.03125,y_center:.78125},{w:1,h:1,x_center:.09375,y_center:.78125},{w:1,h:1,x_center:.09375,y_center:.78125},{w:1,h:1,x_center:.15625,y_center:.78125},{w:1,h:1,x_center:.15625,y_center:.78125},{w:1,h:1,x_center:.21875,y_center:.78125},{w:1,h:1,x_center:.21875,y_center:.78125},{w:1,h:1,x_center:.28125,y_center:.78125},{w:1,h:1,x_center:.28125,y_center:.78125},{w:1,h:1,x_center:.34375,y_center:.78125},{w:1,h:1,x_center:.34375,y_center:.78125},{w:1,h:1,x_center:.40625,y_center:.78125},{w:1,h:1,x_center:.40625,y_center:.78125},{w:1,h:1,x_center:.46875,y_center:.78125},{w:1,h:1,x_center:.46875,y_center:.78125},{w:1,h:1,x_center:.53125,y_center:.78125},{w:1,h:1,x_center:.53125,y_center:.78125},{w:1,h:1,x_center:.59375,y_center:.78125},{w:1,h:1,x_center:.59375,y_center:.78125},{w:1,h:1,x_center:.65625,y_center:.78125},{w:1,h:1,x_center:.65625,y_center:.78125},{w:1,h:1,x_center:.71875,y_center:.78125},{w:1,h:1,x_center:.71875,y_center:.78125},{w:1,h:1,x_center:.78125,y_center:.78125},{w:1,h:1,x_center:.78125,y_center:.78125},{w:1,h:1,x_center:.84375,y_center:.78125},{w:1,h:1,x_center:.84375,y_center:.78125},{w:1,h:1,x_center:.90625,y_center:.78125},{w:1,h:1,x_center:.90625,y_center:.78125},{w:1,h:1,x_center:.96875,y_center:.78125},{w:1,h:1,x_center:.96875,y_center:.78125},{w:1,h:1,x_center:.03125,y_center:.84375},{w:1,h:1,x_center:.03125,y_center:.84375},{w:1,h:1,x_center:.09375,y_center:.84375},{w:1,h:1,x_center:.09375,y_center:.84375},{w:1,h:1,x_center:.15625,y_center:.84375},{w:1,h:1,x_center:.15625,y_center:.84375},{w:1,h:1,x_center:.21875,y_center:.84375},{w:1,h:1,x_center:.21875,y_center:.84375},{w:1,h:1,x_center:.28125,y_center:.84375},{w:1,h:1,x_center:.28125,y_center:.84375},{w:1,h:1,x_center:.34375,y_center:.84375},{w:1,h:1,x_center:.34375,y_center:.84375},{w:1,h:1,x_center:.40625,y_center:.84375},{w:1,h:1,x_center:.40625,y_center:.84375},{w:1,h:1,x_center:.46875,y_center:.84375},{w:1,h:1,x_center:.46875,y_center:.84375},{w:1,h:1,x_center:.53125,y_center:.84375},{w:1,h:1,x_center:.53125,y_center:.84375},{w:1,h:1,x_center:.59375,y_center:.84375},{w:1,h:1,x_center:.59375,y_center:.84375},{w:1,h:1,x_center:.65625,y_center:.84375},{w:1,h:1,x_center:.65625,y_center:.84375},{w:1,h:1,x_center:.71875,y_center:.84375},{w:1,h:1,x_center:.71875,y_center:.84375},{w:1,h:1,x_center:.78125,y_center:.84375},{w:1,h:1,x_center:.78125,y_center:.84375},{w:1,h:1,x_center:.84375,y_center:.84375},{w:1,h:1,x_center:.84375,y_center:.84375},{w:1,h:1,x_center:.90625,y_center:.84375},{w:1,h:1,x_center:.90625,y_center:.84375},{w:1,h:1,x_center:.96875,y_center:.84375},{w:1,h:1,x_center:.96875,y_center:.84375},{w:1,h:1,x_center:.03125,y_center:.90625},{w:1,h:1,x_center:.03125,y_center:.90625},{w:1,h:1,x_center:.09375,y_center:.90625},{w:1,h:1,x_center:.09375,y_center:.90625},{w:1,h:1,x_center:.15625,y_center:.90625},{w:1,h:1,x_center:.15625,y_center:.90625},{w:1,h:1,x_center:.21875,y_center:.90625},{w:1,h:1,x_center:.21875,y_center:.90625},{w:1,h:1,x_center:.28125,y_center:.90625},{w:1,h:1,x_center:.28125,y_center:.90625},{w:1,h:1,x_center:.34375,y_center:.90625},{w:1,h:1,x_center:.34375,y_center:.90625},{w:1,h:1,x_center:.40625,y_center:.90625},{w:1,h:1,x_center:.40625,y_center:.90625},{w:1,h:1,x_center:.46875,y_center:.90625},{w:1,h:1,x_center:.46875,y_center:.90625},{w:1,h:1,x_center:.53125,y_center:.90625},{w:1,h:1,x_center:.53125,y_center:.90625},{w:1,h:1,x_center:.59375,y_center:.90625},{w:1,h:1,x_center:.59375,y_center:.90625},{w:1,h:1,x_center:.65625,y_center:.90625},{w:1,h:1,x_center:.65625,y_center:.90625},{w:1,h:1,x_center:.71875,y_center:.90625},{w:1,h:1,x_center:.71875,y_center:.90625},{w:1,h:1,x_center:.78125,y_center:.90625},{w:1,h:1,x_center:.78125,y_center:.90625},{w:1,h:1,x_center:.84375,y_center:.90625},{w:1,h:1,x_center:.84375,y_center:.90625},{w:1,h:1,x_center:.90625,y_center:.90625},{w:1,h:1,x_center:.90625,y_center:.90625},{w:1,h:1,x_center:.96875,y_center:.90625},{w:1,h:1,x_center:.96875,y_center:.90625},{w:1,h:1,x_center:.03125,y_center:.96875},{w:1,h:1,x_center:.03125,y_center:.96875},{w:1,h:1,x_center:.09375,y_center:.96875},{w:1,h:1,x_center:.09375,y_center:.96875},{w:1,h:1,x_center:.15625,y_center:.96875},{w:1,h:1,x_center:.15625,y_center:.96875},{w:1,h:1,x_center:.21875,y_center:.96875},{w:1,h:1,x_center:.21875,y_center:.96875},{w:1,h:1,x_center:.28125,y_center:.96875},{w:1,h:1,x_center:.28125,y_center:.96875},{w:1,h:1,x_center:.34375,y_center:.96875},{w:1,h:1,x_center:.34375,y_center:.96875},{w:1,h:1,x_center:.40625,y_center:.96875},{w:1,h:1,x_center:.40625,y_center:.96875},{w:1,h:1,x_center:.46875,y_center:.96875},{w:1,h:1,x_center:.46875,y_center:.96875},{w:1,h:1,x_center:.53125,y_center:.96875},{w:1,h:1,x_center:.53125,y_center:.96875},{w:1,h:1,x_center:.59375,y_center:.96875},{w:1,h:1,x_center:.59375,y_center:.96875},{w:1,h:1,x_center:.65625,y_center:.96875},{w:1,h:1,x_center:.65625,y_center:.96875},{w:1,h:1,x_center:.71875,y_center:.96875},{w:1,h:1,x_center:.71875,y_center:.96875},{w:1,h:1,x_center:.78125,y_center:.96875},{w:1,h:1,x_center:.78125,y_center:.96875},{w:1,h:1,x_center:.84375,y_center:.96875},{w:1,h:1,x_center:.84375,y_center:.96875},{w:1,h:1,x_center:.90625,y_center:.96875},{w:1,h:1,x_center:.90625,y_center:.96875},{w:1,h:1,x_center:.96875,y_center:.96875},{w:1,h:1,x_center:.96875,y_center:.96875},{w:1,h:1,x_center:.0625,y_center:.0625},{w:1,h:1,x_center:.0625,y_center:.0625},{w:1,h:1,x_center:.0625,y_center:.0625},{w:1,h:1,x_center:.0625,y_center:.0625},{w:1,h:1,x_center:.0625,y_center:.0625},{w:1,h:1,x_center:.0625,y_center:.0625},{w:1,h:1,x_center:.1875,y_center:.0625},{w:1,h:1,x_center:.1875,y_center:.0625},{w:1,h:1,x_center:.1875,y_center:.0625},{w:1,h:1,x_center:.1875,y_center:.0625},{w:1,h:1,x_center:.1875,y_center:.0625},{w:1,h:1,x_center:.1875,y_center:.0625},{w:1,h:1,x_center:.3125,y_center:.0625},{w:1,h:1,x_center:.3125,y_center:.0625},{w:1,h:1,x_center:.3125,y_center:.0625},{w:1,h:1,x_center:.3125,y_center:.0625},{w:1,h:1,x_center:.3125,y_center:.0625},{w:1,h:1,x_center:.3125,y_center:.0625},{w:1,h:1,x_center:.4375,y_center:.0625},{w:1,h:1,x_center:.4375,y_center:.0625},{w:1,h:1,x_center:.4375,y_center:.0625},{w:1,h:1,x_center:.4375,y_center:.0625},{w:1,h:1,x_center:.4375,y_center:.0625},{w:1,h:1,x_center:.4375,y_center:.0625},{w:1,h:1,x_center:.5625,y_center:.0625},{w:1,h:1,x_center:.5625,y_center:.0625},{w:1,h:1,x_center:.5625,y_center:.0625},{w:1,h:1,x_center:.5625,y_center:.0625},{w:1,h:1,x_center:.5625,y_center:.0625},{w:1,h:1,x_center:.5625,y_center:.0625},{w:1,h:1,x_center:.6875,y_center:.0625},{w:1,h:1,x_center:.6875,y_center:.0625},{w:1,h:1,x_center:.6875,y_center:.0625},{w:1,h:1,x_center:.6875,y_center:.0625},{w:1,h:1,x_center:.6875,y_center:.0625},{w:1,h:1,x_center:.6875,y_center:.0625},{w:1,h:1,x_center:.8125,y_center:.0625},{w:1,h:1,x_center:.8125,y_center:.0625},{w:1,h:1,x_center:.8125,y_center:.0625},{w:1,h:1,x_center:.8125,y_center:.0625},{w:1,h:1,x_center:.8125,y_center:.0625},{w:1,h:1,x_center:.8125,y_center:.0625},{w:1,h:1,x_center:.9375,y_center:.0625},{w:1,h:1,x_center:.9375,y_center:.0625},{w:1,h:1,x_center:.9375,y_center:.0625},{w:1,h:1,x_center:.9375,y_center:.0625},{w:1,h:1,x_center:.9375,y_center:.0625},{w:1,h:1,x_center:.9375,y_center:.0625},{w:1,h:1,x_center:.0625,y_center:.1875},{w:1,h:1,x_center:.0625,y_center:.1875},{w:1,h:1,x_center:.0625,y_center:.1875},{w:1,h:1,x_center:.0625,y_center:.1875},{w:1,h:1,x_center:.0625,y_center:.1875},{w:1,h:1,x_center:.0625,y_center:.1875},{w:1,h:1,x_center:.1875,y_center:.1875},{w:1,h:1,x_center:.1875,y_center:.1875},{w:1,h:1,x_center:.1875,y_center:.1875},{w:1,h:1,x_center:.1875,y_center:.1875},{w:1,h:1,x_center:.1875,y_center:.1875},{w:1,h:1,x_center:.1875,y_center:.1875},{w:1,h:1,x_center:.3125,y_center:.1875},{w:1,h:1,x_center:.3125,y_center:.1875},{w:1,h:1,x_center:.3125,y_center:.1875},{w:1,h:1,x_center:.3125,y_center:.1875},{w:1,h:1,x_center:.3125,y_center:.1875},{w:1,h:1,x_center:.3125,y_center:.1875},{w:1,h:1,x_center:.4375,y_center:.1875},{w:1,h:1,x_center:.4375,y_center:.1875},{w:1,h:1,x_center:.4375,y_center:.1875},{w:1,h:1,x_center:.4375,y_center:.1875},{w:1,h:1,x_center:.4375,y_center:.1875},{w:1,h:1,x_center:.4375,y_center:.1875},{w:1,h:1,x_center:.5625,y_center:.1875},{w:1,h:1,x_center:.5625,y_center:.1875},{w:1,h:1,x_center:.5625,y_center:.1875},{w:1,h:1,x_center:.5625,y_center:.1875},{w:1,h:1,x_center:.5625,y_center:.1875},{w:1,h:1,x_center:.5625,y_center:.1875},{w:1,h:1,x_center:.6875,y_center:.1875},{w:1,h:1,x_center:.6875,y_center:.1875},{w:1,h:1,x_center:.6875,y_center:.1875},{w:1,h:1,x_center:.6875,y_center:.1875},{w:1,h:1,x_center:.6875,y_center:.1875},{w:1,h:1,x_center:.6875,y_center:.1875},{w:1,h:1,x_center:.8125,y_center:.1875},{w:1,h:1,x_center:.8125,y_center:.1875},{w:1,h:1,x_center:.8125,y_center:.1875},{w:1,h:1,x_center:.8125,y_center:.1875},{w:1,h:1,x_center:.8125,y_center:.1875},{w:1,h:1,x_center:.8125,y_center:.1875},{w:1,h:1,x_center:.9375,y_center:.1875},{w:1,h:1,x_center:.9375,y_center:.1875},{w:1,h:1,x_center:.9375,y_center:.1875},{w:1,h:1,x_center:.9375,y_center:.1875},{w:1,h:1,x_center:.9375,y_center:.1875},{w:1,h:1,x_center:.9375,y_center:.1875},{w:1,h:1,x_center:.0625,y_center:.3125},{w:1,h:1,x_center:.0625,y_center:.3125},{w:1,h:1,x_center:.0625,y_center:.3125},{w:1,h:1,x_center:.0625,y_center:.3125},{w:1,h:1,x_center:.0625,y_center:.3125},{w:1,h:1,x_center:.0625,y_center:.3125},{w:1,h:1,x_center:.1875,y_center:.3125},{w:1,h:1,x_center:.1875,y_center:.3125},{w:1,h:1,x_center:.1875,y_center:.3125},{w:1,h:1,x_center:.1875,y_center:.3125},{w:1,h:1,x_center:.1875,y_center:.3125},{w:1,h:1,x_center:.1875,y_center:.3125},{w:1,h:1,x_center:.3125,y_center:.3125},{w:1,h:1,x_center:.3125,y_center:.3125},{w:1,h:1,x_center:.3125,y_center:.3125},{w:1,h:1,x_center:.3125,y_center:.3125},{w:1,h:1,x_center:.3125,y_center:.3125},{w:1,h:1,x_center:.3125,y_center:.3125},{w:1,h:1,x_center:.4375,y_center:.3125},{w:1,h:1,x_center:.4375,y_center:.3125},{w:1,h:1,x_center:.4375,y_center:.3125},{w:1,h:1,x_center:.4375,y_center:.3125},{w:1,h:1,x_center:.4375,y_center:.3125},{w:1,h:1,x_center:.4375,y_center:.3125},{w:1,h:1,x_center:.5625,y_center:.3125},{w:1,h:1,x_center:.5625,y_center:.3125},{w:1,h:1,x_center:.5625,y_center:.3125},{w:1,h:1,x_center:.5625,y_center:.3125},{w:1,h:1,x_center:.5625,y_center:.3125},{w:1,h:1,x_center:.5625,y_center:.3125},{w:1,h:1,x_center:.6875,y_center:.3125},{w:1,h:1,x_center:.6875,y_center:.3125},{w:1,h:1,x_center:.6875,y_center:.3125},{w:1,h:1,x_center:.6875,y_center:.3125},{w:1,h:1,x_center:.6875,y_center:.3125},{w:1,h:1,x_center:.6875,y_center:.3125},{w:1,h:1,x_center:.8125,y_center:.3125},{w:1,h:1,x_center:.8125,y_center:.3125},{w:1,h:1,x_center:.8125,y_center:.3125},{w:1,h:1,x_center:.8125,y_center:.3125},{w:1,h:1,x_center:.8125,y_center:.3125},{w:1,h:1,x_center:.8125,y_center:.3125},{w:1,h:1,x_center:.9375,y_center:.3125},{w:1,h:1,x_center:.9375,y_center:.3125},{w:1,h:1,x_center:.9375,y_center:.3125},{w:1,h:1,x_center:.9375,y_center:.3125},{w:1,h:1,x_center:.9375,y_center:.3125},{w:1,h:1,x_center:.9375,y_center:.3125},{w:1,h:1,x_center:.0625,y_center:.4375},{w:1,h:1,x_center:.0625,y_center:.4375},{w:1,h:1,x_center:.0625,y_center:.4375},{w:1,h:1,x_center:.0625,y_center:.4375},{w:1,h:1,x_center:.0625,y_center:.4375},{w:1,h:1,x_center:.0625,y_center:.4375},{w:1,h:1,x_center:.1875,y_center:.4375},{w:1,h:1,x_center:.1875,y_center:.4375},{w:1,h:1,x_center:.1875,y_center:.4375},{w:1,h:1,x_center:.1875,y_center:.4375},{w:1,h:1,x_center:.1875,y_center:.4375},{w:1,h:1,x_center:.1875,y_center:.4375},{w:1,h:1,x_center:.3125,y_center:.4375},{w:1,h:1,x_center:.3125,y_center:.4375},{w:1,h:1,x_center:.3125,y_center:.4375},{w:1,h:1,x_center:.3125,y_center:.4375},{w:1,h:1,x_center:.3125,y_center:.4375},{w:1,h:1,x_center:.3125,y_center:.4375},{w:1,h:1,x_center:.4375,y_center:.4375},{w:1,h:1,x_center:.4375,y_center:.4375},{w:1,h:1,x_center:.4375,y_center:.4375},{w:1,h:1,x_center:.4375,y_center:.4375},{w:1,h:1,x_center:.4375,y_center:.4375},{w:1,h:1,x_center:.4375,y_center:.4375},{w:1,h:1,x_center:.5625,y_center:.4375},{w:1,h:1,x_center:.5625,y_center:.4375},{w:1,h:1,x_center:.5625,y_center:.4375},{w:1,h:1,x_center:.5625,y_center:.4375},{w:1,h:1,x_center:.5625,y_center:.4375},{w:1,h:1,x_center:.5625,y_center:.4375},{w:1,h:1,x_center:.6875,y_center:.4375},{w:1,h:1,x_center:.6875,y_center:.4375},{w:1,h:1,x_center:.6875,y_center:.4375},{w:1,h:1,x_center:.6875,y_center:.4375},{w:1,h:1,x_center:.6875,y_center:.4375},{w:1,h:1,x_center:.6875,y_center:.4375},{w:1,h:1,x_center:.8125,y_center:.4375},{w:1,h:1,x_center:.8125,y_center:.4375},{w:1,h:1,x_center:.8125,y_center:.4375},{w:1,h:1,x_center:.8125,y_center:.4375},{w:1,h:1,x_center:.8125,y_center:.4375},{w:1,h:1,x_center:.8125,y_center:.4375},{w:1,h:1,x_center:.9375,y_center:.4375},{w:1,h:1,x_center:.9375,y_center:.4375},{w:1,h:1,x_center:.9375,y_center:.4375},{w:1,h:1,x_center:.9375,y_center:.4375},{w:1,h:1,x_center:.9375,y_center:.4375},{w:1,h:1,x_center:.9375,y_center:.4375},{w:1,h:1,x_center:.0625,y_center:.5625},{w:1,h:1,x_center:.0625,y_center:.5625},{w:1,h:1,x_center:.0625,y_center:.5625},{w:1,h:1,x_center:.0625,y_center:.5625},{w:1,h:1,x_center:.0625,y_center:.5625},{w:1,h:1,x_center:.0625,y_center:.5625},{w:1,h:1,x_center:.1875,y_center:.5625},{w:1,h:1,x_center:.1875,y_center:.5625},{w:1,h:1,x_center:.1875,y_center:.5625},{w:1,h:1,x_center:.1875,y_center:.5625},{w:1,h:1,x_center:.1875,y_center:.5625},{w:1,h:1,x_center:.1875,y_center:.5625},{w:1,h:1,x_center:.3125,y_center:.5625},{w:1,h:1,x_center:.3125,y_center:.5625},{w:1,h:1,x_center:.3125,y_center:.5625},{w:1,h:1,x_center:.3125,y_center:.5625},{w:1,h:1,x_center:.3125,y_center:.5625},{w:1,h:1,x_center:.3125,y_center:.5625},{w:1,h:1,x_center:.4375,y_center:.5625},{w:1,h:1,x_center:.4375,y_center:.5625},{w:1,h:1,x_center:.4375,y_center:.5625},{w:1,h:1,x_center:.4375,y_center:.5625},{w:1,h:1,x_center:.4375,y_center:.5625},{w:1,h:1,x_center:.4375,y_center:.5625},{w:1,h:1,x_center:.5625,y_center:.5625},{w:1,h:1,x_center:.5625,y_center:.5625},{w:1,h:1,x_center:.5625,y_center:.5625},{w:1,h:1,x_center:.5625,y_center:.5625},{w:1,h:1,x_center:.5625,y_center:.5625},{w:1,h:1,x_center:.5625,y_center:.5625},{w:1,h:1,x_center:.6875,y_center:.5625},{w:1,h:1,x_center:.6875,y_center:.5625},{w:1,h:1,x_center:.6875,y_center:.5625},{w:1,h:1,x_center:.6875,y_center:.5625},{w:1,h:1,x_center:.6875,y_center:.5625},{w:1,h:1,x_center:.6875,y_center:.5625},{w:1,h:1,x_center:.8125,y_center:.5625},{w:1,h:1,x_center:.8125,y_center:.5625},{w:1,h:1,x_center:.8125,y_center:.5625},{w:1,h:1,x_center:.8125,y_center:.5625},{w:1,h:1,x_center:.8125,y_center:.5625},{w:1,h:1,x_center:.8125,y_center:.5625},{w:1,h:1,x_center:.9375,y_center:.5625},{w:1,h:1,x_center:.9375,y_center:.5625},{w:1,h:1,x_center:.9375,y_center:.5625},{w:1,h:1,x_center:.9375,y_center:.5625},{w:1,h:1,x_center:.9375,y_center:.5625},{w:1,h:1,x_center:.9375,y_center:.5625},{w:1,h:1,x_center:.0625,y_center:.6875},{w:1,h:1,x_center:.0625,y_center:.6875},{w:1,h:1,x_center:.0625,y_center:.6875},{w:1,h:1,x_center:.0625,y_center:.6875},{w:1,h:1,x_center:.0625,y_center:.6875},{w:1,h:1,x_center:.0625,y_center:.6875},{w:1,h:1,x_center:.1875,y_center:.6875},{w:1,h:1,x_center:.1875,y_center:.6875},{w:1,h:1,x_center:.1875,y_center:.6875},{w:1,h:1,x_center:.1875,y_center:.6875},{w:1,h:1,x_center:.1875,y_center:.6875},{w:1,h:1,x_center:.1875,y_center:.6875},{w:1,h:1,x_center:.3125,y_center:.6875},{w:1,h:1,x_center:.3125,y_center:.6875},{w:1,h:1,x_center:.3125,y_center:.6875},{w:1,h:1,x_center:.3125,y_center:.6875},{w:1,h:1,x_center:.3125,y_center:.6875},{w:1,h:1,x_center:.3125,y_center:.6875},{w:1,h:1,x_center:.4375,y_center:.6875},{w:1,h:1,x_center:.4375,y_center:.6875},{w:1,h:1,x_center:.4375,y_center:.6875},{w:1,h:1,x_center:.4375,y_center:.6875},{w:1,h:1,x_center:.4375,y_center:.6875},{w:1,h:1,x_center:.4375,y_center:.6875},{w:1,h:1,x_center:.5625,y_center:.6875},{w:1,h:1,x_center:.5625,y_center:.6875},{w:1,h:1,x_center:.5625,y_center:.6875},{w:1,h:1,x_center:.5625,y_center:.6875},{w:1,h:1,x_center:.5625,y_center:.6875},{w:1,h:1,x_center:.5625,y_center:.6875},{w:1,h:1,x_center:.6875,y_center:.6875},{w:1,h:1,x_center:.6875,y_center:.6875},{w:1,h:1,x_center:.6875,y_center:.6875},{w:1,h:1,x_center:.6875,y_center:.6875},{w:1,h:1,x_center:.6875,y_center:.6875},{w:1,h:1,x_center:.6875,y_center:.6875},{w:1,h:1,x_center:.8125,y_center:.6875},{w:1,h:1,x_center:.8125,y_center:.6875},{w:1,h:1,x_center:.8125,y_center:.6875},{w:1,h:1,x_center:.8125,y_center:.6875},{w:1,h:1,x_center:.8125,y_center:.6875},{w:1,h:1,x_center:.8125,y_center:.6875},{w:1,h:1,x_center:.9375,y_center:.6875},{w:1,h:1,x_center:.9375,y_center:.6875},{w:1,h:1,x_center:.9375,y_center:.6875},{w:1,h:1,x_center:.9375,y_center:.6875},{w:1,h:1,x_center:.9375,y_center:.6875},{w:1,h:1,x_center:.9375,y_center:.6875},{w:1,h:1,x_center:.0625,y_center:.8125},{w:1,h:1,x_center:.0625,y_center:.8125},{w:1,h:1,x_center:.0625,y_center:.8125},{w:1,h:1,x_center:.0625,y_center:.8125},{w:1,h:1,x_center:.0625,y_center:.8125},{w:1,h:1,x_center:.0625,y_center:.8125},{w:1,h:1,x_center:.1875,y_center:.8125},{w:1,h:1,x_center:.1875,y_center:.8125},{w:1,h:1,x_center:.1875,y_center:.8125},{w:1,h:1,x_center:.1875,y_center:.8125},{w:1,h:1,x_center:.1875,y_center:.8125},{w:1,h:1,x_center:.1875,y_center:.8125},{w:1,h:1,x_center:.3125,y_center:.8125},{w:1,h:1,x_center:.3125,y_center:.8125},{w:1,h:1,x_center:.3125,y_center:.8125},{w:1,h:1,x_center:.3125,y_center:.8125},{w:1,h:1,x_center:.3125,y_center:.8125},{w:1,h:1,x_center:.3125,y_center:.8125},{w:1,h:1,x_center:.4375,y_center:.8125},{w:1,h:1,x_center:.4375,y_center:.8125},{w:1,h:1,x_center:.4375,y_center:.8125},{w:1,h:1,x_center:.4375,y_center:.8125},{w:1,h:1,x_center:.4375,y_center:.8125},{w:1,h:1,x_center:.4375,y_center:.8125},{w:1,h:1,x_center:.5625,y_center:.8125},{w:1,h:1,x_center:.5625,y_center:.8125},{w:1,h:1,x_center:.5625,y_center:.8125},{w:1,h:1,x_center:.5625,y_center:.8125},{w:1,h:1,x_center:.5625,y_center:.8125},{w:1,h:1,x_center:.5625,y_center:.8125},{w:1,h:1,x_center:.6875,y_center:.8125},{w:1,h:1,x_center:.6875,y_center:.8125},{w:1,h:1,x_center:.6875,y_center:.8125},{w:1,h:1,x_center:.6875,y_center:.8125},{w:1,h:1,x_center:.6875,y_center:.8125},{w:1,h:1,x_center:.6875,y_center:.8125},{w:1,h:1,x_center:.8125,y_center:.8125},{w:1,h:1,x_center:.8125,y_center:.8125},{w:1,h:1,x_center:.8125,y_center:.8125},{w:1,h:1,x_center:.8125,y_center:.8125},{w:1,h:1,x_center:.8125,y_center:.8125},{w:1,h:1,x_center:.8125,y_center:.8125},{w:1,h:1,x_center:.9375,y_center:.8125},{w:1,h:1,x_center:.9375,y_center:.8125},{w:1,h:1,x_center:.9375,y_center:.8125},{w:1,h:1,x_center:.9375,y_center:.8125},{w:1,h:1,x_center:.9375,y_center:.8125},{w:1,h:1,x_center:.9375,y_center:.8125},{w:1,h:1,x_center:.0625,y_center:.9375},{w:1,h:1,x_center:.0625,y_center:.9375},{w:1,h:1,x_center:.0625,y_center:.9375},{w:1,h:1,x_center:.0625,y_center:.9375},{w:1,h:1,x_center:.0625,y_center:.9375},{w:1,h:1,x_center:.0625,y_center:.9375},{w:1,h:1,x_center:.1875,y_center:.9375},{w:1,h:1,x_center:.1875,y_center:.9375},{w:1,h:1,x_center:.1875,y_center:.9375},{w:1,h:1,x_center:.1875,y_center:.9375},{w:1,h:1,x_center:.1875,y_center:.9375},{w:1,h:1,x_center:.1875,y_center:.9375},{w:1,h:1,x_center:.3125,y_center:.9375},{w:1,h:1,x_center:.3125,y_center:.9375},{w:1,h:1,x_center:.3125,y_center:.9375},{w:1,h:1,x_center:.3125,y_center:.9375},{w:1,h:1,x_center:.3125,y_center:.9375},{w:1,h:1,x_center:.3125,y_center:.9375},{w:1,h:1,x_center:.4375,y_center:.9375},{w:1,h:1,x_center:.4375,y_center:.9375},{w:1,h:1,x_center:.4375,y_center:.9375},{w:1,h:1,x_center:.4375,y_center:.9375},{w:1,h:1,x_center:.4375,y_center:.9375},{w:1,h:1,x_center:.4375,y_center:.9375},{w:1,h:1,x_center:.5625,y_center:.9375},{w:1,h:1,x_center:.5625,y_center:.9375},{w:1,h:1,x_center:.5625,y_center:.9375},{w:1,h:1,x_center:.5625,y_center:.9375},{w:1,h:1,x_center:.5625,y_center:.9375},{w:1,h:1,x_center:.5625,y_center:.9375},{w:1,h:1,x_center:.6875,y_center:.9375},{w:1,h:1,x_center:.6875,y_center:.9375},{w:1,h:1,x_center:.6875,y_center:.9375},{w:1,h:1,x_center:.6875,y_center:.9375},{w:1,h:1,x_center:.6875,y_center:.9375},{w:1,h:1,x_center:.6875,y_center:.9375},{w:1,h:1,x_center:.8125,y_center:.9375},{w:1,h:1,x_center:.8125,y_center:.9375},{w:1,h:1,x_center:.8125,y_center:.9375},{w:1,h:1,x_center:.8125,y_center:.9375},{w:1,h:1,x_center:.8125,y_center:.9375},{w:1,h:1,x_center:.8125,y_center:.9375},{w:1,h:1,x_center:.9375,y_center:.9375},{w:1,h:1,x_center:.9375,y_center:.9375},{w:1,h:1,x_center:.9375,y_center:.9375},{w:1,h:1,x_center:.9375,y_center:.9375},{w:1,h:1,x_center:.9375,y_center:.9375},{w:1,h:1,x_center:.9375,y_center:.9375}]});var o4=ot($2=>{var a4=Pe(G6()),s4=Pe(t4()),i4=Pe(r4()),D2={thumb:[1,2,3,4],indexFinger:[5,6,7,8],middleFinger:[9,10,11,12],ringFinger:[13,14,15,16],pinky:[17,18,19,20],palmBase:[0]},z2=class{constructor(t){this.handPipeline=t}static getAnnotations(){return D2}async estimateHands(t,n){let r=await this.handPipeline.estimateHands(t,n);if(!r)return[];let a=[];for(let s of r){let i={};if(s.landmarks)for(let l of Object.keys(D2))i[l]=D2[l].map(c=>s.landmarks[c]);let o=s.box?[Math.max(0,s.box.topLeft[0]),Math.max(0,s.box.topLeft[1]),Math.min(t.shape[2],s.box.bottomRight[0])-s.box.topLeft[0],Math.min(t.shape[1],s.box.bottomRight[1])-s.box.topLeft[1]]:0;a.push({confidence:s.confidence,box:o,landmarks:s.landmarks,annotations:i})}return a}};$2.HandPose=z2;async function Vse(e){let[t,n]=await Promise.all([e.hand.enabled?Ht(e.hand.detector.modelPath,{fromTFHub:e.hand.detector.modelPath.includes("tfhub.dev")}):null,e.hand.landmarks?Ht(e.hand.skeleton.modelPath,{fromTFHub:e.hand.skeleton.modelPath.includes("tfhub.dev")}):null]),r=new a4.HandDetector(t,e.hand.inputSize,i4.anchors),a=new s4.HandPipeline(r,n,e.hand.inputSize),s=new z2(a);return e.hand.enabled&&Fe(`load model: ${e.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),e.hand.landmarks&&Fe(`load model: ${e.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),s}$2.load=Vse});var l4=ot(Jc=>{Jc.body=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let r=e[n].keypoints.find(l=>l.part==="leftWrist"),a=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&r&&a&&r.position.y<s.position.y&&a.position.y<s.position.y?t.push({body:n,gesture:"i give up"}):s&&r&&r.position.y<s.position.y?t.push({body:n,gesture:"raise left hand"}):s&&a&&a.position.y<s.position.y&&t.push({body:n,gesture:"raise right hand"});let i=e[n].keypoints.find(l=>l.part==="leftShoulder"),o=e[n].keypoints.find(l=>l.part==="rightShoulder");i&&o&&t.push({body:n,gesture:`leaning ${i.position.y>o.position.y?"left":"right"}`})}return t};Jc.face=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>0){let r=e[n].mesh[35][2]-e[n].mesh[263][2];Math.abs(r)<10?t.push({face:n,gesture:"facing camera"}):t.push({face:n,gesture:`facing ${r<0?"right":"left"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let o=e[n].mesh[152][2];Math.abs(o)>10&&t.push({face:n,gesture:`head ${o<0?"up":"down"}`})}return t};Jc.iris=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.rightEyeIris)continue;let r=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],a=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],s=Math.abs(r*a),i=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],o=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(i*o);Math.abs(s-l)/Math.max(s,l)<.25&&t.push({iris:n,gesture:"looking at camera"})}return t};Jc.hand=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let r=[];for(let[a,s]of Object.entries(e[n].annotations))a!=="palmBase"&&r.push({name:a.toLowerCase(),position:s[0]});if(r&&r.length>0){let a=r.reduce((i,o)=>i.position[2]<o.position[2]?i:o),s=r.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:n,gesture:`${a.name} forward ${s.name} up`})}}return t}});var c4=ot(u4=>{var Use=function(e,t,n){let r=function(o,l,c){let u=new RegExp("\\b"+l+" \\w+ (\\w+)","ig");o.replace(u,(h,p)=>(c[p]=0,h))},a=function(o,l){let c=e.createShader(l);if(e.shaderSource(c,o),e.compileShader(c),!e.getShaderParameter(c,e.COMPILE_STATUS))throw new Error("Filter: GL compile failed",e.getShaderInfoLog(c));return c};this.uniform={},this.attribute={};let s=a(t,e.VERTEX_SHADER),i=a(n,e.FRAGMENT_SHADER);if(this.id=e.createProgram(),e.attachShader(this.id,s),e.attachShader(this.id,i),e.linkProgram(this.id),!e.getProgramParameter(this.id,e.LINK_STATUS))throw new Error("Filter: GL link failed",e.getProgramInfoLog(this.id));e.useProgram(this.id),r(t,"attribute",this.attribute);for(let o in this.attribute)this.attribute[o]=e.getAttribLocation(this.id,o);r(t,"uniform",this.uniform),r(n,"uniform",this.uniform);for(let o in this.uniform)this.uniform[o]=e.getUniformLocation(this.id,o)},jse=function(e){e||(e={});let t=0,n=null,r=!1,a=-1,s=[null,null],i=[],o=-1,l=-1,c=null,u=null,h=e.canvas||document.createElement("canvas"),p={},d=h.getContext("webgl");if(!d)throw new Error("Filter: getContext() failed");this.addFilter=function(b){let N=Array.prototype.slice.call(arguments,1),T=w[b];i.push({func:T,args:N})},this.reset=function(){i=[]},this.apply=function(b){if(f(b.width,b.height),t=0,n||(n=d.createTexture()),d.bindTexture(d.TEXTURE_2D,n),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_WRAP_S,d.CLAMP_TO_EDGE),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_WRAP_T,d.CLAMP_TO_EDGE),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_MIN_FILTER,d.NEAREST),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_MAG_FILTER,d.NEAREST),d.texImage2D(d.TEXTURE_2D,0,d.RGBA,d.RGBA,d.UNSIGNED_BYTE,b),i.length===0)return y(),h;for(let N=0;N<i.length;N++){r=N===i.length-1;let T=i[N];T.func.apply(this,T.args||[])}return h};let f=function(b,N){if(!(b===o&&N===l)){if(h.width=b,o=b,h.height=N,l=N,!c){let T=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);c=d.createBuffer(),d.bindBuffer(d.ARRAY_BUFFER,c),d.bufferData(d.ARRAY_BUFFER,T,d.STATIC_DRAW),d.pixelStorei(d.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}d.viewport(0,0,o,l),s=[null,null]}},m=function(b){return s[b]=s[b]||A(o,l),s[b]},A=function(b,N){let T=d.createFramebuffer();d.bindFramebuffer(d.FRAMEBUFFER,T);let E=d.createRenderbuffer();d.bindRenderbuffer(d.RENDERBUFFER,E);let M=d.createTexture();return d.bindTexture(d.TEXTURE_2D,M),d.texImage2D(d.TEXTURE_2D,0,d.RGBA,b,N,0,d.RGBA,d.UNSIGNED_BYTE,null),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_MAG_FILTER,d.LINEAR),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_MIN_FILTER,d.LINEAR),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_WRAP_S,d.CLAMP_TO_EDGE),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_WRAP_T,d.CLAMP_TO_EDGE),d.framebufferTexture2D(d.FRAMEBUFFER,d.COLOR_ATTACHMENT0,d.TEXTURE_2D,M,0),d.bindTexture(d.TEXTURE_2D,null),d.bindFramebuffer(d.FRAMEBUFFER,null),{fbo:T,texture:M}},y=function(b){var M,$;let N=null,T=null,E=!1;t===0?N=n:N=(M=m(a))==null?void 0:M.texture,t++,r&&!(b&_.INTERMEDIATE)?(T=null,E=t%2==0):(a=(a+1)%2,T=($=m(a))==null?void 0:$.fbo),d.bindTexture(d.TEXTURE_2D,N),d.bindFramebuffer(d.FRAMEBUFFER,T),d.uniform1f(u.uniform.flipY,E?-1:1),d.drawArrays(d.TRIANGLES,0,6)},g=function(b){if(p[b])return u=p[b],d.useProgram(u.id),u;u=new Use(d,x.VERTEX_IDENTITY,b);let N=Float32Array.BYTES_PER_ELEMENT,T=4*N;return d.enableVertexAttribArray(u.attribute.pos),d.vertexAttribPointer(u.attribute.pos,2,d.FLOAT,!1,T,0*N),d.enableVertexAttribArray(u.attribute.uv),d.vertexAttribPointer(u.attribute.uv,2,d.FLOAT,!1,T,2*N),p[b]=u,u},_={INTERMEDIATE:1},x={};x.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(`
`),x.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(`
`);let w={};w.colorMatrix=function(b){let N=new Float32Array(b);N[4]/=255,N[9]/=255,N[14]/=255,N[19]/=255;let T=N[18]===1&&N[3]===0&&N[8]===0&&N[13]===0&&N[15]===0&&N[16]===0&&N[17]===0&&N[19]===0?w.colorMatrix.SHADER.WITHOUT_ALPHA:w.colorMatrix.SHADER.WITH_ALPHA,E=g(T);d.uniform1fv(E.uniform.m,N),y()},w.colorMatrix.SHADER={},w.colorMatrix.SHADER.WITH_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];","gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];","}"].join(`
`),w.colorMatrix.SHADER.WITHOUT_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];","gl_FragColor.a = c.a;","}"].join(`
`),w.brightness=function(b){let N=(b||0)+1;w.colorMatrix([N,0,0,0,0,0,N,0,0,0,0,0,N,0,0,0,0,0,1,0])},w.saturation=function(b){let N=(b||0)*2/3+1,T=(N-1)*-.5;w.colorMatrix([N,T,T,0,0,T,N,T,0,0,T,T,N,0,0,0,0,0,1,0])},w.desaturate=function(){w.saturation(-1)},w.contrast=function(b){let N=(b||0)+1,T=-128*(N-1);w.colorMatrix([N,0,0,0,T,0,N,0,0,T,0,0,N,0,T,0,0,0,1,0])},w.negative=function(){w.contrast(-2)},w.hue=function(b){b=(b||0)/180*Math.PI;let N=Math.cos(b),T=Math.sin(b),E=.213,M=.715,$=.072;w.colorMatrix([E+N*(1-E)+T*-E,M+N*-M+T*-M,$+N*-$+T*(1-$),0,0,E+N*-E+T*.143,M+N*(1-M)+T*.14,$+N*-$+T*-.283,0,0,E+N*-E+T*-(1-E),M+N*-M+T*M,$+N*(1-$)+T*$,0,0,0,0,0,1,0])},w.desaturateLuminance=function(){w.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},w.sepia=function(){w.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},w.brownie=function(){w.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},w.vintagePinhole=function(){w.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},w.kodachrome=function(){w.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},w.technicolor=function(){w.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},w.polaroid=function(){w.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},w.shiftToBGR=function(){w.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},w.convolution=function(b){let N=new Float32Array(b),T=1/o,E=1/l,M=g(w.convolution.SHADER);d.uniform1fv(M.uniform.m,N),d.uniform2f(M.uniform.px,T,E),y()},w.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(`
`),w.detectEdges=function(){w.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},w.sobelX=function(){w.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},w.sobelY=function(){w.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},w.sharpen=function(b){let N=b||1;w.convolution.call(this,[0,-1*N,0,-1*N,1+4*N,-1*N,0,-1*N,0])},w.emboss=function(b){let N=b||1;w.convolution.call(this,[-2*N,-1*N,0,-1*N,1,1*N,0,1*N,2*N])},w.blur=function(b){let N=b/7/o,T=b/7/l,E=g(w.blur.SHADER);d.uniform2f(E.uniform.px,0,T),y(_.INTERMEDIATE),d.uniform2f(E.uniform.px,N,0),y()},w.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;","}"].join(`
`),w.pixelate=function(b){let N=b/o,T=b/l,E=g(w.pixelate.SHADER);d.uniform2f(E.uniform.size,N,T),y()},w.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(`
`)};u4.Canvas=jse});var p4=ot(h4=>{var d4=Pe(c4()),St=null,Qt=null;function Hse(e,t){let n;if(e instanceof Ke)n=tr(e);else{let r=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,a=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0,s=r,i=a;if(t.filter.width>0?s=t.filter.width:t.filter.height>0&&(s=r*(t.filter.height/a)),t.filter.height>0?i=t.filter.height:t.filter.width>0&&(i=a*(t.filter.width/r)),!s||!i)return Fe("Human: invalid input",e),null;(!St||St.width!==s||St.height!==i)&&(St=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(s,i):document.createElement("canvas"),St.width!==s&&(St.width=s),St.height!==i&&(St.height=i));let o=St.getContext("2d");if(e instanceof ImageData?o.putImageData(e,0,0):o.drawImage(e,0,0,r,a,0,0,St.width,St.height),t.filter.enabled){if((!this.fx||!Qt||St.width!==Qt.width||St.height!==Qt.height)&&(Qt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(St.width,St.height):document.createElement("canvas"),Qt.width!==St.width&&(Qt.width=St.width),Qt.height!==St.height&&(Qt.height=St.height),this.fx=an.flags.IS_BROWSER?new d4.Canvas({canvas:Qt}):null),!this.fx)return St;this.fx.reset(),this.fx.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&this.fx.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&this.fx.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&this.fx.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&this.fx.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&this.fx.addFilter("hue",t.filter.hue),t.filter.negative&&this.fx.addFilter("negative"),t.filter.sepia&&this.fx.addFilter("sepia"),t.filter.vintage&&this.fx.addFilter("brownie"),t.filter.sepia&&this.fx.addFilter("sepia"),t.filter.kodachrome&&this.fx.addFilter("kodachrome"),t.filter.technicolor&&this.fx.addFilter("technicolor"),t.filter.polaroid&&this.fx.addFilter("polaroid"),t.filter.pixelate!==0&&this.fx.addFilter("pixelate",t.filter.pixelate),this.fx.apply(St)}else Qt=St;let l;if(Qt.data){let u=[Qt.height,Qt.width,3];l=fd(Qt.data,u,"int32")}else if(t.backend==="webgl"||Qt instanceof ImageData)l=sl.fromPixels(Qt);else{let u=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(s,i):document.createElement("canvas");u.width=s,u.height=i;let h=u.getContext("2d");h==null||h.drawImage(Qt,0,0);let p=h==null?void 0:h.getImageData(0,0,s,i);l=sl.fromPixels(p)}let c=l.toFloat();n=c.expandDims(0),l.dispose(),c.dispose()}return{tensor:n,canvas:t.filter.return?Qt:null}}h4.process=Hse});function Fe(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}var W1={};tg(W1,{Abs:()=>Zi,Acos:()=>Yi,Acosh:()=>Ji,AdadeltaOptimizer:()=>Zd,AdagradOptimizer:()=>Yd,AdamOptimizer:()=>Jd,AdamaxOptimizer:()=>Qd,Add:()=>wa,AddN:()=>is,All:()=>Eh,Any:()=>Ch,ArgMax:()=>os,ArgMin:()=>bu,Asin:()=>Qi,Asinh:()=>eo,Atan:()=>to,Atan2:()=>ro,Atanh:()=>no,AvgPool:()=>ls,AvgPool3D:()=>vu,AvgPool3DGrad:()=>Fh,AvgPoolGrad:()=>Rh,BackendWasm:()=>Gb,BatchMatMul:()=>us,BatchToSpaceND:()=>ku,Bincount:()=>Mh,BroadcastTo:()=>gg,Callback:()=>P7,CallbackList:()=>D3,Cast:()=>cs,Ceil:()=>ao,ClipByValue:()=>_a,Complex:()=>Oh,ComplexAbs:()=>Iu,Concat:()=>so,Conv2D:()=>hs,Conv2DBackpropFilter:()=>$h,Conv2DBackpropInput:()=>ds,Conv3D:()=>Nu,Conv3DBackpropFilterV2:()=>Dh,Conv3DBackpropInputV2:()=>zh,Cos:()=>ps,Cosh:()=>io,CropAndResize:()=>oo,Cumsum:()=>fs,CustomCallback:()=>P3,DataStorage:()=>Ih,DenseBincount:()=>Ph,DepthToSpace:()=>lo,DepthwiseConv2dNative:()=>ms,DepthwiseConv2dNativeBackpropFilter:()=>Lh,DepthwiseConv2dNativeBackpropInput:()=>Wh,Diag:()=>Bh,Dilation2D:()=>Su,Dilation2DBackpropFilter:()=>Uh,Dilation2DBackpropInput:()=>Vh,ENV:()=>an,EarlyStopping:()=>W7,Elu:()=>uo,EluGrad:()=>jh,Environment:()=>Ag,Equal:()=>ho,Erf:()=>co,Exp:()=>ys,ExpandDims:()=>po,Expm1:()=>fo,FFT:()=>Hh,Fill:()=>Tu,FlipLeftRight:()=>mo,Floor:()=>gs,FloorDiv:()=>xs,FromPixels:()=>sd,FusedBatchNorm:()=>ws,FusedConv2D:()=>Ys,FusedDepthwiseConv2D:()=>Js,GPGPUContext:()=>mp,GatherNd:()=>yo,GatherV2:()=>Ao,GraphModel:()=>mv,Greater:()=>go,GreaterEqual:()=>_s,History:()=>z3,IFFT:()=>Gh,Identity:()=>xo,Imag:()=>qh,InputSpec:()=>jt,IsFinite:()=>wo,IsInf:()=>_o,IsNan:()=>bo,KernelBackend:()=>xu,LRN:()=>Ru,LRNGrad:()=>Kh,LayerVariable:()=>R3,LayersModel:()=>ca,LeakyRelu:()=>bs,Less:()=>vo,LessEqual:()=>ko,LinSpace:()=>Xh,Log:()=>vs,Log1p:()=>Io,LogSoftmax:()=>xg,LogicalAnd:()=>No,LogicalNot:()=>Eu,LogicalOr:()=>Cu,MathBackendCPU:()=>cx,MathBackendWebGL:()=>yp,Max:()=>ks,MaxPool:()=>Ns,MaxPool3D:()=>Fu,MaxPool3DGrad:()=>Yh,MaxPoolGrad:()=>Zh,MaxPoolWithArgmax:()=>Jh,Maximum:()=>Is,Mean:()=>Ss,Min:()=>Ts,Minimum:()=>Es,MirrorPad:()=>Mu,Mod:()=>So,MomentumOptimizer:()=>ep,Multinomial:()=>Qh,Multiply:()=>Cs,Neg:()=>To,NonMaxSuppressionV3:()=>Co,NonMaxSuppressionV4:()=>Ro,NonMaxSuppressionV5:()=>Fo,NotEqual:()=>Eo,OP_SCOPE_SUFFIX:()=>Cg,OneHot:()=>Rs,OnesLike:()=>Mo,Optimizer:()=>ia,Pack:()=>Oo,PadV2:()=>Fs,Pool:()=>Gk,Pow:()=>Ms,Prelu:()=>Os,Prod:()=>$o,RMSPropOptimizer:()=>tp,RNN:()=>jr,Range:()=>Ou,Rank:()=>J1,Real:()=>ed,RealDiv:()=>As,Reciprocal:()=>Do,Reduction:()=>on,Relu:()=>$s,Relu6:()=>zs,Reshape:()=>zo,ResizeBilinear:()=>Ds,ResizeBilinearGrad:()=>nd,ResizeNearestNeighbor:()=>$u,ResizeNearestNeighborGrad:()=>td,Reverse:()=>Ps,RotateWithOffset:()=>Yo,Round:()=>Ls,Rsqrt:()=>Ws,SGDOptimizer:()=>dc,ScatterNd:()=>Po,Select:()=>Lo,Selu:()=>Wo,Sequential:()=>Bl,Sigmoid:()=>Vs,Sign:()=>Uo,Sin:()=>Bs,Sinh:()=>Vo,Slice:()=>Bo,Softmax:()=>Hs,Softplus:()=>jo,SpaceToBatchND:()=>Du,SparseToDense:()=>rd,SplitV:()=>Ho,Sqrt:()=>Us,Square:()=>zu,SquaredDifference:()=>Gs,Step:()=>va,StridedSlice:()=>Go,Sub:()=>qs,Sum:()=>js,SymbolicTensor:()=>wr,Tan:()=>qo,Tanh:()=>Xs,Tensor:()=>Ke,TensorBuffer:()=>Ft,Tile:()=>ba,TopK:()=>Xo,Transpose:()=>Ks,Unique:()=>ad,Unpack:()=>Ko,UnsortedSegmentSum:()=>Pu,Variable:()=>ju,ZerosLike:()=>Zo,_FusedMatMul:()=>Zs,abs:()=>Mt,acos:()=>Nf,acosh:()=>Sf,add:()=>se,addN:()=>ll,all:()=>xd,any:()=>Xu,argMax:()=>Ku,argMin:()=>Tf,asin:()=>Ef,asinh:()=>Cf,atan:()=>Rf,atan2:()=>Ff,atanh:()=>Mf,avgPool:()=>Yu,avgPool3d:()=>Df,backend:()=>If,backend_util:()=>C,basicLSTMCell:()=>mN,batchNorm:()=>si,batchNorm2d:()=>y5,batchNorm3d:()=>g5,batchNorm4d:()=>x5,batchToSpaceND:()=>Ju,bincount:()=>w5,booleanMaskAsync:()=>wE,broadcastTo:()=>Qu,browser:()=>sl,buffer:()=>Le,callbacks:()=>Nne,cast:()=>me,ceil:()=>zf,clipByValue:()=>mn,clone:()=>tr,complex:()=>ka,concat:()=>rt,concat1d:()=>_5,concat2d:()=>ii,concat3d:()=>b5,concat4d:()=>v5,constraints:()=>a3,conv1d:()=>_d,conv2d:()=>na,conv2dTranspose:()=>bd,conv3d:()=>Lf,conv3dTranspose:()=>zN,copyRegisteredKernels:()=>Kk,cos:()=>ec,cosh:()=>vd,cosineWindow:()=>dm,cumsum:()=>kd,customGrad:()=>Or,data:()=>Av,denseBincount:()=>I5,deprecationWarn:()=>vf,depthToSpace:()=>Wf,depthwiseConv2d:()=>hl,deregisterOp:()=>Tne,device_util:()=>hd,diag:()=>HN,dilation2d:()=>Bf,disableDeprecationWarnings:()=>FI,dispose:()=>Se,disposeVariables:()=>MI,div:()=>be,divNoNan:()=>Vf,dot:()=>N5,dropout:()=>q5,elu:()=>dl,enableDebugMode:()=>RI,enableProdMode:()=>h5,enclosingPowerOfTwo:()=>X5,engine:()=>Vn,env:()=>Q,equal:()=>Ea,erf:()=>Uf,exp:()=>Un,expandDims:()=>Nn,expm1:()=>jf,eye:()=>Hf,fft:()=>cc,fill:()=>tc,findBackend:()=>kf,findBackendFactory:()=>DI,floor:()=>pl,floorDiv:()=>gd,forceHalfFloat:()=>s_,fused:()=>Ma,gather:()=>oi,gatherND:()=>G5,gather_util:()=>Af,getBackend:()=>yd,getGradient:()=>X1,getKernel:()=>id,getKernelsForBackend:()=>Qo,gpgpu_util:()=>Ew,grad:()=>wS,grads:()=>_S,greater:()=>nr,greaterEqual:()=>Ra,ifft:()=>gl,imag:()=>Id,image:()=>at,inTopKAsync:()=>RE,initializers:()=>h3,input:()=>k3,io:()=>fn,irfft:()=>Bd,isFinite:()=>S5,isInf:()=>T5,isNaN:()=>E5,keep:()=>Wt,kernel_impls:()=>Pr,layers:()=>v3,leakyRelu:()=>nc,less:()=>Nd,lessEqual:()=>li,linalg:()=>ix,linspace:()=>C5,loadGraphModel:()=>Ht,loadLayersModel:()=>Gte,localResponseNormalization:()=>Gf,log:()=>Sn,log1p:()=>Sd,logSigmoid:()=>F5,logSoftmax:()=>Ed,logSumExp:()=>Kf,logicalAnd:()=>rr,logicalNot:()=>rc,logicalOr:()=>Cd,logicalXor:()=>D5,losses:()=>GC,matMul:()=>He,math:()=>Gg,max:()=>jn,maxPool:()=>ac,maxPool3d:()=>Zf,maxPoolWithArgmax:()=>z5,maximum:()=>$r,mean:()=>bt,memory:()=>Ad,metrics:()=>$7,min:()=>ml,minimum:()=>Al,mirrorPad:()=>Yf,mod:()=>Jf,model:()=>jte,models:()=>D7,moments:()=>Rd,movingAverage:()=>vE,mul:()=>L,multiRNNCell:()=>ZS,multinomial:()=>P5,neg:()=>_t,nextFrame:()=>np,norm:()=>Hd,notEqual:()=>ci,oneHot:()=>al,ones:()=>Dr,onesLike:()=>Tn,op:()=>z,outerProduct:()=>tT,pad:()=>ra,pad1d:()=>aT,pad2d:()=>iT,pad3d:()=>lT,pad4d:()=>cT,pool:()=>L5,pow:()=>aa,prelu:()=>ic,print:()=>Wg,prod:()=>Fd,profile:()=>ta,rand:()=>xT,randomGamma:()=>vT,randomNormal:()=>W5,randomUniform:()=>yl,range:()=>Md,ready:()=>p5,real:()=>oc,reciprocal:()=>tm,registerBackend:()=>ol,registerCallbackConstructor:()=>qte,registerGradient:()=>wg,registerKernel:()=>Qs,registerOp:()=>Sne,regularizers:()=>z7,relu:()=>zr,relu6:()=>Od,removeBackend:()=>$I,reshape:()=>q,reverse:()=>En,reverse1d:()=>FT,reverse2d:()=>OT,reverse3d:()=>DT,reverse4d:()=>PT,rfft:()=>hc,round:()=>nm,rsqrt:()=>$d,scalar:()=>ke,scatterND:()=>H5,scatter_util:()=>yf,selu:()=>Dd,separableConv2d:()=>rm,sequential:()=>Hte,serialization:()=>re,setBackend:()=>d5,setPlatform:()=>zI,setWasmPath:()=>BZ,setWasmPaths:()=>Xb,setWebGLContext:()=>hp,setdiff1dAsync:()=>B5,shared:()=>Am,sigmoid:()=>In,sign:()=>am,signal:()=>HC,sin:()=>zd,sinh:()=>Pd,slice:()=>Te,slice1d:()=>Ld,slice2d:()=>sm,slice3d:()=>Wd,slice4d:()=>lc,slice_util:()=>sn,softmax:()=>uc,softplus:()=>fl,spaceToBatchND:()=>sc,sparseToDense:()=>hm,spectral:()=>jC,split:()=>Kt,sqrt:()=>Zt,square:()=>lt,squaredDifference:()=>Vd,squeeze:()=>Fa,stack:()=>Cn,step:()=>xl,stridedSlice:()=>im,sub:()=>Ae,sum:()=>Ie,sumOutType:()=>cd,tan:()=>om,tanh:()=>cl,tensor:()=>fr,tensor1d:()=>Bt,tensor2d:()=>yn,tensor3d:()=>fd,tensor4d:()=>cE,tensor5d:()=>hE,tensor6d:()=>dE,tensor_util:()=>pr,test_util:()=>o5,tidy:()=>W,tile:()=>Ca,time:()=>OI,topk:()=>lm,train:()=>di,transpose:()=>nt,truncatedNormal:()=>Ud,unique:()=>jd,unregisterGradient:()=>Xk,unregisterKernel:()=>qk,unsortedSegmentSum:()=>um,unstack:()=>ar,upcastType:()=>er,util:()=>k,valueAndGrad:()=>bS,valueAndGrads:()=>vS,variable:()=>V5,variableGrads:()=>R5,version:()=>fae,version_converter:()=>Tre,version_core:()=>c5,version_cpu:()=>zx,version_layers:()=>$A,version_wasm:()=>Kb,version_webgl:()=>a_,webgl:()=>uL,webgl_util:()=>rw,where:()=>An,whereAsync:()=>cm,zeros:()=>Tt,zerosLike:()=>Ve});var P8=Object.create,kh=Object.defineProperty,L8=Object.getPrototypeOf,W8=Object.prototype.hasOwnProperty,B8=Object.getOwnPropertyNames,V8=Object.getOwnPropertyDescriptor,ng=e=>kh(e,"__esModule",{value:!0}),et=(e,t)=>()=>(t||(t={exports:{}},e(t.exports,t)),t.exports),$e=(e,t)=>{ng(e);for(var n in t)kh(e,n,{get:t[n],enumerable:!0})},U8=(e,t,n)=>{if(ng(e),t&&typeof t=="object"||typeof t=="function")for(let r of B8(t))!W8.call(e,r)&&r!=="default"&&kh(e,r,{get:()=>t[r],enumerable:!(n=V8(t,r))||n.enumerable});return e},qi=e=>e&&e.__esModule?e:U8(kh(e!=null?P8(L8(e)):{},"default",{value:e,enumerable:!0}),e),j8=et(()=>{}),H8=et((e,t)=>{(function(n,r,a){function s(c){var u=this,h=l();u.next=function(){var p=2091639*u.s0+u.c*23283064365386963e-26;return u.s0=u.s1,u.s1=u.s2,u.s2=p-(u.c=p|0)},u.c=1,u.s0=h(" "),u.s1=h(" "),u.s2=h(" "),u.s0-=h(c),u.s0<0&&(u.s0+=1),u.s1-=h(c),u.s1<0&&(u.s1+=1),u.s2-=h(c),u.s2<0&&(u.s2+=1),h=null}function i(c,u){return u.c=c.c,u.s0=c.s0,u.s1=c.s1,u.s2=c.s2,u}function o(c,u){var h=new s(c),p=u&&u.state,d=h.next;return d.int32=function(){return h.next()*4294967296|0},d.double=function(){return d()+(d()*2097152|0)*11102230246251565e-32},d.quick=d,p&&(typeof p=="object"&&i(p,h),d.state=function(){return i(h,{})}),d}function l(){var c=4022871197,u=function(h){h=h.toString();for(var p=0;p<h.length;p++){c+=h.charCodeAt(p);var d=.02519603282416938*c;c=d>>>0,d-=c,d*=c,c=d>>>0,d-=c,c+=d*4294967296}return(c>>>0)*23283064365386963e-26};return u}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),G8=et((e,t)=>{(function(n,r,a){function s(l){var c=this,u="";c.x=0,c.y=0,c.z=0,c.w=0,c.next=function(){var p=c.x^c.x<<11;return c.x=c.y,c.y=c.z,c.z=c.w,c.w^=c.w>>>19^p^p>>>8},l===(l|0)?c.x=l:u+=l;for(var h=0;h<u.length+64;h++)c.x^=u.charCodeAt(h)|0,c.next()}function i(l,c){return c.x=l.x,c.y=l.y,c.z=l.z,c.w=l.w,c}function o(l,c){var u=new s(l),h=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var d=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(d+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,h&&(typeof h=="object"&&i(h,u),p.state=function(){return i(u,{})}),p}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),q8=et((e,t)=>{(function(n,r,a){function s(l){var c=this,u="";c.next=function(){var p=c.x^c.x>>>2;return c.x=c.y,c.y=c.z,c.z=c.w,c.w=c.v,(c.d=c.d+362437|0)+(c.v=c.v^c.v<<4^(p^p<<1))|0},c.x=0,c.y=0,c.z=0,c.w=0,c.v=0,l===(l|0)?c.x=l:u+=l;for(var h=0;h<u.length+64;h++)c.x^=u.charCodeAt(h)|0,h==u.length&&(c.d=c.x<<10^c.x>>>4),c.next()}function i(l,c){return c.x=l.x,c.y=l.y,c.z=l.z,c.w=l.w,c.v=l.v,c.d=l.d,c}function o(l,c){var u=new s(l),h=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var d=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(d+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,h&&(typeof h=="object"&&i(h,u),p.state=function(){return i(u,{})}),p}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),X8=et((e,t)=>{(function(n,r,a){function s(l){var c=this;c.next=function(){var h=c.x,p=c.i,d,f,m;return d=h[p],d^=d>>>7,f=d^d<<24,d=h[p+1&7],f^=d^d>>>10,d=h[p+3&7],f^=d^d>>>3,d=h[p+4&7],f^=d^d<<7,d=h[p+7&7],d=d^d<<13,f^=d^d<<9,h[p]=f,c.i=p+1&7,f};function u(h,p){var d,f,m=[];if(p===(p|0))f=m[0]=p;else for(p=""+p,d=0;d<p.length;++d)m[d&7]=m[d&7]<<15^p.charCodeAt(d)+m[d+1&7]<<13;for(;m.length<8;)m.push(0);for(d=0;d<8&&m[d]===0;++d);for(d==8?f=m[7]=-1:f=m[d],h.x=m,h.i=0,d=256;d>0;--d)h.next()}u(c,l)}function i(l,c){return c.x=l.x.slice(),c.i=l.i,c}function o(l,c){l==null&&(l=+new Date);var u=new s(l),h=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var d=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(d+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,h&&(h.x&&i(h,u),p.state=function(){return i(u,{})}),p}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorshift7=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),K8=et((e,t)=>{(function(n,r,a){function s(l){var c=this;c.next=function(){var h=c.w,p=c.X,d=c.i,f,m;return c.w=h=h+1640531527|0,m=p[d+34&127],f=p[d=d+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=p[d]=m^f,c.i=d,m+(h^h>>>16)|0};function u(h,p){var d,f,m,A,y,g=[],_=128;for(p===(p|0)?(f=p,p=null):(p=p+"\0",f=0,_=Math.max(_,p.length)),m=0,A=-32;A<_;++A)p&&(f^=p.charCodeAt((A+32)%p.length)),A===0&&(y=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,A>=0&&(y=y+1640531527|0,d=g[A&127]^=f+y,m=d==0?m+1:0);for(m>=128&&(g[(p&&p.length||0)&127]=-1),m=127,A=4*128;A>0;--A)f=g[m+34&127],d=g[m=m+1&127],f^=f<<13,d^=d<<17,f^=f>>>15,d^=d>>>12,g[m]=f^d;h.w=y,h.X=g,h.i=m}u(c,l)}function i(l,c){return c.i=l.i,c.w=l.w,c.X=l.X.slice(),c}function o(l,c){l==null&&(l=+new Date);var u=new s(l),h=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var d=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(d+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,h&&(h.X&&i(h,u),p.state=function(){return i(u,{})}),p}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),Z8=et((e,t)=>{(function(n,r,a){function s(l){var c=this,u="";c.next=function(){var p=c.b,d=c.c,f=c.d,m=c.a;return p=p<<25^p>>>7^d,d=d-f|0,f=f<<24^f>>>8^m,m=m-p|0,c.b=p=p<<20^p>>>12^d,c.c=d=d-f|0,c.d=f<<16^d>>>16^m,c.a=m-p|0},c.a=0,c.b=0,c.c=2654435769|0,c.d=1367130551,l===Math.floor(l)?(c.a=l/4294967296|0,c.b=l|0):u+=l;for(var h=0;h<u.length+20;h++)c.b^=u.charCodeAt(h)|0,c.next()}function i(l,c){return c.a=l.a,c.b=l.b,c.c=l.c,c.d=l.d,c}function o(l,c){var u=new s(l),h=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var d=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(d+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,h&&(typeof h=="object"&&i(h,u),p.state=function(){return i(u,{})}),p}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.tychei=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),B1=et(()=>{}),Y8=et((e,t)=>{(function(n,r){var a=this,s=256,i=6,o=52,l="random",c=r.pow(s,i),u=r.pow(2,o),h=u*2,p=s-1,d;function f(w,b,N){var T=[];b=b==!0?{entropy:!0}:b||{};var E=g(y(b.entropy?[w,x(n)]:w==null?_():w,3),T),M=new m(T),$=function(){for(var P=M.g(i),V=c,G=0;P<u;)P=(P+G)*s,V*=s,G=M.g(1);for(;P>=h;)P/=2,V/=2,G>>>=1;return(P+G)/V};return $.int32=function(){return M.g(4)|0},$.quick=function(){return M.g(4)/4294967296},$.double=$,g(x(M.S),n),(b.pass||N||function(P,V,G,U){return U&&(U.S&&A(U,M),P.state=function(){return A(M,{})}),G?(r[l]=P,V):P})($,E,"global"in b?b.global:this==r,b.state)}r["seed"+l]=f;function m(w){var b,N=w.length,T=this,E=0,M=T.i=T.j=0,$=T.S=[];for(N||(w=[N++]);E<s;)$[E]=E++;for(E=0;E<s;E++)$[E]=$[M=p&M+w[E%N]+(b=$[E])],$[M]=b;(T.g=function(P){for(var V,G=0,U=T.i,K=T.j,X=T.S;P--;)V=X[U=p&U+1],G=G*s+X[p&(X[U]=X[K=p&K+V])+(X[K]=V)];return T.i=U,T.j=K,G})(s)}function A(w,b){return b.i=w.i,b.j=w.j,b.S=w.S.slice(),b}function y(w,b){var N=[],T=typeof w,E;if(b&&T=="object")for(E in w)try{N.push(y(w[E],b-1))}catch(M){}return N.length?N:T=="string"?w:w+"\0"}function g(w,b){for(var N=w+"",T,E=0;E<N.length;)b[p&E]=p&(T^=b[p&E]*19)+N.charCodeAt(E++);return x(b)}function _(){try{var w;return d&&(w=d.randomBytes)?w=w(s):(w=new Uint8Array(s),(a.crypto||a.msCrypto).getRandomValues(w)),x(w)}catch(T){var b=a.navigator,N=b&&b.plugins;return[+new Date,a,N,a.screen,x(n)]}}function x(w){return String.fromCharCode.apply(0,w)}if(g(r.random(),n),typeof t=="object"&&t.exports){t.exports=f;try{d=B1()}catch(w){}}else typeof define=="function"&&define.amd&&define(function(){return f})})([],Math)}),J8=et((e,t)=>{var n=H8(),r=G8(),a=q8(),s=X8(),i=K8(),o=Z8(),l=Y8();l.alea=n,l.xor128=r,l.xorwow=a,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),Q8=et((e,t)=>{(function(n,r,a){function s(c){var u=this,h=l();u.next=function(){var p=2091639*u.s0+u.c*23283064365386963e-26;return u.s0=u.s1,u.s1=u.s2,u.s2=p-(u.c=p|0)},u.c=1,u.s0=h(" "),u.s1=h(" "),u.s2=h(" "),u.s0-=h(c),u.s0<0&&(u.s0+=1),u.s1-=h(c),u.s1<0&&(u.s1+=1),u.s2-=h(c),u.s2<0&&(u.s2+=1),h=null}function i(c,u){return u.c=c.c,u.s0=c.s0,u.s1=c.s1,u.s2=c.s2,u}function o(c,u){var h=new s(c),p=u&&u.state,d=h.next;return d.int32=function(){return h.next()*4294967296|0},d.double=function(){return d()+(d()*2097152|0)*11102230246251565e-32},d.quick=d,p&&(typeof p=="object"&&i(p,h),d.state=function(){return i(h,{})}),d}function l(){var c=4022871197,u=function(h){h=h.toString();for(var p=0;p<h.length;p++){c+=h.charCodeAt(p);var d=.02519603282416938*c;c=d>>>0,d-=c,d*=c,c=d>>>0,d-=c,c+=d*4294967296}return(c>>>0)*23283064365386963e-26};return u}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),ek=et((e,t)=>{(function(n,r,a){function s(l){var c=this,u="";c.x=0,c.y=0,c.z=0,c.w=0,c.next=function(){var p=c.x^c.x<<11;return c.x=c.y,c.y=c.z,c.z=c.w,c.w^=c.w>>>19^p^p>>>8},l===(l|0)?c.x=l:u+=l;for(var h=0;h<u.length+64;h++)c.x^=u.charCodeAt(h)|0,c.next()}function i(l,c){return c.x=l.x,c.y=l.y,c.z=l.z,c.w=l.w,c}function o(l,c){var u=new s(l),h=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var d=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(d+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,h&&(typeof h=="object"&&i(h,u),p.state=function(){return i(u,{})}),p}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),tk=et((e,t)=>{(function(n,r,a){function s(l){var c=this,u="";c.next=function(){var p=c.x^c.x>>>2;return c.x=c.y,c.y=c.z,c.z=c.w,c.w=c.v,(c.d=c.d+362437|0)+(c.v=c.v^c.v<<4^(p^p<<1))|0},c.x=0,c.y=0,c.z=0,c.w=0,c.v=0,l===(l|0)?c.x=l:u+=l;for(var h=0;h<u.length+64;h++)c.x^=u.charCodeAt(h)|0,h==u.length&&(c.d=c.x<<10^c.x>>>4),c.next()}function i(l,c){return c.x=l.x,c.y=l.y,c.z=l.z,c.w=l.w,c.v=l.v,c.d=l.d,c}function o(l,c){var u=new s(l),h=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var d=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(d+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,h&&(typeof h=="object"&&i(h,u),p.state=function(){return i(u,{})}),p}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),nk=et((e,t)=>{(function(n,r,a){function s(l){var c=this;c.next=function(){var h=c.x,p=c.i,d,f,m;return d=h[p],d^=d>>>7,f=d^d<<24,d=h[p+1&7],f^=d^d>>>10,d=h[p+3&7],f^=d^d>>>3,d=h[p+4&7],f^=d^d<<7,d=h[p+7&7],d=d^d<<13,f^=d^d<<9,h[p]=f,c.i=p+1&7,f};function u(h,p){var d,f,m=[];if(p===(p|0))f=m[0]=p;else for(p=""+p,d=0;d<p.length;++d)m[d&7]=m[d&7]<<15^p.charCodeAt(d)+m[d+1&7]<<13;for(;m.length<8;)m.push(0);for(d=0;d<8&&m[d]===0;++d);for(d==8?f=m[7]=-1:f=m[d],h.x=m,h.i=0,d=256;d>0;--d)h.next()}u(c,l)}function i(l,c){return c.x=l.x.slice(),c.i=l.i,c}function o(l,c){l==null&&(l=+new Date);var u=new s(l),h=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var d=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(d+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,h&&(h.x&&i(h,u),p.state=function(){return i(u,{})}),p}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorshift7=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),rk=et((e,t)=>{(function(n,r,a){function s(l){var c=this;c.next=function(){var h=c.w,p=c.X,d=c.i,f,m;return c.w=h=h+1640531527|0,m=p[d+34&127],f=p[d=d+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=p[d]=m^f,c.i=d,m+(h^h>>>16)|0};function u(h,p){var d,f,m,A,y,g=[],_=128;for(p===(p|0)?(f=p,p=null):(p=p+"\0",f=0,_=Math.max(_,p.length)),m=0,A=-32;A<_;++A)p&&(f^=p.charCodeAt((A+32)%p.length)),A===0&&(y=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,A>=0&&(y=y+1640531527|0,d=g[A&127]^=f+y,m=d==0?m+1:0);for(m>=128&&(g[(p&&p.length||0)&127]=-1),m=127,A=4*128;A>0;--A)f=g[m+34&127],d=g[m=m+1&127],f^=f<<13,d^=d<<17,f^=f>>>15,d^=d>>>12,g[m]=f^d;h.w=y,h.X=g,h.i=m}u(c,l)}function i(l,c){return c.i=l.i,c.w=l.w,c.X=l.X.slice(),c}function o(l,c){l==null&&(l=+new Date);var u=new s(l),h=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var d=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(d+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,h&&(h.X&&i(h,u),p.state=function(){return i(u,{})}),p}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),ak=et((e,t)=>{(function(n,r,a){function s(l){var c=this,u="";c.next=function(){var p=c.b,d=c.c,f=c.d,m=c.a;return p=p<<25^p>>>7^d,d=d-f|0,f=f<<24^f>>>8^m,m=m-p|0,c.b=p=p<<20^p>>>12^d,c.c=d=d-f|0,c.d=f<<16^d>>>16^m,c.a=m-p|0},c.a=0,c.b=0,c.c=2654435769|0,c.d=1367130551,l===Math.floor(l)?(c.a=l/4294967296|0,c.b=l|0):u+=l;for(var h=0;h<u.length+20;h++)c.b^=u.charCodeAt(h)|0,c.next()}function i(l,c){return c.a=l.a,c.b=l.b,c.c=l.c,c.d=l.d,c}function o(l,c){var u=new s(l),h=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var d=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(d+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,h&&(typeof h=="object"&&i(h,u),p.state=function(){return i(u,{})}),p}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.tychei=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),sk=et((e,t)=>{(function(n,r){var a=this,s=256,i=6,o=52,l="random",c=r.pow(s,i),u=r.pow(2,o),h=u*2,p=s-1,d;function f(w,b,N){var T=[];b=b==!0?{entropy:!0}:b||{};var E=g(y(b.entropy?[w,x(n)]:w==null?_():w,3),T),M=new m(T),$=function(){for(var P=M.g(i),V=c,G=0;P<u;)P=(P+G)*s,V*=s,G=M.g(1);for(;P>=h;)P/=2,V/=2,G>>>=1;return(P+G)/V};return $.int32=function(){return M.g(4)|0},$.quick=function(){return M.g(4)/4294967296},$.double=$,g(x(M.S),n),(b.pass||N||function(P,V,G,U){return U&&(U.S&&A(U,M),P.state=function(){return A(M,{})}),G?(r[l]=P,V):P})($,E,"global"in b?b.global:this==r,b.state)}r["seed"+l]=f;function m(w){var b,N=w.length,T=this,E=0,M=T.i=T.j=0,$=T.S=[];for(N||(w=[N++]);E<s;)$[E]=E++;for(E=0;E<s;E++)$[E]=$[M=p&M+w[E%N]+(b=$[E])],$[M]=b;(T.g=function(P){for(var V,G=0,U=T.i,K=T.j,X=T.S;P--;)V=X[U=p&U+1],G=G*s+X[p&(X[U]=X[K=p&K+V])+(X[K]=V)];return T.i=U,T.j=K,G})(s)}function A(w,b){return b.i=w.i,b.j=w.j,b.S=w.S.slice(),b}function y(w,b){var N=[],T=typeof w,E;if(b&&T=="object")for(E in w)try{N.push(y(w[E],b-1))}catch(M){}return N.length?N:T=="string"?w:w+"\0"}function g(w,b){for(var N=w+"",T,E=0;E<N.length;)b[p&E]=p&(T^=b[p&E]*19)+N.charCodeAt(E++);return x(b)}function _(){try{var w;return d&&(w=d.randomBytes)?w=w(s):(w=new Uint8Array(s),(a.crypto||a.msCrypto).getRandomValues(w)),x(w)}catch(T){var b=a.navigator,N=b&&b.plugins;return[+new Date,a,N,a.screen,x(n)]}}function x(w){return String.fromCharCode.apply(0,w)}if(g(r.random(),n),typeof t=="object"&&t.exports){t.exports=f;try{d=B1()}catch(w){}}else typeof define=="function"&&define.amd&&define(function(){return f})})([],Math)}),ik=et((e,t)=>{var n=Q8(),r=ek(),a=tk(),s=nk(),i=rk(),o=ak(),l=sk();l.alea=n,l.xor128=r,l.xorwow=a,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),gu=et(()=>{}),ok=et(()=>{}),lk=et(()=>{}),uk=et((e,t)=>{var n=function(){var r=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(r=r||__filename),function(a){a=a||{};function s(){return J.buffer!=Ye&&kn(J.buffer),bn}function i(){return J.buffer!=Ye&&kn(J.buffer),Xt}function o(){return J.buffer!=Ye&&kn(J.buffer),pn}function l(){return J.buffer!=Ye&&kn(J.buffer),rn}function c(){return J.buffer!=Ye&&kn(J.buffer),Sr}var u=typeof a!="undefined"?a:{},h={},p;for(p in u)u.hasOwnProperty(p)&&(h[p]=u[p]);var d=[],f="./this.program",m=function(v,S){throw S},A=!1,y=!1,g=!1,_=!1;A=typeof window=="object",y=typeof importScripts=="function",g=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",_=!A&&!g&&!y;var x=u.ENVIRONMENT_IS_PTHREAD||!1;x&&(Ye=u.buffer,Zn=u.DYNAMIC_BASE,dr=u.DYNAMICTOP_PTR);var w="";function b(v){return u.locateFile?u.locateFile(v,w):w+v}var N,T,E,M,$,P;if(g){y?w=gu().dirname(w)+"/":w=__dirname+"/",N=function(v,S){return $||($=require("fs")),P||(P=gu()),v=P.normalize(v),$.readFileSync(v,S?null:"utf8")},E=function(v){var S=N(v,!0);return S.buffer||(S=new Uint8Array(S)),_e(S.buffer),S},process.argv.length>1&&(f=process.argv[1].replace(/\\/g,"/")),d=process.argv.slice(2),process.on("uncaughtException",function(v){if(!(v instanceof J2))throw v}),process.on("unhandledRejection",Zr),m=function(v){process.exit(v)},u.inspect=function(){return"[Emscripten Module object]"};var V;try{V=ok()}catch(v){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),v}Worker=V.Worker}else _?(typeof read!="undefined"&&(N=function(v){return read(v)}),E=function(v){var S;return typeof readbuffer=="function"?new Uint8Array(readbuffer(v)):(S=read(v,"binary"),_e(typeof S=="object"),S)},typeof scriptArgs!="undefined"?d=scriptArgs:typeof arguments!="undefined"&&(d=arguments),typeof quit=="function"&&(m=function(v){quit(v)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(A||y)&&(y?w=self.location.href:document.currentScript&&(w=document.currentScript.src),typeof r!="undefined"&&r&&(w=r),w.indexOf("blob:")!==0?w=w.substr(0,w.lastIndexOf("/")+1):w="",g?(N=function(v,S){return $||($=require("fs")),P||(P=gu()),v=P.normalize(v),$.readFileSync(v,S?null:"utf8")},E=function(v){var S=N(v,!0);return S.buffer||(S=new Uint8Array(S)),_e(S.buffer),S}):(N=function(v){var S=new XMLHttpRequest;return S.open("GET",v,!1),S.send(null),S.responseText},y&&(E=function(v){var S=new XMLHttpRequest;return S.open("GET",v,!1),S.responseType="arraybuffer",S.send(null),new Uint8Array(S.response)}),T=function(v,S,O){var H=new XMLHttpRequest;H.open("GET",v,!0),H.responseType="arraybuffer",H.onload=function(){if(H.status==200||H.status==0&&H.response){S(H.response);return}O()},H.onerror=O,H.send(null)}),M=function(v){document.title=v});g&&typeof performance=="undefined"&&(performance=lk().performance);var G=u.print||console.log.bind(console),U=u.printErr||console.warn.bind(console);for(p in h)h.hasOwnProperty(p)&&(u[p]=h[p]);h=null,u.arguments&&(d=u.arguments),u.thisProgram&&(f=u.thisProgram),u.quit&&(m=u.quit);var K=Atomics.load,X=Atomics.store,ee=Atomics.compareExchange,Z;u.wasmBinary&&(Z=u.wasmBinary);var ae;u.noExitRuntime&&(ae=u.noExitRuntime),typeof WebAssembly!="object"&&U("no native wasm support detected");var J,oe=new WebAssembly.Table({initial:169,maximum:169+0,element:"anyfunc"}),ne,ce=0,ue=0,pe=!1,fe=0;function _e(v,S){v||Zr("Assertion failed: "+S)}function Ne(v){var S=u["_"+v];return _e(S,"Cannot call unknown function "+v+", make sure it is exported"),S}function Ee(v,S,O,H,he){var le={string:function(Wn){var ya=0;if(Wn!=null&&Wn!==0){var yu=(Wn.length<<2)+1;ya=Ui(yu),it(Wn,ya,yu)}return ya},array:function(Wn){var ya=Ui(Wn.length);return ut(Wn,ya),ya}};function ie(Wn){return S==="string"?Be(Wn):S==="boolean"?Boolean(Wn):Wn}var xe=Ne(v),Je=[],Ct=0;if(H)for(var en=0;en<H.length;en++){var Hi=le[O[en]];Hi?(Ct===0&&(Ct=fu()),Je[en]=Hi(H[en])):Je[en]=H[en]}var Au=xe.apply(null,Je);return Au=ie(Au),Ct!==0&&ji(Ct),Au}function Oe(v,S,O,H){O=O||[];var he=O.every(function(ie){return ie==="number"}),le=S!=="string";return le&&he&&!H?Ne(v):function(){return Ee(v,S,O,arguments,H)}}function qe(v,S,O){for(var H=S+O,he="";!(S>=H);){var le=v[S++];if(!le)return he;if(!(le&128)){he+=String.fromCharCode(le);continue}var ie=v[S++]&63;if((le&224)==192){he+=String.fromCharCode((le&31)<<6|ie);continue}var xe=v[S++]&63;if((le&240)==224?le=(le&15)<<12|ie<<6|xe:le=(le&7)<<18|ie<<12|xe<<6|v[S++]&63,le<65536)he+=String.fromCharCode(le);else{var Je=le-65536;he+=String.fromCharCode(55296|Je>>10,56320|Je&1023)}}return he}function Be(v,S){return v?qe(i(),v,S):""}function tt(v,S,O,H){if(!(H>0))return 0;for(var he=O,le=O+H-1,ie=0;ie<v.length;++ie){var xe=v.charCodeAt(ie);if(xe>=55296&&xe<=57343){var Je=v.charCodeAt(++ie);xe=65536+((xe&1023)<<10)|Je&1023}if(xe<=127){if(O>=le)break;S[O++]=xe}else if(xe<=2047){if(O+1>=le)break;S[O++]=192|xe>>6,S[O++]=128|xe&63}else if(xe<=65535){if(O+2>=le)break;S[O++]=224|xe>>12,S[O++]=128|xe>>6&63,S[O++]=128|xe&63}else{if(O+3>=le)break;S[O++]=240|xe>>18,S[O++]=128|xe>>12&63,S[O++]=128|xe>>6&63,S[O++]=128|xe&63}}return S[O]=0,O-he}function it(v,S,O){return tt(v,i(),S,O)}function Ue(v){for(var S=0,O=0;O<v.length;++O){var H=v.charCodeAt(O);H>=55296&&H<=57343&&(H=65536+((H&1023)<<10)|v.charCodeAt(++O)&1023),H<=127?++S:H<=2047?S+=2:H<=65535?S+=3:S+=4}return S}function ut(v,S){s().set(v,S)}var ct=65536;function Pn(v,S){return v%S>0&&(v+=S-v%S),v}var Ye,bn,Xt,vn,Xn,pn,rn,Kn,Sr;function kn(v){Ye=v,u.HEAP8=bn=new Int8Array(v),u.HEAP16=vn=new Int16Array(v),u.HEAP32=pn=new Int32Array(v),u.HEAPU8=Xt=new Uint8Array(v),u.HEAPU16=Xn=new Uint16Array(v),u.HEAPU32=rn=new Uint32Array(v),u.HEAPF32=Kn=new Float32Array(v),u.HEAPF64=Sr=new Float64Array(v)}var Oi=5256480,Ql=Oi,hr=13600,Zn=5256480,dr=12672,$i=u.INITIAL_MEMORY||16777216;if(x)J=u.wasmMemory,Ye=u.buffer;else if(u.wasmMemory)J=u.wasmMemory;else if(J=new WebAssembly.Memory({initial:$i/ct,maximum:2147483648/ct,shared:!0}),!(J.buffer instanceof SharedArrayBuffer))throw U("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),g&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");J&&(Ye=J.buffer),$i=Ye.byteLength,kn(Ye),x||(o()[dr>>2]=Zn);function Di(v){for(;v.length>0;){var S=v.shift();if(typeof S=="function"){S(u);continue}var O=S.func;typeof O=="number"?S.arg===void 0?u.dynCall_v(O):u.dynCall_vi(O,S.arg):O(S.arg===void 0?null:S.arg)}}var Ya=[],eu=[],R0=[],tu=[],nh=[],nu=!1;x&&(nu=!0);function Yn(){if(!x){if(u.preRun)for(typeof u.preRun=="function"&&(u.preRun=[u.preRun]);u.preRun.length;)O0(u.preRun.shift());Di(Ya)}}function rh(){nu=!0,Di(eu)}function F0(){x||Di(R0)}function M0(){if(!x){if(u.postRun)for(typeof u.postRun=="function"&&(u.postRun=[u.postRun]);u.postRun.length;)Ja(u.postRun.shift());Di(nh)}}function O0(v){Ya.unshift(v)}function Ja(v){nh.unshift(v)}var zi=Math.ceil,$0=Math.floor,Kr=0,ru=null,Qa=null;function D0(v){_e(!x,"addRunDependency cannot be used in a pthread worker"),Kr++,u.monitorRunDependencies&&u.monitorRunDependencies(Kr)}function z0(v){if(Kr--,u.monitorRunDependencies&&u.monitorRunDependencies(Kr),Kr==0&&(ru!==null&&(clearInterval(ru),ru=null),Qa)){var S=Qa;Qa=null,S()}}u.preloadedImages={},u.preloadedAudios={};function Zr(v){throw u.onAbort&&u.onAbort(v),x&&console.error("Pthread aborting at "+new Error().stack),v+="",G(v),U(v),pe=!0,fe=1,v="abort("+v+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(v)}function au(v,S){return String.prototype.startsWith?v.startsWith(S):v.indexOf(S)===0}var P0="data:application/octet-stream;base64,";function ah(v){return au(v,P0)}var L0="file://";function sh(v){return au(v,L0)}var Jn="tfjs-backend-wasm-threaded-simd.wasm";ah(Jn)||(Jn=b(Jn));function ih(){try{if(Z)return new Uint8Array(Z);if(E)return E(Jn);throw"both async and sync fetching of the wasm failed"}catch(v){Zr(v)}}function W0(){return!Z&&(A||y)&&typeof fetch=="function"&&!sh(Jn)?fetch(Jn,{credentials:"same-origin"}).then(function(v){if(!v.ok)throw"failed to load wasm binary file at '"+Jn+"'";return v.arrayBuffer()}).catch(function(){return ih()}):new Promise(function(v,S){v(ih())})}function B0(){var v={a:F1};function S(ie,xe){var Je=ie.exports;if(u.asm=Je,ne=xe,!x){var Ct=de.unusedWorkers.length;de.unusedWorkers.forEach(function(en){de.loadWasmModuleToWorker(en,function(){--Ct||z0("wasm-instantiate")})})}}x||D0("wasm-instantiate");function O(ie){S(ie.instance,ie.module)}function H(ie){return W0().then(function(xe){return WebAssembly.instantiate(xe,v)}).then(ie,function(xe){U("failed to asynchronously prepare wasm: "+xe),Zr(xe)})}function he(){if(!Z&&typeof WebAssembly.instantiateStreaming=="function"&&!ah(Jn)&&!sh(Jn)&&typeof fetch=="function")fetch(Jn,{credentials:"same-origin"}).then(function(ie){var xe=WebAssembly.instantiateStreaming(ie,v);return xe.then(O,function(Je){U("wasm streaming compile failed: "+Je),U("falling back to ArrayBuffer instantiation"),H(O)})});else return H(O)}if(u.instantiateWasm)try{var le=u.instantiateWasm(v,S);return le}catch(ie){return U("Module.instantiateWasm callback failed with error: "+ie),!1}return he(),{}}var V0={};function U0(){de.initRuntime()}x||eu.push({func:function(){ou()}});var oh=0,lh=0,uh=0;function Pi(v,S,O){v=v|0,S=S|0,O=O|0,oh=v,uh=S,lh=O}u.__register_pthread_ptr=Pi;var su={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135},Li=13584;function Wi(v,S){if(v<=0||v>s().length||v&!0||S<0)return-28;if(S==0)return 0;S>=2147483647&&(S=Infinity);var O=Atomics.load(o(),Li>>2),H=0;if(O==v){var he=Atomics.compareExchange(o(),Li>>2,O,0);if(he==O&&(--S,H=1,S<=0))return 1}var le=Atomics.notify(o(),v>>2,S);if(le>=0)return le+H;throw"Atomics.notify returned an unexpected value "+le}u._emscripten_futex_wake=Wi;function j0(v){if(x)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!v)throw"Internal Error! Null pthread_ptr in _kill_thread!";o()[v+12>>2]=0;var S=de.pthreads[v];S.worker.terminate(),de.freeThreadData(S),de.runningWorkers.splice(de.runningWorkers.indexOf(S.worker),1),S.worker.pthread=void 0}function H0(v){if(x)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!v)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var S=de.pthreads[v];S.worker.postMessage({cmd:"cancel"})}function G0(v){if(x)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!v)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";o()[v+12>>2]=0;var S=de.pthreads[v];if(S){var O=S.worker;de.returnWorkerToPool(O)}}var de={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){Pi(de.mainThreadBlock,!y,1),X2(de.mainThreadBlock)},initMainThreadBlock:function(){for(var v=8,S=0;S<v;++S)de.allocateUnusedWorker();de.mainThreadBlock=12832;for(var S=0;S<232/4;++S)l()[de.mainThreadBlock/4+S]=0;o()[de.mainThreadBlock+12>>2]=de.mainThreadBlock;var O=de.mainThreadBlock+156;o()[O>>2]=O;for(var H=13072,S=0;S<128;++S)l()[H/4+S]=0;Atomics.store(l(),de.mainThreadBlock+104>>2,H),Atomics.store(l(),de.mainThreadBlock+40>>2,de.mainThreadBlock),Atomics.store(l(),de.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(de.exitHandlers!==null){for(;de.exitHandlers.length>0;)de.exitHandlers.pop()();de.exitHandlers=null}x&&ce&&q2()},threadExit:function(v){var S=Tr();S&&(Atomics.store(l(),S+4>>2,v),Atomics.store(l(),S+0>>2,1),Atomics.store(l(),S+60>>2,1),Atomics.store(l(),S+64>>2,0),de.runExitHandlers(),Wi(S+0,2147483647),Pi(0,0,0),ce=0,x&&postMessage({cmd:"exit"}))},threadCancel:function(){de.runExitHandlers(),Atomics.store(l(),ce+4>>2,-1),Atomics.store(l(),ce+0>>2,1),Wi(ce+0,2147483647),ce=ue=0,Pi(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var v in de.pthreads){var S=de.pthreads[v];S&&S.worker&&de.returnWorkerToPool(S.worker)}de.pthreads={};for(var O=0;O<de.unusedWorkers.length;++O){var H=de.unusedWorkers[O];H.terminate()}de.unusedWorkers=[];for(var O=0;O<de.runningWorkers.length;++O){var H=de.runningWorkers[O],S=H.pthread;de.freeThreadData(S),H.terminate()}de.runningWorkers=[]},freeThreadData:function(v){if(v){if(v.threadInfoStruct){var S=o()[v.threadInfoStruct+104>>2];o()[v.threadInfoStruct+104>>2]=0,pu(S),pu(v.threadInfoStruct)}v.threadInfoStruct=0,v.allocatedOwnStack&&v.stackBase&&pu(v.stackBase),v.stackBase=0,v.worker&&(v.worker.pthread=null)}},returnWorkerToPool:function(v){delete de.pthreads[v.pthread.thread],de.unusedWorkers.push(v),de.runningWorkers.splice(de.runningWorkers.indexOf(v),1),de.freeThreadData(v.pthread),v.pthread=void 0},receiveObjectTransfer:function(v){},loadWasmModuleToWorker:function(v,S){v.onmessage=function(O){var H=O.data,he=H.cmd;if(v.pthread&&(de.currentProxiedOperationCallerThread=v.pthread.threadInfoStruct),H.targetThread&&H.targetThread!=Tr()){var le=de.pthreads[H.targetThread];le?le.worker.postMessage(O.data,H.transferList):console.error('Internal error! Worker sent a message "'+he+'" to target pthread '+H.targetThread+", but that thread no longer exists!"),de.currentProxiedOperationCallerThread=void 0;return}if(he==="processQueuedMainThreadWork")$1();else if(he==="spawnThread")mh(O.data);else if(he==="cleanupThread")G0(H.thread);else if(he==="killThread")j0(H.thread);else if(he==="cancelThread")H0(H.thread);else if(he==="loaded")v.loaded=!0,S&&S(v),v.runPthread&&(v.runPthread(),delete v.runPthread);else if(he==="print")G("Thread "+H.threadId+": "+H.text);else if(he==="printErr")U("Thread "+H.threadId+": "+H.text);else if(he==="alert")alert("Thread "+H.threadId+": "+H.text);else if(he==="exit"){var ie=v.pthread&&Atomics.load(l(),v.pthread.thread+68>>2);ie&&de.returnWorkerToPool(v)}else he==="cancelDone"?de.returnWorkerToPool(v):he==="objectTransfer"?de.receiveObjectTransfer(O.data):O.data.target==="setimmediate"?v.postMessage(O.data):U("worker sent an unknown command "+he);de.currentProxiedOperationCallerThread=void 0},v.onerror=function(O){U("pthread sent an error! "+O.filename+":"+O.lineno+": "+O.message)},g&&(v.on("message",function(O){v.onmessage({data:O})}),v.on("error",function(O){v.onerror(O)}),v.on("exit",function(O){console.log("worker exited - TODO: update the worker queue?")})),v.postMessage({cmd:"load",urlOrBlob:u.mainScriptUrlOrBlob||r,wasmMemory:J,wasmModule:ne,DYNAMIC_BASE:Zn,DYNAMICTOP_PTR:dr})},allocateUnusedWorker:function(){var v=b("tfjs-backend-wasm-threaded-simd.worker.js");de.unusedWorkers.push(new Worker(v))},getNewWorker:function(){return de.unusedWorkers.length==0&&(de.allocateUnusedWorker(),de.loadWasmModuleToWorker(de.unusedWorkers[0])),de.unusedWorkers.length>0?de.unusedWorkers.pop():null},busySpinWait:function(v){for(var S=performance.now()+v;performance.now()<S;);}};function q0(v,S){Oi=Ql=v,hr=S,ji(v)}u.establishStackSpace=q0;function X0(){return ae}u.getNoExitRuntime=X0;function K0(v,S,O,H){Zr("Assertion failed: "+Be(v)+", at: "+[S?Be(S):"unknown filename",O,H?Be(H):"unknown function"])}function Z0(v,S){var O=_main(v,S)}var es;g?es=function(){var v=process.hrtime();return v[0]*1e3+v[1]/1e6}:x?es=function(){return performance.now()-u.__performance_now_clock_drift}:typeof dateNow!="undefined"?es=dateNow:es=function(){return performance.now()};function Y0(v){return o()[j2()>>2]=v,v}function J0(v,S){if(x)return fa(1,1,v,S);tu.unshift({func:v,arg:S})}function Q0(v,S){if(v==S)postMessage({cmd:"processQueuedMainThreadWork"});else if(x)postMessage({targetThread:v,cmd:"processThreadQueue"});else{var O=de.pthreads[v],H=O&&O.worker;if(!H)return;H.postMessage({cmd:"processThreadQueue"})}return 1}function e1(){Zr()}function t1(v,S){v=v|0,S=S|0}function n1(v,S,O){if(v<=0||v>s().length||v&!0)return-28;if(y){var H=Atomics.wait(o(),v>>2,S,O);if(H==="timed-out")return-73;if(H==="not-equal")return-6;if(H==="ok")return 0;throw"Atomics.wait returned an unexpected value "+H}else{var he=Atomics.load(o(),v>>2);if(S!=he)return-6;var le=performance.now(),ie=le+O;Atomics.store(o(),Li>>2,v);for(var xe=v;v==xe;){if(le=performance.now(),le>ie)return-73;$1(),v=Atomics.load(o(),Li>>2)}return 0}}function r1(){return uh|0}function a1(){return lh|0}function s1(v,S,O){i().copyWithin(v,S,S+O)}function i1(){return navigator.hardwareConcurrency}function fa(v,S){for(var O=arguments.length-2,H=fu(),he=Ui(O*8),le=he>>3,ie=0;ie<O;ie++)c()[le+ie]=arguments[2+ie];var xe=Z2(v,O,he,S);return ji(H),xe}var ts=[];function Bi(v,S){Bi.array||(Bi.array=[]);var O=Bi.array;O.length=0;for(var H;H=i()[v++];)H===100||H===102?(S=S+7&~7,O.push(c()[S>>3]),S+=8):(S=S+3&~3,O.push(o()[S>>2]),S+=4);return O}function o1(v,S,O){ts.length=S;for(var H=O>>3,he=0;he<S;he++)ts[he]=c()[H+he];var le=v<0,ie=le?V0[-v-1]:R1[v];if(le){var xe=ts[1],Je=ts[2],Ct=Bi(xe,Je);return ie.apply(null,Ct)}return ie.apply(null,ts)}function l1(){return i().length}function u1(v){try{return J.grow(v-Ye.byteLength+65535>>>16),kn(J.buffer),1}catch(S){}}function c1(v){v=v>>>0;var S=l1();if(v<=S)return!1;var O=65536,H=2147483648;if(v>H)return!1;for(var he=16777216,le=1;le<=4;le*=2){var ie=S*(1+.2/le);ie=Math.min(ie,v+100663296);var xe=Math.min(H,Pn(Math.max(he,v,ie),O)),Je=u1(xe);if(Je)return!0}return!1}var ze={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:!1,removeAllEventListeners:function(){for(var v=ze.eventHandlers.length-1;v>=0;--v)ze._removeHandler(v);ze.eventHandlers=[],ze.deferredCalls=[]},registerRemoveEventListeners:function(){ze.removeEventListenersRegistered||(tu.push(ze.removeAllEventListeners),ze.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(v,S,O){function H(ie,xe){if(ie.length!=xe.length)return!1;for(var Je in ie)if(ie[Je]!=xe[Je])return!1;return!0}for(var he in ze.deferredCalls){var le=ze.deferredCalls[he];if(le.targetFunction==v&&H(le.argsList,O))return}ze.deferredCalls.push({targetFunction:v,precedence:S,argsList:O}),ze.deferredCalls.sort(function(ie,xe){return ie.precedence<xe.precedence})},removeDeferredCalls:function(v){for(var S=0;S<ze.deferredCalls.length;++S)ze.deferredCalls[S].targetFunction==v&&(ze.deferredCalls.splice(S,1),--S)},canPerformEventHandlerRequests:function(){return ze.inEventHandler&&ze.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(ze.canPerformEventHandlerRequests())for(var v=0;v<ze.deferredCalls.length;++v){var S=ze.deferredCalls[v];ze.deferredCalls.splice(v,1),--v,S.targetFunction.apply(null,S.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(v,S){for(var O=0;O<ze.eventHandlers.length;++O)ze.eventHandlers[O].target==v&&(!S||S==ze.eventHandlers[O].eventTypeString)&&ze._removeHandler(O--)},_removeHandler:function(v){var S=ze.eventHandlers[v];S.target.removeEventListener(S.eventTypeString,S.eventListenerFunc,S.useCapture),ze.eventHandlers.splice(v,1)},registerOrRemoveHandler:function(v){var S=function(H){++ze.inEventHandler,ze.currentEventHandler=v,ze.runDeferredCalls(),v.handlerFunc(H),ze.runDeferredCalls(),--ze.inEventHandler};if(v.callbackfunc)v.eventListenerFunc=S,v.target.addEventListener(v.eventTypeString,S,v.useCapture),ze.eventHandlers.push(v),ze.registerRemoveEventListeners();else for(var O=0;O<ze.eventHandlers.length;++O)ze.eventHandlers[O].target==v.target&&ze.eventHandlers[O].eventTypeString==v.eventTypeString&&ze._removeHandler(O--)},queueEventHandlerOnThread_iiii:function(v,S,O,H,he){var le=fu(),ie=Ui(12);o()[ie>>2]=O,o()[ie+4>>2]=H,o()[ie+8>>2]=he,D1(v,637534208,S,H,ie),ji(le)},getTargetThreadForEventCallback:function(v){switch(v){case 1:return 0;case 2:return de.currentProxiedOperationCallerThread;default:return v}},getNodeNameForTarget:function(v){return v?v==window?"#window":v==screen?"#screen":v&&v.nodeName?v.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function h1(v){var S=Ue(v)+1,O=du(S);return it(v,O,S),O}function d1(v,S,O,H){var he=fu(),le=Ui(12),ie=0;S&&(ie=h1(S)),o()[le>>2]=ie,o()[le+4>>2]=O,o()[le+8>>2]=H,D1(v,657457152,0,ie,le),ji(he)}function p1(v,S,O,H){S=S?Be(S):"",d1(v,S,O,H)}function f1(v){return v>2?Be(v):v}var m1=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function A1(v){v=f1(v);var S=m1[v]||(typeof document!="undefined"?document.querySelector(v):void 0);return S}function iu(v){return A1(v)}function ch(v,S,O){var H=iu(v);if(!H)return-4;if(H.canvasSharedPtr&&(o()[H.canvasSharedPtr>>2]=S,o()[H.canvasSharedPtr+4>>2]=O),H.offscreenCanvas||!H.controlTransferredOffscreen){H.offscreenCanvas&&(H=H.offscreenCanvas);var he=!1;if(H.GLctxObject&&H.GLctxObject.GLctx){var le=H.GLctxObject.GLctx.getParameter(2978);he=le[0]===0&&le[1]===0&&le[2]===H.width&&le[3]===H.height}H.width=S,H.height=O,he&&H.GLctxObject.GLctx.viewport(0,0,S,O)}else if(H.canvasSharedPtr){var ie=o()[H.canvasSharedPtr+8>>2];return p1(ie,v,S,O),1}else return-4;return 0}function hh(v,S,O){return x?fa(2,1,v,S,O):ch(v,S,O)}function y1(v,S,O){var H=iu(v);return H?ch(v,S,O):hh(v,S,O)}function g1(v){v=v|0}function x1(v,S){v=v|0,S=S|0}function w1(v){var S=v.getExtension("ANGLE_instanced_arrays");if(S)return v.vertexAttribDivisor=function(O,H){S.vertexAttribDivisorANGLE(O,H)},v.drawArraysInstanced=function(O,H,he,le){S.drawArraysInstancedANGLE(O,H,he,le)},v.drawElementsInstanced=function(O,H,he,le,ie){S.drawElementsInstancedANGLE(O,H,he,le,ie)},1}function _1(v){var S=v.getExtension("OES_vertex_array_object");if(S)return v.createVertexArray=function(){return S.createVertexArrayOES()},v.deleteVertexArray=function(O){S.deleteVertexArrayOES(O)},v.bindVertexArray=function(O){S.bindVertexArrayOES(O)},v.isVertexArray=function(O){return S.isVertexArrayOES(O)},1}function b1(v){var S=v.getExtension("WEBGL_draw_buffers");if(S)return v.drawBuffers=function(O,H){S.drawBuffersWEBGL(O,H)},1}var We={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){for(var v=new Float32Array(We.MINI_TEMP_BUFFER_SIZE),S=0;S<We.MINI_TEMP_BUFFER_SIZE;S++)We.miniTempBufferFloatViews[S]=v.subarray(0,S+1);for(var O=new Int32Array(We.MINI_TEMP_BUFFER_SIZE),S=0;S<We.MINI_TEMP_BUFFER_SIZE;S++)We.miniTempBufferIntViews[S]=O.subarray(0,S+1)},recordError:function(v){We.lastError||(We.lastError=v)},getNewId:function(v){for(var S=We.counter++,O=v.length;O<S;O++)v[O]=null;return S},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(v,S,O,H){for(var he="",le=0;le<S;++le){var ie=H?o()[H+le*4>>2]:-1;he+=Be(o()[O+le*4>>2],ie<0?void 0:ie)}return he},createContext:function(v,S){var O=v.getContext("webgl",S);if(!O)return 0;var H=We.registerContext(O,S);return H},registerContext:function(v,S){var O=du(8);o()[O+4>>2]=Tr();var H={handle:O,attributes:S,version:S.majorVersion,GLctx:v};return v.canvas&&(v.canvas.GLctxObject=H),We.contexts[O]=H,(typeof S.enableExtensionsByDefault=="undefined"||S.enableExtensionsByDefault)&&We.initExtensions(H),O},makeContextCurrent:function(v){return We.currentContext=We.contexts[v],u.ctx=ma=We.currentContext&&We.currentContext.GLctx,!(v&&!ma)},getContext:function(v){return We.contexts[v]},deleteContext:function(v){We.currentContext===We.contexts[v]&&(We.currentContext=null),typeof ze=="object"&&ze.removeAllHandlersOnTarget(We.contexts[v].GLctx.canvas),We.contexts[v]&&We.contexts[v].GLctx.canvas&&(We.contexts[v].GLctx.canvas.GLctxObject=void 0),pu(We.contexts[v].handle),We.contexts[v]=null},initExtensions:function(v){if(v||(v=We.currentContext),!v.initExtensionsDone){v.initExtensionsDone=!0;var S=v.GLctx;w1(S),_1(S),b1(S),S.disjointTimerQueryExt=S.getExtension("EXT_disjoint_timer_query");var O=["OES_texture_float","OES_texture_half_float","OES_standard_derivatives","OES_vertex_array_object","WEBGL_compressed_texture_s3tc","WEBGL_depth_texture","OES_element_index_uint","EXT_texture_filter_anisotropic","EXT_frag_depth","WEBGL_draw_buffers","ANGLE_instanced_arrays","OES_texture_float_linear","OES_texture_half_float_linear","EXT_blend_minmax","EXT_shader_texture_lod","EXT_texture_norm16","WEBGL_compressed_texture_pvrtc","EXT_color_buffer_half_float","WEBGL_color_buffer_float","EXT_sRGB","WEBGL_compressed_texture_etc1","EXT_disjoint_timer_query","WEBGL_compressed_texture_etc","WEBGL_compressed_texture_astc","EXT_color_buffer_float","WEBGL_compressed_texture_s3tc_srgb","EXT_disjoint_timer_query_webgl2","WEBKIT_WEBGL_compressed_texture_pvrtc"],H=S.getSupportedExtensions()||[];H.forEach(function(he){O.indexOf(he)!=-1&&S.getExtension(he)})}},populateUniformTable:function(v){for(var S=We.programs[v],O=We.programInfos[v]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},H=O.uniforms,he=ma.getProgramParameter(S,35718),le=0;le<he;++le){var ie=ma.getActiveUniform(S,le),xe=ie.name;O.maxUniformLength=Math.max(O.maxUniformLength,xe.length+1),xe.slice(-1)=="]"&&(xe=xe.slice(0,xe.lastIndexOf("[")));var Je=ma.getUniformLocation(S,xe);if(Je){var Ct=We.getNewId(We.uniforms);H[xe]=[ie.size,Ct],We.uniforms[Ct]=Je;for(var en=1;en<ie.size;++en){var Hi=xe+"["+en+"]";Je=ma.getUniformLocation(S,Hi),Ct=We.getNewId(We.uniforms),We.uniforms[Ct]=Je}}}}},v1=["default","low-power","high-performance"];function k1(v,S){var O={},H=S>>2;O.alpha=!!o()[H+(0>>2)],O.depth=!!o()[H+(4>>2)],O.stencil=!!o()[H+(8>>2)],O.antialias=!!o()[H+(12>>2)],O.premultipliedAlpha=!!o()[H+(16>>2)],O.preserveDrawingBuffer=!!o()[H+(20>>2)];var he=o()[H+(24>>2)];O.powerPreference=v1[he],O.failIfMajorPerformanceCaveat=!!o()[H+(28>>2)],O.majorVersion=o()[H+(32>>2)],O.minorVersion=o()[H+(36>>2)],O.enableExtensionsByDefault=o()[H+(40>>2)],O.explicitSwapControl=o()[H+(44>>2)],O.proxyContextToMainThread=o()[H+(48>>2)],O.renderViaOffscreenBackBuffer=o()[H+(52>>2)];var le=iu(v);if(!le)return-4;if(O.explicitSwapControl)return-1;var ie=We.createContext(le,O);return ie}function I1(v,S){return k1(v,S)}var ns={splitPath:function(v){var S=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return S.exec(v).slice(1)},normalizeArray:function(v,S){for(var O=0,H=v.length-1;H>=0;H--){var he=v[H];he==="."?v.splice(H,1):he===".."?(v.splice(H,1),O++):O&&(v.splice(H,1),O--)}if(S)for(;O;O--)v.unshift("..");return v},normalize:function(v){var S=v.charAt(0)==="/",O=v.substr(-1)==="/";return v=ns.normalizeArray(v.split("/").filter(function(H){return!!H}),!S).join("/"),!v&&!S&&(v="."),v&&O&&(v+="/"),(S?"/":"")+v},dirname:function(v){var S=ns.splitPath(v),O=S[0],H=S[1];return!O&&!H?".":(H&&(H=H.substr(0,H.length-1)),O+H)},basename:function(v){if(v==="/")return"/";var S=v.lastIndexOf("/");return S===-1?v:v.substr(S+1)},extname:function(v){return ns.splitPath(v)[3]},join:function(){var v=Array.prototype.slice.call(arguments,0);return ns.normalize(v.join("/"))},join2:function(v,S){return ns.normalize(v+"/"+S)}},Vi={mappings:{},buffers:[null,[],[]],printChar:function(v,S){var O=Vi.buffers[v];S===0||S===10?((v===1?G:U)(qe(O,0)),O.length=0):O.push(S)},varargs:void 0,get:function(){Vi.varargs+=4;var v=o()[Vi.varargs-4>>2];return v},getStr:function(v){var S=Be(v);return S},get64:function(v,S){return v}};function dh(v){return x?fa(3,1,v):0}function ph(v,S,O,H,he){if(x)return fa(4,1,v,S,O,H,he)}function fh(v,S,O,H){if(x)return fa(5,1,v,S,O,H);for(var he=0,le=0;le<O;le++){for(var ie=o()[S+le*8>>2],xe=o()[S+(le*8+4)>>2],Je=0;Je<xe;Je++)Vi.printChar(v,i()[ie+Je]);he+=xe}return o()[H>>2]=he,0}function N1(v){var S=de.exitHandlers.pop();v&&S()}function S1(v,S){de.exitHandlers===null&&(de.exitHandlers=[]),de.exitHandlers.push(function(){Y2(v,S)})}function mh(v){if(x)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var S=de.getNewWorker();if(S.pthread!==void 0)throw"Internal error!";if(!v.pthread_ptr)throw"Internal error, no pthread ptr!";de.runningWorkers.push(S);for(var O=du(128*4),H=0;H<128;++H)o()[O+H*4>>2]=0;var he=v.stackBase+v.stackSize,le=de.pthreads[v.pthread_ptr]={worker:S,stackBase:v.stackBase,stackSize:v.stackSize,allocatedOwnStack:v.allocatedOwnStack,thread:v.pthread_ptr,threadInfoStruct:v.pthread_ptr},ie=le.threadInfoStruct>>2;Atomics.store(l(),ie+(0>>2),0),Atomics.store(l(),ie+(4>>2),0),Atomics.store(l(),ie+(8>>2),0),Atomics.store(l(),ie+(68>>2),v.detached),Atomics.store(l(),ie+(104>>2),O),Atomics.store(l(),ie+(48>>2),0),Atomics.store(l(),ie+(40>>2),le.threadInfoStruct),Atomics.store(l(),ie+(44>>2),42),Atomics.store(l(),ie+(108>>2),v.stackSize),Atomics.store(l(),ie+(84>>2),v.stackSize),Atomics.store(l(),ie+(80>>2),he),Atomics.store(l(),ie+(108+8>>2),he),Atomics.store(l(),ie+(108+12>>2),v.detached),Atomics.store(l(),ie+(108+20>>2),v.schedPolicy),Atomics.store(l(),ie+(108+24>>2),v.schedPrio);var xe=H2(),Je=xe+40;Atomics.store(l(),ie+(176>>2),Je),S.pthread=le;var Ct={cmd:"run",start_routine:v.startRoutine,arg:v.arg,threadInfoStruct:v.pthread_ptr,selfThreadId:v.pthread_ptr,parentThreadId:v.parent_pthread_ptr,stackBase:v.stackBase,stackSize:v.stackSize};S.runPthread=function(){Ct.time=performance.now(),S.postMessage(Ct,v.transferList)},S.loaded&&(S.runPthread(),delete S.runPthread)}function T1(v,S,O){if(!S&&!O)return su.EINVAL;if(!v)return U("pthread_getschedparam called with a null thread pointer!"),su.ESRCH;var H=o()[v+12>>2];if(H!==v)return U("pthread_getschedparam attempted on thread "+v+", which does not point to a valid thread, or does not exist anymore!"),su.ESRCH;var he=Atomics.load(l(),v+108+20>>2),le=Atomics.load(l(),v+108+24>>2);return S&&(o()[S>>2]=he),O&&(o()[O>>2]=le),0}function Tr(){return oh|0}u._pthread_self=Tr;function E1(v,S,O,H){if(typeof SharedArrayBuffer=="undefined")return U("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!v)return U("pthread_create called with a null thread pointer!"),28;var he=[],le=0;if(x&&(he.length===0||le))return K2(687865856,v,S,O,H);if(le)return le;var ie=0,xe=0,Je=0,Ct=0,en=0;if(S){ie=o()[S>>2],ie+=81920,xe=o()[S+8>>2],Je=o()[S+12>>2]!==0;var Hi=o()[S+16>>2]===0;if(Hi){var Au=o()[S+20>>2],Wn=o()[S+24>>2],ya=de.currentProxiedOperationCallerThread?de.currentProxiedOperationCallerThread:Tr();T1(ya,S+20,S+24),Ct=o()[S+20>>2],en=o()[S+24>>2],o()[S+20>>2]=Au,o()[S+24>>2]=Wn}else Ct=o()[S+20>>2],en=o()[S+24>>2]}else ie=2097152;var yu=xe==0;yu?xe=G2(16,ie):(xe-=ie,_e(xe>0));for(var Gi=du(232),P1=0;P1<232>>2;++P1)l()[(Gi>>2)+P1]=0;o()[v>>2]=Gi,o()[Gi+12>>2]=Gi;var Q2=Gi+156;o()[Q2>>2]=Q2;var L1={stackBase:xe,stackSize:ie,allocatedOwnStack:yu,schedPolicy:Ct,schedPrio:en,detached:Je,startRoutine:O,pthread_ptr:Gi,parent_pthread_ptr:Tr(),arg:H,transferList:he};return x?(L1.cmd="spawnThread",postMessage(L1,he)):mh(L1),0}function C1(v){return v=+v,v>=0?+$0(v+.5):+zi(v-.5)}function Ah(v){if(x)return fa(6,1,v);switch(v){case 30:return 16384;case 85:var S=2147483648;return S/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return Y0(28),-1}x?de.initWorker():de.initMainThreadBlock();var ma;We.init();var R1=[null,J0,hh,dh,ph,fh,Ah],F1={e:K0,r:Z0,w:Q0,a:e1,l:t1,d:n1,c:Wi,h:es,g:r1,x:a1,q:s1,B:i1,t:o1,A:c1,u:y1,k:g1,s:x1,v:I1,m:dh,o:ph,i:fh,p:U0,memory:J||u.wasmMemory,y:N1,z:S1,j:E1,b:Tr,f:C1,n:Ah,table:oe},yh=B0();u.asm=yh;var ou=u.___wasm_call_ctors=function(){return(ou=u.___wasm_call_ctors=u.asm.C).apply(null,arguments)},lu=u._init=function(){return(lu=u._init=u.asm.D).apply(null,arguments)},gh=u._register_tensor=function(){return(gh=u._register_tensor=u.asm.E).apply(null,arguments)},rs=u._dispose_data=function(){return(rs=u._dispose_data=u.asm.F).apply(null,arguments)},uu=u._dispose=function(){return(uu=u._dispose=u.asm.G).apply(null,arguments)},M1=u._Abs=function(){return(M1=u._Abs=u.asm.H).apply(null,arguments)},O1=u._Add=function(){return(O1=u._Add=u.asm.I).apply(null,arguments)},cu=u._AddN=function(){return(cu=u._AddN=u.asm.J).apply(null,arguments)},xh=u._ArgMax=function(){return(xh=u._ArgMax=u.asm.K).apply(null,arguments)},wh=u._AvgPool=function(){return(wh=u._AvgPool=u.asm.L).apply(null,arguments)},j=u._BatchMatMul=function(){return(j=u._BatchMatMul=u.asm.M).apply(null,arguments)},te=u._ClipByValue=function(){return(te=u._ClipByValue=u.asm.N).apply(null,arguments)},ve=u._Conv2D=function(){return(ve=u._Conv2D=u.asm.O).apply(null,arguments)},Ce=u._Conv2DBackpropInput=function(){return(Ce=u._Conv2DBackpropInput=u.asm.P).apply(null,arguments)},Qe=u._Cos=function(){return(Qe=u._Cos=u.asm.Q).apply(null,arguments)},kt=u._CropAndResize=function(){return(kt=u._CropAndResize=u.asm.R).apply(null,arguments)},Xe=u._Cumsum=function(){return(Xe=u._Cumsum=u.asm.S).apply(null,arguments)},je=u._DepthToSpace=function(){return(je=u._DepthToSpace=u.asm.T).apply(null,arguments)},Pt=u._DepthwiseConv2dNative=function(){return(Pt=u._DepthwiseConv2dNative=u.asm.U).apply(null,arguments)},Yr=u._Equal=function(){return(Yr=u._Equal=u.asm.V).apply(null,arguments)},Jr=u._Exp=function(){return(Jr=u._Exp=u.asm.W).apply(null,arguments)},_h=u._FlipLeftRight=function(){return(_h=u._FlipLeftRight=u.asm.X).apply(null,arguments)},hu=u._Floor=function(){return(hu=u._Floor=u.asm.Y).apply(null,arguments)},Ln=u._FloorDiv=function(){return(Ln=u._FloorDiv=u.asm.Z).apply(null,arguments)},Aa=u._FusedBatchNorm=function(){return(Aa=u._FusedBatchNorm=u.asm._).apply(null,arguments)},bh=u._FusedConv2D=function(){return(bh=u._FusedConv2D=u.asm.$).apply(null,arguments)},g4=u._FusedDepthwiseConv2D=function(){return(g4=u._FusedDepthwiseConv2D=u.asm.aa).apply(null,arguments)},x4=u._Gather=function(){return(x4=u._Gather=u.asm.ba).apply(null,arguments)},w4=u._GatherNd=function(){return(w4=u._GatherNd=u.asm.ca).apply(null,arguments)},_4=u._Greater=function(){return(_4=u._Greater=u.asm.da).apply(null,arguments)},b4=u._GreaterEqual=function(){return(b4=u._GreaterEqual=u.asm.ea).apply(null,arguments)},v4=u._LeakyRelu=function(){return(v4=u._LeakyRelu=u.asm.fa).apply(null,arguments)},k4=u._Less=function(){return(k4=u._Less=u.asm.ga).apply(null,arguments)},I4=u._LessEqual=function(){return(I4=u._LessEqual=u.asm.ha).apply(null,arguments)},N4=u._Log=function(){return(N4=u._Log=u.asm.ia).apply(null,arguments)},S4=u._LogicalAnd=function(){return(S4=u._LogicalAnd=u.asm.ja).apply(null,arguments)},T4=u._Max=function(){return(T4=u._Max=u.asm.ka).apply(null,arguments)},E4=u._MaxPool=function(){return(E4=u._MaxPool=u.asm.la).apply(null,arguments)},C4=u._Maximum=function(){return(C4=u._Maximum=u.asm.ma).apply(null,arguments)},R4=u._Mean=function(){return(R4=u._Mean=u.asm.na).apply(null,arguments)},F4=u._Min=function(){return(F4=u._Min=u.asm.oa).apply(null,arguments)},M4=u._Minimum=function(){return(M4=u._Minimum=u.asm.pa).apply(null,arguments)},O4=u._Multiply=function(){return(O4=u._Multiply=u.asm.qa).apply(null,arguments)},$4=u._Neg=function(){return($4=u._Neg=u.asm.ra).apply(null,arguments)},D4=u._NonMaxSuppressionV3=function(){return(D4=u._NonMaxSuppressionV3=u.asm.sa).apply(null,arguments)},z4=u._NonMaxSuppressionV4=function(){return(z4=u._NonMaxSuppressionV4=u.asm.ta).apply(null,arguments)},P4=u._NonMaxSuppressionV5=function(){return(P4=u._NonMaxSuppressionV5=u.asm.ua).apply(null,arguments)},L4=u._NotEqual=function(){return(L4=u._NotEqual=u.asm.va).apply(null,arguments)},W4=u._OneHot=function(){return(W4=u._OneHot=u.asm.wa).apply(null,arguments)},B4=u._PadV2=function(){return(B4=u._PadV2=u.asm.xa).apply(null,arguments)},V4=u._Pow=function(){return(V4=u._Pow=u.asm.ya).apply(null,arguments)},U4=u._Prelu=function(){return(U4=u._Prelu=u.asm.za).apply(null,arguments)},j4=u._Prod=function(){return(j4=u._Prod=u.asm.Aa).apply(null,arguments)},H4=u._RealDiv=function(){return(H4=u._RealDiv=u.asm.Ba).apply(null,arguments)},G4=u._Relu=function(){return(G4=u._Relu=u.asm.Ca).apply(null,arguments)},q4=u._Relu6=function(){return(q4=u._Relu6=u.asm.Da).apply(null,arguments)},X4=u._ResizeBilinear=function(){return(X4=u._ResizeBilinear=u.asm.Ea).apply(null,arguments)},K4=u._Reverse=function(){return(K4=u._Reverse=u.asm.Fa).apply(null,arguments)},Z4=u._RotateWithOffset=function(){return(Z4=u._RotateWithOffset=u.asm.Ga).apply(null,arguments)},Y4=u._Round=function(){return(Y4=u._Round=u.asm.Ha).apply(null,arguments)},J4=u._Rsqrt=function(){return(J4=u._Rsqrt=u.asm.Ia).apply(null,arguments)},Q4=u._ScatterNd=function(){return(Q4=u._ScatterNd=u.asm.Ja).apply(null,arguments)},e8=u._SelectV2=function(){return(e8=u._SelectV2=u.asm.Ka).apply(null,arguments)},t8=u._Sigmoid=function(){return(t8=u._Sigmoid=u.asm.La).apply(null,arguments)},n8=u._Sin=function(){return(n8=u._Sin=u.asm.Ma).apply(null,arguments)},r8=u._Softmax=function(){return(r8=u._Softmax=u.asm.Na).apply(null,arguments)},a8=u._Sqrt=function(){return(a8=u._Sqrt=u.asm.Oa).apply(null,arguments)},s8=u._Square=function(){return(s8=u._Square=u.asm.Pa).apply(null,arguments)},i8=u._SquaredDifference=function(){return(i8=u._SquaredDifference=u.asm.Qa).apply(null,arguments)},o8=u._Step=function(){return(o8=u._Step=u.asm.Ra).apply(null,arguments)},l8=u._StridedSlice=function(){return(l8=u._StridedSlice=u.asm.Sa).apply(null,arguments)},u8=u._Sub=function(){return(u8=u._Sub=u.asm.Ta).apply(null,arguments)},c8=u._Sum=function(){return(c8=u._Sum=u.asm.Ua).apply(null,arguments)},h8=u._Tanh=function(){return(h8=u._Tanh=u.asm.Va).apply(null,arguments)},d8=u._Tile=function(){return(d8=u._Tile=u.asm.Wa).apply(null,arguments)},p8=u._TopK=function(){return(p8=u._TopK=u.asm.Xa).apply(null,arguments)},f8=u._Transpose=function(){return(f8=u._Transpose=u.asm.Ya).apply(null,arguments)},m8=u.__FusedMatMul=function(){return(m8=u.__FusedMatMul=u.asm.Za).apply(null,arguments)},du=u._malloc=function(){return(du=u._malloc=u.asm._a).apply(null,arguments)},pu=u._free=function(){return(pu=u._free=u.asm.$a).apply(null,arguments)},A8=u.___em_js__initPthreadsJS=function(){return(A8=u.___em_js__initPthreadsJS=u.asm.ab).apply(null,arguments)},j2=u.___errno_location=function(){return(j2=u.___errno_location=u.asm.bb).apply(null,arguments)},H2=u._emscripten_get_global_libc=function(){return(H2=u._emscripten_get_global_libc=u.asm.cb).apply(null,arguments)},G2=u._memalign=function(){return(G2=u._memalign=u.asm.db).apply(null,arguments)},q2=u.___pthread_tsd_run_dtors=function(){return(q2=u.___pthread_tsd_run_dtors=u.asm.eb).apply(null,arguments)},$1=u._emscripten_main_thread_process_queued_calls=function(){return($1=u._emscripten_main_thread_process_queued_calls=u.asm.fb).apply(null,arguments)},y8=u._emscripten_current_thread_process_queued_calls=function(){return(y8=u._emscripten_current_thread_process_queued_calls=u.asm.gb).apply(null,arguments)},X2=u._emscripten_register_main_browser_thread_id=function(){return(X2=u._emscripten_register_main_browser_thread_id=u.asm.hb).apply(null,arguments)},g8=u._emscripten_main_browser_thread_id=function(){return(g8=u._emscripten_main_browser_thread_id=u.asm.ib).apply(null,arguments)},x8=u._emscripten_async_run_in_main_thread=function(){return(x8=u._emscripten_async_run_in_main_thread=u.asm.jb).apply(null,arguments)},w8=u._emscripten_sync_run_in_main_thread=function(){return(w8=u._emscripten_sync_run_in_main_thread=u.asm.kb).apply(null,arguments)},_8=u._emscripten_sync_run_in_main_thread_0=function(){return(_8=u._emscripten_sync_run_in_main_thread_0=u.asm.lb).apply(null,arguments)},b8=u._emscripten_sync_run_in_main_thread_1=function(){return(b8=u._emscripten_sync_run_in_main_thread_1=u.asm.mb).apply(null,arguments)},v8=u._emscripten_sync_run_in_main_thread_2=function(){return(v8=u._emscripten_sync_run_in_main_thread_2=u.asm.nb).apply(null,arguments)},k8=u._emscripten_sync_run_in_main_thread_xprintf_varargs=function(){return(k8=u._emscripten_sync_run_in_main_thread_xprintf_varargs=u.asm.ob).apply(null,arguments)},I8=u._emscripten_sync_run_in_main_thread_3=function(){return(I8=u._emscripten_sync_run_in_main_thread_3=u.asm.pb).apply(null,arguments)},K2=u._emscripten_sync_run_in_main_thread_4=function(){return(K2=u._emscripten_sync_run_in_main_thread_4=u.asm.qb).apply(null,arguments)},N8=u._emscripten_sync_run_in_main_thread_5=function(){return(N8=u._emscripten_sync_run_in_main_thread_5=u.asm.rb).apply(null,arguments)},S8=u._emscripten_sync_run_in_main_thread_6=function(){return(S8=u._emscripten_sync_run_in_main_thread_6=u.asm.sb).apply(null,arguments)},T8=u._emscripten_sync_run_in_main_thread_7=function(){return(T8=u._emscripten_sync_run_in_main_thread_7=u.asm.tb).apply(null,arguments)},Z2=u._emscripten_run_in_main_runtime_thread_js=function(){return(Z2=u._emscripten_run_in_main_runtime_thread_js=u.asm.ub).apply(null,arguments)},D1=u._emscripten_async_queue_on_thread_=function(){return(D1=u._emscripten_async_queue_on_thread_=u.asm.vb).apply(null,arguments)},E8=u._emscripten_tls_init=function(){return(E8=u._emscripten_tls_init=u.asm.wb).apply(null,arguments)},fu=u.stackSave=function(){return(fu=u.stackSave=u.asm.xb).apply(null,arguments)},Ui=u.stackAlloc=function(){return(Ui=u.stackAlloc=u.asm.yb).apply(null,arguments)},ji=u.stackRestore=function(){return(ji=u.stackRestore=u.asm.zb).apply(null,arguments)},Y2=u.dynCall_vi=function(){return(Y2=u.dynCall_vi=u.asm.Ab).apply(null,arguments)},C8=u.dynCall_v=function(){return(C8=u.dynCall_v=u.asm.Bb).apply(null,arguments)},R8=u.dynCall_ii=function(){return(R8=u.dynCall_ii=u.asm.Cb).apply(null,arguments)};u.asm=yh,u.cwrap=Oe,u.PThread=de,u.PThread=de,u._pthread_self=Tr,u.wasmMemory=J,u.ExitStatus=J2;var mu;u.then=function(v){if(mu)v(u);else{var S=u.onRuntimeInitialized;u.onRuntimeInitialized=function(){S&&S(),v(u)}}return u};function J2(v){this.name="ExitStatus",this.message="Program terminated with exit("+v+")",this.status=v}Qa=function v(){mu||z1(),mu||(Qa=v)};function z1(v){if(v=v||d,Kr>0||(Yn(),Kr>0))return;function S(){mu||(mu=!0,u.calledRun=!0,!pe&&(rh(),F0(),u.onRuntimeInitialized&&u.onRuntimeInitialized(),M0()))}u.setStatus?(u.setStatus("Running..."),setTimeout(function(){setTimeout(function(){u.setStatus("")},1),S()},1)):S()}if(u.run=z1,u.preInit)for(typeof u.preInit=="function"&&(u.preInit=[u.preInit]);u.preInit.length>0;)u.preInit.pop()();return x||(ae=!0),x||z1(),a}}();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModuleThreadedSimd=n)}),ck=et((e,t)=>{var n=function(){var r=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(r=r||__filename),function(a){a=a||{};var s=typeof a!="undefined"?a:{},i={},o;for(o in s)s.hasOwnProperty(o)&&(i[o]=s[o]);var l=[],c="./this.program",u=function(j,te){throw te},h=!1,p=!1,d=!1,f=!1;h=typeof window=="object",p=typeof importScripts=="function",d=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",f=!h&&!d&&!p;var m="";function A(j){return s.locateFile?s.locateFile(j,m):m+j}var y,g,_,x,w,b;d?(p?m=gu().dirname(m)+"/":m=__dirname+"/",y=function(j,te){return w||(w=require("fs")),b||(b=gu()),j=b.normalize(j),w.readFileSync(j,te?null:"utf8")},_=function(j){var te=y(j,!0);return te.buffer||(te=new Uint8Array(te)),U(te.buffer),te},process.argv.length>1&&(c=process.argv[1].replace(/\\/g,"/")),l=process.argv.slice(2),process.on("uncaughtException",function(j){if(!(j instanceof uu))throw j}),process.on("unhandledRejection",Ya),u=function(j){process.exit(j)},s.inspect=function(){return"[Emscripten Module object]"}):f?(typeof read!="undefined"&&(y=function(j){return read(j)}),_=function(j){var te;return typeof readbuffer=="function"?new Uint8Array(readbuffer(j)):(te=read(j,"binary"),U(typeof te=="object"),te)},typeof scriptArgs!="undefined"?l=scriptArgs:typeof arguments!="undefined"&&(l=arguments),typeof quit=="function"&&(u=function(j){quit(j)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(h||p)&&(p?m=self.location.href:document.currentScript&&(m=document.currentScript.src),r&&(m=r),m.indexOf("blob:")!==0?m=m.substr(0,m.lastIndexOf("/")+1):m="",y=function(j){var te=new XMLHttpRequest;return te.open("GET",j,!1),te.send(null),te.responseText},p&&(_=function(j){var te=new XMLHttpRequest;return te.open("GET",j,!1),te.responseType="arraybuffer",te.send(null),new Uint8Array(te.response)}),g=function(j,te,ve){var Ce=new XMLHttpRequest;Ce.open("GET",j,!0),Ce.responseType="arraybuffer",Ce.onload=function(){if(Ce.status==200||Ce.status==0&&Ce.response){te(Ce.response);return}ve()},Ce.onerror=ve,Ce.send(null)},x=function(j){document.title=j});var N=s.print||console.log.bind(console),T=s.printErr||console.warn.bind(console);for(o in i)i.hasOwnProperty(o)&&(s[o]=i[o]);i=null,s.arguments&&(l=s.arguments),s.thisProgram&&(c=s.thisProgram),s.quit&&(u=s.quit);var E;s.wasmBinary&&(E=s.wasmBinary);var M;s.noExitRuntime&&(M=s.noExitRuntime),typeof WebAssembly!="object"&&T("no native wasm support detected");var $,P=new WebAssembly.Table({initial:151,maximum:151+0,element:"anyfunc"}),V=!1,G=0;function U(j,te){j||Ya("Assertion failed: "+te)}function K(j){var te=s["_"+j];return U(te,"Cannot call unknown function "+j+", make sure it is exported"),te}function X(j,te,ve,Ce,Qe){var kt={string:function(Ln){var Aa=0;if(Ln!=null&&Ln!==0){var bh=(Ln.length<<2)+1;Aa=lu(bh),ne(Ln,Aa,bh)}return Aa},array:function(Ln){var Aa=lu(Ln.length);return ce(Ln,Aa),Aa}};function Xe(Ln){return te==="string"?J(Ln):te==="boolean"?Boolean(Ln):Ln}var je=K(j),Pt=[],Yr=0;if(Ce)for(var Jr=0;Jr<Ce.length;Jr++){var _h=kt[ve[Jr]];_h?(Yr===0&&(Yr=ou()),Pt[Jr]=_h(Ce[Jr])):Pt[Jr]=Ce[Jr]}var hu=je.apply(null,Pt);return hu=Xe(hu),Yr!==0&&gh(Yr),hu}function ee(j,te,ve,Ce){ve=ve||[];var Qe=ve.every(function(Xe){return Xe==="number"}),kt=te!=="string";return kt&&Qe&&!Ce?K(j):function(){return X(j,te,ve,arguments,Ce)}}var Z=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function ae(j,te,ve){for(var Ce=te+ve,Qe=te;j[Qe]&&!(Qe>=Ce);)++Qe;if(Qe-te>16&&j.subarray&&Z)return Z.decode(j.subarray(te,Qe));for(var kt="";te<Qe;){var Xe=j[te++];if(!(Xe&128)){kt+=String.fromCharCode(Xe);continue}var je=j[te++]&63;if((Xe&224)==192){kt+=String.fromCharCode((Xe&31)<<6|je);continue}var Pt=j[te++]&63;if((Xe&240)==224?Xe=(Xe&15)<<12|je<<6|Pt:Xe=(Xe&7)<<18|je<<12|Pt<<6|j[te++]&63,Xe<65536)kt+=String.fromCharCode(Xe);else{var Yr=Xe-65536;kt+=String.fromCharCode(55296|Yr>>10,56320|Yr&1023)}}return kt}function J(j,te){return j?ae(fe,j,te):""}function oe(j,te,ve,Ce){if(!(Ce>0))return 0;for(var Qe=ve,kt=ve+Ce-1,Xe=0;Xe<j.length;++Xe){var je=j.charCodeAt(Xe);if(je>=55296&&je<=57343){var Pt=j.charCodeAt(++Xe);je=65536+((je&1023)<<10)|Pt&1023}if(je<=127){if(ve>=kt)break;te[ve++]=je}else if(je<=2047){if(ve+1>=kt)break;te[ve++]=192|je>>6,te[ve++]=128|je&63}else if(je<=65535){if(ve+2>=kt)break;te[ve++]=224|je>>12,te[ve++]=128|je>>6&63,te[ve++]=128|je&63}else{if(ve+3>=kt)break;te[ve++]=240|je>>18,te[ve++]=128|je>>12&63,te[ve++]=128|je>>6&63,te[ve++]=128|je&63}}return te[ve]=0,ve-Qe}function ne(j,te,ve){return oe(j,fe,te,ve)}function ce(j,te){pe.set(j,te)}var ue,pe,fe,_e,Ne,Ee,Oe,qe,Be;function tt(j){ue=j,s.HEAP8=pe=new Int8Array(j),s.HEAP16=_e=new Int16Array(j),s.HEAP32=Ee=new Int32Array(j),s.HEAPU8=fe=new Uint8Array(j),s.HEAPU16=Ne=new Uint16Array(j),s.HEAPU32=Oe=new Uint32Array(j),s.HEAPF32=qe=new Float32Array(j),s.HEAPF64=Be=new Float64Array(j)}var it=s.INITIAL_MEMORY||16777216;function Ue(j){for(;j.length>0;){var te=j.shift();if(typeof te=="function"){te(s);continue}var ve=te.func;typeof ve=="number"?te.arg===void 0?s.dynCall_v(ve):s.dynCall_vi(ve,te.arg):ve(te.arg===void 0?null:te.arg)}}var ut=[],ct=[],Pn=[],Ye=[],bn=!1,Xt=!1;function vn(){if(s.preRun)for(typeof s.preRun=="function"&&(s.preRun=[s.preRun]);s.preRun.length;)Sr(s.preRun.shift());Ue(ut)}function Xn(){bn=!0,Ue(ct)}function pn(){Ue(Pn)}function rn(){Xt=!0}function Kn(){if(s.postRun)for(typeof s.postRun=="function"&&(s.postRun=[s.postRun]);s.postRun.length;)kn(s.postRun.shift());Ue(Ye)}function Sr(j){ut.unshift(j)}function kn(j){Ye.unshift(j)}var Oi=Math.ceil,Ql=Math.floor,hr=0,Zn=null,dr=null;function $i(j){hr++,s.monitorRunDependencies&&s.monitorRunDependencies(hr)}function Di(j){if(hr--,s.monitorRunDependencies&&s.monitorRunDependencies(hr),hr==0&&(Zn!==null&&(clearInterval(Zn),Zn=null),dr)){var te=dr;dr=null,te()}}s.preloadedImages={},s.preloadedAudios={};function Ya(j){throw s.onAbort&&s.onAbort(j),j+="",N(j),T(j),V=!0,G=1,j="abort("+j+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(j)}function eu(j,te){return String.prototype.startsWith?j.startsWith(te):j.indexOf(te)===0}var R0="data:application/octet-stream;base64,";function tu(j){return eu(j,R0)}var nh="file://";function nu(j){return eu(j,nh)}var Yn="tfjs-backend-wasm.wasm";tu(Yn)||(Yn=A(Yn));function rh(){try{if(E)return new Uint8Array(E);if(_)return _(Yn);throw"both async and sync fetching of the wasm failed"}catch(j){Ya(j)}}function F0(){return!E&&(h||p)&&typeof fetch=="function"&&!nu(Yn)?fetch(Yn,{credentials:"same-origin"}).then(function(j){if(!j.ok)throw"failed to load wasm binary file at '"+Yn+"'";return j.arrayBuffer()}).catch(function(){return rh()}):new Promise(function(j,te){j(rh())})}function M0(){var j={env:Zr,wasi_snapshot_preview1:Zr};function te(Xe,je){var Pt=Xe.exports;s.asm=Pt,$=Pt.memory,tt($.buffer),Di("wasm-instantiate")}$i("wasm-instantiate");function ve(Xe){te(Xe.instance)}function Ce(Xe){return F0().then(function(je){return WebAssembly.instantiate(je,j)}).then(Xe,function(je){T("failed to asynchronously prepare wasm: "+je),Ya(je)})}function Qe(){if(!E&&typeof WebAssembly.instantiateStreaming=="function"&&!tu(Yn)&&!nu(Yn)&&typeof fetch=="function")fetch(Yn,{credentials:"same-origin"}).then(function(Xe){var je=WebAssembly.instantiateStreaming(Xe,j);return je.then(ve,function(Pt){T("wasm streaming compile failed: "+Pt),T("falling back to ArrayBuffer instantiation"),Ce(ve)})});else return Ce(ve)}if(s.instantiateWasm)try{var kt=s.instantiateWasm(j,te);return kt}catch(Xe){return T("Module.instantiateWasm callback failed with error: "+Xe),!1}return Qe(),{}}ct.push();function O0(j){tt($.buffer)}var Ja={splitPath:function(j){var te=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return te.exec(j).slice(1)},normalizeArray:function(j,te){for(var ve=0,Ce=j.length-1;Ce>=0;Ce--){var Qe=j[Ce];Qe==="."?j.splice(Ce,1):Qe===".."?(j.splice(Ce,1),ve++):ve&&(j.splice(Ce,1),ve--)}if(te)for(;ve;ve--)j.unshift("..");return j},normalize:function(j){var te=j.charAt(0)==="/",ve=j.substr(-1)==="/";return j=Ja.normalizeArray(j.split("/").filter(function(Ce){return!!Ce}),!te).join("/"),!j&&!te&&(j="."),j&&ve&&(j+="/"),(te?"/":"")+j},dirname:function(j){var te=Ja.splitPath(j),ve=te[0],Ce=te[1];return!ve&&!Ce?".":(Ce&&(Ce=Ce.substr(0,Ce.length-1)),ve+Ce)},basename:function(j){if(j==="/")return"/";var te=j.lastIndexOf("/");return te===-1?j:j.substr(te+1)},extname:function(j){return Ja.splitPath(j)[3]},join:function(){var j=Array.prototype.slice.call(arguments,0);return Ja.normalize(j.join("/"))},join2:function(j,te){return Ja.normalize(j+"/"+te)}},zi={mappings:{},buffers:[null,[],[]],printChar:function(j,te){var ve=zi.buffers[j];te===0||te===10?((j===1?N:T)(ae(ve,0)),ve.length=0):ve.push(te)},varargs:void 0,get:function(){zi.varargs+=4;var j=Ee[zi.varargs-4>>2];return j},getStr:function(j){var te=J(j);return te},get64:function(j,te){return j}};function $0(j){return 0}function Kr(j,te,ve,Ce,Qe){}function ru(j,te,ve,Ce){for(var Qe=0,kt=0;kt<ve;kt++){for(var Xe=Ee[te+kt*8>>2],je=Ee[te+(kt*8+4)>>2],Pt=0;Pt<je;Pt++)zi.printChar(j,fe[Xe+Pt]);Qe+=je}return Ee[Ce>>2]=Qe,0}function Qa(j){xh(j)}function D0(j){Qa(j)}function z0(j){return j=+j,j>=0?+Ql(j+.5):+Oi(j-.5)}var Zr={emscripten_notify_memory_growth:O0,fd_close:$0,fd_seek:Kr,fd_write:ru,proc_exit:D0,roundf:z0},au=M0();s.asm=au;var P0=s._init=function(){return(P0=s._init=s.asm.init).apply(null,arguments)},ah=s._register_tensor=function(){return(ah=s._register_tensor=s.asm.register_tensor).apply(null,arguments)},L0=s._dispose_data=function(){return(L0=s._dispose_data=s.asm.dispose_data).apply(null,arguments)},sh=s._dispose=function(){return(sh=s._dispose=s.asm.dispose).apply(null,arguments)},Jn=s._Abs=function(){return(Jn=s._Abs=s.asm.Abs).apply(null,arguments)},ih=s._Add=function(){return(ih=s._Add=s.asm.Add).apply(null,arguments)},W0=s._AddN=function(){return(W0=s._AddN=s.asm.AddN).apply(null,arguments)},B0=s._ArgMax=function(){return(B0=s._ArgMax=s.asm.ArgMax).apply(null,arguments)},V0=s._AvgPool=function(){return(V0=s._AvgPool=s.asm.AvgPool).apply(null,arguments)},U0=s._BatchMatMul=function(){return(U0=s._BatchMatMul=s.asm.BatchMatMul).apply(null,arguments)},oh=s._ClipByValue=function(){return(oh=s._ClipByValue=s.asm.ClipByValue).apply(null,arguments)},lh=s._Conv2D=function(){return(lh=s._Conv2D=s.asm.Conv2D).apply(null,arguments)},uh=s._Conv2DBackpropInput=function(){return(uh=s._Conv2DBackpropInput=s.asm.Conv2DBackpropInput).apply(null,arguments)},Pi=s._Cos=function(){return(Pi=s._Cos=s.asm.Cos).apply(null,arguments)},su=s._CropAndResize=function(){return(su=s._CropAndResize=s.asm.CropAndResize).apply(null,arguments)},Li=s._Cumsum=function(){return(Li=s._Cumsum=s.asm.Cumsum).apply(null,arguments)},Wi=s._DepthToSpace=function(){return(Wi=s._DepthToSpace=s.asm.DepthToSpace).apply(null,arguments)},j0=s._DepthwiseConv2dNative=function(){return(j0=s._DepthwiseConv2dNative=s.asm.DepthwiseConv2dNative).apply(null,arguments)},H0=s._Equal=function(){return(H0=s._Equal=s.asm.Equal).apply(null,arguments)},G0=s._Exp=function(){return(G0=s._Exp=s.asm.Exp).apply(null,arguments)},de=s._FlipLeftRight=function(){return(de=s._FlipLeftRight=s.asm.FlipLeftRight).apply(null,arguments)},q0=s._Floor=function(){return(q0=s._Floor=s.asm.Floor).apply(null,arguments)},X0=s._FloorDiv=function(){return(X0=s._FloorDiv=s.asm.FloorDiv).apply(null,arguments)},K0=s._FusedBatchNorm=function(){return(K0=s._FusedBatchNorm=s.asm.FusedBatchNorm).apply(null,arguments)},Z0=s._FusedConv2D=function(){return(Z0=s._FusedConv2D=s.asm.FusedConv2D).apply(null,arguments)},es=s._FusedDepthwiseConv2D=function(){return(es=s._FusedDepthwiseConv2D=s.asm.FusedDepthwiseConv2D).apply(null,arguments)},Y0=s._Gather=function(){return(Y0=s._Gather=s.asm.Gather).apply(null,arguments)},J0=s._GatherNd=function(){return(J0=s._GatherNd=s.asm.GatherNd).apply(null,arguments)},Q0=s._Greater=function(){return(Q0=s._Greater=s.asm.Greater).apply(null,arguments)},e1=s._GreaterEqual=function(){return(e1=s._GreaterEqual=s.asm.GreaterEqual).apply(null,arguments)},t1=s._LeakyRelu=function(){return(t1=s._LeakyRelu=s.asm.LeakyRelu).apply(null,arguments)},n1=s._Less=function(){return(n1=s._Less=s.asm.Less).apply(null,arguments)},r1=s._LessEqual=function(){return(r1=s._LessEqual=s.asm.LessEqual).apply(null,arguments)},a1=s._Log=function(){return(a1=s._Log=s.asm.Log).apply(null,arguments)},s1=s._LogicalAnd=function(){return(s1=s._LogicalAnd=s.asm.LogicalAnd).apply(null,arguments)},i1=s._Max=function(){return(i1=s._Max=s.asm.Max).apply(null,arguments)},fa=s._MaxPool=function(){return(fa=s._MaxPool=s.asm.MaxPool).apply(null,arguments)},ts=s._Maximum=function(){return(ts=s._Maximum=s.asm.Maximum).apply(null,arguments)},Bi=s._Mean=function(){return(Bi=s._Mean=s.asm.Mean).apply(null,arguments)},o1=s._Min=function(){return(o1=s._Min=s.asm.Min).apply(null,arguments)},l1=s._Minimum=function(){return(l1=s._Minimum=s.asm.Minimum).apply(null,arguments)},u1=s._Multiply=function(){return(u1=s._Multiply=s.asm.Multiply).apply(null,arguments)},c1=s._Neg=function(){return(c1=s._Neg=s.asm.Neg).apply(null,arguments)},ze=s._NonMaxSuppressionV3=function(){return(ze=s._NonMaxSuppressionV3=s.asm.NonMaxSuppressionV3).apply(null,arguments)},h1=s._NonMaxSuppressionV4=function(){return(h1=s._NonMaxSuppressionV4=s.asm.NonMaxSuppressionV4).apply(null,arguments)},d1=s._NonMaxSuppressionV5=function(){return(d1=s._NonMaxSuppressionV5=s.asm.NonMaxSuppressionV5).apply(null,arguments)},p1=s._NotEqual=function(){return(p1=s._NotEqual=s.asm.NotEqual).apply(null,arguments)},f1=s._OneHot=function(){return(f1=s._OneHot=s.asm.OneHot).apply(null,arguments)},m1=s._PadV2=function(){return(m1=s._PadV2=s.asm.PadV2).apply(null,arguments)},A1=s._Pow=function(){return(A1=s._Pow=s.asm.Pow).apply(null,arguments)},iu=s._Prelu=function(){return(iu=s._Prelu=s.asm.Prelu).apply(null,arguments)},ch=s._Prod=function(){return(ch=s._Prod=s.asm.Prod).apply(null,arguments)},hh=s._RealDiv=function(){return(hh=s._RealDiv=s.asm.RealDiv).apply(null,arguments)},y1=s._Relu=function(){return(y1=s._Relu=s.asm.Relu).apply(null,arguments)},g1=s._Relu6=function(){return(g1=s._Relu6=s.asm.Relu6).apply(null,arguments)},x1=s._ResizeBilinear=function(){return(x1=s._ResizeBilinear=s.asm.ResizeBilinear).apply(null,arguments)},w1=s._Reverse=function(){return(w1=s._Reverse=s.asm.Reverse).apply(null,arguments)},_1=s._RotateWithOffset=function(){return(_1=s._RotateWithOffset=s.asm.RotateWithOffset).apply(null,arguments)},b1=s._Round=function(){return(b1=s._Round=s.asm.Round).apply(null,arguments)},We=s._Rsqrt=function(){return(We=s._Rsqrt=s.asm.Rsqrt).apply(null,arguments)},v1=s._ScatterNd=function(){return(v1=s._ScatterNd=s.asm.ScatterNd).apply(null,arguments)},k1=s._SelectV2=function(){return(k1=s._SelectV2=s.asm.SelectV2).apply(null,arguments)},I1=s._Sigmoid=function(){return(I1=s._Sigmoid=s.asm.Sigmoid).apply(null,arguments)},ns=s._Sin=function(){return(ns=s._Sin=s.asm.Sin).apply(null,arguments)},Vi=s._Softmax=function(){return(Vi=s._Softmax=s.asm.Softmax).apply(null,arguments)},dh=s._Sqrt=function(){return(dh=s._Sqrt=s.asm.Sqrt).apply(null,arguments)},ph=s._Square=function(){return(ph=s._Square=s.asm.Square).apply(null,arguments)},fh=s._SquaredDifference=function(){return(fh=s._SquaredDifference=s.asm.SquaredDifference).apply(null,arguments)},N1=s._Step=function(){return(N1=s._Step=s.asm.Step).apply(null,arguments)},S1=s._StridedSlice=function(){return(S1=s._StridedSlice=s.asm.StridedSlice).apply(null,arguments)},mh=s._Sub=function(){return(mh=s._Sub=s.asm.Sub).apply(null,arguments)},T1=s._Sum=function(){return(T1=s._Sum=s.asm.Sum).apply(null,arguments)},Tr=s._Tanh=function(){return(Tr=s._Tanh=s.asm.Tanh).apply(null,arguments)},E1=s._Tile=function(){return(E1=s._Tile=s.asm.Tile).apply(null,arguments)},C1=s._TopK=function(){return(C1=s._TopK=s.asm.TopK).apply(null,arguments)},Ah=s._Transpose=function(){return(Ah=s._Transpose=s.asm.Transpose).apply(null,arguments)},ma=s.__FusedMatMul=function(){return(ma=s.__FusedMatMul=s.asm._FusedMatMul).apply(null,arguments)},R1=s._malloc=function(){return(R1=s._malloc=s.asm.malloc).apply(null,arguments)},F1=s._free=function(){return(F1=s._free=s.asm.free).apply(null,arguments)},yh=s.__start=function(){return(yh=s.__start=s.asm._start).apply(null,arguments)},ou=s.stackSave=function(){return(ou=s.stackSave=s.asm.stackSave).apply(null,arguments)},lu=s.stackAlloc=function(){return(lu=s.stackAlloc=s.asm.stackAlloc).apply(null,arguments)},gh=s.stackRestore=function(){return(gh=s.stackRestore=s.asm.stackRestore).apply(null,arguments)};s.asm=au,s.cwrap=ee;var rs;s.then=function(j){if(rs)j(s);else{var te=s.onRuntimeInitialized;s.onRuntimeInitialized=function(){te&&te(),j(s)}}return s};function uu(j){this.name="ExitStatus",this.message="Program terminated with exit("+j+")",this.status=j}var M1=!1;dr=function j(){rs||cu(),rs||(dr=j)};function O1(j){var te=s.__start;try{te();var ve=0;xh(ve,!0)}catch(Qe){if(Qe instanceof uu)return;if(Qe=="unwind"){M=!0;return}else{var Ce=Qe;Qe&&typeof Qe=="object"&&Qe.stack&&(Ce=[Qe,Qe.stack]),T("exception thrown: "+Ce),u(1,Qe)}}finally{M1=!0}}function cu(j){if(j=j||l,hr>0||(vn(),hr>0))return;function te(){rs||(rs=!0,s.calledRun=!0,!V&&(Xn(),pn(),s.onRuntimeInitialized&&s.onRuntimeInitialized(),wh&&O1(j),Kn()))}s.setStatus?(s.setStatus("Running..."),setTimeout(function(){setTimeout(function(){s.setStatus("")},1),te()},1)):te()}s.run=cu;function xh(j,te){te&&M&&j===0||(M||(V=!0,G=j,rn(),s.onExit&&s.onExit(j)),u(j,new uu(j)))}if(s.preInit)for(typeof s.preInit=="function"&&(s.preInit=[s.preInit]);s.preInit.length>0;)s.preInit.pop()();var wh=!0;return s.noInitialRun&&(wh=!1),M=!0,cu(),a}}();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModule=n)}),hk=et((e,t)=>{(function(n,r,a){function s(c){var u=this,h=l();u.next=function(){var p=2091639*u.s0+u.c*23283064365386963e-26;return u.s0=u.s1,u.s1=u.s2,u.s2=p-(u.c=p|0)},u.c=1,u.s0=h(" "),u.s1=h(" "),u.s2=h(" "),u.s0-=h(c),u.s0<0&&(u.s0+=1),u.s1-=h(c),u.s1<0&&(u.s1+=1),u.s2-=h(c),u.s2<0&&(u.s2+=1),h=null}function i(c,u){return u.c=c.c,u.s0=c.s0,u.s1=c.s1,u.s2=c.s2,u}function o(c,u){var h=new s(c),p=u&&u.state,d=h.next;return d.int32=function(){return h.next()*4294967296|0},d.double=function(){return d()+(d()*2097152|0)*11102230246251565e-32},d.quick=d,p&&(typeof p=="object"&&i(p,h),d.state=function(){return i(h,{})}),d}function l(){var c=4022871197,u=function(h){h=String(h);for(var p=0;p<h.length;p++){c+=h.charCodeAt(p);var d=.02519603282416938*c;c=d>>>0,d-=c,d*=c,c=d>>>0,d-=c,c+=d*4294967296}return(c>>>0)*23283064365386963e-26};return u}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),dk=et((e,t)=>{(function(n,r,a){function s(l){var c=this,u="";c.x=0,c.y=0,c.z=0,c.w=0,c.next=function(){var p=c.x^c.x<<11;return c.x=c.y,c.y=c.z,c.z=c.w,c.w^=c.w>>>19^p^p>>>8},l===(l|0)?c.x=l:u+=l;for(var h=0;h<u.length+64;h++)c.x^=u.charCodeAt(h)|0,c.next()}function i(l,c){return c.x=l.x,c.y=l.y,c.z=l.z,c.w=l.w,c}function o(l,c){var u=new s(l),h=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var d=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(d+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,h&&(typeof h=="object"&&i(h,u),p.state=function(){return i(u,{})}),p}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),pk=et((e,t)=>{(function(n,r,a){function s(l){var c=this,u="";c.next=function(){var p=c.x^c.x>>>2;return c.x=c.y,c.y=c.z,c.z=c.w,c.w=c.v,(c.d=c.d+362437|0)+(c.v=c.v^c.v<<4^(p^p<<1))|0},c.x=0,c.y=0,c.z=0,c.w=0,c.v=0,l===(l|0)?c.x=l:u+=l;for(var h=0;h<u.length+64;h++)c.x^=u.charCodeAt(h)|0,h==u.length&&(c.d=c.x<<10^c.x>>>4),c.next()}function i(l,c){return c.x=l.x,c.y=l.y,c.z=l.z,c.w=l.w,c.v=l.v,c.d=l.d,c}function o(l,c){var u=new s(l),h=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var d=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(d+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,h&&(typeof h=="object"&&i(h,u),p.state=function(){return i(u,{})}),p}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),fk=et((e,t)=>{(function(n,r,a){function s(l){var c=this;c.next=function(){var h=c.x,p=c.i,d,f,m;return d=h[p],d^=d>>>7,f=d^d<<24,d=h[p+1&7],f^=d^d>>>10,d=h[p+3&7],f^=d^d>>>3,d=h[p+4&7],f^=d^d<<7,d=h[p+7&7],d=d^d<<13,f^=d^d<<9,h[p]=f,c.i=p+1&7,f};function u(h,p){var d,f,m=[];if(p===(p|0))f=m[0]=p;else for(p=""+p,d=0;d<p.length;++d)m[d&7]=m[d&7]<<15^p.charCodeAt(d)+m[d+1&7]<<13;for(;m.length<8;)m.push(0);for(d=0;d<8&&m[d]===0;++d);for(d==8?f=m[7]=-1:f=m[d],h.x=m,h.i=0,d=256;d>0;--d)h.next()}u(c,l)}function i(l,c){return c.x=l.x.slice(),c.i=l.i,c}function o(l,c){l==null&&(l=+new Date);var u=new s(l),h=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var d=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(d+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,h&&(h.x&&i(h,u),p.state=function(){return i(u,{})}),p}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorshift7=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),mk=et((e,t)=>{(function(n,r,a){function s(l){var c=this;c.next=function(){var h=c.w,p=c.X,d=c.i,f,m;return c.w=h=h+1640531527|0,m=p[d+34&127],f=p[d=d+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=p[d]=m^f,c.i=d,m+(h^h>>>16)|0};function u(h,p){var d,f,m,A,y,g=[],_=128;for(p===(p|0)?(f=p,p=null):(p=p+"\0",f=0,_=Math.max(_,p.length)),m=0,A=-32;A<_;++A)p&&(f^=p.charCodeAt((A+32)%p.length)),A===0&&(y=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,A>=0&&(y=y+1640531527|0,d=g[A&127]^=f+y,m=d==0?m+1:0);for(m>=128&&(g[(p&&p.length||0)&127]=-1),m=127,A=4*128;A>0;--A)f=g[m+34&127],d=g[m=m+1&127],f^=f<<13,d^=d<<17,f^=f>>>15,d^=d>>>12,g[m]=f^d;h.w=y,h.X=g,h.i=m}u(c,l)}function i(l,c){return c.i=l.i,c.w=l.w,c.X=l.X.slice(),c}function o(l,c){l==null&&(l=+new Date);var u=new s(l),h=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var d=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(d+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,h&&(h.X&&i(h,u),p.state=function(){return i(u,{})}),p}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),Ak=et((e,t)=>{(function(n,r,a){function s(l){var c=this,u="";c.next=function(){var p=c.b,d=c.c,f=c.d,m=c.a;return p=p<<25^p>>>7^d,d=d-f|0,f=f<<24^f>>>8^m,m=m-p|0,c.b=p=p<<20^p>>>12^d,c.c=d=d-f|0,c.d=f<<16^d>>>16^m,c.a=m-p|0},c.a=0,c.b=0,c.c=2654435769|0,c.d=1367130551,l===Math.floor(l)?(c.a=l/4294967296|0,c.b=l|0):u+=l;for(var h=0;h<u.length+20;h++)c.b^=u.charCodeAt(h)|0,c.next()}function i(l,c){return c.a=l.a,c.b=l.b,c.c=l.c,c.d=l.d,c}function o(l,c){var u=new s(l),h=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var d=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(d+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,h&&(typeof h=="object"&&i(h,u),p.state=function(){return i(u,{})}),p}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.tychei=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),yk=et((e,t)=>{(function(n,r,a){var s=256,i=6,o=52,l="random",c=a.pow(s,i),u=a.pow(2,o),h=u*2,p=s-1,d;function f(w,b,N){var T=[];b=b==!0?{entropy:!0}:b||{};var E=g(y(b.entropy?[w,x(r)]:w==null?_():w,3),T),M=new m(T),$=function(){for(var P=M.g(i),V=c,G=0;P<u;)P=(P+G)*s,V*=s,G=M.g(1);for(;P>=h;)P/=2,V/=2,G>>>=1;return(P+G)/V};return $.int32=function(){return M.g(4)|0},$.quick=function(){return M.g(4)/4294967296},$.double=$,g(x(M.S),r),(b.pass||N||function(P,V,G,U){return U&&(U.S&&A(U,M),P.state=function(){return A(M,{})}),G?(a[l]=P,V):P})($,E,"global"in b?b.global:this==a,b.state)}function m(w){var b,N=w.length,T=this,E=0,M=T.i=T.j=0,$=T.S=[];for(N||(w=[N++]);E<s;)$[E]=E++;for(E=0;E<s;E++)$[E]=$[M=p&M+w[E%N]+(b=$[E])],$[M]=b;(T.g=function(P){for(var V,G=0,U=T.i,K=T.j,X=T.S;P--;)V=X[U=p&U+1],G=G*s+X[p&(X[U]=X[K=p&K+V])+(X[K]=V)];return T.i=U,T.j=K,G})(s)}function A(w,b){return b.i=w.i,b.j=w.j,b.S=w.S.slice(),b}function y(w,b){var N=[],T=typeof w,E;if(b&&T=="object")for(E in w)try{N.push(y(w[E],b-1))}catch(M){}return N.length?N:T=="string"?w:w+"\0"}function g(w,b){for(var N=w+"",T,E=0;E<N.length;)b[p&E]=p&(T^=b[p&E]*19)+N.charCodeAt(E++);return x(b)}function _(){try{var w;return d&&(w=d.randomBytes)?w=w(s):(w=new Uint8Array(s),(n.crypto||n.msCrypto).getRandomValues(w)),x(w)}catch(T){var b=n.navigator,N=b&&b.plugins;return[+new Date,n,N,n.screen,x(r)]}}function x(w){return String.fromCharCode.apply(0,w)}if(g(a.random(),r),typeof t=="object"&&t.exports){t.exports=f;try{d=B1()}catch(w){}}else typeof define=="function"&&define.amd?define(function(){return f}):a["seed"+l]=f})(typeof self!="undefined"?self:e,[],Math)}),rg=et((e,t)=>{var n=hk(),r=dk(),a=pk(),s=fk(),i=mk(),o=Ak(),l=yk();l.alea=n,l.xor128=r,l.xorwow=a,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),gk=et(()=>{}),xk="3.0.0",wk="3.0.0",_k="3.0.0",bk="3.0.0",vk="3.0.0",kk=1e-7,Ik=1e-4,Ih=class{constructor(e,t){this.backend=e,this.dataMover=t,this.data=new WeakMap,this.dataIdsCount=0}get(e){return this.data.has(e)||this.dataMover.moveData(this.backend,e),this.data.get(e)}set(e,t){this.dataIdsCount++,this.data.set(e,t)}has(e){return this.data.has(e)}delete(e){return this.dataIdsCount--,this.data.delete(e)}numDataIds(){return this.dataIdsCount}},xu=class{decComplexRef(e){}time(e){return Er("time")}read(e){return Er("read")}readSync(e){return Er("readSync")}numDataIds(){return Er("numDataIds")}disposeData(e){return Er("disposeData")}write(e,t,n){return Er("write")}move(e,t,n,r){return Er("move")}memory(){return Er("memory")}floatPrecision(){return Er("floatPrecision")}epsilon(){return this.floatPrecision()===32?kk:Ik}dispose(){return Er("dispose")}};function Er(e){throw new Error(`'${e}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`)}function ag(e){let t=e.length,n=0,r=0;for(;t>0;)r=Math.random()*t|0,t--,n=e[t],e[t]=e[r],e[r]=n}function Nk(e,t){if(e.length!==t.length)throw Error(`Array sizes must match to be shuffled together First array length was ${e.length}Second array length was ${t.length}`);let n=e.length,r,a,s=0;for(;n>0;)s=Math.random()*n|0,n--,r=e[n],a=t[n],e[n]=e[s],t[n]=t[s],e[s]=r,t[s]=a}function wu(e,t,n){return Math.max(e,Math.min(t,n))}function Sk(e){return e%2==0?e:e+1}function Tk(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function Ek(e,t){let n=Math.random();return t*n+(1-n)*e}function Ck(e,t){let n=0;for(let r=0;r<e.length;r++){let a=Number(e[r])-Number(t[r]);n+=a*a}return n}function F(e,t){if(!e)throw new Error(typeof t=="string"?t:t())}function tn(e,t,n=""){F(Qr(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function as(e){F(e!=null,()=>"The input to the tensor constructor must be a non-null value.")}function ss(e,t=[],n=!1){if(t==null&&(t=[]),Array.isArray(e)||nn(e)&&!n)for(let r=0;r<e.length;++r)ss(e[r],t,n);else t.push(e);return t}function Rt(e){if(e.length===0)return 1;let t=e[0];for(let n=1;n<e.length;n++)t*=e[n];return t}function Rk(e){return e.length===0}function Qr(e,t){if(e===t)return!0;if(e==null||t==null||e.length!==t.length)return!1;for(let n=0;n<e.length;n++)if(e[n]!==t[n])return!1;return!0}function Lt(e){return e%1==0}function Fk(e){if(Math.tanh!=null)return Math.tanh(e);if(e===Infinity)return 1;if(e===-Infinity)return-1;{let t=Math.exp(2*e);return(t-1)/(t+1)}}function Mk(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function Ok(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return ag(t),t}function _u(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function $k(e,t=r=>0,n){return new Promise((r,a)=>{let s=0,i=()=>{if(e()){r();return}s++;let o=t(s);if(n!=null&&s>=n){a();return}setTimeout(i,o)};i()})}function Dk(e,t){let n=1,r=-1;for(let s=0;s<e.length;++s)if(e[s]>=0)n*=e[s];else if(e[s]===-1){if(r!==-1)throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${r} and dim ${s}`);r=s}else if(e[s]<0)throw Error(`Shapes can not be < 0. Found ${e[s]} at dim ${s}`);if(r===-1){if(t>0&&t!==n)throw Error(`Size(${t}) must match the product of shape ${e}`);return e}if(n===0)throw Error(`Cannot infer the missing size in [${e}] when there are 0 elements`);if(t%n!=0)throw Error(`The implicit shape can't be a fractional number. Got ${t} / ${n}`);let a=e.slice();return a[r]=t/n,a}function Qn(e,t){let n=t.length;return e=e==null?t.map((r,a)=>a):[].concat(e),F(e.every(r=>r>=-n&&r<n),()=>`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),F(e.every(r=>Lt(r)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(r=>r<0?n+r:r)}function sg(e,t){let n=[],r=[],a=t!=null&&Array.isArray(t)&&t.length===0,s=t==null||a?null:Qn(t,e).sort(),i=0;for(let o=0;o<e.length;++o){if(s!=null){if(s[i]===o&&e[o]!==1)throw new Error(`Can't squeeze axis ${o} since its dim '${e[o]}' is not 1`);(s[i]==null||s[i]>o)&&e[o]===1&&(n.push(e[o]),r.push(o)),s[i]<=o&&i++}e[o]!==1&&(n.push(e[o]),r.push(o))}return{newShape:n,keptDims:r}}function ig(e,t){let n=null;if(e==null||e==="float32")n=new Float32Array(t);else if(e==="int32")n=new Int32Array(t);else if(e==="bool")n=new Uint8Array(t);else throw new Error(`Unknown data type ${e}`);return n}function og(e,t){let n=null;if(e==null||e==="float32")n=new Float32Array(t);else if(e==="int32")n=new Int32Array(t);else if(e==="bool")n=new Uint8Array(t);else if(e==="string")n=new Array(t);else throw new Error(`Unknown data type ${e}`);return n}function lg(e,t){for(let n=0;n<e.length;n++){let r=e[n];if(isNaN(r)||!isFinite(r))throw Error(`A tensor of type ${t} being uploaded contains ${r}.`)}}function ug(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function zk(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function nn(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function cg(e){if(e==="float32"||e==="int32")return 4;if(e==="complex64")return 8;if(e==="bool")return 1;throw new Error(`Unknown dtype ${e}`)}function hg(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function ga(e){return typeof e=="string"||e instanceof String}function dg(e){return typeof e=="boolean"}function pg(e){return typeof e=="number"}function Nh(e){return Array.isArray(e)?Nh(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":pg(e)?"float32":ga(e)?"string":dg(e)?"bool":"float32"}function xa(e){return!!(e&&e.constructor&&e.call&&e.apply)}function Sh(e,t){for(let n=t;n<e;++n)if(e%n==0)return n;return e}function Xi(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let r=t-3;r>=0;--r)n[r]=n[r+1]*e[r+1];return n}function fg(e,t,n){let r=new Array;if(t.length===1){let a=t[0];for(let s=0;s<a;s++)r[s]=n[e+s]}else{let a=t[0],s=t.slice(1),i=s.reduce((o,l)=>o*l);for(let o=0;o<a;o++)r[o]=fg(e+o*i,s,n)}return r}function Ki(e,t){if(e.length===0)return t[0];let n=e.reduce((r,a)=>r*a);if(n===0)return[];if(n!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}.`);return fg(0,e,t)}function V1(e,t){let n=Th(e,t);for(let r=0;r<n.length;r++)n[r]=1;return n}function Th(e,t){if(t==null||t==="float32"||t==="complex64")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool")return new Uint8Array(e);throw new Error(`Unknown data type ${t}`)}function Pk(e,t){let n=e.reduce((r,a)=>r*a,1);if(t==null||t==="float32")return Ki(e,new Float32Array(n));if(t==="int32")return Ki(e,new Int32Array(n));if(t==="bool")return Ki(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function U1(e){e.forEach(t=>{F(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function Lk(e,t,n){if(t===0)return 0;if(t===1)return e[0];let r=e[e.length-1];for(let a=0;a<e.length-1;++a)r+=n[a]*e[a];return r}function Wk(e,t,n){if(t===0)return[];if(t===1)return[e];let r=new Array(t);for(let a=0;a<r.length-1;++a)r[a]=Math.floor(e/n[a]),e-=r[a]*n[a];return r[r.length-1]=e,r}function j1(e){return e&&e.then&&typeof e.then=="function"}var mg="tfjsflags",Ag=class{constructor(e){this.global=e,this.flags={},this.flagRegistry={},this.urlFlags={},this.populateURLFlags()}setPlatform(e,t){this.platform!=null&&console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${t}.`),this.platformName=e,this.platform=t}registerFlag(e,t,n){if(this.flagRegistry[e]={evaluationFn:t,setHook:n},this.urlFlags[e]!=null){let r=this.urlFlags[e];console.warn(`Setting feature override from URL ${e}: ${r}.`),this.set(e,r)}}async getAsync(e){return e in this.flags?this.flags[e]:(this.flags[e]=await this.evaluateFlag(e),this.flags[e])}get(e){if(e in this.flags)return this.flags[e];let t=this.evaluateFlag(e);if(j1(t))throw new Error(`Flag ${e} cannot be synchronously evaluated. Please use getAsync() instead.`);return this.flags[e]=t,this.flags[e]}getNumber(e){return this.get(e)}getBool(e){return this.get(e)}getFlags(){return this.flags}get features(){return this.flags}set(e,t){if(this.flagRegistry[e]==null)throw new Error(`Cannot set flag ${e} as it has not been registered.`);this.flags[e]=t,this.flagRegistry[e].setHook!=null&&this.flagRegistry[e].setHook(t)}evaluateFlag(e){if(this.flagRegistry[e]==null)throw new Error(`Cannot evaluate flag '${e}': no evaluation function found.`);return this.flagRegistry[e].evaluationFn()}setFlags(e){this.flags=Object.assign({},e)}reset(){this.flags={},this.urlFlags={},this.populateURLFlags()}populateURLFlags(){if(typeof this.global=="undefined"||typeof this.global.location=="undefined"||typeof this.global.location.search=="undefined")return;let e=Bk(this.global.location.search);mg in e&&e[mg].split(",").forEach(t=>{let[n,r]=t.split(":");this.urlFlags[n]=Vk(n,r)})}};function Bk(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...r)=>(Uk(t,r[0],r[1]),r.join("="))),t}function Uk(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function Vk(e,t){if(t=t.toLowerCase(),t==="true"||t==="false")return t==="true";if(`${+t}`===t)return+t;throw new Error(`Could not parse value flag value ${t} for flag ${e}.`)}function Q(){return an}var an=null;function jk(e){an=e}var H1;function yg(){if(H1==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");H1=e}return H1}function Hk(){let e=yg();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function G1(e,t){let n=Hk();if(n.has(e))return n.get(e);{let r=t();return n.set(e,r),n.get(e)}}var Zi="Abs",Yi="Acos",Ji="Acosh",wa="Add",is="AddN",Eh="All",Ch="Any",os="ArgMax",bu="ArgMin",Qi="Asin",eo="Asinh",to="Atan",no="Atanh",ro="Atan2",ls="AvgPool",Rh="AvgPoolGrad",vu="AvgPool3D",Fh="AvgPool3DGrad",us="BatchMatMul",ku="BatchToSpaceND",Mh="Bincount",gg="BroadcastTo",cs="Cast",ao="Ceil",_a="ClipByValue",Oh="Complex",Iu="ComplexAbs",so="Concat",hs="Conv2D",$h="Conv2DBackpropFilter",ds="Conv2DBackpropInput",Nu="Conv3D",Dh="Conv3DBackpropFilterV2",zh="Conv3DBackpropInputV2",ps="Cos",io="Cosh",fs="Cumsum",oo="CropAndResize",Ph="DenseBincount",lo="DepthToSpace",ms="DepthwiseConv2dNative",Lh="DepthwiseConv2dNativeBackpropFilter",Wh="DepthwiseConv2dNativeBackpropInput",Bh="Diag",Su="Dilation2D",Vh="Dilation2DBackpropInput",Uh="Dilation2DBackpropFilter",As="RealDiv",uo="Elu",jh="EluGrad",co="Erf",ho="Equal",ys="Exp",po="ExpandDims",fo="Expm1",Hh="FFT",Tu="Fill",mo="FlipLeftRight",gs="Floor",xs="FloorDiv",ws="FusedBatchNorm",Ao="GatherV2",yo="GatherNd",go="Greater",_s="GreaterEqual",xo="Identity",Gh="IFFT",qh="Imag",wo="IsFinite",_o="IsInf",bo="IsNan",bs="LeakyRelu",vo="Less",ko="LessEqual",Xh="LinSpace",vs="Log",Io="Log1p",No="LogicalAnd",Eu="LogicalNot",Cu="LogicalOr",xg="LogSoftmax",Ru="LRN",Kh="LRNGrad",ks="Max",Is="Maximum",Ns="MaxPool",Zh="MaxPoolGrad",Fu="MaxPool3D",Yh="MaxPool3DGrad",Jh="MaxPoolWithArgmax",Ss="Mean",Ts="Min",Es="Minimum",Mu="MirrorPad",So="Mod",Qh="Multinomial",Cs="Multiply",To="Neg",Eo="NotEqual",Co="NonMaxSuppressionV3",Ro="NonMaxSuppressionV4",Fo="NonMaxSuppressionV5",Mo="OnesLike",Rs="OneHot",Oo="Pack",Fs="PadV2",Gk="Pool",Ms="Pow",Os="Prelu",$o="Prod",Ou="Range",ed="Real",Do="Reciprocal",$s="Relu",zo="Reshape",$u="ResizeNearestNeighbor",td="ResizeNearestNeighborGrad",Ds="ResizeBilinear",nd="ResizeBilinearGrad",zs="Relu6",Ps="Reverse",Ls="Round",Ws="Rsqrt",Po="ScatterNd",Lo="Select",Wo="Selu",Bo="Slice",Bs="Sin",Vo="Sinh",Uo="Sign",Vs="Sigmoid",jo="Softplus",Us="Sqrt",js="Sum",Du="SpaceToBatchND",Ho="SplitV",Hs="Softmax",Gs="SquaredDifference",zu="Square",qs="Sub",rd="SparseToDense",Go="StridedSlice",qo="Tan",Xs="Tanh",ba="Tile",Xo="TopK",Ks="Transpose",ad="Unique",Ko="Unpack",Pu="UnsortedSegmentSum",Zo="ZerosLike",va="Step",sd="FromPixels",Yo="RotateWithOffset",Zs="_FusedMatMul",Ys="FusedConv2D",Js="FusedDepthwiseConv2D",Jo=G1("kernelRegistry",()=>new Map),Lu=G1("gradRegistry",()=>new Map);function id(e,t){let n=q1(e,t);return Jo.get(n)}function X1(e){return Lu.get(e)}function Qo(e){let t=Jo.entries(),n=[];for(;;){let{done:r,value:a}=t.next();if(r)break;let[s,i]=a,[o]=s.split("_");o===e&&n.push(i)}return n}function Qs(e){let{kernelName:t,backendName:n}=e,r=q1(t,n);Jo.has(r)&&console.warn(`The kernel '${t}' for backend '${n}' is already registered`),Jo.set(r,e)}function wg(e){let{kernelName:t}=e;Lu.has(t)&&Q().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${t}'`),Lu.set(t,e)}function qk(e,t){let n=q1(e,t);if(!Jo.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);Jo.delete(n)}function Xk(e){if(!Lu.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);Lu.delete(e)}function Kk(e,t){Qo(e).forEach(n=>{let r=Object.assign({},n,{backendName:t});Qs(r)})}function q1(e,t){return`${t}_${e}`}var k={};$e(k,{arraysEqual:()=>Qr,assert:()=>F,assertNonNegativeIntegerDimensions:()=>U1,assertNonNull:()=>as,assertShapesMatch:()=>tn,bytesFromStringArray:()=>hg,bytesPerElement:()=>cg,checkConversionForErrors:()=>lg,clamp:()=>wu,computeStrides:()=>Xi,createScalarValue:()=>Zk,createShuffledIndices:()=>Ok,decodeString:()=>ld,distSquared:()=>Ck,encodeString:()=>Wu,fetch:()=>Yk,flatten:()=>ss,getArrayFromDType:()=>og,getTypedArrayFromDType:()=>ig,hasEncodingLoss:()=>zk,indexToLoc:()=>Wk,inferDtype:()=>Nh,inferFromImplicitShape:()=>Dk,isBoolean:()=>dg,isFunction:()=>xa,isInt:()=>Lt,isNumber:()=>pg,isPromise:()=>j1,isScalarShape:()=>Rk,isString:()=>ga,isTypedArray:()=>nn,isValidDtype:()=>ug,locToIndex:()=>Lk,makeOnesTypedArray:()=>V1,makeZerosNestedTypedArray:()=>Pk,makeZerosTypedArray:()=>Th,nearestDivisor:()=>Sh,nearestLargerEven:()=>Sk,now:()=>K1,parseAxisParam:()=>Qn,randUniform:()=>Ek,repeatedTry:()=>$k,rightPad:()=>_u,shuffle:()=>ag,shuffleCombo:()=>Nk,sizeFromShape:()=>Rt,sizeToSquarishShape:()=>Mk,squeezeShape:()=>sg,sum:()=>Tk,tanh:()=>Fk,toNestedArray:()=>Ki,toTypedArray:()=>od});function Zk(e,t){return t==="string"?Wu(e):od([e],t)}function Jk(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function od(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=ss(e)),Q().getBool("DEBUG")&&lg(e,t),Jk(e,t))return e;if(t==null||t==="float32"||t==="complex64")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"){let n=new Uint8Array(e.length);for(let r=0;r<n.length;++r)Math.round(e[r])!==0&&(n[r]=1);return n}else throw new Error(`Unknown data type ${t}`)}function K1(){return Q().platform.now()}function Yk(e,t){return Q().platform.fetch(e,t)}function Wu(e,t="utf-8"){return t=t||"utf-8",Q().platform.encode(e,t)}function ld(e,t="utf-8"){return t=t||"utf-8",Q().platform.decode(e,t)}var t9=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new e9)}profileKernel(e,t,n){let r,a=()=>{r=n()},s=this.backendTimer.time(a);if(Q().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let i=0;i<r.length;i++){let o=r[i];o.data().then(l=>{Qk(l,o.dtype,e)})}return{kernelName:e,outputs:r,inputs:t,timeMs:s.then(i=>i.kernelMs),extraInfo:s.then(i=>i.getExtraProfileInfo!=null?i.getExtraProfileInfo():"")}}logKernelProfile(e){let{kernelName:t,outputs:n,timeMs:r,inputs:a,extraInfo:s}=e;n.forEach(i=>{Promise.all([i.data(),r,s]).then(o=>{this.logger.logKernelProfile(t,i,o[0],o[1],a,o[2])})})}};function Qk(e,t,n){if(t!=="float32")return!1;for(let r=0;r<e.length;r++){let a=e[r];if(isNaN(a)||!isFinite(a))return console.warn(`Found ${a} in the result of '${n}'`),!0}return!1}var e9=class{logKernelProfile(e,t,n,r,a,s){let i=typeof r=="number"?_u(`${r}ms`,9):r.error,o=_u(e,25),l=t.rank,c=t.size,u=_u(t.shape.toString(),14),h="";for(let p in a){let d=a[p];if(d!=null){let f=d.shape||t.shape,m=f.length;h+=`${p}: ${m}D ${m>0?f:""} `}}console.log(`%c${o} %c${i} %c${l}D ${u} %c${c} %c${h} %c${s}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function n9(e,t,n){let r={},a={};for(let l=0;l<t.length;l++)r[t[l].id]=!0;for(let l=0;l<e.length;l++){let c=e[l],u=c.inputs;for(let h in u){let p=u[h],d=!1;for(let f=0;f<t.length;f++)if(r[p.id]){c.outputs.forEach(m=>r[m.id]=!0),d=!0,a[c.id]=!0;break}if(d)break}}let s={};s[n.id]=!0;let i={};for(let l=e.length-1;l>=0;l--){let c=e[l],u=c.inputs;for(let h=0;h<c.outputs.length;h++)if(s[c.outputs[h].id]){for(let p in u)s[u[p].id]=!0,i[c.id]=!0;break}}let o=[];for(let l=0;l<e.length;l++){let c=e[l];if(a[c.id]&&i[c.id]){let u={};for(let p in c.inputs){let d=c.inputs[p];r[d.id]&&(u[p]=d)}let h=Object.assign({},c);h.inputs=u,h.outputs=c.outputs,o.push(h)}}return o}function r9(e,t,n,r){for(let a=t.length-1;a>=0;a--){let s=t[a],i=[];if(s.outputs.forEach(l=>{let c=e[l.id];c!=null?i.push(c):i.push(null)}),s.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${s.kernelName}.`);let o=s.gradient(i);for(let l in s.inputs){if(!(l in o))throw new Error(`Cannot backprop through input ${l}. Available gradients found: ${Object.keys(o)}.`);let c=n(()=>o[l]());if(c.dtype!=="float32")throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${l} must have 'float32' dtype, but has '${c.dtype}'`);let u=s.inputs[l];if(!Qr(c.shape,u.shape))throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${l}' has shape '${c.shape}', which does not match the shape of the input '${u.shape}'`);if(e[u.id]==null)e[u.id]=c;else{let h=e[u.id];e[u.id]=r(h,c),h.dispose()}}}}var _g=20,Bu=3,Z1=7;function s9(e,t,n,r){let a=Xi(t),s=a9(e,t,n,a),i=t.length,o=ud(e,t,n,a,s),l=["Tensor"];return r&&(l.push(` dtype: ${n}`),l.push(` rank: ${i}`),l.push(` shape: [${t}]`),l.push(" values:")),l.push(o.map(c=>" "+c).join(`
`)),l.join(`
`)}function a9(e,t,n,r){let a=Rt(t),s=r[r.length-1],i=new Array(s).fill(0),o=t.length,l=n==="complex64"?Uu(e):e;if(o>1)for(let c=0;c<a/s;c++){let u=c*s;for(let h=0;h<s;h++)i[h]=Math.max(i[h],Vu(l[u+h],0,n).length)}return i}function Vu(e,t,n){let r;return Array.isArray(e)?r=`${parseFloat(e[0].toFixed(Z1))} + ${parseFloat(e[1].toFixed(Z1))}j`:ga(e)?r=`'${e}'`:n==="bool"?r=bg(e):r=parseFloat(e.toFixed(Z1)).toString(),_u(r,t)}function bg(e){return e===0?"false":"true"}function ud(e,t,n,r,a,s=!0){let i=n==="complex64"?2:1,o=t[0],l=t.length;if(l===0){if(n==="complex64"){let m=Uu(e);return[Vu(m[0],0,n)]}return n==="bool"?[bg(e[0])]:[e[0].toString()]}if(l===1){if(o>_g){let A=Bu*i,y=Array.from(e.slice(0,A)),g=Array.from(e.slice((o-Bu)*i,o*i));return n==="complex64"&&(y=Uu(y),g=Uu(g)),["["+y.map((_,x)=>Vu(_,a[x],n)).join(", ")+", ..., "+g.map((_,x)=>Vu(_,a[o-Bu+x],n)).join(", ")+"]"]}let m=n==="complex64"?Uu(e):Array.from(e);return["["+m.map((A,y)=>Vu(A,a[y],n)).join(", ")+"]"]}let c=t.slice(1),u=r.slice(1),h=r[0]*i,p=[];if(o>_g){for(let m=0;m<Bu;m++){let A=m*h,y=A+h;p.push(...ud(e.slice(A,y),c,n,u,a,!1))}p.push("...");for(let m=o-Bu;m<o;m++){let A=m*h,y=A+h;p.push(...ud(e.slice(A,y),c,n,u,a,m===o-1))}}else for(let m=0;m<o;m++){let A=m*h,y=A+h;p.push(...ud(e.slice(A,y),c,n,u,a,m===o-1))}let d=l===2?",":"";p[0]="["+p[0]+d;for(let m=1;m<p.length-1;m++)p[m]=" "+p[m]+d;let f=`,
`;for(let m=2;m<l;m++)f+=`
`;return p[p.length-1]=" "+p[p.length-1]+"]"+(s?"":f),p}function Uu(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var Ft=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Rt(e),n!=null){let r=n.length;F(r===this.size,()=>`Length of values '${r}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||og(t,this.size),this.strides=Xi(e)}set(e,...t){t.length===0&&(t=[0]),F(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let r of e){if(r<0||r>=this.shape[t]){let a=`Requested out of range element at ${e}. Buffer shape=${this.shape}`;throw new Error(a)}t++}let n=e[e.length-1];for(let r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Cr().makeTensor(this.values,this.shape,this.dtype)}},Cr=null,el=null,i9=null;function o9(e){Cr=e}function l9(e){el=e}function u9(e){i9=e}var Ke=class{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Rt(e),this.strides=Xi(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return el.buffer(this.shape,this.dtype,e)}bufferSync(){return el.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return Ki(this.shape,e)}arraySync(){return Ki(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let e=Cr().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>ld(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();let e=Cr().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>ld(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await Cr().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Cr().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return el.print(this,e)}clone(){return this.throwIfDisposed(),el.clone(this)}toString(e=!1){let t=this.dataSync();return s9(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),el.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Cr().makeVariable(this,e,t,n)}};Object.defineProperty(Ke,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function Y(){return G1("Tensor",()=>Ke)}Y();var ju=class extends Ke{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!Qr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Cr().disposeTensor(this),this.dataId=e.dataId,Cr().incRef(this,null)}dispose(){Cr().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(ju,Symbol.hasInstance,{value:e=>e instanceof Ke&&e.assign!=null&&e.assign instanceof Function});var pr={};$e(pr,{assertTypesMatch:()=>vg,getTensorsInContainer:()=>Y1,isTensorInList:()=>c9,makeTypesMatch:()=>wt});var J1;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(J1||(J1={}));var Q1;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(Q1||(Q1={}));var ef;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(ef||(ef={}));var tf;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(tf||(tf={}));var nf;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(nf||(nf={}));var h9={float32:tf,int32:Q1,bool:ef,complex64:nf};function er(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return h9[e][t]}function cd(e){return er(e,"int32")}function wt(e,t){if(e.dtype===t.dtype)return[e,t];let n=er(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function vg(e,t){F(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function c9(e,t){return t.some(n=>n.id===e.id)}function Y1(e){let t=[],n=new Set;return kg(e,t,n),t}function kg(e,t,n){if(e==null)return;if(e instanceof Ke){t.push(e);return}if(!d9(e))return;let r=e;for(let a in r){let s=r[a];n.has(s)||(n.add(s),kg(s,t,n))}}function d9(e){return Array.isArray(e)||typeof e=="object"}function rf(e){return e.kernelName!=null}var Ig=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Hu=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Ig}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new t9(this.backendInstance),!0}setupRegisteredKernels(){Qo(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Qo(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof xu)&&typeof n.then=="function"){let r=++this.pendingBackendInitId,a=n.then(s=>r<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t);r.disposeData(t),n.backend=e,e.move(t,a,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let r;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(r),()=>(r=t(),r instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),r))}scopedRun(e,t,n){e();try{let r=n();return t(),r}catch(r){throw t(),r}}nextTensorId(){return Hu.nextTensorId++}nextVariableId(){return Hu.nextVariableId++}clone(e){let t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),n={x:e},r=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return D.runKernel(cs,o,l)}}),a=[];return this.addTapeNode(this.state.activeScope.name,n,[t],r,a,{}),t}runKernel(e,t,n){if(id(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=rf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(rf(e)){let{kernelName:d,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let A=id(d,this.backendName);F(A!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:f,attrs:m,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,y,g);let _=g.map(x=>{if(x.rank!=null)return x;let{dataId:w,shape:b,dtype:N}=x;return this.makeTensorFromDataId(w,b,N)});if(r){let x=this.getTensorsForGradient(d,f,_);n=this.saveTensorsForBackwardMode(x)}return _}}else{let{forwardFunc:d}=e,f=m=>{!r||(n=m.map(A=>this.keep(this.clone(A))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>d(this.backend,f));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,A),A}}let{inputs:c,attrs:u}=e,h=rf(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(p=this.profiler.profileKernel(l,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),r&&this.addTapeNode(l,c,t,h,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:t.map(d=>d.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=X1(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=n.filter((l,c)=>s[c]);return i.concat(o)}return[]}makeTensor(e,t,n,r){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let a=e;n==="string"&&ga(e[0])&&(a=e.map(o=>Wu(o)));let s=r.write(a,t,n),i=new Ke(t,n,s,this.nextTensorId());if(this.incRef(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=hg(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new Ke(t,n,e,this.nextTensorId());return this.incRef(a,r),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));let a=new ju(e,t,n,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}incRef(e,t){let n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let r=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(r=e.size*cg(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:r,refCount:0}),this.state.numBytes+=r}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof ju||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;let t=this.state.tensorInfo.get(e.dataId);t.refCount<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):(t.backend.decComplexRef(e.dataId),this.state.tensorInfo.get(e.dataId).refCount--)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let r of this.state.activeProfile.kernels)r.kernelTimeMs=await r.kernelTimeMs,r.extraInfo=await r.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,r,a,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:a},o=X1(e);o!=null&&(r=o.gradFunc),r!=null&&(i.gradient=l=>(l=l.map((c,u)=>{if(c==null){let h=n[u],p=Th(h.size,h.dtype);return this.makeTensor(p,h.shape,h.dtype)}return c}),r(l.length>1?l:l[0],a,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Y1(e),n=new Set(t.map(a=>a.id));for(let a=0;a<this.state.activeScope.track.length;a++){let s=this.state.activeScope.track[a];!s.kept&&!n.has(s.id)&&s.dispose()}let r=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(a=>{!a.kept&&a.scopeId===r.id&&this.track(a)})}gradients(e,t,n,r=!1){if(F(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));F(a instanceof Ke,()=>"The result y returned by f() must be a tensor.");let s=n9(this.state.activeTape,t,a);if(!r&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let i={};i[a.id]=n==null?p9(a.shape):n,r9(i,s,l=>this.tidy(l),f9);let o=t.map(l=>i[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let c of l.saved)c.dispose()}),this.state.activeTape=null),{value:a,grads:o}})}customGrad(e){return F(xa(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{F(t.every(i=>i instanceof Ke),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,r={};t.forEach((i,o)=>{r[o]=i});let a=(i,o)=>(n=e(...t,o),F(n.value instanceof Ke,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),F(xa(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),s=(i,o)=>{let l=n.gradFunc(i,o),c=Array.isArray(l)?l:[l];F(c.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),F(c.every(h=>h instanceof Ke),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let u={};return c.forEach((h,p)=>{u[p]=()=>h}),u};return this.runKernelFunc({forwardFunc:a,backwardsFunc:s,inputs:r})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}async time(e){let t=K1(),n=await this.backend.time(e);return n.wallMs=K1()-t,n}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new Ig;for(let e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Hu.nextTensorId=0;Hu.nextVariableId=0;function p9(e){let t=V1(Rt(e),"float32");return D.makeTensor(t,e,"float32")}function Ng(){let e=yg();if(e._tfengine==null){let t=new Ag(e);e._tfengine=new Hu(t)}return jk(e._tfengine.ENV),o9(()=>e._tfengine),e._tfengine}var D=Ng();function f9(e,t){let n={a:e,b:t};return D.runKernel(wa,n)}var hd={};$e(hd,{isBrowser:()=>Sg,isMobile:()=>m9});function A9(){return typeof navigator!="undefined"&&navigator!=null}function m9(){if(A9()){let e=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(e)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(e.substr(0,4))}return!1}function Sg(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var Rr=Q();Rr.registerFlag("DEBUG",()=>!1,e=>{e&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});Rr.registerFlag("IS_BROWSER",()=>Sg());Rr.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");Rr.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));Rr.registerFlag("PROD",()=>!1);Rr.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>Rr.getBool("DEBUG"));Rr.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);Rr.registerFlag("IS_TEST",()=>!1);Rr.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);function Fr(e,t){let n=e;if(nn(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let r=[];for(;Array.isArray(n)||nn(n)&&t!=="string";)r.push(n.length),n=n[0];return Array.isArray(e)&&Q().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&Tg(e,r,[]),r}function Tg(e,t,n){if(n=n||[],!Array.isArray(e)&&!nn(e)){F(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}F(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),F(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);let r=t.slice(1);for(let a=0;a<e.length;++a)Tg(e[a],r,n.concat(a))}function Eg(e,t,n,r){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${r}' must be ${e} tensor, but got ${t} tensor`)}}function R(e,t,n,r="numeric"){if(e instanceof Ke)return Eg(r,e.dtype,t,n),e;let a=Nh(e);if(a!=="string"&&["bool","int32","float32"].indexOf(r)>=0&&(a=r),Eg(r,a,t,n),e==null||!nn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let o=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${o}'`)}let s=Fr(e,a);!nn(e)&&!Array.isArray(e)&&(e=[e]);let i=a!=="string"?od(e,a):ss(e,[],!0);return D.makeTensor(i,s,a)}function Gu(e,t,n,r="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((a,s)=>R(a,`${t}[${s}]`,n,r))}var Cg="__op";function z(e){let t=Object.keys(e);if(t.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${t.length} keys.`);let n=t[0],r=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+Cg;let a=(...s)=>{D.startScope(n);try{let i=r(...s);return j1(i)&&console.error("Cannot return a Promise inside of tidy."),D.endScope(i),i}catch(i){throw D.endScope(null),i}};return Object.defineProperty(a,"name",{value:n,configurable:!0}),a}function y9(e,t){let n=R(e,"real","complex"),r=R(t,"imag","complex");tn(n.shape,r.shape,`real and imag shapes, ${n.shape} and ${r.shape}, must match in call to tf.complex().`);let a={real:n,imag:r};return D.runKernel(Oh,a)}var ka=z({complex_:y9});function Ia(e,t,n,r){if(r==null&&(r=Nh(e)),r==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!nn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){U1(t);let a=Rt(t),s=Rt(n);F(a===s,()=>`Based on the provided shape, [${t}], the tensor should have ${a} values but has ${s}`);for(let i=0;i<n.length;++i){let o=n[i],l=i===n.length-1?o!==Rt(t.slice(i)):!0;F(n[i]===t[i]||!l,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!nn(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=r!=="string"?od(e,r):ss(e,[],!0),D.makeTensor(e,t,r)}function fr(e,t,n){let r=Fr(e,n);return Ia(e,t,r,n)}var af={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},dd=4;async function x9(e,t){let n=[],r=[],a=Array.isArray(e)?e.map(i=>i.name):Object.keys(e);for(let i=0;i<a.length;++i){let o=a[i],l=Array.isArray(e)?e[i].tensor:e[o];if(l.dtype!=="float32"&&l.dtype!=="int32"&&l.dtype!=="bool"&&l.dtype!=="string"&&l.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${o}': ${l.dtype}`);let c={name:o,shape:l.shape,dtype:l.dtype};if(l.dtype==="string"){let u=new Promise(async h=>{let p=await l.bytes(),d=p.reduce((A,y)=>A+y.length,0)+dd*p.length,f=new Uint8Array(d),m=0;for(let A=0;A<p.length;A++){let y=p[A],g=new Uint8Array(new Uint32Array([y.length]).buffer);f.set(g,m),m+=dd,f.set(y,m),m+=y.length}h(f)});r.push(u)}else r.push(l.data());t!=null&&(c.group=t),n.push(c)}let s=await Promise.all(r);return{data:g9(s),specs:n}}function Rg(e,t){let n={},r,a=0;for(let s of t){let i=s.name,o=s.dtype,l=s.shape,c=Rt(l),u;if("quantization"in s){let h=s.quantization;if(h.dtype==="uint8"||h.dtype==="uint16"){if(!("min"in h&&"scale"in h))throw new Error(`Weight ${s.name} with quantization ${h.dtype} doesn't have corresponding metadata min and scale.`)}else if(h.dtype==="float16"){if(o!=="float32")throw new Error(`Weight ${s.name} is quantized with ${h.dtype} which only supports weights of type float32 not ${o}.`)}else throw new Error(`Weight ${s.name} has unknown quantization dtype ${h.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let p=af[h.dtype],d=e.slice(a,a+c*p),f=h.dtype==="uint8"?new Uint8Array(d):new Uint16Array(d);if(o==="float32")if(h.dtype==="uint8"||h.dtype==="uint16"){u=new Float32Array(f.length);for(let m=0;m<f.length;m++){let A=f[m];u[m]=A*h.scale+h.min}}else if(h.dtype==="float16")r===void 0&&(r=w9()),u=r(f);else throw new Error(`Unsupported quantization type ${h.dtype} for weight type float32.`);else if(o==="int32"){if(h.dtype!=="uint8"&&h.dtype!=="uint16")throw new Error(`Unsupported quantization type ${h.dtype} for weight type int32.`);u=new Int32Array(f.length);for(let m=0;m<f.length;m++){let A=f[m];u[m]=Math.round(A*h.scale+h.min)}}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);a+=c*p}else if(o==="string"){let h=Rt(s.shape);u=[];for(let p=0;p<h;p++){let d=new Uint32Array(e.slice(a,a+dd))[0];a+=dd;let f=new Uint8Array(e.slice(a,a+d));u.push(f),a+=d}}else{let h=af[o],p=e.slice(a,a+c*h);if(o==="float32")u=new Float32Array(p);else if(o==="int32")u=new Int32Array(p);else if(o==="bool")u=new Uint8Array(p);else if(o==="complex64"){u=new Float32Array(p);let d=new Float32Array(u.length/2),f=new Float32Array(u.length/2);for(let y=0;y<d.length;y++)d[y]=u[y*2],f[y]=u[y*2+1];let m=fr(d,l,"float32"),A=fr(f,l,"float32");n[i]=ka(m,A),m.dispose(),A.dispose()}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);a+=c*h}o!=="complex64"&&(n[i]=fr(u,l,o))}return n}function g9(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0,n=[];e.forEach(s=>{if(t+=s.byteLength,n.push(s.byteLength===s.buffer.byteLength?s:new s.constructor(s)),!(s instanceof Float32Array||s instanceof Int32Array||s instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${s.constructor.name}`)});let r=new Uint8Array(t),a=0;return n.forEach(s=>{r.set(new Uint8Array(s.buffer),a),a+=s.byteLength}),r.buffer}var sf=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function Fg(e){return sf?Buffer.byteLength(e):new Blob([e]).size}function _9(e){if(sf)return Buffer.from(e).toString("base64");let t=new Uint8Array(e),n="";for(let r=0,a=t.length;r<a;r++)n+=String.fromCharCode(t[r]);return btoa(n)}function b9(e){if(sf){let r=Buffer.from(e,"base64");return r.buffer.slice(r.byteOffset,r.byteOffset+r.byteLength)}let t=atob(e),n=new Uint8Array(t.length);for(let r=0;r<t.length;++r)n.set([t.charCodeAt(r)],r);return n.buffer}function of(e){if(e.length===1)return e[0];let t=0;e.forEach(a=>{t+=a.byteLength});let n=new Uint8Array(t),r=0;return e.forEach(a=>{n.set(new Uint8Array(a),r),r+=a.byteLength}),n.buffer}function Mg(e){let t="/";for(e=e.trim();e.endsWith(t);)e=e.slice(0,e.length-1);let n=e.split(t);return n[n.length-1]}function qu(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:e.modelTopology==null?0:Fg(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:Fg(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function v9(){let e=n=>{let r=n<<13,a=0;for(;(r&8388608)==0;)a-=8388608,r<<=1;return r&=~8388608,a+=947912704,r|a},t=new Uint32Array(2048);t[0]=0;for(let n=1;n<1024;n++)t[n]=e(n);for(let n=1024;n<2048;n++)t[n]=939524096+(n-1024<<13);return t}function k9(){let e=new Uint32Array(64);e[0]=0,e[31]=1199570944,e[32]=2147483648,e[63]=3347054592;for(let t=1;t<31;t++)e[t]=t<<23;for(let t=33;t<63;t++)e[t]=2147483648+(t-32<<23);return e}function I9(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function w9(){let e=v9(),t=k9(),n=I9();return r=>{let a=new ArrayBuffer(4*r.length),s=new Uint32Array(a);for(let i=0;i<r.length;i++){let o=r[i],l=e[n[o>>10]+(o&1023)]+t[o>>10];s[i]=l}return new Float32Array(a)}}var It=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return It.instance==null&&(It.instance=new It),It.instance}static registerSaveRouter(e){It.getInstance().saveRouters.push(e)}static registerLoadRouter(e){It.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return It.getHandlers(e,"save")}static getLoadHandlers(e,t){return It.getHandlers(e,"load",t)}static getHandlers(e,t,n){let r=[];return(t==="load"?It.getInstance().loadRouters:It.getInstance().saveRouters).forEach(a=>{let s=a(e,n);s!==null&&r.push(s)}),r}},N9=e=>It.registerSaveRouter(e),S9=e=>It.registerLoadRouter(e),T9=e=>It.getSaveHandlers(e),E9=(e,t)=>It.getLoadHandlers(e,t),lf="tensorflowjs",uf=1,ei="models_store",Na="model_info_store";function Og(){if(!Q().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let e=typeof window=="undefined"?self:window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(t==null)throw new Error("The current browser does not appear to support IndexedDB.");return t}function cf(e){let t=e.result;t.createObjectStore(ei,{keyPath:"modelPath"}),t.createObjectStore(Na,{keyPath:"modelPath"})}var ti=class{constructor(e){if(this.indexedDB=Og(),e==null||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,e)}async load(){return this.databaseAction(this.modelPath)}databaseAction(e,t){return new Promise((n,r)=>{let a=this.indexedDB.open(lf,uf);a.onupgradeneeded=()=>cf(a),a.onsuccess=()=>{let s=a.result;if(t==null){let i=s.transaction(ei,"readonly"),o=i.objectStore(ei).get(this.modelPath);o.onsuccess=()=>{if(o.result==null)return s.close(),r(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(o.result.modelArtifacts)},o.onerror=l=>(s.close(),r(o.error)),i.oncomplete=()=>s.close()}else{let i=qu(t),o=s.transaction(Na,"readwrite"),l=o.objectStore(Na),c=l.put({modelPath:this.modelPath,modelArtifactsInfo:i}),u;c.onsuccess=()=>{u=s.transaction(ei,"readwrite");let h=u.objectStore(ei).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:i});h.onsuccess=()=>n({modelArtifactsInfo:i}),h.onerror=p=>{l=o.objectStore(Na);let d=l.delete(this.modelPath);d.onsuccess=()=>(s.close(),r(h.error)),d.onerror=f=>(s.close(),r(h.error))}},c.onerror=h=>(s.close(),r(c.error)),o.oncomplete=()=>{u==null?s.close():u.oncomplete=()=>s.close()}}},a.onerror=s=>r(a.error)})}};ti.URL_SCHEME="indexeddb://";var $g=e=>Q().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(ti.URL_SCHEME)?C9(e.slice(ti.URL_SCHEME.length)):null;It.registerSaveRouter($g);It.registerLoadRouter($g);function C9(e){return new ti(e)}function R9(e){return e.startsWith(ti.URL_SCHEME)?e.slice(ti.URL_SCHEME.length):e}var F9=class{constructor(){this.indexedDB=Og()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open(lf,uf);n.onupgradeneeded=()=>cf(n),n.onsuccess=()=>{let r=n.result,a=r.transaction(Na,"readonly"),s=a.objectStore(Na).getAll();s.onsuccess=()=>{let i={};for(let o of s.result)i[o.modelPath]=o.modelArtifactsInfo;e(i)},s.onerror=i=>(r.close(),t(s.error)),a.oncomplete=()=>r.close()},n.onerror=r=>t(n.error)})}async removeModel(e){return e=R9(e),new Promise((t,n)=>{let r=this.indexedDB.open(lf,uf);r.onupgradeneeded=()=>cf(r),r.onsuccess=()=>{let a=r.result,s=a.transaction(Na,"readwrite"),i=s.objectStore(Na),o=i.get(e),l;o.onsuccess=()=>{if(o.result==null)return a.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{let c=i.delete(e),u=()=>{l=a.transaction(ei,"readwrite");let h=l.objectStore(ei).delete(e);h.onsuccess=()=>t(o.result.modelArtifactsInfo),h.onerror=p=>n(o.error)};c.onsuccess=u,c.onerror=h=>(u(),a.close(),n(o.error))}},o.onerror=c=>(a.close(),n(o.error)),s.oncomplete=()=>{l==null?a.close():l.oncomplete=()=>a.close()}},r.onerror=a=>n(r.error)})}},ea="/",tl="tensorflowjs_models",Dg="info",M9="model_topology",O9="weight_specs",$9="weight_data",D9="model_metadata";function zg(e){return{info:[tl,e,Dg].join(ea),topology:[tl,e,M9].join(ea),weightSpecs:[tl,e,O9].join(ea),weightData:[tl,e,$9].join(ea),modelMetadata:[tl,e,D9].join(ea)}}function z9(e){let t=e.split(ea);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(ea)}function P9(e){return e.startsWith(ni.URL_SCHEME)?e.slice(ni.URL_SCHEME.length):e}var ni=class{constructor(e){if(!Q().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=zg(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),r=qu(e);try{this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,_9(e.weightData));let a={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};return e.signature!=null&&(a.signature=e.signature),e.userDefinedMetadata!=null&&(a.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(a.modelInitializer=e.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(a)),{modelArtifactsInfo:r}}catch(a){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${r.modelTopologyBytes}, weightSpecsBytes=${r.weightSpecsBytes}, weightDataBytes=${r.weightDataBytes}.`)}}}async load(){let e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(e.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let t={},n=JSON.parse(this.LS.getItem(this.keys.topology));if(n==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);t.modelTopology=n;let r=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(r==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=r;let a=this.LS.getItem(this.keys.modelMetadata);if(a!=null){let i=JSON.parse(a);t.format=i.format,t.generatedBy=i.generatedBy,t.convertedBy=i.convertedBy,i.signature!=null&&(t.signature=i.signature),i.userDefinedMetadata!=null&&(t.userDefinedMetadata=i.userDefinedMetadata),i.modelInitializer!=null&&(t.modelInitializer=i.modelInitializer)}let s=this.LS.getItem(this.keys.weightData);if(s==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=b9(s),t}};ni.URL_SCHEME="localstorage://";var Pg=e=>Q().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(ni.URL_SCHEME)?L9(e.slice(ni.URL_SCHEME.length)):null;It.registerSaveRouter(Pg);It.registerLoadRouter(Pg);function L9(e){return new ni(e)}var W9=class{constructor(){F(Q().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),F(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=tl+ea,n=ea+Dg;for(let r=0;r<this.LS.length;++r){let a=this.LS.key(r);if(a.startsWith(t)&&a.endsWith(n)){let s=z9(a);e[s]=JSON.parse(this.LS.getItem(a))}}return e}async removeModel(e){e=P9(e);let t=zg(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);let n=JSON.parse(this.LS.getItem(t.info));return this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),n}},nl="://",Bn=class{constructor(){this.managers={}}static getInstance(){return Bn.instance==null&&(Bn.instance=new Bn),Bn.instance}static registerManager(e,t){F(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(nl)&&(e=e.slice(0,e.indexOf(nl))),F(e.length>0,()=>"scheme must not be an empty string.");let n=Bn.getInstance();F(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),n.managers[e]=t}static getManager(e){let t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}};function pd(e){if(e.indexOf(nl)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Bn.getSchemes().join(",")}`);return{scheme:e.split(nl)[0],path:e.split(nl)[1]}}async function Lg(e,t,n=!1){F(e!==t,()=>`Old path and new path are the same: '${e}'`);let r=It.getLoadHandlers(e);F(r.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),F(r.length<2,()=>`Copying failed because more than one (${r.length}) load handlers for source URL ${e}.`);let a=r[0],s=It.getSaveHandlers(t);F(s.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),F(s.length<2,()=>`Copying failed because more than one (${r.length}) save handlers for destination URL ${t}.`);let i=s[0],o=pd(e).scheme,l=pd(e).path,c=o===pd(e).scheme,u=await a.load();n&&c&&await Bn.getManager(o).removeModel(l);let h=await i.save(u);return n&&!c&&await Bn.getManager(o).removeModel(l),h.modelArtifactsInfo}async function B9(){let e=Bn.getSchemes(),t={};for(let n of e){let r=await Bn.getManager(n).listModels();for(let a in r){let s=n+nl+a;t[s]=r[a]}}return t}async function V9(e){let t=pd(e);return Bn.getManager(t.scheme).removeModel(t.path)}async function U9(e,t){return Lg(e,t,!1)}async function j9(e,t){return Lg(e,t,!0)}var H9=class{fetch(e,t){return fetch(e,t)}now(){return performance.now()}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${t}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(e)}decode(e,t){return new TextDecoder(t).decode(e)}};if(Q().get("IS_BROWSER")){Q().setPlatform("browser",new H9);try{Bn.registerManager(ni.URL_SCHEME,new W9)}catch(e){}try{Bn.registerManager(ti.URL_SCHEME,new F9)}catch(e){}}var G9={importFetch:()=>j8()},hf,q9=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return Q().global.fetch!=null?Q().global.fetch(e,t):(hf==null&&(hf=G9.importFetch()),hf(e,t))}now(){let e=process.hrtime();return e[0]*1e3+e[1]/1e6}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${t}`);return this.textEncoder.encode(e)}decode(e,t){return e.length===0?"":new this.util.TextDecoder(t).decode(e)}};Q().get("IS_NODE")&&Q().setPlatform("node",new q9);function Le(e,t="float32",n){return t=t||"float32",U1(e),new Ft(e,t,n)}function X9(e,t){let n=R(e,"x","cast");if(!ug(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");let r={x:n},a={dtype:t};return D.runKernel(cs,r,a)}var me=z({cast_:X9});function K9(e){let t={x:R(e,"x","clone","string_or_numeric")};return D.runKernel(xo,t)}var tr=z({clone_:K9});function Wg(e,t=!1){console.log(e.toString(t))}Ng();var Z9={buffer:Le,cast:me,clone:tr,print:Wg};l9(Z9);var fn={};$e(fn,{browserFiles:()=>Y9,browserHTTPRequest:()=>Q9,concatenateArrayBuffers:()=>of,copyModel:()=>U9,decodeWeights:()=>Rg,encodeWeights:()=>x9,fromMemory:()=>eI,getLoadHandlers:()=>E9,getModelArtifactsInfoForJSON:()=>qu,getSaveHandlers:()=>T9,http:()=>pf,isHTTPScheme:()=>df,listModels:()=>B9,loadWeights:()=>J9,moveModel:()=>j9,registerLoadRouter:()=>S9,registerSaveRouter:()=>N9,removeModel:()=>V9,weightsLoaderFactory:()=>Bg,withSaveHandler:()=>tI});var nI="model",rI=".json",aI=".weights.bin";function Vg(e){return new Promise(t=>setTimeout(t)).then(e)}var rl=class{constructor(e){if(!Q().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(rl.URL_SCHEME)&&(e=e.slice(rl.URL_SCHEME.length)),(e==null||e.length===0)&&(e=nI),this.modelTopologyFileName=e+rI,this.weightDataFileName=e+aI}async save(e){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer);let a=window.URL.createObjectURL(new Blob([JSON.stringify(r)],{type:"application/json"})),s=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(s.download=this.modelTopologyFileName,s.href=a,await Vg(()=>s.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){let i=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;i.download=this.weightDataFileName,i.href=t,await Vg(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:qu(e)}}}};rl.URL_SCHEME="downloads://";var sI=class{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.files=e}async load(){let e=this.files[0],t=this.files.slice(1);return new Promise((n,r)=>{let a=new FileReader;a.onload=s=>{let i=JSON.parse(s.target.result),o=i.modelTopology;if(o==null){r(new Error(`modelTopology field is missing from file ${e.name}`));return}t.length===0&&n({modelTopology:o});let l=i.weightsManifest;if(l==null){r(new Error(`weightManifest field is missing from file ${e.name}`));return}let c;try{c=this.checkManifestAndWeightFiles(l,t)}catch(d){r(d);return}let u=[],h=[],p=[];l.forEach(d=>{d.paths.forEach(f=>{h.push(f),p.push(null)}),u.push(...d.weights)}),l.forEach(d=>{d.paths.forEach(f=>{let m=new FileReader;m.onload=A=>{let y=A.target.result,g=h.indexOf(f);if(p[g]=y,p.indexOf(null)===-1){let _={modelTopology:o,weightSpecs:u,weightData:of(p),format:i.format,generatedBy:i.generatedBy,convertedBy:i.convertedBy};i.signature!=null&&(_.signature=i.signature),i.userDefinedMetadata!=null&&(_.userDefinedMetadata=i.userDefinedMetadata),i.modelInitializer!=null&&(_.modelInitializer=i.modelInitializer),n(_)}},m.onerror=A=>r(`Failed to weights data from file of path '${f}'.`),m.readAsArrayBuffer(c[f])})})},a.onerror=s=>r(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),a.readAsText(e)})}checkManifestAndWeightFiles(e,t){let n=[],r=t.map(s=>Mg(s.name)),a={};for(let s of e)s.paths.forEach(i=>{let o=Mg(i);if(n.indexOf(o)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${o}'`);if(n.push(o),r.indexOf(o)===-1)throw new Error(`Weight file with basename '${o}' is not provided.`);a[i]=t[r.indexOf(o)]});if(n.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${n.length}) and the number of weight files provided (${t.length}).`);return a}},oI=e=>Q().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(rl.URL_SCHEME)?iI(e.slice(rl.URL_SCHEME.length)):null;It.registerSaveRouter(oI);function iI(e="model"){return new rl(e)}function Y9(e){return new sI(e)}function Ug(e,t,n,r){i(e),n=n==null?0:n,r=r==null?1:r,o(n,r);let a=0,s=l=>(l.then(c=>{let u=n+ ++a/e.length*(r-n);return t(u),c}),l);function i(l){F(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function o(l,c){F(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),F(c>=0&&c<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${c}`),F(c>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${c}`)}return Promise.all(e.map(s))}async function jg(e,t){t==null&&(t={});let n=t.fetchFunc==null?Q().platform.fetch:t.fetchFunc,r=e.map(c=>n(c,t.requestInit,{isBinary:!0})),a=0,s=.5,i=(t.onProgress==null?await Promise.all(r):await Ug(r,t.onProgress,a,s)).map(c=>c.arrayBuffer()),o=.5,l=1;return t.onProgress==null?await Promise.all(i):await Ug(i,t.onProgress,o,l)}async function J9(e,t="",n,r){return Bg(a=>jg(a,{requestInit:r}))(e,t,n)}function Bg(e){return async(t,n="",r)=>{let a=t.map(()=>!1),s={},i=r!=null?r.map(()=>!1):[],o=[];if(t.forEach((d,f)=>{let m=0;d.weights.forEach(A=>{let y="quantization"in A?A.quantization.dtype:A.dtype,g=af[y]*Rt(A.shape),_=()=>{a[f]=!0,s[f]==null&&(s[f]=[]),s[f].push({manifestEntry:A,groupOffset:m,sizeBytes:g})};r!=null?r.forEach((x,w)=>{x===A.name&&(_(),i[w]=!0)}):_(),o.push(A.name),m+=g})}),!i.every(d=>d)){let d=r.filter((f,m)=>!i[m]);throw new Error(`Could not find weights in manifest with names: ${d.join(", ")}.
Manifest JSON has weights with names: ${o.join(", ")}.`)}let l=a.reduce((d,f,m)=>(f&&d.push(m),d),[]),c=[];l.forEach(d=>{t[d].paths.forEach(f=>{let m=n+(n.endsWith("/")?"":"/")+f;c.push(m)})});let u=await e(c),h={},p=0;return l.forEach(d=>{let f=t[d].paths.length,m=0;for(let _=0;_<f;_++)m+=u[p+_].byteLength;let A=new ArrayBuffer(m),y=new Uint8Array(A),g=0;for(let _=0;_<f;_++){let x=new Uint8Array(u[p+_]);y.set(x,g),g+=x.byteLength}s[d].forEach(_=>{let x=A.slice(_.groupOffset,_.groupOffset+_.sizeBytes),w=Rg(x,[_.manifestEntry]);for(let b in w)h[b]=w[b]}),p+=f}),h}}var lI="application/octet-stream",uI="application/json",ff=class{constructor(e,t){if(this.DEFAULT_METHOD="POST",t==null&&(t={}),this.weightPathPrefix=t.weightPathPrefix,this.onProgress=t.onProgress,this.weightUrlConverter=t.weightUrlConverter,t.fetchFunc!=null?(F(typeof t.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=t.fetchFunc):this.fetch=Q().platform.fetch,F(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&F(e.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${e.length}).`),this.path=e,t.requestInit!=null&&t.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t.requestInit||{}}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);t.body=new FormData;let n=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer),t.body.append("model.json",new Blob([JSON.stringify(r)],{type:uI}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:lI}),"model.weights.bin");let a=await this.fetch(this.path,t);if(a.ok)return{modelArtifactsInfo:qu(e),responses:[a]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${a.status}.`)}async load(){let e=await this.fetch(this.path,this.requestInit);if(!e.ok)throw new Error(`Request to ${this.path} failed with status code ${e.status}. Please verify this URL points to the model JSON of the model to load.`);let t;try{t=await e.json()}catch(d){let f=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?f+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":f+=" Please make sure the server is serving valid JSON for this request.",new Error(f)}let n=t.modelTopology,r=t.weightsManifest,a=t.generatedBy,s=t.convertedBy,i=t.format,o=t.signature,l=t.userDefinedMetadata;if(n==null&&r==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let c,u;r!=null&&([c,u]=await this.loadWeights(r));let h={modelTopology:n,weightSpecs:c,weightData:u,generatedBy:a,convertedBy:s,format:i};o!=null&&(h.signature=o),l!=null&&(h.userDefinedMetadata=l);let p=t.modelInitializer;return p&&(h.modelInitializer=p),h}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[n,r]=cI(t),a=this.weightPathPrefix||n,s=[];for(let c of e)s.push(...c.weights);let i=[],o=[];for(let c of e)for(let u of c.paths)this.weightUrlConverter!=null?o.push(this.weightUrlConverter(u)):i.push(a+u+r);this.weightUrlConverter&&i.push(...await Promise.all(o));let l=await jg(i,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[s,of(l)]}};ff.URL_SCHEME_REGEX=/^https?:\/\//;function cI(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),r=e.substring(0,t),a=n>t?e.substring(n):"";return[r+"/",a]}function df(e){return e.match(ff.URL_SCHEME_REGEX)!=null}var Hg=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(r=>df(r)):n=df(e),n)return pf(e,t)}return null};It.registerSaveRouter(Hg);It.registerLoadRouter(Hg);function pf(e,t){return new ff(e,t)}function Q9(e,t){return pf(e,t)}var mf=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},hI=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function eI(e,t,n,r){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new mf(e):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new mf({modelTopology:e})):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new mf({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:r}))}function tI(e){return new hI(e)}var Gg={};$e(Gg,{confusionMatrix:()=>dI});function pI(e,t,n=!1,r=!1){let a=R(e,"a","matMul"),s=R(t,"b","matMul");[a,s]=wt(a,s);let i={a,b:s},o={transposeA:n,transposeB:r};return D.runKernel(us,i,o)}var He=z({matMul_:pI});function fI(e,t,n=1,r=0){if(t<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);let a={indices:R(e,"indices","oneHot","int32")},s={depth:t,onValue:n,offValue:r};return D.runKernel(Rs,a,s)}var al=z({oneHot_:fI});function mI(e,t){let n=R(e,"x","transpose");if(t==null&&(t=n.shape.map((s,i)=>i).reverse()),F(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(s=>{F(s>=0&&s<n.rank,()=>`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let r={x:n},a={perm:t};return D.runKernel(Ks,r,a)}var nt=z({transpose_:mI});function AI(e,t,n){let r=R(e,"labels","confusionMatrix"),a=R(t,"predictions","confusionMatrix");F(n==null||n>0&&Number.isInteger(n),()=>`If provided, numClasses must be a positive integer, but got ${n}`),F(r.rank===1,()=>`Expected the rank of labels to be 1, but got ${r.rank}`),F(a.rank===1,()=>`Expected the rank of predictions to be 1, but got ${a.rank}`),F(r.shape[0]===a.shape[0],()=>`Mismatch in the number of examples: ${r.shape[0]} vs. ${a.shape[0]}. Labels and predictions should have the same number of elements.`),F(n>0&&Number.isInteger(n),()=>`numClasses is required to be a positive integer, but got ${n}`);let s=al(me(r,"int32"),n),i=al(me(a,"int32"),n),o=nt(s),l=He(o,i);return me(l,"int32")}var dI=z({confusionMatrix_:AI}),sl={};$e(sl,{fromPixels:()=>gI,toPixels:()=>yI});function fd(e,t,n){if(as(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let r=Fr(e,n);if(r.length!==3&&r.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return Ia(e,t,r,n)}var il;function xI(e,t=3){if(t>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(e==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let n=!1,r=!1,a=!1,s=!1,i=!1,o=!1;if(e.data instanceof Uint8Array)n=!0;else if(typeof ImageData!="undefined"&&e instanceof ImageData)r=!0;else if(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)a=!0;else if(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)s=!0;else if(e.getContext!=null)i=!0;else if(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)o=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`);if(a){let p=2;if(a&&e.readyState<p)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(id(sd,D.backendName)!=null){let p={pixels:e},d={numChannels:t};return D.runKernel(sd,p,d)}let[l,c]=a?[e.videoWidth,e.videoHeight]:[e.width,e.height],u;i?u=e.getContext("2d").getImageData(0,0,l,c).data:r||n?u=e.data:(s||a||o)&&(il==null&&(il=document.createElement("canvas").getContext("2d")),il.canvas.width=l,il.canvas.height=c,il.drawImage(e,0,0,l,c),u=il.getImageData(0,0,l,c).data);let h;if(t===4)h=new Int32Array(u);else{let p=l*c;h=new Int32Array(p*t);for(let d=0;d<p;d++)for(let f=0;f<t;++f)h[d*t+f]=u[d*4+f]}return fd(h,[c,l,t],"int32")}async function yI(e,t){let n=R(e,"img","toPixels");if(!(e instanceof Ke)){let c=n;n=me(c,"int32"),c.dispose()}if(n.rank!==2&&n.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${n.rank}.`);let[r,a]=n.shape.slice(0,2),s=n.rank===2?1:n.shape[2];if(s>4||s===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${s}`);if(n.dtype!=="float32"&&n.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${n.dtype}. Please use float32 or int32 tensors.`);let i=await n.data(),o=n.dtype==="float32"?255:1,l=new Uint8ClampedArray(a*r*4);for(let c=0;c<r*a;++c){let u=[0,0,0,255];for(let p=0;p<s;p++){let d=i[c*s+p];if(n.dtype==="float32"){if(d<0||d>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${d}.`)}else if(n.dtype==="int32"&&(d<0||d>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${d}.`);s===1?(u[0]=d*o,u[1]=d*o,u[2]=d*o):u[p]=d*o}let h=c*4;l[h+0]=Math.round(u[0]),l[h+1]=Math.round(u[1]),l[h+2]=Math.round(u[2]),l[h+3]=Math.round(u[3])}if(t!=null){t.width=a,t.height=r;let c=t.getContext("2d"),u=new ImageData(l,a,r);c.putImageData(u,0,0)}return n!==e&&n.dispose(),l}var gI=z({fromPixels_:xI}),Af={};$e(Af,{prepareAndValidate:()=>qg});function qg(e,t){let n=e.shape.length,r=t.shape.length;if(n<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${n}.`);if(r<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${r}.`);if(t.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${t.dtype}.`);if(t.shape[r-1]>n)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[r-1]} vs. ${n}`);if(Rt(e.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);let a=t.shape,s=a[a.length-1],i=1;for(let h=0;h<a.length-1;++h)i*=a[h];let o=e.shape,l=a.slice();l.pop();let c=1;for(let h=s;h<n;++h)c*=o[h],l.push(o[h]);let u=[...Xi(e.shape).map(h=>h/c),1].slice(0,s);return[l,i,c,u]}var yf={};$e(yf,{calculateShapes:()=>Xg,validateInput:()=>xf,validateUpdateShape:()=>gf});function gf(e,t,n){let r=t.rank>1?t.shape[t.rank-1]:1,a=t.rank>1?t.rank-1:1,s=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${r}, and batchDim: ${a}.`;if(n.rank<a)throw new Error(s+` update.rank < ${a}. `);if(e.length<r+(n.rank-a))throw new Error(s+` Output shape length < ${r+(n.rank-a)}`);if(n.rank!==a+e.length-r)throw new Error(s+` update.rank != ${a+e.length-r}`);for(let i=0;i<a;++i)if(n.shape[i]!==t.shape[i])throw new Error(s+` updates.shape[${i}] (${n.shape[i]}) != indices.shape[${i}] (${t.shape[i]}).`);for(let i=0;i<n.rank-a;++i)if(n.shape[i+a]!==e[i+r])throw new Error(s+` updates.shape[${i+a}] (${n.shape[i+a]}) != shape[${i+a}] (${e[i+a]})`)}function xf(e,t,n){if(t.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${t.rank}.`);if(e.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${e.rank}.`);if(t.dtype!=="int32")throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${t.dtype}`);if(n.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${n}`);if(n.length===0){if(t.size===0)throw new Error(`Indices specified for empty output. indices shape: ${t.shape}`);if(e.size===0)throw new Error(`Updates specified for empty output. updates shape: ${e.shape}`)}gf(n,t,e)}function Xg(e,t,n){let r=t.shape.length,a=r>1?t.shape[r-1]:1,s=n.length,i=1;for(let h=a;h<s;++h)i*=n[h];let o=a<1?1:a,l=Rt(t.shape)/o,c=[...Xi(n.slice(0,a)),1],u=Rt(n);return{sliceRank:a,numUpdates:l,sliceSize:i,strides:c,outputSize:u}}var sn={};$e(sn,{assertParamsValid:()=>wI,computeFlatOffset:()=>bI,computeOutShape:()=>Kg,getNormalizedAxes:()=>Yg,isSliceContinous:()=>_I,maskToAxes:()=>md,parseSliceParams:()=>r5,sliceInfo:()=>vI,startForAxis:()=>t5,startIndicesWithElidedDims:()=>Jg,stopForAxis:()=>n5,stopIndicesWithElidedDims:()=>Qg,stridesForAxis:()=>e5,stridesWithElidedDims:()=>Zg});function wI(e,t,n){let r=e.shape.length;F(r===t.length,()=>`Error in slice${r}D: Length of begin ${t} must match the rank of the array (${r}).`),F(r===n.length,()=>`Error in slice${r}D: Length of size ${n} must match the rank of the array (${r}).`);for(let a=0;a<r;++a)F(t[a]+n[a]<=e.shape[a],()=>`Error in slice${r}D: begin[${a}] + size[${a}] (${t[a]+n[a]}) would overflow input.shape[${a}] (${e.shape[a]})`)}function md(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function Kg(e,t,n){let r=[];for(let a=0;a<e.length;a++)r[a]=Math.ceil((t[a]-e[a])/n[a]);return r}function Zg(e,t,n,r){let a=[...e];for(let s=a.length;s<r.length;s++)a.push(1);for(let s=0;s<n;s++)s===0?a[t]=1:(a.splice(t,0,1),a.pop());return a}function a5(e,t,n){return n<=e?n:n-(t-1)}function s5(e,t){let n=[];for(let r=0;r<e;r++)n.push(t+r);return n}function Yg(e,t,n,r,a,s,i,o,l){let c=e.length,u=new Array(c),h=new Array(c),p=new Array(c);if(t.length&&n>0){let d=t[0],f=n+1;u=Jg(i,d,f,r,e),h=Qg(o,d,f,a,e),p=Zg(s,d,f,e)}else for(let d=0;d<c;d++)u[d]=t5(i,r,s,e,d,l),h[d]=n5(o,a,s,e,d,l),p[d]=e5(s,d,l);return{begin:u,end:h,strides:p}}function Jg(e,t,n,r,a){let s=[...a],i=s5(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=0;else{let l=a5(t,n,o),c=r[l];e&1<<l&&(c=0),s[o]=c}return s}function Qg(e,t,n,r,a){let s=[...a],i=s5(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=Number.MAX_SAFE_INTEGER;else{let l=a5(t,n,o),c=r[l];e&1<<l&&(c=Number.MAX_SAFE_INTEGER),s[o]=c}for(let o=0;o<s.length;o++){let l=a[o];s[o]<0&&(s[o]+=l),s[o]=wu(0,s[o],a[o])}return s}function e5(e,t,n){let r=e[t];return(n&1<<t||r==null)&&(r=1),r}function t5(e,t,n,r,a,s){let i=t[a],o=n[a]||1;(e&1<<a||s&1<<a||i==null)&&(o>0?i=Number.MIN_SAFE_INTEGER:i=Number.MAX_SAFE_INTEGER);let l=r[a];return i<0&&(i+=l),i=wu(0,i,l-1),i}function n5(e,t,n,r,a,s){let i=t[a],o=n[a]||1;(e&1<<a||s&1<<a||i==null)&&(o>0?i=Number.MAX_SAFE_INTEGER:i=Number.MIN_SAFE_INTEGER);let l=r[a];return i<0&&(i+=l),o>0?i=wu(0,i,l):i=wu(-1,i,l-1),i}function _I(e,t,n){let r=n.length;for(let a=0;a<n.length;a++)if(n[a]>1){r=a;break}for(let a=r+1;a<n.length;a++)if(t[a]>0||n[a]!==e[a])return!1;return!0}function bI(e,t){let n=e.length>0?e[e.length-1]:1;for(let r=0;r<e.length-1;r++)n+=e[r]*t[r];return n}function r5(e,t,n){let r,a=e.shape.length;typeof t=="number"?r=[t,...new Array(a-1).fill(0)]:t.length<a?r=t.concat(new Array(a-t.length).fill(0)):r=t.slice(),r.forEach(i=>{F(i!==-1,()=>"slice() does not support negative begin indexing.")});let s;return n==null?s=new Array(a).fill(-1):typeof n=="number"?s=[n,...new Array(a-1).fill(-1)]:n.length<a?s=n.concat(new Array(a-n.length).fill(-1)):s=n,s=s.map((i,o)=>i>=0?i:(F(i===-1,()=>`Negative size values should be exactly -1 but got ${i} for the slice() size at index ${o}.`),e.shape[o]-r[o])),[r,s]}function vI(e,t,n,r,a,s,i,o,l){let c=t.slice(),u=n.slice(),h=r;r==null&&(h=new Array(c.length));let p=md(i);if(p.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(i!==0&&o!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(i!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let d=e.length-c.length,f=md(o),m=e.slice();f.forEach(b=>{c[b]=0,u[b]=1,m.splice(b,0,1)});let{begin:A,end:y,strides:g}=Yg(m,p,d,c,u,h,a,s,i);c=A,u=y,h=g;let _=md(l);_.forEach(b=>{u[b]=c[b]+1,h[b]=1});let x=Kg(c,u,h),w=x.filter((b,N)=>_.indexOf(N)===-1);return{nonStrided:h.every(b=>b===1),$begin:c,$end:u,$strides:h,size:x,newShape:m,outShape:w}}var re={};$e(re,{Serializable:()=>i5,SerializationMap:()=>ri,registerClass:()=>Sa});var i5=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},ri=class{constructor(){this.classNameMap={}}static getMap(){return ri.instance==null&&(ri.instance=new ri),ri.instance}static register(e){ri.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function Sa(e){F(e.className!=null,()=>"Class being registered does not have the static className property defined."),F(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),F(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),ri.register(e)}var o5={};$e(o5,{TEST_EPSILON_FLOAT16:()=>l5,encodeStrings:()=>u5,expectArrayBuffersEqual:()=>EI,expectArraysClose:()=>kI,expectArraysEqual:()=>NI,expectNumbersClose:()=>SI,expectPromiseToFail:()=>II,expectValuesInRange:()=>TI,testEpsilon:()=>wf});var CI=.001,l5=.1;function kI(e,t,n){return n==null&&(n=wf()),_f(e,t,(r,a)=>bf(r,a,n))}function wf(){return D.backend.floatPrecision()===32?CI:l5}function _f(e,t,n){let r=!0;if((nn(e)||nn(t))&&(r=!1),nn(e)&&nn(t)&&(r=!0),r){let i=e.constructor.name,o=t.constructor.name;if(i!==o)throw new Error(`Arrays are of different type. Actual: ${i}. Expected: ${o}`)}if(Array.isArray(e)&&Array.isArray(t)){let i=Fr(e),o=Fr(t);if(!Qr(i,o))throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`)}let a=nn(e)?e:ss(e),s=nn(t)?t:ss(t);if(a.length!==s.length)throw new Error(`Arrays have different lengths actual: ${a.length} vs expected: ${s.length}.
Actual: ${a}.
Expected: ${s}.`);for(let i=0;i<s.length;++i){let o=a[i],l=s[i];if(!n(o,l))throw new Error(`Arrays differ: actual[${i}] = ${o}, expected[${i}] = ${l}.
Actual: ${a}.
Expected: ${s}.`)}}function II(e,t){e().then(()=>t.fail(),()=>t())}function NI(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return ga(e)||ga(e[0])||ga(t)||ga(t[0])?_f(e,n,(r,a)=>r==a):_f(e,t,(r,a)=>bf(r,a,0))}function SI(e,t,n){if(n==null&&(n=wf()),!bf(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function bf(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function TI(e,t,n){for(let r=0;r<e.length;r++)if(e[r]<t||e[r]>n)throw new Error(`Value out of range:${e[r]} low: ${t}, high: ${n}`)}function EI(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function u5(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?u5(n):e[t]=Wu(n)}return e}var c5="3.0.0";function h5(){Q().set("PROD",!0)}function RI(){Q().set("DEBUG",!0)}function FI(){Q().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function vf(e){Q().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}u9(vf);function MI(){D.disposeVariables()}function Vn(){return D}function Ad(){return D.memory()}function ta(e){return D.profile(e)}function W(e,t){return D.tidy(e,t)}function Se(e){Y1(e).forEach(t=>t.dispose())}function Wt(e){return D.keep(e)}function OI(e){return D.time(e)}function d5(e){return D.setBackend(e)}function p5(){return D.ready()}function yd(){return D.backendName}function $I(e){D.removeBackend(e)}function kf(e){return D.findBackend(e)}function DI(e){return D.findBackendFactory(e)}function ol(e,t,n=1){return D.registerBackend(e,t,n)}function If(){return D.backend}function zI(e,t){Q().setPlatform(e,t)}function PI(e,t){let n=R(e,"a","add"),r=R(t,"b","add");[n,r]=wt(n,r);let a={a:n,b:r};return D.runKernel(wa,a)}var se=z({add_:PI});function LI(e,t){let n=R(e,"a","floorDiv"),r=R(t,"b","floorDiv");[n,r]=wt(n,r);let a={a:n,b:r};return D.runKernel(xs,a)}var gd=z({floorDiv_:LI});function WI(e,t){let n=R(e,"a","div"),r=R(t,"b","div");if([n,r]=wt(n,r),n.dtype==="int32"&&r.dtype==="int32")return gd(n,r);let a={a:n,b:r},s={};return D.runKernel(As,a,s)}var be=z({div_:WI});function BI(e,t){let n=R(e,"a","mul"),r=R(t,"b","mul");[n,r]=wt(n,r);let a={a:n,b:r};return D.runKernel(Cs,a)}var L=z({mul_:BI});function VI(e){let t=R(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return D.runKernel(Iu,n)}else{let n={x:t};return D.runKernel(Zi,n)}}var Mt=z({abs_:VI});function UI(e){let t={x:R(e,"x","acos")};return D.runKernel(Yi,t)}var Nf=z({acos_:UI});function jI(e){let t={x:R(e,"x","acosh")};return D.runKernel(Ji,t)}var Sf=z({acosh_:jI});function HI(e){F(Array.isArray(e),()=>"The argument passed to tf.addN() must be a list of tensors"),F(e.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`);let t=e.map((a,s)=>R(a,`tensors${s}`,"addN")),n=t[0];t.forEach(a=>{if(a.dtype!==n.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),t.forEach(a=>{if(!Qr(a.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let r=t;return D.runKernel(is,r)}var ll=z({addN_:HI});function GI(e,t=null,n=!1){let r={x:R(e,"x","all","bool")},a={axis:t,keepDims:n};return D.runKernel(Eh,r,a)}var xd=z({all_:GI});function qI(e,t=null,n=!1){let r={x:R(e,"x","any","bool")},a={axis:t,keepDims:n};return D.runKernel(Ch,r,a)}var Xu=z({any_:qI});function XI(e,t=0){let n={x:R(e,"x","argMax")},r={axis:t};return D.runKernel(os,n,r)}var Ku=z({argMax_:XI});function KI(e,t=0){let n={x:R(e,"x","argMin")},r={axis:t};return D.runKernel(bu,n,r)}var Tf=z({argMin_:KI});function ZI(e){let t={x:R(e,"x","asin")};return D.runKernel(Qi,t)}var Ef=z({asin_:ZI});function YI(e){let t={x:R(e,"x","asinh")};return D.runKernel(eo,t)}var Cf=z({asinh_:YI});function JI(e){let t={x:R(e,"x","atan")};return D.runKernel(to,t)}var Rf=z({atan_:JI});function QI(e,t){let n=R(e,"a","atan2"),r=R(t,"b","atan2");[n,r]=wt(n,r);let a={a:n,b:r};return D.runKernel(ro,a)}var Ff=z({atan2_:QI});function eN(e){let t={x:R(e,"x","atanh")};return D.runKernel(no,t)}var Mf=z({atanh_:eN});function tN(e,t,n,r,a="NHWC",s){let i=e[3],o=[...t,i],l=f5(a);return Zu(e,o,n,s,r,null,null,l)}function m5(e,t,n,r,a,s,i="channelsLast"){let[o,l]=wd(t),c;if(i==="channelsLast")c=[o,l,e[3],e[3]];else if(i==="channelsFirst")c=[o,l,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return Zu(e,c,n,r,a,s,!1,i)}function nN(e,t,n,r,a,s,i="NDHWC"){let[o,l,c]=Of(t),u,h;if(i==="NDHWC")h="channelsLast",u=[o,l,c,e[4],e[4]];else if(i==="NCDHW")h="channelsFirst",u=[o,l,c,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return A5(e,u,n,r,a,!1,h,s)}function Zu(e,t,n,r,a,s,i=!1,o="channelsLast"){let[l,c,u,h]=[-1,-1,-1,-1];if(o==="channelsLast")[l,c,u,h]=e;else if(o==="channelsFirst")[l,h,c,u]=e;else throw new Error(`Unknown dataFormat ${o}`);let[p,d,,f]=t,[m,A]=wd(n),[y,g]=wd(r),_=ul(p,y),x=ul(d,g),{padInfo:w,outHeight:b,outWidth:N}=rN(a,c,u,m,A,_,x,s,o),T=i?f*h:f,E;return o==="channelsFirst"?E=[l,T,b,N]:o==="channelsLast"&&(E=[l,b,N,T]),{batchSize:l,dataFormat:o,inHeight:c,inWidth:u,inChannels:h,outHeight:b,outWidth:N,outChannels:T,padInfo:w,strideHeight:m,strideWidth:A,filterHeight:p,filterWidth:d,effectiveFilterHeight:_,effectiveFilterWidth:x,dilationHeight:y,dilationWidth:g,inShape:e,outShape:E,filterShape:t}}function A5(e,t,n,r,a,s=!1,i="channelsLast",o){let[l,c,u,h,p]=[-1,-1,-1,-1,-1];if(i==="channelsLast")[l,c,u,h,p]=e;else if(i==="channelsFirst")[l,p,c,u,h]=e;else throw new Error(`Unknown dataFormat ${i}`);let[d,f,m,,A]=t,[y,g,_]=Of(n),[x,w,b]=Of(r),N=ul(d,x),T=ul(f,w),E=ul(m,b),{padInfo:M,outDepth:$,outHeight:P,outWidth:V}=aN(a,c,u,h,y,g,_,N,T,E,o),G=s?A*p:A,U;return i==="channelsFirst"?U=[l,G,$,P,V]:i==="channelsLast"&&(U=[l,$,P,V,G]),{batchSize:l,dataFormat:i,inDepth:c,inHeight:u,inWidth:h,inChannels:p,outDepth:$,outHeight:P,outWidth:V,outChannels:G,padInfo:M,strideDepth:y,strideHeight:g,strideWidth:_,filterDepth:d,filterHeight:f,filterWidth:m,effectiveFilterDepth:N,effectiveFilterHeight:T,effectiveFilterWidth:E,dilationDepth:x,dilationHeight:w,dilationWidth:b,inShape:e,outShape:U,filterShape:t}}function sN(e,t,n,r,a){r==null&&(r=$f(e,t,n));let s=e[0],i=e[1],o=ai((s-t+2*r)/n+1,a),l=ai((i-t+2*r)/n+1,a);return[o,l]}function iN(e,t,n,r,a,s){a==null&&(a=$f(e,t,r));let i=e[0],o=e[1],l=e[2],c=ai((i-t+2*a)/r+1,s),u=ai((o-t+2*a)/r+1,s),h=ai((l-t+2*a)/r+1,s);return[c,u,h,n]}function $f(e,t,n,r=1){let a=ul(t,r);return Math.floor((e[0]*(n-1)-n+a)/2)}function wd(e){return typeof e=="number"?[e,e,e]:e.length===2?[e[0],e[1],1]:e}function Of(e){return typeof e=="number"?[e,e,e]:e}function ul(e,t){return t<=1?e:e+(e-1)*(t-1)}function rN(e,t,n,r,a,s,i,o,l){let c,u,h;if(typeof e=="number"){c={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let p=sN([t,n],s,r,e,o);u=p[0],h=p[1]}else if(e==="same"){u=Math.ceil(t/r),h=Math.ceil(n/a);let p=Math.max(0,(u-1)*r+s-t),d=Math.max(0,(h-1)*a+i-n),f=Math.floor(p/2),m=p-f,A=Math.floor(d/2),y=d-A;c={top:f,bottom:m,left:A,right:y,type:"SAME"}}else if(e==="valid")c={top:0,bottom:0,left:0,right:0,type:"VALID"},u=Math.ceil((t-s+1)/r),h=Math.ceil((n-i+1)/a);else if(typeof e=="object"){let p=l==="channelsLast"?e[1][0]:e[2][0],d=l==="channelsLast"?e[1][1]:e[2][1],f=l==="channelsLast"?e[2][0]:e[3][0],m=l==="channelsLast"?e[2][1]:e[3][1];c={top:p,bottom:d,left:f,right:m,type:p===0&&d===0&&f===0&&m===0?"VALID":"EXPLICIT"},u=ai((t-s+p+d)/r+1,o),h=ai((n-i+f+m)/a+1,o)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:c,outHeight:u,outWidth:h}}function aN(e,t,n,r,a,s,i,o,l,c,u){let h,p,d,f;if(typeof e=="number"){h={top:e,bottom:e,left:e,right:e,front:e,back:e,type:e===0?"VALID":"NUMBER"};let m=iN([t,n,r,1],o,1,a,e,u);p=m[0],d=m[1],f=m[2]}else if(e==="same"){p=Math.ceil(t/a),d=Math.ceil(n/s),f=Math.ceil(r/i);let m=(p-1)*a+o-t,A=(d-1)*s+l-n,y=(f-1)*i+c-r,g=Math.floor(m/2),_=m-g,x=Math.floor(A/2),w=A-x,b=Math.floor(y/2),N=y-b;h={top:x,bottom:w,left:b,right:N,front:g,back:_,type:"SAME"}}else if(e==="valid")h={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},p=Math.ceil((t-o+1)/a),d=Math.ceil((n-l+1)/s),f=Math.ceil((r-c+1)/i);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:h,outDepth:p,outHeight:d,outWidth:f}}function ai(e,t){if(!t)return Math.trunc(e);switch(t){case"round":return Math.round(e);case"ceil":return Math.ceil(e);case"floor":return Math.floor(e);default:throw new Error(`Unknown roundingMode ${t}`)}}function Ta(e){let[t,n,r]=wd(e);return t===1&&n===1&&r===1}function Mr(e,t){return Ta(e)||Ta(t)}function f5(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function oN(e,t){let n={x:R(e,"x","reshape","string_or_numeric")},r={shape:t};return D.runKernel(zo,n,r)}var q=z({reshape_:oN});function lN(e,t,n,r,a){let s=R(e,"x","avgPool","float32"),i=1;F(Mr(n,i),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`);let o=s,l=!1;s.rank===3&&(l=!0,o=q(s,[1,s.shape[0],s.shape[1],s.shape[2]])),F(o.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${o.rank}.`),a!=null&&F(Lt(r),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let c={x:o},u={filterSize:t,strides:n,pad:r,dimRoundingMode:a},h=D.runKernel(ls,c,u);return h=me(h,s.dtype),l?q(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Yu=z({avgPool_:lN});function uN(e,t,n,r,a,s="NDHWC"){let i=R(e,"x","avgPool3d","float32"),o=i,l=!1;i.rank===4&&(l=!0,o=q(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(o.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`),F(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),a!=null&&F(Lt(r),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let c={x:o},u={filterSize:t,strides:n,pad:r,dimRoundingMode:a,dataFormat:s},h=D.runKernel(vu,c,u);return h=me(h,o.dtype),l?q(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var Df=z({avgPool3d_:uN});function cN(e,t=0){F(e.length>=1,()=>"Pass at least one tensor to concat");let n=Gu(e,"tensors","concat","string_or_numeric");if(n[0].dtype==="complex64"&&n.forEach(s=>{if(s.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
with dtype ${s.dtype}. `)}),n.length===1)return tr(n[0]);let r=n,a={axis:t};return D.runKernel(so,r,a)}var rt=z({concat_:cN});function hN(e){let t={x:R(e,"x","sigmoid")};return D.runKernel(Vs,t)}var In=z({sigmoid_:hN});function dN(e,t,n){let r=R(e,"x","slice","string_or_numeric");if(r.rank===0)throw new Error("Slicing scalar is not possible");let a={x:r},s={begin:t,size:n};return D.runKernel(Bo,a,s)}var Te=z({slice_:dN});function pN(e){let t={x:R(e,"x","tanh")};return D.runKernel(Xs,t)}var cl=z({tanh_:pN});function fN(e,t,n,r,a,s){let i=R(e,"forgetBias","basicLSTMCell"),o=R(t,"lstmKernel","basicLSTMCell"),l=R(n,"lstmBias","basicLSTMCell"),c=R(r,"data","basicLSTMCell"),u=R(a,"c","basicLSTMCell"),h=R(s,"h","basicLSTMCell"),p=rt([c,h],1),d=He(p,o),f=se(d,l),m=f.shape[0],A=f.shape[1]/4,y=[m,A],g=Te(f,[0,0],y),_=Te(f,[0,A],y),x=Te(f,[0,A*2],y),w=Te(f,[0,A*3],y),b=se(L(In(g),cl(_)),L(u,In(se(i,x)))),N=L(cl(b),In(w));return[b,N]}var mN=z({basicLSTMCell_:fN});function AN(e,t,n){let r=R(e,"x","batchToSpaceND"),a=t.reduce((o,l)=>o*l);F(r.rank>=1+t.length,()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`),F(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),F(r.shape[0]%a==0,()=>`input tensor batch is ${r.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${a}`);let s={x:r},i={blockShape:t,crops:n};return D.runKernel(ku,s,i)}var Ju=z({batchToSpaceND_:AN});function yN(e){let t;return e.rank===0||e.rank===1?t=q(e,[1,1,1,e.size]):e.rank===2?t=q(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=q(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function gN(e,t,n,r,a,s){s==null&&(s=.001);let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;r!=null&&(u=R(r,"offset","batchNorm")),F(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),F(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),F(c==null||o.rank===c.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let h={x:yN(i),scale:c,offset:u,mean:o,variance:l},p={varianceEpsilon:s},d=D.runKernel(ws,h,p);return q(d,i.shape)}var si=z({batchNorm_:gN});function xN(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;return r!=null&&(u=R(r,"offset","batchNorm")),F(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),F(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),F(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&F(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${c.rank}.`),u!=null&&F(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${u.rank}.`),si(i,o,l,u,c,s)}var y5=z({batchNorm2d_:xN});function wN(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;return r!=null&&(u=R(r,"offset","batchNorm")),F(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),F(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),F(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&F(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`),u!=null&&F(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${u.rank}.`),si(i,o,l,u,c,s)}var g5=z({batchNorm3d_:wN});function _N(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;return r!=null&&(u=R(r,"offset","batchNorm")),F(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),F(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),F(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&F(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),u!=null&&F(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${u.rank}.`),si(i,o,l,u,c,s)}var x5=z({batchNorm4d_:_N});function bN(e,t,n){let r=R(e,"x","bincount"),a=R(t,"weights","bincount");F(r.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(a.size===r.size||a.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${r.shape}, weights shape: ${a.shape}.`);let s={x:r,weights:a},i={size:n};return D.runKernel(Mh,s,i)}var w5=z({bincount_:bN});function vN(e,t){let n=R(e,"broadcastTo","x"),r=n.shape;if(t.some(l=>!(l>0)||l%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let l=n.shape.slice();for(;l.length<t.length;)l.unshift(1);n=q(n,l)}let a=n.shape,s=Array.from(t);for(let l=t.length-1;l>=0;l--)if(a[l]===t[l])s[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${t}].`);if(s.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return tr(n);let i={x:n},o={reps:s};return D.runKernel(ba,i,o)}var Qu=z({broadcastTo_:vN});function kN(e){let t={x:R(e,"x","ceil")};return D.runKernel(ao,t)}var zf=z({ceil_:kN});function IN(e,t,n){let r=R(e,"x","clipByValue");F(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let a={x:r},s={clipValueMin:t,clipValueMax:n};return D.runKernel(_a,a,s)}var mn=z({clipByValue_:IN});function NN(e){return rt(e,0)}var _5=z({concat1d_:NN});function SN(e,t){return rt(e,t)}var ii=z({concat2d_:SN});function TN(e,t){return rt(e,t)}var b5=z({concat3d_:TN});function EN(e,t){return rt(e,t)}var v5=z({concat4d_:EN});function CN(e,t,n,r,a="NHWC",s=[1,1],i){let o=R(e,"x","conv2d"),l=R(t,"filter","conv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=q(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(c.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`),F(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&F(Lt(r),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let h=a==="NHWC"?c.shape[3]:c.shape[1];F(h===l.shape[2],()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${l.shape[2]}.`),F(Mr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`);let p={x:c,filter:l},d={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i},f=D.runKernel(hs,p,d);return u?q(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var na=z({conv2d_:CN});function RN(e,t,n,r,a="NWC",s=1,i){let o=R(e,"x","conv1d"),l=R(t,"filter","conv1d"),c=o,u=!1;o.rank===2&&(u=!0,c=q(o,[1,o.shape[0],o.shape[1]])),F(c.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${c.rank}.`),F(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),i!=null&&F(Lt(r),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),F(c.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${c.shape[2]}) must match input depth for filter ${l.shape[1]}.`),F(Mr(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),F(a==="NWC",()=>`Error in conv1d: got dataFormat of ${a} but only NWC is currently supported.`);let h=q(l,[1,l.shape[0],l.shape[1],l.shape[2]]),p=q(c,[c.shape[0],1,c.shape[1],c.shape[2]]),d=na(p,h,[1,n],r,"NHWC",[1,s],i);return u?q(d,[d.shape[2],d.shape[3]]):q(d,[d.shape[0],d.shape[2],d.shape[3]])}var _d=z({conv1d_:RN});function FN(e,t,n,r,a,s="NHWC",i){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,c=!1;t.rank===3&&(c=!0,l=q(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),F(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),F(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),F(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let u=s==="NHWC"?o[3]:o[1],h=s==="NHWC"?l.shape[3]:l.shape[1];F(u===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[2]}.`),F(h===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${h}) must match output depth for filter ${n.shape[3]}.`),i!=null&&F(Lt(a),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let p={dy:l,filter:n},d={strides:r,pad:a,dataFormat:s,dimRoundingMode:i,inputShape:o},f=D.runKernel(ds,p,d);return c?q(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Pf=z({conv2DBackpropInput_:FN});function MN(e,t,n,r,a,s){let i=R(e,"x","conv2dTranspose"),o=R(t,"filter","conv2dTranspose");return Pf(n,i,o,r,a,"NHWC",s)}var bd=z({conv2dTranspose_:MN});function ON(e,t,n,r,a="NDHWC",s=[1,1,1]){let i=R(e,"x","conv3d"),o=R(t,"filter","conv3d"),l=i,c=!1;i.rank===4&&(c=!0,l=q(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),F(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),F(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),F(Mr(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),F(a==="NDHWC",()=>`Error in conv3d: got dataFormat of ${a} but only NDHWC is currently supported.`);let u={x:l,filter:o},h={strides:n,pad:r,dataFormat:a,dilations:s},p=D.runKernel(Nu,u,h);return c?q(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Lf=z({conv3d_:ON});function $N(e,t,n,r,a){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,i=t,o=!1;t.rank===4&&(o=!0,i=q(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,e[0],e[1],e[2],e[3]]);let l=s[4],c=i.shape[4];F(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),F(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),F(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),F(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),F(c===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[4]}.`);let u={dy:i,filter:n},h={pad:a,strides:r,inputShape:s},p=D.runKernel(zh,u,h);return o?q(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var k5=z({conv3DBackpropInput_:$N});function DN(e,t,n,r,a){let s=R(e,"x","conv3dTranspose"),i=R(t,"filter","conv3dTranspose");return k5(n,s,i,r,a)}var zN=z({conv3dTranspose_:DN});function PN(e){let t={x:R(e,"x","cos")};return D.runKernel(ps,t)}var ec=z({cos_:PN});function LN(e){let t={x:R(e,"x","cosh")};return D.runKernel(io,t)}var vd=z({cosh_:LN});function WN(e,t=0,n=!1,r=!1){let a={x:R(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:r};return D.runKernel(fs,a,s)}var kd=z({cumsum_:WN});function BN(e,t,n,r=!1){let a=R(e,"x","denseBincount"),s=R(t,"weights","denseBincount");F(a.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${a.dtype}`),F(a.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${a.rank}.`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(s.size===a.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${a.shape}, weights shape: ${s.shape}.`);let i={x:a,weights:s},o={size:n,binaryOutput:r};return D.runKernel(Ph,i,o)}var I5=z({denseBincount_:BN});function VN(e,t,n="NHWC"){let r=R(e,"x","depthToSpace"),a=n==="NHWC"?r.shape[1]:r.shape[2],s=n==="NHWC"?r.shape[2]:r.shape[3],i=n==="NHWC"?r.shape[3]:r.shape[1];F(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
${a} and ${t} for depthToSpace with input shape
${r.shape}`),F(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
${s} and ${t} for depthToSpace with input shape
${r.shape}`),F(i%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${r.shape}`);let o={x:r},l={blockSize:t,dataFormat:n};return D.runKernel(lo,o,l)}var Wf=z({depthToSpace_:VN});function UN(e,t,n,r,a="NHWC",s=[1,1],i){let o=R(e,"x","depthwiseConv2d"),l=R(t,"filter","depthwiseConv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=q(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(c.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${c.rank}.`),F(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),F(c.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${c.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),i!=null&&F(Lt(r),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let h={x:c,filter:l},p={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i},d=D.runKernel(ms,h,p);return u?q(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var hl=z({depthwiseConv2d_:UN});function jN(e){let t={x:R(e,"x","diag")};return D.runKernel(Bh,t)}var HN=z({diag_:jN});function GN(e,t,n,r,a=[1,1],s="NHWC"){let i=R(e,"x","dilation2d"),o=R(t,"filter","dilation2d");F(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),F(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),F(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=i,c=!1;i.rank===3&&(l=q(i,[1,i.shape[0],i.shape[1],i.shape[2]]),c=!0);let u={x:l,filter:o},h={strides:n,pad:r,dilations:a},p=D.runKernel(Su,u,h);return c?q(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var Bf=z({dilation2d_:GN});function qN(e,t){let n=e.length,r=[];for(let a=0;a<n;a++){let s=n-1-a,i=e[s]||1;(t[t.length-1-a]||1)>1&&i===1&&r.unshift(s)}return r}function Ot(e,t){let n=[];for(let r=0;r<t.length;r++){let a=e[e.length-r-1],s=t.length-r-1,i=t[s];(a==null||a===1&&i>1)&&n.unshift(s)}return n}function At(e,t){let n=[],r=Math.max(e.length,t.length);for(let a=0;a<r;a++){let s=e[e.length-a-1];s==null&&(s=1);let i=t[t.length-a-1];if(i==null&&(i=1),s===1)n.unshift(i);else if(i===1)n.unshift(s);else if(s!==i){let o=`Operands could not be broadcast together with shapes ${e} and ${t}.`;throw Error(o)}else n.unshift(s)}return n}function XN(e,t){let n=R(e,"a","equal"),r=R(t,"b","equal");[n,r]=wt(n,r),At(n.shape,r.shape);let a={a:n,b:r};return D.runKernel(ho,a)}var Ea=z({equal_:XN});function KN(e,t,n){let r=R(t,"a","where"),a=R(n,"b","where"),s=R(e,"condition","where","bool"),i=At(r.shape,a.shape),o=Qu(r,i),l=Qu(a,i);s.rank===1&&F(s.shape[0]===r.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),s.rank!==1&&tn(s.shape,l.shape,"Error in where: ");let c={condition:s,t:o,e:l};return D.runKernel(Lo,c)}var An=z({where_:KN});function ZN(e){let t={x:R(e,"x","zerosLike")};return D.runKernel(Zo,t)}var Ve=z({zerosLike_:ZN});function YN(e,t){let n=R(e,"a","div"),r=R(t,"b","div");[n,r]=wt(n,r);let a=be(n,r),s=Ve(a),i=Ea(r,s);return An(i,s,a)}var Vf=z({divNoNan_:YN});function JN(e,t){let n=R(e,"t1","dot"),r=R(t,"t2","dot");F((n.rank===1||n.rank===2)&&(r.rank===1||r.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${r.rank}.`);let a=n.rank===1?n.size:n.shape[1],s=r.rank===1?r.size:r.shape[0];if(F(a===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${a} and ${s}.`),n.rank===1&&r.rank===1){let i=q(n,[1,-1]),o=q(r,[-1,1]),l=He(i,o);return q(l,[])}else if(n.rank===1&&r.rank===2){let i=q(n,[1,-1]),o=q(r,[r.shape[0],r.shape[1]]),l=He(i,o);return q(l,[l.size])}else if(n.rank===2&&r.rank===1){let i=q(r,[-1,1]),o=He(n,i);return q(o,[o.size])}else{let i=q(r,[r.shape[0],r.shape[1]]);return He(n,i)}}var N5=z({dot_:JN});function QN(e){let t={x:R(e,"x","elu")};return D.runKernel(uo,t)}var dl=z({elu_:QN});function eS(e){let t=R(e,"x","erf");F(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=me(t,"float32"));let n={x:t};return D.runKernel(co,n)}var Uf=z({erf_:eS});function tS(e){let t={x:R(e,"x","exp")};return D.runKernel(ys,t)}var Un=z({exp_:tS});function nS(e,t=0){let n=R(e,"x","expandDims","string_or_numeric");F(t<=n.rank,()=>"Axis must be <= rank of the tensor");let r={input:n},a={dim:t};return D.runKernel(po,r,a)}var Nn=z({expandDims_:nS});function rS(e){let t={x:R(e,"x","expm1")};return D.runKernel(fo,t)}var jf=z({expm1_:rS});function aS(e,t){let n=R(e,"x","tile","string_or_numeric");F(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let r={x:n},a={reps:t};return D.runKernel(ba,r,a)}var Ca=z({tile_:aS});function sS(e,t,n,r="float32"){t==null&&(t=e);let a=Le([e,t],r),s=e<=t?e:t;for(let o=0;o<s;++o)a.set(1,o,o);let i=q(a.toTensor(),[e,t]);if(n==null)return i;if(n.length===1)return Ca(Nn(i,0),[n[0],1,1]);if(n.length===2)return Ca(Nn(Nn(i,0),0),[n[0],n[1],1,1]);if(n.length===3)return Ca(Nn(Nn(Nn(i,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`)}var Hf=z({eye_:sS});function tc(e,t,n){let r={shape:e,value:t,dtype:n};return D.runKernel(Tu,{},r)}function iS(e){let t={x:R(e,"x","floor")};return D.runKernel(gs,t)}var pl=z({floor_:iS});function oS(e,t,n=0,r=0){let a=R(e,"x","gather"),s=R(t,"indices","gather","int32"),i={x:a,indices:s},o={axis:n,batchDims:r};return D.runKernel(Ao,i,o)}var oi=z({gather_:oS});function lS(e,t){let n=R(e,"a","greater"),r=R(t,"b","greater");[n,r]=wt(n,r),At(n.shape,r.shape);let a={a:n,b:r};return D.runKernel(go,a)}var nr=z({greater_:lS});function uS(e,t){let n=R(e,"a","greaterEqual"),r=R(t,"b","greaterEqual");[n,r]=wt(n,r),At(n.shape,r.shape);let a={a:n,b:r};return D.runKernel(_s,a)}var Ra=z({greaterEqual_:uS});function cS(e){let t={input:R(e,"input","imag")};return D.runKernel(qh,t)}var Id=z({imag_:cS});function hS(e){let t={x:R(e,"x","isFinite")};return D.runKernel(wo,t)}var S5=z({isFinite_:hS});function dS(e){let t={x:R(e,"x","isInf")};return D.runKernel(_o,t)}var T5=z({isInf_:dS});function pS(e){let t={x:R(e,"x","isNaN")};return D.runKernel(bo,t)}var E5=z({isNaN_:pS});function fS(e,t=.2){let n={x:R(e,"x","leakyRelu")},r={alpha:t};return D.runKernel(bs,n,r)}var nc=z({leakyRelu_:fS});function mS(e,t){let n=R(e,"a","less"),r=R(t,"b","less");[n,r]=wt(n,r),At(n.shape,r.shape);let a={a:n,b:r};return D.runKernel(vo,a)}var Nd=z({less_:mS});function AS(e,t){let n=R(e,"a","lessEqual"),r=R(t,"b","lessEqual");[n,r]=wt(n,r),At(n.shape,r.shape);let a={a:n,b:r};return D.runKernel(ko,a)}var li=z({lessEqual_:AS});function C5(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");let r={start:e,stop:t,num:n};return D.runKernel(Xh,{},r)}function yS(e,t=5,n=1,r=1,a=.5){let s=R(e,"x","localResponseNormalization");F(s.rank===4||s.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
rank ${s.rank}.`),F(Lt(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let i=s,o=!1;s.rank===3&&(o=!0,i=q(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let l={x:i},c={depthRadius:t,bias:n,alpha:r,beta:a},u=D.runKernel(Ru,l,c);return o?q(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var Gf=z({localResponseNormalization_:yS});function gS(e){let t={x:R(e,"x","log")};return D.runKernel(vs,t)}var Sn=z({log_:gS});function xS(e){let t={x:R(e,"x","log1p")};return D.runKernel(Io,t)}var Sd=z({log1p_:xS});function wS(e){return F(xa(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let r=R(t,"x","tf.grad","string_or_numeric"),a=n!=null?R(n,"dy","tf.grad"):null;return D.tidy(()=>{let{value:s,grads:i}=D.gradients(()=>e(r),[r],a);return a!=null&&tn(s.shape,a.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),Td(i),i[0]})}}function _S(e){return F(xa(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{F(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let r=Gu(t,"args","tf.grads","string_or_numeric"),a=n!=null?R(n,"dy","tf.grads"):null;return D.tidy(()=>{let{value:s,grads:i}=D.gradients(()=>e(...r),r,a);return a!=null&&tn(s.shape,a.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Td(i),i})}}function bS(e){return F(xa(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{F(t instanceof Ke,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),F(n==null||n instanceof Ke,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:r,value:a}=D.gradients(()=>e(t),[t],n);return Td(r),{grad:r[0],value:a}}}function vS(e){return F(xa(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{F(Array.isArray(t)&&t.every(a=>a instanceof Ke),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),F(n==null||n instanceof Ke,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let r=D.gradients(()=>e(...t),t,n);return n!=null&&tn(r.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Td(r.grads),r}}function R5(e,t){F(xa(e),()=>"The f passed in variableGrads(f) must be a function"),F(t==null||Array.isArray(t)&&t.every(c=>c instanceof ju),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let c in D.registeredVariables)t.push(D.registeredVariables[c])}let r=n?t.filter(c=>!c.trainable):null,a=t.length;t=t.filter(c=>c.trainable),F(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${a} variables is trainable.`);let s=!0,{value:i,grads:o}=D.gradients(e,t,null,s);F(o.some(c=>c!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),F(i.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);let l={};return t.forEach((c,u)=>{o[u]!=null&&(l[c.name]=o[u])}),r!=null&&r.forEach(c=>l[c.name]=null),{value:i,grads:l}}function Or(e){return D.customGrad(e)}function Td(e){if(e.filter(t=>t==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
the f you passed encloses all operations that lead from x to y.`)}function kS(e){let t={x:R(e,"x","neg")};return D.runKernel(To,t)}var _t=z({neg_:kS});function IS(e){let t={x:R(e,"x","softplus")};return D.runKernel(jo,t)}var fl=z({softplus_:IS});function NS(e){let t=R(e,"x","logSigmoid");return Or(n=>({value:_t(fl(_t(n))),gradFunc:r=>L(r,In(_t(n)))}))(t)}var F5=z({logSigmoid_:NS});function SS(e,t=null,n=!1){let r={x:R(e,"x","max")},a={reductionIndices:t,keepDims:n};return D.runKernel(ks,r,a)}var jn=z({max_:SS});function TS(e,t){let n=R(e,"a","sub"),r=R(t,"b","sub");[n,r]=wt(n,r);let a={a:n,b:r};return D.runKernel(qs,a)}var Ae=z({sub_:TS});function ES(e,t=null,n=!1){let r=R(e,"x","sum");r.dtype==="bool"&&(r=me(r,"int32"));let a={x:r},s={axis:t,keepDims:n};return D.runKernel(js,a,s)}var Ie=z({sum_:ES});function CS(e,t=-1){let n=R(e,"logits","logSoftmax");if(t===-1&&(t=n.rank-1),t!==n.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and axis was ${t}`);return Or((r,a)=>{let s=!0,i=jn(r,t,!0),o=Ae(r,i),l=Ae(me(o,"float32"),Sn(Ie(Un(o),t,s)));return a([l]),{value:l,gradFunc:(c,u)=>{let[h]=u,p=!0,d=Un(h);return Ae(c,L(Ie(c,t,p),d))}}})(n)}var Ed=z({logSoftmax_:CS});function qf(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function M5(e,t,n){let r=e.length+t.length,a=[],s=0,i=0;for(let o=0;o<r;o++)n.indexOf(o)===-1?a.push(e[s++]):a.push(t[i++]);return a}function O5(e,t){let n=[],r=e.length;for(let s=0;s<r;s++)t.indexOf(s)===-1&&n.push(e[s]);let a=t.map(s=>e[s]);return[n,a]}function ui(e,t){let n=t.map(r=>1);return M5(e,n,t)}function RS(e,t,n){F(qf(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function $5(e,t){if(qf(e,t))return null;let n=[];for(let r=0;r<t;++r)e.indexOf(r)===-1&&n.push(r);return e.forEach(r=>n.push(r)),n}function Xf(e){return e.map((t,n)=>[n,t]).sort((t,n)=>t[1]-n[1]).map(t=>t[0])}function FS(e,t){let n=[];for(let r=t-e;r<t;++r)n.push(r);return n}function MS(e,t=null,n=!1){let r=R(e,"x","logSumExp"),a=Qn(t,r.shape),s=jn(r,a,!0),i=Ae(r,s),o=Un(i),l=Ie(o,a),c=Sn(l),u=se(q(s,c.shape),c);if(n){let h=ui(u.shape,a);return q(u,h)}return u}var Kf=z({logSumExp_:MS});function OS(e,t){let n=R(e,"a","logicalAnd","bool"),r=R(t,"b","logicalAnd","bool");At(n.shape,r.shape);let a={a:n,b:r};return D.runKernel(No,a)}var rr=z({logicalAnd_:OS});function $S(e){let t={x:R(e,"x","logicalNot","bool")};return D.runKernel(Eu,t)}var rc=z({logicalNot_:$S});function DS(e,t){let n=R(e,"a","logicalOr","bool"),r=R(t,"b","logicalOr","bool");At(n.shape,r.shape);let a={a:n,b:r};return D.runKernel(Cu,a)}var Cd=z({logicalOr_:DS});function zS(e,t){let n=R(e,"a","logicalXor","bool"),r=R(t,"b","logicalXor","bool");return At(n.shape,r.shape),rr(Cd(e,t),rc(rr(e,t)))}var D5=z({logicalXor_:zS});function PS(e,t,n,r,a){let s=R(e,"x","maxPool"),i=1,o=s,l=!1;s.rank===3&&(l=!0,o=q(s,[1,s.shape[0],s.shape[1],s.shape[2]])),F(o.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.rank}.`),F(Mr(n,i),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`),a!=null&&F(Lt(r),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let c={x:o},u={filterSize:t,strides:n,pad:r,dimRoundingMode:a},h=D.runKernel(Ns,c,u);return l?q(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var ac=z({maxPool_:PS});function LS(e,t=[1,1,1],n,r,a,s="NDHWC"){let i=R(e,"x","maxPool3d"),o=i,l=!1;i.rank===4&&(l=!0,o=q(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(o.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${o.rank}.`),F(s==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),a!=null&&F(Lt(r),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let c={x:o},u={filterSize:t,strides:n,pad:r,dimRoundingMode:a,dataFormat:s},h=D.runKernel(Fu,c,u);return l?q(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var Zf=z({maxPool3d_:LS});function WS(e,t,n,r,a=!1){let s={x:R(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:n,pad:r,includeBatchInIndex:a},o=D.runKernel(Jh,s,i);return{result:o[0],indexes:o[1]}}var z5=z({maxPoolWithArgmax_:WS});function BS(e,t){let n=R(e,"a","maximum"),r=R(t,"b","maximum");[n,r]=wt(n,r),n.dtype==="bool"&&(n=me(n,"int32"),r=me(r,"int32")),At(n.shape,r.shape);let a={a:n,b:r};return D.runKernel(Is,a)}var $r=z({maximum_:BS});function VS(e,t=null,n=!1){let r={x:R(e,"x","mean")},a={axis:t,keepDims:n};return D.runKernel(Ss,r,a)}var bt=z({mean_:VS});function US(e,t=null,n=!1){let r={x:R(e,"x","min")},a={axis:t,keepDims:n};return D.runKernel(Ts,r,a)}var ml=z({min_:US});function jS(e,t){let n=R(e,"a","minimum"),r=R(t,"b","minimum");[n,r]=wt(n,r),n.dtype==="bool"&&(n=me(n,"int32"),r=me(r,"int32")),At(n.shape,r.shape);let a={a:n,b:r};return D.runKernel(Es,a)}var Al=z({minimum_:jS});function HS(e,t,n){F(n==="reflect"||n==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);let r=R(e,"x","mirrorPad");if(r.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");F(t.length===r.rank,()=>`Padding doesn't match input. Must be ${r.rank}. Got ${t.length}.`);let a=n==="reflect"?1:0;for(let o=0;o<r.rank;o++)F(t[o].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),F(t[o][0]>=0&&t[o][0]<=r.shape[o]-a&&t[o][1]>=0&&t[o][1]<=r.shape[o]-a,()=>`Padding in dimension ${o} cannot be greater than or equal to ${r.shape[o]-a} or less than 0 for input of shape ${r.shape}`);let s={paddings:t,mode:n},i={x:r};return D.runKernel(Mu,i,s)}var Yf=z({mirrorPad_:HS});function GS(e,t){let n=R(e,"a","mod"),r=R(t,"b","mod");[n,r]=wt(n,r);let a={a:n,b:r};return D.runKernel(So,a)}var Jf=z({mod_:GS});function qS(e){let t=R(e,"x","square"),n={};return D.runKernel("Square",{x:t},n)}var lt=z({square_:qS});function XS(e,t=null,n=!1){e=R(e,"x","moments");let r=Qn(t,e.shape),a=bt(e,r,n),s=a.shape;n||(s=ui(a.shape,r));let i=lt(Ae(me(e,"float32"),q(a,s))),o=bt(i,r,n);return{mean:a,variance:o}}var Rd=z({moments_:XS});function KS(e,t,n,r){let a=R(t,"data","multiRNNCell"),s=Gu(n,"c","multiRNNCell"),i=Gu(r,"h","multiRNNCell"),o=a,l=[];for(let h=0;h<e.length;h++){let p=e[h](o,s[h],i[h]);l.push(p[0]),l.push(p[1]),o=p[1]}let c=[],u=[];for(let h=0;h<l.length;h+=2)c.push(l[h]),u.push(l[h+1]);return[c,u]}var ZS=z({multiRNNCell_:KS});function YS(e,t,n,r=!1){let a=R(e,"logits","multinomial"),s=a.size,i=a.rank;if(s<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${s}.`);if(i>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${i}`);n=n||Math.random();let o={logits:i===1?q(a,[1,-1]):a},l={numSamples:t,seed:n,normalized:r},c=D.runKernel(Qh,o,l);return i===1?q(c,[c.size]):c}var P5=z({multinomial_:YS});function JS(e,t){let n=R(e,"a","notEqual"),r=R(t,"b","notEqual");[n,r]=wt(n,r),At(n.shape,r.shape);let a={a:n,b:r};return D.runKernel(Eo,a)}var ci=z({notEqual_:JS});function Tt(e,t="float32"){if(t==="complex64"){let r=Tt(e,"float32"),a=Tt(e,"float32");return ka(r,a)}let n=Th(Rt(e),t);return D.makeTensor(n,e,t)}function Dr(e,t="float32"){if(t==="complex64"){let r=Dr(e,"float32"),a=Tt(e,"float32");return ka(r,a)}let n=V1(Rt(e),t);return D.makeTensor(n,e,t)}function QS(e){let t={x:R(e,"x","onesLike")};return D.runKernel(Mo,t)}var Tn=z({onesLike_:QS});function eT(e,t){let n=R(e,"v1","outerProduct"),r=R(t,"v2","outerProduct");F(n.rank===1&&r.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${r.rank}.`);let a=q(n,[-1,1]),s=q(r,[1,-1]);return He(a,s)}var tT=z({outerProduct_:eT});function nT(e,t,n=0){let r=R(e,"x","pad");if(r.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let a={paddings:t,constantValue:n},s={x:r};return D.runKernel(Fs,s,a)}var ra=z({pad_:nT});function rT(e,t,n=0){return F(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),ra(e,[t],n)}var aT=z({pad1d_:rT});function sT(e,t,n=0){return F(t.length===2&&t[0].length===2&&t[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),ra(e,t,n)}var iT=z({pad2d_:sT});function oT(e,t,n=0){return F(t.length===3&&t[0].length===2&&t[1].length===2&&t[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),ra(e,t,n)}var lT=z({pad3d_:oT});function uT(e,t,n=0){return F(t.length===4&&t[0].length===2&&t[1].length===2&&t[2].length===2&&t[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),ra(e,t,n)}var cT=z({pad4d_:uT});function hT(e,t,n){let r=R(e,"x","spaceToBatchND");F(r.rank>=1+t.length,()=>`input rank ${r.rank} should be > than [blockShape] ${t.length}`),F(n.length===t.length,()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`),F(r.shape.reduce((i,o,l)=>l>0&&l<=t.length?i&&(o+n[l-1][0]+n[l-1][1])%t[l-1]==0:i,!0),()=>`input spatial dimensions ${r.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);let a={x:r},s={blockShape:t,paddings:n};return D.runKernel(Du,a,s)}var sc=z({spaceToBatchND_:hT});function fT(e,t,n,r,a,s){a==null&&(a=[1,1]),s==null&&(s=1),r===0&&(r="valid");let i=R(e,"x","maxPool"),o=i,l=!1;i.rank===3&&(l=!0,o=q(i,[1,i.shape[0],i.shape[1],i.shape[2]])),F(Mr(s,a),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${a}'`);let c=m5(o.shape,t,s,a,r),u=[c.dilationHeight,c.dilationWidth],h;r==="same"?h=pT([c.filterHeight,c.filterWidth],u):h=[[0,0],[0,0]];let p=u[0]===1&&u[1]===1,[d,f]=dT([c.inHeight,c.inWidth],u,h),m=p?r:"valid",A=p?o:sc(o,u,d),y=(n==="avg"?()=>Yu(A,t,s,m):()=>ac(A,t,s,m))(),g=p?y:Ju(y,u,f);return l?q(g,[g.shape[1],g.shape[2],g.shape[3]]):g}function dT(e,t,n){let r=n.map(u=>u[0]),a=n.map(u=>u[1]),s=e.concat(r,a),i=t.map((u,h)=>(u-s[h]%u)%u),o=a.map((u,h)=>u+i[h]),l=t.map((u,h)=>[r[h],o[h]]),c=t.map((u,h)=>[0,i[h]]);return[l,c]}function pT(e,t){let n=e.map((s,i)=>s+(s-1)*(t[i]-1)).map(s=>s-1),r=n.map(s=>Math.floor(s/2)),a=n.map((s,i)=>s-r[i]);return n.map((s,i)=>[r[i],a[i]])}var L5=z({pool_:fT});function mT(e,t){let n=R(e,"base","pow"),r=R(t,"exp","pow");[n,r]=wt(n,r);let a={a:n,b:r};return D.runKernel(Ms,a)}var aa=z({pow_:mT});function AT(e,t){let n=R(e,"x","prelu"),r=R(t,"alpha","prelu"),a={x:n,alpha:r};return D.runKernel(Os,a)}var ic=z({prelu_:AT});function yT(e,t=null,n=!1){let r=R(e,"x","prod");r.dtype==="bool"&&(r=me(r,"int32"));let a={x:r},s={axis:t,keepDims:n};return D.runKernel($o,a,s)}var Fd=z({prod_:yT});function gT(e,t,n){let r=Rt(e),a=null;if(n==null||n==="float32")a=new Float32Array(r);else if(n==="int32")a=new Int32Array(r);else if(n==="bool")a=new Uint8Array(r);else throw new Error(`Unknown data type ${n}`);for(let s=0;s<r;s++)a[s]=t();return D.makeTensor(a,e,n)}var xT=z({rand_:gT}),Qf=qi(J8()),em=class{constructor(e,t,n,r,a){this.mean=e,this.stdDev=t,this.dtype=n,this.nextVal=NaN,this.truncated=r,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let s=a||Math.random();this.random=Qf.alea(s.toString())}nextValue(){if(!isNaN(this.nextVal)){let r=this.nextVal;return this.nextVal=NaN,r}let e,t,n=!1;for(;!n;){let r,a,s;do r=2*this.random()-1,a=2*this.random()-1,s=r*r+a*a;while(s>=1||s===0);let i=Math.sqrt(-2*Math.log(s)/s);e=this.mean+this.stdDev*r*i,t=this.mean+this.stdDev*a*i,(!this.truncated||this.isValidTruncated(e))&&(n=!0)}return(!this.truncated||this.isValidTruncated(t))&&(this.nextVal=this.convertValue(t)),this.convertValue(e)}convertValue(e){return this.dtype==null||this.dtype==="float32"?e:Math.round(e)}isValidTruncated(e){return e<=this.upper&&e>=this.lower}},wT=class{constructor(e,t,n,r){this.alpha=e,this.beta=1/t,this.dtype=n;let a=r||Math.random();this.randu=Qf.alea(a.toString()),this.randn=new em(0,1,n,!1,this.randu()),e<1?this.d=e+2/3:this.d=e-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let e,t,n,r,a,s;for(;;){do r=this.randn.nextValue(),s=1+this.c*r;while(s<=0);if(s*=s*s,e=r*r,t=1-.331*e*e,n=.5*e+this.d*(1-s+Math.log(s)),a=this.randu(),a<t||Math.log(a)<n)break}return s=1/this.beta*this.d*s,this.alpha<1&&(s*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(s)}convertValue(e){return this.dtype==="float32"?e:Math.round(e)}},_T=class{constructor(e=0,t=1,n,r){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=e,this.range=t-e,this.dtype=n,r==null&&(r=Math.random()),typeof r=="number"&&(r=r.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);this.random=Qf.alea(r)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function bT(e,t,n=1,r="float32",a){if(n==null&&(n=1),r==null&&(r="float32"),r!=="float32"&&r!=="int32")throw new Error(`Unsupported data type ${r}`);let s=new wT(t,n,r,a),i=Le(e,r);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var vT=z({randomGamma_:bT});function kT(e,t=0,n=1,r,a){if(r!=null&&r==="bool")throw new Error(`Unsupported data type ${r}`);let s=new em(t,n,r,!1,a),i=Le(e,r);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var W5=z({randomNormal_:kT});function IT(e,t=0,n=1,r="float32",a){let s=Le(e,r),i=new _T(t,n,null,a);for(let o=0;o<s.values.length;o++)s.values[o]=i.nextValue();return s.toTensor()}var yl=z({randomUniform_:IT});function Md(e,t,n=1,r="float32"){if(n===0)throw new Error("Cannot have a step of zero");let a={start:e,stop:t,step:n,dtype:r};return D.runKernel(Ou,{},a)}function NT(e){let t={input:R(e,"input","real")};return D.runKernel(ed,t)}var oc=z({real_:NT});function ST(e){let t={x:R(e,"x","reciprocal")};return D.runKernel(Do,t)}var tm=z({reciprocal_:ST});function TT(e){let t={x:R(e,"x","relu")};return D.runKernel($s,t)}var zr=z({relu_:TT});function ET(e){let t={x:R(e,"x","relu6")};return D.runKernel(zs,t)}var Od=z({relu6_:ET});function CT(e,t){let n={x:R(e,"x","reverse")},r={dims:t};return D.runKernel(Ps,n,r)}var En=z({reverse_:CT});function RT(e){let t=R(e,"x","reverse");return F(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),En(t,0)}var FT=z({reverse1d_:RT});function MT(e,t){let n=R(e,"x","reverse");return F(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),En(n,t)}var OT=z({reverse2d_:MT});function $T(e,t){let n=R(e,"x","reverse");return F(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),En(n,t)}var DT=z({reverse3d_:$T});function zT(e,t){let n=R(e,"x","reverse");return F(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),En(n,t)}var PT=z({reverse4d_:zT});function LT(e){let t={x:R(e,"x","round")};return D.runKernel(Ls,t)}var nm=z({round_:LT});function WT(e){let t={x:R(e,"x","rsqrt")};return D.runKernel(Ws,t)}var $d=z({rsqrt_:WT});function ke(e,t){if((nn(e)&&t!=="string"||Array.isArray(e))&&t!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(t==="string"&&nn(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return Ia(e,[],[],t)}function BT(e){let t={x:R(e,"x","selu")};return D.runKernel(Wo,t)}var Dd=z({selu_:BT});function VT(e,t,n,r,a,s=[1,1],i="NHWC"){let o=R(e,"x","separableConv2d"),l=R(t,"depthwiseFilter","separableConv2d"),c=R(n,"pointwiseFilter","separableConv2d"),u=o,h=!1;if(o.rank===3&&(h=!0,u=q(o,[1,o.shape[0],o.shape[1],o.shape[2]])),i==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");F(u.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${u.rank}.`),F(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),F(c.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),F(c.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${c.shape[0]}.`),F(c.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${c.shape[1]}.`);let p=l.shape[2],d=l.shape[3];F(c.shape[2]===p*d,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${p*d}, but got ${c.shape[2]}.`);let f=hl(u,l,r,a,i,s),m=na(f,c,1,"valid",i);return h?q(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var rm=z({separableConv2d_:VT});async function UT(e,t){let n=R(e,"x","setdiff1d"),r=R(t,"y","setdiff1d");F(n.dtype===r.dtype,()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${r.dtype}).`),F(n.rank===1,()=>`x should be 1D tensor, but got x (${n.shape}).`),F(r.rank===1,()=>`y should be 1D tensor, but got y (${r.shape}).`);let a=await n.data(),s=await r.data(),i=new Set(s),o=0;for(let u=0;u<a.length;u++)i.has(a[u])||o++;let l=new Ft([o],n.dtype),c=new Ft([o],"int32");for(let u=0,h=0;u<a.length;u++)i.has(a[u])||(l.values[h]=a[u],c.values[h]=u,h++);return[l.toTensor(),c.toTensor()]}var B5=UT;function jT(e){let t={x:R(e,"x","sign")};return D.runKernel(Uo,t)}var am=z({sign_:jT});function HT(e){let t={x:R(e,"x","sin")};return D.runKernel(Bs,t)}var zd=z({sin_:HT});function GT(e){let t={x:R(e,"x","sinh")};return D.runKernel(Vo,t)}var Pd=z({sinh_:GT});function qT(e,t,n){let r=R(e,"x","slice1d");return F(r.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${r.rank} tensor`),Te(r,[t],[n])}var Ld=z({slice1d_:qT});function XT(e,t,n){let r=R(e,"x","slice2d");return F(r.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${r.rank} tensor`),Te(r,t,n)}var sm=z({slice2d_:XT});function KT(e,t,n){let r=R(e,"x","slice3d");return F(r.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${r.rank} tensor`),Te(r,t,n)}var Wd=z({slice3d_:KT});function ZT(e,t,n){let r=R(e,"x","slice4d");return F(r.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${r.rank} tensor`),Te(r,t,n)}var lc=z({slice4d_:ZT});function YT(e,t=-1){let n=R(e,"logits","softmax","float32");if(t===-1&&(t=n.rank-1),t!==n.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and dim was ${t}`);let r={logits:n},a={dim:t};return D.runKernel(Hs,r,a)}var uc=z({softmax_:YT});function JT(e){F(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);let t={input:e};return D.runKernel(Hh,t)}var cc=z({fft_:JT});function QT(e){F(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return D.runKernel(Gh,t)}var gl=z({ifft_:QT});function eE(e){let t=e.shape[e.shape.length-1],n=e.size/t,r;if(t<=2){let a=q(e,[n,t]);r=gl(a)}else{let a=[n,2*(t-1)],s=q(oc(e),[n,t]),i=q(Id(e),[n,t]),o=En(Te(s,[0,1],[n,t-2]),1),l=L(En(Te(i,[0,1],[n,t-2]),1),ke(-1)),c=rt([s,o],1),u=rt([i,l],1),h=q(ka(c,u),[a[0],a[1]]);r=gl(h)}if(r=oc(r),e.rank===3&&e.shape[0]!==0){let a=r,s=e.shape[0];r=q(r,[s,r.shape[0]/s,r.shape[1]]),a.dispose()}return r}var Bd=z({irfft_:eE});function tE(e,t,n=0){let r={x:R(e,"x","split")},a={numOrSizeSplits:t,axis:n};return D.runKernel(Ho,r,a)}var Kt=z({split_:tE});function nE(e,t){F(e.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${e.dtype}`);let n=e.shape[e.shape.length-1],r=e.size/n,a;if(t!=null&&t<n){let f=e.shape.map(A=>0),m=e.shape.map(A=>A);m[e.shape.length-1]=t,a=Te(e,f,m),n=t}else if(t!=null&&t>n){let f=e.shape.map(m=>m);f[e.shape.length-1]=t-n,a=rt([e,Tt(f)],e.shape.length-1),n=t}else a=e;let s=Ve(a),i=q(ka(a,s),[r,n]),o=cc(i),l=Math.floor(n/2)+1,c=oc(o),u=Id(o),h=Kt(c,[l,n-l],c.shape.length-1),p=Kt(u,[l,n-l],u.shape.length-1),d=a.shape.slice();return d[a.shape.length-1]=l,q(ka(h[0],p[0]),d)}var hc=z({rfft_:nE});function rE(e){let t={x:R(e,"x","sqrt")};return D.runKernel(Us,t)}var Zt=z({sqrt_:rE});function aE(e,t){let n=R(e,"a","squaredDifference"),r=R(t,"b","squaredDifference");[n,r]=wt(n,r),At(n.shape,r.shape);let a={a:n,b:r},s={};return D.runKernel(Gs,a,s)}var Vd=z({squaredDifference_:aE});function sE(e,t){let n=R(e,"x","squeeze");return q(n,sg(n.shape,t).newShape)}var Fa=z({squeeze_:sE});function iE(e,t=0){let n=Gu(e,"tensors","stack","string_or_numeric");F(n.length>=1,()=>"Pass at least one tensor to tf.stack"),n.length>0&&F(t<=n[0].rank,()=>"Axis must be <= rank of the tensor");let r=n,a={axis:t};return D.runKernel(Oo,r,a)}var Cn=z({stack_:iE});function oE(e,t=0){let n={x:R(e,"x","step")},r={alpha:t};return D.runKernel(va,n,r)}var xl=z({step_:oE});function lE(e,t,n,r,a=0,s=0,i=0,o=0,l=0){let c={x:R(e,"x","stridedSlice")},u={begin:t,end:n,strides:r,beginMask:a,endMask:s,ellipsisMask:i,newAxisMask:o,shrinkAxisMask:l};return D.runKernel(Go,c,u)}var im=z({stridedSlice_:lE});function uE(e){let t={x:R(e,"x","tan")};return D.runKernel(qo,t)}var om=z({tan_:uE});function Bt(e,t){as(e);let n=Fr(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return Ia(e,null,n,t)}function yn(e,t,n){if(as(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let r=Fr(e,n);if(r.length!==2&&r.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return Ia(e,t,r,n)}function cE(e,t,n){if(as(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let r=Fr(e,n);if(r.length!==4&&r.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return Ia(e,t,r,n)}function hE(e,t,n){if(as(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let r=Fr(e,n);if(r.length!==5&&r.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return Ia(e,t,r,n)}function dE(e,t,n){if(as(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let r=Fr(e,n);if(r.length!==6&&r.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return t=t||r,Ia(e,t,r,n)}function pE(e,t=1,n=!0){let r=R(e,"x","topk");if(r.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let a=r.shape[r.shape.length-1];if(t>a)throw new Error(`'k' passed to topk() must be <= the last dimension (${a}) but got ${t}`);let s={x:r},i={k:t,sorted:n},[o,l]=D.runKernel(Xo,s,i);return{values:o,indices:l}}var lm=z({topk_:pE});function fE(e,t=0,n=1,r,a){if(r!=null&&r==="bool")throw new Error("Unsupported data type $ { dtype }");let s=new em(t,n,r,!0,a),i=Le(e,r);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var Ud=z({truncatedNormal_:fE});function mE(e,t=0){let n=R(e,"x","unique","string_or_numeric");F(n.rank>0,()=>"The input tensor must be at least 1D");let r={x:n},a={axis:t},[s,i]=D.runKernel(ad,r,a);return{values:s,indices:i}}var jd=z({unique_:mE});function AE(e,t,n){let r=R(e,"x","unsortedSegmentSum"),a=R(t,"segmentIds","unsortedSegmentSum","int32");F(Lt(n),()=>"numSegments must be of dtype int");let s={x:r,segmentIds:a},i={numSegments:n};return D.runKernel(Pu,s,i)}var um=z({unsortedSegmentSum_:AE});function yE(e,t=0){let n=R(e,"x","unstack","string_or_numeric");F(t>=-n.shape.length&&t<n.shape.length,()=>`Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);let r={value:n},a={axis:t};return D.runKernel(Ko,r,a)}var ar=z({unstack_:yE});function V5(e,t=!0,n,r){return D.makeVariable(e,t,n,r)}function U5(e,t){let n=[];for(let s=0;s<t.length;s++)t[s]&&n.push(s);let r=Le(e,"int32"),a=Le([n.length,e.length],"int32");for(let s=0;s<n.length;s++){let i=r.indexToLoc(n[s]),o=s*e.length;a.values.set(i,o)}return a.toTensor()}async function gE(e){let t=R(e,"condition","whereAsync","bool"),n=await t.data(),r=U5(t.shape,n);return e!==t&&t.dispose(),r}var cm=gE;async function xE(e,t,n){let r=R(e,"tensor","boolMask"),a=R(t,"mask","boolMask","bool"),s=n==null?0:n,i=a.rank,o=r.shape;F(i>0,()=>"mask cannot be scalar"),tn(o.slice(s,s+i),a.shape,"mask's shape must match the first K dimensions of tensor's shape,");let l=1;for(let m=s;m<s+i;m++)l*=o[m];let c=o.slice(0,s).concat([l],o.slice(s+i)),u=q(r,c),h=q(a,[-1]),p=await cm(h),d=Fa(p,[1]),f=oi(u,d,s);return e!==r&&r.dispose(),t!==a&&a.dispose(),d.dispose(),u.dispose(),h.dispose(),p.dispose(),f}var wE=xE;function _E(e,t="euclidean",n=null,r=!1){e=R(e,"x","norm");let a=j5(e,t,n),s=a.shape;if(r){let i=Qn(n,e.shape);s=ui(a.shape,i)}return q(a,s)}function j5(e,t,n=null){if(e.rank===0)return Mt(e);if(e.rank!==1&&n===null)return j5(q(e,[-1]),t,n);if(e.rank===1||typeof n=="number"||Array.isArray(n)&&n.length===1){if(t===1)return Ie(Mt(e),n);if(t===Infinity)return jn(Mt(e),n);if(t===-Infinity)return ml(Mt(e),n);if(t==="euclidean"||t===2)return Zt(Ie(aa(Mt(e),ke(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&n.length===2){if(t===1)return jn(Ie(Mt(e),n[0]),n[1]-1);if(t===Infinity)return jn(Ie(Mt(e),n[1]),n[0]);if(t===-Infinity)return ml(Ie(Mt(e),n[1]),n[0]);if(t==="fro"||t==="euclidean")return Zt(Ie(lt(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}var Hd=z({norm_:_E});function bE(e,t,n,r,a=!0){let s=R(e,"v","movingAverage"),i=R(t,"x","movingAverage"),o=R(n,"decay","movingAverage");vg(s,i),F(Qr(s.shape,i.shape),()=>"Shape mismatch in v and x");let l=ke(1),c=Ae(l,o),u=L(Ae(i,s),c);if(a){F(r!=null,()=>"When using zeroDebias: true, step is required.");let h=R(r,"step","movingAverage");u=be(u,Ae(l,aa(o,h)))}return se(s,u)}var vE=z({movingAverage_:bE});function kE(e,t,n){let r=R(e,"indices","scatterND","int32"),a=R(t,"updates","scatterND");xf(a,r,n);let s={indices:r,updates:a},i={shape:n};return D.runKernel(Po,s,i)}var H5=z({scatterND_:kE});function IE(e,t,n,r){if(e.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${e.dtype}.`);if(e.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${e.shape}.`);let a=e.rank>0?e.shape[0]:1,s=e.rank>1?e.shape[1]:1;if(n.length!==s)throw new Error(`outputShape has incorrect number of elements:, ${n.length}, should be: ${s}.`);let i=t.size;if(!(t.rank===0||t.rank===1&&i===a))throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${a}]`);if(t.dtype!==r.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function NE(e,t,n,r=0){let a=R(e,"sparseIndices","sparseToDense","int32"),s=R(t,"sparseValues","sparseToDense"),i=R(r,"defaultValue","sparseToDense",s.dtype);IE(a,s,n,i);let o={sparseIndices:a,sparseValues:s,defaultValue:i},l={outputShape:n};return D.runKernel(rd,o,l)}var hm=z({sparseToDense_:NE});function SE(e,t){let n=R(t,"indices","gatherND","int32"),r={params:R(e,"x","gatherND"),indices:n};return D.runKernel(yo,r)}var G5=z({gatherND_:SE});function TE(e,t){if(t==null)return e.shape.slice();if(Qr(e.shape,t))return t;if(e.shape.length===t.length){let n=[];for(let r=0;r<e.shape.length;r++)t[r]==null&&e.shape[r]!=null?n.push(e.shape[r]):n.push(t[r]);return n}return t}function EE(e,t,n,r){let a=R(e,"x","dropout");if(F(a.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${a.dtype} tensor instead.`),F(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof Ke?a.clone():a;let s=TE(a,n),i=1-t,o=be(pl(se(yl(s,0,1,"float32",r),i)),i);return L(a,o)}var q5=z({dropout_:EE});function X5(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function dm(e,t,n){let r=1-e%2,a=new Float32Array(e);for(let s=0;s<e;++s){let i=2*Math.PI*s/(e+r-1);a[s]=t-n*Math.cos(i)}return Bt(a,"float32")}async function CE(e,t,n=1){let r=R(e,"predictions","inTopK"),a=R(t,"targets","inTopK");F(r.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${r.rank}`),F(r.rank-1===a.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${r.rank} and targets rank ${a.rank}`),tn(r.shape.slice(0,r.shape.length-1),a.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let s=r.shape[r.shape.length-1];F(n>0&&n<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${n}`);let i=await r.data(),o=await a.data(),[l,c]=[i.length/s,s],u=ig("bool",l);for(let h=0;h<l;h++){let p=h*c,d=i.subarray(p,p+c),f=[];for(let m=0;m<d.length;m++)f.push({value:d[m],index:m});f.sort((m,A)=>A.value-m.value),u[h]=0;for(let m=0;m<n;m++)if(f[m].index===o[h]){u[h]=1;break}}return e!==r&&r.dispose(),t!==a&&a.dispose(),fr(u,a.shape,"bool")}var RE=CE,Ma={};$e(Ma,{conv2d:()=>FE,depthwiseConv2d:()=>ME,matMul:()=>OE});function $E(e,t,n,r,a,s="NHWC",i){let o=e;e.rank===3&&(o=q(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=q(t,[1,t.shape[0],t.shape[1],t.shape[2]])),F(o.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`),F(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),F(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let c=s==="NHWC"?o.shape[3]:o.shape[1],u=s==="NHWC"?l.shape[3]:l.shape[1];F(c===n[2],()=>`Error in conv2dDerFilter: depth of input ${c}) must match input depth in filter (${n[2]}.`),F(u===n[3],()=>`Error in conv2dDerFilter: depth of dy (${u}) must match output depth for filter (${n[3]}).`),i!=null&&F(Lt(a),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let h={x:o,dy:l},p={strides:r,pad:a,dataFormat:s,dimRoundingMode:i,filterShape:n};return D.runKernel($h,h,p)}var pm=z({conv2DBackpropFilter_:$E});function Gd(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return L(e,xl(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function qd(e,t){let n=t,r=Ot(e.shape,t.shape);return r.length>0&&(n=Ie(n,r)),q(n,e.shape)}function Xd(e,t,n,r){if(t==="linear")return e;if(t==="relu")return zr(e);if(t==="elu")return dl(e);if(t==="relu6")return Od(e);if(t==="prelu")return ic(e,n);if(t==="leakyrelu")return nc(e,r);throw new Error(`Unknown fused activation ${t}.`)}var Kd=(e,t)=>!(e>0)||t==="linear";function DE({x:e,filter:t,strides:n,pad:r,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(l=l||"linear",Kd(D.state.gradientDepth,l)===!1){let w=na(e,t,n,r,a,s,i);return o!=null&&(w=se(w,o)),Xd(w,l,c,u)}let h=R(e,"x","conv2d"),p=R(t,"filter","conv2d"),d=h,f=!1;h.rank===3&&(f=!0,d=q(h,[1,h.shape[0],h.shape[1],h.shape[2]])),F(d.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${d.rank}.`),F(p.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${p.rank}.`),i!=null&&F(Lt(r),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),F(d.shape[3]===p.shape[2],()=>`Error in conv2d: depth of input (${d.shape[3]}) must match input depth for filter ${p.shape[2]}.`),F(Mr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),F(a==="NHWC",()=>`Error in conv2d: got dataFormat of ${a} but only NHWC is currently supported.`);let m=Zu(d.shape,p.shape,n,s,r,i),A;o!=null&&(A=R(o,"bias","fused conv2d"),[A]=wt(A,h),At(m.outShape,A.shape));let y;c!=null&&(y=R(c,"prelu weights","fused conv2d"));let g=(w,b)=>{let[N,T,E,M]=b,$=Gd(w,E,l);F(Ta(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let P=Pf(T.shape,$,N,n,r),V=pm(T,$,N.shape,n,r),G=[P,V];if(M!=null){let U=qd(M,$);G.push(U)}return G},_={x:d,filter:p,bias:A,preluActivationWeights:y},x={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:u};return o==null?Or((w,b,N)=>{let T=D.runKernel(Ys,_,x);return N([b,w,T]),f&&(T=q(T,[T.shape[1],T.shape[2],T.shape[3]])),{value:T,gradFunc:g}})(d,p):Or((w,b,N,T)=>{let E=D.runKernel(Ys,_,x);return T([b,w,E,N]),f&&(E=q(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:g}})(d,p,A)}var FE=z({fusedConv2d_:DE});function zE(e,t,n,r,a,s=[1,1],i){let o=e;e.rank===3&&(o=q(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=q(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let c={x:o,dy:l},u={strides:r,pad:a,dimRoundingMode:i,dilations:s,filterShape:n};return D.runKernel(Lh,c,u)}var K5=z({depthwiseConv2dNativeBackpropFilter_:zE});function PE(e,t,n,r,a,s=[1,1],i){let o=t,l=!1;t.rank===3&&(l=!0,o=q(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let c={dy:o,filter:n},u={strides:r,pad:a,dimRoundingMode:i,dilations:s,inputShape:e},h=D.runKernel(Wh,c,u);return l?q(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Z5=z({depthwiseConv2dNativeBackpropInput_:PE});function LE({x:e,filter:t,strides:n,pad:r,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(Kd(D.state.gradientDepth,l)===!1){let w=hl(e,t,n,r,a,s,i);return o!=null&&(w=se(w,o)),Xd(w,l,c,u)}let h=R(e,"x","depthwiseConv2d"),p=R(t,"filter","depthwiseConv2d"),d=h,f=!1;h.rank===3&&(f=!0,d=q(h,[1,h.shape[0],h.shape[1],h.shape[2]])),F(d.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${d.rank}.`),F(p.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${p.rank}.`),F(d.shape[3]===p.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${d.shape[3]}) must match the inChannels dimension in filter ${p.shape[2]}.`),s==null&&(s=[1,1]),F(Mr(n,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),i!=null&&F(Lt(r),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${i} but got pad ${r}.`);let m=Zu(d.shape,p.shape,n,s,r,i,!0),A;o!=null&&(A=R(o,"bias","fused conv2d"),[A]=wt(A,h),At(m.outShape,A.shape));let y;c!=null&&(y=R(c,"prelu weights","fused depthwiseConv2d"));let g=(w,b)=>{F(Ta(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[N,T,E,M]=b,$=Gd(w,E,l),P=Z5(T.shape,$,N,n,r,s,i),V=K5(T,$,N.shape,n,r,s,i);if(M!=null){let G=qd(A,$);return[P,V,G]}return[P,V]},_={x:d,filter:p,bias:A,preluActivationWeights:y},x={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:u};return o==null?Or((w,b,N)=>{let T=D.runKernel(Js,_,x);return N([b,w,T]),f&&(T=q(T,[T.shape[1],T.shape[2],T.shape[3]])),{value:T,gradFunc:g}})(d,p):Or((w,b,N,T)=>{let E=D.runKernel(Js,_,x);return T([b,w,E,N]),f&&(E=q(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:g}})(d,p,A)}var ME=z({fusedDepthwiseConv2d_:LE});function WE({a:e,b:t,transposeA:n=!1,transposeB:r=!1,bias:a,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:o}){if(Kd(D.state.gradientDepth,s)===!1){let M=He(e,t,n,r);return a!=null&&(M=se(M,a)),Xd(M,s,i,o)}let l=R(e,"a","fused matMul"),c=R(t,"b","fused matMul");[l,c]=wt(l,c);let u=n?l.shape[l.rank-2]:l.shape[l.rank-1],h=r?c.shape[c.rank-1]:c.shape[c.rank-2],p=n?l.shape[l.rank-1]:l.shape[l.rank-2],d=r?c.shape[c.rank-2]:c.shape[c.rank-1],f=l.shape.slice(0,-2),m=c.shape.slice(0,-2),A=Rt(f),y=Rt(m);F(l.rank>=2&&c.rank>=2&&l.rank===c.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${l.rank} and ${c.rank}.`),F(Qr(f,m),()=>`Error in fused matMul: outer dimensions (${f}) and (${m}) of Tensors with shapes ${l.shape} and ${c.shape} must match.`),F(u===h,()=>`Error in fused matMul: inner shapes (${u}) and (${h}) of Tensors with shapes ${l.shape} and ${c.shape} and transposeA=${n} and transposeB=${r} must match.`);let g=l.shape.slice(0,-2).concat([p,d]),_=n?q(l,[A,u,p]):q(l,[A,p,u]),x=r?q(c,[y,d,h]):q(c,[y,h,d]),w;a!=null&&(w=R(a,"bias","fused matMul"),[w]=wt(w,l),At(g,w.shape));let b;i!=null&&(b=R(i,"prelu weights","fused matMul"));let N=(M,$)=>{let[P,V,G,U]=$,K=Gd(q(M,G.shape),G,s),X,ee;if(!n&&!r?(X=He(K,V,!1,!0),ee=He(P,K,!0,!1)):!n&&r?(X=He(K,V,!1,!1),ee=He(K,P,!0,!1)):n&&!r?(X=He(V,K,!1,!0),ee=He(P,K,!1,!1)):(X=He(V,K,!0,!0),ee=He(K,P,!0,!0)),a!=null){let Z=qd(U,K);return[X,ee,Z]}else return[X,ee]},T={a:_,b:x,bias:w,preluActivationWeights:b},E={transposeA:n,transposeB:r,activation:s,leakyreluAlpha:o};return a==null?Or((M,$,P)=>{let V=D.runKernel(Zs,T,E);return P([M,$,V]),{value:q(V,g),gradFunc:N}})(_,x):Or((M,$,P,V)=>{let G=D.runKernel(Zs,T,E);return V([M,$,G,P]),{value:q(G,g),gradFunc:N}})(_,x,w)}var OE=z({fusedMatMul_:WE});function BE(e){return dm(e,.54,.46)}var VE=z({hammingWindow_:BE});function UE(e){return dm(e,.5,.5)}var Y5=z({hannWindow_:UE});function jE(e,t,n,r=!1,a=0){let s=0,i=[];for(;s+t<=e.size;)i.push(Te(e,s,t)),s+=n;if(r)for(;s<e.size;){let o=s+t-e.size,l=rt([Te(e,s,t-o),tc([o],a)]);i.push(l),s+=n}return i.length===0?yn([],[0,t]):q(rt(i),[i.length,t])}var J5=z({frame_:jE});function HE(e,t,n,r,a=Y5){r==null&&(r=X5(t));let s=J5(e,t,n),i=L(s,a(t)),o=[];for(let l=0;l<s.shape[0];l++)o.push(hc(Te(i,[l,0],[1,t]),r));return rt(o)}var GE=z({stft_:HE});function qE(e,t,n,r,a="bilinear",s=0){let i=R(e,"image","cropAndResize"),o=R(t,"boxes","cropAndResize","float32"),l=R(n,"boxInd","cropAndResize","int32"),c=o.shape[0];F(i.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),F(o.rank===2&&o.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${c},4] but had shape ${o.shape}.`),F(l.rank===1&&l.shape[0]===c,()=>`Error in cropAndResize: boxInd must be have size [${c}] but had shape ${o.shape}.`),F(r.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${r.length}.`),F(r[0]>=1&&r[1]>=1,()=>`cropSize must be atleast [1,1], but was ${r}`),F(a==="bilinear"||a==="nearest",()=>`method must be bilinear or nearest, but was ${a}`);let u={image:i,boxes:o,boxInd:l},h={method:a,extrapolationValue:s,cropSize:r};return D.runKernel(oo,u,h)}var XE=z({cropAndResize_:qE});function KE(e){let t=R(e,"image","flipLeftRight","float32");F(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let n={image:t};return D.runKernel(mo,n,{})}var ZE=z({flipLeftRight_:KE});function YE(e,t,n=0,r=.5){let a=R(e,"image","rotateWithOffset","float32");F(a.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${a.rank}.`);let s={image:a},i={radians:t,fillValue:n,center:r};return D.runKernel(Yo,s,i)}var JE=z({rotateWithOffset_:YE});function wl(e,t,n,r,a,s){r==null&&(r=.5),a==null&&(a=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),F(0<=r&&r<=1,()=>`iouThreshold must be in [0, 1], but was '${r}'`),F(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),F(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),F(t.rank===1,()=>"scores must be a 1D tensor"),F(t.shape[0]===i,()=>`scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`),F(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:n,iouThreshold:r,scoreThreshold:a,softNmsSigma:s}}function QE(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){let s=R(e,"boxes","nonMaxSuppression"),i=R(t,"scores","nonMaxSuppression"),o=wl(s,i,n,r,a);n=o.maxOutputSize,r=o.iouThreshold,a=o.scoreThreshold;let l={maxOutputSize:n,iouThreshold:r,scoreThreshold:a};return D.runKernel(Co,{boxes:s,scores:i},l)}var eC=z({nonMaxSuppression_:QE});function nC(e,t,n){let r=tC(e,t,n),a=r<0?-(r+1):r;e.splice(a,0,t)}function tC(e,t,n){return aC(e,t,n||rC)}function rC(e,t){return e>t?1:e<t?-1:0}function aC(e,t,n){let r=0,a=e.length,s=0,i=!1;for(;r<a;){s=r+(a-r>>>1);let o=n(t,e[s]);o>0?r=s+1:(a=s,i=!o)}return i?r:-r-1}function Q5(e,t,n,r,a){return fm(e,t,n,r,a,0)}function ex(e,t,n,r,a,s){return fm(e,t,n,r,a,0,!1,s,!0)}function tx(e,t,n,r,a,s){return fm(e,t,n,r,a,s,!0)}function fm(e,t,n,r,a,s,i=!1,o=!1,l=!1){let c=[];for(let A=0;A<t.length;A++)t[A]>a&&c.push({score:t[A],boxIndex:A,suppressBeginIndex:0});c.sort(nx);let u=s>0?-.5/s:0,h=[],p=[];for(;h.length<n&&c.length>0;){let A=c.pop(),{score:y,boxIndex:g,suppressBeginIndex:_}=A;if(y<a)break;let x=!1;for(let w=h.length-1;w>=_;--w){let b=sC(e,g,h[w]);if(b>=r){x=!0;break}if(A.score=A.score*iC(r,u,b),A.score<=a)break}A.suppressBeginIndex=h.length,x||(A.score===y?(h.push(g),p.push(A.score)):A.score>a&&nC(c,A,nx))}let d=h.length,f=n-d;o&&f>0&&(h.push(...new Array(f).fill(0)),p.push(...new Array(f).fill(0)));let m={selectedIndices:h};return i&&(m.selectedScores=p),l&&(m.validOutputs=d),m}function sC(e,t,n){let r=e.subarray(t*4,t*4+4),a=e.subarray(n*4,n*4+4),s=Math.min(r[0],r[2]),i=Math.min(r[1],r[3]),o=Math.max(r[0],r[2]),l=Math.max(r[1],r[3]),c=Math.min(a[0],a[2]),u=Math.min(a[1],a[3]),h=Math.max(a[0],a[2]),p=Math.max(a[1],a[3]),d=(o-s)*(l-i),f=(h-c)*(p-u);if(d<=0||f<=0)return 0;let m=Math.max(s,c),A=Math.max(i,u),y=Math.min(o,h),g=Math.min(l,p),_=Math.max(y-m,0)*Math.max(g-A,0);return _/(d+f-_)}function iC(e,t,n){let r=Math.exp(t*n*n);return n<=e?r:0}function nx(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function oC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){let s=R(e,"boxes","nonMaxSuppressionAsync"),i=R(t,"scores","nonMaxSuppressionAsync"),o=wl(s,i,n,r,a);n=o.maxOutputSize,r=o.iouThreshold,a=o.scoreThreshold;let l=await Promise.all([s.data(),i.data()]),c=l[0],u=l[1],{selectedIndices:h}=Q5(c,u,n,r,a);return s!==e&&s.dispose(),i!==t&&i.dispose(),Bt(h,"int32")}var lC=oC;function uC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){let i=R(e,"boxes","nonMaxSuppression"),o=R(t,"scores","nonMaxSuppression"),l=wl(i,o,n,r,a,s);n=l.maxOutputSize,r=l.iouThreshold,a=l.scoreThreshold,s=l.softNmsSigma;let c={boxes:i,scores:o},u={maxOutputSize:n,iouThreshold:r,scoreThreshold:a,softNmsSigma:s},h=D.runKernel(Fo,c,u);return{selectedIndices:h[0],selectedScores:h[1]}}var cC=z({nonMaxSuppressionWithScore_:uC});async function hC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){let i=R(e,"boxes","nonMaxSuppressionAsync"),o=R(t,"scores","nonMaxSuppressionAsync"),l=wl(i,o,n,r,a,s);n=l.maxOutputSize,r=l.iouThreshold,a=l.scoreThreshold,s=l.softNmsSigma;let c=await Promise.all([i.data(),o.data()]),u=c[0],h=c[1],{selectedIndices:p,selectedScores:d}=tx(u,h,n,r,a,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:Bt(p,"int32"),selectedScores:Bt(d)}}var dC=hC;function pC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=R(e,"boxes","nonMaxSuppression"),o=R(t,"scores","nonMaxSuppression"),l=wl(i,o,n,r,a,null),c=l.maxOutputSize,u=l.iouThreshold,h=l.scoreThreshold,p={boxes:i,scores:o},d={maxOutputSize:c,iouThreshold:u,scoreThreshold:h,padToMaxOutputSize:s},f=D.runKernel(Ro,p,d);return{selectedIndices:f[0],validOutputs:f[1]}}var fC=z({nonMaxSuppressionPadded_:pC});async function mC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=R(e,"boxes","nonMaxSuppressionAsync"),o=R(t,"scores","nonMaxSuppressionAsync"),l=wl(i,o,n,r,a,null),c=l.maxOutputSize,u=l.iouThreshold,h=l.scoreThreshold,[p,d]=await Promise.all([i.data(),o.data()]),{selectedIndices:f,validOutputs:m}=ex(p,d,c,u,h,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:Bt(f,"int32"),validOutputs:ke(m,"int32")}}var AC=mC;function yC(e,t,n=!1,r=!1){let a=R(e,"images","resizeBilinear");F(a.rank===3||a.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${a.rank}.`),F(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),F(r===!1||n===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=a,i=!1;a.rank===3&&(i=!0,s=q(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},c=D.runKernel(Ds,o,l);return i?q(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var rx=z({resizeBilinear_:yC});function gC(e,t,n=!1,r=!1){let a=R(e,"images","resizeNearestNeighbor");F(a.rank===3||a.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${a.rank}.`),F(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),F(a.dtype==="float32"||a.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),F(r===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=a,i=!1;a.rank===3&&(i=!0,s=q(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},c=D.runKernel($u,o,l);return i?q(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var ax=z({resizeNearestNeighbor_:gC});function xC(e,t,n){F(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),F(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let r=R(e,"a","bandPart");F(r.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${r.rank}.`);let a=r.shape,[s,i]=r.shape.slice(-2);if(!(t<=s))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`);if(!(n<=i))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);t<0&&(t=s),n<0&&(n=i);let o=q(Md(0,s,1,"int32"),[-1,1]),l=Md(0,i,1,"int32"),c=Ae(o,l),u=rr(li(c,ke(+t,"int32")),Ra(c,ke(-n,"int32"))),h=Tt([s,i],r.dtype);return q(Cn(ar(q(r,[-1,s,i])).map(p=>An(u,p,h))),a)}var wC=z({bandPart_:xC});function _C(e){let t;if(Array.isArray(e)){t=!1,F(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let a=e[0].shape[0];for(let s=1;s<e.length;++s)F(e[s].shape[0]===a,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[s].shape[0]} vs. ${a})`)}else t=!0,e=Kt(e,e.shape[0],0).map(a=>Fa(a,[0]));F(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],r=e;for(let a=0;a<e.length;++a)n.push(D.tidy(()=>{let s=r[a];if(a>0)for(let i=0;i<a;++i){let o=L(Ie(L(n[i],s)),n[i]);s=Ae(s,o)}return be(s,Hd(s,"euclidean"))}));return t?Cn(n,0):n}var bC=z({gramSchmidt_:_C});function vC(e,t=!1){if(F(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return sx(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,c)=>l*c),r=ar(q(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),a=[],s=[];r.forEach(l=>{let[c,u]=sx(l,t);a.push(c),s.push(u)});let i=q(Cn(a,0),e.shape),o=q(Cn(s,0),e.shape);return[i,o]}}function sx(e,t=!1){return D.tidy(()=>{F(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],r=e.shape[1],a=Hf(n),s=tr(e),i=yn([[1]],[1,1]),o=tr(i),l=n>=r?r:n;for(let c=0;c<l;++c){let u=s,h=o,p=a;[o,s,a]=D.tidy(()=>{let d=Te(s,[c,c],[n-c,1]),f=Hd(d),m=Te(s,[c,c],[1,1]),A=An(nr(m,0),yn([[-1]]),yn([[1]])),y=Ae(m,L(A,f)),g=be(d,y);g.shape[0]===1?o=tr(i):o=rt([i,Te(g,[1,0],[g.shape[0]-1,g.shape[1]])],0);let _=_t(be(He(A,y),f)),x=Te(s,[c,0],[n-c,r]),w=L(_,o),b=nt(o);if(c===0)s=Ae(x,He(w,He(b,x)));else{let E=Ae(x,He(w,He(b,x)));s=rt([Te(s,[0,0],[c,r]),E],0)}let N=nt(w),T=Te(a,[0,c],[n,a.shape[1]-c]);if(c===0)a=Ae(T,He(He(T,o),N));else{let E=Ae(T,He(He(T,o),N));a=rt([Te(a,[0,0],[n,c]),E],1)}return[o,s,a]}),Se([u,h,p])}return!t&&n>r&&(a=Te(a,[0,0],[n,r]),s=Te(s,[0,0],[r,r])),[a,s]})}var kC=z({qr_:vC}),on;(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(on||(on={}));function IC(e,t,n=on.SUM_BY_NONZERO_WEIGHTS){let r=R(e,"losses","computeWeightedLoss"),a=null;t!=null&&(a=R(t,"weights","computeWeightedLoss"));let s=a==null?r:L(r,a);if(n===on.NONE)return s;if(n===on.SUM)return Ie(s);if(n===on.MEAN){if(a==null)return bt(s);{let i=r.size/a.size,o=be(Ie(s),Ie(a));return i>1?be(o,ke(i)):o}}if(n===on.SUM_BY_NONZERO_WEIGHTS){if(a==null)return be(Ie(s),ke(r.size));{let i=L(a,Dr(r.shape)),o=me(Ie(ci(i,ke(0))),"float32");return be(Ie(s),o)}}throw Error(`Unknown reduction: ${n}`)}var sa=z({computeWeightedLoss_:IC});function NC(e,t,n,r=on.SUM_BY_NONZERO_WEIGHTS){let a=R(e,"labels","absoluteDifference"),s=R(t,"predictions","absoluteDifference"),i=null;n!=null&&(i=R(n,"weights","absoluteDifference")),tn(a.shape,s.shape,"Error in absoluteDifference: ");let o=Mt(Ae(a,s));return sa(o,i,r)}var SC=z({absoluteDifference_:NC});function TC(e,t,n,r,a=on.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"labels","cosineDistance"),i=R(t,"predictions","cosineDistance"),o=null;r!=null&&(o=R(r,"weights","cosineDistance")),tn(s.shape,i.shape,"Error in cosineDistance: ");let l=ke(1),c=Ae(l,Ie(L(s,i),n,!0));return sa(c,o,a)}var EC=z({cosineDistance_:TC});function CC(e,t,n,r=on.SUM_BY_NONZERO_WEIGHTS){let a=R(e,"labels","hingeLoss"),s=R(t,"predictions","hingeLoss"),i=null;n!=null&&(i=R(n,"weights","hingeLoss")),tn(a.shape,s.shape,"Error in hingeLoss: ");let o=ke(1);a=Ae(L(ke(2),a),o);let l=zr(Ae(o,L(a,s)));return sa(l,i,r)}var RC=z({hingeLoss_:CC});function FC(e,t,n,r=1,a=on.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"labels","huberLoss"),i=R(t,"predictions","huberLoss"),o=null;n!=null&&(o=R(n,"weights","huberLoss")),tn(s.shape,i.shape,"Error in huberLoss: ");let l=ke(r),c=Mt(Ae(i,s)),u=Al(c,l),h=Ae(c,u),p=se(L(ke(.5),lt(u)),L(l,h));return sa(p,o,a)}var MC=z({huberLoss_:FC});function OC(e,t,n,r=1e-7,a=on.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"labels","logLoss"),i=R(t,"predictions","logLoss"),o=null;n!=null&&(o=R(n,"weights","logLoss")),tn(s.shape,i.shape,"Error in logLoss: ");let l=ke(1),c=ke(r),u=_t(L(s,Sn(se(i,c)))),h=L(Ae(l,s),Sn(se(Ae(l,i),c))),p=Ae(u,h);return sa(p,o,a)}var $C=z({logLoss_:OC});function DC(e,t,n,r=on.SUM_BY_NONZERO_WEIGHTS){let a=R(e,"labels","meanSquaredError"),s=R(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=R(n,"weights","meanSquaredError")),tn(a.shape,s.shape,"Error in meanSquaredError: ");let o=Vd(a,s);return sa(o,i,r)}var zC=z({meanSquaredError_:DC});function PC(e,t){let n=R(e,"labels","sigmoidCrossEntropyWithLogits"),r=R(t,"logits","sigmoidCrossEntropyWithLogits");tn(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");let a=zr(r),s=L(r,n),i=Sd(Un(_t(Mt(r))));return se(Ae(a,s),i)}function LC(e,t,n,r=0,a=on.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"multiClassLabels","sigmoidCrossEntropy"),i=R(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=R(n,"weights","sigmoidCrossEntropy")),tn(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),r>0){let c=ke(r),u=ke(1),h=ke(.5);s=se(L(s,Ae(u,c)),L(h,c))}let l=PC(s,i);return sa(l,o,a)}var WC=z({sigmoidCrossEntropy_:LC});function BC(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);return Or((r,a,s)=>{let i=Kf(a,[n],!0),o=Ae(me(a,"float32"),i);s([r,o]);let l=_t(L(o,r));return{value:Ie(l,[n]),gradFunc:(c,u)=>{let[h,p]=u,d=ui(c.shape,[n]);return[L(q(c,d),Ae(me(h,"float32"),Un(p))),L(q(c,d),Ae(Un(p),me(h,"float32")))]}}})(e,t)}function VC(e,t,n,r=0,a=on.SUM_BY_NONZERO_WEIGHTS){let s=R(e,"onehotLabels","softmaxCrossEntropy"),i=R(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=R(n,"weights","softmaxCrossEntropy")),tn(s.shape,i.shape,"Error in softmaxCrossEntropy: "),r>0){let c=ke(r),u=ke(1),h=ke(s.shape[1]);s=se(L(s,Ae(u,c)),be(c,h))}let l=BC(s,i);return sa(l,o,a)}var UC=z({softmaxCrossEntropy_:VC}),jC={fft:cc,ifft:gl,rfft:hc,irfft:Bd},HC={hammingWindow:VE,hannWindow:Y5,frame:J5,stft:GE},at={flipLeftRight:ZE,resizeNearestNeighbor:ax,resizeBilinear:rx,rotateWithOffset:JE,cropAndResize:XE,nonMaxSuppression:eC,nonMaxSuppressionAsync:lC,nonMaxSuppressionWithScore:cC,nonMaxSuppressionWithScoreAsync:dC,nonMaxSuppressionPadded:fC,nonMaxSuppressionPaddedAsync:AC},ix={bandPart:wC,gramSchmidt:bC,qr:kC},GC={absoluteDifference:SC,computeWeightedLoss:sa,cosineDistance:EC,hingeLoss:RC,huberLoss:MC,logLoss:$C,meanSquaredError:zC,sigmoidCrossEntropy:WC,softmaxCrossEntropy:UC},ia=class extends i5{minimize(e,t=!1,n){let{value:r,grads:a}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:a[i.name]}));this.applyGradients(s)}else this.applyGradients(a);return Se(a),t?r:(r.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return R5(e,t)}dispose(){this.iterations_!=null&&Se(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ke(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(ia,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Zd=class extends ia{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=D.registeredVariables[t],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:W(()=>Ve(r).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:W(()=>Ve(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;W(()=>{let l=se(L(i,this.rho),L(lt(s),1-this.rho)),c=L(be(Zt(se(o,this.epsilon)),Zt(se(i,this.epsilon))),s),u=se(L(o,this.rho),L(lt(c),1-this.rho));i.assign(l),o.assign(u);let h=se(L(c,-this.learningRate),r);r.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Se(this.accumulatedGrads.map(e=>e.variable)),Se(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Zd.className="Adadelta";Sa(Zd);var Yd=class extends ia{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=D.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:W(()=>tc(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[n].variable;W(()=>{let i=se(s,lt(a));s.assign(i);let o=se(L(be(a,Zt(se(i,D.backend.epsilon()))),-this.learningRate),r);r.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Se(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Yd.className="Adagrad";Sa(Yd);var Jd=class extends ia{constructor(e,t,n,r=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],W(()=>{this.accBeta1=ke(t).variable(),this.accBeta2=ke(n).variable()}),r==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);W(()=>{let n=Ae(1,this.accBeta1),r=Ae(1,this.accBeta2);t.forEach((a,s)=>{let i=D.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:W(()=>Ve(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${a}/v`,variable:W(()=>Ve(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let c=this.accumulatedFirstMoment[s].variable,u=this.accumulatedSecondMoment[s].variable,h=se(L(c,this.beta1),L(l,1-this.beta1)),p=se(L(u,this.beta2),L(lt(l),1-this.beta2)),d=be(h,n),f=be(p,r);c.assign(h),u.assign(p);let m=se(L(be(d,se(Zt(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Se(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Se(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),W(()=>{this.accBeta1.assign(aa(this.beta1,this.iterations_+1)),this.accBeta2.assign(aa(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Jd.className="Adam";Sa(Jd);var Qd=class extends ia{constructor(e,t,n,r=null,a=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],W(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),r==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);W(()=>{let n=Ae(1,this.accBeta1),r=be(-this.learningRate,se(L(this.iteration,this.decay),1));t.forEach((a,s)=>{let i=D.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:Ve(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${a}/v`,variable:Ve(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let c=this.accumulatedFirstMoment[s].variable,u=this.accumulatedWeightedInfNorm[s].variable,h=se(L(c,this.beta1),L(l,1-this.beta1)),p=L(u,this.beta2),d=Mt(l),f=$r(p,d);c.assign(h),u.assign(f);let m=se(L(be(r,n),be(h,se(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(se(this.iteration,1)),this.accBeta1.assign(L(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Se(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Se(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Qd.className="Adamax";Sa(Qd);var dc=class extends ia{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let a=D.registeredVariables[t];W(()=>{let s=se(L(this.c,r),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Wt(ke(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};dc.className="SGD";Sa(dc);var ep=class extends dc{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=ke(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=D.registeredVariables[t];if(this.accumulations[n]==null){let i=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:W(()=>Ve(r).variable(i))}}let a=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];s!=null&&W(()=>{let i,o=se(L(this.m,a),s);this.useNesterov?i=se(L(this.c,se(s,L(o,this.m))),r):i=se(L(this.c,o),r),a.assign(o),r.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Se(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};ep.className="Momentum";Sa(ep);var tp=class extends ia{constructor(e,t=.9,n=0,r=null,a=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,r==null&&(this.epsilon=D.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=D.registeredVariables[t],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:W(()=>Ve(r).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:W(()=>Ve(r).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:W(()=>Ve(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;W(()=>{let l=se(L(i,this.decay),L(lt(s),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[n].variable,u=se(L(c,this.decay),L(s,1-this.decay)),h=be(L(s,this.learningRate),Zt(Ae(l,se(lt(u),this.epsilon)))),p=se(L(o,this.momentum),h);i.assign(l),c.assign(u),o.assign(p);let d=Ae(r,p);r.assign(d)}else{let c=se(L(i,this.decay),L(lt(s),1-this.decay)),u=se(L(o,this.momentum),be(L(s,this.learningRate),Zt(se(c,this.epsilon))));i.assign(c),o.assign(u);let h=Ae(r,u);r.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Se(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Se(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Se(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};tp.className="RMSProp";Sa(tp);var hi=class{static sgd(e){return new dc(e)}static momentum(e,t,n=!1){return new ep(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,a=!1){return new tp(e,t,n,r,a)}static adam(e=.001,t=.9,n=.999,r=null){return new Jd(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new Zd(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,a=0){return new Qd(e,t,n,r,a)}static adagrad(e,t=.1){return new Yd(e,t)}},di={sgd:hi.sgd,momentum:hi.momentum,adadelta:hi.adadelta,adagrad:hi.adagrad,rmsprop:hi.rmsprop,adamax:hi.adamax,adam:hi.adam},qC=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function np(){return new Promise(e=>qC(()=>e()))}var C={};$e(C,{ERF_A1:()=>aR,ERF_A2:()=>sR,ERF_A3:()=>iR,ERF_A4:()=>oR,ERF_A5:()=>lR,ERF_P:()=>rR,PARALLELIZE_THRESHOLD:()=>mm,SELU_SCALE:()=>lx,SELU_SCALEALPHA:()=>ox,applyActivation:()=>Xd,assertAndGetBroadcastShape:()=>At,assertAxesAreInnerMostDims:()=>RS,assertParamsConsistent:()=>XC,assignToTypedArray:()=>AR,axesAreInnerMostDims:()=>qf,calculateShapes:()=>Xg,combineLocations:()=>M5,complexWithEvenIndex:()=>pR,complexWithOddIndex:()=>fR,computeConv2DInfo:()=>Zu,computeConv3DInfo:()=>A5,computeDefaultPad:()=>$f,computeDilation2DInfo:()=>tN,computeOptimalWindowSize:()=>ZC,computeOutAndReduceShapes:()=>O5,computeOutShape:()=>KC,computePool2DInfo:()=>m5,computePool3DInfo:()=>nN,convertConv2DDataFormat:()=>f5,eitherStridesOrDilationsAreOne:()=>Mr,expandShapeToKeepDim:()=>ui,exponent:()=>gR,exponents:()=>yR,fromStringArrayToUint8:()=>_R,fromUint8ToStringArray:()=>wR,getAxesPermutation:()=>$5,getBroadcastDims:()=>qN,getComplexWithIndex:()=>mR,getFusedBiasGradient:()=>qd,getFusedDyActivation:()=>Gd,getImageCenter:()=>YC,getInnerMostAxes:()=>FS,getPermuted:()=>QC,getReductionAxes:()=>Ot,getReshaped:()=>JC,getReshapedPermuted:()=>eR,getSliceBeginCoords:()=>tR,getSliceSize:()=>nR,getUndoAxesPermutation:()=>Xf,log:()=>cR,mergeRealAndImagArrays:()=>hR,prepareAndValidate:()=>qg,prepareSplitSize:()=>xR,segment_util:()=>ux,shouldFuse:()=>Kd,slice_util:()=>sn,splitRealAndImagArrays:()=>dR,tupleValuesAreOne:()=>Ta,upcastType:()=>er,validateInput:()=>xf,validateUpdateShape:()=>gf,warn:()=>uR});function XC(e,t){let n=e[0].length;e.forEach((a,s)=>{F(a.length===n,()=>`Error in concat${n}D: rank of tensors[${s}] must be the same as the rank of the rest (${n})`)}),F(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let r=e[0];e.forEach((a,s)=>{for(let i=0;i<n;i++)F(i===t||a[i]===r[i],()=>`Error in concat${n}D: Shape of tensors[${s}] (${a}) does not match the shape of the rest (${r}) along the non-concatenated axis ${s}.`)})}function KC(e,t){let n=e[0].slice();for(let r=1;r<e.length;r++)n[t]+=e[r][t];return n}var mm=30;function ZC(e){return e<=mm?e:Sh(e,Math.floor(Math.sqrt(e)))}function YC(e,t,n){let r=n*(typeof e=="number"?e:e[0]),a=t*(typeof e=="number"?e:e[1]);return[r,a]}function JC(e,t,n,r=!0){let a=[];if(r)a=a.concat(t.slice(0)),a.push(e[0]/n),a=a.concat(e.slice(1));else{a=a.concat(e[0]);let s=t.length;for(let i=0;i<s;++i)a=a.concat([e[i+1]/t[i],t[i]]);a=a.concat(e.slice(s+1))}return a}function QC(e,t,n=!0){let r=[];if(n){r.push(t);for(let a=t+1;a<e;++a)a<=2*t?(r.push(a),r.push(a-(t+1))):r.push(a)}else{let a=[],s=[];for(let i=1;i<e;++i)i>=t*2+1||i%2==1?s.push(i):a.push(i);r.push(...a),r.push(0),r.push(...s)}return r}function eR(e,t,n,r=!0){let a=[];r?a.push(e[0]/n):a.push(e[0]*n);for(let s=1;s<e.length;++s)s<=t.length?r?a.push(t[s-1]*e[s]):a.push(e[s]/t[s-1]):a.push(e[s]);return a}function tR(e,t){let n=[0];for(let r=0;r<t;++r)n.push(e[r][0]);return n}function nR(e,t,n){let r=e.slice(0,1);for(let a=0;a<n;++a)r.push(e[a+1]-t[a][0]-t[a][1]);return r}var ox=1.7580993408473768,lx=1.0507009873554805,rR=.3275911,aR=.254829592,sR=-.284496736,iR=1.421413741,oR=-1.453152027,lR=1.061405429;function uR(...e){Q().getBool("IS_TEST")||console.warn(...e)}function cR(...e){Q().getBool("IS_TEST")||console.log(...e)}function hR(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n}function dR(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return{real:t,imag:n}}function pR(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let a=0;a<e.length;a+=4)n[Math.floor(a/4)]=e[a],r[Math.floor(a/4)]=e[a+1];return{real:n,imag:r}}function fR(e){let t=Math.floor(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let a=2;a<e.length;a+=4)n[Math.floor(a/4)]=e[a],r[Math.floor(a/4)]=e[a+1];return{real:n,imag:r}}function mR(e,t){let n=e[t*2],r=e[t*2+1];return{real:n,imag:r}}function AR(e,t,n,r){e[r*2]=t,e[r*2+1]=n}function yR(e,t){let n=new Float32Array(e/2),r=new Float32Array(e/2);for(let a=0;a<Math.ceil(e/2);a++){let s=(t?2:-2)*Math.PI*(a/e);n[a]=Math.cos(s),r[a]=Math.sin(s)}return{real:n,imag:r}}function gR(e,t,n){let r=(n?2:-2)*Math.PI*(e/t),a=Math.cos(r),s=Math.sin(r);return{real:a,imag:s}}function xR(e,t,n=0){let r=[];if(typeof t=="number")F(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),r=new Array(t).fill(e.shape[n]/t);else{let a=t.reduce((i,o)=>(o===-1&&(i+=1),i),0);F(a<=1,()=>"There should be only one negative value in split array.");let s=t.indexOf(-1);if(s!==-1){let i=t.reduce((o,l)=>l>0?o+l:o);t[s]=e.shape[n]-i}F(e.shape[n]===t.reduce((i,o)=>i+o),()=>"The sum of sizes must match the size of the axis dimension."),r=t}return r}var ux={};$e(ux,{collectGatherOpShapeInfo:()=>kR,computeOutShape:()=>vR,segOpComputeOptimalWindowSize:()=>bR});function bR(e,t){let n=!1,r;for(e<=mm?(r=e,n=!0):r=Sh(e,Math.floor(Math.sqrt(e)));!n;)r>t||r===e?n=!0:r=Sh(e,r+1);return r}function vR(e,t,n){let r=[],a=e.length;for(let s=0;s<a;s++)s!==t?r.push(e[s]):r.push(n);return r}function kR(e,t,n,r){let a=t.shape.length,s=e.shape.length;if(r!==0&&(r<-a||r>a))throw new Error(`Expect batchDims in the range of [-${a}, ${a}], but got ${r}`);if(r<0&&(r+=a),r>s)throw new Error(`batchDims (${r}) must be less than rank(x) (
${s}).`);if(n<r)throw new Error(`batchDims (${r}) must be less than or equal to axis (${n}).`);for(let h=0;h<r;++h)if(e.shape[h]!==t.shape[h])throw new Error(`x.shape[${h}]: ${e.shape[h]} should be equal to indices.shape[${h}]: ${t.shape[h]}.`);let i=e.shape[n],o=[],l=1,c=1,u=1;for(let h=0;h<r;++h)o.push(e.shape[h]),l*=e.shape[h];for(let h=r;h<n;h++)o.push(e.shape[h]),c*=e.shape[h];for(let h=r;h<a;h++)o.push(t.shape[h]);for(let h=n+1;h<s;h++)o.push(e.shape[h]),u*=e.shape[h];return{batchSize:l,sliceSize:u,outerSize:c,dimSize:i,outputShape:o}}function wR(e){try{return e.map(t=>ld(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function _R(e){return e.map(t=>Wu(t))}var Pr={};$e(Pr,{nonMaxSuppressionV3Impl:()=>Q5,nonMaxSuppressionV4Impl:()=>ex,nonMaxSuppressionV5Impl:()=>tx,whereImpl:()=>U5});function we(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var IR=Pr.whereImpl,cx=class extends xu{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Ih(this,Vn())}write(e,t,n){this.firstUse&&(this.firstUse=!1,Q().get("IS_NODE")&&C.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
============================`));let r={};return this.data.set(r,{values:e,dtype:n,refCount:1}),r}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let a=n.map(s=>k.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return{dataId:r,shape:e,dtype:t}}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,r){this.data.set(e,{values:t,dtype:r,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let r=this.readSync(n.real.dataId),a=this.readSync(n.imag.dataId);return C.mergeRealAndImagArrays(r,a)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>k.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,n)}makeOutput(e,t,n){let r=this.write(e,t,n);return Vn().makeTensorFromDataId(r,t,n,this)}disposeData(e){if(this.data.has(e)){let{complexTensorInfos:t}=this.data.get(e);t!=null&&(this.disposeData(t.real.dataId),this.disposeData(t.imag.dataId)),this.data.delete(e)}}disposeIntermediateTensorInfo(e){let t=e.dataId;if(this.data.has(t)){let n=this.data.get(t);n.refCount--,n.refCount<1&&this.disposeData(t)}}async time(e){let t=k.now();return e(),{kernelMs:k.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){we([e],"where");let t=this.readSync(e.dataId);return IR(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}},Am={};$e(Am,{addImpl:()=>dx,bincountImpl:()=>ym,bincountReduceImpl:()=>px,ceilImpl:()=>fx,concatImpl:()=>gm,expImpl:()=>mx,expm1Impl:()=>Ax,floorImpl:()=>yx,gatherV2Impl:()=>gx,greaterImpl:()=>xx,lessImpl:()=>wx,linSpaceImpl:()=>_x,logImpl:()=>bx,maxImpl:()=>vx,maximumImpl:()=>kx,minimumImpl:()=>Ix,multiplyImpl:()=>xm,negImpl:()=>Nx,notEqualImpl:()=>Sx,prodImpl:()=>Tx,rangeImpl:()=>_m,rsqrtImpl:()=>Ex,simpleAbsImpl:()=>hx,sliceImpl:()=>rp,squaredDifferenceImpl:()=>Cx,stridedSliceImpl:()=>Rx,subImpl:()=>Fx,tileImpl:()=>Mx,topKImpl:()=>Ox,transposeImpl:()=>wm,uniqueImpl:()=>$x});function hx(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var NR=e=>{let{x:t}=e.inputs,n=e.backend;we(t,"abs");let r=new Float32Array(k.sizeFromShape(t.shape)),a=n.data.get(t.dataId).values;return r=hx(a),n.makeOutput(r,t.shape,"float32")},SR={kernelName:Zi,backendName:"cpu",kernelFunc:NR};function Et(e){return(t,n,r,a,s)=>{let i=C.assertAndGetBroadcastShape(t,n),o=i.length,l=k.computeStrides(i),c=k.sizeFromShape(i),u=k.getTypedArrayFromDType(s,c),h=t.length,p=n.length,d=k.computeStrides(t),f=k.computeStrides(n),m=C.getBroadcastDims(t,i),A=C.getBroadcastDims(n,i);if(m.length+A.length===0)for(let y=0;y<u.length;++y)u[y]=e(r[y%r.length],a[y%a.length]);else for(let y=0;y<u.length;++y){let g=k.indexToLoc(y,o,l),_=g.slice(-h);m.forEach(N=>_[N]=0);let x=k.locToIndex(_,h,d),w=g.slice(-p);A.forEach(N=>w[N]=0);let b=k.locToIndex(w,p,f);u[y]=e(r[x],a[b])}return[u,i]}}function Rn(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.data.get(r.dataId).values,i=n.data.get(a.dataId).values,o=n.makeTensorInfo(r.shape,"complex64"),l=n.data.get(o.dataId);return l.complexTensorInfos={real:n.makeTensorInfo(r.shape,"float32",s),imag:n.makeTensorInfo(a.shape,"float32",i)},o}var TR={kernelName:Oh,backendName:"cpu",kernelFunc:Rn};function ap(e,t,n="float32"){if(n==="complex64"){let a=ap(e,t,"float32"),s=ap(e,t,"float32");return Rn({inputs:{real:a,imag:s},backend:e})}let r=k.makeZerosTypedArray(k.sizeFromShape(t),n);return e.makeTensorInfo(t,n,r)}function Lr(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var ER={kernelName:xo,backendName:"cpu",kernelFunc:Lr};function pi(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.data.get(r.dataId).complexTensorInfos.real,s=n.data.get(a.dataId).values;return n.makeTensorInfo(a.shape,a.dtype,s)}var CR={kernelName:ed,backendName:"cpu",kernelFunc:pi};function Oa(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Lr({inputs:{x:a},backend:n});let i=ap(n,a.shape,a.dtype),o=Oa({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Rn({inputs:{real:o,imag:i},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=pi({inputs:{input:a},backend:n}),o=Oa({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(a.dtype,s)){let i=Lr({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32"){let i=n.data.get(a.dataId).values,o=Int32Array.from(i);return n.makeTensorInfo(a.shape,"int32",o)}if(s==="bool"){let i=n.data.get(a.dataId).values,o=k.toTypedArray([0],a.dtype),[l,c]=Et((u,h)=>u!==h?1:0)(a.shape,[],i,o,"bool");return n.makeTensorInfo(c,"bool",l)}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var RR={kernelName:cs,backendName:"cpu",kernelFunc:Oa};function Vt(e,t,n,r){return n==null?({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;we([i,o],e);let c=l.data.get(i.dataId).values,u=l.data.get(o.dataId).values,h=r||i.dtype,[p,d]=t(i.shape,o.shape,c,u,h);return l.makeTensorInfo(d,h,p)}:({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;if(i.dtype==="complex64"||o.dtype==="complex64"){let c=Oa({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),u=l.data.get(c.dataId),h=u.complexTensorInfos.real,p=u.complexTensorInfos.imag,d=l.data.get(h.dataId).values,f=l.data.get(p.dataId).values,m=Oa({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),A=l.data.get(m.dataId),y=A.complexTensorInfos.real,g=A.complexTensorInfos.imag,_=l.data.get(y.dataId).values,x=l.data.get(g.dataId).values,[w,b,N]=n(i.shape,o.shape,d,f,_,x),T=l.makeTensorInfo(N,"float32",w),E=l.makeTensorInfo(N,"float32",b),M=Rn({inputs:{real:T,imag:E},backend:l});return l.disposeIntermediateTensorInfo(c),l.disposeIntermediateTensorInfo(m),l.disposeIntermediateTensorInfo(T),l.disposeIntermediateTensorInfo(E),M}else{let c=l.data.get(i.dataId).values,u=l.data.get(o.dataId).values,h=r||i.dtype,[p,d]=t(i.shape,o.shape,c,u,h);return l.makeTensorInfo(d,h,p)}}}function bm(e){return(t,n,r,a,s,i)=>{let o=C.assertAndGetBroadcastShape(t,n),l=k.sizeFromShape(o),c=o.length,u=k.computeStrides(o),h=k.getTypedArrayFromDType("float32",l),p=k.getTypedArrayFromDType("float32",l),d=C.getBroadcastDims(t,o),f=C.getBroadcastDims(n,o),m=C.mergeRealAndImagArrays(r,a),A=C.mergeRealAndImagArrays(s,i),y=t.length,g=k.computeStrides(t),_=n.length,x=k.computeStrides(n);if(d.length+f.length===0)for(let w=0;w<h.length;w++){let b=w%m.length,N=w%A.length,T=e(m[b*2],m[b*2+1],A[N*2],A[N*2+1]);h[w]=T.real,p[w]=T.imag}else for(let w=0;w<h.length;w++){let b=k.indexToLoc(w,c,u),N=b.slice(-y);d.forEach(P=>N[P]=0);let T=k.locToIndex(N,y,g),E=b.slice(-_);f.forEach(P=>E[P]=0);let M=k.locToIndex(E,_,x),$=e(m[T*2],m[T*2+1],A[M*2],A[M*2+1]);h[w]=$.real,p[w]=$.imag}return[h,p,o]}}var dx=Et((e,t)=>e+t),FR=bm((e,t,n,r)=>({real:e+n,imag:t+r})),pc=Vt(wa,dx,FR),MR={kernelName:wa,backendName:"cpu",kernelFunc:pc};function ym(e,t,n,r,a){let s=k.sizeFromShape(r),i=k.makeZerosTypedArray(a,n);for(let o=0;o<e.length;o++){let l=e[o];if(l<0)throw new Error("Input x must be non-negative!");l>=a||(s>0?i[l]+=t[o]:i[l]+=1)}return i}function px(e,t,n,r=!1){let a=e.shape[0],s=e.shape[1],i=Le([a,n],t.dtype);for(let o=0;o<a;o++)for(let l=0;l<s;l++){let c=e.get(o,l);if(c<0)throw new Error("Input x must be non-negative!");c>=n||(r?i.set(1,o,c):t.size>0?i.set(i.get(o,c)+t.get(o,l),o,c):i.set(i.get(o,c)+1,o,c))}return i}function _l(e){return(t,n,r)=>{let a=k.getTypedArrayFromDType(n,t.length);for(let s=0;s<t.length;++s)a[s]=e(t[s],r);return a}}function st(e,t,n){return({inputs:r,attrs:a,backend:s})=>{let{x:i}=r;if(we(i,e),i.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,l=o.data.get(i.dataId).values,c=k.sizeFromShape(i.shape),u=n||i.dtype,h=k.getArrayFromDType(u,c);for(let p=0;p<c;++p)h[p]=t(l[p],a);return o.makeTensorInfo(i.shape,u,h)}}function bl(e,t,n){return({inputs:r,attrs:a,backend:s})=>{let{x:i}=r;if(we(i,e),i.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,l=o.data.get(i.dataId).values,c=n||i.dtype,u=t(l,c,a);return o.makeTensorInfo(i.shape,c,u)}}var fx=_l(e=>Math.ceil(e)),OR=bl(ao,fx),$R={kernelName:ao,backendName:"cpu",kernelFunc:OR};function gm(e,t,n,r){let a=k.getArrayFromDType(n,k.sizeFromShape(t));if(r&&n!=="string"){let s=0;e.forEach(i=>{let o=k.sizeFromShape(i.shape);a.set(i.vals,s),s+=o})}else{let s=0;e.forEach(i=>{let o=n==="string"?C.fromUint8ToStringArray(i.vals):i.vals,l=0;for(let c=0;c<i.shape[0];++c){let u=c*t[1]+s;for(let h=0;h<i.shape[1];++h)a[u+h]=o[l++]}s+=i.shape[1]})}return a}var mx=_l(e=>Math.exp(e)),Dx=bl(ys,mx),DR={kernelName:ys,backendName:"cpu",kernelFunc:Dx},Ax=_l(e=>Math.expm1(e)),zR=bl(fo,Ax),PR={kernelName:fo,backendName:"cpu",kernelFunc:zR},yx=_l(e=>Math.floor(e)),LR=bl(gs,yx),WR={kernelName:gs,backendName:"cpu",kernelFunc:LR};function gx(e,t,n){let r=Le(n,e.dtype);for(let a=0;a<r.size;++a){let s=r.indexToLoc(a).slice(),i=s[0],o=s[2],l=t.locToIndex([i,o]);s[2]=t.values[l];let c=e.locToIndex(s);r.values[a]=e.values[c]}return r}var xx=Et((e,t)=>e>t?1:0),BR=Vt(go,xx,null,"bool"),VR={kernelName:go,backendName:"cpu",kernelFunc:BR},wx=Et((e,t)=>e<t?1:0),UR=Vt(vo,wx,null,"bool"),jR={kernelName:vo,backendName:"cpu",kernelFunc:UR};function _x(e,t,n){let r=(t-e)/(n-1),a=k.makeZerosTypedArray(n,"float32");a[0]=e;for(let s=1;s<a.length;s++)a[s]=a[s-1]+r;return a}var bx=_l(e=>Math.log(e)),HR=bl(vs,bx),GR={kernelName:vs,backendName:"cpu",kernelFunc:HR};function vx(e,t,n,r){let a=k.getTypedArrayFromDType(r,k.sizeFromShape(n));for(let s=0;s<a.length;++s){let i=s*t,o=e[i];for(let l=0;l<t;++l){let c=e[i+l];c>o&&(o=c)}a[s]=o}return a}var kx=Et((e,t)=>Math.max(e,t)),qR=Vt(Is,kx),XR={kernelName:Is,backendName:"cpu",kernelFunc:qR},Ix=Et((e,t)=>Math.min(e,t)),KR=Vt(Es,Ix),ZR={kernelName:Es,backendName:"cpu",kernelFunc:KR},xm=Et((e,t)=>e*t),YR=bm((e,t,n,r)=>({real:e*n-t*r,imag:e*r+t*n})),vm=Vt(Cs,xm,YR),JR={kernelName:Cs,backendName:"cpu",kernelFunc:vm};function Nx(e,t,n){let r=k.createScalarValue(-1,n);return xm([],t,r,e,n)}function QR(e){let{inputs:t,backend:n}=e,{x:r}=t;we(r,"neg");let a=n.data.get(r.dataId).values,[s,i]=Nx(a,r.shape,r.dtype);return n.makeTensorInfo(i,r.dtype,s)}var eF={kernelName:To,backendName:"cpu",kernelFunc:QR},Sx=Et((e,t)=>e!==t?1:0),tF=Vt(Eo,Sx,null,"bool"),nF={kernelName:Eo,backendName:"cpu",kernelFunc:tF};function wm(e,t,n,r,a){let s=t.length,i=k.sizeFromShape(t),o=k.computeStrides(t),l=k.computeStrides(a),c=k.getTypedArrayFromDType(n,k.sizeFromShape(a));for(let u=0;u<i;++u){let h=k.indexToLoc(u,s,o),p=new Array(h.length);for(let f=0;f<p.length;f++)p[f]=h[r[f]];let d=k.locToIndex(p,s,l);c[d]=e[u]}return c}function sr(e){let{inputs:t,attrs:n,backend:r}=e,{x:a}=t,{perm:s}=n;we(a,"transpose");let i=a.shape.length,o=new Array(i);for(let u=0;u<o.length;u++)o[u]=a.shape[s[u]];let l=r.data.get(a.dataId).values,c=wm(l,a.shape,a.dtype,s,o);return{dataId:r.write(c,o,a.dtype),shape:o,dtype:a.dtype}}var rF={kernelName:Ks,backendName:"cpu",kernelFunc:sr};function Tx(e,t,n,r){let[a,s]=C.computeOutAndReduceShapes(e,r),i=er(t,"int32"),o=k.makeZerosTypedArray(k.sizeFromShape(a),i),l=k.sizeFromShape(s);for(let c=0;c<o.length;++c){let u=c*l,h=1;for(let p=0;p<l;++p)h*=n[u+p];o[c]=h}return{outVals:o,outShape:a,outDtype:i}}function aF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;we(a,"prod");let o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=C.getAxesPermutation(l,o),u=l,h=a,p=[];c!=null&&(h=sr({inputs:{x:a},backend:n,attrs:{perm:c}}),p.push(h),u=C.getInnerMostAxes(u.length,o));let d=n.data.get(h.dataId).values,{outVals:f,outShape:m,outDtype:A}=Tx(h.shape,h.dtype,d,u),y=m;return i&&(y=C.expandShapeToKeepDim(m,l)),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(y,A,f)}var sF={kernelName:$o,backendName:"cpu",kernelFunc:aF};function _m(e,t,n,r){let a=e===t,s=e<t&&n<0,i=t<e&&n>1;if(a||s||i)return k.makeZerosTypedArray(0,r);let o=Math.abs(Math.ceil((t-e)/n)),l=k.makeZerosTypedArray(o,r);t<e&&n===1&&(n=-1),l[0]=e;for(let c=1;c<l.length;c++)l[c]=l[c-1]+n;return l}var Ex=_l(e=>1/Math.sqrt(e)),iF=bl(Ws,Ex),oF={kernelName:Ws,backendName:"cpu",kernelFunc:iF};function rp(e,t,n,r,a){let s=sn.isSliceContinous(r,t,n),i=k.sizeFromShape(n),o=k.computeStrides(r);if(s){let h=sn.computeFlatOffset(t,o);return a==="string"?e.slice(h,h+i):e.subarray(h,h+i)}let l=a==="string"?C.fromUint8ToStringArray(e):e,c=Le(r,a,l),u=Le(n,a);for(let h=0;h<u.size;++h){let p=u.indexToLoc(h),d=p.map((f,m)=>f+t[m]);u.set(c.get(...d),...p)}return a==="string"?C.fromStringArrayToUint8(u.values):u.values}function fi(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r;we(a,"slice");let[o,l]=sn.parseSliceParams(a,s,i);sn.assertParamsValid(a,o,l);let c=n.data.get(a.dataId).values,u=rp(c,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,u)}var lF={kernelName:Bo,backendName:"cpu",kernelFunc:fi},Cx=Et((e,t)=>{let n=e-t;return n*n}),uF=Vt(Gs,Cx),cF={kernelName:Gs,backendName:"cpu",kernelFunc:uF};function Rx(e,t,n,r){let a=Le(e,t.dtype);for(let s=0;s<a.size;s++){let i=a.indexToLoc(s),o=new Array(i.length);for(let l=0;l<o.length;l++)o[l]=i[l]*n[l]+r[l];a.set(t.get(...o),...i)}return a}var Fx=Et((e,t)=>e-t),hF=bm((e,t,n,r)=>({real:e-n,imag:t-r})),km=Vt(qs,Fx,hF),dF={kernelName:qs,backendName:"cpu",kernelFunc:km};function Mx(e,t){let n=new Array(e.rank);for(let a=0;a<n.length;a++)n[a]=e.shape[a]*t[a];let r=Le(n,e.dtype);for(let a=0;a<r.values.length;++a){let s=r.indexToLoc(a),i=new Array(e.rank);for(let l=0;l<i.length;l++)i[l]=s[l]%e.shape[l];let o=e.locToIndex(i);r.values[a]=e.values[o]}return r}function Ox(e,t,n,r,a){let s=t[t.length-1],[i,o]=[e.length/s,s],l=k.getTypedArrayFromDType(n,i*r),c=k.getTypedArrayFromDType("int32",i*r);for(let h=0;h<i;h++){let p=h*o,d=e.subarray(p,p+o),f=[];for(let g=0;g<d.length;g++)f.push({value:d[g],index:g});f.sort((g,_)=>_.value-g.value);let m=h*r,A=l.subarray(m,m+r),y=c.subarray(m,m+r);for(let g=0;g<r;g++)A[g]=f[g].value,y[g]=f[g].index}let u=t.slice();return u[u.length-1]=r,[Le(u,n,l),Le(u,"int32",c)]}function $x(e,t,n,r){let a=k.parseAxisParam(t,n)[0],s=[1,n[0],1];for(let f=0;f<a;f++)s[0]*=n[f];s[1]=n[a];for(let f=a+1;f<n.length;f++)s[2]*=n[f];let i={},o=new Int32Array(n[a]),l=new Ft(s,r,e),c=[],u=s[0]===1&&s[2]===1;for(let f=0;f<n[a];f++){let m;if(u)m=e[f].toString();else{let A=[];for(let y=0;y<s[0];y++)for(let g=0;g<s[2];g++)A.push(l.get(y,f,g));m=A.join(",")}if(i[m]!==void 0)o[f]=i[m];else{let A=Object.keys(i).length;i[m]=A,o[f]=A,c.push(f)}}let h=s.slice();h[1]=Object.keys(i).length;let p=new Ft(h,r);c.forEach((f,m)=>{for(let A=0;A<s[0];A++)for(let y=0;y<s[2];y++)p.set(l.get(A,f,y),A,m,y)});let d=n.slice();return d[a]=h[1],{outputValues:p.values,outputShape:d,indices:o}}var zx="3.0.0";ol("cpu",()=>new cx,1);var Px=st(uo,e=>e>=0?e:Math.exp(e)-1),pF={kernelName:uo,backendName:"cpu",kernelFunc:Px};function Lx(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r;we([a],"leakyRelu");let i=k.sizeFromShape(a.shape),o=n.data.get(a.dataId).values,l=k.getTypedArrayFromDType("float32",i);for(let c=0;c<o.length;c++)l[c]=o[c]<0?s*o[c]:o[c];return n.makeTensorInfo(a.shape,"float32",l)}var fF={kernelName:bs,backendName:"cpu",kernelFunc:Lx},mF=Et((e,t)=>e<0?t*e:e);function Wx(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t;we([r,a],"prelu");let s=n.data.get(r.dataId).values,i=n.data.get(a.dataId).values,[o,l]=mF(r.shape,a.shape,s,i,r.dtype);return n.makeTensorInfo(l,r.dtype,o)}var AF={kernelName:Os,backendName:"cpu",kernelFunc:Wx},Bx=st($s,e=>Math.max(0,e)),yF={kernelName:$s,backendName:"cpu",kernelFunc:Bx},Vx=st(zs,e=>Math.min(Math.max(0,e),6)),gF={kernelName:zs,backendName:"cpu",kernelFunc:Vx};function Im(e,t,n,r,a){if(n==="linear")return Lr({inputs:{x:t},backend:e});if(n==="relu")return Bx({inputs:{x:t},backend:e});if(n==="elu")return Px({inputs:{x:t},backend:e});if(n==="relu6")return Vx({inputs:{x:t},backend:e});if(n==="prelu")return Wx({inputs:{x:t,alpha:r},backend:e});if(n==="leakyrelu")return Lx({inputs:{x:t},backend:e,attrs:{alpha:a}});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function yt(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=k.sizeFromShape(a.shape),o=k.inferFromImplicitShape(s,i),l=k.sizeFromShape(o);k.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${a.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`),n.incRef(a.dataId);let c=n.data.get(a.dataId);if(c.complexTensorInfos!=null){let u=c.complexTensorInfos.real,h=c.complexTensorInfos.imag;u.shape=o,h.shape=o}return{dataId:a.dataId,shape:o,dtype:a.dtype}}var xF={kernelName:zo,backendName:"cpu",kernelFunc:yt};function Ux(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;we([a,s],"matMul");let l=a.shape.length,c=s.shape.length,u=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[c-1]:s.shape[c-2],p=i?a.shape[l-1]:a.shape[l-2],d=o?s.shape[c-2]:s.shape[c-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),A=k.sizeFromShape(f),y=k.sizeFromShape(m),g=A===y||A===1||y===1;k.assert(l>=2&&c>=2&&g,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let _=(A>y?a.shape.slice(0,-2):s.shape.slice(0,-2)).concat([p,d]);k.assert(u===h,()=>`Error in matMul: inner shapes (${u}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[A,u,p]:[A,p,u],w=o?[y,d,h]:[y,h,d],b=yt({inputs:{x:a},backend:n,attrs:{shape:x}}),N=yt({inputs:{x:s},backend:n,attrs:{shape:w}}),T=i?b.shape[1]:b.shape[2],E=i?b.shape[2]:b.shape[1],M=o?N.shape[1]:N.shape[2],$=Math.max(A,y),P=n.data.get(b.dataId).values,V=n.data.get(N.dataId).values,G=k.computeStrides(b.shape),U=k.computeStrides(N.shape),[K,X,ee]=i?[G[0],1,G[1]]:[G[0],G[1],1],[Z,ae,J]=o?[1,U[1],U[0]]:[U[1],1,U[0]],oe=E*M,ne=Le([$,E,M],b.dtype),ce=ne.values,ue=n.blockSize;for(let pe=0;pe<$;pe++)for(let fe=0;fe<E;fe+=ue)for(let _e=0;_e<M;_e+=ue)for(let Ne=0;Ne<T;Ne+=ue){let Ee=Math.min(fe+ue,E),Oe=Math.min(_e+ue,M),qe=Math.min(Ne+ue,T);for(let Be=fe;Be<Ee;Be++)for(let tt=_e;tt<Oe;tt++){let it=0;for(let Ue=Ne;Ue<qe;Ue++){let ut=Math.min(pe,A-1)*K,ct=Math.min(pe,y-1)*J,Pn=P[ut+Be*X+Ue*ee],Ye=V[Ue*Z+tt*ae+ct];it+=Pn*Ye}ce[pe*oe+(Be*M+tt)]+=it}}return n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(N),n.makeTensorInfo(_,ne.dtype,ne.values)}var wF={kernelName:us,backendName:"cpu",kernelFunc:Ux};function _F(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r,p,d,f,m=[];p=Ux({inputs:{a,b:s},attrs:{transposeA:l,transposeB:c},backend:n}),i&&(d=pc({inputs:{a:p,b:i},backend:n}),m.push(p),p=d),u&&(f=Im(n,p,u,o,h),m.push(p),p=f);for(let A of m)n.disposeIntermediateTensorInfo(A);return p}var bF={kernelName:Zs,backendName:"cpu",kernelFunc:_F},vF=st(Yi,e=>Math.acos(e)),kF={kernelName:Yi,backendName:"cpu",kernelFunc:vF},IF=st(Ji,e=>Math.acosh(e)),NF={kernelName:Ji,backendName:"cpu",kernelFunc:IF};function SF(e){let{inputs:t,backend:n}=e,r=t;we(t,"addN");let a=r.map(o=>n.data.get(o.dataId).values),s=Le(r[0].shape,r[0].dtype),i=s.values;for(let o=0;o<r.length;o++){let l=a[o];for(let c=0;c<i.length;c++)i[c]+=l[c]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var TF={kernelName:is,backendName:"cpu",kernelFunc:SF};function EF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;we(a,"all");let o=k.parseAxisParam(s,a.shape),l=o,c=C.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=sr({inputs:{x:a},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("all",l,u.shape.length);let[h,p]=C.computeOutAndReduceShapes(u.shape,l),d=k.sizeFromShape(p),f=k.makeZerosTypedArray(k.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let g=y*d,_=m[g];for(let x=0;x<d;++x){let w=m[g+x];_=_&&w}f[y]=_}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,f);if(i){let y=C.expandShapeToKeepDim(h,o),g=yt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var CF={kernelName:Eh,backendName:"cpu",kernelFunc:EF};function RF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;we(a,"any");let o=k.parseAxisParam(s,a.shape),l=o,c=C.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=sr({inputs:{x:a},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("any",l,u.shape.length);let[h,p]=C.computeOutAndReduceShapes(u.shape,l),d=k.sizeFromShape(p),f=k.makeZerosTypedArray(k.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let g=y*d,_=m[g];for(let x=0;x<d;++x){let w=m[g+x];_=_||w}f[y]=_}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,f);if(i){let y=C.expandShapeToKeepDim(h,o),g=yt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var FF={kernelName:Ch,backendName:"cpu",kernelFunc:RF};function MF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;we(a,"argMax");let i=k.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=sr({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[u,h]=C.computeOutAndReduceShapes(l.shape,i),p=k.sizeFromShape(u),d=k.makeZerosTypedArray(p,"int32"),f=k.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let A=0;A<d.length;++A){let y=A*f,g=m[y],_=0;for(let x=0;x<f;++x){let w=m[y+x];w>g&&(g=w,_=x)}d[A]=_}return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(u,"int32",d)}var OF={kernelName:os,backendName:"cpu",kernelFunc:MF};function $F(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;we(a,"argMin");let i=k.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=sr({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[u,h]=C.computeOutAndReduceShapes(l.shape,i),p=k.sizeFromShape(u),d=k.makeZerosTypedArray(p,"int32"),f=k.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let A=0;A<d.length;++A){let y=A*f,g=m[y],_=0;for(let x=0;x<f;++x){let w=m[y+x];w<g&&(g=w,_=x)}d[A]=_}return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(u,"int32",d)}var DF={kernelName:bu,backendName:"cpu",kernelFunc:$F},zF=st(Qi,e=>Math.asin(e)),PF={kernelName:Qi,backendName:"cpu",kernelFunc:zF},LF=st(eo,e=>Math.asinh(e)),WF={kernelName:eo,backendName:"cpu",kernelFunc:LF},BF=st(to,e=>Math.atan(e)),VF={kernelName:to,backendName:"cpu",kernelFunc:BF},UF=Et((e,t)=>Math.atan2(e,t)),jF=Vt(ro,UF),HF={kernelName:ro,backendName:"cpu",kernelFunc:jF},GF=st(no,e=>Math.atanh(e)),qF={kernelName:no,backendName:"cpu",kernelFunc:GF};function Nm(e,t,n,r,a,s){let i=a.strideHeight,o=a.strideWidth,l=a.dilationHeight,c=a.dilationWidth,u=a.effectiveFilterHeight,h=a.effectiveFilterWidth,p=a.padInfo.top,d=a.padInfo.left,f=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=Le(a.outShape,n),A=m.values,y=a.outShape[1]*a.outShape[2]*a.outShape[3],g=a.outShape[2]*a.outShape[3],_=a.outShape[3];for(let x=0;x<a.batchSize;++x){let w=x*y,b=x*r[0];for(let N=0;N<a.inChannels;++N)for(let T=0;T<a.outHeight;++T){let E=T*i-p,M=Math.max(0,E),$=Math.min(a.inHeight,u+E),P=w+T*g;for(let V=0;V<a.outWidth;++V){let G=V*o-d,U=Math.max(0,G),K=Math.min(a.inWidth,h+G),X=f,ee=0,Z=0;for(let J=M;J<$;J+=l){let oe=b+J*r[1];for(let ne=U;ne<K;ne+=c){let ce=oe+ne*r[2],ue=e[ce+N];s==="max"&&ue>X?X=ue:s==="avg"&&(ee+=ue,Z++)}if(isNaN(X))break}let ae=P+V*_+N;A[ae]=s==="avg"?ee/Z:X}}}return m}function jx(e,t,n,r,a=!1,s=!1){let i=Le(r.outShape,"int32"),o=r.strideHeight,l=r.strideWidth,c=r.dilationHeight,u=r.dilationWidth,h=r.effectiveFilterHeight,p=r.effectiveFilterWidth,d=r.padInfo.top,f=r.padInfo.left,m=Le(t,n,e);for(let A=0;A<r.batchSize;++A)for(let y=0;y<r.inChannels;++y)for(let g=0;g<r.outHeight;++g){let _=g*o-d,x=_;for(;x<0;)x+=c;let w=Math.min(r.inHeight,h+_);for(let b=0;b<r.outWidth;++b){let N=b*l-f,T=N;for(;T<0;)T+=u;let E=Math.min(r.inWidth,p+N),M=Number.NEGATIVE_INFINITY,$=-1;for(let P=x;P<w;P+=c){let V=P-_;for(let G=T;G<E;G+=u){let U=G-N,K=m.get(A,P,G,y);K>M&&(M=K,a?$=s?((A*r.inHeight+P)*r.inWidth+G)*r.inChannels+y:(P*r.inWidth+G)*r.inChannels+y:$=V*p+U)}}i.set($,A,g,b,y)}}return i}function Hx(e,t,n,r,a,s){let i=a.strideDepth,o=a.strideHeight,l=a.strideWidth,c=a.dilationDepth,u=a.dilationHeight,h=a.dilationWidth,p=a.effectiveFilterDepth,d=a.effectiveFilterHeight,f=a.effectiveFilterWidth,m=a.padInfo.front,A=a.padInfo.top,y=a.padInfo.left,g=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,_=Le(a.outShape,n),x=_.values,w=a.outShape[1]*a.outShape[2]*a.outShape[3]*a.outShape[4],b=a.outShape[2]*a.outShape[3]*a.outShape[4],N=a.outShape[3]*a.outShape[4],T=a.outShape[4];for(let E=0;E<a.batchSize;++E){let M=E*w,$=E*r[0];for(let P=0;P<a.inChannels;++P)for(let V=0;V<a.outDepth;++V){let G=V*i-m,U=G;for(;U<0;)U+=c;let K=Math.min(a.inDepth,p+G),X=M+V*b;for(let ee=0;ee<a.outHeight;++ee){let Z=ee*o-A,ae=Z;for(;ae<0;)ae+=u;let J=Math.min(a.inHeight,d+Z),oe=X+ee*N;for(let ne=0;ne<a.outWidth;++ne){let ce=ne*l-y,ue=ce;for(;ue<0;)ue+=h;let pe=Math.min(a.inWidth,f+ce),fe=oe+ne*T,_e=g,Ne=0,Ee=0;for(let qe=U;qe<K;qe+=c){let Be=$+qe*r[1];for(let tt=ae;tt<J;tt+=u){let it=Be+tt*r[2];for(let Ue=ue;Ue<pe;Ue+=h){let ut=it+Ue*r[3],ct=e[ut+P];if(s==="max"&&ct>_e?_e=ct:s==="avg"&&(Ne+=ct,Ee++),isNaN(_e))break}if(isNaN(_e))break}if(isNaN(_e))break}let Oe=fe+P;x[Oe]=s==="avg"?Ne/Ee:_e}}}}return _}function XF(e,t){let n=Le(t.outShape,"int32"),r=t.strideDepth,a=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,u=t.effectiveFilterHeight,h=t.effectiveFilterWidth,p=t.padInfo.front,d=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let A=0;A<t.inChannels;++A)for(let y=0;y<t.outDepth;++y){let g=y*r-p,_=g;for(;_<0;)_+=i;let x=Math.min(t.inDepth,c+g);for(let w=0;w<t.outHeight;++w){let b=w*a-d,N=b;for(;N<0;)N+=o;let T=Math.min(t.inHeight,u+b);for(let E=0;E<t.outWidth;++E){let M=E*s-f,$=M;for(;$<0;)$+=l;let P=Math.min(t.inWidth,h+M),V=Number.NEGATIVE_INFINITY,G=-1;for(let U=_;U<x;U+=i){let K=U-g;for(let X=N;X<T;X+=o){let ee=X-b;for(let Z=$;Z<P;Z+=l){let ae=Z-M,J=e.get(m,U,X,Z,A);J>=V&&(V=J,G=K*u*h+ee*u+ae)}}}n.set(G,m,y,w,E,A)}}}return n}function KF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;we(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;k.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l),h;if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))h=Lr({inputs:{x:a},backend:n});else{let p=n.data.get(a.dataId).values,d=k.computeStrides(a.shape),f=Nm(p,a.shape,a.dtype,d,u,"avg");h=n.makeTensorInfo(u.outShape,a.dtype,f.values)}return h}var ZF={kernelName:ls,backendName:"cpu",kernelFunc:KF};function YF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r;we(a,"avgPool3d");let u=C.computePool3DInfo(a.shape,s,i,1,o,l,c),h=n.data.get(a.dataId).values,p=Hx(h,a.shape,a.dtype,k.computeStrides(a.shape),u,"avg");return n.makeTensorInfo(p.shape,"float32",p.values)}var JF={kernelName:vu,backendName:"cpu",kernelFunc:YF};function QF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=r;we([a,s],"avgPool3DGrad");let u=C.computePool3DInfo(s.shape,i,o,1,l,c),h=u.strideDepth,p=u.strideHeight,d=u.strideWidth,f=u.filterDepth,m=u.filterHeight,A=u.filterWidth,y=u.dilationDepth,g=u.dilationHeight,_=u.dilationWidth,x=u.effectiveFilterDepth,w=u.effectiveFilterHeight,b=u.effectiveFilterWidth,N=x-1-u.padInfo.front,T=b-1-u.padInfo.left,E=w-1-u.padInfo.top,M=Le(s.shape,"float32"),$=1/(f*m*A),P=n.bufferSync(a);for(let V=0;V<u.batchSize;++V)for(let G=0;G<u.inChannels;++G)for(let U=0;U<u.inDepth;++U)for(let K=0;K<u.inHeight;++K)for(let X=0;X<u.inWidth;++X){let ee=U-N,Z=K-E,ae=X-T,J=0;for(let oe=0;oe<x;oe+=y){let ne=(ee+oe)/h;if(!(ne<0||ne>=u.outDepth||Math.floor(ne)!==ne))for(let ce=0;ce<w;ce+=g){let ue=(Z+ce)/p;if(!(ue<0||ue>=u.outHeight||Math.floor(ue)!==ue))for(let pe=0;pe<b;pe+=_){let fe=(ae+pe)/d;fe<0||fe>=u.outWidth||Math.floor(fe)!==fe||(J+=P.get(V,ne,ue,fe,G))}}}M.set(J*$,V,U,K,X,G)}return n.makeTensorInfo(M.shape,M.dtype,M.values)}var eM={kernelName:Fh,backendName:"cpu",kernelFunc:QF};function tM(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;we([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=C.computePool2DInfo(i.shape,o,l,1,c),h=u.strideHeight,p=u.strideWidth,d=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,A=u.dilationWidth,y=u.effectiveFilterHeight,g=u.effectiveFilterWidth,_=g-1-u.padInfo.left,x=y-1-u.padInfo.top,w=Le(i.shape,"float32"),b=1/(d*f),N=n.data.get(a.dataId).values,T=Le(a.shape,"float32",N);for(let E=0;E<u.batchSize;++E)for(let M=0;M<u.inChannels;++M)for(let $=0;$<u.inHeight;++$)for(let P=0;P<u.inWidth;++P){let V=$-x,G=P-_,U=0;for(let K=0;K<y;K+=m){let X=(V+K)/h;if(!(X<0||X>=u.outHeight||Math.floor(X)!==X))for(let ee=0;ee<g;ee+=A){let Z=(G+ee)/p;Z<0||Z>=u.outWidth||Math.floor(Z)!==Z||(U+=T.get(E,X,Z,M))}}w.set(U*b,E,$,P,M)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var nM={kernelName:Rh,backendName:"cpu",kernelFunc:tM};function rM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;k.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),we([a,o,l,s,i],"batchNorm");let{varianceEpsilon:c}=r;c==null&&(c=.001);let u=n.data.get(a.dataId).values,h=n.data.get(o.dataId).values,p=n.data.get(l.dataId).values,d=s?n.data.get(s.dataId).values:new Float32Array([1]),f=i?n.data.get(i.dataId).values:new Float32Array([0]),m=new Float32Array(u.length),A=f.length,y=d.length,g=p.length,_=h.length,x=0,w=0,b=0,N=0;for(let T=0;T<u.length;++T)m[T]=f[x++]+(u[T]-h[w++])*d[b++]/Math.sqrt(p[N++]+c),x>=A&&(x=0),w>=_&&(w=0),b>=y&&(b=0),N>=g&&(N=0);return n.makeTensorInfo(a.shape,a.dtype,m)}var aM={kernelName:ws,backendName:"cpu",kernelFunc:rM};function sM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;we([a],"batchToSpaceND");let o=s.reduce((y,g)=>y*g),l=C.getReshaped(a.shape,s,o),c=C.getPermuted(l.length,s.length),u=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(u,i,s.length),d=yt({inputs:{x:a},backend:n,attrs:{shape:l}}),f=sr({inputs:{x:d},backend:n,attrs:{perm:c}}),m=yt({inputs:{x:f},backend:n,attrs:{shape:u}}),A=fi({inputs:{x:m},backend:n,attrs:{begin:h,size:p}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var iM={kernelName:ku,backendName:"cpu",kernelFunc:sM};function oM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,c=ym(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var lM={kernelName:Mh,backendName:"cpu",kernelFunc:oM},uM=st(_a,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),cM={kernelName:_a,backendName:"cpu",kernelFunc:uM},hM=e=>{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(k.sizeFromShape(t.shape)),a=n.data.get(t.dataId),s=a.complexTensorInfos.real,i=a.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let c=0;c<o.length;c++){let u=o[c],h=l[c];r[c]=Math.hypot(u,h)}return n.makeOutput(r,t.shape,"float32")},dM={kernelName:Iu,backendName:"cpu",kernelFunc:hM};function vl(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.data.get(r.dataId).complexTensorInfos.imag,s=n.data.get(a.dataId).values;return n.makeTensorInfo(a.shape,a.dtype,s)}var pM={kernelName:qh,backendName:"cpu",kernelFunc:vl};function kl(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=k.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(m=>m.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(m=>k.sizeFromShape(m.shape)>0);if(o.length===1)return Lr({inputs:{x:o[0]},backend:n});let l=o.map(m=>m.shape);if(C.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let m=o.map(x=>pi({inputs:{input:x},backend:n})),A=o.map(x=>vl({inputs:{input:x},backend:n})),y=kl({inputs:m,backend:n,attrs:{axis:s}}),g=kl({inputs:A,backend:n,attrs:{axis:s}}),_=Rn({inputs:{real:y,imag:g},backend:n});return m.forEach(x=>n.disposeIntermediateTensorInfo(x)),A.forEach(x=>n.disposeIntermediateTensorInfo(x)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),_}let c=o.map(m=>{let A=k.sizeFromShape(m.shape.slice(s));return yt({inputs:{x:m},backend:n,attrs:{shape:[-1,A]}})}),u=c.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));i=C.computeOutShape(c.map(m=>m.shape),1);let h=c[0].shape[0]===1,p=gm(u,i,t[0].dtype,h),d=C.computeOutShape(o.map(m=>m.shape),s),f=n.makeTensorInfo(d,t[0].dtype,p);return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var fM={kernelName:so,backendName:"cpu",kernelFunc:kl};function Gx(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r;we([a,s],"conv2d");let h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),d=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,A=p.dilationWidth,y=p.padInfo.left,g=p.padInfo.top,_=p.dataFormat==="channelsLast",x=new Ft(p.outShape,a.dtype),w=k.computeStrides(a.shape),b=k.computeStrides(s.shape),N=w[0],T=_?w[1]:w[2],E=_?w[2]:1,M=_?1:w[1],$=x.strides[0],P=_?x.strides[1]:x.strides[2],V=_?x.strides[2]:1,G=_?1:x.strides[1],U=n.data.get(a.dataId).values,K=n.data.get(s.dataId).values,X=x.values;for(let ee=0;ee<p.batchSize;++ee){let Z=ee*N,ae=ee*$;for(let J=0;J<p.outHeight;++J){let oe=ae+J*P,ne=J*p.strideHeight-g;for(let ce=0;ce<d;++ce){let ue=ne+ce*m;if(ue<0||ue>=p.inHeight)continue;let pe=ce*b[0],fe=Z+ue*T;for(let _e=0;_e<p.outWidth;++_e){let Ne=oe+_e*V,Ee=_e*p.strideWidth-y;for(let Oe=0;Oe<f;++Oe){let qe=Ee+Oe*A;if(qe<0||qe>=p.inWidth)continue;let Be=pe+Oe*b[1],tt=fe+qe*E,it=Be;for(let Ue=0;Ue<p.inChannels;++Ue){let ut=U[tt+Ue*M];for(let ct=0;ct<p.outChannels;++ct)X[Ne+ct*G]+=ut*K[it+ct];it+=p.outChannels}}}}}}return n.makeTensorInfo(x.shape,x.dtype,X)}var mM={kernelName:hs,backendName:"cpu",kernelFunc:Gx};function AM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=r;we([a,s],"conv2dBackpropFilter");let h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),{strideHeight:d,strideWidth:f,filterHeight:m,filterWidth:A}=p,y=p.dataFormat==="channelsLast",g=new Ft(p.filterShape,"float32"),_=p.padInfo.left,x=p.padInfo.top,w=n.data.get(a.dataId).values,b=n.data.get(s.dataId).values,N=new Ft(a.shape,a.dtype,w),T=new Ft(s.shape,s.dtype,b);for(let E=0;E<m;++E){let M=Math.max(0,Math.ceil((x-E)/d)),$=Math.min(p.outHeight,(p.inHeight+x-E)/d);for(let P=0;P<A;++P){let V=Math.max(0,Math.ceil((_-P)/f)),G=Math.min(p.outWidth,(p.inWidth+_-P)/f);for(let U=0;U<p.inChannels;++U)for(let K=0;K<p.outChannels;++K){let X=0;for(let ee=0;ee<p.batchSize;++ee)for(let Z=M;Z<$;++Z){let ae=E+Z*d-x;for(let J=V;J<G;++J){let oe=P+J*f-_;y?X+=N.get(ee,ae,oe,U)*T.get(ee,Z,J,K):X+=N.get(ee,U,ae,oe)*T.get(ee,K,Z,J)}}g.set(X,E,P,U,K)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var yM={kernelName:$h,backendName:"cpu",kernelFunc:AM};function gM(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r;we([a,s],"conv2dBackpropInput");let h=k.computeStrides(s.shape),p=k.computeStrides(a.shape),d=C.convertConv2DDataFormat(c),f=C.computeConv2DInfo(i,s.shape,o,1,l,u,!1,d),m=new Ft(f.inShape,"float32"),A=m.values,y=n.data.get(a.dataId).values,g=n.data.get(s.dataId).values,[_,x,w]=h,{batchSize:b,filterHeight:N,filterWidth:T,inChannels:E,inHeight:M,inWidth:$,outChannels:P,outHeight:V,outWidth:G,strideHeight:U,strideWidth:K}=f;d=f.dataFormat;let X=N-1-f.padInfo.top,ee=T-1-f.padInfo.left,Z=d==="channelsLast",ae=m.strides[0],J=Z?m.strides[1]:m.strides[2],oe=Z?m.strides[2]:1,ne=Z?1:m.strides[1],ce=p[0],ue=Z?p[1]:p[2],pe=Z?p[2]:1,fe=Z?1:p[1];for(let _e=0;_e<b;++_e)for(let Ne=0;Ne<E;++Ne)for(let Ee=0;Ee<M;++Ee){let Oe=Ee-X,qe=Math.max(0,Math.ceil(Oe/U)),Be=Math.min(V,(N+Oe)/U);for(let tt=0;tt<$;++tt){let it=tt-ee,Ue=Math.max(0,Math.ceil(it/K)),ut=Math.min(G,(T+it)/K),ct=0;for(let Ye=qe;Ye<Be;++Ye){let bn=Ye*U-Oe;for(let Xt=Ue;Xt<ut;++Xt){let vn=Xt*K-it,Xn=ce*_e+ue*Ye+pe*Xt,pn=_*(N-1-bn)+x*(T-1-vn)+w*Ne;for(let rn=0;rn<P;++rn){let Kn=y[Xn+fe*rn],Sr=g[pn+rn];ct+=Kn*Sr}}}let Pn=ae*_e+J*Ee+oe*tt+ne*Ne;A[Pn]=ct}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var xM={kernelName:ds,backendName:"cpu",kernelFunc:gM};function wM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r;we([a,s],"conv3d");let c=C.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:h,filterWidth:p,dilationDepth:d,dilationHeight:f,dilationWidth:m,padInfo:A}=c,y=A.front,g=A.left,_=A.top,x=new Ft(c.outShape,a.dtype),w=n.data.get(a.dataId).values,b=n.data.get(s.dataId).values,N=x.values,T=k.computeStrides(a.shape),E=k.computeStrides(s.shape);for(let M=0;M<c.batchSize;++M){let $=M*T[0],P=M*x.strides[0];for(let V=0;V<c.outDepth;++V){let G=P+V*x.strides[1],U=V*c.strideDepth-y;for(let K=0;K<u;++K){let X=U+K*d;if(X<0||X>=c.inDepth)continue;let ee=K*E[0],Z=$+X*T[1];for(let ae=0;ae<c.outHeight;++ae){let J=G+ae*x.strides[2],oe=ae*c.strideHeight-_;for(let ne=0;ne<h;++ne){let ce=oe+ne*f;if(ce<0||ce>=c.inHeight)continue;let ue=ee+ne*E[1],pe=Z+ce*T[2];for(let fe=0;fe<c.outWidth;++fe){let _e=J+fe*c.outChannels,Ne=fe*c.strideWidth-g;for(let Ee=0;Ee<p;++Ee){let Oe=Ne+Ee*m;if(Oe<0||Oe>=c.inWidth)continue;let qe=ue+Ee*E[2],Be=pe+Oe*c.inChannels,tt=qe;for(let it=0;it<c.inChannels;++it){let Ue=w[Be+it];for(let ut=0;ut<c.outChannels;++ut)N[_e+ut]+=Ue*b[tt+ut];tt+=c.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var _M={kernelName:Nu,backendName:"cpu",kernelFunc:wM};function bM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r;we([a,s],"conv3dBackpropFilterV2");let c=k.computeStrides(a.shape),u=k.computeStrides(s.shape),h=C.computeConv3DInfo(a.shape,l,i,1,o),p=h.strideDepth,d=h.strideHeight,f=h.strideWidth,m=h.filterDepth,A=h.filterHeight,y=h.filterWidth,g=new Ft(h.filterShape,"float32"),_=g.values,[x,w,b,N]=g.strides,T=n.data.get(s.dataId).values,[E,M,$,P]=u,V=n.data.get(a.dataId).values,[G,U,K,X]=c,ee=h.padInfo.front,Z=h.padInfo.left,ae=h.padInfo.top;for(let J=0;J<m;++J){let oe=Math.max(0,Math.ceil((ee-J)/p)),ne=Math.min(h.outDepth,(h.inDepth+ee-J)/p),ce=J*x;for(let ue=0;ue<A;++ue){let pe=Math.max(0,Math.ceil((ae-ue)/d)),fe=Math.min(h.outHeight,(h.inHeight+ae-ue)/d),_e=ue*w+ce;for(let Ne=0;Ne<y;++Ne){let Ee=Math.max(0,Math.ceil((Z-Ne)/f)),Oe=Math.min(h.outWidth,(h.inWidth+Z-Ne)/f),qe=Ne*b+_e;for(let Be=0;Be<h.inChannels;++Be){let tt=Be*N+qe;for(let it=0;it<h.outChannels;++it){let Ue=0;for(let ut=0;ut<h.batchSize;++ut){let ct=ut*G,Pn=ut*E;for(let Ye=oe;Ye<ne;++Ye){let bn=(J+Ye*p-ee)*U+ct,Xt=Ye*M+Pn;for(let vn=pe;vn<fe;++vn){let Xn=(ue+vn*d-ae)*K+bn,pn=vn*$+Xt;for(let rn=Ee;rn<Oe;++rn){let Kn=(Ne+rn*f-Z)*X+Xn,Sr=rn*P+pn;Ue+=V[Kn+Be]*T[Sr+it]}}}}_[tt+it]=Ue}}}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var vM={kernelName:Dh,backendName:"cpu",kernelFunc:bM};function kM(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r;we([a],"conv3dBackpropInputV2");let c=k.computeStrides(a.shape),u=k.computeStrides(s.shape),h=C.computeConv3DInfo(l,s.shape,o,1,i),p=new Ft(h.inShape,"float32"),d=p.values,[f,m,A,y]=p.strides,g=n.data.get(a.dataId).values,[_,x,w,b]=c,N=n.data.get(s.dataId).values,[T,E,M,$]=u,{batchSize:P,filterDepth:V,filterHeight:G,filterWidth:U,inChannels:K,inDepth:X,inHeight:ee,inWidth:Z,outChannels:ae,outDepth:J,outHeight:oe,outWidth:ne,strideDepth:ce,strideHeight:ue,strideWidth:pe}=h,fe=V-1-h.padInfo.front,_e=G-1-h.padInfo.top,Ne=U-1-h.padInfo.left;for(let Ee=0;Ee<P;++Ee)for(let Oe=0;Oe<K;++Oe)for(let qe=0;qe<X;++qe){let Be=qe-fe,tt=Math.max(0,Math.ceil(Be/ce)),it=Math.min(J,(V+Be)/ce);for(let Ue=0;Ue<ee;++Ue){let ut=Ue-_e,ct=Math.max(0,Math.ceil(ut/ue)),Pn=Math.min(oe,(G+ut)/ue);for(let Ye=0;Ye<Z;++Ye){let bn=Ye-Ne,Xt=Math.max(0,Math.ceil(bn/pe)),vn=Math.min(ne,(U+bn)/pe),Xn=0;for(let pn=tt;pn<it;++pn){let rn=pn*ce-Be;for(let Kn=ct;Kn<Pn;++Kn){let Sr=Kn*ue-ut;for(let kn=Xt;kn<vn;++kn){let Oi=kn*pe-bn,Ql=_*Ee+x*pn+w*Kn+b*kn,hr=T*(V-1-rn)+E*(G-1-Sr)+M*(U-1-Oi)+$*Oe;for(let Zn=0;Zn<ae;++Zn){let dr=g[Ql+Zn],$i=N[hr+Zn];Xn+=dr*$i}}}}d[f*Ee+m*qe+A*Ue+y*Ye+Oe]=Xn}}}return n.makeTensorInfo(p.shape,p.dtype,p.values)}var IM={kernelName:zh,backendName:"cpu",kernelFunc:kM},NM=st(ps,e=>Math.cos(e)),SM={kernelName:ps,backendName:"cpu",kernelFunc:NM},TM=st(io,e=>Math.cosh(e)),EM={kernelName:io,backendName:"cpu",kernelFunc:TM};function CM(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,[u,h,p,d]=a.shape,f=s.shape[0],[m,A]=o,y=Le([f,m,A,d],"float32"),g=n.data.get(s.dataId).values,_=n.data.get(i.dataId).values,x=n.data.get(a.dataId).values,w=k.computeStrides(a.shape),b=k.computeStrides(y.shape);for(let N=0;N<f;N++){let T=N*4,E=g[T],M=g[T+1],$=g[T+2],P=g[T+3],V=_[N];if(V>=u)continue;let G=m>1?($-E)*(h-1)/(m-1):0,U=A>1?(P-M)*(p-1)/(A-1):0;for(let K=0;K<m;K++){let X=m>1?E*(h-1)+K*G:.5*(E+$)*(h-1);if(X<0||X>h-1){for(let ee=0;ee<A;ee++)for(let Z=0;Z<d;Z++){let ae=Z+ee*b[2]+K*b[1]+N*b[0];y.values[ae]=c}continue}if(l==="bilinear"){let ee=Math.floor(X),Z=Math.ceil(X),ae=X-ee;for(let J=0;J<A;J++){let oe=A>1?M*(p-1)+J*U:.5*(M+P)*(p-1);if(oe<0||oe>p-1){for(let pe=0;pe<d;pe++){let fe=pe+J*b[2]+K*b[1]+N*b[0];y.values[fe]=c}continue}let ne=Math.floor(oe),ce=Math.ceil(oe),ue=oe-ne;for(let pe=0;pe<d;pe++){let fe=pe+ne*w[2]+ee*w[1]+V*w[0],_e=x[fe];fe=pe+ce*w[2]+ee*w[1]+V*w[0];let Ne=x[fe];fe=pe+ne*w[2]+Z*w[1]+V*w[0];let Ee=x[fe];fe=pe+ce*w[2]+Z*w[1]+V*w[0];let Oe=x[fe],qe=_e+(Ne-_e)*ue,Be=Ee+(Oe-Ee)*ue;fe=pe+J*b[2]+K*b[1]+N*b[0],y.values[fe]=qe+(Be-qe)*ae}}}else for(let ee=0;ee<A;++ee){let Z=A>1?M*(p-1)+ee*U:.5*(M+P)*(p-1);if(Z<0||Z>p-1){for(let oe=0;oe<d;oe++){let ne=oe+ee*b[2]+K*b[1]+N*b[0];y.values[ne]=c}continue}let ae=Math.round(Z),J=Math.round(X);for(let oe=0;oe<d;oe++){let ne=oe+ae*w[2]+J*w[1]+V*w[0],ce=oe+ee*b[2]+K*b[1]+N*b[0];y.values[ce]=x[ne]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var RM={kernelName:oo,backendName:"cpu",kernelFunc:CM};function FM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r;we(a,"cumsum");let l=C.getAxesPermutation([s],a.shape.length),c=a;l!=null&&(c=sr({inputs:{x:a},backend:n,attrs:{perm:l}}));let u=C.getInnerMostAxes(1,a.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let h=er(c.dtype,"int32"),p=k.makeZerosTypedArray(k.sizeFromShape(c.shape),h),d=n.data.get(c.dataId).values,f=c.shape[c.shape.length-1],m=o?(y,g)=>y+f-g-1:(y,g)=>y+g;for(let y=0;y<d.length;y+=f)for(let g=0;g<f;g++){let _=m(y,g);if(g===0)p[_]=i?0:d[_];else{let x=m(y,g-1);p[_]=i?d[x]+p[x]:d[_]+p[x]}}let A=n.makeTensorInfo(c.shape,h,p);if(l!=null){let y=C.getUndoAxesPermutation(l),g=sr({inputs:{x:A},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(c),g}return A}var MM={kernelName:fs,backendName:"cpu",kernelFunc:FM};function OM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.data.get(a.dataId).values,c=n.data.get(s.dataId).values,u=ym(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=px(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var $M={kernelName:Ph,backendName:"cpu",kernelFunc:OM};function DM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;k.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=a.shape[1],c=a.shape[2],u=a.shape[3],h=l*s,p=c*s,d=u/(s*s),f=n.data.get(a.dataId).values,m=new Float32Array(o*h*p*d),A=0;for(let y=0;y<o;++y)for(let g=0;g<h;++g){let _=Math.floor(g/s),x=g%s;for(let w=0;w<p;++w){let b=Math.floor(w/s),N=w%s,T=(x*s+N)*d;for(let E=0;E<d;++E){let M=E+T+u*(b+c*(_+l*y));m[A++]=f[M]}}}return n.makeTensorInfo([o,h,p,d],a.dtype,m)}var zM={kernelName:lo,backendName:"cpu",kernelFunc:DM};function qx(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r;we([a,s],"depthwiseConv2DNative");let u=k.computeStrides(a.shape),h=k.computeStrides(s.shape),p=l;p==null&&(p=[1,1]),k.assert(C.eitherStridesOrDilationsAreOne(i,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let d=C.computeConv2DInfo(a.shape,s.shape,i,p,o,c,!0),{filterHeight:f,filterWidth:m,dilationHeight:A,dilationWidth:y,padInfo:g}=d,_=g.left,x=g.top,w=d.outChannels/d.inChannels,b=new Ft(d.outShape,a.dtype),N=n.data.get(a.dataId).values,T=n.data.get(s.dataId).values,E=b.values;for(let M=0;M<d.batchSize;++M){let $=M*u[0],P=M*b.strides[0];for(let V=0;V<d.outHeight;++V){let G=P+V*b.strides[1],U=V*d.strideHeight-_;for(let K=0;K<f;++K){let X=U+K*A;if(X<0||X>=d.inHeight)continue;let ee=K*h[0],Z=$+X*u[1];for(let ae=0;ae<d.outWidth;++ae){let J=G+ae*b.strides[2],oe=ae*d.strideWidth-x;for(let ne=0;ne<m;++ne){let ce=oe+ne*y;if(ce<0||ce>=d.inWidth)continue;let ue=ee+ne*h[1],pe=Z+ce*d.inChannels,fe=J,_e=ue;for(let Ne=0;Ne<d.inChannels;++Ne){let Ee=N[pe+Ne];for(let Oe=0;Oe<w;++Oe)E[fe+Oe]+=Ee*T[_e+Oe];fe+=w,_e+=w}}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var PM={kernelName:ms,backendName:"cpu",kernelFunc:qx};function LM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r;we([a,s],"depthwiseConv2dNativeBackpropFilter");let h=C.computeConv2DInfo(a.shape,u,i,o,l,c,!0),{strideHeight:p,strideWidth:d,filterHeight:f,filterWidth:m}=h,A=new Ft(h.filterShape,"float32"),y=h.padInfo.left,g=h.padInfo.top,_=h.outChannels/h.inChannels,x=n.data.get(a.dataId).values,w=new Ft(a.shape,a.dtype,x),b=n.data.get(s.dataId).values,N=new Ft(s.shape,s.dtype,b);for(let T=0;T<f;++T){let E=Math.max(0,Math.ceil((g-T)/p)),M=Math.min(h.outHeight,(h.inHeight+g-T)/p);for(let $=0;$<m;++$){let P=Math.max(0,Math.ceil((y-$)/d)),V=Math.min(h.outWidth,(h.inWidth+y-$)/d);for(let G=0;G<h.outChannels;++G){let U=Math.trunc(G/_),K=G%_,X=0;for(let ee=0;ee<h.batchSize;++ee)for(let Z=E;Z<M;++Z){let ae=T+Z*p-g;for(let J=P;J<V;++J){let oe=$+J*d-y;X+=w.get(ee,ae,oe,U)*N.get(ee,Z,J,G)}}A.set(X,T,$,U,K)}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var WM={kernelName:Lh,backendName:"cpu",kernelFunc:LM};function BM(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r;we([a,s],"depthwiseConv2DNativeBackpropInput");let h=k.computeStrides(a.shape),p=k.computeStrides(s.shape),d=C.computeConv2DInfo(u,s.shape,i,o,l,c,!0),f=new Ft(d.inShape,"float32"),m=f.values,[A,y,g]=f.strides,_=n.data.get(a.dataId).values,[x,w,b]=h,N=n.data.get(s.dataId).values,[T,E,M]=p,{batchSize:$,filterHeight:P,filterWidth:V,inChannels:G,inHeight:U,inWidth:K,outChannels:X,outHeight:ee,outWidth:Z,strideHeight:ae,strideWidth:J}=d,oe=P-1-d.padInfo.top,ne=V-1-d.padInfo.left,ce=X/G;for(let ue=0;ue<$;++ue)for(let pe=0;pe<G;++pe)for(let fe=0;fe<U;++fe){let _e=fe-oe,Ne=Math.max(0,Math.ceil(_e/ae)),Ee=Math.min(ee,(P+_e)/ae);for(let Oe=0;Oe<K;++Oe){let qe=Oe-ne,Be=Math.max(0,Math.ceil(qe/J)),tt=Math.min(Z,(V+qe)/J),it=0;for(let Ue=Ne;Ue<Ee;++Ue){let ut=Ue*ae-_e;for(let ct=Be;ct<tt;++ct){let Pn=ct*J-qe,Ye=x*ue+w*Ue+b*ct,bn=T*(P-1-ut)+E*(V-1-Pn)+M*pe;for(let Xt=0;Xt<ce;++Xt){let vn=pe*ce+Xt,Xn=_[Ye+vn],pn=N[bn+Xt];it+=Xn*pn}}}m[A*ue+y*fe+g*Oe+pe]=it}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var VM={kernelName:Wh,backendName:"cpu",kernelFunc:BM};function UM(e){let{inputs:t,backend:n}=e,{x:r}=t,a=k.sizeFromShape(r.shape),s=n.data.get(r.dataId).values,i=Le([a,a],r.dtype),o=i.values;for(let c=0;c<s.length;c++)o[c*a+c]=s[c];let l=[...r.shape,...r.shape];return n.makeTensorInfo(l,i.dtype,i.values)}var jM={kernelName:Bh,backendName:"cpu",kernelFunc:UM},HM={kernelName:Su,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a}=e,{strides:s,pad:i,dilations:o}=n,l=t,c=l.data.get(r.dataId).values,u=r.shape.length,h=l.data.get(a.dataId).values,p=a.shape.length,{batchSize:d,inHeight:f,inWidth:m,inChannels:A,outHeight:y,outWidth:g,padInfo:_,strideHeight:x,strideWidth:w,filterHeight:b,filterWidth:N,dilationHeight:T,dilationWidth:E,outShape:M}=C.computeDilation2DInfo(r.shape,a.shape,s,i,"NHWC",o),$=k.sizeFromShape(M),P=M.length,V=k.getArrayFromDType(r.dtype,$);for(let G=0;G<d;++G)for(let U=0;U<y;++U){let K=U*x-_.top;for(let X=0;X<g;++X){let ee=X*w-_.left;for(let Z=0;Z<A;++Z){let ae=Number.MIN_SAFE_INTEGER;for(let oe=0;oe<b;++oe){let ne=K+oe*T;if(ne>=0&&ne<f)for(let ce=0;ce<N;++ce){let ue=ee+ce*E;if(ue>=0&&ue<m){let pe=k.locToIndex([G,ne,ue,Z],u,k.computeStrides(r.shape)),fe=k.locToIndex([oe,ce,Z],p,k.computeStrides(a.shape)),_e=c[pe]+h[fe];_e>ae&&(ae=_e)}}}let J=k.locToIndex([G,U,X,Z],P,k.computeStrides(M));V[J]=ae}}}return{dataId:l.write(k.toTypedArray(V,r.dtype),M,r.dtype),shape:M,dtype:r.dtype}}},GM={kernelName:Uh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,c=t,u=k.toNestedArray(r.shape,c.data.get(r.dataId).values),h=k.toNestedArray(a.shape,c.data.get(a.dataId).values),{batchSize:p,inHeight:d,inWidth:f,inChannels:m,outHeight:A,outWidth:y,padInfo:g,strideHeight:_,strideWidth:x,filterHeight:w,filterWidth:b,dilationHeight:N,dilationWidth:T,outShape:E}=C.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);k.assert(s.rank===E.length,()=>`Error in ${Uh}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let M=k.toNestedArray(E,c.data.get(s.dataId).values),$=k.makeZerosNestedTypedArray(a.shape,a.dtype);for(let P=0;P<p;++P)for(let V=0;V<A;++V){let G=V*_-g.top;for(let U=0;U<y;++U){let K=U*x-g.left;for(let X=0;X<m;++X){let ee=Number.MIN_SAFE_INTEGER,Z=0,ae=0;for(let J=0;J<w;++J){let oe=G+J*N;if(oe>=0&&oe<d)for(let ne=0;ne<b;++ne){let ce=K+ne*T;if(ce>=0&&ce<f){let ue=u[P][oe][ce][X]+h[J][ne][X];ue>ee&&(ee=ue,Z=J,ae=ne)}}}$[Z][ae][X]+=M[P][V][U][X]}}}return{dataId:c.write(k.toTypedArray($,r.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},qM={kernelName:Vh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,c=t,u=k.toNestedArray(r.shape,c.data.get(r.dataId).values),h=k.toNestedArray(a.shape,c.data.get(a.dataId).values),{batchSize:p,inHeight:d,inWidth:f,inChannels:m,outHeight:A,outWidth:y,padInfo:g,strideHeight:_,strideWidth:x,filterHeight:w,filterWidth:b,dilationHeight:N,dilationWidth:T,outShape:E}=C.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);k.assert(s.rank===E.length,()=>`Error in ${Vh}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let M=k.toNestedArray(E,c.data.get(s.dataId).values),$=k.makeZerosNestedTypedArray(r.shape,r.dtype);for(let P=0;P<p;++P)for(let V=0;V<A;++V){let G=V*_-g.top;for(let U=0;U<y;++U){let K=U*x-g.left;for(let X=0;X<m;++X){let ee=Number.MIN_SAFE_INTEGER,Z=G<0?0:G,ae=K<0?0:K;for(let J=0;J<w;++J){let oe=G+J*N;if(oe>=0&&oe<d)for(let ne=0;ne<b;++ne){let ce=K+ne*T;if(ce>=0&&ce<f){let ue=u[P][oe][ce][X]+h[J][ne][X];ue>ee&&(ee=ue,Z=oe,ae=ce)}}}$[P][Z][ae][X]+=M[P][V][U][X]}}}return{dataId:c.write(k.toTypedArray($,r.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}};function XM(e){let{inputs:t,backend:n}=e,{dy:r,y:a}=t;we([r,a],"eluGrad");let s=new Float32Array(k.sizeFromShape(a.shape)),i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values;for(let l=0;l<i.length;++l){let c=i[l];c>=1?s[l]=o[l]:s[l]=o[l]*(c+1)}return n.makeTensorInfo(a.shape,"float32",s)}var KM={kernelName:jh,backendName:"cpu",kernelFunc:XM},ZM=Et((e,t)=>e===t?1:0),Xx=Vt(ho,ZM,null,"bool"),YM={kernelName:ho,backendName:"cpu",kernelFunc:Xx},JM=C.ERF_P,QM=C.ERF_A1,eO=C.ERF_A2,tO=C.ERF_A3,nO=C.ERF_A4,rO=C.ERF_A5,aO=st(co,e=>{let t=Math.sign(e),n=Math.abs(e),r=1/(1+JM*n);return t*(1-((((rO*r+nO)*r+tO)*r+eO)*r+QM)*r*Math.exp(-n*n))}),sO={kernelName:co,backendName:"cpu",kernelFunc:aO};function sp(e){let{inputs:t,backend:n,attrs:r}=e,{input:a}=t,{dim:s}=r,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),yt({inputs:{x:a},backend:n,attrs:{shape:o}})}var iO={kernelName:po,backendName:"cpu",kernelFunc:sp},oO=Et((e,t)=>e/t),Sm=Vt(As,oO),Tm={kernelName:As,backendName:"cpu",kernelFunc:Sm};function Kx(e,t,n){let r=e.shape,a=r[0],s=r[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,c=[a,s],u=k.sizeFromShape(c),h=k.getTypedArrayFromDType("float32",u),p=k.getTypedArrayFromDType("float32",u);for(let A=0;A<a;A++){let y=fi({inputs:{x:o},backend:n,attrs:{begin:[A,0],size:[1,s]}}),g=fi({inputs:{x:l},backend:n,attrs:{begin:[A,0],size:[1,s]}}),_=Rn({inputs:{real:y,imag:g},backend:n}),{real:x,imag:w}=lO(_,t,n),b=C.mergeRealAndImagArrays(x,w);for(let N=0;N<s;N++){let T=C.getComplexWithIndex(b,N);h[A*s+N]=T.real,p[A*s+N]=T.imag}n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(_)}let d=n.makeTensorInfo(c,"float32",h),f=n.makeTensorInfo(c,"float32",p),m=Rn({inputs:{real:d,imag:f},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}function lO(e,t,n){let r=k.sizeFromShape(e.shape),a=n.data.get(e.dataId),s=n.data.get(a.complexTensorInfos.real.dataId).values,i=n.data.get(a.complexTensorInfos.imag.dataId).values;if(uO(r)){let o=Em(s,i,r,t,n),l=[e.shape[0],e.shape[1]];if(t){let c=n.makeTensorInfo(l,"float32",o.real),u=n.makeTensorInfo(l,"float32",o.imag),h=n.makeTensorInfo([],"float32",k.createScalarValue(r,"float32")),p=Lr({inputs:{x:h},backend:n}),d=Tm.kernelFunc({inputs:{a:c,b:h},backend:n}),f=Tm.kernelFunc({inputs:{a:u,b:p},backend:n}),m=n.data.get(d.dataId).values,A=n.data.get(f.dataId).values;return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),{real:m,imag:A}}return o}else{let o=C.mergeRealAndImagArrays(s,i),l=cO(o,r,t);return C.splitRealAndImagArrays(l)}}function uO(e){return(e&e-1)==0}function Em(e,t,n,r,a){if(n===1)return{real:e,imag:t};let s=C.mergeRealAndImagArrays(e,t),i=n/2,o=C.complexWithEvenIndex(s),l=o.real,c=o.imag,u=[l.length],h=a.makeTensorInfo(u,"float32",l),p=a.makeTensorInfo(u,"float32",c),d=Rn({inputs:{real:h,imag:p},backend:a}),f=C.complexWithOddIndex(s),m=f.real,A=f.imag,y=[m.length],g=a.makeTensorInfo(y,"float32",m),_=a.makeTensorInfo(y,"float32",A),x=Rn({inputs:{real:g,imag:_},backend:a}),w=Em(l,c,i,r,a),b=w.real,N=w.imag,T=[b.length],E=a.makeTensorInfo(T,"float32",b),M=a.makeTensorInfo(T,"float32",N),$=Rn({inputs:{real:E,imag:M},backend:a}),P=Em(m,A,i,r,a),V=P.real,G=P.imag,U=[V.length],K=a.makeTensorInfo(U,"float32",V),X=a.makeTensorInfo(U,"float32",G),ee=Rn({inputs:{real:K,imag:X},backend:a}),Z=C.exponents(n,r),ae=[Z.real.length],J=a.makeTensorInfo(ae,"float32",Z.real),oe=a.makeTensorInfo(ae,"float32",Z.imag),ne=Rn({inputs:{real:J,imag:oe},backend:a}),ce=vm({inputs:{a:ne,b:ee},backend:a}),ue=pc({inputs:{a:$,b:ce},backend:a}),pe=km({inputs:{a:$,b:ce},backend:a}),fe=pi({inputs:{input:ue},backend:a}),_e=pi({inputs:{input:pe},backend:a}),Ne=vl({inputs:{input:ue},backend:a}),Ee=vl({inputs:{input:pe},backend:a}),Oe=kl({inputs:[fe,_e],backend:a,attrs:{axis:0}}),qe=kl({inputs:[Ne,Ee],backend:a,attrs:{axis:0}}),Be=a.data.get(Oe.dataId).values,tt=a.data.get(qe.dataId).values;return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(_),a.disposeIntermediateTensorInfo(x),a.disposeIntermediateTensorInfo(E),a.disposeIntermediateTensorInfo(M),a.disposeIntermediateTensorInfo($),a.disposeIntermediateTensorInfo(K),a.disposeIntermediateTensorInfo(X),a.disposeIntermediateTensorInfo(ee),a.disposeIntermediateTensorInfo(J),a.disposeIntermediateTensorInfo(oe),a.disposeIntermediateTensorInfo(ne),a.disposeIntermediateTensorInfo(ce),a.disposeIntermediateTensorInfo(ue),a.disposeIntermediateTensorInfo(pe),a.disposeIntermediateTensorInfo(fe),a.disposeIntermediateTensorInfo(Ne),a.disposeIntermediateTensorInfo(_e),a.disposeIntermediateTensorInfo(Ee),a.disposeIntermediateTensorInfo(Oe),a.disposeIntermediateTensorInfo(qe),{real:Be,imag:tt}}function cO(e,t,n){let r=new Float32Array(t*2);for(let a=0;a<t;a++){let s=0,i=0;for(let o=0;o<t;o++){let l=C.exponent(a*o,t,n),c=C.getComplexWithIndex(e,o);s+=c.real*l.real-c.imag*l.imag,i+=c.real*l.imag+c.imag*l.real}n&&(s/=t,i/=t),C.assignToTypedArray(r,s,i,a)}return r}function hO(e){let{inputs:t,backend:n}=e,{input:r}=t,a=k.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=yt({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=Kx(o,!1,n),c=yt({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var dO={kernelName:Hh,backendName:"cpu",kernelFunc:hO};function Cm(e){let{backend:t,attrs:n}=e,{shape:r,value:a,dtype:s}=n,i=s||k.inferDtype(a),o=k.getArrayFromDType(i,k.sizeFromShape(r));return pO(o,a,i),t.makeTensorInfo(r,i,o)}var fO={kernelName:Tu,backendName:"cpu",kernelFunc:Cm};function pO(e,t,n){e.fill(t)}var mO={kernelName:mo,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,a=n,s=k.getTypedArrayFromDType(r.dtype,k.sizeFromShape(r.shape)),[i,o,l,c]=r.shape,u=a.data.get(r.dataId).values;for(let h=0;h<i;h++){let p=h*l*o*c;for(let d=0;d<o;d++){let f=d*(l*c);for(let m=0;m<l;m++){let A=m*c;for(let y=0;y<c;y++){let g=[i,d,m,y][2],_=Math.round(l-g),x=p+f+A+y,w=u[x];if(_>=0&&_<l){let b=_*c,N=p+f+b+y;w=u[N]}s[x]=w}}}}return{dataId:a.write(s,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},AO=Et((e,t)=>Math.floor(e/t)),yO=Vt(xs,AO,null,"int32"),gO={kernelName:xs,backendName:"cpu",kernelFunc:yO};function xO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:p,activation:d,leakyreluAlpha:f}=r,m=Gx({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:p}});if(i){let A=m;m=pc({inputs:{a:m,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(d){let A=m;m=Im(n,m,d,o,f),n.disposeIntermediateTensorInfo(A)}return m}var wO={kernelName:Ys,backendName:"cpu",kernelFunc:xO};function _O(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:p,activation:d,leakyreluAlpha:f}=r,m=qx({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:p}});if(i){let A=m;m=pc({inputs:{a:m,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(d){let A=m;m=Im(n,m,d,o,f),n.disposeIntermediateTensorInfo(A)}return m}var bO={kernelName:Js,backendName:"cpu",kernelFunc:_O};function vO(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=k.sizeFromShape(r.shape),i=a.shape,o=i[i.length-1],[l,c,u,h]=C.prepareAndValidate(r,a);if(c===0)return n.makeTensorInfo(l,r.dtype,[]);let p=Le([c,u],r.dtype),d=n.data.get(a.dataId).values,f=n.data.get(r.dataId).values;for(let m=0;m<c;m++){let A=[],y=0;for(let g=0;g<o;g++){let _=d[m*o+g];y+=_*h[g],A.push(_)}if(y<0||y>=s/u)throw new Error(`Invalid indices: ${A} does not index into ${r.shape}`);for(let g=0;g<u;g++)p.values[m*u+g]=f[y*u+g]}return n.makeTensorInfo(l,p.dtype,p.values)}var kO={kernelName:yo,backendName:"cpu",kernelFunc:vO};function IO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r;we([a,s],"gatherV2");let l=o;o==null&&(l=0);let c=k.sizeFromShape(s.shape),u=k.parseAxisParam(i,a.shape)[0],h=C.segment_util.collectGatherOpShapeInfo(a,s,u,l),p=yt({inputs:{x:a},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),d=yt({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,c/h.batchSize]}}),f=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],m=n.bufferSync(d),A=n.bufferSync(p),y=gx(A,m,f);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.makeTensorInfo(h.outputShape,y.dtype,y.values)}var NO={kernelName:Ao,backendName:"cpu",kernelFunc:IO},SO=Et((e,t)=>e>=t?1:0),TO=Vt(_s,SO,null,"bool"),EO={kernelName:_s,backendName:"cpu",kernelFunc:TO};function CO(e){let{inputs:t,backend:n}=e,{input:r}=t,a=k.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=yt({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=Kx(o,!0,n),c=yt({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var RO={kernelName:Gh,backendName:"cpu",kernelFunc:CO},FO=st(wo,e=>Number.isFinite(e)?1:0,"bool"),MO={kernelName:wo,backendName:"cpu",kernelFunc:FO},OO=st(_o,e=>Math.abs(e)===Infinity?1:0,"bool"),$O={kernelName:_o,backendName:"cpu",kernelFunc:OO},DO=st(bo,e=>Number.isNaN(e)?1:0,"bool"),zO={kernelName:bo,backendName:"cpu",kernelFunc:DO},PO=Et((e,t)=>e<=t?1:0),LO=Vt(ko,PO,null,"bool"),WO={kernelName:ko,backendName:"cpu",kernelFunc:LO};function BO(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=_x(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var VO={kernelName:Xh,backendName:"cpu",kernelFunc:BO},UO=st(Io,e=>Math.log1p(e)),jO={kernelName:Io,backendName:"cpu",kernelFunc:UO},HO=Et((e,t)=>e&&t),GO=Vt(No,HO,null,"bool"),qO={kernelName:No,backendName:"cpu",kernelFunc:GO},XO=st(Eu,e=>e?0:1,"bool"),KO={kernelName:Eu,backendName:"cpu",kernelFunc:XO},ZO=Et((e,t)=>e||t),YO=Vt(Cu,ZO,null,"bool"),JO={kernelName:Cu,backendName:"cpu",kernelFunc:YO};function QO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r;we(a,"LRN");let c=a.shape[3],u=c-1,h=n.data.get(a.dataId).values,p=k.sizeFromShape(a.shape),d=new Float32Array(p);function f(m){let A=m%c,y=m-A+Math.max(0,A-s),g=m-A+Math.min(A+s,u),_=0;for(;y<=g;y++){let x=h[y];_+=x*x}return _}for(let m=0;m<p;m++){let A=f(m),y=h[m]*Math.pow(i+o*A,-l);d[m]=y}return n.makeTensorInfo(a.shape,a.dtype,d)}var e$={kernelName:Ru,backendName:"cpu",kernelFunc:QO};function t$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=r;we(i,"LRNGrad");let h=k.sizeFromShape(i.shape),p=i.shape[3],d=n.data.get(i.dataId).values,f=n.data.get(a.dataId).values,m=n.data.get(s.dataId).values,A=new Float32Array(h),y=h;for(let g=0;g<y;g++){let _=g%p,x=g-_+Math.max(0,_-o),w=g-_+Math.min(p,_+o+1),b=0;for(let N=x;N<w;N++)b+=Math.pow(f[N],2);b=c*b+l;for(let N=x;N<w;N++){let T=-2*c*u*f[N]*m[g]/b;g===N&&(T+=Math.pow(b,-u)),T*=d[g],A[N]+=T}}return n.makeTensorInfo(i.shape,a.dtype,A)}var n$={kernelName:Kh,backendName:"cpu",kernelFunc:t$};function Zx(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=n,l=a.shape,c=l.length,u=k.parseAxisParam(s,l),h=u,p=C.getAxesPermutation(h,c),d=o.data.get(a.dataId).values;if(p!=null){let x=new Array(c);for(let w=0;w<x.length;w++)x[w]=l[p[w]];d=wm(d,l,a.dtype,p,x),h=C.getInnerMostAxes(h.length,c),l=x}we(a,"max"),C.assertAxesAreInnerMostDims("max",h,c);let[f,m]=C.computeOutAndReduceShapes(l,h),A=k.sizeFromShape(m),y=vx(d,A,f,a.dtype),g=o.write(y,f,a.dtype),_=f;return i&&(_=C.expandShapeToKeepDim(f,u)),{dataId:g,shape:_,dtype:a.dtype}}var r$={kernelName:ks,backendName:"cpu",kernelFunc:Zx};function a$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;we(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;k.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l),h;if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))h=Lr({inputs:{x:a},backend:n});else{let p=n.data.get(a.dataId).values,d=k.computeStrides(a.shape),f=Nm(p,a.shape,a.dtype,d,u,"max");h=n.makeTensorInfo(u.outShape,a.dtype,f.values)}return h}var s$={kernelName:Ns,backendName:"cpu",kernelFunc:a$};function i$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r;we(a,"maxPool3d");let u=C.computePool3DInfo(a.shape,s,i,1,o,l,c),h=n.data.get(a.dataId).values,p=Hx(h,a.shape,a.dtype,k.computeStrides(a.shape),u,"max");return n.makeTensorInfo(p.shape,"float32",p.values)}var o$={kernelName:Fu,backendName:"cpu",kernelFunc:i$};function l$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=r;we([a,s],"maxPool3DGrad");let u=C.computePool3DInfo(s.shape,i,o,1,l,c),h=n.bufferSync(s),p=XF(h,u),d=u.strideDepth,f=u.strideHeight,m=u.strideWidth,A=u.dilationDepth,y=u.dilationHeight,g=u.dilationWidth,_=u.effectiveFilterDepth,x=u.effectiveFilterHeight,w=u.effectiveFilterWidth,b=_-1-u.padInfo.front,N=w-1-u.padInfo.left,T=x-1-u.padInfo.top,E=Le(s.shape,"float32"),M=n.bufferSync(a);for(let $=0;$<u.batchSize;++$)for(let P=0;P<u.inChannels;++P)for(let V=0;V<u.inDepth;++V)for(let G=0;G<u.inHeight;++G)for(let U=0;U<u.inWidth;++U){let K=V-b,X=G-T,ee=U-N,Z=0;for(let ae=0;ae<_;ae+=A){let J=(K+ae)/d;if(!(J<0||J>=u.outDepth||Math.floor(J)!==J))for(let oe=0;oe<x;oe+=y){let ne=(X+oe)/f;if(!(ne<0||ne>=u.outHeight||Math.floor(ne)!==ne))for(let ce=0;ce<w;ce+=g){let ue=(ee+ce)/m;if(ue<0||ue>=u.outWidth||Math.floor(ue)!==ue)continue;let pe=_*x*w-1-p.get($,J,ne,ue,P),fe=ae*x*w+oe*w+ce,_e=pe===fe?1:0;_e!==0&&(Z+=M.get($,J,ne,ue,P)*_e)}}}E.set(Z,$,V,G,U,P)}return n.makeTensorInfo(E.shape,E.dtype,E.values)}var u$={kernelName:Yh,backendName:"cpu",kernelFunc:l$};function c$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;we([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,p=C.computePool2DInfo(o.shape,l,c,1,u,h),d=n.data.get(o.dataId).values,f=Le(p.outShape,o.dtype,jx(d,o.shape,o.dtype,p).values),m=p.strideHeight,A=p.strideWidth,y=p.dilationHeight,g=p.dilationWidth,_=p.effectiveFilterHeight,x=p.effectiveFilterWidth,w=x-1-p.padInfo.left,b=_-1-p.padInfo.top,N=Le(o.shape,"float32"),T=n.data.get(a.dataId).values,E=Le(a.shape,"float32",T);for(let M=0;M<p.batchSize;++M)for(let $=0;$<p.inChannels;++$)for(let P=0;P<p.inHeight;++P)for(let V=0;V<p.inWidth;++V){let G=P-b,U=V-w,K=0;for(let X=0;X<_;X+=y){let ee=(G+X)/m;if(!(ee<0||ee>=p.outHeight||Math.floor(ee)!==ee))for(let Z=0;Z<x;Z+=g){let ae=(U+Z)/A;if(ae<0||ae>=p.outWidth||Math.floor(ae)!==ae)continue;let J=_*x-1-f.get(M,ee,ae,$),oe=X*x+Z,ne=J===oe?1:0;ne!==0&&(K+=E.get(M,ee,ae,$)*ne)}}N.set(K,M,P,V,$)}return n.makeTensorInfo(N.shape,N.dtype,N.values)}var h$={kernelName:Zh,backendName:"cpu",kernelFunc:c$};function d$(e,t,n,r,a){let s=k.computeStrides(t),i=Nm(e,t,n,s,a,"max"),o=jx(e,t,n,a,!0,r);return[i.values,o.values]}var p$={kernelName:Jh,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;we(r,"MaxPoolWithArgmax");let c=l.data.get(r.dataId).values,u=C.computePool2DInfo(r.shape,a,s,[1,1],i),[h,p]=d$(c,r.shape,r.dtype,o,u),d=l.write(h,u.outShape,r.dtype),f=l.write(p,u.outShape,r.dtype);return[{dataId:d,shape:u.outShape,dtype:r.dtype},{dataId:f,shape:u.outShape,dtype:"int32"}]}};function ip(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;we(a,"sum");let o;a.dtype==="bool"?o=Oa({inputs:{x:a},backend:n,attrs:{dtype:"int32"}}):o=Lr({inputs:{x:a},backend:n});let l=o.shape.length,c=k.parseAxisParam(s,o.shape),u=C.getAxesPermutation(c,l),h=c,p=o;u!=null&&(p=sr({inputs:{x:o},backend:n,attrs:{perm:u}}),h=C.getInnerMostAxes(h.length,l)),C.assertAxesAreInnerMostDims("sum",h,p.shape.length);let[d,f]=C.computeOutAndReduceShapes(p.shape,h),m=C.upcastType(p.dtype,"int32"),A=ap(n,d,m),y=k.sizeFromShape(f),g=n.data.get(A.dataId).values,_=n.data.get(p.dataId).values;for(let x=0;x<g.length;++x){let w=x*y,b=0;for(let N=0;N<y;++N)b+=_[w+N];g[x]=b}if(i){let x=C.expandShapeToKeepDim(A.shape,c),w=A;A=yt({inputs:{x:A},backend:n,attrs:{shape:x}}),n.disposeIntermediateTensorInfo(w)}return n.disposeIntermediateTensorInfo(o),u!=null&&n.disposeIntermediateTensorInfo(p),A}var f$={kernelName:js,backendName:"cpu",kernelFunc:ip};function m$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=k.parseAxisParam(s,a.shape),l=C.computeOutAndReduceShapes(a.shape,o)[1],c=k.sizeFromShape(l),u=[],h=n.makeTensorInfo([],"float32",new Float32Array([c]));u.push(h);let p=Oa({inputs:{x:a},backend:n,attrs:{dtype:"float32"}});u.push(p);let d=Sm({inputs:{a:p,b:h},backend:n});u.push(d);let f=ip({inputs:{x:d},backend:n,attrs:{axis:s,keepDims:i}});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var A$={kernelName:Ss,backendName:"cpu",kernelFunc:m$};function y$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;we(a,"min");let o=k.parseAxisParam(s,a.shape),l=o,c=C.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=sr({inputs:{x:a},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("min",l,u.shape.length);let[h,p]=C.computeOutAndReduceShapes(u.shape,l),d=k.sizeFromShape(p),f=k.makeZerosTypedArray(k.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let g=y*d,_=m[g];for(let x=0;x<d;++x){let w=m[g+x];w<_&&(_=w)}f[y]=_}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,f);if(i){let y=C.expandShapeToKeepDim(h,o),g=yt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var g$={kernelName:Ts,backendName:"cpu",kernelFunc:y$};function x$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,mode:i}=r;we(a,"mirrorPad");let o=s.map((g,_)=>g[0]+a.shape[_]+g[1]),l=s.map(g=>g[0]),c=s.map((g,_)=>g[0]+a.shape[_]),u=i==="reflect"?0:1,h=n.data.get(a.dataId).values,p=a.shape.length,d=k.computeStrides(a.shape),f=k.sizeFromShape(o),m=o.length,A=k.computeStrides(o),y=k.getTypedArrayFromDType(a.dtype,f);for(let g=0;g<f;g++){let _=k.indexToLoc(g,m,A);for(let w=0;w<m;w++)_[w]<l[w]?_[w]=l[w]*2-_[w]-u:_[w]>=c[w]&&(_[w]=(c[w]-1)*2-_[w]+u);_=_.map((w,b)=>w-l[b]);let x=k.locToIndex(_,p,d);y[g]=h[x]}return{dataId:n.write(y,o,a.dtype),shape:o,dtype:a.dtype}}var w$={kernelName:Mu,backendName:"cpu",kernelFunc:x$},_$=Et((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),b$=Vt(So,_$),v$={kernelName:So,backendName:"cpu",kernelFunc:b$},k$=qi(ik());function Yx(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=a.shape.length,o=s;if(o===-1&&(o=i-1),o!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${o}`);let l=k.parseAxisParam([o],a.shape),c=Zx({inputs:{x:a},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),u=C.expandShapeToKeepDim(c.shape,l),h=yt({inputs:{x:c},backend:n,attrs:{shape:u}}),p=km({inputs:{a,b:h},backend:n}),d=Dx({inputs:{x:p},backend:n}),f=ip({inputs:{x:d},backend:n,attrs:{axis:l,keepDims:!1}}),m=yt({inputs:{x:f},backend:n,attrs:{shape:u}}),A=Sm({inputs:{a:d,b:m},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var I$={kernelName:Hs,backendName:"cpu",kernelFunc:Yx};function N$(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r;we(a,"multinomial");let l=o?a:Yx({inputs:{logits:a},backend:n,attrs:{dim:-1}}),c=l.shape[0],u=l.shape[1],h=n.data.get(l.dataId).values,p=[c,s],d=k.makeZerosTypedArray(k.sizeFromShape(p),"int32");for(let f=0;f<c;++f){let m=f*u,A=new Float32Array(u-1);A[0]=h[m];for(let _=1;_<A.length;++_)A[_]=A[_-1]+h[m+_];let y=k$.alea(i.toString()),g=f*s;for(let _=0;_<s;++_){let x=y();d[g+_]=A.length;for(let w=0;w<A.length;w++)if(x<A[w]){d[g+_]=w;break}}}return o||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(p,"int32",d)}var S$={kernelName:Qh,backendName:"cpu",kernelFunc:N$},T$=Pr.nonMaxSuppressionV3Impl;function E$(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r;we(a,"NonMaxSuppression");let c=n.data.get(a.dataId).values,u=n.data.get(s.dataId).values,{selectedIndices:h}=T$(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var C$={kernelName:Co,backendName:"cpu",kernelFunc:E$},R$=Pr.nonMaxSuppressionV4Impl;function F$(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r;we(a,"NonMaxSuppressionPadded");let u=n.data.get(a.dataId).values,h=n.data.get(s.dataId).values,{selectedIndices:p,validOutputs:d}=R$(u,h,i,o,l,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([d]))]}var M$={kernelName:Ro,backendName:"cpu",kernelFunc:F$},O$=Pr.nonMaxSuppressionV5Impl;function $$(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r;we(a,"NonMaxSuppressionWithScore");let u=n.data.get(a.dataId).values,h=n.data.get(s.dataId).values,p=i,d=o,f=l,m=c,{selectedIndices:A,selectedScores:y}=O$(u,h,p,d,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var D$={kernelName:Fo,backendName:"cpu",kernelFunc:$$};function z$(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r;we(a,"oneHot");let l=k.sizeFromShape(a.shape),c=new Float32Array(l*s);c.fill(o);let u=n.data.get(a.dataId).values;for(let h=0;h<l;++h)u[h]>=0&&u[h]<s&&(c[h*s+u[h]]=i);return n.makeTensorInfo([...a.shape,s],"int32",c)}var P$={kernelName:Rs,backendName:"cpu",kernelFunc:z$};function op(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(r.dtype==="complex64"){let a=pi({inputs:{input:r},backend:n}),s=op({inputs:{x:a},backend:n}),i=vl({inputs:{input:r},backend:n}),o=op({inputs:{x:i},backend:n}),l=Rn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Cm({backend:n,attrs:{shape:r.shape,value:0,dtype:r.dtype}})}var L$={kernelName:Zo,backendName:"cpu",kernelFunc:op};function Jx(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(r.dtype==="complex64"){let a=pi({inputs:{input:r},backend:n}),s=Jx({inputs:{x:a},backend:n}),i=vl({inputs:{input:r},backend:n}),o=op({inputs:{x:i},backend:n}),l=Rn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Cm({backend:n,attrs:{shape:r.shape,value:1,dtype:r.dtype}})}var W$={kernelName:Mo,backendName:"cpu",kernelFunc:Jx};function Qx(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return sp({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{k.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=sp({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=kl({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var B$={kernelName:Oo,backendName:"cpu",kernelFunc:Qx};function V$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r;we(a,"pad");let o=s.map((y,g)=>y[0]+a.shape[g]+y[1]),l=s.map(y=>y[0]),c=n.data.get(a.dataId).values,u=k.sizeFromShape(a.shape),h=a.shape.length,p=k.computeStrides(a.shape),d=k.sizeFromShape(o),f=o.length,m=k.computeStrides(o),A=k.getTypedArrayFromDType(a.dtype,d);i!==0&&A.fill(i);for(let y=0;y<u;y++){let g=k.indexToLoc(y,h,p).map((x,w)=>x+l[w]),_=k.locToIndex(g,f,m);A[_]=c[y]}return{dataId:n.write(A,o,a.dtype),shape:o,dtype:a.dtype}}var ew={kernelName:Fs,backendName:"cpu",kernelFunc:V$},U$=Et((e,t)=>Math.pow(e,t)),j$=Vt(Ms,U$),H$={kernelName:Ms,backendName:"cpu",kernelFunc:j$};function G$(e){let{backend:t,attrs:n}=e,{start:r,stop:a,dtype:s,step:i}=n,o=_m(r,a,i,s);return t.makeTensorInfo([o.length],s,o)}var q$={kernelName:Ou,backendName:"cpu",kernelFunc:G$},X$=st(Do,e=>1/e),K$={kernelName:Do,backendName:"cpu",kernelFunc:X$};function Z$(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r;we(a,"resizeBilinear");let l=k.computeStrides(a.shape),[c,u]=o,[h,p,d,f]=a.shape,m=n.data.get(a.dataId).values,A=new Float32Array(k.sizeFromShape([h,c,u,f])),y=[s&&c>1?p-1:p,s&&u>1?d-1:d],g=[s&&c>1?c-1:c,s&&u>1?u-1:u],_=0,x=y[0]/g[0],w=y[1]/g[1];for(let b=0;b<h;b++)for(let N=0;N<c;N++){let T;i?T=x*(N+.5)-.5:T=x*N;let E=Math.max(0,Math.floor(T)),M=T-E,$=Math.min(p-1,Math.ceil(T)),P=b*l[0]+E*l[1],V=b*l[0]+$*l[1];for(let G=0;G<u;G++){let U;i?U=w*(G+.5)-.5:U=w*G;let K=Math.max(0,Math.floor(U)),X=U-K,ee=Math.min(d-1,Math.ceil(U)),Z=P+K*l[2],ae=V+K*l[2],J=P+ee*l[2],oe=V+ee*l[2];for(let ne=0;ne<f;ne++){let ce=m[Z+ne],ue=m[ae+ne],pe=m[J+ne],fe=m[oe+ne],_e=ce+(pe-ce)*X,Ne=ue+(fe-ue)*X,Ee=_e+(Ne-_e)*M;A[_++]=Ee}}}return n.makeTensorInfo([h,c,u,f],"float32",A)}var Y$={kernelName:Ds,backendName:"cpu",kernelFunc:Z$};function J$(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r;we([s,a],"resizeBilinearGrad");let o=k.computeStrides(a.shape),[l,c,u,h]=a.shape,[,p,d]=s.shape,f=new Float32Array(l*c*u*h),m=[i&&p>1?c-1:c,i&&d>1?u-1:u],A=[i&&p>1?p-1:p,i&&d>1?d-1:d],y=m[0]/A[0],g=m[1]/A[1],_=n.data.get(s.dataId).values,x=0;for(let w=0;w<l;w++){let b=w*o[0];for(let N=0;N<p;N++){let T=N*y,E=Math.floor(T),M=Math.min(Math.ceil(T),c-1),$=b+E*o[1],P=b+M*o[1],V=T-E,G=1-V;for(let U=0;U<d;U++){let K=U*g,X=Math.floor(K),ee=Math.min(Math.ceil(K),u-1),Z=K-X,ae=1-Z,J=$+X*o[2],oe=$+ee*o[2],ne=P+X*o[2],ce=P+ee*o[2],ue=G*ae,pe=G*Z,fe=V*ae,_e=V*Z;for(let Ne=0;Ne<h;Ne++){let Ee=_[x++];f[J+Ne]+=Ee*ue,f[oe+Ne]+=Ee*pe,f[ne+Ne]+=Ee*fe,f[ce+Ne]+=Ee*_e}}}}return n.makeTensorInfo([l,u,c,h],"float32",f)}var Q$={kernelName:nd,backendName:"cpu",kernelFunc:J$};function eD(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r;we(a,"resizeNearestNeighbor");let l=k.computeStrides(a.shape),[c,u]=o,[h,p,d,f]=a.shape,m=n.data.get(a.dataId).values,A=new Float32Array(h*c*u*f),y=[s&&c>1?p-1:p,s&&u>1?d-1:d],g=[s&&c>1?c-1:c,s&&u>1?u-1:u],_=y[0]/g[0],x=y[1]/g[1],w=0;for(let b=0;b<h;b++){let N=b*l[0];for(let T=0;T<c;T++){let E=i?_*(T+.5):_*T,M=Math.min(p-1,s?Math.round(E):Math.floor(E));i&&(M=Math.max(0,M));let $=N+M*l[1];for(let P=0;P<u;P++){let V=i?x*(P+.5):x*P,G=Math.min(d-1,s?Math.round(V):Math.floor(V));i&&(G=Math.max(0,G));let U=$+G*l[2];for(let K=0;K<f;K++){let X=m[U+K];A[w++]=X}}}}return n.makeTensorInfo([h,c,u,f],a.dtype,A)}var tD={kernelName:$u,backendName:"cpu",kernelFunc:eD};function nD(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r;we([s,a],"resizeNearestNeighborGrad");let o=k.computeStrides(a.shape),l=k.computeStrides(s.shape),[c,u,h,p]=a.shape,[,d,f]=s.shape,m=new Float32Array(c*u*h*p),A=n.data.get(s.dataId).values,y=[i&&d>1?u-1:u,i&&f>1?h-1:h],g=[i&&d>1?d-1:d,i&&f>1?f-1:f],_=y[0]/g[0],x=y[1]/g[1],w=1/_,b=1/x,N=Math.ceil(w)*2+2,T=Math.ceil(b)*2+2;for(let E=0;E<c;E++){let M=E*o[0];for(let $=0;$<u;$++){let P=M+$*o[1],V=Math.floor($*w),G=Math.floor(V-N/2);for(let U=0;U<h;U++){let K=P+U*o[2],X=Math.floor(U*b),ee=Math.floor(X-T/2);for(let Z=0;Z<p;Z++){let ae=0;for(let J=0;J<N;J++){let oe=J+G;if(oe<0||oe>=d)continue;let ne=M+oe*l[1],ce=oe*_,ue=Math.min(u-1,i?Math.round(ce):Math.floor(ce));if($===ue)for(let pe=0;pe<T;pe++){let fe=pe+ee;if(fe<0||fe>=f)continue;let _e=ne+fe*l[2],Ne=fe*x,Ee=Math.min(h-1,i?Math.round(Ne):Math.floor(Ne));U===Ee&&(ae+=A[_e+Z])}}m[K+Z]=ae}}}}return n.makeTensorInfo(a.shape,a.dtype,m)}var rD={kernelName:td,backendName:"cpu",kernelFunc:nD};function aD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r;we(a,"reverse");let i=a.shape.length,o=k.parseAxisParam(s,a.shape);if(i===0)return Lr({inputs:{x:a},backend:n});let l=new Ft(a.shape,a.dtype),c=n.bufferSync(a);for(let u=0;u<l.size;u++){let h=l.indexToLoc(u),p=h.slice();o.forEach(d=>p[d]=a.shape[d]-1-p[d]),l.set(c.get(...p),...h)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var sD={kernelName:Ps,backendName:"cpu",kernelFunc:aD},iD={kernelName:Yo,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=k.getTypedArrayFromDType(r.dtype,k.sizeFromShape(r.shape)),[c,u,h,p]=r.shape,[d,f]=C.getImageCenter(i,u,h),m=255,A=Math.sin(a),y=Math.cos(a),g=o.data.get(r.dataId).values;for(let _=0;_<c;_++){let x=_*h*u*p;for(let w=0;w<u;w++){let b=w*(h*p);for(let N=0;N<h;N++){let T=N*p;for(let E=0;E<p;E++){let M=[c,w,N,E],$=M[2],P=M[1],V=($-d)*y-(P-f)*A,G=($-d)*A+(P-f)*y;V=Math.round(V+d),G=Math.round(G+f);let U=s;if(typeof s!="number"&&(E===3?U=m:U=s[E]),V>=0&&V<h&&G>=0&&G<u){let X=G*(h*p),ee=V*p,Z=x+X+ee+E;U=g[Z]}let K=x+b+T+E;l[K]=U}}}}return{dataId:o.write(l,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},oD=st(Ls,e=>{let t=Math.floor(e);return e-t<.5?Math.floor(e):e-t>.5?Math.ceil(e):t%2==0?t:t+1}),lD={kernelName:Ls,backendName:"cpu",kernelFunc:oD};function tw(e,t,n,r,a,s,i,o,l,c){let u=[r/a,a],h=e.values,p=t.values;if(r===0)return Le(n,t.dtype);let d=Le(u,t.dtype);d.values.fill(l);for(let f=0;f<s;f++){let m=[],A=0;for(let y=0;y<i;y++){let g=h[f*i+y];m.push(g),A+=g*o[y]}if(A<0||A>=r/a)throw new Error(`Invalid indices: ${m} does not index into ${n}`);for(let y=0;y<a;y++)c?d.values[A*a+y]+=p[f*a+y]:d.values[A*a+y]=t.rank===0?p[0]:p[f*a+y]}return d}function uD(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:h}=C.calculateShapes(s,a,i),p=!0,d=n.bufferSync(a),f=n.bufferSync(s),m=tw(d,f,i,h,c,l,o,u,0,p);return n.makeTensorInfo(i,m.dtype,m.values)}var cD={kernelName:Po,backendName:"cpu",kernelFunc:uD};function hD(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t;we([r,a,s],"select");let i=r.shape.length,o=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=n.data.get(s.dataId).values,u=er(a.dtype,s.dtype),h=k.makeZerosTypedArray(k.sizeFromShape(a.shape),u),p=0,d=i===0||i>1||a.shape.length===1?1:k.sizeFromShape(a.shape.slice(1));for(let f=0;f<o.length;f++)for(let m=0;m<d;m++)o[f]===1?h[p++]=l[f]:h[p++]=c[f];return n.makeTensorInfo(a.shape,u,h)}var dD={kernelName:Lo,backendName:"cpu",kernelFunc:hD},pD=C.SELU_SCALEALPHA,fD=C.SELU_SCALE,mD=st(Wo,e=>e>=0?fD*e:pD*(Math.exp(e)-1)),AD={kernelName:Wo,backendName:"cpu",kernelFunc:mD},yD=st(Vs,e=>1/(1+Math.exp(-e))),gD={kernelName:Vs,backendName:"cpu",kernelFunc:yD},xD=st(Uo,e=>e<0?-1:e>0?1:0),wD={kernelName:Uo,backendName:"cpu",kernelFunc:xD},_D=st(Bs,e=>Math.sin(e)),bD={kernelName:Bs,backendName:"cpu",kernelFunc:_D},vD=st(Vo,e=>Math.sinh(e)),kD={kernelName:Vo,backendName:"cpu",kernelFunc:vD},ID=11920928955078125e-23,nw=Math.log(ID)+2,ND=st(jo,e=>{let t=e>-nw,n=e<nw,r=Math.exp(e),a;return n?a=r:t?a=e:a=Math.log(1+r),a}),SD={kernelName:jo,backendName:"cpu",kernelFunc:ND};function TD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;we([a],"spaceToBatchND");let o=k.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let A=1+s.length;A<a.shape.length;++A)l.push([0,0]);let c=ew.kernelFunc({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),u=C.getReshaped(c.shape,s,o,!1),h=C.getPermuted(u.length,s.length,!1),p=C.getReshapedPermuted(c.shape,s,o,!1),d=yt({inputs:{x:c},backend:n,attrs:{shape:u}}),f=sr({inputs:{x:d},backend:n,attrs:{perm:h}}),m=yt({inputs:{x:f},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var ED={kernelName:Du,backendName:"cpu",kernelFunc:TD};function CD(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:c,sliceSize:u,strides:h,outputSize:p}=C.calculateShapes(s,a,o),d=!1,f=n.bufferSync(a),m=n.bufferSync(s),A=n.data.get(i.dataId).values[0],y=tw(f,m,o,p,u,c,l,h,A,d);return n.makeTensorInfo(o,y.dtype,y.values)}var RD={kernelName:rd,backendName:"cpu",kernelFunc:CD};function FD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=k.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),c=new Array(a.shape.length).fill(0),u=a.shape.slice();return l.map(h=>{let p=[...u];p[o]=h;let d=fi({inputs:{x:a},backend:n,attrs:{begin:c,size:p}});return c[o]+=h,d})}var MD={kernelName:Ho,backendName:"cpu",kernelFunc:FD},OD=st(Us,e=>Math.sqrt(e)),$D={kernelName:Us,backendName:"cpu",kernelFunc:OD},DD={kernelName:zu,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,r=t;we(n,"square");let a=r.data.get(n.dataId).values,s=new Float32Array(a.length);for(let i=0;i<a.length;++i){let o=a[i];s[i]=o*o}return{dataId:r.write(s,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},zD=st(va,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),PD={kernelName:va,backendName:"cpu",kernelFunc:zD};function LD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:h,shrinkAxisMask:p}=r;we(a,"stridedSlice");let{nonStrided:d,$begin:f,$strides:m,size:A,newShape:y,outShape:g}=sn.sliceInfo(a.shape,s,i,o,l,c,u,h,p),_=yt({inputs:{x:a},backend:n,attrs:{shape:y}}),x;if(d){let b=fi({inputs:{x:_},backend:n,attrs:{begin:f,size:A}});x=yt({inputs:{x:b},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(b)}else if(g.some(b=>b===0))x=n.makeTensorInfo(g,a.dtype,[]);else{let b=n.bufferSync(_),N=Rx(g,b,m,f);x=n.makeTensorInfo(N.shape,N.dtype,N.values)}let w=yt({inputs:{x},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(_),n.disposeIntermediateTensorInfo(x),w}var WD={kernelName:Go,backendName:"cpu",kernelFunc:LD},BD=st(qo,e=>Math.tan(e)),VD={kernelName:qo,backendName:"cpu",kernelFunc:BD},UD=st(Xs,e=>Math.tanh(e)),jD={kernelName:Xs,backendName:"cpu",kernelFunc:UD};function HD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;we(a,"tile");let i=Mx(n.bufferSync(a),s);return n.makeTensorInfo(i.shape,i.dtype,i.values)}var GD={kernelName:ba,backendName:"cpu",kernelFunc:HD};function qD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r;we(a,"topk");let o=n.data.get(a.dataId).values,[l,c]=Ox(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var XD={kernelName:Xo,backendName:"cpu",kernelFunc:qD};function KD(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;we(s,"unique");let i=r.data.get(s.dataId).values,{outputValues:o,outputShape:l,indices:c}=$x(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([c.length],"int32",c)]}var ZD={kernelName:ad,backendName:"cpu",kernelFunc:KD};function YD(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a.shape.length,o=a.shape[s],l=new Array(i-1),c=0;for(let d=0;d<i;d++)d!==s&&(l[c++]=a.shape[d]);let u=new Array(i).fill(0),h=a.shape.slice();h[s]=1;let p=new Array(o);for(let d=0;d<p.length;d++){u[s]=d;let f=fi({inputs:{x:a},backend:n,attrs:{begin:u,size:h}});p[d]=yt({inputs:{x:f},backend:n,attrs:{shape:l}}),n.disposeIntermediateTensorInfo(f)}return p}var JD={kernelName:Ko,backendName:"cpu",kernelFunc:YD};function QD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r;we(a,"unsortedSegmentSum");let o=a.shape.length,l=s.shape.length,c=[],u=[],h=o-l,p=s;for(let f=0;f<h;++f){let m=sp({inputs:{input:p},backend:n,attrs:{dim:f+1}});p=m,u.push(m)}for(let f=0;f<i;++f){let m=k.createScalarValue(f,"int32"),A=n.makeTensorInfo([],"int32",m),y=Xx({inputs:{a:A,b:p},backend:n}),g=Oa({inputs:{x:y},backend:n,attrs:{dtype:"float32"}}),_=vm({inputs:{a:g,b:a},backend:n}),x=ip({inputs:{x:_},backend:n,attrs:{axis:0,keepDims:!1}});c.push(x),u.push(A),u.push(y),u.push(g),u.push(_),u.push(x)}let d=Qx({inputs:c,backend:n,attrs:{axis:0}});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),d}var ez={kernelName:Pu,backendName:"cpu",kernelFunc:QD},tz=[bF,SR,kF,NF,MR,TF,CF,FF,OF,DF,PF,WF,VF,HF,qF,ZF,JF,eM,nM,wF,aM,iM,lM,RR,$R,cM,TR,dM,fM,yM,xM,mM,vM,IM,_M,SM,EM,RM,MM,$M,zM,PM,WM,VM,jM,HM,qM,GM,Tm,pF,KM,YM,sO,DR,iO,PR,dO,fO,mO,WR,gO,wO,bO,kO,NO,VR,EO,ER,RO,pM,MO,$O,zO,fF,jR,WO,VO,GR,jO,qO,KO,JO,e$,n$,XR,s$,o$,u$,h$,p$,r$,A$,g$,ZR,w$,v$,S$,JR,eF,C$,M$,D$,nF,P$,W$,B$,ew,H$,AF,sF,q$,CR,K$,yF,gF,xF,Y$,Q$,tD,rD,sD,iD,lD,oF,cD,dD,AD,gD,wD,bD,kD,lF,I$,SD,ED,RD,MD,$D,DD,cF,PD,WD,dF,f$,VD,jD,GD,XD,rF,ZD,JD,ez,L$];for(let e of tz)Qs(e);var rw={};$e(rw,{assertNotComplex:()=>Il,bindCanvasToFramebuffer:()=>az,bindColorTextureToFramebuffer:()=>up,bindTextureToProgramUniformSampler:()=>gw,bindTextureUnit:()=>mw,bindVertexBufferToProgramAttribute:()=>Rm,callAndCheck:()=>ge,canBeRepresented:()=>aw,createFragmentShader:()=>ow,createFramebuffer:()=>fw,createProgram:()=>lw,createStaticIndexBuffer:()=>hw,createStaticVertexBuffer:()=>cw,createTexture:()=>dw,createVertexShader:()=>iw,getBatchDim:()=>mi,getExtensionOrThrow:()=>fc,getFramebufferErrorMessage:()=>xw,getMaxTexturesInShader:()=>bw,getNumChannels:()=>nz,getProgramUniformLocation:()=>yw,getProgramUniformLocationOrThrow:()=>Aw,getRowsCols:()=>Ai,getShapeAs3D:()=>cp,getTextureShapeFromLogicalShape:()=>ww,getWebGLDisjointQueryTimerVersion:()=>vw,getWebGLErrorMessage:()=>sw,getWebGLMaxTextureSize:()=>_w,hasExtension:()=>Hn,isCapableOfRenderingToFloatTexture:()=>kw,isDownloadFloatTextureEnabled:()=>Iw,isReshapeFree:()=>Ac,isWebGLFenceEnabled:()=>Nw,isWebGLVersionEnabled:()=>Mm,linkProgram:()=>uw,resetMaxTextureSize:()=>sz,resetMaxTexturesInShader:()=>iz,unbindColorTextureFromFramebuffer:()=>Fm,unbindTextureUnit:()=>rz,validateFramebuffer:()=>mc,validateProgram:()=>lp,validateTextureSize:()=>pw});var yi={},Om={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function hp(e,t){yi[e]=t}function Wr(e){if(!(e in yi)){let n=oz(e);if(n!==null)yi[e]=n;else return console.log("Could not get context for WebGL version",e),null}let t=yi[e];return t.isContextLost()?(delete yi[e],Wr(e)):(t.disable(t.DEPTH_TEST),t.disable(t.STENCIL_TEST),t.disable(t.BLEND),t.disable(t.DITHER),t.disable(t.POLYGON_OFFSET_FILL),t.disable(t.SAMPLE_COVERAGE),t.enable(t.SCISSOR_TEST),t.enable(t.CULL_FACE),t.cullFace(t.BACK),yi[e])}function lz(e){if(typeof OffscreenCanvas!="undefined"&&e===2)return new OffscreenCanvas(300,150);if(typeof document!="undefined")return document.createElement("canvas");throw new Error("Cannot create a canvas in this context")}function oz(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=lz(e);return t.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete yi[e]},!1),e===1?t.getContext("webgl",Om)||t.getContext("experimental-webgl",Om):t.getContext("webgl2",Om)}var yc;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(yc||(yc={}));var Gn;(function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD"})(Gn||(Gn={}));var Yt;(function(e){e[e.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",e[e.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",e[e.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",e[e.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",e[e.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(Yt||(Yt={}));function gc(e,t){return[t,e]}function uz(e,t){return e*t}function xc(e){let t=k.sizeFromShape(e),n=Math.ceil(t/4);return k.sizeToSquarishShape(n)}function Nl(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function cz(e,t){let[n,r]=Nl(e,t);return n*r*4}function $m(e,t){let n=e,r,a,s,i,o,l,c,u,h,p;return Q().getNumber("WEBGL_VERSION")===2?(r=n.R32F,a=n.R16F,s=n.RGBA16F,i=n.RGBA32F,o=n.RED,c=4,u=1,h=n.HALF_FLOAT,p=n.FLOAT):(r=e.RGBA,a=e.RGBA,s=e.RGBA,i=n.RGBA,o=e.RGBA,c=4,u=4,h=t!=null?t.HALF_FLOAT_OES:null,p=e.FLOAT),l=e.RGBA,{internalFormatFloat:r,internalFormatHalfFloat:a,internalFormatPackedHalfFloat:s,internalFormatPackedFloat:i,textureFormatFloat:o,downloadTextureFormat:l,downloadUnpackNumChannels:c,defaultNumChannels:u,textureTypeHalfFloat:h,textureTypeFloat:p}}function ge(e,t){let n=t();return Q().getBool("DEBUG")&&hz(e),n}function hz(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+sw(e,t))}var dz=596e-10,pz=65504;function aw(e){return!!(Q().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||dz<Math.abs(e)&&Math.abs(e)<pz)}function sw(e,t){switch(t){case e.NO_ERROR:return"NO_ERROR";case e.INVALID_ENUM:return"INVALID_ENUM";case e.INVALID_VALUE:return"INVALID_VALUE";case e.INVALID_OPERATION:return"INVALID_OPERATION";case e.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case e.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case e.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return`Unknown error code ${t}`}}function fc(e,t){return oa(e,()=>e.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function iw(e,t){let n=oa(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(ge(e,()=>e.shaderSource(n,t)),ge(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw console.log(e.getShaderInfoLog(n)),new Error("Failed to compile vertex shader.");return n}function ow(e,t){let n=oa(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(ge(e,()=>e.shaderSource(n,t)),ge(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw fz(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var mz=/ERROR: [0-9]+:([0-9]+):/g;function fz(e,t){let n=mz.exec(t);if(n==null){console.log(`Couldn't parse line number in error: ${t}`),console.log(e);return}let r=+n[1],a=e.split(`
`),s=a.length.toString().length+2,i=a.map((h,p)=>k.rightPad((p+1).toString(),s)+h),o=0;for(let h=0;h<i.length;h++)o=Math.max(i[h].length,o);let l=i.slice(0,r-1),c=i.slice(r-1,r),u=i.slice(r);console.log(l.join(`
`)),console.log(t.split(`
`)[0]),console.log(`%c ${k.rightPad(c[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(u.join(`
`))}function lw(e){return oa(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function uw(e,t){if(ge(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function lp(e,t){if(ge(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function cw(e,t){let n=oa(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return ge(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),ge(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function hw(e,t){let n=oa(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return ge(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),ge(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function nz(){return Q().getNumber("WEBGL_VERSION")===2?1:4}function dw(e){return oa(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function pw(e,t){let n=Q().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let r=`[${e}x${t}]`;throw new Error("Requested texture size "+r+" is invalid.")}if(e>n||t>n){let r=`[${e}x${t}]`,a=`[${n}x${n}]`;throw new Error("Requested texture size "+r+" greater than WebGL maximum on this browser / GPU "+a+".")}}function fw(e){return oa(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function Rm(e,t,n,r,a,s,i){let o=e.getAttribLocation(t,n);return o===-1?!1:(ge(e,()=>e.bindBuffer(e.ARRAY_BUFFER,r)),ge(e,()=>e.vertexAttribPointer(o,a,e.FLOAT,!1,s,i)),ge(e,()=>e.enableVertexAttribArray(o)),!0)}function mw(e,t,n){Sw(e,n),ge(e,()=>e.activeTexture(e.TEXTURE0+n)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function rz(e,t){Sw(e,t),ge(e,()=>e.activeTexture(e.TEXTURE0+t)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Aw(e,t,n){return oa(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function yw(e,t,n){return e.getUniformLocation(t,n)}function gw(e,t,n,r){ge(e,()=>mw(e,t,r)),ge(e,()=>e.uniform1i(n,r))}function az(e){ge(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ge(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),ge(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function up(e,t,n){ge(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),ge(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function Fm(e,t){ge(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),ge(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function mc(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+xw(e,t))}function xw(e,t){switch(t){case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case e.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${t}`}}function oa(e,t,n){let r=ge(e,()=>t());if(r==null)throw new Error(n);return r}function Sw(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n){let a=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${a}.`)}}function mi(e,t=2){return k.sizeFromShape(e.slice(0,e.length-t))}function Ai(e){if(e.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[e.length>1?e[e.length-2]:1,e[e.length-1]]}function cp(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[mi(e),...Ai(e)]),t}function ww(e,t=!1){let n=Q().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((a,s)=>s>=e.length-2?k.nearestLargerEven(e[s]):e[s]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=k.squeezeShape(e).newShape);let r=k.sizeFromShape(e);if(e.length<=1&&r<=n)return[1,r];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){let a=mi(e),s=2,i=2;return e.length&&([s,i]=Ai(e)),r=a*(s/2)*(i/2),k.sizeToSquarishShape(r).map(o=>o*2)}return k.sizeToSquarishShape(r)}function dp(e){return e%2==0}function Ac(e,t){if(e=e.slice(-2),t=t.slice(-2),k.arraysEqual(e,t)||!e.length||!t.length||e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){let n=e.slice(-1)[0],r=t.slice(-1)[0];if(n===r||dp(n)&&dp(r)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&dp(e[0])&&dp(t[0])}var pp,fp;function _w(e){if(pp==null){let t=Wr(e);pp=t.getParameter(t.MAX_TEXTURE_SIZE)}return pp}function sz(){pp=null}function iz(){fp=null}function bw(e){if(fp==null){let t=Wr(e);fp=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,fp)}function vw(e){if(e===0)return 0;let t,n=Wr(e);return Hn(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:Hn(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function Hn(e,t){return e.getExtension(t)!=null}function Mm(e){try{if(Wr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function kw(e){if(e===0)return!1;let t=Wr(e);if(e===1){if(!Hn(t,"OES_texture_float"))return!1}else if(!Hn(t,"EXT_color_buffer_float"))return!1;return Dm(t)}function Iw(e){if(e===0)return!1;let t=Wr(e);if(e===1){if(!Hn(t,"OES_texture_float")||!Hn(t,"WEBGL_color_buffer_float"))return!1}else{if(Hn(t,"EXT_color_buffer_float"))return Dm(t);let n="EXT_color_buffer_half_float";if(Hn(t,n)){let r=t.getExtension(n);return Az(t,r)}return!1}return Dm(t)}function Dm(e){let t=$m(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,r,a,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(s),i}function Az(e,t){let n=$m(e,t),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);let a=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,a,s,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(i),o}function Nw(e){return e!==2?!1:Wr(e).fenceSync!=null}function Il(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Re=Q();Re.registerFlag("HAS_WEBGL",()=>Re.getNumber("WEBGL_VERSION")>0);Re.registerFlag("WEBGL_VERSION",()=>Mm(2)?2:Mm(1)?1:0);Re.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Re.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Re.get("WEBGL_VERSION")===2);Re.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Re.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Re.registerFlag("WEBGL_PACK",()=>Re.getBool("HAS_WEBGL"));Re.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_CLIP",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);Re.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_REDUCE",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_LAZILY_UNPACK",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_CONV_IM2COL",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>_w(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>bw(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Re.getNumber("WEBGL_VERSION");return e===0?0:vw(e)});Re.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Re.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!hd.isMobile());Re.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>kw(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Re.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Re.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Re.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>Iw(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_FENCE_API_ENABLED",()=>Nw(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Re.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Re.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});function ln(){let e,t,n,r,a,s,i,o,l,c;return Q().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",a="texture",s="outputColor",i="out vec4 outputColor;",o=`
bool isnan_custom(float val) {
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`,l="",c=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(e="",t="attribute",n="varying",r="varying",a="texture2D",s="gl_FragColor",i="",o=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,l=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,c=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:c}}function gi(e,t,n="index"){let r=k.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function zm(e){let t=k.computeStrides(e).map(n=>n.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}var Tw=`
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
`,yz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=yc.DENSE;let t=xc(e),n=ln();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${gi(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${n.output} = result;
}
`}},gz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=yc.DENSE;let t=xc(e),n=ln();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${gi(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${n.output} = result;
}
`}},xz=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Gn.DOWNLOAD;let t=ln();this.outputShape=e,this.userCode=`
${Tw}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},wz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Gn.DOWNLOAD;let t=ln();this.outputShape=e,this.userCode=`
${Tw}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},_z=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=ln(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
${zm(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / ${s};
int c = imod(flatIndex, ${s});
vec2 uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${a}.0);
vec4 values = ${r.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${r.output} = vec4(${i}, 0., 0., 0.);
}
`}},bz=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=ln(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let c=0;c<=1;c++){let u=l*2+c;i+=`
localCoords = coords;
if(localCoords[2] + ${c} < ${e[2]}) {
localCoords[2] += ${c};
if(localCoords[1] + ${l} < ${e[1]}) {
localCoords[1] += ${l};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
r = flatIndex / ${s};
c = imod(flatIndex, ${s});
uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${a}.0);
values = ${r.texture2D}(A, uv);
if(offset == 0) {
result[${u}] = values[0];
} else if(offset == 1) {
result[${u}] = values[1];
} else if(offset == 2) {
result[${u}] = values[2];
} else {
result[${u}] = values[3];
}
}
}
`}this.userCode=`
${zm(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${i}
${r.output} = ${o};
}
`}},Ew={};$e(Ew,{bindVertexProgramAttributeStreams:()=>Pw,createBufferFromOutputTexture:()=>Bw,createFloat16MatrixTexture:()=>Ow,createFloat16PackedMatrixTexture:()=>zw,createFloat32MatrixTexture:()=>Mw,createIndexBuffer:()=>Fw,createPackedMatrixTexture:()=>Dw,createUnsignedBytesMatrixTexture:()=>$w,createVertexBuffer:()=>Rw,createVertexShader:()=>Cw,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Uw,downloadFloat32MatrixFromBuffer:()=>Vw,downloadMatrixFromPackedOutputTexture:()=>Hw,downloadPackedMatrixFromBuffer:()=>jw,getInternalFormatForFloat16MatrixTexture:()=>Lm,getInternalFormatForFloat16PackedMatrixTexture:()=>Vm,getInternalFormatForFloat32MatrixTexture:()=>Pm,getInternalFormatForPackedMatrixTexture:()=>Bm,getInternalFormatForUnsignedBytesMatrixTexture:()=>Wm,uploadDenseMatrixToTexture:()=>Lw,uploadPixelDataToTexture:()=>Ww});function Cw(e){let t=ln(),n=`${t.version}
precision highp float;
${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return iw(e,n)}function Rw(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return cw(e,t)}function Fw(e){let t=new Uint16Array([0,1,2,2,1,3]);return hw(e,t)}function wc(e,t,n,r,a,s){pw(t,n);let i=dw(e),o=e.TEXTURE_2D;return ge(e,()=>e.bindTexture(o,i)),ge(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ge(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ge(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),ge(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),ge(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function Pm(e){return e.internalFormatFloat}function Mw(e,t,n,r){let[a,s]=gc(t,n);return wc(e,a,s,Pm(r),r.textureFormatFloat,e.FLOAT)}function Lm(e){return e.internalFormatHalfFloat}function Ow(e,t,n,r){let[a,s]=gc(t,n);return wc(e,a,s,Lm(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function Wm(e){return e.downloadTextureFormat}function $w(e,t,n,r){let[a,s]=gc(t,n);return wc(e,a,s,Wm(r),e.RGBA,e.UNSIGNED_BYTE)}function Bm(e){return e.internalFormatPackedFloat}function Dw(e,t,n,r){let[a,s]=Nl(t,n);return wc(e,a,s,Bm(r),e.RGBA,e.FLOAT)}function Vm(e){return e.internalFormatPackedHalfFloat}function zw(e,t,n,r){let[a,s]=Nl(t,n);return wc(e,a,s,Vm(r),e.RGBA,r.textureTypeHalfFloat)}function Pw(e,t,n){let r=0,a=3*4,s=3*4+2*4;return ge(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Rm(e,t,"clipSpacePos",n,3,s,r)&&Rm(e,t,"uv",n,2,s,a)}function Lw(e,t,n,r,a,s){ge(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;a instanceof Uint8Array?(i=new Uint8Array(n*r*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*r*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(a),ge(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Ww(e,t,n){ge(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?ge(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):ge(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),ge(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Bw(e,t,n,r){let a=e.createBuffer();ge(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return ge(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),ge(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),ge(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function Vw(e,t,n){let r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}function Uw(e,t,n,r){let[a,s]=gc(t,n),i=4,o=new Uint8Array(uz(t*n,i));return ge(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function jw(e,t,n,r,a,s,i,o){let l=e,c=new Float32Array(cz(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function Hw(e,t,n){let r=new Float32Array(t*n*4);return ge(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var mp=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=Q().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,hp(t,e)):this.gl=Wr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(Q().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=fc(this.gl,a),Hn(this.gl,s))this.textureHalfFloatExtension=fc(this.gl,s);else if(Q().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Hn(this.gl,r))this.colorBufferHalfFloatExtension=fc(this.gl,r);else if(Q().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Hn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Hn(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=Rw(this.gl),this.indexBuffer=Fw(this.gl),this.framebuffer=fw(this.gl),this.textureConfig=$m(this.gl,this.textureHalfFloatExtension)}get debug(){return Q().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;ge(e,()=>e.finish()),ge(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ge(e,()=>e.deleteFramebuffer(this.framebuffer)),ge(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ge(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ge(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),Mw(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),Ow(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),$w(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Ww(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),Lw(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),zw(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Dw(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Fm(this.gl,this.framebuffer),this.outputTexture=null),ge(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Uw(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return jw(this.gl,e,t,n,r,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return Vw(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=Bw(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(Q().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,a=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=r.clientWaitSync(a,0,0);return s===r.ALREADY_SIGNALED||s===r.CONDITION_SATISFIED},t=a}else Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Hw(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=ow(t,e),r=Cw(t),a=lw(t);return ge(t,()=>t.attachShader(a,r)),ge(t,()=>t.attachShader(a,n)),uw(t,a),this.debug&&lp(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=Pw(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&ge(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&lp(this.gl,this.program),ge(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?Aw(this.gl,e,t):yw(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ge(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),gw(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,a]=Nl(t,n);this.setOutputMatrixTextureDriver(e,r,a)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&lp(this.gl,this.program),mc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),ge(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ge(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=fc(this.gl,Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.createQuery();return n.beginQuery(r.TIME_ELAPSED_EXT,a),a}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await k.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=vz(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&k.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),up(this.gl,e,this.framebuffer),this.debug&&mc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(up(this.gl,this.outputTexture,this.framebuffer),this.debug&&mc(this.gl)):Fm(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;up(r,e,this.framebuffer),this.debug&&mc(r),this.outputTexture=e,ge(r,()=>r.viewport(0,0,t,n)),ge(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),ge(this.gl,()=>this.gl.scissor(e,t,n,r))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function vz(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:Gw}=C;function Fz(e,t,n,r){let a=[];e.forEach(d=>{let f=k.sizeFromShape(d.shapeInfo.logicalShape);d.shapeInfo.isUniform?a.push(`uniform float ${d.name}${f>1?`[${f}]`:""};`):(a.push(`uniform sampler2D ${d.name};`),a.push(`uniform int offset${d.name};`))});let s=a.join(`
`),i=e.map(d=>kz(d,t,r)).join(`
`),o=t.texShape,l=ln(),c=Sz(l),u,h,p=Cz(l);return t.isPacked?(u=Iz(t.logicalShape,o),h=Ez(l)):(u=Nz(t.logicalShape,o),h=Tz(l)),r&&(p+=Rz),[p,c,h,s,u,i,n].join(`
`)}function Sl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return Mz(e);case 1:return Oz(e);case 2:return $z(e);case 3:return Dz(e);case 4:return zz(e);case 5:return Pz(e);case 6:return Lz(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function qw(e){switch(e.shapeInfo.logicalShape.length){case 0:return Wz(e);case 1:return Bz(e);case 2:return Vz(e);case 3:return Uz(e);default:return jz(e)}}function kz(e,t,n=!1){let r="";n?r+=qw(e):r+=Sl(e);let a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(n?r+=Hz(e,t):r+=Gz(e,t)),r}function Iz(e,t){switch(e.length){case 0:return Xw();case 1:return qz(e,t);case 2:return Zz(e,t);case 3:return Xz(e,t);default:return Kz(e,t)}}function Nz(e,t){switch(e.length){case 0:return Xw();case 1:return Yz(e,t);case 2:return nP(e,t);case 3:return Jz(e,t);case 4:return Qz(e,t);case 5:return eP(e,t);case 6:return tP(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function Sz(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function Tz(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function Ez(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function Cz(e){return`${e.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${e.varyingFs} vec2 resultUV;
${e.defineOutput}
const vec2 halfCR = vec2(0.5, 0.5);
struct ivec5
{
int x;
int y;
int z;
int w;
int u;
};
struct ivec6
{
int x;
int y;
int z;
int w;
int u;
int v;
};
uniform float NAN;
${e.defineSpecialNaN}
${e.defineSpecialInf}
${e.defineRound}
int imod(int x, int y) {
return x - y * (x / y);
}
int idiv(int a, int b, float sign) {
int res = a / b;
int mod = imod(a, b);
if (sign < 0. && mod != 0) {
res -= 1;
}
return res;
}
//Based on the work of Dave Hoskins
//https://www.shadertoy.com/view/4djSRW
#define HASHSCALE1 443.8975
float random(float seed){
vec2 p = resultUV * seed;
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
p3 += dot(p3, p3.yzx + 19.19);
return fract((p3.x + p3.y) * p3.z);
}
${rP}
${aP}
${sP}
`}var rP=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
int texelIndex = index / 2;
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,aP=`
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
int texNumC, int row, int col) {
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,sP=`
vec2 packedUVfrom3D(int texNumR, int texNumC,
int texelsInBatch, int texelsInLogicalRow, int b,
int row, int col) {
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,Rz=`
float getChannel(vec4 frag, vec2 innerDims) {
vec2 modCoord = mod(innerDims, 2.);
return modCoord.x == 0. ?
(modCoord.y == 0. ? frag.r : frag.g) :
(modCoord.y == 0. ? frag.b : frag.a);
}
float getChannel(vec4 frag, int dim) {
float modCoord = mod(float(dim), 2.);
return modCoord == 0. ? frag.r : frag.g;
}
`;function Xw(){return`
int getOutputCoords() {
return 0;
}
`}function qz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
int getOutputCoords() {
return 2 * int(resultUV.x * ${n[1]}.0);
}
`:n[1]===1?`
int getOutputCoords() {
return 2 * int(resultUV.y * ${n[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
}
`}function Yz(e,t){return t[0]===1?`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function Xz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec3(b, r, c);
}
`}function Jz(e,t){let n=gi(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function Kz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
int b${l} = index / ${s};
index -= b${l} * ${s};
`+i,o=`b${l}, `+o;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
${i}
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec${e.length}(${o});
}
`}function Qz(e,t){let n=gi(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function eP(e,t){let n=gi(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function tP(e,t){let n=gi(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function Zz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
}
`;let r=Math.ceil(e[1]/2);return`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec2(r, c);
}
`}function nP(e,t){return k.arraysEqual(e,t)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function xi(e){return`offset${e}`}function Wz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=ln();return`
vec4 ${n}() {
return ${r.texture2D}(${t}, halfCR);
}
`}function Mz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
float ${n}() {
return sampleTexture(${t}, halfCR);
}
`;let[s,i]=e.shapeInfo.texShape,o=xi(t);return`
float ${n}() {
vec2 uv = uvFromFlat(${s}, ${i}, ${o});
return sampleTexture(${t}, uv);
}
`}function Bz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,a=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=ln();return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${a[0]}, ${a[1]}, index);
return ${s.texture2D}(${t}, uv);
}
`}function Oz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
float ${n}(int index) {
${Tl(e)}
}
`;let r=e.shapeInfo.texShape,a=r[0],s=r[1];if(s===1&&a===1)return`
float ${n}(int index) {
return sampleTexture(${t}, halfCR);
}
`;let i=xi(t);return s===1?`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
return sampleTexture(${t}, uv);
}
`:a===1?`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${s}.0, 0.5);
return sampleTexture(${t}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = uvFromFlat(${a}, ${s}, index + ${i});
return sampleTexture(${t}, uv);
}
`}function Vz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=a[0],i=a[1],o=ln();if(a!=null&&k.arraysEqual(t,a))return`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
return ${o.texture2D}(${n}, uv);
}
`;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(t[1]/2);return`
vec4 ${r}(int row, int col) {
vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
return ${o.texture2D}(${n}, uv);
}
`}function $z(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape;if(a!=null&&k.arraysEqual(t,a)){let h=a[0],p=a[1];return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${p}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`}let{newShape:s,keptDims:i}=k.squeezeShape(t),o=s;if(o.length<t.length){let h=El(e,o),p=["row","col"];return`
${Sl(h)}
float ${r}(int row, int col) {
return ${r}(${Cl(p,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${Tl(e)}
}
`;let l=a[0],c=a[1],u=xi(n);return c===1?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
return sampleTexture(${n}, uv);
}
`:l===1?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${t[1]} + col + ${u};
vec2 uv = uvFromFlat(${l}, ${c}, index);
return sampleTexture(${n}, uv);
}
`}function Uz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(t[0]===1){let h=t.slice(1),p=[1,2],d=El(e,h),f=["b","row","col"];return`
${qw(d)}
vec4 ${r}(int b, int row, int col) {
return ${r}(${Cl(f,p)});
}
`}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),u=ln();return`
vec4 ${r}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${i}, ${o}, ${c}, ${l}, b, row, col);
return ${u.texture2D}(${n}, uv);
}
`}function Dz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=k.squeezeShape(t),l=i;if(l.length<t.length){let f=El(e,l),m=["row","col","depth"];return`
${Sl(f)}
float ${r}(int row, int col, int depth) {
return ${r}(${Cl(m,o)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${a}, ${s}, 1)));
${Tl(e)}
}
`;let c=e.shapeInfo.texShape,u=c[0],h=c[1],p=e.shapeInfo.flatOffset;if(h===a&&p==null)return`
float ${r}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${u}.0);
return sampleTexture(${n}, uv);
}
`;if(h===s&&p==null)return`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${u}.0);
return sampleTexture(${n}, uv);
}
`;let d=xi(n);return`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${s} + depth + ${d};
vec2 uv = uvFromFlat(${u}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function jz(e){let t=e.shapeInfo.logicalShape,n=t.length,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],c=Math.ceil(t[n-1]/2),u=c*Math.ceil(t[n-2]/2),h="int b, int row, int col",p=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let f=2;f<n-1;f++)h=`int b${f}, `+h,u*=t[n-f-1],p=`b${f} * ${u} + `+p;let d=ln();return`
vec4 ${a}(${h}) {
int index = ${p};
int texR = index / ${l};
int texC = index - texR * ${l};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
return ${d.texture2D}(${r}, uv);
}
`}function zz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[3],s=t[2]*a,i=t[1]*s,{newShape:o,keptDims:l}=k.squeezeShape(t);if(o.length<t.length){let f=El(e,o),m=["row","col","depth","depth2"];return`
${Sl(f)}
float ${r}(int row, int col, int depth, int depth2) {
return ${r}(${Cl(m,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${s}, ${a}, 1)));
${Tl(e)}
}
`;let c=e.shapeInfo.flatOffset,u=e.shapeInfo.texShape,h=u[0],p=u[1];if(p===i&&c==null)return`
float ${r}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${s}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(p===a&&c==null)return`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${t[1]*t[2]}, ${t[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let d=xi(n);return`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${s} +
depth * ${a} + depth2;
vec2 uv = uvFromFlat(${h}, ${p}, index + ${d});
return sampleTexture(${n}, uv);
}
`}function Pz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=k.squeezeShape(t);if(l.length<t.length){let m=El(e,l),A=["row","col","depth","depth2","depth3"];return`
${Sl(m)}
float ${r}(int row, int col, int depth, int depth2, int depth3) {
return ${r}(${Cl(A,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${s}, ${a})) +
depth3;
${Tl(e)}
}
`;let u=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,p=h[0],d=h[1];if(d===o&&u==null)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${i}, ${s}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(d===a&&u==null)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let f=xi(n);return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${i} + depth * ${s} +
depth2 * ${a} + depth3 + ${f};
vec2 uv = uvFromFlat(${p}, ${d}, index);
return sampleTexture(${n}, uv);
}
`}function Lz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=k.squeezeShape(t);if(a.length<t.length){let A=El(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
${Sl(A)}
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${r}(${Cl(y,s)});
}
`}let i=t[5],o=t[4]*i,l=t[3]*o,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${u}, ${c}, ${l}, ${o})) +
dot(
vec2(depth3, depth4),
vec2(${i}, 1)));
${Tl(e)}
}
`;let h=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],f=p[1];if(f===u&&h==null)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${c}, ${l}, ${o}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;if(f===i&&h==null)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;let m=xi(n);return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${u} + col * ${c} + depth * ${l} +
depth2 * ${o} + depth3 * ${i} + depth4 + ${m};
vec2 uv = uvFromFlat(${d}, ${f}, index);
return sampleTexture(${n}, uv);
}
`}function Tl(e){let t=e.name,n=k.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function Hz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=Gw(e.shapeInfo.logicalShape,t.logicalShape),l=ht(i),c=i-s,u,h=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(A=>`coords.${h[A+c]} = 0;`).join(`
`);let p="";i<2&&s>0?p="coords":p=e.shapeInfo.logicalShape.map((A,y)=>`coords.${h[y+c]}`).join(", ");let d="return outputValue;",f=k.sizeFromShape(e.shapeInfo.logicalShape)===1,m=k.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)d=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(f&&!m)i===1?d=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:d=`
return vec4(outputValue.x);
`;else if(o.length){let A=s-2,y=s-1;o.indexOf(A)>-1&&o.indexOf(y)>-1?d="return vec4(outputValue.x);":o.indexOf(A)>-1?d="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(d="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${a}() {
${l} coords = getOutputCoords();
${u}
vec4 outputValue = get${r}(${p});
${d}
}
`}function Gz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&k.arraysEqual(i,s))return`
float ${a}() {
return sampleTexture(${n}, resultUV);
}
`;let c=ht(l),u=Gw(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,p,d=["x","y","z","w","u","v"];o===0?p="":l<2&&u.length>=1?p="coords = 0;":p=u.map(m=>`coords.${d[m+h]} = 0;`).join(`
`);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,A)=>`coords.${d[A+h]}`).join(", "),`
float ${a}() {
${c} coords = getOutputCoords();
${p}
return get${r}(${f});
}
`}function ht(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function El(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Cl(e,t){return t.map(n=>e[n]).join(", ")}function iP(e,t,n,r){let a=t.userCode,s=n.map((d,f)=>{let m={logicalShape:d.shape,texShape:d.isUniform?null:d.texData.texShape,isUniform:d.isUniform,isPacked:d.isUniform?!1:d.texData.isPacked,flatOffset:null};return d.texData!=null&&d.texData.slice!=null&&d.texData.slice.flatOffset>0&&(m.flatOffset=d.texData.slice.flatOffset),{name:t.variableNames[f],shapeInfo:m}}),i=s.map(d=>d.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=Fz(s,o,a,t.packedInputs),c=e.createProgram(l),u=null,h=e.getUniformLocation(c,"NAN",!1);Q().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let p={};for(let d=0;d<t.variableNames.length;d++){let f=t.variableNames[d],m=!1;p[f]=e.getUniformLocation(c,f,m),p[`offset${f}`]=e.getUniformLocation(c,`offset${f}`,m)}return{program:t,source:l,webGLProgram:c,uniformLocations:p,inShapeInfos:i,outShapeInfo:o,infLoc:u,nanLoc:h}}function Kw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let a=n.logicalShape,s=t[r],i=s.shape;if(!k.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!k.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function oP(e,t,n,r,a){Kw(t.inShapeInfos,n),Kw([t.outShapeInfo],[r]);let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),Q().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],h=t.uniformLocations[`offset${c}`];if(u!=null){if(o.isUniform){if(k.sizeFromShape(o.shape)<2)e.gl.uniform1f(u,o.uniformValues[0]);else{let p=o.uniformValues;p instanceof Float32Array||(p=new Float32Array(p)),e.gl.uniform1fv(u,p)}return}o.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,u,l)}}),a!=null&&a(e,t.webGLProgram),e.executeProgram()}function lP(e,t,n){let r="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,l=i.isUniform?"uniform":i.texData.texShape;r+=`${i.shape}_${l}_${o}`});let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a,s}var{addImpl:uP,bincountImpl:Zw,bincountReduceImpl:cP,ceilImpl:hP,concatImpl:dP,expImpl:pP,expm1Impl:fP,floorImpl:mP,gatherV2Impl:AP,greaterImpl:yP,lessImpl:gP,linSpaceImpl:xP,logImpl:wP,maxImpl:_P,maximumImpl:bP,minimumImpl:vP,multiplyImpl:kP,negImpl:IP,prodImpl:NP,rangeImpl:SP,rsqrtImpl:TP,simpleAbsImpl:Yw,sliceImpl:EP,stridedSliceImpl:CP,subImpl:RP,tileImpl:FP,topKImpl:MP,transposeImpl:Um,uniqueImpl:OP}=Am;function Jw(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function un(e,t){return t===1?[e]:Jw(e,t)}function $P(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var LP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let n=un("rc",t),r=ht(t),a=DP(t,e,n),s=zP(t,e[e.length-1],e[e.length-2],n),i=PP(e,n);this.userCode=`
void main() {
${r} rc = getOutputCoords();
if(${a}) {
setOutput(vec4(0));
} else {
${s}
setOutput(vec4(${i}));
}
}
`}}};function WP(e,t){let n=[];for(let r=0;r<=1;r++)for(let a=0;a<=1;a++){let s=`${r===0?"r":"rp1"}, ${a===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function DP(e,t,n){if(e===1)return`rc > ${t[0]}`;let r="";for(let a=e-2;a<e;a++)r+=`${n[a]} >= ${t[a]}`,a<e-1&&(r+="||");return r}function zP(e,t,n,r){if(e===1)return"";let a=r.slice(-2);return`
int r = ${a[0]};
int c = ${a[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${t};
bool rEdge = rp1 >= ${n};
`}function PP(e,t){let n=e.length,r=WP(n,t);return n===1?`getA(rc),
rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
0, 0`:`getA(${r[0]}),
cEdge ? 0. : getA(${r[1]}),
rEdge ? 0. : getA(${r[2]}),
rEdge || cEdge ? 0. : getA(${r[3]})`}var Qw=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let a="thisRC = rc;";r%2==1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),n+=`
${a}
${r>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${r}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${r>0?"}":""}
`}this.userCode=`
${BP(t)}
${zm(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${e[1]};
int cols = ${e[2]};
${n}
setOutput(result);
}
`}};function BP(e){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${gi(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var VP=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=t_(t,n),a=n_(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=e_(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===Yt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===Yt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===Yt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===Yt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===Yt.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=t_(n,r),s=n_(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=e_(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=Q().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function UP(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function e_(e,t,n,r,a){let s=jP(t,r),i;if(a){let[l,c]=Nl(e[0],e[1]);i=l*c}else{let[l,c]=gc(e[0],e[1]);i=l*c}let o=UP(n,s);return i*o}function jP(e,t){switch(e){case Yt.PACKED_2X2_FLOAT32:return Bm(t);case Yt.PACKED_2X2_FLOAT16:return Vm(t);case Yt.UNPACKED_FLOAT32:return Pm(t);case Yt.UNPACKED_FLOAT16:return Lm(t);case Yt.PACKED_4X1_UNSIGNED_BYTE:return Wm(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function HP(e){return Q().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Yt.PACKED_2X2_FLOAT32:Yt.UNPACKED_FLOAT32:e?Yt.PACKED_2X2_FLOAT16:Yt.UNPACKED_FLOAT16}function t_(e,t){if(e===Gn.UPLOAD)return Yt.PACKED_2X2_FLOAT32;if(e===Gn.RENDER||e==null)return HP(t);if(e===Gn.DOWNLOAD||e===Gn.PIXELS)return Yt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function n_(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var $a=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},mr="if (isnan(x)) return x;",GP="return x;",r_="return abs(x);",qP="return (x >= 0.0) ? x : (exp(x) - 1.0);",XP=mr+`
return (x < 0.0) ? 0.0 : x;
`,KP=mr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,Ap="return x;",ZP="return x;",YP=`
vec4 result;
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
return result;
`,JP=`
vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,QP=`
vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,Rl=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},eL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=un("rc",t),r=ht(t),a=$P(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
void main() {
${r} rc = getOutputCoords();
vec4 packedInput = getA(${a});
setOutput(getChannel(packedInput, ${i}));
}
`}},tL=Pr.whereImpl,nL=1e-7,rL=1e-4,jm={};function aL(e){return e in jm||(jm[e]={}),jm[e]}var sL=128,iL=600;function oL(){return Q().global.screen==null?1024:Q().global.screen.height*Q().global.screen.width*window.devicePixelRatio*iL/1024/1024}var yp=class extends xu{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!Q().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Wr(Q().getNumber("WEBGL_VERSION"));this.binaryCache=aL(Q().getNumber("WEBGL_VERSION")),this.gpgpu=new mp(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new VP(this.gpgpu),this.numMBBeforeWarning=oL(),this.texData=new Ih(this,Vn())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((Q().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Q().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:Gn.UPLOAD,refCount:1,complexParentRefCount:0}),r}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}decComplexRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.complexParentRefCount>0&&t.refCount--}}move(e,t,n,r){if(Q().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:Gn.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(e){let t=e.dataId;if(this.texData.has(t)){let n=this.texData.get(t);n.refCount--,n.refCount<1&&this.disposeData(t)}}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new Rl(i,Ap):h=new $a(i,Ap);let p=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),d=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),d}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,c;l&&(c=k.now());let u;if(r==="complex64"){let h=this.readSync(a.real.dataId),p=this.readSync(a.imag.dataId);u=C.mergeRealAndImagArrays(h,p)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=k.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let d=this.pendingRead.get(e);return new Promise(f=>d.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let d;o?d=new Rl(r,Ap):d=new $a(r,Ap);let f=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!Q().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Q().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(s!=="complex64"&&Q().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture,...xc(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let d=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=d[0],m=d[1];u=C.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let d=k.sizeFromShape(r);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}c!=null&&this.disposeIntermediateTensorInfo(c);let h=this.convertAndCacheOnCPU(e,u),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(d=>d(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>k.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Le(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!aw(n))throw Q().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=k.sizeFromShape(t);if(Q().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),p=this.texData.get(h.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(p.texture,...xc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),d}let s=Q().getBool("WEBGL_PACK")&&r===!0,i=s?cp(t):t,o=s?new wz(i):new xz(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),u}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;if(this.texData.get(e).complexParentRefCount>0){this.texData.get(e).refCount--;return}this.releaseGPUData(e);let{complexTensorInfos:t}=this.texData.get(e);t!=null&&(this.texData.get(t.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.real),this.texData.get(t.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.imag)),this.texData.delete(e)}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return Q().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Vn().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=sL){let n=this.getCPUBackend();return!Q().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&k.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return tL(e.shape,t)}packedUnaryOp(e,t,n){let r=new Rl(e.shape,t);return this.compileAndRun(r,[e],n)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=Yw(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(Q().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,r_,e.dtype);let t=new $a(e.shape,r_);return this.compileAndRun(t,[e])}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let a=n.map(s=>k.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Vn().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new eL(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new LP(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[mi(e.shape),...Ai(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[mi(t),...Ai(t)],s=new Qw(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=cp(r),i;n?i=new gz(s):i=new yz(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===yc.DENSE){let f=xc(e.outputShape);i.texShape=f.map(m=>m*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),k.sizeFromShape(s.shape)===0)return i.values=k.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(f=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let m=this.texData.get(f.dataId);if(m.texture==null){if(!e.packedInputs&&k.sizeFromShape(f.shape)<=Q().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:f.shape,texData:null,isUniform:!0,uniformValues:m.values};e.packedInputs&&(m.isPacked=!0,m.shape=f.shape)}else if(!!m.isPacked!=!!e.packedInputs)f=m.isPacked?this.unpackTensor(f):this.packTensor(f),o.push(f),m=this.texData.get(f.dataId);else if(m.isPacked&&!Ac(m.shape,f.shape)){let A=f,y=f.shape;f.shape=m.shape,f=this.packedReshape(f,y),o.push(f),m=this.texData.get(f.dataId),A.shape=y}return this.uploadToGPU(f.dataId),{shape:f.shape,texData:m,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=lP(e,l,c),h=this.getAndSaveBinary(u,()=>iP(this.gpgpu,e,l,c)),p=this.activeTimers!=null,d;if(p&&(d=this.startTimer()),oP(this.gpgpu,h,l,c,r),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),p&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)})),!Q().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let f=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),f}return s}compileAndRun(e,t,n,r,a=!1){n=n||t[0].dtype;let s=this.runWebGLProgram(e,t,n,r,a);return Vn().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Q().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=W(()=>{if(!Q().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Q().getBool("DEBUG");Q().set("DEBUG",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(Q().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?nL:rL}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,c;l&&(c=k.now());let u=t.texShape;if(u==null&&(u=ww(n,o),t.texShape=u),a!=null){let h=cp(n),p,d=u[1],f=u[0],m=a instanceof Uint8Array;o?([d,f]=Nl(u[0],u[1]),p=new bz(h,[f,d],m)):p=new _z(h,[f,d],m);let A=this.makeTensorInfo([f,d],r);m?this.texData.get(A.dataId).usage=Gn.PIXELS:this.texData.get(A.dataId).usage=Gn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),d,f,a);let y=!0,g=this.runWebGLProgram(p,[A],r,null,y),_=this.texData.get(g.dataId);t.texture=_.texture,t.texShape=_.texShape,t.isPacked=_.isPacked,t.usage=_.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=k.now()-c)}else{let h=this.acquireTexture(u,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=lL(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*k.bytesPerElement(t)}};function lL(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var a_="3.0.0";function s_(){Q().set("WEBGL_FORCE_F16_TEXTURES",!0)}hd.isBrowser()&&ol("webgl",()=>new yp,2);var uL={forceHalfFloat:s_},i_=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Fl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},gp=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`,_c=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||k.sizeFromShape(this.outputShape)===1)s=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(s=`
${ht(a)} coords = getOutputCoords();
`,a===1)s+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let i=un("coords",a);s+=`
bool nextRowOutOfBounds =
(${i[a-2]} + 1) >= ${this.outputShape[a-2]};
bool nextColOutOfBounds =
(${i[a-1]} + 1) >= ${this.outputShape[a-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${s}
setOutput(result);
}
`}};function Fn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var cL={kernelName:xo,backendName:"webgl",kernelFunc:Fn};function Da(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=Fn({inputs:{x:r},backend:n}),l=n.texData.get(o.dataId);l.complexParentRefCount++;let c=Fn({inputs:{x:a},backend:n}),u=n.texData.get(c.dataId);return u.complexParentRefCount++,i.complexTensorInfos={real:o,imag:c},s}var hL={kernelName:Oh,backendName:"webgl",kernelFunc:Da},o_="return (a < 0.) ? b * a : a;",l_=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function dL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new _c(l_,a.shape,i.shape):new Fl(o_,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var pL={kernelName:bs,backendName:"webgl",kernelFunc:dL},u_="return (a < 0.) ? b * a : a;",c_=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function fL(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new _c(c_,r.shape,a.shape):new Fl(u_,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var mL={kernelName:Os,backendName:"webgl",kernelFunc:fL},h_="if (isnan(x)) return x;",AL=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,yL=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;function Ze({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),p=n(h.values,l);return o.makeTensorInfo(i.shape,l,p)}let c=Q().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new Rl(i.shape,t):u=new $a(i.shape,e),o.runWebGLProgram(u,[i],l)}}function Jt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(r&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(_=>{let[x,w]=_,b={dataId:x.dataId,dtype:x.dtype,shape:l.shape},N={dataId:w.dataId,dtype:w.dtype,shape:c.shape},T=new Fl(e,l.shape,c.shape);return u.runWebGLProgram(T,[b,N],er(x.dtype,w.dtype))}),g=Da({inputs:{real:A,imag:y},backend:u});return u.disposeIntermediateTensorInfo(A),u.disposeIntermediateTensorInfo(y),g}let h=s||er(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&a!=null){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=a(l.shape,c.shape,f.values,m.values,h),g=u.makeTensorInfo(y,h),_=u.texData.get(g.dataId);return _.values=A,g}let p=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,d;return p?d=new _c(t,l.shape,c.shape,n):d=new Fl(e,l.shape,c.shape),u.runWebGLProgram(d,[l,c],h)}}function xp(e,t=!1){if(e==="linear")return t?ZP:GP;if(e==="relu")return t?JP:XP;if(e==="elu")return t?YP:qP;if(e==="relu6")return t?QP:KP;if(e==="prelu")return t?c_:u_;if(e==="leakyrelu")return t?l_:o_;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var d_=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=r?e[1]:e[2],u=Math.ceil(c/2),h=r?"i * 2, rc.y":"rc.y, i * 2",p=a?"rc.z, i * 2":"i * 2, rc.z",d=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",A="";i&&(o?m=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${i}
}`:l?m=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${i}
}`:m=`vec4 activation(vec4 x) {
${i}
}`,A="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",_="rc.x";e[0]<t[0]?g=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(_=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${m}
const float sharedDimension = ${u}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${u}; i++) {
int batchA = ${g};
int batchB = ${_};
vec4 a = getMatrixA(batchA, ${h});
vec4 b = getMatrixB(batchB, ${p});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${d[0]} * ${f[0]});
result += (${d[1]} * ${f[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${y}
${A}
setOutput(result);
}
`}},p_={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},f_=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},m_="return a * b;";function A_(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=C.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),c=new f_(p_.REAL,r.shape,a.shape),u=new f_(p_.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],p=n.runWebGLProgram(c,h,"float32"),d=n.runWebGLProgram(u,h,"float32"),f=Da({inputs:{real:p,imag:d},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[c,u]=kP(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(u,s),p=n.texData.get(h.dataId);return p.values=c,h}let i;return Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new _c(m_,r.shape,a.shape):i=new Fl(m_,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var gL={kernelName:Cs,backendName:"webgl",kernelFunc:A_};function xL(e,t,n){let r=[mi(e.shape),...Ai(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[mi(t),...Ai(t)],i=new Qw(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function ye(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=k.sizeFromShape(a.shape),l=k.inferFromImplicitShape(s,o),c=k.sizeFromShape(l);k.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(a.dataId);return u.isPacked&&!Ac(a.shape,l)&&!(u.texture!==null&&Ac(u.shape,l))?xL(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var wL={kernelName:zo,backendName:"webgl",kernelFunc:ye},y_=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${k.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";a%n>0&&(c=`
if (inIdx < 0 || inIdx >= ${a}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${i}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${l}
}
int inIdx = inOffset + ${i};
if (${o===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${l}
} else if (${o===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${l}
} else if (${o===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${l}
}
setOutput(sumValue);
}
`}},_L=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,u=n%4,h=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${o}(values, minMaxValue);
}
`,p="vec4";t==="all"?(i="1.0",h=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,p="bvec4"):t==="any"&&(i="0.0",h=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,p="bvec4");let d="";a%n>0&&(d=`
if (inIdx < 0 || inIdx >= ${a}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${i};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${d}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${i});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${h}
}
int inIdx = inOffset + ${c};
if (${u===1}) {
${p} values = ${p}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${h}
} else if (${u===2}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${h}
} else if (${u===3}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${h}
}
setOutput(${l});
}
`}};function bL(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=C.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function wi(e,t,n,r){let a=bL(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:c}=a[i],u,h;n==="mean"?u=i===0?new y_({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new y_({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new _L({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),h=s,s=r.runWebGLProgram(u,[s],t),h.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(h)}return s}var kL=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let r=ht(this.rank),a=vL(t);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function vL(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a<e.length;a++)r[e[a]]=n[a];return r.join()}var IL=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=ht(this.rank),a=Jw("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=a[c];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
void main() {
${r} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${l};
if(${o}) {
result[1] = ${l};
}
--${a[this.rank-1]};
if(++${a[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${l};
if(${o}) {
result[3] = ${l};
}
}
setOutput(result);
}
`}};function wp(e,t,n){let r=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new IL(e.shape,t):new kL(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function NL(e,t,n,r){let a=t,s=e.shape.length,i=k.parseAxisParam(a,e.shape),o=i,l=C.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=wp(e,l,r),o=C.getInnerMostAxes(o.length,s)),C.assertAxesAreInnerMostDims("sum",o,s);let[h,p]=C.computeOutAndReduceShapes(u.shape,o),d=h;n&&(d=C.expandShapeToKeepDim(h,i));let f=k.sizeFromShape(p),m=k.sizeFromShape(e.shape)/f,A=ye({inputs:{x:u},attrs:{shape:[m,f]},backend:r}),y=cd(e.dtype),g=wi(A,y,"sum",r),_=ye({inputs:{x:g},attrs:{shape:d},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),c&&r.disposeIntermediateTensorInfo(u),_}function Hm(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return NL(a,s,i,n)}var SL={kernelName:js,backendName:"webgl",kernelFunc:Hm};function gn(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=a.shape[s[u]];let c;if(i.shouldExecuteOnCPU([a])){let u=i.texData.get(a.dataId).values,h=Um(u,a.shape,a.dtype,s,l);c=i.makeTensorInfo(l,a.dtype);let p=i.texData.get(c.dataId);p.values=h}else c=wp(a,s,i);return c}var TL={kernelName:Ks,backendName:"webgl",kernelFunc:gn},g_=1e3;function _p({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,h=n?e.shape[c-2]:e.shape[c-1],p=r?t.shape[u-1]:t.shape[u-2],d=n?e.shape[c-1]:e.shape[c-2],f=r?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),A=t.shape.slice(0,-2),y=k.sizeFromShape(m),g=k.sizeFromShape(A),_=y===g||y===1||g===1;k.assert(c>=2&&u>=2&&_,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${A}).`);let x=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([d,f]);k.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let w=n?[y,h,d]:[y,d,h],b=r?[g,f,p]:[g,p,f],N=ye({inputs:{x:e},backend:a,attrs:{shape:w}}),T=ye({inputs:{x:t},backend:a,attrs:{shape:b}}),E=[N,T],M=Math.max(y,g),$=n?N.shape[1]:N.shape[2],P=s!=null,V=i!=null,G=l==="leakyrelu",U=l!=null?xp(l,!0):null,K=P||V||G||U!=null,X;if((d===1||f===1)&&$>g_&&K===!1){let Z=N,ae=T;n&&(Z=gn({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),E.push(Z)),r&&(ae=gn({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(ae));let J=f!==1,oe=f===1,ne=Z;J&&(ne=ye({inputs:{x:Z},backend:a,attrs:{shape:[M,$,1]}}),E.push(ne));let ce=f===1?2:1,ue=ae;oe&&(ue=ye({inputs:{x:ae},backend:a,attrs:{shape:[M,1,$]}}),E.push(ue));let pe=A_({inputs:{a:ne,b:ue},backend:a});X=Hm({inputs:{x:pe},backend:a,attrs:{axis:ce,keepDims:!0}}),E.push(pe)}else{let Z=er(e.dtype,t.dtype),ae=new d_(w,b,[M,d,f],n,r,P,U,V,G),J=[N,T];if(s!=null&&J.push(s),V&&J.push(i),G){let oe=a.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));J.push(oe),E.push(oe)}X=a.runWebGLProgram(ae,J,Z)}let ee=ye({inputs:{x:X},backend:a,attrs:{shape:x}});E.push(X);for(let Z of E)a.disposeIntermediateTensorInfo(Z);return ee}function EL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r;return _p({a,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:u})}var CL={kernelName:Zs,backendName:"webgl",kernelFunc:EL},x_="return abs(x);";function RL(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=Yw(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return Q().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Rl(r.shape,x_):a=new $a(r.shape,x_),n.runWebGLProgram(a,[r],r.dtype)}var FL={kernelName:Zi,backendName:"webgl",kernelFunc:RL},ML=mr+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,OL=Ze({opSnippet:ML}),$L={kernelName:Yi,backendName:"webgl",kernelFunc:OL},DL=mr+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,zL=Ze({opSnippet:DL}),PL={kernelName:Ji,backendName:"webgl",kernelFunc:zL},w_="return a + b;",LL=Jt({opSnippet:w_,packedOpSnippet:w_,supportsComplex:!0,cpuKernelImpl:uP}),WL={kernelName:wa,backendName:"webgl",kernelFunc:LL},BL=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${r};
setOutput(result);
}
`}},VL=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${r};
setOutput(result);
}
`}};function bp(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Fn({inputs:{x:r[0]},backend:n});if(r.length>Q().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=bp({inputs:r.slice(0,o),backend:n}),c=bp({inputs:r.slice(o),backend:n});return bp({inputs:[l,c],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>er(o,l)),s=r.map(o=>o.shape),i=Q().getBool("WEBGL_PACK")?new VL(r[0].shape,s):new BL(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var UL={kernelName:is,backendName:"webgl",kernelFunc:bp};function jL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=gn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,o)),C.assertAxesAreInnerMostDims("all",c,o);let[p,d]=C.computeOutAndReduceShapes(h.shape,c),f=k.sizeFromShape(d),m=ye({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=wi(m,m.dtype,"all",n),y;if(i){let g=C.expandShapeToKeepDim(p,l);y=ye({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=ye({inputs:{x:A},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var HL={kernelName:Eh,backendName:"webgl",kernelFunc:jL};function GL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=gn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,o)),C.assertAxesAreInnerMostDims("any",c,o);let[p,d]=C.computeOutAndReduceShapes(h.shape,c),f=k.sizeFromShape(d),m=ye({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=wi(m,m.dtype,"any",n),y;if(i){let g=C.expandShapeToKeepDim(p,l);y=ye({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=ye({inputs:{x:A},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var qL={kernelName:Ch,backendName:"webgl",kernelFunc:GL},XL=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${r};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${r}; i++) {
int inIdx = ${o};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},KL=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ht(o),c=un("coords",o),u,h;if(s===1){h=o+1;let N=ht(h);u=`
${N} sourceLocR = ${N}(${c.join()}, 0);
++${c[o-1]};
${N} sourceLocG = ${N}(${c.join()}, 0);
++${c[o-2]};
${N} sourceLocA = ${N}(${c.join()}, 0);
--${c[o-1]};
${N} sourceLocB = ${N}(${c.join()}, 0);
--${c[o-2]};`}else h=o,u=`
${l} sourceLocR = coords;
++${c[o-1]};
${l} sourceLocG = coords;
++${c[o-2]};
${l} sourceLocA = coords;
--${c[o-1]};
${l} sourceLocB = coords;
--${c[o-2]};`;let p=["x","y","z","w","u","v"].slice(0,h),d="."+p[h-1],f=p.map(N=>"int "+N),m=un("sourceLocR",h-1).concat("inIdx.r"),A=un("sourceLocG",h-1).concat("inIdx.g"),y=un("sourceLocB",h-1).concat("inIdx.b"),g=un("sourceLocA",h-1).concat("inIdx.a"),_=n==="max"?"greaterThan":"lessThan",x=r?"":`
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
getBestIndicesAChannel(${A.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${g.join()})));`,w=`vec4(
getAChannel(${m.join()}),
hasNextCol ? getAChannel(${A.join()}) : 0.,
hasNextRow ? getAChannel(${y.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${g.join()}) : 0.)`,b=r?"":`
float getBestIndicesAChannel(${f.join()}) {
return getChannel(getBestIndicesA(${p.join()}),
vec2(${p.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${f.join()}) {
return getChannel(getA(${p.join()}),
vec2(${p.slice(-2).join()}));
}
${b}
void main() {
${l} coords = getOutputCoords();
bool hasNextCol = ${c[o-1]} < ${i[o-1]-1};
bool hasNextRow = ${c[o-2]} < ${i[o-2]-1};
${u}
ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},
sourceLocB${d}, sourceLocA${d}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${w};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${x}
vec4 candidate = ${w};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${_}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
replace.y ? candidate.y : bestValue.y,
replace.z ? candidate.z : bestValue.z,
replace.w ? candidate.w : bestValue.w);
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
srcIdx++;
}
setOutput(bestIndex);
}
`}};function __(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new XL(o,n,r==null),c=[t];r!=null&&c.push(r);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let h=__(e,t,n,u);return e.disposeIntermediateTensorInfo(u),h}function b_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=C.computeOptimalWindowSize(s),o=new KL(a,i,n,r==null),l=r==null?[t]:[t,r],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=b_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function v_(e,t,n,r){let a=[n];if(C.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!Q().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=C.computeOutAndReduceShapes(t.shape,a),l=k.sizeFromShape(o),c=ye({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=__(e,c,r);s.push(u);let h=ye({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(p=>e.disposeIntermediateTensorInfo(p)),h}return b_(e,t,r)}function ZL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=k.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=gn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=v_(n,l,i[0],"max");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var YL={kernelName:os,backendName:"webgl",kernelFunc:ZL};function JL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=k.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=gn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=v_(n,l,i[0],"min");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var QL={kernelName:bu,backendName:"webgl",kernelFunc:JL},eW=mr+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,tW=Ze({opSnippet:eW}),nW={kernelName:Qi,backendName:"webgl",kernelFunc:tW},rW=mr+"return log(x + sqrt(x * x + 1.0));",aW=Ze({opSnippet:rW}),sW={kernelName:eo,backendName:"webgl",kernelFunc:aW},iW=mr+`
return atan(x);
`,oW=Ze({opSnippet:iW}),lW={kernelName:to,backendName:"webgl",kernelFunc:oW},uW=AL+`
return atan(a, b);
`,cW=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+yL+`
return result;
`,hW=Jt({opSnippet:uW,packedOpSnippet:cW}),dW={kernelName:ro,backendName:"webgl",kernelFunc:hW},pW=mr+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fW=Ze({opSnippet:pW}),mW={kernelName:no,backendName:"webgl",kernelFunc:fW},bc=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,h=e.effectiveFilterWidth,p=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${p}, ${d});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${u};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${h};
wC += ${c}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${N} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${r?a?m:A:`wR * ${h} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let g="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let x=Math.floor(s/4)*4,w=s%4,b=`
if (${f}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${g}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${p}, ${d});
const float initializationValue = ${y};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${y});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${u};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${x}; wC += 4) {
int xC = xCCorner + wC * ${c};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
getValue(batch, xR, xC + 3 * ${c}, d)
);
${b}
}
int xC = xCCorner + ${x};
if (${w===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${b}
} else if (${w===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${b}
} else if (${w===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${b}
}
}
setOutput(${_});
}
`}},Gm=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,h=e.dilationWidth,p=e.effectiveFilterDepth,d=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",_="0.0";if(g||(_="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${m}, ${A}, ${y});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
for (int wD = 0; wD < ${p};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f};
wC += ${h}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${E} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${r?a?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${f} +
wR * ${f} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let x="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let b=Math.floor(s/4)*4,N=s%4,T=`
if (${g}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${x}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${m}, ${A}, ${y});
const float initializationValue = ${_};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${_});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${p};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${b}; wC += 4) {
int xC = xCCorner + wC * ${h};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${h}, ch),
getValue(batch, xD, xR, xC + 2 * ${h}, ch),
getValue(batch, xD, xR, xC + 3 * ${h}, ch)
);
${T}
}
int xC = xCCorner + ${b};
if (${N===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${T}
} else if (${N===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${h}, ch),
initializationValue,
initializationValue
);
${T}
} else if (${N===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${h}, ch),
getValue(batch, xD, xR, xC + 2 * ${h}, ch),
initializationValue
);
${T}
}
}
setOutput(${w});
}
}
`}};function AW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Il(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;k.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Fn({inputs:{x:a},backend:n});let h=new bc(u,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var yW={kernelName:ls,backendName:"webgl",kernelFunc:AW};function gW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r,u=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,u,o,l,c),p=new Gm(h,"avg",!1);return n.runWebGLProgram(p,[a],"float32")}var xW={kernelName:vu,backendName:"webgl",kernelFunc:gW},wW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.top,u=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${c}, ${u});
const float avgMultiplier = float(${h});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${o};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${r}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${l};
wC+= ${i}) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},_W=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,h=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=u-1-e.padInfo.front,f=h-1-e.padInfo.top,m=p-1-e.padInfo.left,A=1/(t*n*r);this.userCode=`
const ivec3 pads = ivec3(${d}, ${f}, ${m});
const float avgMultiplier = float(${A});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${u};
wD += ${o}) {
float dyD = float(dyDCorner + wD) / ${a}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${h};
wR += ${l}) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${p};
wC += ${c}) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function bW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],p=C.computePool3DInfo(i.shape,o,l,h,c,u),d=new _W(p);return n.runWebGLProgram(d,[a],i.dtype)}var vW={kernelName:Fh,backendName:"webgl",kernelFunc:bW};function kW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;Il([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=C.computePool2DInfo(i.shape,o,l,1,c),h=new wW(u);return n.runWebGLProgram(h,[a],i.dtype)}var IW={kernelName:Rh,backendName:"webgl",kernelFunc:kW};function NW(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return _p({a,b:s,transposeA:i,transposeB:o,backend:n})}var SW={kernelName:us,backendName:"webgl",kernelFunc:NW},TW=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${i};
float scale = ${o};
float inv = scale * inversesqrt(variance + float(${s}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},EW=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${i};
vec4 scale = ${o};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${s}));
setOutput((x - mean) * inv + offset);
}
`}},CW=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;k.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[r,a,s],u=null;i!=null&&(u=i.shape,c.push(i));let h=null;o!=null&&(h=o.shape,c.push(o));let p=Q().getBool("WEBGL_PACK_NORMALIZATION")?new EW(r.shape,a.shape,s.shape,u,h,l):new TW(r.shape,a.shape,s.shape,u,h,l);return t.runWebGLProgram(p,c,c[0].dtype)},RW={kernelName:ws,backendName:"webgl",kernelFunc:CW},MW=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ht(this.rank),n=`uniform int start[${this.rank}];`,r=FW(this.rank),a,s=e.map((i,o)=>`sourceLoc.${qm[o]} = start[${o}] + coords.${qm[o]};`);a=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${s.join(`
`)}
`,this.userCode=`
${n}
void main() {
${a}
setOutput(getSource(${r}));
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},qm=["x","y","z","w","u","v"];function FW(e){if(e===1)return"sourceLoc";if(e<=6)return qm.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var OW=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ht(this.rank),n=un("coords",this.rank),r=un("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=`
result.x = ${s};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${r[this.rank-1]};
result.y = ${s};
--${r[this.rank-1]};
}
`,o=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${r[this.rank-2]};
result.z = ${s};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${r[this.rank-1]};
result.w = ${s};
}
}
`,l=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${r[u]} = ${n[u]} + start[${u}];`).join(`
`);this.userCode=`
uniform int start[${this.rank}];
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${l}
vec4 result = vec4(0.);
${i}
${o}
setOutput(result);
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function $W(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.complexParentRefCount=0,i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=sn.computeFlatOffset(t,k.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function vc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=sn.parseSliceParams(a,s,i);if(sn.assertParamsValid(a,o,l),k.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),p=EP(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,p)}let{isPacked:c}=n.texData.get(a.dataId),u=sn.isSliceContinous(a.shape,o,l);if(c||!u){let h=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new OW(l):new MW(l),p=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,p)}return n.uploadToGPU(a.dataId),$W(a,o,l,n)}var DW={kernelName:Bo,backendName:"webgl",kernelFunc:vc},zW=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;k.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,_)=>g*_),l=C.getReshaped(a.shape,s,o),c=C.getPermuted(l.length,s.length),u=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),p=C.getSliceSize(u,i,s.length),d=[],f=ye({inputs:{x:a},backend:n,attrs:{shape:l}}),m=gn({inputs:{x:f},backend:n,attrs:{perm:c}}),A=ye({inputs:{x:m},backend:n,attrs:{shape:u}}),y=vc({inputs:{x:A},backend:n,attrs:{begin:h,size:p}});return d.push(f),d.push(m),d.push(A),d.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},PW={kernelName:ku,backendName:"webgl",kernelFunc:zW};function LW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),c=Zw(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var WW={kernelName:Mh,backendName:"webgl",kernelFunc:LW},BW="return float(a != b);",k_=Jt({opSnippet:BW,dtype:"bool"}),VW={kernelName:Eo,backendName:"webgl",kernelFunc:k_};function kc(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Fn({inputs:{x:a.complexTensorInfos.real},backend:n})}var UW={kernelName:ed,backendName:"webgl",kernelFunc:kc},jW="return float(int(x));";function HW(e,t){let n=new $a(e.shape,jW),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function Xm(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Fn({inputs:{x:a},backend:n});let i=Tt(a.shape),o=Xm({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Da({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=kc({inputs:{input:a},backend:n}),o=Xm({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(a.dtype,s)){let i=Fn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return HW(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=k_({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var GW={kernelName:cs,backendName:"webgl",kernelFunc:Xm},I_="return ceil(x);",qW=Ze({opSnippet:I_,packedOpSnippet:I_,cpuKernelImpl:hP}),XW={kernelName:ao,backendName:"webgl",kernelFunc:qW},KW=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},ZW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function YW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;Q().getBool("WEBGL_PACK_CLIP")?o=new ZW(a.shape):o=new KW(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var JW={kernelName:_a,backendName:"webgl",kernelFunc:YW},QW=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`}};function N_(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function eB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new QW(r.shape),i=[N_(r,a.complexTensorInfos.real),N_(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var tB={kernelName:Iu,backendName:"webgl",kernelFunc:eB},nB=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},rB=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=C.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=ht(r),s=un("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],c=i.slice(-2),u=i.join(),h=`if (${l} < ${o[0]}) {
return getChannel(
getT0(${u}), vec2(${c.join()}));
}`;for(let f=1;f<o.length;f++){let m=o[f-1];h+=`
if (${l} < ${o[f]} && ${l} >= ${o[f-1]}) {
return getChannel(
getT${f}(${vp(i,l,m)}),
vec2(${vp(c,l,m)}));
}`}let p=o.length,d=o[o.length-1];h+=`
return getChannel(
getT${p}(${vp(i,l,d)}),
vec2(${vp(c,l,d)}));`,this.userCode=`
float getValue(${i.map(f=>"int "+f)}) {
${h}
}
void main() {
${a} coords = getOutputCoords();
vec4 result = vec4(getValue(${s}), 0., 0., 0.);
${s[r-1]} = ${s[r-1]} + 1;
if (${s[r-1]} < ${n[r-1]}) {
result.g = getValue(${s});
}
${s[r-2]} = ${s[r-2]} + 1;
if (${s[r-2]} < ${n[r-2]}) {
result.a = getValue(${s});
}
${s[r-1]} = ${s[r-1]} - 1;
if (${s[r-2]} < ${n[r-2]} &&
${s[r-1]} < ${n[r-1]}) {
result.b = getValue(${s});
}
setOutput(result);
}
`}};function vp(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function kp(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Fn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var aB={kernelName:qh,backendName:"webgl",kernelFunc:kp};function Ml(e,t,n){let r=e[0].dtype;if(r==="complex64"){let c=e.map(f=>kc({inputs:{input:f},backend:n})),u=e.map(f=>kp({inputs:{input:f},backend:n})),h=Ml(c,t,n),p=Ml(u,t,n),d=Da({inputs:{real:h,imag:p},backend:n});return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),u.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),d}if(r==="string"){let{tensors2D:c,outShape:u}=S_(e,t,n),h=c.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),p=c[0].shape[0]===1,d=dP(h,u,r,p),f=C.computeOutShape(e.map(A=>A.shape),t),m=n.makeTensorInfo(f,r,d);return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}if(e.length>Q().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=Ml(e.slice(0,c),t,n),h=Ml(e.slice(c),t,n),p=Ml([u,h],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),p}if(Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new rB(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,r)}let{tensors2D:a,outShape:s}=S_(e,t,n),i=new nB(a.map(c=>c.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=ye({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function S_(e,t,n){let r=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ye({inputs:{x:a},attrs:{shape:[-1,k.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function T_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=k.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(c=>c.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>k.sizeFromShape(c.shape)>0);if(o.length===1)return Fn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return C.assertParamsConsistent(l,s),Ml(o,s,n)}var sB={kernelName:so,backendName:"webgl",kernelFunc:T_},E_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,h=e.filterHeight,p=e.filterWidth,d=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",A=m?1:2,y=m?2:3,g=m?3:1,_="",x="";n&&(r?_=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:a?_=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:_=`
float activation(float x) {
${n}
}
`,x="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${_}
const ivec2 strides = ivec2(${o}, ${l});
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${g}];
ivec2 xRCCorner =
ivec2(coords[${A}], coords[${y}]) * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${h}; wR++) {
int xR = xRCorner + wR * ${c};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p}; wC++) {
int xC = xCCorner + wC * ${u};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${d}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${m}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${f===1}) {
if (${m}) {
dotProd +=
getX(batch, xR, xC, ${d}) *
getW(wR, wC, ${d}, d2);
} else {
dotProd +=
getX(batch, ${d}, xR, xC) *
getW(wR, wC, ${d}, d2);
}
} else if (${f===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${d}, d2),
getW(wR, wC, ${d} + 1, d2)
);
if (${m}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${d}),
getX(batch, xR, xC, ${d} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${d}, xR, xC),
getX(batch, ${d} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${f===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${d}, d2),
getW(wR, wC, ${d} + 1, d2),
getW(wR, wC, ${d} + 2, d2)
);
if (${m}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${d}),
getX(batch, xR, xC, ${d} + 1),
getX(batch, xR, xC, ${d} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${d}, xR, xC),
getX(batch, ${d} + 1, xR, xC),
getX(batch, ${d} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${w}
${x}
setOutput(result);
}
`}},iB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,h=e.filterHeight,p=e.filterWidth,d=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${a}, ${s}, ${i});
const ivec3 pads = ivec3(${t}, ${n}, ${r});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${u}; wF++) {
int xF = xFCorner + wF * ${o};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h}; wR++) {
int xR = xRCorner + wR * ${l};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${d}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${f===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${d}) *
getW(wF, wR, wC, ${d}, d2);
} else if (${f===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${d}),
getX(batch, xF, xR, xC, ${d} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${d}, d2),
getW(wF, wR, wC, ${d} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${f===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${d}),
getX(batch, xF, xR, xC, ${d} + 1),
getX(batch, xF, xR, xC, ${d} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${d}, d2),
getW(wF, wR, wC, ${d} + 1, d2),
getW(wF, wR, wC, ${d} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},oB=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:c,dilationHeight:u,dataFormat:h}=n,{left:p,top:d}=o,f=a*r,m=ln(),A=h==="channelsLast",y=A?0:1,g=A?1:2,_="";for(let x=0;x<=1;x++)for(let w=0;w<=1;w++)_+=`
blockIndex = rc.y + ${w};
pos = rc.x + ${x};
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
offsetY = int(blockIndex / (${l})) * ${i} - ${d};
d0 = offsetY + ${u} * (pos / ${f});
if(d0 < ${t[y]} && d0 >= 0) {
offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${p}.);
d1 = offsetX + ${c} * (int(mod(float(pos), ${f}.) / ${a}.));
if(d1 < ${t[g]} && d1 >= 0) {
ch = int(mod(float(pos), ${a}.));
if (${A}) {
innerDims = vec2(d1, ch);
result[${x*2+w}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${x*2+w}] = getChannel(
getA(ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;this.userCode=`
void main() {
ivec2 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${_}
${m.output} = result;
}
`}};function C_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=r.texData.get(e.dataId),u=n.inChannels,h=l[0]*l[1]*l[2],p=n.outChannels,d=n.dataFormat==="channelsLast",f=!1,m=!1,A,y=[],g=(h===1||p===1)&&u>g_,_=l[2]%2!=0&&!!c.isPacked;if(g||!Q().getBool("WEBGL_LAZILY_UNPACK")||!Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!_){let x=d?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=ye({inputs:{x:e},backend:r,attrs:{shape:[1,x,n.inChannels]}}),b=ye({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=_p({a:w,b,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=ye({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),y.push(w),y.push(b),y.push(N)}else{let x=d?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),w={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},b=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k.assert(Ac(c.shape,w.shape),()=>`packed reshape ${c.shape} to ${w.shape} isn't free`);let N=ye({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=_p({a:w,b:N,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=r.texData.get(T.dataId);k.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=b,E.shape=n.outShape,A=Fn({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let x of y)r.disposeIntermediateTensorInfo(x);return A}function R_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:h,outHeight:p,dataFormat:d}=n,f=d==="channelsLast",m=l*c*u,A=p*h,y=[m,A],g=!0,_=!1,x=[],w=ye({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),b=ye({inputs:{x:t},backend:r,attrs:{shape:[1,m,k.sizeFromShape(t.shape)/m]}});x.push(w),x.push(b);let N=new oB(y,w.shape,n),T=r.runWebGLProgram(N,[w],"float32"),E=ye({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});x.push(T),x.push(E);let M=a!=null,$=s!=null,P=o==="leakyrelu",V=o?xp(o,!0):null,G=new d_(E.shape,b.shape,[1,A,n.outChannels],g,_,M,V,$,P),U=[E,b];if(a&&U.push(a),$&&U.push(s),P){let Z=r.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));U.push(Z),x.push(Z)}let K=r.runWebGLProgram(G,U,"float32"),X=f?[1,p,h,n.outChannels]:[1,n.outChannels,p,h],ee=ye({inputs:{x:K},backend:r,attrs:{shape:X}});x.push(K);for(let Z of x)r.disposeIntermediateTensorInfo(Z);return ee}function lB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r,h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),d;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))d=C_({x:a,filter:s,convInfo:p,backend:n});else if(Q().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)d=R_({x:a,filter:s,convInfo:p,backend:n});else{let m=new E_(p);d=n.runWebGLProgram(m,[a,s],"float32")}let f=ye({inputs:{x:d},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(d),f}var uB={kernelName:hs,backendName:"webgl",kernelFunc:lB},cB=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${r};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${a};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${s}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},hB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,c=s?2:3,u=s?3:1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${u}];
ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${r}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${s}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}},dB=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${a};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${r} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},pB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${o}, ${l}, ${c});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${a}.0;
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${t} - 1 - wF;
for (int wR = 0; wR < ${n}; wR++) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${n} - 1 - wR;
for (int wC = 0; wC < ${r}; wC++) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${r} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function fB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=r,h=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),d=new cB(p);return n.runWebGLProgram(d,[a,s],"float32")}var mB={kernelName:$h,backendName:"webgl",kernelFunc:fB};function AB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r,h=C.convertConv2DDataFormat(c),p=C.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),d=new hB(p);return n.runWebGLProgram(d,[a,s],"float32")}var yB={kernelName:ds,backendName:"webgl",kernelFunc:AB};function gB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=C.computeConv3DInfo(a.shape,s.shape,i,l,o),u=new iB(c);return n.runWebGLProgram(u,[a,s],"float32")}var xB={kernelName:Nu,backendName:"webgl",kernelFunc:gB};function wB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,c=C.computeConv3DInfo(a.shape,l,i,1,o),u=new dB(c);return n.runWebGLProgram(u,[a,s],"float32")}var _B={kernelName:Dh,backendName:"webgl",kernelFunc:wB};function bB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,c=C.computeConv3DInfo(l,s.shape,o,1,i),u=new pB(c);return n.runWebGLProgram(u,[a,s],"float32")}var vB={kernelName:zh,backendName:"webgl",kernelFunc:bB},kB=h_+`
return cos(x);
`,IB=Ze({opSnippet:kB}),NB={kernelName:ps,backendName:"webgl",kernelFunc:IB},SB=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,TB=Ze({opSnippet:SB}),EB={kernelName:io,backendName:"webgl",kernelFunc:TB},CB=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,h]=n;this.outputShape=[c,u,h,l];let p=r==="bilinear"?1:0,[d,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[g,_,x]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
const float height_ratio = float(${m});
const float width_ratio = float(${g});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${s}) {
return;
}
float height_scale = ${A};
float width_scale = ${_};
float in_y = ${y};
if( in_y < 0.0 || in_y > ${d} ) {
setOutput(float(${a}));
return;
}
float in_x = ${x};
if( in_x < 0.0 || in_x > ${f} ) {
setOutput(float(${a}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${p} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},RB=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,u=new CB(a.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[a,s,i],"float32")},FB={kernelName:oo,backendName:"webgl",kernelFunc:RB},O_=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${F_(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
uniform float index;
void main() {
${ht(r)} coords = getOutputCoords();
int end = ${M_(r,"coords")};
float val = ${a};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${o};
${M_(r,"coords")} = idx;
val += getX(${F_(r,"coords")});
}
setOutput(val);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function F_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function M_(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function MB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,c=C.getAxesPermutation([s],l),u=a;c!=null&&(u=gn({inputs:{x:a},backend:n,attrs:{perm:c}}));let h=C.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let p=a.shape[h],d=Fn({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new O_(u.shape,!1,o),A=m.getCustomSetupFunc(f),y=d;d=n.runWebGLProgram(m,[d],d.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new O_(u.shape,i,o),m=d;d=n.runWebGLProgram(f,[d],d.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=C.getUndoAxesPermutation(c),m=gn({inputs:{x:d},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(u),m}return d}var OB={kernelName:fs,backendName:"webgl",kernelFunc:MB};function $B(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),c=n.readSync(s.dataId),u=Zw(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=cP(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var DB={kernelName:Ph,backendName:"webgl",kernelFunc:$B},zB=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int h = ${this.getHeightCoordString()};
int w = ${this.getWidthCoordString()};
int d = ${this.getDepthCoordString()};
int in_h = h / ${t};
int offset_h = imod(h, ${t});
int in_w = w / ${t};
int offset_w = imod(w, ${t});
int offset_d = (offset_h * ${t} + offset_w) *
${this.getOutputDepthSize()};
int in_d = d + offset_d;
float result = ${this.getInputSamplingString()};
setOutput(result);
}
`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function PB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=c*s,d=u/(s*s),f=i==="NHWC"?[o,h,p,d]:[o,d,h,p],m=new zB(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var LB={kernelName:lo,backendName:"webgl",kernelFunc:PB},$_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,p=e.dilationWidth,d=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:a?A=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:A=`
float activation(float x) {
${n}
}
`,y="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${A}
const ivec2 strides = ivec2(${c}, ${u});
const ivec2 pads = ivec2(${o}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${m};
int q = d2 - d1 * ${m};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
for (int wR = 0; wR < ${d}; wR++) {
int xR = xRCorner + wR * ${h};
if (xR < 0 || xR >= ${s}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${p};
if (xC < 0 || xC >= ${i}) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${g}
${y}
setOutput(result);
}
`}},D_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,p=e.dilationWidth,d=e.filterHeight,f=e.filterWidth,m=f,A="int xR; int xC; int xCOffset;";for(let x=0;x<d;x++)for(let w=0;w<f;w++)A+=`
vec4 xTexelR${x}C${w*2} = vec4(0.);
vec4 wR${x}C${w} = vec4(0.);
vec4 xR${x}C${w} = vec4(0.);`;for(let x=0;x<d;x++)for(let w=0;w<m;w++){let b=w*2;if(A+=`
xR = xRCorner + ${x*h};
xC = xCCorner + ${b*p};
`,u===1){if(b<f&&(l%2==1?A+=`
xCOffset = xC + 1;
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${b} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
xTexelR${x}C${b}.zw = vec2(0.);
}
} else {
xTexelR${x}C${b} = vec4(0.);
}
xCOffset = xC + 1 - 2;
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
vec4 previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
previous.zw = vec2(0.);
}
xR${x}C${b} = vec4(previous.zw, xTexelR${x}C${b}.xy);
} else {
xR${x}C${b} = vec4(0, 0, xTexelR${x}C${b}.xy);
}
`:A+=`
if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${i}) {
xTexelR${x}C${b} = getX(batch, xR, xC, d1);
} else {
xTexelR${x}C${b} = vec4(0.);
}
xR${x}C${b} = xTexelR${x}C${b};
`,b+1<f)){let N=l%2==0?k.nearestLargerEven(p):p;p%2==0&&l%2==1||p%2!=0&&l%2!=1?(A+=`
xCOffset = xC + ${l%2} + ${N};
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${b+2} = getX(batch, xR, xCOffset, d1);
}
`,p>1&&(A+=`
xCOffset -= 2;
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${b} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${x}C${b} = vec4(0.);
}
`),A+=`
xR${x}C${b+1} = vec4(
xTexelR${x}C${b}.zw, xTexelR${x}C${b+2}.xy);
`):A+=`
xCOffset = xC + ${N};
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${b+2} = getX(batch, xR, xCOffset, d1);
}
xR${x}C${b+1} = xTexelR${x}C${b+2};
`}}else b<f&&(A+=`
if(xR >= 0 && xR < ${s}) {
`,l%2==1?(A+=`
xCOffset = xC + 1 - ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${b} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${x}C${b} = vec4(0.);
}
if(xC + 1 >= 0 && xC + 1 < ${i}) {
xTexelR${x}C${b+2} = getX(batch, xR, xC + 1, d1);
} else {
xTexelR${x}C${b+2} = vec4(0.);
}
xR${x}C${b} = vec4(
xTexelR${x}C${b}.zw, xTexelR${x}C${b+2}.zw);
`,b+1<f&&(A+=`
vec4 final = vec4(0.);
xCOffset = xC + 1 + ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
final = getX(batch, xR, xCOffset, d1);
}
xR${x}C${b+1} = vec4(xTexelR${x}C${b+2}.xy, final.xy);
`)):(A+=`
if(xC >= 0 && xC < ${i}) {
xTexelR${x}C${b} = getX(batch, xR, xC, d1);
} else {
xTexelR${x}C${b} = vec4(0.);
}
xCOffset = xC + ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${b+2} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${x}C${b+2} = vec4(0.);
}
xR${x}C${b} = vec4(
xTexelR${x}C${b}.xy, xTexelR${x}C${b+2}.xy);
`,b+1<f&&(A+=`
xR${x}C${b+1} = vec4(
xTexelR${x}C${b}.zw, xTexelR${x}C${b+2}.zw);
`)),A+="}");b<f&&(A+=`
vec4 wTexelR${x}C${b} = getW(${x}, ${b}, d1, q);
wR${x}C${b} = vec4(wTexelR${x}C${b}.xz, wTexelR${x}C${b}.xz);
`,b+1<f&&(A+=`
vec4 wTexelR${x}C${b+1} = getW(${x}, ${b+1}, d1, q);
wR${x}C${b+1} =
vec4(wTexelR${x}C${b+1}.xz, wTexelR${x}C${b+1}.xz);`))}for(let x=0;x<d;x++)for(let w=0;w<f;w++)A+=`dotProd += xR${x}C${w} * wR${x}C${w};`;let y="",g="";n&&(r?y=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:a?y=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:y=`vec4 activation(vec4 x) {
${n}
}`,g="result = activation(result);");let _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${y}
const ivec2 strides = ivec2(${c}, ${u});
const ivec2 pads = ivec2(${o}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2;
int q = 0;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
vec4 dotProd = vec4(0.);
${A}
vec4 result = dotProd;
${_}
${g}
setOutput(result);
}
`}};function WB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r,u=l;u==null&&(u=[1,1]),k.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let h=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!0),p;return Q().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?p=new D_(h):p=new $_(h),n.runWebGLProgram(p,[a,s],"float32")}var BB={kernelName:ms,backendName:"webgl",kernelFunc:WB},VB=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${s} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${r};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${a};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},UB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${r}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${o}; dm++) {
int d2 = d1 * ${o} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function jB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r,h=C.computeConv2DInfo(a.shape,u,i,o,l,c,!0),p=new VB(h);return n.runWebGLProgram(p,[a,s],"float32")}var HB={kernelName:Lh,backendName:"webgl",kernelFunc:jB};function GB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r,h=C.computeConv2DInfo(u,s.shape,i,o,l,c,!0),p=new UB(h);return n.runWebGLProgram(p,[a,s],"float32")}var qB={kernelName:Wh,backendName:"webgl",kernelFunc:GB},XB=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function KB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=k.sizeFromShape(r.shape),i=ye({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new XB(s),l=n.runWebGLProgram(o,[i],i.dtype),c=ye({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var ZB={kernelName:Bh,backendName:"webgl",kernelFunc:KB},YB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:h}=r;this.userCode=`
const ivec2 strides = ivec2(${a}, ${s});
const ivec2 pads = ivec2(${u}, ${h});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${i}; h++) {
int hIn = hBeg + h * ${l};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${o}; w++) {
int wIn = wBeg + w * ${c};
if (wIn >= 0 && wIn < ${n}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}};function JB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=C.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),u,h=new YB(c);u=n.runWebGLProgram(h,[a,s],"float32");let p=ye({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),p}var QB={kernelName:Su,backendName:"webgl",kernelFunc:JB},eV="return (x >= 0.0) ? x : (exp(x) - 1.0);",tV=`
vec4 result;
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
return result;
`,nV=Ze({opSnippet:eV,packedOpSnippet:tV}),rV={kernelName:uo,backendName:"webgl",kernelFunc:nV},aV="return (b >= 1.0) ? a : a * (b + 1.0);",sV=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,iV=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new _c(sV,r.shape,a.shape):new Fl(aV,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},oV={kernelName:jh,backendName:"webgl",kernelFunc:iV},lV=`
return vec4(equal(a, b));
`,uV="return float(a == b);",cV=Jt({opSnippet:uV,packedOpSnippet:lV,dtype:"bool"}),hV={kernelName:ho,backendName:"webgl",kernelFunc:cV},dV=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${C.ERF_P};
float a1 = ${C.ERF_A1};
float a2 = ${C.ERF_A2};
float a3 = ${C.ERF_A3};
float a4 = ${C.ERF_A4};
float a5 = ${C.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,pV=Ze({opSnippet:dV}),fV={kernelName:co,backendName:"webgl",kernelFunc:pV},z_="return exp(x);",P_=Ze({opSnippet:z_,packedOpSnippet:z_,cpuKernelImpl:pP}),mV={kernelName:ys,backendName:"webgl",kernelFunc:P_};function Km(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(k.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),ye({inputs:{x:s},backend:r,attrs:{shape:o}})}var AV={kernelName:po,backendName:"webgl",kernelFunc:Km},L_="return exp(x) - 1.0;",yV=Ze({opSnippet:L_,packedOpSnippet:L_,cpuKernelImpl:fP}),gV={kernelName:fo,backendName:"webgl",kernelFunc:yV},W_=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${a};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${r});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${r}; i++) {
// x = (-2|2 * PI / N) * index * i;
float x = exponentMultiplierTimesIndexRatio * float(i);
float expR = cos(x);
float expI = sin(x);
float real = getReal(batch, i);
float imag = getImag(batch, i);
result +=
unaryOpComplex(real, expR, imag, expI) / ${s};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function B_(e,t,n){let r=n.texData.get(e.dataId),a=k.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=ye({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new W_("real",l,t),u=new W_("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],p=n.runWebGLProgram(c,h,"float32"),d=n.runWebGLProgram(u,h,"float32"),f=Da({inputs:{real:p,imag:d},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d);let m=ye({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function xV(e){let{inputs:t,backend:n}=e,{input:r}=t;return B_(r,!1,n)}var wV={kernelName:Hh,backendName:"webgl",kernelFunc:xV},_V=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
uniform float value;
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function Zm(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||k.inferDtype(a),s==="string"){let i=k.getArrayFromDType(s,k.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new _V(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var bV={kernelName:Tu,backendName:"webgl",kernelFunc:Zm},vV=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},kV={kernelName:mo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new vV(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},V_="return floor(x);",IV=Ze({opSnippet:V_,packedOpSnippet:V_,cpuKernelImpl:mP}),NV={kernelName:gs,backendName:"webgl",kernelFunc:IV},SV=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,TV=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,EV=Jt({opSnippet:SV,packedOpSnippet:TV,dtype:"int32"}),CV={kernelName:xs,backendName:"webgl",kernelFunc:EV},RV=class{constructor(e){this.variableNames=["A"];let t=ln(),[n,r]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},FV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=ln(),[n,r]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
result[row * 2 + col] = floor(value * 255.0 + 0.5);
}
}
${t.output} = result;
}
`}},OV={kernelName:sd,backendName:"webgl",kernelFunc:MV},Ol;function MV(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[c,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],h=[u,c],p=[u,c,s];(o||i||l)&&(Ol==null&&(Ol=document.createElement("canvas").getContext("2d")),Ol.canvas.width=c,Ol.canvas.height=u,Ol.drawImage(a,0,0,c,u),a=Ol.canvas);let d=n.makeTensorInfo(h,"int32");n.texData.get(d.dataId).usage=Gn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let f=Q().getBool("WEBGL_PACK")?new FV(p):new RV(p),m=n.runWebGLProgram(f,[d],"int32");return n.disposeData(d.dataId),m}function $V(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:p,activation:d,leakyreluAlpha:f}=r,m=C.convertConv2DDataFormat(u),A=C.computeConv2DInfo(a.shape,s.shape,l,h,c,p,!1,m),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=C_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:d,preluActivationWeights:o,leakyreluAlpha:f});else if(Q().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=R_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:d,preluActivationWeights:o,leakyreluAlpha:f});else{let x=i!=null,w=o!=null,b=d==="leakyrelu",N=d?xp(d,!1):null,T=new E_(A,x,N,w,b),E=[a,s];if(i&&E.push(i),o&&E.push(o),b){let M=n.makeTensorInfo([],"float32",k.createScalarValue(f,"float32"));E.push(M),g.push(M)}y=n.runWebGLProgram(T,E,"float32")}let _=ye({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(x=>n.disposeIntermediateTensorInfo(x)),_}var DV={kernelName:Ys,backendName:"webgl",kernelFunc:$V};function zV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:h,activation:p,leakyreluAlpha:d}=r,f=[],m=u;m==null&&(m=[1,1]),k.assert(C.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=C.computeConv2DInfo(a.shape,s.shape,l,m,c,h,!0),y=Q().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=p?xp(p,y):null,_=[a,s],x=i!=null,w=o!=null,b=p==="leakyrelu";if(x&&_.push(i),w&&_.push(o),b){let E=n.makeTensorInfo([],"float32",k.createScalarValue(d,"float32"));_.push(E),f.push(E)}let N;y?N=new D_(A,x,g,w,b):N=new $_(A,x,g,w,b);let T=n.runWebGLProgram(N,_,"float32");return f.forEach(E=>n.disposeIntermediateTensorInfo(E)),T}var PV={kernelName:Js,backendName:"webgl",kernelFunc:zV},LV=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=ht(t.length),a=ht(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${r} strides = ${r}(${this.strides});
void main() {
${a} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${s};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function WV(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,c,u]=C.prepareAndValidate(r,a),h=ye({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),p=ye({inputs:{x:r},backend:n,attrs:{shape:[k.sizeFromShape(r.shape)/c,c]}}),d=new LV(i,u,[l,c]),f=n.runWebGLProgram(d,[p,h],p.dtype),m=ye({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),m}var BV={kernelName:yo,backendName:"webgl",kernelFunc:WV},UV=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ht(this.rank),r=VV(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function VV(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;a<e.length;a++)a===2?r.push("int(getIndices(resRC.x, resRC.z))"):r.push(`${n[a]}`);return r.join()}function jV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=k.parseAxisParam(i,a.shape)[0],c=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=k.sizeFromShape(s.shape),h=[],p=ye({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),d=ye({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});h.push(p),h.push(d);let f=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let g=n.bufferSync(d),_=n.bufferSync(p),x=AP(_,g,f);return h.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(c.outputShape,x.dtype,x.values)}let m=new UV(p.shape,f),A=n.runWebGLProgram(m,[p,d],p.dtype);h.push(A);let y=ye({inputs:{x:A},backend:n,attrs:{shape:c.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var HV={kernelName:Ao,backendName:"webgl",kernelFunc:jV},GV="return float(a > b);",qV=`
return vec4(greaterThan(a, b));
`,XV=Jt({opSnippet:GV,packedOpSnippet:qV,cpuKernelImpl:yP,dtype:"bool"}),KV={kernelName:go,backendName:"webgl",kernelFunc:XV},ZV="return float(a >= b);",YV=`
return vec4(greaterThanEqual(a, b));
`,JV=Jt({opSnippet:ZV,packedOpSnippet:YV,dtype:"bool"}),QV={kernelName:_s,backendName:"webgl",kernelFunc:JV};function eU(e){let{inputs:t,backend:n}=e,{input:r}=t;return B_(r,!0,n)}var tU={kernelName:Gh,backendName:"webgl",kernelFunc:eU},nU="return float(!isnan(x) && !isinf(x));",rU=Ze({opSnippet:nU,dtype:"bool"}),aU={kernelName:wo,backendName:"webgl",kernelFunc:rU},sU="return float(isinf(x));",iU=Ze({opSnippet:sU,dtype:"bool"}),oU={kernelName:_o,backendName:"webgl",kernelFunc:iU},lU="return float(isnan(x));",uU=Ze({opSnippet:lU,dtype:"bool"}),cU={kernelName:bo,backendName:"webgl",kernelFunc:uU},hU="return float(a < b);",dU=`
return vec4(lessThan(a, b));
`,pU=Jt({opSnippet:hU,packedOpSnippet:dU,cpuKernelImpl:gP,dtype:"bool"}),fU={kernelName:vo,backendName:"webgl",kernelFunc:pU},mU="return float(a <= b);",AU=`
return vec4(lessThanEqual(a, b));
`,yU=Jt({opSnippet:mU,packedOpSnippet:AU,dtype:"bool"}),gU={kernelName:ko,backendName:"webgl",kernelFunc:yU};function xU(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=xP(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var wU={kernelName:Xh,backendName:"webgl",kernelFunc:xU},_U=`if (x < 0.0) return NAN;
return log(x);`,bU=`
vec4 result = log(x);
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
result.r = isNaN.r == 1.0 ? NAN : result.r;
result.g = isNaN.g == 1.0 ? NAN : result.g;
result.b = isNaN.b == 1.0 ? NAN : result.b;
result.a = isNaN.a == 1.0 ? NAN : result.a;
return result;
`,vU=Ze({opSnippet:_U,packedOpSnippet:bU,cpuKernelImpl:wP}),kU={kernelName:vs,backendName:"webgl",kernelFunc:vU},IU="return log(1.0 + x);",NU=Ze({opSnippet:IU}),SU={kernelName:Io,backendName:"webgl",kernelFunc:NU},TU="return float(a >= 1.0 && b >= 1.0);",EU=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,CU=Jt({opSnippet:TU,packedOpSnippet:EU,dtype:"bool"}),RU={kernelName:No,backendName:"webgl",kernelFunc:CU},FU="return float(!(x >= 1.0));",MU=Ze({opSnippet:FU}),OU={kernelName:Eu,backendName:"webgl",kernelFunc:MU},$U="return float(a >= 1.0 || b >= 1.0);",DU=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,zU=Jt({opSnippet:$U,packedOpSnippet:DU,dtype:"bool"}),PU={kernelName:Cu,backendName:"webgl",kernelFunc:zU},LU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${s}; j <= ${s}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${i}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${o};
setOutput(val);
}
`}},WU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${s};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${s}; j <= ${s}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${o};
setOutput(result);
}
`}},BU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,c=Q().getBool("WEBGL_PACK_NORMALIZATION")?new WU(a.shape,s,i,o,l):new LU(a.shape,s,i,o,l);return n.runWebGLProgram(c,[a],a.dtype)},VU={kernelName:Ru,backendName:"webgl",kernelFunc:BU},UU=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${t})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${r}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${r})
* float(${a})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${a});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},jU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=r,h=new UU(a.shape,o,l,c,u);return n.runWebGLProgram(h,[a,s,i],a.dtype)},HU={kernelName:Kh,backendName:"webgl",kernelFunc:jU};function GU(e,t,n,r){let a=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/a,i=ye({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=wi(i,e.dtype,"max",r),l=ye({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function U_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=u!=null,p=n.shouldExecuteOnCPU([a]),d=a;if(h){if(p){let g=n.texData.get(d.dataId).values,_=new Array(o);for(let b=0;b<_.length;b++)_[b]=a.shape[u[b]];let x=Um(g,a.shape,a.dtype,u,_);d=n.makeTensorInfo(_,a.dtype);let w=n.texData.get(d.dataId);w.values=x}else d=wp(a,u,n);c=C.getInnerMostAxes(c.length,o)}C.assertAxesAreInnerMostDims("max",c,o);let[f,m]=C.computeOutAndReduceShapes(d.shape,c),A=f;i&&(A=C.expandShapeToKeepDim(f,l));let y;if(p){let g=n.texData.get(d.dataId).values,_=_P(g,k.sizeFromShape(m),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let x=n.texData.get(y.dataId);x.values=_}else y=GU(d,m,A,n);return h&&n.disposeIntermediateTensorInfo(d),y}var qU={kernelName:ks,backendName:"webgl",kernelFunc:U_},XU=i_+`
return max(a, b);
`,KU=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+gp+`
return result;
`,ZU=Jt({opSnippet:XU,packedOpSnippet:KU,cpuKernelImpl:bP}),YU={kernelName:Is,backendName:"webgl",kernelFunc:ZU};function JU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Il(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;k.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Fn({inputs:{x:a},backend:n});let h=new bc(u,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var QU={kernelName:Ns,backendName:"webgl",kernelFunc:JU};function ej(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=r,u=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,u,o,c,l),p=new Gm(h,"max",!1);return n.runWebGLProgram(p,[a],a.dtype)}var tj={kernelName:Fu,backendName:"webgl",kernelFunc:ej},nj=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${a};
wR += ${r}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${s}; wC++) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${s} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},rj=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,h=l-1-e.padInfo.top,p=c-1-e.padInfo.left,d=o*l*c-1;this.userCode=`
const ivec3 pads = ivec3(${u}, ${h}, ${p});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${o};
wD += ${a}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${l};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c};
wC += ${i}) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${d} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${l} * ${c} +
wR * ${c} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function aj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],p=C.computePool3DInfo(i.shape,o,l,h,c,u),d=new Gm(p,"max",!0),f=n.runWebGLProgram(d,[i],i.dtype),m=new rj(p),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var sj={kernelName:Yh,backendName:"webgl",kernelFunc:aj};function ij(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;Il([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,p=C.computePool2DInfo(o.shape,l,c,1,u,h),d=!0,f=new bc(p,"max",d),m=n.runWebGLProgram(f,[o],o.dtype),A=new nj(p),y=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var oj={kernelName:Zh,backendName:"webgl",kernelFunc:ij};function lj(e,t,n,r){let a=new bc(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new bc(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var uj={kernelName:Jh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;k.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];k.assert(C.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=C.computePool2DInfo(r.shape,a,s,c,i),[h,p]=lj(r,o,u,l);return[h,p]}};function cj(e,t,n,r){let a=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/a,i=ye({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=wi(i,"float32","mean",r),l=ye({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var hj={kernelName:Ss,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=C.getAxesPermutation(c,o),h=u!=null,p=i.shouldExecuteOnCPU([r]),d=[],f=r;if(h){if(p){let _=i.texData.get(f.dataId).values,x=new Array(o);for(let N=0;N<x.length;N++)x[N]=r.shape[u[N]];let w=Um(_,r.shape,r.dtype,u,x);f=i.makeTensorInfo(x,r.dtype);let b=i.texData.get(f.dataId);b.values=w}else f=wp(r,u,i);d.push(f),c=C.getInnerMostAxes(c.length,o)}C.assertAxesAreInnerMostDims("sum",c,o);let[m,A]=C.computeOutAndReduceShapes(f.shape,c),y=m;a&&(y=C.expandShapeToKeepDim(m,l));let g=cj(f,A,y,i);for(let _ of d)i.disposeIntermediateTensorInfo(_);return g}};function dj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=gn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,a.shape.length)),C.assertAxesAreInnerMostDims("min",c,o);let[p,d]=C.computeOutAndReduceShapes(h.shape,c),f=k.sizeFromShape(d),m=ye({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=wi(m,m.dtype,"min",n),y;if(i){let g=C.expandShapeToKeepDim(p,l);y=ye({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=ye({inputs:{x:A},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var pj={kernelName:Ts,backendName:"webgl",kernelFunc:dj},fj=i_+`
return min(a, b);
`,mj=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+gp+`
return result;
`,Aj=Jt({opSnippet:fj,packedOpSnippet:mj,cpuKernelImpl:vP}),yj={kernelName:Es,backendName:"webgl",kernelFunc:Aj},gj=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let r=e.length,a=ht(r),s=t.map(c=>c[0]).join(","),i=t.map((c,u)=>c[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=`
int start = ${s};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${l};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${l};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${a} start = ${a}(${s});
${a} end = ${a}(${i});
void main() {
${a} outC = getOutputCoords();
for (int i = 0; i < ${r}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${l};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
}
}
${a} coords = outC - start;
setOutput(getX(${o}));
}
`}},xj=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((d,f)=>d[0]+e[f]+d[1]);let r=e.length,a=ht(r),s=t.map(d=>d[0]).join(","),i=t.map((d,f)=>d[0]+e[f]).join(","),o=un("rc",r),l=un("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,p="";if(r===1){let d=`
${a} source = rc;
if (source < start) {
source = start * 2 - source - ${h};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${h};
}
source -= start;
`;p=`
${a} rc = outputLoc;
${d}
result[0] = getChannel(getX(${l.join()}), ${u});
${o[r-1]} += 1;
if(${c}) {
${d}
result[1] = getChannel(getX(${l.join()}), ${u});
}
`}else{let d=`
${a} source = rc;
${a} lt = ${a}(lessThan(source, start));
${a} gte = ${a}(greaterThanEqual(source, end));
${a} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${h}) +
gte * ((end - 1) * 2 - source + ${h});
source -= start;
`;p=`
${a} rc = outputLoc;
${d}
result[0] = getChannel(getX(${l.join()}), ${u});
${o[r-1]} += 1;
if(${c}) {
${d}
result[1] = getChannel(getX(${l.join()}), ${u});
}
rc = outputLoc;
${o[r-2]} += 1;
if(${o[r-2]} < ${this.outputShape[r-2]}) {
${d}
result[2] = getChannel(getX(${l.join()}), ${u});
${o[r-1]} += 1;
if(${c}) {
${d}
result[3] = getChannel(getX(${l.join()}), ${u});
}
}
`}this.userCode=`
const ${a} start = ${a}(${s});
const ${a} end = ${a}(${i});
void main() {
${a} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${p}
setOutput(result);
}
`}},wj=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xj(r.shape,a,s):new gj(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},_j={kernelName:Mu,backendName:"webgl",kernelFunc:wj},bj=`if (b == 0.0) return NAN;
return mod(a, b);`,vj=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+gp+`
return result;
`,kj=Jt({opSnippet:bj,packedOpSnippet:vj}),Ij={kernelName:So,backendName:"webgl",kernelFunc:kj},Nj=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
uniform float seed;
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},Sj=`
if (a == b) {
return 1.0;
};
return a / b;`,Tj=`
// vec4 one = vec4(equal(a, b));
// return one + (vec4(1.0) - one) * a / b;
vec4 result = a / b;
if(a.x == b.x) {
result.x = 1.;
}
if(a.y == b.y) {
result.y = 1.;
}
if(a.z == b.z) {
result.z = 1.;
}
if(a.w == b.w) {
result.w = 1.;
}
return result;
`,j_=Jt({opSnippet:Sj,packedOpSnippet:Tj,checkOutOfBounds:!0}),Ej={kernelName:As,backendName:"webgl",kernelFunc:j_},H_="return a - b;",G_=Jt({opSnippet:H_,packedOpSnippet:H_,supportsComplex:!0,cpuKernelImpl:RP}),Cj={kernelName:qs,backendName:"webgl",kernelFunc:G_};function q_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=k.parseAxisParam([s],a.shape),o=U_({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),c=ye({inputs:{x:o},backend:n,attrs:{shape:l}}),u=G_({inputs:{a,b:c},backend:n}),h=P_({inputs:{x:u},backend:n}),p=Hm({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),d=ye({inputs:{x:p},backend:n,attrs:{shape:l}}),f=j_({inputs:{a:h,b:d},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),f}var Rj={kernelName:Hs,backendName:"webgl",kernelFunc:q_};function Fj(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:q_({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),c=l.shape[0],u=l.shape[1],h=new Nj(c,u,s),p=h.getCustomSetupFunc(i),d=n.runWebGLProgram(h,[l],"int32",p);return o||n.disposeIntermediateTensorInfo(l),d}var Mj={kernelName:Qh,backendName:"webgl",kernelFunc:Fj},X_="return -x;";function Oj(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=IP(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return Q().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Rl(r.shape,X_):a=new $a(r.shape,X_),n.runWebGLProgram(a,[r],r.dtype)}var $j={kernelName:To,backendName:"webgl",kernelFunc:Oj},Dj=Pr.nonMaxSuppressionV3Impl;function zj(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,c=n.readSync(a.dataId),u=n.readSync(s.dataId),{selectedIndices:h}=Dj(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var Pj={kernelName:Co,backendName:"webgl",kernelFunc:zj},Lj=Pr.nonMaxSuppressionV4Impl;function Wj(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:p,validOutputs:d}=Lj(u,h,i,o,l,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([d]))]}var Bj={kernelName:Ro,backendName:"webgl",kernelFunc:Wj},Vj=Pr.nonMaxSuppressionV5Impl;function Uj(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),p=i,d=o,f=l,m=c,{selectedIndices:A,selectedScores:y}=Vj(u,h,p,d,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var jj={kernelName:Fo,backendName:"webgl",kernelFunc:Uj},Hj=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${r}), float(${n}),
float(index == coords.y)));
}
`}},Gj=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=k.sizeFromShape(a.shape),c=new Hj(l,s,i,o),u=ye({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(c,[u],a.dtype);n.disposeIntermediateTensorInfo(u);let p=[...a.shape,s],d=ye({inputs:{x:h},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(h),d},qj={kernelName:Rs,backendName:"webgl",kernelFunc:Gj};function Ip(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=kc({inputs:{input:r},backend:n}),s=Ip({inputs:{x:a},backend:n}),i=kp({inputs:{input:r},backend:n}),o=Ip({inputs:{x:i},backend:n}),l=Da({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Zm({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var Xj={kernelName:Zo,backendName:"webgl",kernelFunc:Ip};function K_(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=kc({inputs:{input:r},backend:n}),s=K_({inputs:{x:a},backend:n}),i=kp({inputs:{input:r},backend:n}),o=Ip({inputs:{x:i},backend:n}),l=Da({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Zm({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var Kj={kernelName:Mo,backendName:"webgl",kernelFunc:K_};function Zj(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return Km({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{k.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=Km({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=T_({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var Yj={kernelName:Oo,backendName:"webgl",kernelFunc:Zj},Jj=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let r=e.length,a=ht(r),s=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
int start = ${s};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(float(${n}));
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${a} start = ${a}(${s});
${a} end = ${a}(${i});
void main() {
${a} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(float(${n}));
} else {
${a} coords = outC - start;
setOutput(getX(${o}));
}
}
`}},Qj=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=ht(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=un("rc",r),l=un("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;
if(${c}) {
`,r===1?"":`}
rc = outputLoc;
${o[r-2]} += 1;
if(${o[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":` ${o[r-1]} += 1;
if(${c}) {`],p=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let f=0,m=r===1?2:4;f<m;f++)d+=`
${h[f]}
if (${p}) {
result[${f}] = float(${n});
} else {
${a} source = rc - start;
result[${f}] = getChannel(getX(${l.join()}), ${u});
}
`;d+=r===1?"} ":"}}",this.userCode=`
const ${a} start = ${a}(${s});
const ${a} end = ${a}(${i});
void main() {
${a} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${d}
setOutput(result);
}
`}},Z_=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Qj(a.shape,s,i):new Jj(a.shape,s,i);return n.runWebGLProgram(o,[a],a.dtype)},eH={kernelName:Fs,backendName:"webgl",kernelFunc:Z_},tH=`
if(a < 0.0 && floor(b) < b){
return NAN;
}
if (b == 0.0) {
return 1.0;
}
return (round(mod(b, 2.0)) != 1) ?
pow(abs(a), b) : sign(a) * pow(abs(a), b);
`,nH=`
// isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
vec4 result = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
bvec4 isExpZero = equal(b, vec4(0.0));
result.r = isExpZero.r ? 1.0 : result.r;
result.g = isExpZero.g ? 1.0 : result.g;
result.b = isExpZero.b ? 1.0 : result.b;
result.a = isExpZero.a ? 1.0 : result.a;
vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
`+gp+`
return result;
`,rH=Jt({opSnippet:tH,packedOpSnippet:nH}),aH={kernelName:Ms,backendName:"webgl",kernelFunc:rH};function sH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],c=k.parseAxisParam(s,a.shape),u=c,h=C.getAxesPermutation(u,o),p=a;h!=null&&(p=gn({inputs:{x:a},backend:n,attrs:{perm:h}}),u=C.getInnerMostAxes(u.length,o),l.push(p)),C.assertAxesAreInnerMostDims("prod",u,o);let d;if(n.shouldExecuteOnCPU([p])){let f=n.texData.get(p.dataId).values,{outVals:m,outShape:A,outDtype:y}=NP(p.shape,p.dtype,f,u);d=n.makeTensorInfo(A,y,m)}else{let[f,m]=C.computeOutAndReduceShapes(p.shape,u),A=k.sizeFromShape(m),y=ye({inputs:{x:p},backend:n,attrs:{shape:[-1,A]}}),g=cd(a.dtype),_=wi(y,g,"prod",n);d=ye({inputs:{x:_},backend:n,attrs:{shape:f}}),l.push(y),l.push(_)}if(i){l.push(d);let f=C.expandShapeToKeepDim(d.shape,c);d=ye({inputs:{x:d},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),d}var iH={kernelName:$o,backendName:"webgl",kernelFunc:sH},Y_=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=SP(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},oH={kernelName:Ou,backendName:"webgl",kernelFunc:Y_},lH="return 1.0 / x;",uH=Ze({opSnippet:lH}),cH={kernelName:Do,backendName:"webgl",kernelFunc:uH},hH=mr+`
return (x < 0.0) ? 0.0 : x;
`,dH=`
vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,pH=Ze({opSnippet:hH,packedOpSnippet:dH}),fH={kernelName:$s,backendName:"webgl",kernelFunc:pH},mH=mr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,AH=`
vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,yH=Ze({opSnippet:mH,packedOpSnippet:AH}),gH={kernelName:zs,backendName:"webgl",kernelFunc:yH},xH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/u[0]},
${c[1]/u[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${h};
// Compute the four integer indices.
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
ivec2 sourceCeilRC = ivec2(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
float top = topLeft + (topRight - topLeft) * fracRC.y;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
float newValue = top + (bottom - top) * fracRC.x;
setOutput(newValue);
}
`}},wH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/u[0]},
${c[1]/u[1]},
${c[1]/u[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
${o}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${h};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${l-1};
bool hasNextRow = coords.z < ${n-1};
// In parallel, construct four corners for all four components in
// packed 2x2 cell.
vec4 topLeft = vec4(
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 bottomLeft = vec4(
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 topRight = vec4(
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec4 bottomRight = vec4(
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
vec4 newValue = mix(top, bottom, fracRC.x);
setOutput(newValue);
}
`}};function _H(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=Q().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new wH(a.shape,l,c,s,i):new xH(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],"float32")}var bH={kernelName:Ds,backendName:"webgl",kernelFunc:_H},vH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,p=1/u,d=Math.ceil(h)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${u});
const float invHeightScale = float(${h});
const float invWidthScale = float(${p});
const int winHeight = int(${d});
const int winWidth = int(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${s}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${r-1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${a-1}.0));
float dxCLerp = dxC - float(leftDxCIndex);
float inverseDxCLerp = 1.0 - dxCLerp;
if (r == topDxRIndex && c == leftDxCIndex) {
// topLeft
accumulator +=
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
}
if (r == topDxRIndex && c == rightDxCIndex) {
// topRight
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
}
if (r == bottomDxRIndex && c == leftDxCIndex) {
// bottomLeft
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
}
if (r == bottomDxRIndex && c == rightDxCIndex) {
// bottomRight
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function kH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new vH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var IH={kernelName:nd,backendName:"webgl",kernelFunc:kH},NH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",p;a?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/u[0]},
${c[1]/u[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${p};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}};function SH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=new NH(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],a.dtype)}var TH={kernelName:$u,backendName:"webgl",kernelFunc:SH},EH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,p=1/u,d=Math.ceil(h)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${u});
const float invHeightScale = float(${h});
const float invWidthScale = float(${p});
const int winHeight = int(${d});
const int winWidth = int(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${s}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float sourceFracRow =
float(${o[0]}) *
(float(dyR) / float(${l[0]}));
float sourceFracCol =
float(${o[1]}) *
(float(dyC) / float(${l[1]}));
int sourceNearestRow = int(min(
float(int(${r}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${a}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function CH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new EH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var RH={kernelName:td,backendName:"webgl",kernelFunc:CH},FH=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let r=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>r(o)).join(","),s=ht(n);this.userCode=`
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${a}));
}
`}},MH=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=un("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ht(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${a}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${i} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${o(r.slice())};
if(${a}){
result.g = ${l(r.slice())};
}
if(${s}) {
result.b = ${c(r.slice())};
if(${a}) {
result.a = ${u(r.slice())};
}
}
setOutput(result);
}
`;function o(d){return h(d)}function l(d){return d[n-1]="("+d[n-1]+" + 1)",h(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",h(d)}function u(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",h(d)}function h(d){let f=e.map((y,g)=>p(g,d)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function p(d,f){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${f[d]} - 1`:`${f[d]}`}}};function OH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=k.parseAxisParam(s,a.shape);if(i===0)return Fn({inputs:{x:a},backend:n});let l=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new MH(a.shape,o):new FH(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var $H={kernelName:Ps,backendName:"webgl",kernelFunc:OH},DH=class{constructor(e,t,n,r){this.variableNames=["Image"],this.outputShape=[];let a=e[1],s=e[2],i=Math.sin(t).toFixed(3),o=Math.cos(t).toFixed(3);this.outputShape=e;let[l,c]=C.getImageCenter(r,a,s),u=l.toFixed(3),h=c.toFixed(3),p="";typeof n=="number"?p=`float outputValue = ${n.toFixed(2)};`:p=`
vec3 fill = vec3(${n.join(",")});
float outputValue = fill[coords[3]];`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - ${u}) * ${o} - (float(y) - ${h}) * ${i};
float coordYFloat = (float(x) - ${u}) * ${i} + (float(y) - ${h}) * ${o};
int coordX = int(round(coordXFloat + ${u}));
int coordY = int(round(coordYFloat + ${h}));
${p}
if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${a}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},zH={kernelName:Yo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new DH(r.shape,a,s,i);return o.runWebGLProgram(l,[r],r.dtype)}},PH=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,LH=Ze({opSnippet:PH}),WH={kernelName:Ls,backendName:"webgl",kernelFunc:LH},BH="return inversesqrt(x);",VH=Ze({opSnippet:BH,cpuKernelImpl:TP}),UH={kernelName:Ws,backendName:"webgl",kernelFunc:VH},J_=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ht(a.length),l=ht(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let p=`getUpdates(${h})`,d=t>1?"strides[j]":"strides";this.userCode=`
${o} strides = ${o}(${a});
void main() {
${l} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${u});
flattenedIndex += index * ${d};
}
if (flattenedIndex == coords[0]) {
sum += ${p};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function jH(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:h}=C.calculateShapes(s,a,i),p=[h/c,c];if(h===0)return n.makeTensorInfo(i,a.dtype);let d=ye({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=ye({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new J_(l,o,d.shape.length,f.shape.length,u,p),y=n.runWebGLProgram(A,[f,d,m],f.dtype),g=ye({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),g}var HH={kernelName:Po,backendName:"webgl",kernelFunc:jH},GH=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);r=o.join(),a=l.join()}let s=ht(n);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
float cVal = getC(${r});
if (cVal >= 1.0) {
setOutput(getA(${a}));
} else {
setOutput(getB(${a}));
}
}
`}};function qH(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new GH(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],er(a.dtype,s.dtype))}var XH={kernelName:Lo,backendName:"webgl",kernelFunc:qH},KH=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${C.SELU_SCALEALPHA};
float scale = ${C.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,ZH=Ze({opSnippet:KH}),YH={kernelName:Wo,backendName:"webgl",kernelFunc:ZH},JH="return 1.0 / (1.0 + exp(-1.0 * x));",QH=Ze({opSnippet:JH}),eG={kernelName:Vs,backendName:"webgl",kernelFunc:QH},tG=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,nG=Ze({opSnippet:tG}),rG={kernelName:Uo,backendName:"webgl",kernelFunc:nG},aG=h_+`
return sin(x);
`,sG=Ze({opSnippet:aG}),iG={kernelName:Bs,backendName:"webgl",kernelFunc:sG},oG=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,lG=Ze({opSnippet:oG}),uG={kernelName:Vo,backendName:"webgl",kernelFunc:lG},cG=`
float epsilon = 1.1920928955078125e-7;
float threshold = log(epsilon) + 2.0;
bool too_large = x > -threshold;
bool too_small = x < threshold;
float result;
float exp_x = exp(x);
if (too_large){
result = x;
}
else if (too_small){
result = exp_x;
}
else{
result = log(exp_x + 1.0);
}
return result;
`,hG=Ze({opSnippet:cG}),dG={kernelName:jo,backendName:"webgl",kernelFunc:hG},pG=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;k.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let c=[],u=Z_({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=C.getReshaped(u.shape,s,o,!1),p=C.getPermuted(h.length,s.length,!1),d=C.getReshapedPermuted(u.shape,s,o,!1),f=ye({inputs:{x:u},backend:n,attrs:{shape:h}}),m=gn({inputs:{x:f},backend:n,attrs:{perm:p}}),A=ye({inputs:{x:m},backend:n,attrs:{shape:d}});return c.push(u),c.push(f),c.push(m),c.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},fG={kernelName:Du,backendName:"webgl",kernelFunc:pG};function mG(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:c,strides:u,outputSize:h}=C.calculateShapes(s,a,o),p=!1,d=new J_(c,l,a.shape.length,s.shape.length,u,[h,1],p),f=n.runWebGLProgram(d,[s,a,i],s.dtype),m=ye({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var AG={kernelName:rd,backendName:"webgl",kernelFunc:mG};function yG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=k.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),c=a.shape.length,u=new Array(c).fill(0),h=a.shape.slice();return l.map(p=>{let d=[...h];d[o]=p;let f=vc({inputs:{x:a},backend:n,attrs:{begin:u,size:d}});return u[o]+=p,f})}var gG={kernelName:Ho,backendName:"webgl",kernelFunc:yG},xG="return sqrt(x);",wG=Ze({opSnippet:xG}),_G={kernelName:Us,backendName:"webgl",kernelFunc:wG},bG="return x * x;",vG=Ze({opSnippet:bG}),kG={kernelName:zu,backendName:"webgl",kernelFunc:vG},Q_="return (a - b) * (a - b);",IG=Jt({opSnippet:Q_,packedOpSnippet:Q_}),NG={kernelName:Gs,backendName:"webgl",kernelFunc:IG};function SG({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=mr+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,s=new $a(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var TG={kernelName:va,backendName:"webgl",kernelFunc:SG},EG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=ht(n.length),s=ht(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${o-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
${a} begin = ${a}(${e});
${a} strides = ${a}(${t});
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}};function CG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:h,shrinkAxisMask:p}=r,{nonStrided:d,$begin:f,$strides:m,size:A,newShape:y,outShape:g}=sn.sliceInfo(a.shape,s,i,o,l,c,u,h,p),_=ye({inputs:{x:a},backend:n,attrs:{shape:y}}),x;if(d){let b=vc({inputs:{x:_},backend:n,attrs:{begin:f,size:A}});x=ye({inputs:{x:b},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(b)}else if(g.some(b=>b===0))x=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([_])){let b=n.texData.get(_.dataId).values,N=Le(_.shape,_.dtype,b),T=CP(g,N,m,f);x=n.makeTensorInfo(g,_.dtype,T.values)}else{let b=new EG(f,m,g);x=n.runWebGLProgram(b,[_],_.dtype)}let w=ye({inputs:{x},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(_),n.disposeIntermediateTensorInfo(x),w}var RG={kernelName:Go,backendName:"webgl",kernelFunc:CG},FG="return tan(x);",MG=Ze({opSnippet:FG}),OG={kernelName:qo,backendName:"webgl",kernelFunc:MG},$G=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,DG=Ze({opSnippet:$G}),zG={kernelName:Xs,backendName:"webgl",kernelFunc:DG},LG=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let r=ht(this.rank),a=PG(e);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function PG(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let a=0;a<e.length;a++)r.push(`imod(${n[a]}, ${e[a]})`);return r.join()}function eb(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;if(a.dtype==="string"){let o=n.readSync(a.dataId).map(u=>k.decodeString(u)),l=Le(a.shape,a.dtype,o),c=FP(l,s);return n.makeTensorInfo(c.shape,c.dtype,c.values)}let i=new LG(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var WG={kernelName:ba,backendName:"webgl",kernelFunc:eb};function BG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,c]=MP(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var VG={kernelName:Xo,backendName:"webgl",kernelFunc:BG};function UG(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;Il(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=r.readSync(s.dataId),{outputValues:o,outputShape:l,indices:c}=OP(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([c.length],"int32",c)]}var jG={kernelName:ad,backendName:"webgl",kernelFunc:UG};function HG(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],c=new Array(o-1),u=0;for(let m=0;m<o;m++)m!==s&&(c[u++]=i.shape[m]);let h=[],p=new Array(o).fill(0),d=i.shape.slice();d[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[s]=m;let A=vc({inputs:{x:i},backend:n,attrs:{begin:p,size:d}}),y=ye({inputs:{x:A},backend:n,attrs:{shape:c}});f[m]=y,h.push(A)}return h.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var GG={kernelName:Ko,backendName:"webgl",kernelFunc:HG},qG=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/n);this.outputShape=[r,i];let o="0.0",l="sumValue",c=Math.floor(n/4)*4,u=n%4,h=`
sumValue += dot(values, segFilter);
`,p="";a%n>0&&(p=`
if (inIdx < 0 || inIdx >= ${a}) {
return initializationValue;
}
`);let d="";a%n>0&&(d=`
if (inIdx < 0 || inIdx >= ${a}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${o};
float getValue(int batch, int inIdx) {
${p}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${d}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${s})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${s})));
float sumValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${h}
}
int inIdx = inOffset + ${c};
if (${u===1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${h}
} else if (${u===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${h}
} else if (${u===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${h}
}
setOutput(${l});
}
`}};function XG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r,o=a.shape.length,l=[],c=0,u=C.getAxesPermutation([c],o),h=a;u!=null&&(h=gn({inputs:{x:a},backend:n,attrs:{perm:u}}),l.push(h),c=C.getInnerMostAxes(1,o)[0]);let p=C.segment_util.computeOutShape(h.shape,c,i),d=k.sizeFromShape([h.shape[c]]),f=ye({inputs:{x:h},backend:n,attrs:{shape:[-1,d]}});l.push(f);let m=cd(a.dtype),A=(x,w,b,N,T)=>{let E=x.shape[0],M=x.shape[1],$=C.segment_util.segOpComputeOptimalWindowSize(M,T),P={windowSize:$,inSize:M,batchSize:E,numSegments:T},V=new qG(P,w),G=n.compileAndRun(V,[x,b],N);if(l.push(G),G.shape[1]===T)return G;let U=Y_({backend:n,attrs:{start:0,stop:T,step:1,dtype:"float32"}}),K=eb({inputs:{x:U},backend:n,attrs:{reps:[M/$]}});return l.push(U),l.push(K),A(G,w,K,N,T)},y=A(f,"unsortedSegmentSum",s,m,i),g=ye({inputs:{x:y},backend:n,attrs:{shape:p}}),_=g;if(u!=null){l.push(g);let x=C.getUndoAxesPermutation(u);_=gn({inputs:{x:_},backend:n,attrs:{perm:x}})}return l.forEach(x=>n.disposeIntermediateTensorInfo(x)),_}var KG={kernelName:Pu,backendName:"webgl",kernelFunc:XG},ZG=[VU,HU,CL,FL,$L,PL,WL,UL,HL,qL,YL,QL,nW,sW,dW,lW,mW,xW,yW,vW,IW,SW,RW,PW,WW,GW,XW,JW,tB,hL,sB,mB,yB,uB,_B,vB,xB,NB,EB,FB,OB,DB,LB,HB,qB,BB,ZB,QB,rV,oV,hV,fV,mV,AV,gV,wV,bV,kV,NV,CV,OV,DV,PV,BV,HV,KV,QV,cL,tU,aB,aU,oU,cU,pL,fU,gU,wU,SU,kU,RU,OU,PU,qU,tj,QU,sj,oj,uj,YU,hj,pj,yj,_j,Ij,Mj,gL,$j,Pj,Bj,jj,VW,qj,Kj,Yj,eH,aH,mL,iH,oH,UW,Ej,cH,gH,fH,wL,bH,IH,TH,RH,$H,zH,WH,UH,HH,XH,YH,eG,rG,iG,uG,DW,Rj,dG,fG,AG,gG,_G,kG,NG,TG,RG,Cj,SL,OG,zG,WG,VG,TL,jG,GG,KG,Xj];for(let e of ZG)Qs(e);var Mn;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Mn||(Mn={}));var Ic;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu"})(Ic||(Ic={}));var tb;function YG(e){tb=e.wasm.cwrap(Zs,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function JG(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r,p=n.dataIdMap.get(a.dataId).id,d=n.dataIdMap.get(s.dataId).id,f=0;if(i!=null){let T=n.dataIdMap.get(i.dataId);if(T.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${T.shape.length}.`);f=T.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,A=Ic[u];if(A==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],g=c?s.shape[1]:s.shape[2],_=a.shape[0],x=n.makeOutput([_,y,g],a.dtype),w=n.dataIdMap.get(x.dataId).id,b=new Uint8Array(new Int32Array(a.shape).buffer),N=new Uint8Array(new Int32Array(s.shape).buffer);return tb(p,b,a.shape.length,d,N,s.shape.length,l,c,A,f,m,h||0,w),x}var QG={kernelName:Zs,backendName:"wasm",setupFunc:YG,kernelFunc:JG};function On(e){let t;function n(a){t=a.wasm.cwrap(e,null,["number","number"])}function r(a){let{backend:s,inputs:{x:i}}=a,o=s.dataIdMap.get(i.dataId).id,l=s.makeOutput(i.shape,i.dtype),c=s.dataIdMap.get(l.dataId).id;return k.sizeFromShape(l.shape)===0||t(o,c),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:r}}var eq=On(Zi);function cn(e,t,n){let r;function a(i){r=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:c,b:u}=l,h=o.dataIdMap.get(c.dataId).id,p=o.dataIdMap.get(u.dataId).id,d=n!=null?n:c.dtype,f=C.assertAndGetBroadcastShape(c.shape,u.shape),m=o.makeOutput(f,d);if(k.sizeFromShape(f)===0)return m;let A=new Uint8Array(new Int32Array(c.shape).buffer),y=new Uint8Array(new Int32Array(u.shape).buffer),g=o.dataIdMap.get(m.dataId).id,_=()=>r(h,A,c.shape.length,p,y,u.shape.length,Mn[c.dtype],g);if(t&&c.dtype==="float32")return _(),m;let x=C.getBroadcastDims(c.shape,f),w=C.getBroadcastDims(u.shape,f),b=x.every((T,E)=>T===E),N=w.every((T,E)=>T===E);if(b&&N)return _(),m;throw new Error(`Broadcasting along outer dims is not yet supported for ${c.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var tq=!0,nq=cn(wa,tq),nb;function rq(e){nb=e.wasm.cwrap(is,null,["array","number","number","number"])}function aq(e){let{inputs:t,backend:n}=e,r=n.makeOutput(t[0].shape,t[0].dtype);if(k.sizeFromShape(r.shape)===0)return r;let a=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=n.dataIdMap.get(r.dataId).id;return nb(s,a.length,Mn[r.dtype],i),r}var sq={kernelName:is,backendName:"wasm",setupFunc:rq,kernelFunc:aq};function Np(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype),a=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(a),r}var iq={kernelName:xo,backendName:"wasm",kernelFunc:Np},rb;function oq(e){rb=e.wasm.cwrap(Ks,null,["number","array","number","number","number","array","number"])}function Sp(e){let{inputs:t,backend:n,attrs:r}=e,[a,s]=uq(t.x.shape,r.perm),i=!0;for(let f=0;f<s.length;f++)s[f]!==f&&(i=!1);let o=lq(t.x.shape,r.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let f=Np({inputs:t,backend:n});return f.shape=o,f}let c=n.makeOutput(o,l.dtype),u=n.dataIdMap.get(l.dataId).id,h=n.dataIdMap.get(c.dataId).id,p=new Uint8Array(new Int32Array(s).buffer),d=new Uint8Array(new Int32Array(l.shape).buffer);return rb(u,d,l.shape.length,Mn[l.dtype],h,p,s.length),c}function lq(e,t){let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];return n}function uq(e,t){let n=[],r=[];for(let a=0;a<e.length;++a)e[a]!==1&&n.push(e[a]),e[t[a]]!==1&&r.push(t[a]);for(let a=0;a<r.length;++a){let s=-1;for(let i=0;i<r.length;++i)r[i]>=a&&(s===-1||r[s]>r[i])&&(s=i);r[s]=a}return[n,r]}var cq={kernelName:Ks,backendName:"wasm",kernelFunc:Sp,setupFunc:oq};function $l(e,t,n){let r=e.shape,a=e.shape.length,s=k.parseAxisParam(t,r),i=s,o=C.getAxesPermutation(i,a),l=null,c=!1;if(o!=null){let u=new Array(a);for(let p=0;p<u.length;p++)u[p]=r[o[p]];i=C.getInnerMostAxes(i.length,a),l=Sp({inputs:{x:e},attrs:{perm:o},backend:n});let h=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==h&&(c=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:c}}var ab;function hq(e){ab=e.wasm.cwrap(os,null,["number","number","number","number","number"])}function dq(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a}=r,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:c,axes:u,inputWasTransposed:h}=$l(s,a,t);if(h){let y=t.dataIdMap.get(c.dataId).id;y!==i&&(l=c,o=y)}let p=l.shape.slice(0,-1),d=t.makeOutput(p,"int32"),f=t.dataIdMap.get(d.dataId).id,m=k.sizeFromShape(d.shape),A=l.shape[u[0]];return ab(o,Mn[l.dtype],m,A,f),h&&t.disposeData(c.dataId),d}var pq={kernelName:os,backendName:"wasm",kernelFunc:dq,setupFunc:hq},sb;function fq(e){sb=e.wasm.cwrap(ls,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function mq(e){let{inputs:t,attrs:n,backend:r}=e,a=t.x,s=r.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=C.computePool2DInfo(a.shape,i,o,1,l,c),h=u.filterHeight,p=u.filterWidth,d=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,A=u.padInfo.left,y=u.strideHeight,g=u.strideWidth,_=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);if(u.dilationWidth!==1||u.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${u.dilationHeight}, ${u.dilationWidth}].`);let x=r.makeOutput(u.outShape,"float32"),w=r.dataIdMap.get(x.dataId).id;return sb(s,a.shape[0],a.shape[1],a.shape[2],h,p,d,f,m,A,y,g,_,w),x}var Aq={kernelName:ls,backendName:"wasm",setupFunc:fq,kernelFunc:mq};function Ar(e){let{inputs:t,attrs:n}=e,{x:r}=t,{shape:a}=n,s=k.sizeFromShape(r.shape),i=k.inferFromImplicitShape(a,s);return k.assert(s===k.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${r.shape}. New shape and old shape must have the same number of elements.`),{dataId:r.dataId,shape:i,dtype:r.dtype}}var yq={kernelName:zo,backendName:"wasm",kernelFunc:Ar},ib;function gq(e){ib=e.wasm.cwrap(us,null,["number","array","number","number","array","number","number","number","number"])}function xq(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,c=s.shape.length,u=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[c-1]:s.shape[c-2],p=i?a.shape[l-1]:a.shape[l-2],d=o?s.shape[c-2]:s.shape[c-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),A=k.sizeFromShape(f),y=k.sizeFromShape(m),g=A===y||A===1||y===1;k.assert(l>=2&&c>=2&&g,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let _=(A>y?a.shape.slice(0,-2):s.shape.slice(0,-2)).concat([p,d]);k.assert(u===h,()=>`Error in matMul: inner shapes (${u}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[A,u,p]:[A,p,u],w=o?[y,d,h]:[y,h,d],b=Ar({inputs:{x:a},backend:n,attrs:{shape:x}}),N=Ar({inputs:{x:s},backend:n,attrs:{shape:w}}),T=n.dataIdMap.get(b.dataId).id,E=n.dataIdMap.get(N.dataId).id,M=i?b.shape[2]:b.shape[1],$=o?N.shape[1]:N.shape[2],P=Math.max(A,y),V=n.makeOutput([P,M,$],b.dtype),G=n.dataIdMap.get(V.dataId).id,U=new Uint8Array(new Int32Array(b.shape).buffer),K=new Uint8Array(new Int32Array(N.shape).buffer);return ib(T,U,b.shape.length,E,K,N.shape.length,i,o,G),V.shape=_,V}var wq={kernelName:us,backendName:"wasm",setupFunc:gq,kernelFunc:xq};function Tp(e){let{inputs:{x:t},attrs:{dtype:n},backend:r}=e,a=r.makeOutput(t.shape,n),s=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(a).set(s),a}var _q={kernelName:cs,backendName:"wasm",kernelFunc:Tp},ob;function bq(e){ob=e.wasm.cwrap(_a,null,["number","number","number","number"])}function vq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o=n.dataIdMap.get(a.dataId).id,l=n.makeOutput(a.shape,a.dtype),c=n.dataIdMap.get(l.dataId).id;return ob(o,s,i,c),l}var kq={kernelName:_a,backendName:"wasm",setupFunc:bq,kernelFunc:vq};function lb(e){let{inputs:t,backend:n}=e,r=k.parseAxisParam(e.attrs.axis,t[0].shape)[0],a=C.computeOutShape(t.map(d=>d.shape),r),s=t.filter(d=>k.sizeFromShape(d.shape)>0);if(s.length===1)return Np({inputs:{x:s[0]},backend:n});let i=n.makeOutput(a,t[0].dtype);if(k.sizeFromShape(a)===0)return i;let o=s.map(d=>d.shape);if(C.assertParamsConsistent(o,r),s[0].dtype==="string"){let d=s.map(_=>{let x=k.sizeFromShape(_.shape.slice(r));return Ar({inputs:{x:_},backend:n,attrs:{shape:[-1,x]}})}),f=d.map(_=>({vals:n.readSync(_.dataId),shape:_.shape}));a=C.computeOutShape(d.map(_=>_.shape),1);let m=d[0].shape[0]===1,A=gm(f,a,t[0].dtype,m),y=C.computeOutShape(s.map(_=>_.shape),r);i.shape=y;let g=n.dataIdMap.get(i.dataId);return g.stringBytes=C.fromStringArrayToUint8(A),i}let l=k.sizeFromShape(s[0].shape.slice(0,r)),c=0,u=s.map(d=>{let f=k.sizeFromShape(d.shape.slice(r));return c+=f,f}),h=s.map(d=>n.typedArrayFromHeap(d)),p=n.typedArrayFromHeap(i);for(let d=0;d<l;d++){let f=d*c;for(let m=0;m<h.length;m++){let A=u[m],y=d*A,g=h[m].subarray(y,y+A);p.set(g,f),f+=A}}return i}var Iq={kernelName:so,backendName:"wasm",kernelFunc:lb},ub;function Nq(e){ub=e.wasm.cwrap(hs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Sq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s}=t,i=r.dataIdMap.get(a.dataId).id,o=r.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:h,dataFormat:p}=n,d=C.convertConv2DDataFormat(p),f=C.computeConv2DInfo(a.shape,s.shape,l,c,u,h,!1,d),m=f.filterHeight,A=f.filterWidth,y=f.padInfo.top,g=f.padInfo.right,_=f.padInfo.bottom,x=f.padInfo.left,w=f.dilationHeight,b=f.dilationWidth,N=f.strideHeight,T=f.strideWidth,E=f.inChannels,M=f.outChannels,$=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let P=r.makeOutput(f.outShape,"float32"),V=r.dataIdMap.get(P.dataId).id;return ub(i,a.shape[0],a.shape[1],a.shape[2],o,m,A,y,g,_,x,$,w,b,N,T,E,M,V),P}var Tq={kernelName:hs,backendName:"wasm",setupFunc:Nq,kernelFunc:Sq},cb;function Eq(e){cb=e.wasm.cwrap(ds,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Cq(e){let{backend:t,inputs:n,attrs:r}=e,{dy:a,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,inputShape:u}=r,h=1,p=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(u,s.shape,i,h,o,c,!1,p),{batchSize:f,filterHeight:m,filterWidth:A,inChannels:y,inHeight:g,inWidth:_,outChannels:x,outHeight:w,outWidth:b,strideHeight:N,strideWidth:T}=d,E=m-1-d.padInfo.top,M=A-1-d.padInfo.left,$=d.dataFormat==="channelsLast",P=k.computeStrides(d.inShape),V=k.computeStrides(a.shape),[G,U,K]=k.computeStrides(s.shape),X=P[0],ee=$?P[1]:P[2],Z=$?P[2]:1,ae=$?1:P[1],J=V[0],oe=$?V[1]:V[2],ne=$?V[2]:1,ce=$?1:V[1],ue=t.makeOutput(d.inShape,"float32"),pe=t.dataIdMap.get(ue.dataId).id,fe=t.dataIdMap.get(a.dataId).id,_e=t.dataIdMap.get(s.dataId).id;return cb(fe,_e,f,m,A,g,_,y,w,b,x,N,T,E,M,G,U,K,X,ee,Z,ae,J,oe,ne,ce,pe),ue}var Rq={kernelName:ds,backendName:"wasm",setupFunc:Eq,kernelFunc:Cq},Fq=On(ps),Ym;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(Ym||(Ym={}));var hb;function Mq(e){hb=e.wasm.cwrap(oo,null,["number","number","number","number","array","number","number","number","number","number"])}function Oq(e){let{backend:t,inputs:n,attrs:r}=e,{method:a,extrapolationValue:s,cropSize:i}=r,{image:o,boxes:l,boxInd:c}=n,u=l.shape[0],[h,p]=i,d=[u,h,p,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!=="float32"&&(m=Tp({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let A=f.id,y=t.dataIdMap.get(l.dataId).id,g=t.dataIdMap.get(c.dataId).id,_=t.makeOutput(d,"float32"),x=t.dataIdMap.get(_.dataId).id,w=new Uint8Array(new Int32Array(o.shape).buffer);return hb(A,y,g,u,w,h,p,Ym[a],s,x),m!=null&&t.disposeData(m.dataId),_}var $q={kernelName:oo,backendName:"wasm",setupFunc:Mq,kernelFunc:Oq},db;function Dq(e){db=e.wasm.cwrap(fs,null,["number","number","number","number","number","number"])}function zq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length;k.assert(a.dtype==="float32"||a.dtype==="int32",()=>`cumsum does not support ${a.dtype} tensors in the WASM backend`);let c=C.getAxesPermutation([s],l),u=a;c!==null&&(u=Sp({inputs:{x:a},attrs:{perm:c},backend:n}));let h=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumsum",[h],l);let p=n.makeOutput(u.shape,u.dtype),d=u.shape[h],f=n.dataIdMap.get(u.dataId).id,m=n.dataIdMap.get(p.dataId).id;db(f,i?1:0,o?1:0,d,m,Mn[a.dtype]);let A=p;if(c!==null){let y=C.getUndoAxesPermutation(c);A=Sp({inputs:{x:p},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(p.dataId)}return A}var Pq={kernelName:fs,backendName:"wasm",setupFunc:Dq,kernelFunc:zq},pb;function Lq(e){pb=e.wasm.cwrap(lo,null,["number","number","number","array","number","array","array","number","number"])}function Wq(e){let{backend:t,inputs:n,attrs:r}=e,{x:a}=n,{blockSize:s,dataFormat:i}=r;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=c*s,d=u/(s*s),f=i==="NHWC"?[o,h,p,d]:[o,d,h,p],m=t.makeOutput(f,"float32"),A=t.dataIdMap.get(a.dataId).id,y=new Uint8Array(new Int32Array(k.computeStrides(a.shape)).buffer),g=new Uint8Array(new Int32Array(f).buffer),_=new Uint8Array(new Int32Array(k.computeStrides(f)).buffer),x=t.dataIdMap.get(m.dataId).id;return pb(A,s,i==="NHWC"?1:0,y,a.shape.length-1,g,_,f.length,x),m}var Bq={kernelName:lo,backendName:"wasm",setupFunc:Lq,kernelFunc:Wq},fb;function Vq(e){fb=e.wasm.cwrap(ms,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Uq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s}=t,i=r.dataIdMap.get(a.dataId).id,o=r.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:h}=n,p=c==null?[1,1]:c,d=C.computeConv2DInfo(a.shape,s.shape,l,p,u,h,!0),f=d.filterHeight,m=d.filterWidth,A=d.padInfo.top,y=d.padInfo.right,g=d.padInfo.bottom,_=d.padInfo.left,x=d.dilationHeight,w=d.dilationWidth,b=d.strideHeight,N=d.strideWidth,T=d.inChannels,E=d.outChannels,M=d.padInfo.type==="SAME"?1:0;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let $=r.makeOutput(d.outShape,"float32"),P=r.dataIdMap.get($.dataId).id;return fb(i,a.shape[0],a.shape[1],a.shape[2],o,f,m,A,y,g,_,M,x,w,b,N,T,E,P),$}var jq={kernelName:ms,backendName:"wasm",setupFunc:Vq,kernelFunc:Uq},Hq=!1,Gq=cn(ho,Hq,"bool"),qq=On(ys);function Jm(e){let{inputs:t,attrs:n,backend:r}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Ar({inputs:{x:a},backend:r,attrs:{shape:o}})}var Xq={kernelName:po,backendName:"wasm",kernelFunc:Jm};function Kq(e){let{attrs:{shape:t,value:n,dtype:r},backend:a}=e,s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var Zq={kernelName:Tu,backendName:"wasm",kernelFunc:Kq},mb;function Yq(e){mb=e.wasm.cwrap(mo,null,["number","number","number","number","number","number"])}function Jq(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,c,u]=r.shape;return mb(s,o,l,c,u,i),a}var Qq={kernelName:mo,backendName:"wasm",kernelFunc:Jq,setupFunc:Yq},eX=On(gs),tX=!1,nX=cn(xs,tX),Ab;function rX(e){Ab=e.wasm.cwrap(ws,null,["number","number","number","number","number","number","number"])}function aX(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,p=t.dataIdMap.get(o.dataId).id,d=l!=null?t.dataIdMap.get(l.dataId).id:0,f=c!=null?t.dataIdMap.get(c.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return m;let A=t.dataIdMap.get(m.dataId).id;return Ab(u,h,p,d,f,a,A),m}var sX={kernelName:ws,backendName:"wasm",setupFunc:rX,kernelFunc:aX},yb;function iX(e){yb=e.wasm.cwrap(Ys,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function oX(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:h,dimRoundingMode:p,activation:d,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(a.shape,s.shape,l,u,c,p),A=Ic[d];if(A==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,_=m.outChannels,x=0;if(i!=null){let ne=r.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==_)throw new Error(`FusedConv2D bias shape (${ne.shape}) does not match the number of output channels (${_})`);x=ne.id}let w=m.filterHeight,b=m.filterWidth,N=m.padInfo.top,T=m.padInfo.right,E=m.padInfo.bottom,M=m.padInfo.left,$=m.dilationHeight,P=m.dilationWidth,V=m.strideHeight,G=m.strideWidth,U=m.inChannels,K=m.padInfo.type==="SAME"?1:0,X=m.batchSize,ee=m.inHeight,Z=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(m.outShape,"float32"),J=r.dataIdMap.get(ae.dataId).id,oe=o==null?0:r.dataIdMap.get(o.dataId).id;return yb(y,X,ee,Z,g,w,b,x,N,T,E,M,K,$,P,V,G,U,_,A,oe,f||0,J),ae}var lX={kernelName:Ys,backendName:"wasm",setupFunc:iX,kernelFunc:oX},gb;function uX(e){gb=e.wasm.cwrap(Js,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function cX(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:h,dimRoundingMode:p,activation:d,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(a.shape,s.shape,l,u,c,p,!0),A=Ic[d];if(A==null)throw new Error(`${d} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,_=m.outChannels,x=0;if(i!=null){let ne=r.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==_)throw new Error(`FusedDepthwiseConv2D bias shape (${ne.shape}) does not match the number of output channels (${_})`);x=ne.id}let w=m.filterHeight,b=m.filterWidth,N=m.padInfo.top,T=m.padInfo.right,E=m.padInfo.bottom,M=m.padInfo.left,$=m.dilationHeight,P=m.dilationWidth,V=m.strideHeight,G=m.strideWidth,U=m.inChannels,K=m.padInfo.type==="SAME"?1:0,X=m.batchSize,ee=m.inHeight,Z=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(m.outShape,"float32"),J=r.dataIdMap.get(ae.dataId).id,oe=o==null?0:r.dataIdMap.get(o.dataId).id;return gb(y,X,ee,Z,g,w,b,x,N,T,E,M,K,$,P,V,G,U,_,A,oe,f||0,J),ae}var hX={kernelName:Js,backendName:"wasm",setupFunc:uX,kernelFunc:cX},xb;function dX(e){xb=e.wasm.cwrap(yo,null,["number","number","number","number","number","number","array","number"])}function pX(e){let{backend:t,inputs:n}=e,{params:r,indices:a}=n,[s,i,o,l]=Af.prepareAndValidate(r,a),c=t.makeOutput(s,r.dtype);if(i===0)return c;let u=a.shape,h=u[u.length-1],p=t.dataIdMap.get(r.dataId).id,d=t.dataIdMap.get(a.dataId).id,f=new Uint8Array(new Int32Array(l).buffer),m=t.dataIdMap.get(c.dataId).id;return xb(p,Mn[r.dtype],d,i,h,o,f,m),c}var fX={kernelName:yo,backendName:"wasm",setupFunc:dX,kernelFunc:pX},wb;function mX(e){wb=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function AX(e){let{backend:t,inputs:n,attrs:r}=e,{x:a,indices:s}=n,{axis:i,batchDims:o}=r,l=k.parseAxisParam(i,a.shape)[0],c=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=Ar({inputs:{x:a},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),h=k.sizeFromShape(s.shape),p=Ar({inputs:{x:s},attrs:{shape:[c.batchSize,h/c.batchSize]},backend:t}),d=[c.batchSize,c.outerSize,h/c.batchSize,c.sliceSize],f=t.makeOutput(d,a.dtype);if(k.sizeFromShape(a.shape)===0)return f;let m=u.shape.length-1,A=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(p.dataId).id,g=t.dataIdMap.get(f.dataId).id,_=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(d)).buffer);return wb(A,Mn[a.dtype],_,m,y,c.batchSize,x,g),f.shape=c.outputShape,f}var yX={kernelName:Ao,backendName:"wasm",setupFunc:mX,kernelFunc:AX},gX=!1,xX=cn(go,gX,"bool"),wX=!1,_X=cn(_s,wX,"bool"),_b;function bX(e){_b=e.wasm.cwrap(bs,null,["number","number","number"])}function vX(e){let{inputs:{x:t},attrs:{alpha:n},backend:r}=e,a=r.dataIdMap.get(t.dataId).id,s=r.makeOutput(t.shape,t.dtype);if(k.sizeFromShape(t.shape)!==0){let i=r.dataIdMap.get(s.dataId).id;_b(a,n,i)}return s}var kX={kernelName:bs,backendName:"wasm",setupFunc:bX,kernelFunc:vX},IX=!1,NX=cn(vo,IX,"bool"),SX=!1,TX=cn(ko,SX,"bool"),EX=On(vs),CX=!1,RX=cn(No,CX,"bool"),bb;function FX(e){bb=e.wasm.cwrap(ks,null,["number, number, number"])}function MX(e){let{backend:t,inputs:n,attrs:r}=e,{reductionIndices:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:h,inputWasTransposed:p}=$l(i,a,t);if(p){let g=t.dataIdMap.get(c.dataId).id;l=c,o=g}let d=l.shape.length;C.assertAxesAreInnerMostDims("max",u,d);let[f,m]=C.computeOutAndReduceShapes(l.shape,u),A=k.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(k.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;bb(o,A,g)}if(p&&t.disposeData(c.dataId),s){let g=C.expandShapeToKeepDim(y.shape,h);y.shape=g}return y}var OX={kernelName:ks,backendName:"wasm",setupFunc:FX,kernelFunc:MX},$X=!1,DX=cn(Is,$X),vb;function zX(e){vb=e.wasm.cwrap(Ns,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function PX(e){let{inputs:t,attrs:n,backend:r}=e,a=t.x,s=r.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=C.computePool2DInfo(a.shape,i,o,1,l,c),h=u.filterHeight,p=u.filterWidth,d=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,A=u.padInfo.left,y=u.dilationHeight,g=u.dilationWidth,_=u.strideHeight,x=u.strideWidth,w=u.inChannels,b=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let N=r.makeOutput(u.outShape,"float32"),T=r.dataIdMap.get(N.dataId).id;return vb(s,a.shape[0],a.shape[1],a.shape[2],h,p,d,f,m,A,y,g,_,x,w,b,T),N}var LX={kernelName:Ns,backendName:"wasm",setupFunc:zX,kernelFunc:PX},kb;function WX(e){kb=e.wasm.cwrap(Ss,null,["number, number, number"])}function BX(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:h,originalAxes:p,inputWasTransposed:d}=$l(i,a,t),f=h;if(d){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x,f=C.getInnerMostAxes(f.length,c.shape.length))}C.assertAxesAreInnerMostDims("mean",f,c.shape.length);let[m,A]=C.computeOutAndReduceShapes(c.shape,f),y=k.sizeFromShape(A),g=c;c.dtype!=="float32"&&(g=Tp({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(g.dataId).id);let _=t.makeOutput(m,"float32");if(k.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(_.dataId).id;kb(l,y,x)}if(d&&t.disposeData(u.dataId),s){let x=C.expandShapeToKeepDim(_.shape,p);_.shape=x}return c.dtype!=="float32"&&t.disposeData(g.dataId),_}var VX={kernelName:Ss,backendName:"wasm",setupFunc:WX,kernelFunc:BX},Ib;function UX(e){Ib=e.wasm.cwrap(Ts,null,["number, number, number"])}function jX(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:h,originalAxes:p,inputWasTransposed:d}=$l(i,a,t);if(d){let _=t.dataIdMap.get(u.dataId).id;_!==o&&(c=u,l=_)}let f=c.shape.length;C.assertAxesAreInnerMostDims("min",h,f);let[m,A]=C.computeOutAndReduceShapes(c.shape,h),y=k.sizeFromShape(A),g=t.makeOutput(m,c.dtype);if(k.sizeFromShape(c.shape)!==0){let _=t.dataIdMap.get(g.dataId).id;Ib(l,y,_)}if(d&&t.disposeData(u.dataId),s){let _=C.expandShapeToKeepDim(g.shape,p);g.shape=_}return g}var HX={kernelName:Ts,backendName:"wasm",setupFunc:UX,kernelFunc:jX},GX=!1,qX=cn(Es,GX),XX=!0,KX=cn(Cs,XX),ZX=On(To);function Qm(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),r=n[0],a=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:r,selectedSize:a,pSelectedScores:s,pValidOutputs:i}}var Nb;function YX(e){Nb=e.wasm.cwrap(Co,"number",["number","number","number","number","number"])}function JX(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i}=r,{boxes:o,scores:l}=n,c=t.dataIdMap.get(o.dataId).id,u=t.dataIdMap.get(l.dataId).id,h=Nb(c,u,s,a,i),{pSelectedIndices:p,selectedSize:d,pSelectedScores:f,pValidOutputs:m}=Qm(t,h);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([d],"int32",p)}var QX={kernelName:Co,backendName:"wasm",setupFunc:YX,kernelFunc:JX},Sb;function eK(e){Sb=e.wasm.cwrap(Ro,"number",["number","number","number","number","number","bool"])}function tK(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=r,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(c.dataId).id,p=Sb(u,h,s,a,i,o),{pSelectedIndices:d,selectedSize:f,pSelectedScores:m,pValidOutputs:A}=Qm(t,p);t.wasm._free(m);let y=t.makeOutput([f],"int32",d),g=t.makeOutput([],"int32",A);return[y,g]}var nK={kernelName:Ro,backendName:"wasm",setupFunc:eK,kernelFunc:tK},Tb;function rK(e){Tb=e.wasm.cwrap(Fo,"number",["number","number","number","number","number","number"])}function aK(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=r,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(c.dataId).id,p=Tb(u,h,s,a,i,o),{pSelectedIndices:d,selectedSize:f,pSelectedScores:m,pValidOutputs:A}=Qm(t,p);t.wasm._free(A);let y=t.makeOutput([f],"int32",d),g=t.makeOutput([f],"float32",m);return[y,g]}var sK={kernelName:Fo,backendName:"wasm",setupFunc:rK,kernelFunc:aK},iK=!1,oK=cn(Eo,iK,"bool"),Eb;function lK(e){Eb=e.wasm.cwrap(Rs,null,["number","number","number","number","number"])}function uK(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=n.makeOutput([...a.shape,s],"int32"),c=n.dataIdMap.get(l.dataId).id,u=n.dataIdMap.get(a.dataId).id;return Eb(u,s,i,o,c),l}var cK={kernelName:Rs,backendName:"wasm",setupFunc:lK,kernelFunc:uK};function hK(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(1),r}var dK={kernelName:Mo,backendName:"wasm",kernelFunc:hK};function pK(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return Jm({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(l=>{k.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=t.map(l=>Jm({inputs:{input:l},backend:n,attrs:{dim:a}}));return lb({inputs:o,backend:n,attrs:{axis:a}})}var fK={kernelName:Oo,backendName:"wasm",kernelFunc:pK},Cb;function mK(e){Cb=e.wasm.cwrap(Fs,null,["number","array","number","number","array","array","number","number"])}function AK(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,constantValue:a}}=e,s=r.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),u=r.map(f=>f[0]),h=r.map(f=>f[1]),p=new Uint8Array(new Int32Array(u).buffer),d=new Uint8Array(new Int32Array(h).buffer);return Cb(i,c,t.shape.length,Mn[t.dtype],p,d,a,l),o}var yK={kernelName:Fs,backendName:"wasm",kernelFunc:AK,setupFunc:mK},gK=!1,xK=cn(Ms,gK),Rb;function wK(e){Rb=e.wasm.cwrap(Os,null,["number","number","number"])}function _K(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,o=n.makeOutput(r.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return Rb(s,i,l),o}var bK={kernelName:Os,backendName:"wasm",setupFunc:wK,kernelFunc:_K},Fb;function vK(e){Fb=e.wasm.cwrap($o,null,["number","number","number","number"])}function kK(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:h,originalAxes:p,inputWasTransposed:d}=$l(i,a,t),f=h;if(d){let _=t.dataIdMap.get(u.dataId).id;_!==o&&(c=u,l=_,f=C.getInnerMostAxes(f.length,c.shape.length))}C.assertAxesAreInnerMostDims("prod",f,c.shape.length);let[m,A]=C.computeOutAndReduceShapes(c.shape,f),y=k.sizeFromShape(A),g=t.makeOutput(m,c.dtype);if(k.sizeFromShape(c.shape)!==0){let _=t.dataIdMap.get(g.dataId).id;Fb(l,y,Mn[g.dtype],_)}if(d&&t.disposeData(u.dataId),s){let _=C.expandShapeToKeepDim(g.shape,p);g.shape=_}return g}var IK={kernelName:$o,backendName:"wasm",setupFunc:vK,kernelFunc:kK},NK=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=_m(r,a,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},SK={kernelName:Ou,backendName:"wasm",kernelFunc:NK},TK=!0,EK=cn(As,TK),CK=On($s),RK=On(zs),Mb;function FK(e){Mb=e.wasm.cwrap(Ds,null,["number","number","number","number","number","number","number","number","number","number"])}function MK(e){let{backend:t,inputs:n,attrs:r}=e,{images:a}=n,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,[u,h,p,d]=a.shape,f=[u,l,c,d],m=t.dataIdMap.get(a.dataId),A;m.dtype!=="float32"&&(A=Tp({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(A.dataId));let y=m.id,g=t.makeOutput(f,"float32");if(k.sizeFromShape(a.shape)===0)return g;let _=t.dataIdMap.get(g.dataId).id;return Mb(y,u,h,p,d,l,c,s?1:0,i?1:0,_),A!=null&&t.disposeData(A.dataId),g}var OK={kernelName:Ds,backendName:"wasm",setupFunc:FK,kernelFunc:MK},Ob;function $K(e){Ob=e.wasm.cwrap(Ps,null,["number","array","number","array","number","number"])}function DK(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=k.parseAxisParam(s,a.shape);if(a.shape.length===0)return Np({inputs:{x:a},backend:n});let o=n.makeOutput(a.shape,a.dtype),l=n.dataIdMap.get(a.dataId).id,c=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(i).buffer),h=new Uint8Array(new Int32Array(a.shape).buffer);return Ob(l,u,i.length,h,a.shape.length,c),Ar({inputs:{x:o},attrs:{shape:a.shape},backend:n})}var zK={kernelName:Ps,backendName:"wasm",kernelFunc:DK,setupFunc:$K},$b;function PK(e){$b=e.wasm.cwrap(Yo,null,["number","number","number","number","number","number","number","number","array","number","number"])}function LK(e){let{inputs:t,backend:n,attrs:r}=e,{image:a}=t,{radians:s,fillValue:i,center:o}=r,l=n.makeOutput(a.shape,a.dtype),c=n.dataIdMap.get(a.dataId).id,u=n.dataIdMap.get(l.dataId).id,[h,p,d,f]=a.shape,[m,A]=C.getImageCenter(o,p,d),y=i===0,g=255,_=typeof i=="number"?[i,i,i,y?0:g]:[...i,g],x=new Uint8Array(new Int32Array(_).buffer);return $b(c,h,p,d,f,s,m,A,x,_.length,u),l}var WK={kernelName:Yo,backendName:"wasm",kernelFunc:LK,setupFunc:PK},BK=On(Ls),VK=On(Ws),Db;function UK(e){Db=e.wasm.cwrap(Po,null,["number","number","number","number","number","number","array","number","number"])}function jK(e){let{backend:t,inputs:n,attrs:r}=e,{indices:a,updates:s}=n,{shape:i}=r,o=t.makeOutput(i,s.dtype);if(k.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:c,sliceSize:u,strides:h,outputSize:p}=yf.calculateShapes(s,a,i),d=t.dataIdMap.get(a.dataId).id,f=t.dataIdMap.get(s.dataId).id,m=new Uint8Array(new Int32Array(h).buffer),A=t.dataIdMap.get(o.dataId).id;return Db(d,f,Mn[s.dtype],l,c,u,m,p,A),o}var HK={kernelName:Po,backendName:"wasm",setupFunc:UK,kernelFunc:jK},zb;function GK(e){zb=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function qK(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(a.dataId).id,l=n.dataIdMap.get(s.dataId).id,c=n.makeOutput(a.shape,a.dtype),u=n.dataIdMap.get(c.dataId).id,h=r.shape.length,p=a.shape.length,d=h===0||h>1||p===1?1:k.sizeFromShape(a.shape.slice(1));return zb(i,o,l,d,u),c}var XK={kernelName:Lo,backendName:"wasm",kernelFunc:qK,setupFunc:GK},Pb;function KK(e){Pb=e.wasm.cwrap(Vs,null,["number","number"])}function ZK(e){let{backend:t,inputs:{x:n}}=e,r=t.dataIdMap.get(n.dataId).id,a=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(a.dataId).id;return k.sizeFromShape(a.shape)===0||Pb(r,s),a}var YK={kernelName:"Sigmoid",backendName:"wasm",setupFunc:KK,kernelFunc:ZK},JK=On(Bs);function Ep(e){let{inputs:{x:t},attrs:{begin:n,size:r},backend:a}=e,[s,i]=sn.parseSliceParams(t,n,r),o=sn.isSliceContinous(t.shape,s,i),l=a.readSync(t.dataId),c=a.makeOutput(i,t.dtype),u=k.computeStrides(t.shape),h=a.dataIdMap.get(c.dataId);if(o){let f=sn.computeFlatOffset(s,u);return t.dtype==="string"?h.stringBytes=l.slice(f,f+k.sizeFromShape(i)):a.typedArrayFromHeap(c).set(l.subarray(f,f+k.sizeFromShape(i))),c}if(t.dtype==="string"){let f=rp(l,s,i,t.shape,t.dtype);return h.stringBytes=f,c}let p=a.typedArrayFromHeap(c),d=t.shape.length;if(d===2)QK(l,u[0],p,s,i);else if(d===3)eZ(l,u[0],u[1],p,s,i);else if(d===4)tZ(l,u[0],u[1],u[2],p,s,i);else{let f=rp(l,s,i,t.shape,t.dtype);p.set(f)}return c}function QK(e,t,n,r,a){let s=0,i=r[0],o=r[1],l=i+a[0];for(let c=i;c<l;c++){let u=c*t+o;n.set(e.subarray(u,u+a[1]),s),s+=a[1]}}function eZ(e,t,n,r,a,s){let i=0,o=a[0],l=a[1],c=a[2],u=o+s[0],h=l+s[1];for(let p=o;p<u;p++)for(let d=l;d<h;d++){let f=p*t+d*n+c;r.set(e.subarray(f,f+s[2]),i),i+=s[2]}}function tZ(e,t,n,r,a,s,i){let o=0,l=s[0],c=s[1],u=s[2],h=l+i[0],p=c+i[1],d=u+i[2],f=s[3];for(let m=l;m<h;m++)for(let A=c;A<p;A++)for(let y=u;y<d;y++){let g=m*t+A*n+y*r+f;a.set(e.subarray(g,g+i[3]),o),o+=i[3]}}var nZ={kernelName:Bo,backendName:"wasm",kernelFunc:Ep},Lb;function rZ(e){Lb=e.wasm.cwrap(Hs,null,["number","number","number","number"])}function aZ(e){let{backend:t,inputs:{logits:n},attrs:{dim:r}}=e,a=t.dataIdMap.get(n.dataId).id,s=t.makeOutput(n.shape,n.dtype),i=t.dataIdMap.get(s.dataId).id,o=n.shape[r],l=k.sizeFromShape(n.shape)/o;return k.sizeFromShape(s.shape)===0||Lb(a,i,o,l),s}var sZ={kernelName:Hs,backendName:"wasm",setupFunc:rZ,kernelFunc:aZ};function iZ(e){let{inputs:t,attrs:n,backend:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=k.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),c=new Array(a.shape.length).fill(0),u=a.shape.slice();return l.map(h=>{let p=[...u];p[o]=h;let d=Ep({inputs:{x:a},attrs:{begin:c,size:p},backend:r});return c[o]+=h,d})}var oZ={kernelName:Ho,backendName:"wasm",kernelFunc:iZ},lZ=On(Us),uZ=On(zu),cZ=!0,hZ=cn(Gs,cZ),Wb;function dZ(e){Wb=e.wasm.cwrap(va,null,["number","number","number"])}function pZ(e){let{backend:t,inputs:n,attrs:r}=e,{alpha:a}=r,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return Wb(i,a,l),o}var fZ={kernelName:va,backendName:"wasm",setupFunc:dZ,kernelFunc:pZ},Bb;function mZ(e){Bb=e.wasm.cwrap(Go,null,["number","array","number","array","array","array","array","array","number","number"])}function AZ(e){let{backend:t,inputs:n,attrs:r}=e,{x:a}=n,{begin:s,end:i,strides:o}=r;o==null&&(o=new Array(s.length));let{beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:h,shrinkAxisMask:p}=r,d=C.slice_util.maskToAxes(u);if(d.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(u!==0&&h!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(u!==0&&p!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let f=a.shape.length-s.length,m=C.slice_util.maskToAxes(h),A=a.shape.slice();m.forEach(E=>{s[E]=0,i[E]=1,A.splice(E,0,1)});let y=Ar({inputs:{x:a},attrs:{shape:A},backend:t}),{begin:g,end:_,strides:x}=C.slice_util.getNormalizedAxes(y.shape,d,f,s,i,o,l,c,u);s=g,i=_,o=x;let w=C.slice_util.maskToAxes(p);w.forEach(E=>{i[E]=s[E]+1,o[E]=1});let b=C.slice_util.computeOutShape(s,i,o),N=b.filter((E,M)=>w.indexOf(M)===-1);if(o.every(E=>E===1)){let E=Ep({inputs:{x:a},attrs:{begin:s,size:b},backend:t});return Ar({inputs:{x:E},attrs:{shape:N},backend:t})}let T=t.makeOutput(N,"float32");if(!N.some(E=>E===0)){let E=t.dataIdMap.get(y.dataId).id,M=new Uint8Array(new Int32Array(k.computeStrides(y.shape)).buffer),$=new Uint8Array(new Int32Array(s).buffer),P=new Uint8Array(new Int32Array(i).buffer),V=new Uint8Array(new Int32Array(o).buffer),G=new Uint8Array(new Int32Array(N).buffer),U=new Uint8Array(new Int32Array(k.computeStrides(N)).buffer),K=t.dataIdMap.get(T.dataId).id;Bb(E,M,y.shape.length,$,P,V,G,U,N.length,K)}return Ar({inputs:{x:T},attrs:{shape:N},backend:t})}var yZ={kernelName:Go,backendName:"wasm",setupFunc:mZ,kernelFunc:AZ},gZ=!0,xZ=cn(qs,gZ),Vb;function wZ(e){Vb=e.wasm.cwrap(js,null,["number, number, number"])}function _Z(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:h,originalAxes:p,inputWasTransposed:d}=$l(i,a,t),f=h;if(d){let _=t.dataIdMap.get(u.dataId).id;_!==o&&(c=u,l=_,f=C.getInnerMostAxes(f.length,c.shape.length))}C.assertAxesAreInnerMostDims("sum",f,c.shape.length);let[m,A]=C.computeOutAndReduceShapes(c.shape,f),y=k.sizeFromShape(A),g=t.makeOutput(m,c.dtype);if(k.sizeFromShape(c.shape)!==0){let _=t.dataIdMap.get(g.dataId).id;Vb(l,y,_)}if(d&&t.disposeData(u.dataId),s){let _=C.expandShapeToKeepDim(g.shape,p);g.shape=_}return g}var bZ={kernelName:js,backendName:"wasm",setupFunc:wZ,kernelFunc:_Z},vZ=On(Xs),Ub;function kZ(e){Ub=e.wasm.cwrap(ba,null,["number","array","number","array","number","number"])}function IZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,s=n.dataIdMap.get(a.dataId).id,{reps:i}=r,o=new Array(a.shape.length);for(let p=0;p<o.length;p++)o[p]=a.shape[p]*i[p];let l=new Uint8Array(new Int32Array(a.shape).buffer),c=new Uint8Array(new Int32Array(o).buffer),u=n.makeOutput(o,a.dtype),h=n.dataIdMap.get(u.dataId).id;return Ub(s,l,a.shape.length,c,o.length,Mn[u.dtype],h),u}var NZ={kernelName:ba,backendName:"wasm",setupFunc:kZ,kernelFunc:IZ},jb;function SZ(e){jb=e.wasm.cwrap(Xo,null,["number","array","number","number","number","bool","number","number"])}var TZ=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{k:a,sorted:s}=n,i=t.dataIdMap.get(r.dataId).id,o=new Uint8Array(new Int32Array(r.shape).buffer),l=r.shape.slice();l[l.length-1]=a;let c=t.makeOutput(l,r.dtype),u=t.dataIdMap.get(c.dataId).id,h=t.makeOutput(l,"int32"),p=t.dataIdMap.get(h.dataId).id;return jb(i,o,r.shape.length,Mn[r.dtype],a,s,u,p),[c,h]},EZ={kernelName:Xo,backendName:"wasm",setupFunc:SZ,kernelFunc:TZ};function CZ(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a.shape[s],o=a.shape.length,l=new Array(o-1),c=0;for(let d=0;d<o;d++)d!==s&&(l[c++]=a.shape[d]);let u=new Array(i),h=new Array(o).fill(0),p=a.shape.slice();p[s]=1;for(let d=0;d<u.length;d++)h[s]=d,u[d]=Ep({inputs:{x:a},attrs:{begin:h,size:p},backend:n});return u.map(({dataId:d,dtype:f})=>({dataId:d,dtype:f,shape:l}))}var RZ={kernelName:Ko,backendName:"wasm",kernelFunc:CZ};function FZ(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(0),r}var MZ={kernelName:Zo,backendName:"wasm",kernelFunc:FZ},OZ=[eq,nq,sq,pq,Aq,wq,_q,kq,Iq,Tq,Rq,Fq,$q,Pq,Bq,jq,Gq,qq,Xq,Zq,Qq,eX,nX,QG,sX,lX,hX,fX,yX,xX,_X,iq,kX,NX,TX,EX,RX,OX,DX,LX,VX,HX,qX,KX,ZX,QX,nK,sK,oK,cK,dK,fK,yK,xK,bK,IK,SK,EK,CK,RK,yq,OK,zK,WK,VK,BK,HK,XK,YK,JK,nZ,sZ,oZ,lZ,uZ,hZ,fZ,yZ,xZ,bZ,vZ,NZ,EZ,cq,RZ,MZ];for(let e of OZ)Qs(e);var eA=Q();eA.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));eA.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(eA.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var Hb=qi(uk()),$Z='var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["dynCall_ii"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',DZ=qi(ck()),Gb=class extends xu{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new Ih(this,Vn())}write(e,t,n){let r={};return this.move(r,e,t,n),r}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=k.now();return e(),{kernelMs:k.now()-t}}move(e,t,n,r){let a=this.dataIdNextNumber++;if(r==="string"){let l=t;this.dataIdMap.set(e,{id:a,stringBytes:l,shape:n,dtype:r,memoryOffset:null});return}let s=k.sizeFromShape(n),i=s*k.bytesPerElement(r),o=this.wasm._malloc(i);this.dataIdMap.set(e,{id:a,memoryOffset:o,shape:n,dtype:r}),this.wasm.tfjs.registerTensor(a,s,o),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,i),o)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:n,shape:r,stringBytes:a}=this.dataIdMap.get(e);if(n==="string")return a;let s=this.wasm.HEAPU8.slice(t,t+k.sizeFromShape(r)*k.bytesPerElement(n));return zZ(s.buffer,n)}disposeData(e){let t=this.dataIdMap.get(e);this.wasm._free(t.memoryOffset),this.wasm.tfjs.disposeData(t.id),this.dataIdMap.delete(e)}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let r;if(n==null)r=this.write(null,e,t);else{r={};let a=this.dataIdNextNumber++;this.dataIdMap.set(r,{id:a,memoryOffset:n,shape:e,dtype:t});let s=k.sizeFromShape(e);this.wasm.tfjs.registerTensor(a,s,n)}return{dataId:r,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let r=this.wasm.HEAPU8.buffer,{memoryOffset:a}=this.dataIdMap.get(n),s=k.sizeFromShape(e);switch(t){case"float32":return new Float32Array(r,a,s);case"int32":return new Int32Array(r,a,s);case"bool":return new Uint8Array(r,a,s);default:throw new Error(`Unknown dtype ${t}`)}}};function PZ(e){return(t,n)=>(k.fetch(e,{credentials:"same-origin"}).then(r=>{r.ok||t.env.a(`failed to load wasm binary file at '${e}'`),r.arrayBuffer().then(a=>{WebAssembly.instantiate(a,t).then(s=>{n(s.instance)})})}),{})}function qb(e,t,n){if(Cp!=null)return Cp;let r="tfjs-backend-wasm.wasm";return e&&t?r="tfjs-backend-wasm-threaded-simd.wasm":e&&(r="tfjs-backend-wasm-simd.wasm"),Nc!=null&&Nc[r]!=null?Nc[r]:n+r}async function LZ(){let[e,t]=await Promise.all([Q().getAsync("WASM_HAS_SIMD_SUPPORT"),Q().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,r)=>{let a={};a.locateFile=(l,c)=>{if(l.endsWith(".worker.js")){let u=$Z,h=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(h)}return l.endsWith(".wasm")?qb(e,t,Sc!=null?Sc:c):c+l},tA&&(a.instantiateWasm=PZ(qb(e,t,Sc!=null?Sc:"")));let s;t&&e&&Cp==null?(s=Hb.default(a),s.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+Hb.default.toString()],{type:"text/javascript"})):s=DZ.default(a);let i=null;s.tfjs={init:s.cwrap("init",null,[]),registerTensor:s.cwrap("register_tensor",null,["number","number","number"]),disposeData:s.cwrap("dispose_data",i,["number"]),dispose:s.cwrap("dispose",i,[])};let o=!1;s.onRuntimeInitialized=()=>{o=!0,Tc=!1,n({wasm:s})},s.onAbort=()=>{o||Tc||(Tc=!0,r({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))}})}function zZ(e,t){switch(t){case"float32":return new Float32Array(e);case"int32":return new Int32Array(e);case"bool":return new Uint8Array(e);default:throw new Error(`Unknown dtype ${t}`)}}var WZ=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],Cp=null,Sc=null,Nc={},Tc=!1,tA=!1;function BZ(e,t=!1){if(vf("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),Tc)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Cp=e,tA=t}function Xb(e,t=!1){if(Tc)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof e=="string")Sc=e;else{Nc=e;let n=WZ.filter(r=>Nc[r]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}tA=t}var Kb="3.0.0",VZ=2;ol("wasm",async()=>{let{wasm:e}=await LZ();return new Gb(e)},VZ);Y().prototype.abs=function(){return this.throwIfDisposed(),Mt(this)};Y().prototype.acos=function(){return this.throwIfDisposed(),Nf(this)};Y().prototype.acosh=function(){return this.throwIfDisposed(),Sf(this)};Y().prototype.add=function(e){return this.throwIfDisposed(),se(this,e)};Y().prototype.all=function(e,t){return this.throwIfDisposed(),xd(this,e,t)};Y().prototype.any=function(e,t){return this.throwIfDisposed(),Xu(this,e,t)};Y().prototype.argMax=function(e){return this.throwIfDisposed(),Ku(this,e)};Y().prototype.argMin=function(e){return this.throwIfDisposed(),Tf(this,e)};Y().prototype.asScalar=function(){return this.throwIfDisposed(),F(this.size===1,()=>"The array must have only 1 element."),q(this,[])};Y().prototype.asType=function(e){return this.throwIfDisposed(),me(this,e)};Y().prototype.as1D=function(){return this.throwIfDisposed(),q(this,[this.size])};Y().prototype.as2D=function(e,t){return this.throwIfDisposed(),q(this,[e,t])};Y().prototype.as3D=function(e,t,n){return this.throwIfDisposed(),q(this,[e,t,n])};Y().prototype.as4D=function(e,t,n,r){return this.throwIfDisposed(),q(this,[e,t,n,r])};Y().prototype.as5D=function(e,t,n,r,a){return this.throwIfDisposed(),q(this,[e,t,n,r,a])};Y().prototype.asin=function(){return this.throwIfDisposed(),Ef(this)};Y().prototype.asinh=function(){return this.throwIfDisposed(),Cf(this)};Y().prototype.atan=function(){return this.throwIfDisposed(),Rf(this)};Y().prototype.atan2=function(e){return this.throwIfDisposed(),Ff(this,e)};Y().prototype.atanh=function(){return this.throwIfDisposed(),Mf(this)};Y().prototype.avgPool=function(e,t,n,r){return this.throwIfDisposed(),Yu(this,e,t,n,r)};Y().prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),Ju(this,e,t)};Y().prototype.batchNorm=function(e,t,n,r,a){return this.throwIfDisposed(),si(this,e,t,n,r,a)};Y().prototype.broadcastTo=function(e){return this.throwIfDisposed(),Qu(this,e)};Y().prototype.cast=function(e){return this.throwIfDisposed(),me(this,e)};Y().prototype.ceil=function(){return this.throwIfDisposed(),zf(this)};Y().prototype.clipByValue=function(e,t){return this.throwIfDisposed(),mn(this,e,t)};Y().prototype.concat=function(e,t){return this.throwIfDisposed(),e instanceof Ke&&(e=[e]),rt([this,...e],t)};Y().prototype.conv1d=function(e,t,n,r,a,s){return this.throwIfDisposed(),_d(this,e,t,n,r,a,s)};Y().prototype.conv2dTranspose=function(e,t,n,r,a){return this.throwIfDisposed(),bd(this,e,t,n,r,a)};Y().prototype.conv2d=function(e,t,n,r,a,s){return this.throwIfDisposed(),na(this,e,t,n,r,a,s)};Y().prototype.cos=function(){return this.throwIfDisposed(),ec(this)};Y().prototype.cosh=function(){return this.throwIfDisposed(),vd(this)};Y().prototype.cumsum=function(e,t,n){return this.throwIfDisposed(),kd(this,e,t,n)};Y().prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),Wf(this,e,t)};Y().prototype.depthwiseConv2d=function(e,t,n,r,a,s){return this.throwIfDisposed(),hl(this,e,t,n,r,a,s)};Y().prototype.dilation2d=function(e,t,n,r,a){return this.throwIfDisposed(),Bf(this,e,t,n,r,a)};Y().prototype.divNoNan=function(e){return this.throwIfDisposed(),Vf(this,e)};Y().prototype.div=function(e){return this.throwIfDisposed(),be(this,e)};Y().prototype.dot=function(e){return this.throwIfDisposed(),N5(this,e)};Y().prototype.elu=function(){return this.throwIfDisposed(),dl(this)};Y().prototype.equal=function(e){return this.throwIfDisposed(),Ea(this,e)};Y().prototype.erf=function(){return this.throwIfDisposed(),Uf(this)};Y().prototype.exp=function(){return this.throwIfDisposed(),Un(this)};Y().prototype.expandDims=function(e){return this.throwIfDisposed(),Nn(this,e)};Y().prototype.expm1=function(){return this.throwIfDisposed(),jf(this)};Y().prototype.fft=function(){return this.throwIfDisposed(),cc(this)};Y().prototype.flatten=function(){return this.throwIfDisposed(),q(this,[this.size])};Y().prototype.floor=function(){return this.throwIfDisposed(),pl(this)};Y().prototype.floorDiv=function(e){return this.throwIfDisposed(),gd(this,e)};Y().prototype.gather=function(e,t){return this.throwIfDisposed(),oi(this,e,t)};Y().prototype.greaterEqual=function(e){return this.throwIfDisposed(),Ra(this,e)};Y().prototype.greater=function(e){return this.throwIfDisposed(),nr(this,e)};Y().prototype.ifft=function(){return this.throwIfDisposed(),gl(this)};Y().prototype.irfft=function(){return this.throwIfDisposed(),Bd(this)};Y().prototype.isFinite=function(){return this.throwIfDisposed(),S5(this)};Y().prototype.isInf=function(){return this.throwIfDisposed(),T5(this)};Y().prototype.isNaN=function(){return this.throwIfDisposed(),E5(this)};Y().prototype.leakyRelu=function(e){return this.throwIfDisposed(),nc(this,e)};Y().prototype.lessEqual=function(e){return this.throwIfDisposed(),li(this,e)};Y().prototype.less=function(e){return this.throwIfDisposed(),Nd(this,e)};Y().prototype.localResponseNormalization=function(e,t,n,r){return this.throwIfDisposed(),Gf(this,e,t,n,r)};Y().prototype.logSigmoid=function(){return this.throwIfDisposed(),F5(this)};Y().prototype.logSoftmax=function(e){return this.throwIfDisposed(),Ed(this,e)};Y().prototype.logSumExp=function(e,t){return this.throwIfDisposed(),Kf(this,e,t)};Y().prototype.log=function(){return this.throwIfDisposed(),Sn(this)};Y().prototype.log1p=function(){return this.throwIfDisposed(),Sd(this)};Y().prototype.logicalAnd=function(e){return this.throwIfDisposed(),rr(this,e)};Y().prototype.logicalNot=function(){return this.throwIfDisposed(),rc(this)};Y().prototype.logicalOr=function(e){return this.throwIfDisposed(),Cd(this,e)};Y().prototype.logicalXor=function(e){return this.throwIfDisposed(),D5(this,e)};Y().prototype.matMul=function(e,t,n){return this.throwIfDisposed(),He(this,e,t,n)};Y().prototype.maxPool=function(e,t,n,r){return this.throwIfDisposed(),ac(this,e,t,n,r)};Y().prototype.max=function(e,t){return this.throwIfDisposed(),jn(this,e,t)};Y().prototype.maximum=function(e){return this.throwIfDisposed(),$r(this,e)};Y().prototype.mean=function(e,t){return this.throwIfDisposed(),bt(this,e,t)};Y().prototype.min=function(e,t){return this.throwIfDisposed(),ml(this,e,t)};Y().prototype.minimum=function(e){return this.throwIfDisposed(),Al(this,e)};Y().prototype.mirrorPad=function(e,t){return this.throwIfDisposed(),Yf(this,e,t)};Y().prototype.mod=function(e){return this.throwIfDisposed(),Jf(this,e)};Y().prototype.mul=function(e){return this.throwIfDisposed(),L(this,e)};Y().prototype.neg=function(){return this.throwIfDisposed(),_t(this)};Y().prototype.norm=function(e,t,n){return this.throwIfDisposed(),Hd(this,e,t,n)};Y().prototype.notEqual=function(e){return this.throwIfDisposed(),ci(this,e)};Y().prototype.oneHot=function(e,t=1,n=0){return this.throwIfDisposed(),al(this,e,t,n)};Y().prototype.onesLike=function(){return this.throwIfDisposed(),Tn(this)};Y().prototype.pad=function(e,t){return this.throwIfDisposed(),ra(this,e,t)};Y().prototype.pool=function(e,t,n,r,a){return this.throwIfDisposed(),L5(this,e,t,n,r,a)};Y().prototype.pow=function(e){return this.throwIfDisposed(),aa(this,e)};Y().prototype.prelu=function(e){return this.throwIfDisposed(),ic(this,e)};Y().prototype.prod=function(e,t){return this.throwIfDisposed(),Fd(this,e,t)};Y().prototype.reciprocal=function(){return this.throwIfDisposed(),tm(this)};Y().prototype.relu=function(){return this.throwIfDisposed(),zr(this)};Y().prototype.relu6=function(){return this.throwIfDisposed(),Od(this)};Y().prototype.reshapeAs=function(e){return this.throwIfDisposed(),q(this,e.shape)};Y().prototype.reshape=function(e){return this.throwIfDisposed(),q(this,e)};Y().prototype.resizeBilinear=function(e,t,n){return this.throwIfDisposed(),rx(this,e,t,n)};Y().prototype.resizeNearestNeighbor=function(e,t,n){return this.throwIfDisposed(),ax(this,e,t,n)};Y().prototype.reverse=function(e){return this.throwIfDisposed(),En(this,e)};Y().prototype.rfft=function(){return this.throwIfDisposed(),hc(this)};Y().prototype.round=function(){return this.throwIfDisposed(),nm(this)};Y().prototype.rsqrt=function(){return this.throwIfDisposed(),$d(this)};Y().prototype.selu=function(){return this.throwIfDisposed(),Dd(this)};Y().prototype.separableConv2d=function(e,t,n,r,a,s){return this.throwIfDisposed(),rm(this,e,t,n,r,a,s)};Y().prototype.sigmoid=function(){return this.throwIfDisposed(),In(this)};Y().prototype.sign=function(){return this.throwIfDisposed(),am(this)};Y().prototype.sin=function(){return this.throwIfDisposed(),zd(this)};Y().prototype.sinh=function(){return this.throwIfDisposed(),Pd(this)};Y().prototype.slice=function(e,t){return this.throwIfDisposed(),Te(this,e,t)};Y().prototype.softmax=function(e){return this.throwIfDisposed(),uc(this,e)};Y().prototype.softplus=function(){return this.throwIfDisposed(),fl(this)};Y().prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),sc(this,e,t)};Y().prototype.split=function(e,t){return this.throwIfDisposed(),Kt(this,e,t)};Y().prototype.sqrt=function(){return this.throwIfDisposed(),Zt(this)};Y().prototype.square=function(){return this.throwIfDisposed(),lt(this)};Y().prototype.squaredDifference=function(e){return this.throwIfDisposed(),Vd(this,e)};Y().prototype.squeeze=function(e){return this.throwIfDisposed(),Fa(this,e)};Y().prototype.stack=function(e,t){this.throwIfDisposed();let n=e instanceof Ke?[this,e]:[this,...e];return Cn(n,t)};Y().prototype.step=function(e){return this.throwIfDisposed(),xl(this,e)};Y().prototype.stridedSlice=function(e,t,n,r,a,s,i,o){return this.throwIfDisposed(),im(this,e,t,n,r,a,s,i,o)};Y().prototype.sub=function(e){return this.throwIfDisposed(),Ae(this,e)};Y().prototype.sum=function(e,t){return this.throwIfDisposed(),Ie(this,e,t)};Y().prototype.tan=function(){return this.throwIfDisposed(),om(this)};Y().prototype.tanh=function(){return this.throwIfDisposed(),cl(this)};Y().prototype.tile=function(e){return this.throwIfDisposed(),Ca(this,e)};Y().prototype.toBool=function(){return this.throwIfDisposed(),me(this,"bool")};Y().prototype.toFloat=function(){return this.throwIfDisposed(),me(this,"float32")};Y().prototype.toInt=function(){return this.throwIfDisposed(),me(this,"int32")};Y().prototype.topk=function(e,t){return this.throwIfDisposed(),lm(this,e,t)};Y().prototype.transpose=function(e){return this.throwIfDisposed(),nt(this,e)};Y().prototype.unique=function(e){return this.throwIfDisposed(),jd(this,e)};Y().prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),um(this,e,t)};Y().prototype.unstack=function(e){return this.throwIfDisposed(),ar(this,e)};Y().prototype.where=function(e,t){return this.throwIfDisposed(),An(e,this,t)};Y().prototype.zerosLike=function(){return this.throwIfDisposed(),Ve(this)};var Zb={kernelName:Zi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,xl(me(n,"float32"),-1))}}},UZ={kernelName:Yi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=lt(me(n,"float32")),a=Zt(Ae(ke(1),r));return _t(be(e,a))}}}},jZ={kernelName:Ji,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=Zt(Ae(lt(me(n,"float32")),1));return be(e,r)}}}},HZ={kernelName:wa,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=At(n.shape,r.shape);return{a:()=>{let s=e,i=Ot(n.shape,a);return i.length>0&&(s=Ie(s,i)),q(s,n.shape)},b:()=>{let s=e,i=Ot(r.shape,a);return i.length>0&&(s=Ie(s,i)),q(s,r.shape)}}}},GZ={kernelName:is,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((r,a)=>{n[a]=()=>e.clone()}),n}},qZ={kernelName:os,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ve(n)}}},XZ={kernelName:bu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ve(n)}}},KZ={kernelName:Qi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>be(e,Zt(Ae(ke(1),lt(me(n,"float32")))))}}},ZZ={kernelName:eo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=Zt(se(ke(1),lt(me(n,"float32"))));return be(e,r)}}}},YZ={kernelName:ro,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=At(n.shape,r.shape);return{a:()=>{let s=se(lt(n),lt(r)),i=L(e,be(r,s)),o=Ot(n.shape,a);return o.length>0&&(i=Ie(i,o)),q(i,n.shape)},b:()=>{let s=se(lt(n),lt(r)),i=_t(L(e,be(n,s))),o=Ot(r.shape,a);return o.length>0&&(i=Ie(i,o)),q(i,r.shape)}}}},JZ={kernelName:to,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>be(e,se(lt(me(n,"float32")),1))}}},QZ={kernelName:no,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>be(e,Ae(ke(1),lt(me(n,"float32"))))}}};function eY(e,t,n,r,a,s){let i=R(e,"dy","avgPool3dGrad"),o=R(t,"input","avgPool3dGrad"),l=i,c=o,u=!1;o.rank===4&&(u=!0,l=q(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]]),c=q(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),F(l.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${l.rank}.`),F(c.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${c.rank}.`),s!=null&&F(Lt(a),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${s} but got pad ${a}.`);let h={dy:l,input:c},p={filterSize:n,strides:r,pad:a,dimRoundingMode:s},d=D.runKernel(Fh,h,p);return u?q(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var tY=z({avgPool3dGrad_:eY}),nY={kernelName:vu,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{filterSize:a,strides:s,pad:i,dimRoundingMode:o}=n;return{x:()=>tY(e,r,a,s,i,o)}}};function rY(e,t,n,r,a){let s=R(e,"dy","avgPoolGrad"),i=R(t,"input","avgPoolGrad");F(i.rank===s.rank,()=>`Rank of input (${i.rank}) does not match rank of dy (${s.rank})`);let o=i,l=s,c=!1;i.rank===3&&(c=!0,o=q(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=q(s,[1,s.shape[0],s.shape[1],s.shape[2]])),F(l.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${l.rank}.`),F(o.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);let u={dy:l,input:o},h={filterSize:n,strides:r,pad:a},p=D.runKernel(Rh,u,h);return c?q(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var aY=z({avgPoolGrad_:rY}),sY={kernelName:ls,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{filterSize:a,strides:s,pad:i}=n;return{x:()=>aY(e,r,a,s,i)}}},iY={kernelName:us,inputsToSave:["a","b"],gradFunc:(e,t,n)=>{let[r,a]=t,{transposeA:s,transposeB:i}=n;return!s&&!i?{a:()=>He(e,a,!1,!0),b:()=>He(r,e,!0,!1)}:!s&&i?{a:()=>He(e,a,!1,!1),b:()=>He(e,r,!0,!1)}:s&&!i?{a:()=>He(a,e,!1,!0),b:()=>He(r,e,!1,!1)}:{a:()=>He(a,e,!0,!0),b:()=>He(e,r,!0,!0)}}},oY={kernelName:ku,gradFunc:(e,t,n)=>{let{blockShape:r,crops:a}=n;return{x:()=>sc(e,r,a)}}},lY={kernelName:gg,gradFunc:(e,t,n)=>{let r=n,a=r.inputShape,s=r.shape,i=Array.from(s);for(let l=a.length-1;l>=0;l--)if(a[l]===s[l])i[l]=1;else if(a[l]!==1)throw new Error(`broadcastTo(): [${a}] cannot be broadcast to [${s}].`);let o=[];for(let l=0;l<i.length;l++)i[l]>1&&o.push(l);return{x:()=>Ie(e,o,!0)}}},uY={kernelName:cs,gradFunc:e=>({x:()=>e.clone()})},cY={kernelName:ao,gradFunc:e=>({x:()=>Ve(e)})},hY={kernelName:_a,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{clipValueMin:a,clipValueMax:s}=n;return{x:()=>An(rr(Ra(r,a),li(r,s)),e,Ve(e))}}},dY={kernelName:Iu,inputsToSave:["x"],gradFunc:Zb.gradFunc},pY={kernelName:so,saveAllInputs:!0,gradFunc:(e,t,n)=>{let r=t.map(o=>o.shape),{axis:a}=n,s=Qn(a,t[0].shape)[0],i=r.map(o=>o[s]);return Kt(e,i,s).map(o=>()=>o)}},fY={kernelName:hs,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,{dilations:s,strides:i,pad:o,dataFormat:l}=n;return F(Ta(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`),{x:()=>Pf(r.shape,e,a,i,o,l),filter:()=>pm(r,e,a.shape,i,o,l)}}},mY={kernelName:ds,inputsToSave:["dy","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,{strides:s,pad:i,dataFormat:o,dimRoundingMode:l}=n;return{dy:()=>na(e,a,s,i,o,1,l),filter:()=>pm(e,r,a.shape,s,i,o,l)}}};function AY(e,t,n,r,a){let s=e;e.rank===4&&(s=q(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]));let i=t;i.rank===4&&(i=q(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]])),F(s.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${s.shape}.`),F(i.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`),F(n.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`),F(s.shape[4]===n[3],()=>`Error in conv3dDerFilter: depth of input ${s.shape[4]}) must match input depth in filter (${n[3]}.`),F(i.shape[4]===n[4],()=>`Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${n[4]}).`);let o={x:s,dy:i},l={strides:r,pad:a,filterShape:n};return D.runKernel(Dh,o,l)}var yY=z({conv3DBackpropFilter_:AY}),gY={kernelName:Nu,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:r,strides:a,pad:s}=n;F(Ta(r),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${r}'`);let[i,o]=t;return{x:()=>k5(i.shape,e,o,a,s),filter:()=>yY(i,e,o.shape,a,s)}}},xY={kernelName:ps,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(_t(zd(me(n,"float32"))),e)}}},wY={kernelName:io,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(Pd(me(n,"float32")),e)}}},_Y={kernelName:fs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{axis:a,exclusive:s,reverse:i}=n;return{x:()=>{let o=$5([a],r.rank),l=kd(e,a,s,!i);return o!=null&&(l=nt(l,o)),l}}}},bY={kernelName:ms,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:r,strides:a,pad:s,dimRoundingMode:i}=n,o=r==null?[1,1]:r;F(Ta(o),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);let[l,c]=t;return F(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${l.rank}.`),F(c.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${c.rank}.`),F(l.shape[3]===c.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${c.shape[2]}.`),F(Mr(a,o),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${o}'.`),i!=null&&F(Lt(s),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`),{x:()=>Z5(l.shape,e,c,a,s,r,i),filter:()=>K5(l,e,c.shape,a,s,r,i)}}},vY={kernelName:Su,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,s={x:r,filter:a,dy:e},i={x:r,filter:a,dy:e};return{x:()=>D.runKernel(Vh,s,n),filter:()=>D.runKernel(Uh,i,n)}}},kY={kernelName:uo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,r={dy:e,y:n};return{x:()=>D.runKernel(jh,r)}}},IY={kernelName:co,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,r=L(Un(_t(lt(n))),2/Math.sqrt(Math.PI));return{x:()=>L(e,r)}}},NY={kernelName:ys,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,n)}}},SY={kernelName:po,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>q(e,n.shape)}}},TY={kernelName:fo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,Un(n))}}},EY={kernelName:gs,gradFunc:e=>({x:()=>Ve(e)})},CY={kernelName:xs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=At(n.shape,r.shape);return{a:()=>{let s=be(e,me(r,"float32")),i=Ot(n.shape,a);return i.length>0?q(Ie(s,i),n.shape):s},b:()=>{let s=L(e,me(n,"float32")),i=Ot(r.shape,a);i.length>0&&(s=q(Ie(s,i),r.shape));let o=lt(r);return _t(be(s,me(o,"float32")))}}}},RY={kernelName:ws,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:r}=n,[a,s,i,o]=t,l=o==null?ke(1):o,c=Ot(s.shape,a.shape),u=[];if(s.rank===1){for(let m=0;m<a.shape.length-1;++m)u.push(a.shape[m]);u.push(1)}let h=Ae(a,s),p=L(e,l),d=$d(se(i,ke(r))),f=L(L(L(d,d),d),ke(-.5));return{x:()=>s.rank===1?q(L(L(e,Ca(q(d,[1,1,1,s.shape[0]]),u)),l),a.shape):q(L(L(e,d),l),a.shape),mean:()=>{let m=L(L(d,ke(-1)),p);return s.rank===1&&(m=Ie(m,c)),q(m,s.shape)},variance:()=>{let m=L(L(f,h),p);return s.rank===1&&(m=Ie(m,c)),q(m,s.shape)},scale:()=>{let m=L(h,d),A=L(e,m);return s.rank===1&&(A=Ie(A,c)),q(A,s.shape)},offset:()=>{let m=e;return s.rank===1&&(m=Ie(m,c)),q(m,s.shape)}}}},FY={kernelName:Ao,inputsToSave:["x","indices"],gradFunc:(e,t,n)=>{let[r,a]=t,{axis:s}=n,i=Qn(s,r.shape)[0];return{x:()=>{let o=r.shape,l=a.size,c=o.slice(0,i),u=c.length,h=o.slice(s,o.length).slice(1),p=h.length,d=Yb(0,u),f=Yb(u+1,u+1+p),m=Jb([c,[l],h]),A=q(e,m),y=q(a,[l]),g=Jb([[u],d,f]),_=nt(A,g),x=um(_,y,r.shape[i]),w=Xf(g);return x=nt(x,w),x},indices:()=>a}}};function Yb(e,t){let n=[];for(let r=e;r<t;++r)n.push(r);return n}function Jb(e){let t=[];for(let n=0;n<e.length;++n)for(let r=0;r<e[n].length;++r)t.push(e[n][r]);return t}var MY={kernelName:_s,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>Ve(n),b:()=>Ve(r)}}},OY={kernelName:xo,gradFunc:e=>({x:()=>me(e,"float32")})},$Y={kernelName:wo,gradFunc:e=>({x:()=>Ve(e)})},DY={kernelName:_o,gradFunc:e=>({x:()=>Ve(e)})},zY={kernelName:bo,gradFunc:e=>({x:()=>Ve(e)})},PY={kernelName:bs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{alpha:a}=n,s=nr(r,0);return{x:()=>An(s,e,L(e,a))}}},LY={kernelName:Io,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>be(e,se(n,1))}}},WY={kernelName:vs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>be(e,me(n,"float32"))}}},BY={kernelName:xg,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r]=t,{axis:a}=n;return{logits:()=>{let s=!0,i=Un(r);return Ae(e,L(Ie(e,a,s),i))}}}};function VY(e,t,n,r=5,a=1,s=1,i=.5){let o={x:e,y:t,dy:n},l={depthRadius:r,bias:a,alpha:s,beta:i};return D.runKernel(Kh,o,l)}var UY=z({localResponseNormalizationBackprop_:VY}),jY={kernelName:Ru,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,a]=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;return{x:()=>UY(r,a,e,s,i,o,l)}}};function Qb(e,t,n,r){return t.rank<n.rank&&(t=q(t,ui(t.shape,r))),e.rank<n.rank&&(e=q(e,ui(e.shape,r))),{x:()=>L(e,me(Ea(n,t),e.dtype))}}var e3={kernelName:ks,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let r=n,{reductionIndices:a}=r,s=t[0],i=t[1],o=Qn(a,s.shape),l=Qb(e,i,s,o);return{x:()=>l.x()}}},HY={kernelName:Is,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>L(e,me(Ra(n,r),"float32")),b:()=>L(e,me(Nd(n,r),"float32"))}}};function GY(e,t,n,r,a,s,i){let o=R(e,"dy","maxPool3dGrad"),l=R(t,"input","maxPool3dGrad"),c=R(n,"output","maxPool3dGrad"),u=o,h=l,p=c,d=!1;l.rank===4&&(d=!0,u=q(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]]),h=q(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]]),p=q(c,[1,c.shape[0],c.shape[1],c.shape[2],c.shape[3]])),F(u.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${u.rank}.`),F(h.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${h.rank}.`),F(p.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${p.rank}.`),i!=null&&F(Lt(s),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let f={dy:u,input:h,output:p},m={filterSize:r,strides:a,pad:s,dimRoundingMode:i},A=D.runKernel(Yh,f,m);return d?q(A,[A.shape[1],A.shape[2],A.shape[3],A.shape[4]]):A}var qY=z({maxPool3dGrad_:GY}),XY={kernelName:Fu,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,a]=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n;return{x:()=>qY(e,r,a,s,i,o,l)}}};function KY(e,t,n,r,a,s,i){let o=R(e,"dy","maxPoolGrad"),l=R(t,"input","maxPoolGrad"),c=R(n,"output","maxPoolGrad");F(l.rank===o.rank,()=>`Rank of input (${l.rank}) does not match rank of dy (${o.rank})`),F(o.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`),F(l.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${l.rank}.`),i!=null&&F(Lt(s),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let u={dy:o,input:l,output:c},h={filterSize:r,strides:a,pad:s,dimRoundingMode:i};return D.runKernel(Zh,u,h)}var ZY=z({maxPoolGrad_:KY}),YY={kernelName:Ns,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,a]=t,{filterSize:s,strides:i,pad:o}=n;return{x:()=>ZY(e,r,a,s,i,o)}}},JY={kernelName:Ss,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{axis:a}=n,s=Qn(a,r.shape),i=O5(r.shape,s)[1],o=Rt(i);return{x:()=>{let l=r.shape.slice();s.forEach(u=>{l[u]=1});let c=q(e,l);return be(L(c,Dr(r.shape,"float32")),o)}}}},QY={kernelName:Ts,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let r=n,{axis:a}=r,[s,i]=t,o=Qn(a,s.shape),l=Qb(e,i,s,o);return{x:()=>l.x()}}},eJ={kernelName:Es,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>L(e,me(li(n,r),"float32")),b:()=>L(e,me(nr(n,r),"float32"))}}},tJ={kernelName:Mu,inputsToSave:["x"],gradFunc:(e,t,n)=>{let r=t[0],{paddings:a}=n,s=a.map(i=>i[0]);return{x:()=>Te(e,s,r.shape)}}},nJ={kernelName:So,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=At(n.shape,r.shape);return{a:()=>{let s=Ot(n.shape,a);return s.length>0?q(Ie(e,s),n.shape):e},b:()=>{let s=L(e,_t(pl(be(n,r)))),i=Ot(r.shape,a);return i.length>0?q(Ie(s,i),r.shape):s}}}},rJ={kernelName:Cs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=At(n.shape,r.shape);return{a:()=>{let s=L(e,me(r,"float32")),i=Ot(n.shape,a);return i.length>0?q(Ie(s,i),n.shape):s},b:()=>{let s=L(e,me(n,"float32")),i=Ot(r.shape,a);return i.length>0?q(Ie(s,i),r.shape):s}}}},aJ={kernelName:To,gradFunc:e=>({x:()=>_t(e)})},sJ={kernelName:Rs,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>Tt(n.shape,"float32")}}},iJ={kernelName:Mo,gradFunc:e=>({x:()=>Ve(e)})},oJ={kernelName:Oo,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:r}=n;return ar(e,r).map(a=>()=>a)}},t3={kernelName:Fs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let r=t[0],{paddings:a}=n,s=a.map(i=>i[0]);return{x:()=>Te(e,s,r.shape)}}},lJ={kernelName:Ms,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,r,a]=t,s=n,i=r,o=At(s.shape,i.shape);return{a:()=>{let l=me(i,"float32"),c=L(e,L(l,aa(s,Ae(l,ke(1))))),u=Ot(s.shape,o);return u.length>0&&(c=Ie(c,u)),q(c,s.shape)},b:()=>{let l=nr(s,0),c=An(l,Sn(s),Ve(s)),u=L(e,L(a,c)),h=Ot(i.shape,o);return h.length>0&&(u=Ie(u,h)),q(u,i.shape)}}}},uJ={kernelName:Os,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,r]=t,a=nr(n,0);return{x:()=>An(a,e,L(e,r)),alpha:()=>{let s=An(a,Ve(e),L(e,n)),i=Ot(r.shape,e.shape);return i.length>0&&(s=Ie(s,i)),q(s,r.shape)}}}},cJ={kernelName:As,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=At(n.shape,r.shape);return{a:()=>{let s=be(e,me(r,"float32")),i=Ot(n.shape,a);return i.length>0?q(Ie(s,i),n.shape):s},b:()=>{let s=L(e,me(n,"float32")),i=Ot(r.shape,a);i.length>0&&(s=q(Ie(s,i),r.shape));let o=lt(r);return _t(be(s,me(o,"float32")))}}}},hJ={kernelName:Do,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>be(e,_t(lt(n)))}}},dJ={kernelName:zs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,r=L(li(n,6),xl(n));return{x:()=>L(e,me(r,"float32"))}}},pJ={kernelName:$s,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,me(xl(n),"float32"))}}},fJ={kernelName:zo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>q(e,n.shape)}}},mJ={kernelName:Ds,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[r]=t,a={dy:e,images:r};return{images:()=>D.runKernel(nd,a,n)}}},AJ={kernelName:$u,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[r]=t,a={dy:e,images:r};return{images:()=>D.runKernel(td,a,n)}}},yJ={kernelName:Ps,gradFunc:(e,t,n)=>{let{dims:r}=n,a=Qn(r,e.shape);return{x:()=>En(e,a)}}},gJ={kernelName:Ls,gradFunc:e=>({x:()=>Ve(e)})},xJ={kernelName:Ws,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>_t(be(e,L(aa(n,1.5),2)))}}},wJ={kernelName:Lo,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>me(Ve(n),"float32"),t:()=>L(e,me(n,e.dtype)),e:()=>L(e,me(rc(n),e.dtype))}}},_J={kernelName:Wo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=nr(n,ke(0)),a=ke(ox),s=ke(lx),i=L(e,s),o=L(L(e,a),Un(me(n,"float32")));return An(r,i,o)}}}},bJ={kernelName:Vs,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,L(n,Ae(ke(1),n)))}}},vJ={kernelName:Uo,gradFunc:e=>({x:()=>Ve(e)})},kJ={kernelName:Bs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(ec(me(n,"float32")),e)}}},IJ={kernelName:Vo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(vd(me(n,"float32")),e)}}},NJ={kernelName:Bo,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{begin:a,size:s}=n,i=r.shape,[o,l]=r5(r,a,s),c=[];for(let u=0;u<e.rank;u++)c.push([o[u],i[u]-o[u]-l[u]]);return{x:()=>ra(e,c)}}},SJ={kernelName:Hs,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r]=t,{dim:a}=n,s=!0,i=L(e,r);return{logits:()=>Ae(i,L(Ie(i,[a],s),r))}}},TJ={kernelName:jo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,In(n))}}},n3={kernelName:Du,gradFunc:(e,t,n)=>{let{blockShape:r,paddings:a}=n;return{x:()=>Ju(e,r,a)}}},r3={kernelName:Ho,gradFunc:(e,t,n)=>{let{axis:r}=n;return{x:()=>rt(e,r)}}},EJ={kernelName:Us,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>be(e,L(Zt(me(n,"float32")),2))}}},CJ={kernelName:zu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,L(me(n,"float32"),2))}}},RJ={kernelName:Gs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=ke(2);return{a:()=>L(e,L(a,Ae(n,r))),b:()=>L(e,L(a,Ae(r,n)))}}},FJ={kernelName:va,gradFunc:e=>({x:()=>Ve(e)})},MJ={kernelName:qs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=At(n.shape,r.shape);return{a:()=>{let s=e,i=Ot(n.shape,a);return i.length>0&&(s=Ie(s,i)),q(s,n.shape)},b:()=>{let s=e,i=Ot(r.shape,a);return i.length>0&&(s=Ie(s,i)),q(_t(s),r.shape)}}}},OJ={kernelName:js,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,a=r.shape.slice(),{axis:s}=n;Qn(s,r.shape).forEach(l=>{a[l]=1});let i=q(e,a),o=L(i,Dr(r.shape,"float32"));return{x:()=>o}}},$J={kernelName:qo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>be(e,lt(ec(n)))}}},DJ={kernelName:Xs,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(Ae(ke(1),lt(n)),e)}}},zJ={kernelName:ba,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{reps:a}=n;return{x:()=>{let s=Ve(r);if(r.rank===1)for(let i=0;i<a[0];++i)s=se(s,Te(e,[i*r.shape[0]],[r.shape[0]]));else if(r.rank===2)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)s=se(s,Te(e,[i*r.shape[0],o*r.shape[1]],[r.shape[0],r.shape[1]]));else if(r.rank===3)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)for(let l=0;l<a[2];++l)s=se(s,Te(e,[i*r.shape[0],o*r.shape[1],l*r.shape[2]],[r.shape[0],r.shape[1],r.shape[2]]));else if(r.rank===4)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)for(let l=0;l<a[2];++l)for(let c=0;c<a[3];++c)s=se(s,Te(e,[i*r.shape[0],o*r.shape[1],l*r.shape[2],c*r.shape[3]],[r.shape[0],r.shape[1],r.shape[2],r.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${r.rank} tensors yet.`);return s}}}},PJ={kernelName:Ks,gradFunc:(e,t,n)=>{let r=n,{perm:a}=r,s=Xf(a);return{x:()=>nt(e,s)}}},LJ={kernelName:Ko,gradFunc:(e,t,n)=>{let r=n,{axis:a}=r;return{value:()=>Cn(e,a)}}},BJ={kernelName:Pu,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>WJ(e,n)}}};function WJ(e,t){let n=$r(t,Ve(t)),r=oi(e,n),a=Ra(t,ke(0,"int32")),s=r.rank-a.rank;for(let o=0;o<s;++o)a=Nn(a,o+1);a=rr(a,Dr(r.shape,"bool"));let i=Ve(r);return An(a,r,i)}var VJ={kernelName:Zo,gradFunc:e=>({x:()=>Ve(e)})},UJ=[Zb,UZ,jZ,HZ,GZ,qZ,XZ,KZ,ZZ,YZ,JZ,QZ,nY,sY,iY,oY,lY,uY,cY,hY,dY,pY,mY,fY,gY,xY,wY,_Y,bY,vY,cJ,kY,IY,NY,SY,TY,CY,EY,RY,FY,MY,OY,$Y,DY,zY,PY,LY,WY,BY,jY,e3,e3,HY,XY,YY,JY,QY,eJ,tJ,nJ,rJ,aJ,sJ,iJ,oJ,t3,t3,lJ,uJ,hJ,dJ,pJ,fJ,mJ,AJ,yJ,gJ,xJ,wJ,_J,bJ,vJ,kJ,IJ,NJ,SJ,TJ,n3,n3,r3,r3,EJ,RJ,CJ,FJ,MJ,OJ,$J,DJ,zJ,PJ,LJ,BJ,VJ];for(let e of UJ)wg(e);var a3={};$e(a3,{maxNorm:()=>jJ,minMaxNorm:()=>qJ,nonNeg:()=>GJ,unitNorm:()=>HJ});var nA;function $t(){return nA==null&&(nA=If().epsilon()),nA}function yr(){return"channelsLast"}var la=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,la.prototype)}},gr=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,gr.prototype)}},B=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,B.prototype)}},Me=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Me.prototype)}},s3=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,s3.prototype)}},XJ=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,XJ.prototype)}};function _i(e,t){if(Array.isArray(e)){let n=[];for(let r=0;r<t;r++)n=n.concat(e);return n}else{let n=new Array(t);return n.fill(e),n}}function Br(e,t){if(!e)throw new s3(t)}function i3(e,t){let n=0;for(let r of e)r===t&&n++;return n}function xn(e){return e.length===1?e[0]:e}function mt(e){return Array.isArray(e)?e:[e]}function ua(e){let t=e.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return t[0]!=="_"?t:"private"+t}function bi(e){return e.length<=1||e.indexOf("_")===-1?e:e.replace(/[_]+(\w|$)/g,(t,n)=>n.toUpperCase())}var ir={};function rA(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function aA(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>aA(t));else{let t=Object.keys(e);for(let n of t){let r=e[n];r!=null&&typeof r=="object"&&(!Array.isArray(r)&&r.type==="ndarray"&&typeof r.value=="number"?e[n]=r.value:aA(r))}}}function Ec(e,t={},n={},r="object",a=!1){if(typeof e=="string"){let s=e,i;if(s in n)i=n[s];else if(s in ir)i=ir[s];else if(i=t[s],i==null)throw new B(`Unknown ${r}: ${e}. This may be due to one of the following reasons:
1. The ${r} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${r} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return i}else{let s=e;if(s.className==null||s.config==null)throw new B(`${r}: Improper config format: ${JSON.stringify(s)}.
'className' and 'config' must set.`);let i=s.className,o,l;if(i in n?[o,l]=n[i]:i in ir?[o,l]=ir.className:i in t&&([o,l]=t[i]),o==null)throw new B(`Unknown ${r}: ${i}. This may be due to one of the following reasons:
1. The ${r} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${r} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(l!=null){let c={};for(let d of Object.keys(ir))c[d]=ir[d];for(let d of Object.keys(n))c[d]=n[d];let u=s.config;u.customObjects=c;let h=Object.assign({},ir);for(let d of Object.keys(n))ir[d]=n[d];aA(s.config);let p=l(o,s.config,n,a);return ir=Object.assign({},h),p}else{let c=Object.assign({},ir);for(let h of Object.keys(n))ir[h]=n[h];let u=new o(s.config);return ir=Object.assign({},c),u}}}function KJ(e,t){return e<t?-1:e>t?1:0}function Rp(e,t){return-1*KJ(e,t)}function za(e){if(e==null)return e;let t=[];for(let n of e)t.indexOf(n)===-1&&t.push(n);return t}function ZJ(e){if(e==null)throw new B(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function vi(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new B(`${n} is not a valid ${t}. Valid values are ${e} or null/undefined.`)}function sA(e,t,n=0,r=Infinity){return Br(n>=0),Br(r>=n),Array.isArray(e)&&e.length>=n&&e.length<=r&&e.every(a=>typeof a===t)}function Ut(e,t){Array.isArray(e)?(k.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((n,r)=>Ut(n,`element ${r+1} of ${t}`))):k.assert(Number.isInteger(e)&&e>0,()=>`Expected ${t} to be a positive integer, but got ${o3(e)}.`)}function o3(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>o3(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function YJ(e,t){let n=k.now(),r;return(...a)=>{let s=k.now();return s-n<t||(n=s,r=e(...a)),r}}function l3(e){return e==="relu"?"relu":e==="linear"?"linear":e==="elu"?"elu":null}function iA(e,t){return W(()=>Zt(Ie(L(e,e),t,!0)))}var Cc=class extends re.Serializable{getConfig(){return{}}},oA=class extends Cc{constructor(e){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=e.maxValue!=null?e.maxValue:this.defaultMaxValue,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return W(()=>{let t=iA(e,this.axis),n=mn(t,0,this.maxValue);return L(e,be(n,se($t(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};oA.className="MaxNorm";re.registerClass(oA);var lA=class extends Cc{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return W(()=>be(e,se($t(),iA(e,this.axis))))}getConfig(){return{axis:this.axis}}};lA.className="UnitNorm";re.registerClass(lA);var uA=class extends Cc{apply(e){return zr(e)}};uA.className="NonNeg";re.registerClass(uA);var cA=class extends Cc{constructor(e){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=e.minValue!=null?e.minValue:this.defaultMinValue,this.maxValue=e.maxValue!=null?e.maxValue:this.defaultMaxValue,this.rate=e.rate!=null?e.rate:this.defaultRate,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return W(()=>{let t=iA(e,this.axis),n=se(L(this.rate,mn(t,this.minValue,this.maxValue)),L(1-this.rate,t));return L(e,be(n,se($t(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};cA.className="MinMaxNorm";re.registerClass(cA);var u3={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Dt(e){return rA(e)}function c3(e,t={}){return Ec(e,re.SerializationMap.getMap().classNameMap,t,"constraint")}function zt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in u3?u3[e]:e,config:{}};return c3(t)}else return e instanceof Cc?e:c3(e)}function jJ(e){return new oA(e)}function HJ(e){return new lA(e)}function GJ(){return new uA}function qJ(e){return new cA(e)}var h3={};$e(h3,{constant:()=>eQ,glorotNormal:()=>oQ,glorotUniform:()=>iQ,heNormal:()=>lQ,heUniform:()=>uQ,identity:()=>aQ,leCunNormal:()=>cQ,leCunUniform:()=>hQ,ones:()=>QJ,orthogonal:()=>dQ,randomNormal:()=>nQ,randomUniform:()=>tQ,truncatedNormal:()=>rQ,varianceScaling:()=>sQ,zeros:()=>JJ});var pQ=["channelsFirst","channelsLast"],fQ=["nearest","bilinear"],mQ=["valid","same","causal"],AQ=["max","avg"],yQ=["sum","mul","concat","ave"],Dl=new Map;function Nt(e){vi(pQ,"DataFormat",e)}function gQ(e){vi(fQ,"InterpolationFormat",e)}function qn(e){vi(mQ,"PaddingMode",e)}function d3(e){vi(AQ,"PoolMode",e)}var Rc=[],p3="/";function ki(e,t){Rc.push(e);try{let n=t();return Rc.pop(),n}catch(n){throw Rc.pop(),n}}function xQ(){return Rc.length===0?"":Rc.join(p3)+p3}function m3(e){if(!f3(e))throw new Error("Not a valid tensor name: '"+e+"'");return xQ()+e}function A3(e){if(!f3(e))throw new Error("Not a valid tensor name: '"+e+"'");Dl.has(e)||Dl.set(e,0);let t=Dl.get(e);if(Dl.set(e,Dl.get(e)+1),t>0){let n=`${e}_${t}`;return Dl.set(n,1),n}else return e}var wQ=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function f3(e){return!!e.match(wQ)}function _Q(e){return e===parseInt(e.toString(),10)}function Pa(e,t,n){t==null&&(t=0),n==null&&(n=e.length);let r=1;for(let a=t;a<n;++a)r*=e[a];return r}function y3(e){return e=Array.isArray(e)?new Float32Array(e):e,Bt(e)}function zl(e){return ml(y3(e)).dataSync()[0]}function La(e){return jn(y3(e)).dataSync()[0]}function xr(e,t){if(t<e)throw new B(`end (${t}) < begin (${e}) is forbidden.`);let n=[];for(let r=e;r<t;++r)n.push(r);return n}function Fc(e,t){return e.asType(t)}function Mc(e,t=-1){let n=e.shape.slice();return t<0&&(t=n.length+t+1),n.splice(t,0,1),e.reshape(n)}function bQ(e,t){return W(()=>{if(e.shape.length!==2)throw new B(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let n=Mc(e,1);return hA(n,[1,t,1])})}function vQ(e){let t=[Pa(e.shape)];return e.reshape(t)}function kQ(e){if(e.rank<=1)throw new B(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],Pa(e.shape,1)];return e.reshape(t)}function Ii(e,t,n){return W(()=>{switch(e.rank){case 1:return Ld(e,t,n);case 2:return sm(e,[t,0],[n,e.shape[1]]);case 3:return Wd(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return lc(e,[t,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3]]);case 5:return Te(e,[t,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return Te(e,[t,0,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4],e.shape[5]]);default:throw new B(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}})}function dA(e,t,n){return W(()=>{switch(e.rank){case 1:return Ld(e,t,n);case 2:return sm(e,[0,t],[e.shape[0],n]);case 3:return Wd(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return lc(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],n]);default:throw new B(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function Fp(e,t,n,r){return W(()=>{switch(e.rank){case 1:return Ld(e,t,n);case 2:switch(r){case 1:return Ii(e,t,n);case 2:return dA(e,t,n);default:throw new B(`The axis is not within the rank of the tensor ${r}`)}case 3:switch(r){case 1:return Ii(e,t,n);case 2:return Wd(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return dA(e,t,n);default:throw new B(`The axis is not within the rank of the tensor ${r}`)}case 4:switch(r){case 1:return Ii(e,t,n);case 2:return lc(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return lc(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return dA(e,t,n);default:throw new B(`The axis is not within the rank of the tensor ${r}`)}default:throw new B(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function pA(e,t=-1){let n;return t<0&&(n=e[0].rank,n!==0?t=n:t=0),t===e[0].rank&&(t=-1),rt(e,t)}function g3(e,t){switch(e.rank){case 1:return _5([e,t]);case 2:return ii([e,t],0);case 3:return b5([e,t],0);case 4:return v5([e,t],0);default:throw new B(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function hA(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new B(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return Ca(e,t)}function Mp(e,t=0,n=1,r,a){return W5(e,t,n,r,a)}function Vr(e,t,n,r){if(e.rank<2||t.rank<2)throw new Me(`dot requires both inputs to be rank >= 2 but got x shape = ${e.shape} and y shape = ${t.shape}`);if(t.rank>=3){let a=e.shape.slice(-1)[0],s=t.shape.slice(-2)[0];if(a!==s)throw new Me(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${e.shape} and y shape = ${t.shape}`)}if(e.rank===2&&t.rank===2){let a=!1,s=!1;return Ma.matMul({a:e,b:t,transposeA:a,transposeB:s,bias:r?fA(e.rank,r,yr()):null,activation:n})}else{let a=e.shape.slice(),s=a.pop();e=e.reshape([-1,s]);let i=t.shape.slice(),o=i.pop(),l=i.pop(),c=[...i,o],u=Array.from({length:t.rank},(f,m)=>m===0?t.rank-2:m<=t.rank-2?m-1:m);t=t.transpose(u).reshape([l,-1]);let h=[...a,...c],p=!1,d=!1;return Ma.matMul({a:e,b:t,transposeA:p,transposeB:d,bias:r?fA(e.rank,r,yr()):null,activation:n}).reshape(h)}}function x3(e,t,n){return W(()=>(Array.isArray(t)?t=Bt(t,"int32"):t=t.toInt(),oi(e,t,n)))}function Oc(e){return L(e,e)}function fA(e,t,n){let r=t.shape;if(t.rank!==1&&t.rank!==e)throw new B(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);if(e===5){if(n==="channelsFirst")return r.length===1?t.reshape([1,r[0],1,1,1]):t.reshape([1,r[3],r[0],r[1],r[2]]);if(n==="channelsLast")return r.length===1?t.reshape([1,1,1,1,r[0]]):t.reshape([1].concat(r))}else if(e===4){if(n==="channelsFirst")return r.length===1?t.reshape([1,r[0],1,1]):t.reshape([1,r[2],r[0],r[1]]);if(n==="channelsLast")return r.length===1?t.reshape([1,1,1,r[0]]):t.reshape([1].concat(r))}else if(e===3){if(n==="channelsFirst")return r.length===1?t.reshape([1,r[0],1]):t.reshape([1,r[1],r[0]]);if(n==="channelsLast")return r.length===1?t.reshape([1,1,r[0]]):t.reshape([1].concat(r))}else if(e<3)return t;throw new B(`Unsupported input rank by biasAdd: ${t.rank}`)}function Ur(e,t,n){return W(()=>(n==null&&(n=yr()),Nt(n),e.add(fA(e.rank,t,n))))}function IQ(e,t=1){if(t!==1)throw new Me(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return dl(e)}function NQ(e){return W(()=>be(e,Mt(e).add(1)))}function w3(e,t,n,r){return W(()=>q5(e,t,n,r))}function SQ(e){return W(()=>{let t=se(.5,L(.2,e));return mn(t,0,1)})}function $c(e,t,n=!1){return n?e():t()}var TQ=["fanIn","fanOut","fanAvg"],EQ=["normal","uniform","truncatedNormal"];function CQ(e){vi(TQ,"FanMode",e)}function RQ(e){vi(EQ,"Distribution",e)}var or=class extends re.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},mA=class extends or{apply(e,t){return Tt(e,t)}};mA.className="Zeros";re.registerClass(mA);var Op=class extends or{apply(e,t){return Dr(e,t)}};Op.className="Ones";re.registerClass(Op);var AA=class extends or{constructor(e){super();if(typeof e!="object")throw new B(`Expected argument of type ConstantConfig but got ${e}`);if(e.value===void 0)throw new B(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return W(()=>L(ke(this.value),Dr(e,t)))}getConfig(){return{value:this.value}}};AA.className="Constant";re.registerClass(AA);var yA=class extends or{constructor(e){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=e.minval||this.DEFAULT_MINVAL,this.maxval=e.maxval||this.DEFAULT_MAXVAL,this.seed=e.seed}apply(e,t){return yl(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};yA.className="RandomUniform";re.registerClass(yA);var gA=class extends or{constructor(e){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=e.mean||this.DEFAULT_MEAN,this.stddev=e.stddev||this.DEFAULT_STDDEV,this.seed=e.seed}apply(e,t){if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Me(`randomNormal does not support dType ${t}.`);return Mp(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};gA.className="RandomNormal";re.registerClass(gA);var xA=class extends or{constructor(e){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=e.mean||this.DEFAULT_MEAN,this.stddev=e.stddev||this.DEFAULT_STDDEV,this.seed=e.seed}apply(e,t){if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Me(`truncatedNormal does not support dType ${t}.`);return Ud(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};xA.className="TruncatedNormal";re.registerClass(xA);var wA=class extends or{constructor(e){super();this.gain=e.gain!=null?e.gain:1}apply(e,t){return W(()=>{if(e.length!==2||e[0]!==e[1])throw new B("Identity matrix initializer can only be used for 2D square matrices.");return L(this.gain,Hf(e[0]))})}getConfig(){return{gain:this.gain}}};wA.className="Identity";re.registerClass(wA);function FQ(e,t="channelsLast"){let n,r;if(Nt(t),e.length===2)n=e[0],r=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){let a=Pa(e,2);n=e[1]*a,r=e[0]*a}else if(t==="channelsLast"){let a=Pa(e,0,e.length-2);n=e[e.length-2]*a,r=e[e.length-1]*a}}else{let a=Pa(e);n=Math.sqrt(a),r=Math.sqrt(a)}return[n,r]}var wn=class extends or{constructor(e){super();if(e.scale<0)throw new B(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,CQ(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,RQ(this.distribution),this.seed=e.seed}apply(e,t){let n=FQ(e),r=n[0],a=n[1],s=this.scale;if(this.mode==="fanIn"?s/=Math.max(1,r):this.mode==="fanOut"?s/=Math.max(1,a):s/=Math.max(1,(r+a)/2),this.distribution==="normal"){let i=Math.sqrt(s);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Me(`${this.getClassName()} does not support dType ${t}.`);return Ud(e,0,i,t,this.seed)}else{let i=Math.sqrt(3*s);return yl(e,-i,i,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};wn.className="VarianceScaling";re.registerClass(wn);var $p=class extends wn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return wn.className}};$p.className="GlorotUniform";re.registerClass($p);var Dp=class extends wn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return wn.className}};Dp.className="GlorotNormal";re.registerClass(Dp);var zp=class extends wn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return wn.className}};zp.className="HeNormal";re.registerClass(zp);var Pp=class extends wn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return wn.className}};Pp.className="HeUniform";re.registerClass(Pp);var Lp=class extends wn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return wn.className}};Lp.className="LeCunNormal";re.registerClass(Lp);var Wp=class extends wn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return wn.className}};Wp.className="LeCunNormal";re.registerClass(Wp);var _A=class extends or{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new Me("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return W(()=>{if(e.length<2)throw new Me("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let n=e[0]>e[1]?[e[1],e[0]]:e,r=Mp(n,0,1,"float32"),a=ix.gramSchmidt(r);return e[0]>e[1]&&(a=a.transpose()),L(this.gain,a)})}getConfig(){return{gain:this.gain,seed:this.seed}}};_A.className="Orthogonal";re.registerClass(_A);var _3={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function b3(e,t={}){return Ec(e,re.SerializationMap.getMap().classNameMap,t,"initializer")}function vt(e){return rA(e)}function gt(e){if(typeof e=="string"){let t=e in _3?_3[e]:e;if(t==="GlorotNormal")return new Dp;if(t==="GlorotUniform")return new $p;if(t==="HeNormal")return new zp;if(t==="HeUniform")return new Pp;if(t==="LeCunNormal")return new Lp;if(t==="LeCunUniform")return new Wp;{let n={};return n.className=t,n.config={},b3(n)}}else return e instanceof or?e:b3(e)}function JJ(){return new mA}function QJ(){return new Op}function eQ(e){return new AA(e)}function tQ(e){return new yA(e)}function nQ(e){return new gA(e)}function rQ(e){return new xA(e)}function aQ(e){return new wA(e)}function sQ(e){return new wn(e)}function iQ(e){return new $p(e)}function oQ(e){return new Dp(e)}function lQ(e){return new zp(e)}function uQ(e){return new Pp(e)}function cQ(e){return new Lp(e)}function hQ(e){return new Wp(e)}function dQ(e){return new _A(e)}var v3={};$e(v3,{Layer:()=>Ge,RNN:()=>jr,RNNCell:()=>Dc,activation:()=>XQ,add:()=>ree,alphaDropout:()=>Wee,average:()=>aee,averagePooling1d:()=>bA,averagePooling2d:()=>vA,averagePooling3d:()=>kA,avgPool1d:()=>pee,avgPool2d:()=>mee,avgPool3d:()=>yee,avgPooling1d:()=>fee,avgPooling2d:()=>Aee,avgPooling3d:()=>gee,batchNormalization:()=>cee,bidirectional:()=>Fee,concatenate:()=>see,conv1d:()=>WQ,conv2d:()=>BQ,conv2dTranspose:()=>VQ,conv3d:()=>UQ,convLstm2d:()=>Tee,convLstm2dCell:()=>Eee,cropping2D:()=>HQ,dense:()=>KQ,depthwiseConv2d:()=>qQ,dot:()=>uee,dropout:()=>ZQ,elu:()=>OQ,embedding:()=>nee,flatten:()=>JQ,gaussianDropout:()=>Lee,gaussianNoise:()=>Pee,globalAveragePooling1d:()=>xee,globalAveragePooling2d:()=>wee,globalMaxPool1d:()=>Oee,globalMaxPool2d:()=>$ee,globalMaxPooling1d:()=>I3,globalMaxPooling2d:()=>N3,gru:()=>bee,gruCell:()=>vee,input:()=>k3,inputLayer:()=>MQ,layerNormalization:()=>hee,leakyReLU:()=>DQ,lstm:()=>kee,lstmCell:()=>Iee,masking:()=>Bee,maxPool1d:()=>Dee,maxPool2d:()=>zee,maxPooling1d:()=>S3,maxPooling2d:()=>T3,maxPooling3d:()=>_ee,maximum:()=>iee,minimum:()=>oee,multiply:()=>lee,permute:()=>tee,prelu:()=>zQ,reLU:()=>$Q,repeatVector:()=>QQ,reshape:()=>eee,rnn:()=>Cee,separableConv2d:()=>jQ,simpleRNN:()=>Nee,simpleRNNCell:()=>See,softmax:()=>PQ,spatialDropout1d:()=>YQ,stackedRNNCells:()=>Ree,thresholdedReLU:()=>LQ,timeDistributed:()=>Mee,upSampling2d:()=>GQ,zeroPadding2d:()=>dee});var Vee=0;function E3(){return Vee++}var Bp={};function Vp(e=""){return e in Bp||(Bp[e]=0),Bp[e]+=1,e+Bp[e].toString()}function IA(e){return Array.isArray(e)&&Array.isArray(e[0])}function Up(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function De(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new B(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function dt(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new B(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function jp(e){let t=0;for(let n of e)n.shape.length===0?t+=1:t+=n.shape.reduce((r,a)=>r*a);return t}var C3="Variable",R3=class{constructor(e,t="float32",n=C3,r=!0,a=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=E3(),n=n==null?C3:n,this.originalName=m3(n),this.name=A3(this.originalName),this.trainable_=r,this.constraint=a,this.val=V5(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),Uee(this.val,e),this.val.id!==e.id&&(this.val.assign(e),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}};function Uee(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function NA(e){return e.map(t=>t.read())}function SA(e){e.forEach(t=>{t[0].write(t[1])})}var jt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},wr=class{constructor(e,t,n,r,a,s,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=r,this.callArgs=a,this.outputTensorIndex=i,this.id=E3(),s!=null&&(this.originalName=m3(s),this.name=A3(this.originalName)),this.rank=t.length}},jee=0,Hp=class{constructor(e,t){this.callArgs=t,this.id=jee++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},Hee=0,Ge=class extends re.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=Hee++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=ua(n)+"_"+Vp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let a=null;e.batchSize!=null&&(a=e.batchSize),n=[a].concat(e.inputShape)}this.batchInputShape=n;let r=e.dtype;r==null&&(r=e.inputDType),r==null&&(r="float32"),this.dtype=r}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new gr(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new B(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return xn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return xn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new la(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new la(`Layer ${this.name} is not connected, no input to return.`);return xn(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new la(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new la(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return xn(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=mt(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=mt(this.inputSpec);if(e.length!==t.length)throw new B(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){let r=e[n],a=t[n];if(a==null)continue;let s=r.rank;if(a.ndim!=null&&s!==a.ndim)throw new B(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${a.ndim}, found ndim=${s}`);if(a.maxNDim!=null&&s>a.maxNDim)throw new B(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${s}`);if(a.minNDim!=null&&s<a.minNDim)throw new B(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${a.minNDim}, found ndim=${s}.`);if(a.dtype!=null&&r.dtype!==a.dtype)throw new B(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${a.dtype}, found dtype=${r.dtype}.`);if(a.axes){let i=r.shape;for(let o in a.axes){let l=Number(o),c=a.axes[o],u=l>=0?i[l]:i[i.length+l];if(c!=null&&[c,null].indexOf(u)===-1)throw new B(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${c} but got shape ${i}.`)}}if(a.shape!=null)for(let i=0;i<a.shape.length;++i){let o=a.shape[i],l=r.shape[i];if(o!=null&&l!=null&&o!==l)throw new B(`Input ${n} is incompatible with layer ${this.name}: expected shape=${a.shape}, found shape=${r.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=mt(e),r=!0;for(let s of n)if(!(s instanceof wr)){r=!1;break}let a=!0;for(let s of n)if(s instanceof wr){a=!1;break}if(r===a)throw new B("Arguments to apply() must be all SymbolicTensors or all Tensors");return ki(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of mt(e))s.push(i.shape);this.build(xn(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let s=this.call(e,t),i=mt(s),o=[];for(let l of i)n.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=xn(o),this.activityRegularizer!=null)throw new Me("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=Gee(e),i=this.computeOutputShape(s),o,l=qee(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((c,u)=>new wr(l,c,this,mt(e),t,this.name,u)):o=new wr(l,i,this,mt(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new Me("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,r)=>{n!=null&&e[r]!=null&&e[r]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new la(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new la(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new gr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return jp(this.weights)}build(e){this.built=!0}getWeights(e=!1){return NA(e?this.trainableWeights:this.weights)}setWeights(e){W(()=>{let t=this.weights;if(t.length!==e.length)throw new B(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],r=NA(t);for(let a=0;a<r.length;++a){let s=r[a],i=t[a],o=e[a];if(!k.arraysEqual(s.shape,o.shape))throw new B(`Layer weight shape ${s.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}SA(n)})}addWeight(e,t,n,r,a,s,i){if(this._addedWeightNames.indexOf(e)!==-1)throw new B(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(r=gt("zeros"));let o=r.apply(t,n),l=new R3(o,n,e,s,i);return o.dispose(),a!=null&&this.addLoss(()=>a.apply(l.read())),s==null&&(s=!0),s?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=mt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,r,a,s,i=null){let o=mt(e);t=mt(t),n=mt(n),r=mt(r),a=Up(a),s=Up(s);let l=[],c=[],u=[];for(let h of o)l.push(h.sourceLayer),c.push(h.nodeIndex),u.push(h.tensorIndex);new Hp({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:u,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:r,inputShapes:a,outputShapes:s},i);for(let h=0;h<t.length;h++)t[h].sourceLayer=this,t[h].nodeIndex=this.inboundNodes.length-1,t[h].tensorIndex=h}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount==0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function Gee(e){e=mt(e);let t=[];for(let n of e)t.push(n.shape);return xn(t)}function qee(e){return"float32"}function F3(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let r=t.inboundNodes[n];if(r.inboundLayers.length===0)return r.inputTensors;{let a=[];for(let s=0;s<r.inboundLayers.length;s++){let i=r.inputTensors[s],o=r.inboundLayers[s],l=r.nodeIndices[s],c=F3(i,o,l);for(let u of c)a.indexOf(u)===-1&&a.push(u)}return a}}}var Pl=class extends Ge{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Vp("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new B("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new B("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new B("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];let r=new wr(this.dtype,this.batchInputShape,this,[],{},this.name);r.nodeIndex=0,r.tensorIndex=0,new Hp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[r],outputTensors:[r],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new B(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};Pl.className="InputLayer";re.registerClass(Pl);function M3(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(e.batchShape!=null&&e.shape!=null)throw new B("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=e.batchShape;e.shape!=null&&t==null&&(t=[null].concat(e.shape));let n=e.dtype;return n==null&&(n="float32"),new Pl({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}async function Wa(e){if(e==null)return;let t=[],n=[],r=[];for(let a in e){let s=e[a];if(typeof s!="number"){let i=s;t.push(i.data()),n.push(a),r.push(i)}}if(t.length>0){let a=await Promise.all(t);for(let s=0;s<a.length;++s)e[n[s]]=a[s][0];Se(r)}}function O3(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var $3;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})($3||($3={}));var Xee=125,Ll=class{constructor(){this.validationData=null}setParams(e){this.params=e}async onEpochBegin(e,t){}async onEpochEnd(e,t){}async onBatchBegin(e,t){}async onBatchEnd(e,t){}async onTrainBegin(e){}async onTrainEnd(e){}setModel(e){}},D3=class{constructor(e,t=10){e==null&&(e=[]),this.callbacks=e,this.queueLength=t}append(e){this.callbacks.push(e)}setParams(e){for(let t of this.callbacks)t.setParams(e)}setModel(e){for(let t of this.callbacks)t.setModel(e)}async onEpochBegin(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onEpochBegin(e,t)}async onEpochEnd(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onEpochEnd(e,t)}async onBatchBegin(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onBatchBegin(e,t)}async onBatchEnd(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onBatchEnd(e,t)}async onTrainBegin(e){e==null&&(e={});for(let t of this.callbacks)await t.onTrainBegin(e)}async onTrainEnd(e){e==null&&(e={});for(let t of this.callbacks)await t.onTrainEnd(e)}},Kee=class extends Ll{constructor(){super()}async onEpochBegin(e){this.seen=0,this.totals={}}async onBatchEnd(e,t){t==null&&(t={});let n=t.size==null?0:t.size;this.seen+=n;for(let r in t){let a=t[r];if(typeof a=="number")this.totals.hasOwnProperty(r)||(this.totals[r]=0),this.totals[r]=this.totals[r]+a*n;else{let s;r in this.totals?s=this.totals[r]:this.totals[r]=0;let i=W(()=>se(this.totals[r],L(a,n)));this.totals[r]=i,s!=null&&s.dispose()}}}async onEpochEnd(e,t){if(t!=null)for(let n of this.params.metrics)this.totals[n]!=null&&(typeof this.totals[n]=="number"?t[n]=this.totals[n]/this.seen:W(()=>{let r=L(be(1,this.seen),this.totals[n]);t[n]=r,this.totals[n].dispose(),Wt(t[n])}))}},z3=class extends Ll{async onTrainBegin(e){this.epoch=[],this.history={}}async onEpochEnd(e,t){t==null&&(t={}),this.epoch.push(e);for(let n in t)this.history[n]==null&&(this.history[n]=[]),this.history[n].push(t[n])}async syncData(){let e=[],t=[],n=[];for(let a in this.history){let s=this.history[a];for(let i=0;i<s.length;++i)if(typeof s[i]!="number"){let o=s[i];e.push(o.data()),t.push(a),n.push(i)}}let r=await Promise.all(e);for(let a=0;a<r.length;++a)this.history[t[a]][n[a]].dispose(),this.history[t[a]][n[a]]=r[a][0]}},P3=class extends Ll{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=Xee),this.yieldEvery==="never"&&e.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");k.isNumber(this.yieldEvery)&&(this.maybeWait=YJ(this.maybeWait.bind(this),this.yieldEvery)),this.trainBegin=e.onTrainBegin,this.trainEnd=e.onTrainEnd,this.epochBegin=e.onEpochBegin,this.epochEnd=e.onEpochEnd,this.batchBegin=e.onBatchBegin,this.batchEnd=e.onBatchEnd,this.yield=e.onYield}async maybeWait(e,t,n){let r=[];this.yield!=null&&(await Wa(n),r.push(this.yield(e,t,n))),r.push(np()),await Promise.all(r)}async onEpochBegin(e,t){this.currentEpoch=e,this.epochBegin!=null&&(await Wa(t),await this.epochBegin(e,t))}async onEpochEnd(e,t){let n=[];this.epochEnd!=null&&(await Wa(t),n.push(this.epochEnd(e,t))),this.yieldEvery==="epoch"&&n.push(np()),await Promise.all(n)}async onBatchBegin(e,t){this.batchBegin!=null&&(await Wa(t),await this.batchBegin(e,t))}async onBatchEnd(e,t){let n=[];this.batchEnd!=null&&(await Wa(t),n.push(this.batchEnd(e,t))),this.yieldEvery==="batch"?n.push(np()):k.isNumber(this.yieldEvery)&&n.push(this.maybeWait(this.currentEpoch,e,t)),await Promise.all(n)}async onTrainBegin(e){this.trainBegin!=null&&(await Wa(e),await this.trainBegin(e))}async onTrainEnd(e){this.trainEnd!=null&&(await Wa(e),await this.trainEnd(e))}};function L3(e,t){return e==null&&(e={}),e instanceof Ll?[e]:Array.isArray(e)&&e[0]instanceof Ll?e:mt(e).map(n=>new P3(n,t))}var lr=class{constructor(){}static registerCallbackConstructor(e,t){k.assert(e>=0&&Number.isInteger(e),()=>`Verbosity level is expected to be an integer >= 0, but got ${e}`),lr.checkForDuplicate(t),lr.constructors[e]==null&&(lr.constructors[e]=[]),lr.constructors[e].push(t)}static checkForDuplicate(e){for(let t in lr.constructors)lr.constructors[+t].forEach(n=>{if(n===e)throw new B("Duplicate callback constructor.")})}static clear(){lr.constructors={}}static createCallbacks(e){let t=[];for(let n in lr.constructors){let r=+n;e>=r&&t.push(...lr.constructors[r])}return t.map(n=>new n)}};lr.constructors={};function W3(e,t,n,r,a,s,i,o,l){let c=new z3,u=[new Kee,...lr.createCallbacks(t)];e!=null&&u.push(...e),u.push(c);let h=new D3(u);return h.setParams({epochs:n,initialEpoch:r,samples:a,steps:s,batchSize:i,verbose:t,doValidation:o,metrics:l}),{callbackList:h,history:c}}function _r(e,t={},n=!1){return Ec(e,re.SerializationMap.getMap().classNameMap,t,"layer",n)}function Gp(e,t){return W(()=>{e.dtype!=="float32"&&(e=e.asType("float32"));let n=Ie(Oc(e),t,!0),r=tc(n.shape,$t()),a=Zt($r(n,r));return be(e,a)})}function Ni(e,t){return W(()=>bt(Oc(Ae(t,e)),-1))}function qp(e,t){return W(()=>bt(Mt(Ae(t,e)),-1))}function Wl(e,t){return W(()=>{let n=Ae(e,t),r=mn(Mt(e),$t(),Number.MAX_VALUE),a=Mt(be(n,r));return L(100,bt(a,-1))})}function Zee(e,t){return W(()=>{let n=mn(t,$t(),Number.MAX_VALUE),r=Sn(se(1,n)),a=mn(e,$t(),Number.MAX_VALUE),s=Sn(se(1,a));return bt(Oc(Ae(r,s)),-1)})}function Yee(e,t){return W(()=>{let n=$r(0,Ae(1,L(e,t)));return bt(Oc(n),-1)})}function Jee(e,t){return W(()=>{let n=$r(0,Ae(1,L(e,t)));return bt(n,-1)})}function Qee(e,t){return W(()=>{let n=Ie(L(e,t),-1),r=jn(L(Ae(1,e),t),-1);return $r(0,se(1,Ae(r,n)))})}function ete(e,t){return W(()=>{let n=Math.log(2),r=Ae(t,e),a=Ae(se(r,fl(L(-2,r))),n);return bt(a,-1)})}function zc(e,t,n=!1){return W(()=>{if(n)t=uc(t);else{let r=Ie(t,t.shape.length-1,!0);t=be(t,r)}return t=mn(t,$t(),1-$t()),_t(Ie(L(e.toFloat(),Sn(t)),t.shape.length-1))})}function Xp(e,t,n=!1){return W(()=>{let r=pl(vQ(e)).toInt();t=mn(t,$t(),1-$t());let a=t.shape,s=al(r,a[a.length-1]).reshape(a);return zc(s,t,n)})}function tte(e,t){if(!k.arraysEqual(e.shape,t.shape))throw new B(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);return W(()=>{let n=t.relu(),r=t.abs().neg();return n.sub(t.mul(e)).add(r.exp().log1p())})}function Kp(e,t){return W(()=>{let n;return n=mn(t,$t(),1-$t()),n=Sn(be(n,Ae(1,n))),bt(tte(e,n),-1)})}function nte(e,t){return W(()=>{let n=mn(e,$t(),1),r=mn(t,$t(),1);return Ie(L(e,Sn(be(n,r))),-1)})}function rte(e,t){return W(()=>{let n=Sn(se($t(),t));return bt(Ae(t,L(e,n)),-1)})}function TA(e,t){return W(()=>{let n=Gp(e,-1),r=Gp(t,-1),a=L(n,r);return _t(Ie(a,-1))})}var Zp={meanSquaredError:Ni,meanAbsoluteError:qp,meanAbsolutePercentageError:Wl,meanSquaredLogarithmicError:Zee,squaredHinge:Yee,hinge:Jee,categoricalHinge:Qee,logcosh:ete,categoricalCrossentropy:zc,sparseCategoricalCrossentropy:Xp,binaryCrossentropy:Kp,kullbackLeiblerDivergence:nte,poisson:rte,cosineProximity:TA};function EA(e){if(typeof e=="string"){if(e in Zp)return Zp[e];let t=`Unknown loss ${e}`;throw e.toLowerCase().includes("softmaxcrossentropy")&&(t=`Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new B(t)}else return e}function CA(e,t){return W(()=>{let n=L(.5,Tn(t)),r=Fc(nr(t,n),e.dtype);return bt(Ea(e,r),-1)})}function RA(e,t){return W(()=>Fc(Ea(Ku(e,-1),Ku(t,-1)),"float32"))}function B3(e,t){return W(()=>rr(e.equal(1),t.equal(1)).sum().cast("float32"))}function ate(e,t){return W(()=>rr(e.equal(1),t.equal(0)).sum().cast("float32"))}function ste(e,t){return W(()=>rr(e.equal(0),t.equal(1)).sum().cast("float32"))}function V3(e,t){return W(()=>{let n=B3(e,t),r=ste(e,t),a=n.add(r);return An(nr(a,0),n.div(a),0).cast("float32")})}function ite(e,t){return W(()=>{let n=B3(e,t),r=ate(e,t),a=n.add(r);return An(nr(a,0),n.div(a),0).cast("float32")})}function U3(e,t){return Kp(e,t)}function j3(e,t){return e.rank===t.rank&&(e=e.squeeze([e.rank-1])),t=t.argMax(-1),t.dtype!==e.dtype&&(t=t.asType(e.dtype)),Ea(e,t).asType("float32")}var ote=Ni,lte=Ni,ute=qp,cte=qp,hte=Wl,dte=Wl,FA=zc,pte=TA,H3=Xp,Yp={binaryAccuracy:CA,categoricalAccuracy:RA,precision:V3,categoricalCrossentropy:FA,sparseCategoricalCrossentropy:H3,mse:ote,MSE:lte,mae:ute,MAE:cte,mape:hte,MAPE:dte,cosine:pte};function fte(e){if(typeof e=="string"&&e in Yp)return Yp[e];if(typeof e!="string"&&e!=null)return e;throw new B(`Unknown metric ${e}`)}function Jp(e){if(Br(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys(Zp))if(Zp[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(Yp))if(Yp[n]===e){t=n;break}return t!==void 0?t:e.name}}function mte(e){let t={Adagrad:()=>di.adagrad(.01),Adadelta:()=>di.adadelta(1,.95,$t()),Adam:()=>di.adam(.001,.9,.999,$t()),Adamax:()=>di.adamax(.002,.9,.999,$t(),0),RMSProp:()=>di.rmsprop(.001,.9,0,$t()),SGD:()=>di.sgd(.01)};if(t.adagrad=t.Adagrad,t.adadelta=t.Adadelta,t.adam=t.Adam,t.adamax=t.Adamax,t.rmsprop=t.RMSProp,t.sgd=t.SGD,e in t)return t[e]();throw new B(`Unknown Optimizer ${e}`)}var G3=1*1024*1024;function q3(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!MA(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let r=JSON.stringify(e);r.length>G3&&console.warn(`User-defined metadata of model "${t}" is too large in size (length=${r.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${G3}.`)}}function MA(e){if(e===null)return!0;if(typeof e=="object")if(Object.getPrototypeOf(e)===Object.prototype){let t=Object.keys(e);for(let n of t)if(typeof n!="string"||!MA(e[n]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!MA(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function wte(e,t,n,r=console.log){let a=yte(e),s=["Layer (type)","Output shape","Param #"];a?(t=t||65,n=n||[.45,.85,1]):(t=t||98,n=n||[.33,.55,.67,1]),n[n.length-1]<=1&&(n=n.map(u=>Math.floor(t*u)));let i;if(!a){s.push("Receives inputs"),i=[];for(let u in e.nodesByDepth)i.push(...e.nodesByDepth[u])}r("_".repeat(t)),Qp(s,n,r),r("=".repeat(t));let o=e.layers;for(let u=0;u<o.length;++u)a?gte(o[u],n,r):xte(o[u],n,i,r),r((u===o.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=Ate(e),c=jp(e.nonTrainableWeights);r(`Total params: ${l+c}`),r(`Trainable params: ${l}`),r(`Non-trainable params: ${c}`),r("_".repeat(t))}function Ate(e){let t;return e.collectedTrainableWeights!=null?t=jp(e.collectedTrainableWeights):t=jp(e.trainableWeights),t}function yte(e){let t=!0,n=[],r=[];for(let a in e.nodesByDepth)n.push(e.nodesByDepth[a]);for(let a of n){if(a.length>1||a.length===1&&a[0].inboundLayers.length>1){t=!1;break}r.push(...a)}if(t)for(let a of e.layers){let s=!1;for(let i of a.inboundNodes)if(r.indexOf(i)!==-1)if(s){t=!1;break}else s=!0;if(!t)break}return t}function Qp(e,t,n=console.log){let r="";for(let a=0;a<e.length;++a)a>0&&(r=r.slice(0,r.length-1)+" "),r+=e[a],r=r.slice(0,t[a]),r+=" ".repeat(t[a]-r.length);n(r)}function gte(e,t,n){let r;try{r=JSON.stringify(e.outputShape)}catch(o){r="multiple"}let a=e.name,s=e.getClassName(),i=[`${a} (${s})`,r,e.countParams().toString()];Qp(i,t,n)}function xte(e,t,n,r){let a;try{a=JSON.stringify(e.outputShape)}catch(u){a="multiple"}let s=[];for(let u of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(u)===-1))for(let h=0;h<u.inboundLayers.length;++h){let p=u.inboundLayers[h].name,d=u.nodeIndices[h],f=u.tensorIndices[h];s.push(`${p}[${d}][${f}]`)}let i=e.name,o=e.getClassName(),l=s.length===0?"":s[0],c=[`${i} (${o})`,a,e.countParams().toString(),l];Qp(c,t,r);for(let u=1;u<s.length;++u)Qp(["","","",s[u]],t,r)}function X3(e,t,n){return(e==="inboundNodes"||e==="outputLayers"||e==="inputLayers")&&t===0&&typeof n=="string"}function Pc(e,t){if(e===null)return null;if(typeof e=="string")return bi(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],r=e.length;for(let a=0;a<r;++a){let s=e[a];X3(t,a,s)?n.push(s):n.push(Pc(s,t))}return n}else{let n={};for(let r of Object.keys(e)){let a=e[r];if(r==="name"&&typeof a=="string")n[r]=a;else{let s=bi(r);n[s]=Pc(a,s)}}return n}}function OA(e,t){if(e==null)return null;if(typeof e=="string")return ua(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],r=e.length;for(let a=0;a<r;++a){let s=e[a];X3(t,a,s)?n.push(s):n.push(OA(s,t))}return n}else{let n={};for(let r of Object.keys(e)){let a=e[r],s=ua(r);(r==="name"||r==="className")&&typeof a=="string"?n[s]=a:n[s]=OA(a,r)}return n}}var $A="3.0.0";function _te(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return me(t,e.dtype)}catch(n){throw new B(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}var Si=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof Si)for(let t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(let t of e)this.add(t.key,t.value)}}add(e,t,n){if(this.id2Value[e.id]==null)this.id2Value[e.id]=_te(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new B(`Duplicate key: name=${e.name}, id=${e.id}`);return this}addFeed(e){this.add(e.key,e.value)}hasKey(e){return this.id2Value[e.id]!=null}names(){return Object.keys(this.name2Id)}getValue(e){if(e instanceof wr){if(this.id2Value[e.id]==null)throw new B(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{let t=this.name2Id[e];if(t==null)throw new B(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof wr){if(this.id2Value[e.id]==null)throw new B(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{let t=this.name2Id[e];if(t==null)throw new B(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&Se(this.id2Mask)}},DA={},K3={};function Lc(e,t,n,r){let a=n==null?!1:n.training,s=Array.isArray(e),i=s?e:[e],o=i.map(f=>f.name),l=[],c=t.names();for(let f of o)c.indexOf(f)!==-1?l.push(t.getValue(f)):l.push(null);r!=null&&(r.maxNumTensors=-Infinity,r.minNumTensors=Infinity);let u=o.join(",")+"|"+t.names().join(","),h,p;if(DA[u]==null){let f=bte(i,t);h=f.sorted,p=f.recipientCounts,DA[u]=h,K3[u]=p}h=DA[u],p={},a||Object.assign(p,K3[u]);let d=new Si(t);for(let f=0;f<h.length;++f){if(r!=null){let E=Ad().numTensors;E>r.maxNumTensors&&(r.maxNumTensors=E),E<r.minNumTensors&&(r.minNumTensors=E)}let m=h[f],A=m.sourceLayer;if(A instanceof Pl)continue;let y=[],g=[],_=[],x=!1;for(let E of m.inputs){let M=d.getValue(E),$=d.getMask(E);y.push(M),g.push($),$!=null&&(x=!0),a||(p[E.name]--,p[E.name]===0&&!t.hasKey(E)&&o.indexOf(E.name)===-1&&!M.isDisposed&&E.sourceLayer.stateful!==!0&&_.push(M))}x&&(n=n||{},n.mask=g[0]);let w=mt(A.apply(y,n)),b=null;A.supportsMasking&&(b=A.computeMask(y,g));let N=vte(m),T=Array.isArray(N)?N:[N];for(let E=0;E<T.length;++E){d.hasKey(T[E])||d.add(T[E],w[E],Array.isArray(b)?b[0]:b);let M=o.indexOf(T[E].name);M!==-1&&(l[M]=w[E])}a||Se(_)}return d.disposeMasks(),s?l:l[0]}function bte(e,t){k.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],r={};if(e.length===1){let a=Z3(e[0],t);n=a.sorted,r=a.recipientMap}else{let a=new Set;for(let s of e){let{sorted:i,recipientMap:o}=Z3(s,t);for(let l of i)a.has(l.name)||(n.push(l),a.add(l.name));for(let l in o)r[l]==null&&(r[l]=new Set),o[l].forEach(c=>r[l].add(c))}}return{sorted:n,recipientCounts:kte(r)}}function kte(e){let t={};for(let n in e)t[n]=e[n].size;return t}function Z3(e,t){let n=new Set,r=[],a={};for(let o of t.names())n.add(o);let s=[],i=[];for(s.push(e);s.length>0;){let o=s[s.length-1];if(n.has(o.name)){s.pop();continue}let l=i[i.length-1]===s.length-1;if(o.inputs.length===0||l)s.pop(),r.push(o),n.add(o.name),l&&i.pop();else{i.push(s.length-1);for(let c of o.inputs)a[c.name]==null&&(a[c.name]=new Set),a[c.name].add(o.name),!n.has(c.name)&&s.push(c)}}return{sorted:r,recipientMap:a}}function vte(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let r=0;r<e.sourceLayer.inboundNodes.length;++r)for(let a of e.sourceLayer.inboundNodes[r].outputTensors)if(a.id===e.id){n=r;break}t=e.sourceLayer.getOutputAt(n)}return t}var Hr=class extends Ge{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=Vp(y)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],za(this.inputs).length!==this.inputs.length)throw new B(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);za(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let g=y.sourceLayer,_=y.nodeIndex,x=y.tensorIndex;this.outputLayers.push(g),this.outputLayersNodeIndices.push(_),this.outputLayersTensorIndices.push(x)}for(let y of this.inputs){let g=y.sourceLayer,_=y.nodeIndex,x=y.tensorIndex;Br(_===0,"input layer has >1 nodes"),Br(x===0,"input layer has >1 tensors"),this.inputLayers.push(g),this.inputLayersNodeIndices.push(_),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let g=this.inputLayers[y];if(!(g instanceof Pl))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${g.getClassName()}.`);this.inputNames.push(g.name),this.feedInputShapes.push(g.batchInputShape),this.feedInputNames.push(g.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},r={},a={},s={},i=[],o=(y,g,_,x,w,b)=>{(x==null||w==null||b==null)&&(x=y.sourceLayer,w=y.nodeIndex,b=y.tensorIndex);let N=x.inboundNodes[w];if(_.indexOf(N)!==-1)throw new gr(`The tensor ${y.name} at layer "${x.name}" is part of a cycle.`);if(g.indexOf(N)!==-1)return;this.containerNodes.add(Hr.nodeKey(x,w)),x.id in s||(s[x.id]=Object.keys(s).length),_.indexOf(N)===-1&&_.push(N);let T=N.inboundLayers.length;for(let E=0;E<T;E++){let M=N.inputTensors[E],$=N.inboundLayers[E],P=N.nodeIndices[E],V=N.tensorIndices[E];o(M,g,_,$,P,V)}for(g.push(N);_.indexOf(N)>=0;)_.splice(_.indexOf(N),1);i.push(N)},l=[],c=[];for(let y of this.outputs)o(y,l,c);let u=i.slice().reverse();for(let y of u){n[y.id]=y,y.id in t||(t[y.id]=0);let g=t[y.id],_=r[y.outboundLayer.id]==null?0:r[y.outboundLayer.id];g=Math.max(g,_),r[y.outboundLayer.id]=g,a[y.outboundLayer.id]=y.outboundLayer,t[y.id]=g;for(let x=0;x<y.inboundLayers.length;x++){let w=y.inboundLayers[x],b=y.nodeIndices[x],N=w.inboundNodes[b],T=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(g+1,T),n[N.id]=N}}let h={};for(let y in t){let g=t[y];g in h||(h[g]=[]),h[g].push(n[y])}let p={};for(let y in r){let g=r[y];g in p||(p[g]=[]),p[g].push(a[y])}let d=Object.keys(p).map(y=>parseInt(y,10)).sort(Rp);this.layers=[];for(let y of d){let g=p[y];g.sort((_,x)=>{let w=s[_.id],b=s[x.id];return w<b?-1:w>b?1:0});for(let _ of g)_ instanceof Hr&&this.internalContainerRefs.push(_),this.layers.push(_)}this.layersByDepth=p,d=Object.keys(h).map(y=>parseInt(y,10)).sort(Rp);let f=this.inputs.slice(),m=[];for(let y of d)for(let g of h[y]){let _=g.outboundLayer;if(_!=null){for(let x of g.inputTensors)if(f.indexOf(x)===-1)throw new gr(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${_.name}". The following previous layers were accessed without issue: ${m}`);for(let x of g.outputTensors)f.push(x);m.push(_.name)}}this.nodesByDepth=h;let A=this.layers.map(y=>y.name);for(let y of A){let g=A.filter(_=>_===y).length;if(g!==1)throw new gr(`The name "${y}" is used ${g} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(A))}this.outboundNodes=[],this.inboundNodes=[],new Hp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new B("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},r=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new B(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,r++}let a=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)a.push([n[i],e[s]]);else if(t)throw new B(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new B(`${s.length} of ${r} weights are not set: ${s}`)}SA(a)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${$A}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=OA(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return W(()=>{e=mt(e);let n=new Si;for(let r=0;r<this.inputs.length;++r)n.add(this.inputs[r],e[r]);return Lc(this.outputs,n,t)})}computeMask(e,t){return W(()=>{e=mt(e);let n;return t==null?n=_i(null,e.length):n=mt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Up(e);if(t.length!==this.inputLayers.length)throw new B(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],c=o.name+"_0_0";n[c]=l}let r=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Rp);if(r.length>1)for(let i of r){let o=this.nodesByDepth[i];for(let l of o){let c=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(c.id)!==-1)continue;let u=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],A=l.nodeIndices[f],y=l.tensorIndices[f],g=`${m.name}_${A}_${y}`,_=n[g];u.push(_)}let h=c.computeOutputShape(xn(u)),p=Up(h),d=c.inboundNodes.indexOf(l);for(let f=0;f<p.length;f++){let m=`${c.name}_${d}_${f}`;n[m]=p[f]}}}let a=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],u=`${o.name}_${l}_${c}`;s.push(u)}for(let i=0;i<s.length;i++){let o=s[i];Br(o in n),a.push(n[o])}return xn(a)}runInternalGraph(e,t){t==null&&(t=_i(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],c=e[o],u=t[o];n[l.id]=[c,u]}let r=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Rp);for(let o of r){let l=this.nodesByDepth[o];for(let c of l){let u=c.outboundLayer,h=c.inputTensors,p=c.outputTensors,d=new Array;for(let f of h)f.id in n&&d.push(n[f.id]);if(d.length===h.length){let f={},m,A,y,g;if(c.callArgs!=null&&(f=c.callArgs),d.length===1){let[_,x]=d[0];f.mask==null&&(f.mask=x),y=mt(u.call(_,f)),g=mt(u.computeMask(_,x)),m=[_],A=[x]}else m=d.map(_=>_[0]),A=d.map(_=>_[1]),f.mask==null&&(f.mask=A),y=mt(u.call(m,f)),g=mt(u.computeMask(m,A));if(u.activityRegularizer)throw new Me("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let _=0;_<p.length;++_){let x=p[_],w=y[_],b=g[_];n[x.id]=[w,b]}}}}let a=[],s=[],i=[];for(let o of this.outputs){Br(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,c]=n[o.id];i.push(l.shape),a.push(l),s.push(c)}return[a,s,i]}buildNodeConversionMap(e){let t={},n;for(let r of this.layers){n=r instanceof Hr?1:0;for(let a=0;a<r.inboundNodes.length;a++){let s=Hr.nodeKey(r,a);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new B(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new B("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new B(`No such layer: ${e}`)}calculateLosses(){return W(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let r=Hr.nodeKey(t,n);this.containerNodes.has(r)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let u=0;u<s.inboundNodes.length;u++){let h=s.inboundNodes[u],p=Hr.nodeKey(s,u),d={};if(this.containerNodes.has(p)){if(h.callArgs)try{JSON.stringify(h.callArgs),d=h.callArgs}catch(f){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${h.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),d={}}if(h.inboundLayers.length>0){let f=[];for(let m=0;m<h.inboundLayers.length;m++){let A=h.inboundLayers[m],y=h.nodeIndices[m],g=h.tensorIndices[m],_=Hr.nodeKey(A,y),x=t[_];x==null&&(x=0),f.push([A.name,x,g,d])}l.push(f)}}}let c={};c.name=s.name,c.className=i,c.config=o,c.inboundNodes=l,n.push(c)}e.layers=n;let r=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=Hr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[s];r.push([i.name,c,u])}e.inputLayers=r;let a=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=Hr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[s];a.push([i.name,c,u])}return e.outputLayers=a,e}static fromConfig(e,t,n={},r=!1){let a={},s={};function i(m,A){m.name in s?s[m.name].push(A):s[m.name]=[A]}function o(m,A){let y=[],g;for(let _ of A){let x=_[0],w=_[1],b=_[2];if(g=_[3]==null?{}:_[3],!(x in a)){i(m,A);return}let N=a[x];if(N.inboundNodes.length<=w){i(m,A);return}let T=N.inboundNodes[w];y.push(T.outputTensors[b])}y.length>0&&m.apply(xn(y),g)}function l(m){let A=m.name,y=_r(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(r),a[A]=y,m.inboundNodes.forEach(g=>{if(!(g instanceof Array))throw new B(`Corrupted configuration, expected array for nodeData: ${g}`);i(y,g)})}let c=t.name,u=t.layers;for(let m of u)l(m);for(;!ZJ(s);)for(let m of u){let A=a[m.name];if(A.name in s){let y=s[A.name];delete s[A.name];for(let g of y)o(A,g)}}let h=[],p=[],d=t.inputLayers;for(let m of d){let A=m[0],y=m[1],g=m[2];Br(A in a);let _=a[A].inboundNodes[y].outputTensors;h.push(_[g])}let f=t.outputLayers;for(let m of f){let A=m[0],y=m[1],g=m[2];Br(A in a);let _=a[A].inboundNodes[y].outputTensors;p.push(_[g])}return new e({inputs:h,outputs:p,name:c})}get stateful(){if(this._stateful)throw new B("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){W(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function Ite(e,t,n){let r=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>null);if(r===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==r)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let a=[];return t.forEach(s=>{s in e?a.push(e[s]):a.push(null)}),a}else throw new Error(`The model has multiple (${r}) outputs, so ${n} must be either an array with ${r} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function Y3(e,t){return Ite(e,t,"classWeight")}async function J3(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let a=W(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await a.data());Se(a);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Bt(i,"float32")}else return null}function Nte(e,t){return L(e,t)}var Ste=32;function e7(e,t){let n,r,a=t;n=a.xs,r=a.ys,k.assert(n!=null&&r!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let s=Q3("input",e.inputNames,n),i=Q3("output",e.outputNames,r),o=s[0].shape[0];k.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),k.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)k.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)k.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function Q3(e,t,n){if(n instanceof Ke)return[n];if(Array.isArray(n))return k.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let r=[];for(let a of t){if(n[a]==null)throw new B(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);r.push(n[a])}return r}}function Tte(e){if(e.length===3)throw new Me("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function Cte(e,t,n){let r=n.batchesPerEpoch!=null;if(k.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),k.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),k.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let a=n.validationData!=null,s,i;if(a)if(t7(n.validationData))k.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let A=Tte(n.validationData);s=A.xs,i=A.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;a?c=l.slice().concat(l.map(A=>"val_"+A)):c=l.slice();let u=L3(n.callbacks,n.yieldEvery),h=n.verbose==null?1:n.verbose,{callbackList:p,history:d}=W3(u,h,n.epochs,null,null,Ete(t,n),null,a,c);p.setModel(e),e.history=d,await p.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let A={};await p.onEpochBegin(f);let y=0,g=0;for(r||(m=await t.iterator());r?y<n.batchesPerEpoch:!0;){let _=await m.next();if(r&&_.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(_.value!=null){let{xs:x,ys:w}=e7(e,_.value),b={};b.batch=g,b.size=x[0].shape[0],await p.onBatchBegin(g,b);let N=[];if(n.classWeight!=null){let M=Y3(n.classWeight,e.outputNames);for(let $=0;$<M.length;++$)N.push(await J3(w[$],null,M[$]))}let T=x.concat(w).concat(N),E=o(T);Se(T);for(let M=0;M<l.length;++M){let $=l[M],P=E[M];b[$]=P,Wt(P)}await p.onBatchEnd(g,b),O3(b),g++,y++}if(r?y>=n.batchesPerEpoch:_.done){if(a){let x;t7(n.validationData)?x=mt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):x=mt(e.evaluate(s,i,{batchSize:n.validationBatchSize==null?Ste:n.validationBatchSize,verbose:0}));for(let w=0;w<e.metricsNames.length;++w)A[`val_${e.metricsNames[w]}`]=x[w]}break}if(e.stopTraining_)break}if(await p.onEpochEnd(f,A),f++,e.stopTraining_)break}return await p.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function Ete(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function t7(e){return typeof e.iterator=="function"}function Rte(e){return typeof e.next=="function"}async function Fte(e,t,n){n=n||{};let r=n.batches!=null,a=e.testFunction,s=[];if(n.verbose>0)throw new Me("Verbose mode is not implemented yet.");k.assert(!r||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);let i=Rte(t)?t:await t.iterator(),o=0,l=0;for(;r?l<n.batches:!0;){let c=await i.next();if(s=W(()=>{if(c.value){let{xs:u,ys:h}=e7(e,c.value),p=u.concat(h),d=W(()=>a(p));if(Se(p),l===0)for(let m=0;m<d.length;++m)s.push(ke(0));let f=p[0].shape[0];for(let m=0;m<d.length;++m){let A=d[m],y=s[m];s[m]=W(()=>se(s[m],L(f,A))),l>0&&Se(y)}Se(d),o+=f,++l}return s}),c.done){r&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let c=0;c<s.length;++c){let u=s[c];s[c]=be(s[c],o),Se(u)}return xn(s)}function zA(e){k.assert(e>0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function Wc(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(r=>Ii(r,t,n-t)):Ii(e,t,n-t)}function PA(e,t){return W(()=>e==null?null:Array.isArray(e)?e.map(n=>PA(n,t)):x3(e,t.dtype==="int32"?t:t.toInt()))}function LA(e,t){let n=[],r=0,a=null;for(;r<e;)a=r+t,a>=e&&(a=e),n.push([r,a]),r=a;return n}async function Mte(e,t,n,r,a,s,i,o,l,c,u,h,p,d,f){a==null&&(a=32),s==null&&(s=1),u==null&&(u=!0),p==null&&(p=0);let m=!1;if(l!=null&&c!=null&&(m=!0),f!=null&&(m=!0,d==null))throw new B("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let A=e.checkNumSamples(n,a,d,"steps_per_epoch"),y;A!=null&&(y=xr(0,A)),i==null&&(i=1);let{callbackList:g,history:_}=W3(o,i,s,p,A,d,a,m,h);g.setModel(e),e.history=_,await g.onTrainBegin(),e.stopTraining_=!1;for(let x=p;x<s;++x){await g.onEpochBegin(x);let w={};if(d!=null)throw new Me("stepsPerEpoch mode is not implemented yet.");{if(u==="batch")throw new Me("batch shuffling is not implemneted yet");u&&k.shuffle(y);let b=Bt(y),N=LA(A,a);for(let T=0;T<N.length;++T){let E={};if(await g.onBatchBegin(T,E),W(()=>{let M=N[T][0],$=N[T][1],P=Ii(b,M,$-M);E.batch=T,E.size=$-M;let V=PA(n,P),G=t(V);for(let U=0;U<r.length;++U){let K=r[U],X=G[U];E[K]=X,Wt(X)}if(T===N.length-1&&m){let U=e.testLoop(l,c,a);for(let K=0;K<r.length;++K){let X=r[K],ee=U[K];Wt(ee),w["val_"+X]=ee}}}),await g.onBatchEnd(T,E),O3(E),e.stopTraining_)break}b.dispose()}if(await g.onEpochEnd(x,w),e.stopTraining_)break}return await g.onTrainEnd(),await e.history.syncData(),e.history}async function Ote(e,t,n,r={}){if(e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;let a,s,i,o,l,c,u;try{let h=r.batchSize==null?32:r.batchSize;zA(h);let p=!1,d=await e.standardizeUserData(t,n,r.sampleWeight,r.classWeight,p,h);a=d[0],s=d[1],u=d[2];let f=!1,m;if(r.validationData!=null&&r.validationData.length>0){if(f=!0,r.validationData.length===2)i=r.validationData[0],o=r.validationData[1];else throw r.validationData.length===3?new Me("validationData including sample weights is not supported yet."):new B(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${r.validationData} is invalid.`);let b=!0,N=await e.standardizeUserData(i,o,null,null,b,h);l=N[0],c=N[1],m=l.concat(c)}else if(r.validationSplit!=null&&r.validationSplit>0&&r.validationSplit<1){f=!0;let b=Math.floor(a[0].shape[0]*(1-r.validationSplit)),N=a[0].shape[0];l=Wc(a,b,N),a=Wc(a,0,b),c=Wc(s,b,N),s=Wc(s,0,b),m=l.concat(c)}else r.validationSteps!=null&&(f=!0);let A=a.concat(s).concat(u);e.checkTrainableWeightsConsistency();let y=e.makeTrainFunction(),g=e.getDedupedMetricsNames(),_,x;f?(e.makeTestFunction(),_=e.testFunction,x=g.slice().concat(g.map(b=>"val_"+b))):(_=null,m=[],x=g.slice());let w=L3(r.callbacks,r.yieldEvery);return await Mte(e,y,A,g,h,r.epochs,r.verbose,w,_,m,r.shuffle,x,r.initialEpoch,null,null)}finally{e.isTraining=!1,Ti(a,t),Ti(s,n),Ti(l,i),Ti(c,o),u!=null&&Se(u)}}function n7(e){let t=[];e instanceof Ke&&(e=[e]);for(let n=0;n<e.length;++n){let r=e[n];if(r.rank===1)t.push(Mc(r,1));else{if(r.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");t.push(r)}}return t}function Ti(e,t){if(e==null)return;let n=[];if(t instanceof Ke)n.push(t.id);else if(Array.isArray(t))t.forEach(a=>n.push(a.id));else if(t!=null)for(let a in t){let s=t[a];n.push(s.id)}let r=[];if(e instanceof Ke)n.indexOf(e.id)===-1&&r.push(e);else if(Array.isArray(e))e.forEach(a=>{n.indexOf(a.id)===-1&&r.push(a)});else if(e!=null)for(let a in e){let s=e[a];n.indexOf(s.id)===-1&&r.push(s)}r.forEach(a=>{a.isDisposed||a.dispose()})}function $te(e){return e instanceof Ke}function WA(e){return Array.isArray(e)}function r7(e){return!$te(e)&&!WA(e)}function a7(e,t,n,r=!0,a=""){if(t==null||t.length===0){if(e!=null){let i=!1;if(WA(e)&&e.length>0)i=!0;else if(r7(e)){for(let o in e)if(e.hasOwnProperty(o)){i=!0;break}}else i=!0;if(i)throw new B(`Error when checking model ${a} expected no data, but got ${e}`)}return[]}if(e==null)return t.map(i=>null);let s;if(r7(e)){e=e,s=[];for(let i of t){if(e[i]==null)throw new B(`No data provided for "${i}". Need data for each key in: ${t}`);s.push(e[i])}}else if(WA(e)){if(e=e,e.length!==t.length)throw new B(`Error when checking model ${a}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${t.length} Tensor(s), but instead got the following list of Tensor(s): ${e}`);s=e}else{if(e=e,t.length>1)throw new B(`The model ${a} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);s=[e]}if(s=n7(s),n!=null)for(let i=0;i<t.length;++i){if(n[i]==null)continue;let o=s[i];if(o.shape.length!==n[i].length)throw new B(`Error when checking ${a}: expected ${t[i]} to have ${n[i].length} dimension(s). but got array with shape ${o.shape}`);for(let l=0;l<n[i].length;++l){if(l===0&&!r)continue;let c=o.shape[l],u=n[i][l];if(u!=null&&u>=0&&c!==u)throw new B(`Error when checking ${a}: expected ${t[i]} to have shape [${n[i]}], but got array with shape [${o.shape}].`)}}return s}function Dte(e,t,n){let r=za(e.map(s=>s.shape[0]));r.sort();let a=za(t.map(s=>s.shape[0]));if(a.sort(),r.length>1)throw new B(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map(s=>s.shape))}`);if(a.length>1)throw new B(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map(s=>s.shape))}`);if(r.length>0&&a.length>0&&!k.arraysEqual(r,a))throw new B(`Input Tensors should have the same number of samples as target Tensors. Found ${r[0]} input sample(s) and ${a[0]} target sample(s).`)}function zte(e,t,n){let r=[Ni,Kp,zc];for(let a=0;a<e.length;++a){let s=e[a],i=t[a],o=n[a];if(i!=null){if(i===zc&&s.shape[s.shape.length-1]===1)throw new B(`You are passing a target array of shape ${s.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(r.indexOf(i)!==-1){let l=s.shape.slice(1),c=o.slice(1);for(let u=0;u<l.length;++u){let h=l[u],p=c[u];if(p!=null&&h!==p)throw new B(`A target Tensor with shape ${s.shape} was passed for an output of shape ${o}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}function s7(e,t,n,r=!0,a=""){let s;if(Array.isArray(e)){if(e.length!==t.length)throw new B(`Error when checking model ${a}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${t.length} Tensor(s), but instead got ${e.length} Tensors(s).`);s=e}else{if(t.length>1)throw new B(`The model expects ${t.length} ${a} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(e.shape)}.`);s=[e]}if(n!=null)for(let i=0;i<t.length;++i){if(n[i]==null)continue;let o=s[i];if(o.shape.length!==n[i].length)throw new B(`Error when checking ${a}: expected ${t[i]} to have ${n[i].length} dimension(s), but got array with shape ${JSON.stringify(o.shape)}`);for(let l=0;l<n[i].length;++l){if(l===0&&!r)continue;let c=o.shape[l],u=n[i][l];if(u!=null&&u!==c)throw new B(`Error when checking ${a}: expected ${t[i]} to have shape ${JSON.stringify(n[i])} but got array with shape ${JSON.stringify(o.shape)}.`)}}}function Pte(e,t){if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>[]);let n;if(typeof e=="string"||typeof e=="function")n=[e];else if(Array.isArray(e)||typeof e=="object")n=e;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${e}`);if(Array.isArray(n))return t.map(r=>n);{let r=[];for(let a of t){let s=n.hasOwnProperty(a)?n[a]:[];Array.isArray(s)||(s=[s]),r.push(s)}return r}}var Lte="layers-model",ca=class extends Hr{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new B("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");wte(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=mte(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof ia))throw new B("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let s in e.loss)if(this.outputNames.indexOf(s)===-1)throw new B(`Unknown entry in loss dictionary: "${s}". Only expected the following keys: ${this.outputNames}`);for(let s of this.outputNames)e.loss[s]==null&&console.warn(`Output "${s}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${s} during training`),t.push(EA(e.loss[s]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new B(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${e.loss}.`);t=e.loss.map(s=>EA(s))}else{let s=EA(e.loss);this.outputs.forEach(i=>{t.push(s)})}this.lossFunctions=t,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let s=0;s<this.outputs.length;++s){let i=this.internalOutputShapes[s],o=this.outputNames[s];this.feedOutputNames.push(o),this.feedOutputShapes.push(i),this.feedLossFns.push(this.lossFunctions[s])}let n=[];this.metrics=e.metrics,this.metricsNames=["loss"],this.metricsTensors=[],ki("loss",()=>{for(let s=0;s<this.outputs.length;++s){if(n.indexOf(s)!==-1)continue;let i=this.lossFunctions[s];this.outputs.length>1&&(this.metricsTensors.push([i,s]),this.metricsNames.push(this.outputNames[s]+"_loss"))}});let r=Pte(e.metrics,this.outputNames),a=(s,i,o)=>{this.outputNames.length>1&&(i=this.outputNames[s]+"_"+i),this.metricsNames.push(i),this.metricsTensors.push([o,s])};ki("metric",()=>{for(let s=0;s<this.outputs.length;++s){if(n.indexOf(s)!==-1)continue;let i=r[s];(o=>{let l="",c,u,h;for(let p of o){if(typeof p=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(p)!==-1){let f=this.internalOutputShapes[s];f[f.length-1]===1||this.lossFunctions[s]===Kp?["accuracy","acc"].indexOf(p)!==-1?u=CA:["crossentropy","ce"].indexOf(p)!==-1&&(u=U3):this.lossFunctions[s]===Xp?["accuracy","acc"].indexOf(p)!==-1?u=j3:["crossentropy","ce"].indexOf(p)!==-1&&(u=H3):["accuracy","acc"].indexOf(p)!==-1?u=RA:["crossentropy","ce"].indexOf(p)!==-1&&(u=FA);let m;["accuracy","acc"].indexOf(p)!==-1?m="acc":["crossentropy","ce"].indexOf(p)!==-1&&(m="ce"),h=u,c=l+m}else h=fte(p),c=l+Jp(p);let d;ki(c,()=>{d=h}),a(s,c,d)}})(i)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(e,t,n={}){let r=n.batchSize==null?32:n.batchSize;zA(r);let a=!0,s=this.standardizeUserDataXY(e,t,a,r);try{let i=s[0].concat(s[1]);this.makeTestFunction();let o=this.testFunction,l=this.testLoop(o,i,r,n.verbose,n.steps);return xn(l)}finally{Ti(s[0],e),Ti(s[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),Fte(this,e,t)}checkNumSamples(e,t,n,r="steps"){let a;if(n!=null){if(a=null,t!=null)throw new B(`If ${r} is set, batchSize must be null or undefined.Got batchSize = ${t}`)}else if(e!=null)Array.isArray(e)?a=e[0].shape[0]:a=e.shape[0];else throw new B(`Either the input data should have a defined shape, or ${r} shoud be specified.`);return a}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new B("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),r=n?t:[t],a=this.retrieveSymbolicTensors(r),s=new Si;if(e instanceof Ke&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new B(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let o=0;o<this.inputs.length;++o)s.add(this.inputs[o],e[o])}else for(let o of this.inputs){let l=e[o.name];if(l==null)throw new B(`No value is provided for the model's input ${o.name}`);s.add(o,l)}let i=Lc(a,s);return n?i:i[0]}retrieveSymbolicTensors(e){let t=_i(null,e.length),n=e.length;for(let r of this.layers){let a=Array.isArray(r.output)?r.output:[r.output],s=a.map(i=>i.name);for(let i=0;i<e.length;++i){let o=s.indexOf(e[i]);if(o!==-1&&(t[i]=a[o],n--),n===0)break}if(n===0)break}if(n>0){let r=[];throw t.forEach((a,s)=>{a==null&&r.push(e[s])}),new B(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(r)}`)}return t}predictLoop(e,t=32,n=!1){return W(()=>{let r=this.checkNumSamples(e);if(n)throw new Me("Verbose predictLoop() is not implemented yet.");let a=LA(r,t),s=this.outputs.map(i=>[]);for(let i=0;i<a.length;++i)W(()=>{let o=a[i][0],l=a[i][1],c=Wc(e,o,l),u=[];if(Array.isArray(c))for(let p=0;p<c.length;++p)u.push({key:this.inputs[p],value:c[p]});else u.push({key:this.inputs[0],value:c});let h=new Si(u);return Lc(this.outputs,h)}).forEach((o,l)=>s[l].push(o));return xn(s.map(i=>rt(i,0)))})}predict(e,t={}){let n=n7(e);s7(n,this.inputNames,this.feedInputShapes,!1);try{let r=t.batchSize==null?32:t.batchSize;return zA(r),this.predictLoop(n,r)}finally{Ti(n,e)}}predictOnBatch(e){s7(e,this.inputNames,this.feedInputShapes,!0);let t=(Array.isArray(e)?e[0]:e).shape[0];return this.predictLoop(e,t)}standardizeUserDataXY(e,t,n=!0,r){if(this.optimizer_==null)throw new gr("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let a=[];for(let s=0;s<this.feedOutputShapes.length;++s){let i=this.feedOutputShapes[s];this.feedLossFns[s]===Xp?a.push(i.slice(0,i.length-1).concat([1])):a.push(i)}if(e=a7(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=a7(t,this.feedOutputNames,a,!1,"target"),Dte(e,t,null),zte(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&r!=null&&r>0&&e[0].shape[0]%r!=0)throw new B(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${r}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,r,a=!0,s){let[i,o]=this.standardizeUserDataXY(e,t,a,s);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(r!=null){let c=Y3(r,this.outputNames);l=[];for(let u=0;u<c.length;++u)l.push(await J3(o[u],null,c[u]))}return[i,o,l]}testLoop(e,t,n,r=0,a){return W(()=>{let s=this.checkNumSamples(t,n,a,"steps"),i=[];if(r>0)throw new Me("Verbose mode is not implemented yet.");if(a!=null)throw new Me("steps mode in testLoop() is not implemented yet");{let o=LA(s,n),l=Bt(xr(0,s));for(let c=0;c<o.length;++c){let u=o[c][0],h=o[c][1],p=Ii(l,u,h-u),d=PA(t,p),f=e(d);if(c===0)for(let m=0;m<f.length;++m)i.push(ke(0));for(let m=0;m<f.length;++m){let A=f[m];i[m]=se(i[m],L(h-u,A))}}for(let c=0;c<i.length;++c)i[c]=be(i[c],s)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let r=e[n],a=r;i3(e,r)>1&&(a+=`_${i3(e.slice(0,n),r)}`),t.push(a)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let c=[];for(let d=0;d<this.inputs.length;++d)c.push({key:this.inputs[d],value:n[d]});let u=new Si(c),h=Lc(this.outputs,u,{training:!0}),p;for(let d=0;d<this.lossFunctions.length;++d){let f=this.lossFunctions[d](r[d],h[d]);a[d]!=null&&(f=Nte(f,a[d]));let m=bt(f);t.push(m),d===0?p=f:p=se(p,f)}for(let d=0;d<this.metricsTensors.length;++d){let f;if(this.outputs.length>1&&d<this.outputs.length)f=t[d];else{let m=this.metricsTensors[d][0],A=this.metricsTensors[d][1];f=bt(m(r[A],h[A]))}Wt(f),s.push(f)}return p=bt(p),this.calculateLosses().forEach(d=>{p=se(p,d)}),p},o=this.collectedTrainableWeights.map(c=>c.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>W(()=>{let t=[],n,r=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:r[l]});let i=new Si(s),o=Lc(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],u=bt(c(a[l],o[l]));l===0?n=u:n=se(n,u),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let c=this.metricsTensors[l][0],u=this.metricsTensors[l][1],h=bt(c(a[u],o[u]));t.push(h)}return t})}async fit(e,t,n={}){return Ote(this,e,t,n)}async fitDataset(e,t){return Cte(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),r=n[0],a=n[1],s=this.makeTrainFunction()(r.concat(a)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return Se(s),xn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,r=n?this.trainableWeights:this.weights,a=this.getWeights(n);for(let s=0;s<r.length;++s)n&&!r[s].trainable||t.push({name:r[s].originalName,tensor:a[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=Ad().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Ad().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=ua(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>ua(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let r of t)if(typeof n[r]=="string")e[r]=ua(n[r]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[ua(Jp(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>ua(Jp(e)));{let e={};for(let t in this.metrics)e[t]=ua(Jp(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Pc(e.optimizer_config),n=_r(t),r;if(typeof e.loss=="string")r=bi(e.loss);else if(Array.isArray(e.loss))r=e.loss.map(s=>bi(s));else if(e.loss!=null){r={};for(let s in e.loss)r[s]=bi(e.loss[s])}let a;if(Array.isArray(e.metrics))a=e.metrics.map(s=>bi(s));else if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=bi(e.metrics[s])}this.compile({loss:r,metrics:a,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=fn.getSaveHandlers(e);if(i.length===0)throw new B(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new B(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new B("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await fn.encodeWeights(this.getNamedWeights(t)),r=!1,a=null,s={modelTopology:this.toJSON(a,r),format:Lte,generatedBy:`TensorFlow.js tfjs-layers v${$A}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await fn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=fn.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;q3(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){q3(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};ca.className="Model";re.registerClass(ca);var i7=class extends ca{};i7.className="Functional";re.registerClass(i7);async function Wte(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=Pc(n),a=_r(r,t);if(e.weightsManifest!=null){let s=await fn.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),Se(s)}return a}async function Vte(e,t){if(t==null&&(t={}),typeof e=="string"){let n=fn.getLoadHandlers(e,t);if(n.length===0)n.push(fn.browserHTTPRequest(e,t));else if(n.length>1)throw new B(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Bte(e,void 0,t)}async function Bte(e,t,n){if(n==null&&(n={}),e.load==null)throw new B("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await e.load(),a=r.modelTopology;a.model_config!=null&&(a=a.model_config);let s=n.strict==null?!0:n.strict,i=r.weightData!=null&&r.weightSpecs!=null&&s,o=_r(Pc(a),t,i),l=r.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),r.userDefinedMetadata!=null&&o.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new B("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:c,optimizerWeights:u}=Ute(r.weightData,r.weightSpecs);o.loadWeights(c,s),o.optimizer!=null&&u.length>0&&await o.optimizer.setWeights(u),Se(c),Se(u.map(h=>h.tensor))}return o}function Ute(e,t){let n=fn.decodeWeights(e,t),r={},a=[];return t.forEach(s=>{s.group==="optimizer"?a.push({name:s.name,tensor:n[s.name]}):r[s.name]=n[s.name]}),{modelWeights:r,optimizerWeights:a}}var Bl=class extends ca{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Vp("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new B(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Bl||e instanceof ca,n;if(t){if(n=e,n.outputs.length!==1)throw new B("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new B("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new B("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let r=M3({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(r)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new B(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new B("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=F3(this.outputs[0])}this.inboundNodes=[],new Hp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:_i(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(r=>r.shape),outputShapes:this.outputs[0].shape})}else{let r=e.apply(this.outputs[0]);if(Array.isArray(r))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[r],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(dt(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new ca({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new gr("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new gr("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new gr("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new gr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new B("Legacy serialization format not supported yet.");a=t}else k.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Bl))throw new Me(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=_r(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new B("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new B("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Bl.className="Sequential";re.registerClass(Bl);function jte(e){return new ca(e)}function Hte(e){return new Bl(e)}function Gte(e,t){return t==null&&(t={}),Vte(e,t)}function k3(e){return M3(e)}function qte(e,t){lr.registerCallbackConstructor(e,t)}var $n=class extends re.Serializable{getConfig(){return{}}},o7=class extends $n{apply(e,t=1){return IQ(e,t)}};o7.className="elu";re.registerClass(o7);var l7=class extends $n{apply(e){return Dd(e)}};l7.className="selu";re.registerClass(l7);var u7=class extends $n{apply(e){return zr(e)}};u7.className="relu";re.registerClass(u7);var c7=class extends $n{apply(e){return W(()=>Al(6,zr(e)))}};c7.className="relu6";re.registerClass(c7);var h7=class extends $n{apply(e){return e}};h7.className="linear";re.registerClass(h7);var d7=class extends $n{apply(e){return In(e)}};d7.className="sigmoid";re.registerClass(d7);var p7=class extends $n{apply(e){return SQ(e)}};p7.className="hardSigmoid";re.registerClass(p7);var f7=class extends $n{apply(e){return fl(e)}};f7.className="softplus";re.registerClass(f7);var m7=class extends $n{apply(e){return NQ(e)}};m7.className="softsign";re.registerClass(m7);var A7=class extends $n{apply(e){return cl(e)}};A7.className="tanh";re.registerClass(A7);var BA=class extends $n{apply(e,t=-1){return uc(e,t)}};BA.className="softmax";re.registerClass(BA);var y7=class extends $n{apply(e,t=-1){return Ed(e,t)}};y7.className="logSoftmax";re.registerClass(y7);var g7=class extends $n{apply(e,t=1){return W(()=>In(e.mul(t)).mul(e))}};g7.className="swish";re.registerClass(g7);function Ba(e){return e.getClassName()}function VA(e,t={}){return Ec(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function Va(e){if(e==null){let t={};return t.className="linear",t.config={},VA(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},VA(t)}else return e instanceof $n?e:VA(e)}function UA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var x7=class extends re.Serializable{},Bc=class extends x7{constructor(e){super();UA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return W(()=>{let t=Tt([1]);return this.hasL1&&(t=se(t,Ie(L(this.l1,Mt(e))))),this.hasL2&&(t=se(t,Ie(L(this.l2,Oc(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Bc.className="L1L2";re.registerClass(Bc);function Xte(e){return UA(e),new Bc({l1:e!=null?e.l1:null,l2:0})}function Kte(e){return UA(e),new Bc({l2:e!=null?e.l2:null,l1:0})}var w7={l1l2:"L1L2"};function pt(e){return rA(e)}function _7(e,t={}){return Ec(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function xt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in w7?w7[e]:e,config:{}};return _7(t)}else return e instanceof x7?e:_7(e)}var jA=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=De(e);let n=zr(e);return this.maxValue!=null&&(n=mn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};jA.className="ReLU";re.registerClass(jA);var HA=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=De(e);return nc(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};HA.className="LeakyReLU";re.registerClass(HA);var GA=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=gt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=xt(e.alphaRegularizer),this.alphaConstraint=zt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new B(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=dt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r<e.length;++r)n[r]=e[r];this.inputSpec=[new jt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=De(e),ic(e,this.alpha.read())}getConfig(){let e={alphaInitializer:vt(this.alphaInitializer),alphaRegularizer:pt(this.alphaRegularizer),alphaConstraint:Dt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};GA.className="PReLU";re.registerClass(GA);var qA=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Me(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=De(e);return dl(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};qA.className="ELU";re.registerClass(qA);var XA=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=De(e);return n.mul(Fc(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};XA.className="ThresholdedReLU";re.registerClass(XA);var KA=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new BA().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=De(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};KA.className="Softmax";re.registerClass(KA);function Vl(e,t,n){if(typeof e=="number")return _i(e,t);if(e.length!==t)throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r<t;++r){let a=e[r];if(!_Q(a))throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function br(e,t,n,r,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+r-1)/r)}function e0(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+La([n-t,0]);else if(r==="same")e=e*t;else throw new B(`Unsupport padding mode: ${r}.`);return e}function ZA(e,t){return W(()=>(Nt(t),t==="channelsFirst"?nt(e,[0,2,3,1]):e))}function b7(e,t){return W(()=>(Nt(t),t==="channelsFirst"?nt(e,[0,2,3,4,1]):e))}function Zte(e,t,n,r=1,a="valid",s,i=1){return W(()=>{if(s==null&&(s=yr()),Nt(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=nt(e,[0,2,1])),a==="causal")throw new Me("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=_d(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ur(o,n)),o})}function v7(e,t,n,r=[1,1],a="valid",s,i,o=null){return W(()=>{if(s==null&&(s=yr()),Nt(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=ZA(e,s);if(a==="causal")throw new Me("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Ma.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=nt(l,[0,3,1,2])),l})}function Yte(e,t,n,r=[1,1,1],a="valid",s,i){return W(()=>{if(s==null&&(s=yr()),Nt(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=b7(e,s);if(a==="causal")throw new Me("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Lf(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ur(o,n)),s==="channelsFirst"&&(o=nt(o,[0,4,1,2,3])),o})}var YA=class extends Ge{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",YA.verifyArgs(t),this.rank=e,Ut(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Me(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Vl(t.kernelSize,e,"kernelSize"),this.strides=Vl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,qn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Nt(this.dataFormat),this.activation=Va(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=gt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=zt(t.biasConstraint),this.biasRegularizer=xt(t.biasRegularizer),this.activityRegularizer=xt(t.activityRegularizer),this.dilationRate=Vl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Br("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!sA(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ba(this.activation),useBias:this.useBias,biasInitializer:vt(this.biasInitializer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(this.activityRegularizer),biasConstraint:Dt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Vc=class extends YA{constructor(e,t){super(e,t);this.kernel=null,Vc.verifyArgs(t),this.filters=t.filters,Ut(this.filters,"filters"),this.kernelInitializer=gt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=zt(t.kernelConstraint),this.kernelRegularizer=xt(t.kernelRegularizer)}build(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return W(()=>{e=De(e);let n,r=this.bias==null?null:this.bias.read(),a=l3(this.activation.getClassName());if(a!=null&&this.rank===2)n=v7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=Zte(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=v7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=Yte(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Me("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=dt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<n.length;++a){let s=br(n[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:vt(this.kernelInitializer),kernelRegularizer:pt(this.kernelRegularizer),kernelConstraint:Dt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new B(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Uc=class extends Vc{constructor(e){super(2,e);Uc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!sA(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Uc.className="Conv2D";re.registerClass(Uc);var t0=class extends Vc{constructor(e){super(3,e);t0.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};t0.className="Conv3D";re.registerClass(t0);var JA=class extends Uc{constructor(e){super(e);if(this.inputSpec=[new jt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=dt(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new jt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return W(()=>{let n=De(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],c=this.kernelSize[0],u=this.kernelSize[1],h=this.strides[0],p=this.strides[1],d=e0(o,h,c,this.padding),f=e0(l,p,u,this.padding),m=[a,d,f,this.filters];this.dataFormat!=="channelsLast"&&(n=nt(n,[0,2,3,1]));let A=bd(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=nt(A,[0,3,1,2])),this.bias!=null&&(A=Ur(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=dt(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=e0(t[r],o,s,this.padding),t[a]=e0(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};JA.className="Conv2DTranspose";re.registerClass(JA);var k7=class extends Vc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=xt(t.depthwiseRegularizer),this.depthwiseConstraint=zt(t.depthwiseConstraint),this.pointwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=xt(t.pointwiseRegularizer),this.pointwiseConstraint=zt(t.pointwiseConstraint)}build(e){if(e=dt(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new jt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return W(()=>{e=De(e);let n;if(this.rank===1)throw new Me("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=nt(e,[0,2,3,1])),n=rm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=nt(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=vt(this.depthwiseInitializer),e.pointwiseInitializer=vt(this.pointwiseInitializer),e.depthwiseRegularizer=pt(this.depthwiseRegularizer),e.pointwiseRegularizer=pt(this.pointwiseRegularizer),e.depthwiseConstraint=Dt(this.depthwiseConstraint),e.pointwiseConstraint=Dt(this.pointwiseConstraint),e}};k7.className="SeparableConv";var QA=class extends k7{constructor(e){super(2,e)}};QA.className="SeparableConv2D";re.registerClass(QA);var n0=class extends Vc{constructor(e){super(1,e);n0.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!sA(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};n0.className="Conv1D";re.registerClass(n0);var ey=class extends Ge{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return W(()=>{if(e=De(e),this.dataFormat==="channelsLast"){let n=Fp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Fp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Fp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Fp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ey.className="Cropping2D";re.registerClass(ey);var ty=class extends Ge{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Nt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,gQ(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return W(()=>{let n=De(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=nt(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return nt(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ty.className="UpSampling2D";re.registerClass(ty);function Jte(e,t,n=[1,1],r="valid",a,s){return W(()=>{a==null&&(a=yr()),Nt(a);let i=ZA(e,a);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=hl(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}var ny=class extends YA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=gt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=zt(e.depthwiseConstraint),this.depthwiseRegularizer=xt(e.depthwiseRegularizer)}build(e){if(e=dt(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return W(()=>{e=De(e);let n=Jte(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=br(t,this.kernelSize[0],this.padding,this.strides[0]),s=br(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=vt(this.depthwiseInitializer),e.depthwiseRegularizer=pt(this.depthwiseRegularizer),e.depthwiseConstraint=Dt(this.depthwiseRegularizer),e}};ny.className="DepthwiseConv2D";re.registerClass(ny);function I7(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function N7(e,t,n,r=!1,a,s,i=!1,o=!1){return W(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(xr(2,l));if(t=nt(t,c),s!=null)throw new Me("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Nn(a,-1)),a=nt(a,c)),r&&(t=En(t,0),a!=null&&(a=En(a,0)));let u=[],h,p=n,d=t.shape[0],f=ar(t),m;a!=null&&(m=ar(a));for(let y=0;y<d;++y){let g=f[y],_=W(()=>e(g,p));if(a==null)h=_[0],p=_[1];else{let x=W(()=>{let w=m[y],b=Tn(w).sub(w),N=_[0].mul(w).add(p[0].mul(b)),T=p.map((E,M)=>_[1][M].mul(w).add(E.mul(b)));return{output:N,newStates:T}});h=x.output,p=x.newStates}o&&u.push(h)}let A;return o&&(A=Cn(u,1)),[h,A,p]})}var jr=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new r0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new jt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return xr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){IA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return W(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Me("Constants support is not implemented in RNN yet.");IA(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new jt({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new Me("Constants support is not implemented in RNN yet.");this.cell.build(a);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!k.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new jt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){W(()=>{if(!this.stateful)throw new la("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Tt([n,r])):this.states_=[Tt([n,this.cell.stateSize])];else if(e==null)Se(this.states_),this.keptStates!=null&&(Se(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Tt([n,r])):this.states_[0]=Tt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Se(this.states_);for(let r=0;r<this.states_.length;++r){let a=e[r],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,i=[n,s];if(!k.arraysEqual(a.shape,i))throw new B(`State ${r} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[r]=a}}this.states_=this.states_.map(r=>Wt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=I7(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new jt({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof wr){let o=[e].concat(s),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=c,u}else return super.apply(e,t)}call(e,t){return W(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=De(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new B(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=N7((p,d)=>{let f=this.cell.call([p].concat(d),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,r);let h=this.returnSequences?c:l;return this.returnState?[h].concat(u):h})}getInitialState(e){return W(()=>{let t=Tt(e.shape);return t=Ie(t,[1,2]),t=Mc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?hA(t,[1,n]):t):this.cell.stateSize>1?[hA(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===jr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=_r(r,n);return new e(Object.assign(t,{cell:a}))}};jr.className="RNN";re.registerClass(jr);var Dc=class extends Ge{},a0=class extends Dc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ut(this.units,"units"),this.activation=Va(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=zt(e.kernelConstraint),this.recurrentConstraint=zt(e.recurrentConstraint),this.biasConstraint=zt(e.biasConstraint),this.dropout=zl([1,La([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=zl([1,La([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=dt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return W(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ua({ones:()=>Tn(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ua({ones:()=>Tn(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Vr(L(e,s),this.kernel.read()):a=Vr(e,this.kernel.read()),this.bias!=null&&(a=Ur(a,this.bias.read())),i!=null&&(n=L(n,i));let o=se(a,Vr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ba(this.activation),useBias:this.useBias,kernelInitializer:vt(this.kernelInitializer),recurrentInitializer:vt(this.recurrentInitializer),biasInitializer:vt(this.biasInitializer),kernelRegularizer:pt(this.kernelRegularizer),recurrentRegularizer:pt(this.recurrentRegularizer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(this.activityRegularizer),kernelConstraint:Dt(this.kernelConstraint),recurrentConstraint:Dt(this.recurrentConstraint),biasConstraint:Dt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};a0.className="SimpleRNNCell";re.registerClass(a0);var ry=class extends jr{constructor(e){e.cell=new a0(e),super(e)}call(e,t){return W(()=>{this.cell.dropoutMask!=null&&(Se(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Se(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};ry.className="SimpleRNN";re.registerClass(ry);var s0=class extends Dc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Ut(this.units,"units"),this.activation=Va(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Va(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=zt(e.kernelConstraint),this.recurrentConstraint=zt(e.recurrentConstraint),this.biasConstraint=zt(e.biasConstraint),this.dropout=zl([1,La([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=zl([1,La([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=dt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return W(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ua({ones:()=>Tn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ua({ones:()=>Tn(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let c=Vr(e,this.kernel.read());this.useBias&&(c=Ur(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=L(r,s[0]));let u=this.recurrentKernel.read(),[h,p]=Kt(u,[2*this.units,this.units],u.rank-1),d=Vr(r,h),[f,m,A]=Kt(c,3,c.rank-1),[y,g]=Kt(d,2,d.rank-1);i=this.recurrentActivation.apply(se(f,y)),o=this.recurrentActivation.apply(se(m,g));let _=Vr(L(o,r),p);l=this.activation.apply(se(A,_));let x=se(L(i,r),L(se(1,_t(i)),l));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ba(this.activation),recurrentActivation:Ba(this.recurrentActivation),useBias:this.useBias,kernelInitializer:vt(this.kernelInitializer),recurrentInitializer:vt(this.recurrentInitializer),biasInitializer:vt(this.biasInitializer),kernelRegularizer:pt(this.kernelRegularizer),recurrentRegularizer:pt(this.recurrentRegularizer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(this.activityRegularizer),kernelConstraint:Dt(this.kernelConstraint),recurrentConstraint:Dt(this.recurrentConstraint),biasConstraint:Dt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};s0.className="GRUCell";re.registerClass(s0);var ay=class extends jr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new s0(e),super(e)}call(e,t){return W(()=>{this.cell.dropoutMask!=null&&(Se(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Se(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};ay.className="GRU";re.registerClass(ay);var jc=class extends Dc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ut(this.units,"units"),this.activation=Va(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Va(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=zt(e.kernelConstraint),this.recurrentConstraint=zt(e.recurrentConstraint),this.biasConstraint=zt(e.biasConstraint),this.dropout=zl([1,La([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=zl([1,La([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=dt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends or{apply(i,o){let l=a.apply([s]),c=new Op().apply([s]),u=a.apply([s*2]);return g3(g3(l,c),u)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return W(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ua({ones:()=>Tn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ua({ones:()=>Tn(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,c,u;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let h=Vr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=L(r,i[0])),h=se(h,Vr(r,this.recurrentKernel.read())),this.useBias&&(h=Ur(h,this.bias.read()));let[p,d,f,m]=Kt(h,4,h.rank-1);o=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(d),c=se(L(l,a),L(o,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let A=L(u,this.activation.apply(c));return[A,A,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ba(this.activation),recurrentActivation:Ba(this.recurrentActivation),useBias:this.useBias,kernelInitializer:vt(this.kernelInitializer),recurrentInitializer:vt(this.recurrentInitializer),biasInitializer:vt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:pt(this.kernelRegularizer),recurrentRegularizer:pt(this.recurrentRegularizer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(this.activityRegularizer),kernelConstraint:Dt(this.kernelConstraint),recurrentConstraint:Dt(this.recurrentConstraint),biasConstraint:Dt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};jc.className="LSTMCell";re.registerClass(jc);var sy=class extends jr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new jc(e),super(e)}call(e,t){return W(()=>{this.cell.dropoutMask!=null&&(Se(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Se(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};sy.className="LSTM";re.registerClass(sy);var r0=class extends Dc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return W(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=r[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),a.push(s.slice(1))}n=[];for(let i of a.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){IA(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{ki(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(_r(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return NA(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],a[s]])}SA(t)}};r0.className="StackedRNNCells";re.registerClass(r0);function Ua(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>w3(t(),n),i=()=>$c(s,t,r);return!a||a<=1?Wt(i().clone()):Array(a).fill(void 0).map(i).map(o=>Wt(o.clone()))}var Qte=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a<r.length;a++)t.indexOf(r[a])<0&&Object.prototype.propertyIsEnumerable.call(e,r[a])&&(n[r[a]]=e[r[a]]);return n},S7=class extends jr{constructor(e){if(e.unroll)throw new Me("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Me("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new jt({ndim:5})]}call(e,t){return W(()=>{if(this.cell.dropoutMask!=null&&(Se(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Se(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return W(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Tt(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){W(()=>{if(!this.stateful)throw new la("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Tt(a)):this.states_=[Tt(a)];else if(e==null)Se(this.states_),this.keptStates!=null&&(Se(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Tt(a)):this.states_[0]=Tt(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Se(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!k.arraysEqual(i.shape,o))throw new B(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Wt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=br(l,r[0],a,s[0],i[0]),h=br(c,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,h]:[u,h,n]]}};S7.className="ConvRNN2D";var i0=class extends jc{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Ut(this.filters,"filters"),this.kernelSize=Vl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Ut(o,"kernelSize")),this.strides=Vl(r||1,2,"strides"),this.strides.forEach(o=>Ut(o,"strides")),this.padding=a||"valid",qn(this.padding),this.dataFormat=s||"channelsLast",Nt(this.dataFormat),this.dilationRate=Vl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Ut(o,"dilationRate"))}build(e){var t;e=dt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;o=new(t=class extends or{apply(u,h){let p=l.apply([c]),d=Dr([c]),f=l.apply([c*2]);return pA([p,d,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return W(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ua({ones:()=>Tn(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Z,ae,J)=>!ae||!ae[J]?Z:L(ae[J],Z),c=l(r,o,0),u=l(r,o,1),h=l(r,o,2),p=l(r,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ua({ones:()=>Tn(a),rate:this.recurrentDropout,training:n,count:i}));let d=this.recurrentDropoutMask,f=l(a,d,0),m=l(a,d,1),A=l(a,d,2),y=l(a,d,3),g=3,[_,x,w,b]=Kt(this.kernel.read(),i,g),[N,T,E,M]=this.useBias?Kt(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,_,N,this.padding),u=this.inputConv(u,x,T,this.padding),h=this.inputConv(h,w,E,this.padding),p=this.inputConv(p,b,M,this.padding);let[$,P,V,G]=Kt(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,$),m=this.recurrentConv(m,P),A=this.recurrentConv(A,V),y=this.recurrentConv(y,G);let U=this.recurrentActivation.apply(se(c,f)),K=this.recurrentActivation.apply(se(u,m)),X=se(L(K,s),L(U,this.activation.apply(se(h,A)))),ee=L(this.recurrentActivation.apply(se(p,y)),this.activation.apply(X));return[ee,ee,X]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Qte(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=na(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ur(a,n,this.dataFormat):a}recurrentConv(e,t){return na(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};i0.className="ConvLSTM2DCell";re.registerClass(i0);var iy=class extends S7{constructor(e){let t=new i0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};iy.className="ConvLSTM2D";re.registerClass(iy);var o0=class extends Ge{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return W(()=>{this.invokeCallHook(e,t);let n=De(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return $c(()=>w3(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};o0.className="Dropout";re.registerClass(o0);var oy=class extends o0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};oy.className="SpatialDropout1D";re.registerClass(oy);var ly=class extends Ge{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Ut(this.units,"units"),this.activation=Va(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=zt(e.kernelConstraint),this.biasConstraint=zt(e.biasConstraint),this.kernelRegularizer=xt(e.kernelRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=dt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=dt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return W(()=>{this.invokeCallHook(e,t);let n=De(e),r=l3(this.activation.getClassName()),a;return r!=null?a=Vr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Vr(n,this.kernel.read()),this.bias!=null&&(a=Ur(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Ba(this.activation),useBias:this.useBias,kernelInitializer:vt(this.kernelInitializer),biasInitializer:vt(this.biasInitializer),kernelRegularizer:pt(this.kernelRegularizer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(this.activityRegularizer),kernelConstraint:Dt(this.kernelConstraint),biasConstraint:Dt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};ly.className="Dense";re.registerClass(ly);var uy=class extends Ge{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=dt(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Pa(e,1)]}call(e,t){return W(()=>{this.invokeCallHook(e,t);let n=De(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let a=2;a<n.rank;++a)r.push(a);r.push(1),n=n.transpose(r)}return kQ(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};uy.className="Flatten";re.registerClass(uy);var cy=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.activation=Va(e.activation)}call(e,t){return W(()=>{this.invokeCallHook(e,t);let n=De(e);return this.activation.apply(n)})}getConfig(){let e={activation:Ba(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};cy.className="Activation";re.registerClass(cy);var hy=class extends Ge{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return W(()=>(e=De(e),bQ(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};hy.className="RepeatVector";re.registerClass(hy);var dy=class extends Ge{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),a=1,s=null;for(let o=0;o<r.length;++o){let l=r[o];if(this.isUnknown(l))if(s===null)s=o;else throw new B("Can only specifiy one unknown dimension.");else a*=l}let i=Pa(e);if(s!==null){if(a===0||i%a!=0)throw new B(n);r[s]=i/a}else if(i!==a)throw new B(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return W(()=>{this.invokeCallHook(e,t);let n=De(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return n.reshape(a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};dy.className="Reshape";re.registerClass(dy);var py=class extends Ge{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=xr(1,e.dims.length+1);if(!k.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new jt({ndim:this.dims.length+1})]}computeOutputShape(e){e=dt(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return nt(De(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};py.className="Permute";re.registerClass(py);var fy=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=De(e),r=-1;return Xu(ci(n,this.maskValue),r)}call(e,t){return W(()=>{this.invokeCallHook(e,t);let n=De(e),r=-1,a=!0,s=Xu(ci(n,this.maskValue),r,a);return n.mul(s.asType(n.dtype))})}};fy.className="Masking";re.registerClass(fy);var my=class extends Ge{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(mt(e.inputLength))}this.inputDim=e.inputDim,Ut(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Ut(this.outputDim,"outputDim"),this.embeddingsInitializer=gt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=xt(e.embeddingsRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.embeddingsConstraint=zt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return W(()=>this.maskZero?(e=De(e),ci(e,Ve(e))):null)}computeOutputShape(e){if(e=dt(e),this.inputLength==null)return[...e,this.outputDim];let t=mt(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let a=t[r],s=e[r+1];if(a!=null&&s!=null&&a!==s)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return W(()=>{this.invokeCallHook(e,t);let n=De(e);return n.dtype!=="int32"&&(n=Fc(n,"int32")),x3(this.embeddings.read(),n.as1D()).reshape(dt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:vt(this.embeddingsInitializer),embeddingsRegularizer:pt(this.embeddingsRegularizer),activityRegularizer:pt(this.activityRegularizer),embeddingsConstraint:Dt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};my.className="Embedding";re.registerClass(my);var Ei=class extends Ge{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Me}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let a=e[e.length-t.length+r],s=t[r];if(a==null||s==null||a<0||s<0)n.push(null);else if(a===1)n.push(s);else if(s===1)n.push(a);else{if(a!==s)throw new B("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(a)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[dt(e)]),e=e,e.length<2)throw new B(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=za(t),t.length>1)throw new B(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let r=e.map(a=>a.length);e.indexOf(null)===-1&&za(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return W(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=La(r);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=Mc(s,1);n.push(s)}return this.mergeFunction(n)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],h=c.slice(1).concat([u]),p=o.reshape([u].concat(Pa(c.slice(1))));p=nt(p,[1,0]),p=p.reshape(h),n.push(p),a=!0}else if(l>1){let c=xr(1,l).concat([0]);n.push(nt(o,c)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=nt(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat(xr(0,i-1));s=nt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let n=[];for(let r of e)r!=null&&r[0]!==null&&n.push(r[0]);return n=za(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return W(()=>{if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an Array");if(!Array.isArray(e))throw new B("`inputs` should be an Array");if(t.length!==e.length)throw new B(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:Nn(r,0));let n=t[0];for(let r=1;r<t.length-1;++r)n=rr(n,t[r]);return n})}},Ay=class extends Ei{constructor(e){super(e)}mergeFunction(e){return W(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return t})}};Ay.className="Add";re.registerClass(Ay);var yy=class extends Ei{constructor(e){super(e)}mergeFunction(e){return W(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=L(t,e[n]);return t})}};yy.className="Multiply";re.registerClass(yy);var gy=class extends Ei{constructor(e){super(e)}mergeFunction(e){return W(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return L(1/e.length,t)})}};gy.className="Average";re.registerClass(gy);var xy=class extends Ei{constructor(e){super(e)}mergeFunction(e){return W(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=$r(t,e[n]);return t})}};xy.className="Maximum";re.registerClass(xy);var wy=class extends Ei{constructor(e){super(e)}mergeFunction(e){return W(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Al(t,e[n]);return t})}};wy.className="Minimum";re.registerClass(wy);var _y=class extends Ei{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new B("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let r of e)if(r!=null){t=!1;break}if(t)return;let n=[];for(let r=0;r<e.length;++r){let a=e[r].slice();a.splice(this.axis,1);let s=!1;for(let i of n)if(k.arraysEqual(i,a)){s=!0;break}s||n.push(a)}if(n.length>1)throw new B("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return W(()=>pA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new B("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(let a of t.slice(1)){if(n[r]==null||a[r]==null){n[r]=null;break}n[r]+=a[r]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new B("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new B(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return W(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let r=[];for(let s=0;s<e.length;++s)t[s]==null?r.push(Tn(e[s]).asType("bool")):t[s].rank<e[s].rank?r.push(Nn(t[s],-1)):r.push(t[s]);let a=rt(r,this.axis);return xd(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};_y.className="Concatenate";re.registerClass(_y);function Hc(e,t){for(;e<0;)e+=t;return e}function ene(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Me("batchDot is not implemented for tensors of 4D or higher rank yet");if(k.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Me("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return W(()=>{let i;if(r>a){i=r-a;let l=[];for(let c=0;c<i;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(a>r){i=a-r;let l=[];for(let c=0;c<i;++c)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,c=s[1]===t.shape.length-1;o=e.matMul(t,l,c)}if(i>0){let l;r>a?l=r+a-3:l=r-1;let c=[];for(let u=l;u<l+i;++u)c.push(u);o=o.squeeze(c)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var by=class extends Ei{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Me("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new B(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((a,s)=>Hc(a,e[s].shape.length)):r=[Hc(this.axes,t.shape.length),Hc(this.axes,n.shape.length)],this.normalize&&(t=Gp(t,r[0]),n=Gp(n,r[1])),ene(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Hc(this.axes,e.length),Hc(this.axes,t.length)],n}computeOutputShape(e){k.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Me("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};by.className="Dot";re.registerClass(by);var vy=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return W(()=>{this.invokeCallHook(e,t);let n=De(e);return $c(()=>Mp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};vy.className="GaussianNoise";re.registerClass(vy);var ky=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return W(()=>{this.invokeCallHook(e,t);let n=De(e);return this.rate>0&&this.rate<1?$c(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(Mp(n.shape,1,r))},()=>n,t.training||!1):n})}};ky.className="GaussianDropout";re.registerClass(ky);var Iy=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||De(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return W(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return $c(()=>{let r=De(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=Ra(yl(n),this.rate);o=Fc(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate;return r.mul(o).add(o.add(-1).mul(i)).mul(l).add(c)},()=>De(e),t.training||!1)}return e})}};Iy.className="AlphaDropout";re.registerClass(Iy);function Gc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=y5(e,t,n,r,a,s);else if(e.rank===3)i=g5(e,t,n,r,a,s);else if(e.rank===4)i=x5(e,t,n,r,a,s);else throw new Me(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function tne(e,t,n,r,a=.001){return W(()=>{let s=Rd(e,r),i=s.mean,o=s.variance;return[Gc(e,i,o,n,t,a),i,o]})}function nne(e,t,n,r,a=.001){return W(()=>{let s=Rd(e,r),i=s.mean,o=s.variance,l=[];for(let d of xr(0,e.rank))r.indexOf(d)!==-1?l.push(1):l.push(e.shape[d]);let c=i.reshape(l),u=o.reshape(l),h=t==null?null:t.reshape(l),p=n==null?null:n.reshape(l);return[Gc(e,c,u,p,h,a),i,o]})}function rne(e,t,n,r,a=.001){return k.arraysEqual(r.slice().sort(),xr(0,e.rank-1))?tne(e,t,n,r,a):nne(e,t,n,r,a)}var Ny=class extends Ge{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.movingMeanInitializer=gt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=gt(e.movingVarianceInitializer||"ones"),this.betaConstraint=zt(e.betaConstraint),this.gammaConstraint=zt(e.gammaConstraint),this.betaRegularizer=xt(e.betaRegularizer),this.gammaRegularizer=xt(e.gammaRegularizer)}build(e){e=dt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new jt({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return W(()=>{let n=t.training==null?!1:t.training,r=De(e),a=r.shape,s=a.length,i=xr(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=_i(1,s);l[o]=a[o];let c=i.slice();c.sort();let u=!k.arraysEqual(c,xr(0,s).slice(0,s-1)),h=()=>{if(u){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,_=this.scale?this.gamma.read().reshape(l):null;return Gc(r,A,y,g,_,this.epsilon)}else return Gc(r,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return h();let[p,d,f]=rne(r,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(A,y,g)=>{W(()=>{let _=1-g,x=A.read(),w=x.sub(y).mul(_);A.write(x.sub(w))})};return(()=>{m(this.movingMean,d,this.momentum),m(this.movingVariance,f,this.momentum)})(),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:vt(this.betaInitializer),gammaInitializer:vt(this.gammaInitializer),movingMeanInitializer:vt(this.movingMeanInitializer),movingVarianceInitializer:vt(this.movingVarianceInitializer),betaRegularizer:pt(this.betaRegularizer),gammaRegularizer:pt(this.gammaRegularizer),betaConstraint:Dt(this.betaConstraint),gammaConstraint:Dt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ny.className="BatchNormalization";re.registerClass(Ny);var Sy=class extends Ge{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.betaRegularizer=xt(e.betaRegularizer),this.gammaRegularizer=xt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=dt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==za(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(a=>e[a]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=De(e),r=n.shape,a=r.length;return W(()=>{let s=!0,{mean:i,variance:o}=Rd(n,this.axis,s),l=_i(1,a);for(let f of this.axis)l[f]=r[f];let c=f=>f!=null&&f.shape.length!==a&&this.axis!==[a-1]?f.reshape(l):f,u=c(this.gamma.read()),h=c(this.beta.read()),p=[],d=[];for(let f=0;f<a;++f)this.axis.indexOf(f)!==-1?(p.push(r[f]),d.push(1)):(p.push(1),d.push(r[f]));return i=i.tile(p),o=o.tile(p),u=u.tile(d),h=h.tile(d),Gc(n,i,o,h,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:vt(this.betaInitializer),gammaInitializer:vt(this.gammaInitializer),betaRegularizer:pt(this.betaRegularizer),gammaRegularizer:pt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Sy.className="LayerNormalization";re.registerClass(Sy);function ane(e,t,n){return W(()=>{if(e.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=yr()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return n==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],ra(e,r)})}var Ty=class extends Ge{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?yr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new B(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new B(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new B(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new jt({ndim:4})]}computeOutputShape(e){e=dt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return W(()=>ane(De(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ty.className="ZeroPadding2D";re.registerClass(Ty);function l0(e,t,n,r,a,s){return W(()=>{Nt(a),d3(s),qn(r),n==null&&(n=[1,1]),r==null&&(r="valid"),a==null&&(a=yr()),s==null&&(s="max"),e=ZA(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=ac(e,t,n,o):i=Yu(e,t,n,o),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}function T7(e,t,n,r,a,s){return W(()=>{Nt(a),d3(s),qn(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=yr()),s==null&&(s="max"),e=b7(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Zf(e,t,n,o):i=Df(e,t,n,o),a==="channelsFirst"&&(i=nt(i,[0,4,1,2,3])),i})}var E7=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Ut(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,qn(this.padding),this.inputSpec=[new jt({ndim:3})]}computeOutputShape(e){e=dt(e);let t=br(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return W(()=>{this.invokeCallHook(e,t),e=Mc(De(e),2);let n=this.poolingFunction(De(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Fa(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Ey=class extends E7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Nt(a),qn(r),l0(e,t,n,r,a,"max")}};Ey.className="MaxPooling1D";re.registerClass(Ey);var Cy=class extends E7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Nt(a),qn(r),l0(e,t,n,r,a,"avg")}};Cy.className="AveragePooling1D";re.registerClass(Cy);var C7=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new B(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Ut(this.poolSize,"poolSize"),Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Nt(this.dataFormat),qn(this.padding),this.inputSpec=[new jt({ndim:4})]}computeOutputShape(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=br(t,this.poolSize[0],this.padding,this.strides[0]),n=br(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return W(()=>(this.invokeCallHook(e,t),this.poolingFunction(De(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ry=class extends C7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Nt(a),qn(r),l0(e,t,n,r,a,"max")}};Ry.className="MaxPooling2D";re.registerClass(Ry);var Fy=class extends C7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Nt(a),qn(r),l0(e,t,n,r,a,"avg")}};Fy.className="AveragePooling2D";re.registerClass(Fy);var R7=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Ut(this.poolSize,"poolSize"),Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Nt(this.dataFormat),qn(this.padding),this.inputSpec=[new jt({ndim:5})]}computeOutputShape(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=br(t,this.poolSize[0],this.padding,this.strides[0]),n=br(n,this.poolSize[1],this.padding,this.strides[1]),r=br(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return W(()=>(this.invokeCallHook(e,t),this.poolingFunction(De(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},My=class extends R7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Nt(a),qn(r),T7(e,t,n,r,a,"max")}};My.className="MaxPooling3D";re.registerClass(My);var Oy=class extends R7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Nt(a),qn(r),T7(e,t,n,r,a,"avg")}};Oy.className="AveragePooling3D";re.registerClass(Oy);var F7=class extends Ge{constructor(e){super(e);this.inputSpec=[new jt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Me}},$y=class extends F7{constructor(e){super(e||{})}call(e,t){return W(()=>{let n=De(e);return bt(n,1)})}};$y.className="GlobalAveragePooling1D";re.registerClass($y);var Dy=class extends F7{constructor(e){super(e||{})}call(e,t){return W(()=>{let n=De(e);return jn(n,1)})}};Dy.className="GlobalMaxPooling1D";re.registerClass(Dy);var M7=class extends Ge{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Nt(this.dataFormat),this.inputSpec=[new jt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Me}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},zy=class extends M7{call(e,t){return W(()=>{let n=De(e);return this.dataFormat==="channelsLast"?bt(n,[1,2]):bt(n,[2,3])})}};zy.className="GlobalAveragePooling2D";re.registerClass(zy);var Py=class extends M7{call(e,t){return W(()=>{let n=De(e);return this.dataFormat==="channelsLast"?jn(n,[1,2]):jn(n,[2,3])})}};Py.className="GlobalMaxPooling2D";re.registerClass(Py);var O7=class extends Ge{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let r=t.layer,a=_r(r,n);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},Ly=class extends O7{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=dt(e),e.length<3)throw new B(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=dt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return W(()=>(e=De(e),N7((n,r)=>[De(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Ly.className="TimeDistributed";re.registerClass(Ly);function sne(e){vi(yQ,"BidirectionalMergeMode",e)}var ine="concat",Wy=class extends O7{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=_r(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=_r(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?ine:e.mergeMode,sne(this.mergeMode),e.weights)throw new Me("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,r,a;return this.returnState&&(a=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(a).concat(a.slice()):[n].concat(a).concat(a.slice()):xn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=I7(e,n,r,this.numConstants);if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new B("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let c=n.map(u=>new jt({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),i.push(...c)}if(r!=null)throw new Me("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof wr;for(let l of s)if(l instanceof wr!==o)throw new B("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),c=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=c;let h=super.apply(l,t);return this.inputSpec=u,h}else return super.apply(e,t)}call(e,t){return W(()=>{let n=t.initialState,r,a;if(n==null)r=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);r=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(r)&&(s=r.slice(1).concat(a.slice(1))),r=r[0],a=a[0]),this.returnSequences&&(a=En(a,1));let i;return this.mergeMode==="concat"?i=pA([r,a]):this.mergeMode==="sum"?i=se(r,a):this.mergeMode==="ave"?i=L(.5,se(r,a)):this.mergeMode==="mul"?i=L(r,a):this.mergeMode==null&&(i=[r,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){ki(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),ki(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=_r(t.layer);if(delete t.layer,t.numConstants!=null)throw new Me("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let r=t;return r.layer=n,new e(r)}};Wy.className="Bidirectional";re.registerClass(Wy);function MQ(e){return new Pl(e)}function OQ(e){return new qA(e)}function $Q(e){return new jA(e)}function DQ(e){return new HA(e)}function zQ(e){return new GA(e)}function PQ(e){return new KA(e)}function LQ(e){return new XA(e)}function WQ(e){return new n0(e)}function BQ(e){return new Uc(e)}function VQ(e){return new JA(e)}function UQ(e){return new t0(e)}function jQ(e){return new QA(e)}function HQ(e){return new ey(e)}function GQ(e){return new ty(e)}function qQ(e){return new ny(e)}function XQ(e){return new cy(e)}function KQ(e){return new ly(e)}function ZQ(e){return new o0(e)}function YQ(e){return new oy(e)}function JQ(e){return new uy(e)}function QQ(e){return new hy(e)}function eee(e){return new dy(e)}function tee(e){return new py(e)}function nee(e){return new my(e)}function ree(e){return new Ay(e)}function aee(e){return new gy(e)}function see(e){return new _y(e)}function iee(e){return new xy(e)}function oee(e){return new wy(e)}function lee(e){return new yy(e)}function uee(e){return new by(e)}function cee(e){return new Ny(e)}function hee(e){return new Sy(e)}function dee(e){return new Ty(e)}function bA(e){return new Cy(e)}function pee(e){return bA(e)}function fee(e){return bA(e)}function vA(e){return new Fy(e)}function mee(e){return vA(e)}function Aee(e){return vA(e)}function kA(e){return new Oy(e)}function yee(e){return kA(e)}function gee(e){return kA(e)}function xee(e){return new $y(e)}function wee(e){return new zy(e)}function I3(e){return new Dy(e)}function N3(e){return new Py(e)}function S3(e){return new Ey(e)}function T3(e){return new Ry(e)}function _ee(e){return new My(e)}function bee(e){return new ay(e)}function vee(e){return new s0(e)}function kee(e){return new sy(e)}function Iee(e){return new jc(e)}function Nee(e){return new ry(e)}function See(e){return new a0(e)}function Tee(e){return new iy(e)}function Eee(e){return new i0(e)}function Cee(e){return new jr(e)}function Ree(e){return new r0(e)}function Fee(e){return new Wy(e)}function Mee(e){return new Ly(e)}var Oee=I3,$ee=N3,Dee=S3,zee=T3;function Pee(e){return new vy(e)}function Lee(e){return new ky(e)}function Wee(e){return new Iy(e)}function Bee(e){return new fy(e)}var $7={};$e($7,{MAPE:()=>yne,MSE:()=>wne,binaryAccuracy:()=>one,binaryCrossentropy:()=>lne,categoricalAccuracy:()=>cne,categoricalCrossentropy:()=>hne,cosineProximity:()=>fne,mape:()=>gne,meanAbsoluteError:()=>mne,meanAbsolutePercentageError:()=>Ane,meanSquaredError:()=>xne,mse:()=>_ne,precision:()=>dne,recall:()=>pne,sparseCategoricalAccuracy:()=>une});function one(e,t){return CA(e,t)}function lne(e,t){return U3(e,t)}function une(e,t){return j3(e,t)}function cne(e,t){return RA(e,t)}function hne(e,t){return FA(e,t)}function dne(e,t){return V3(e,t)}function pne(e,t){return ite(e,t)}function fne(e,t){return TA(e,t)}function mne(e,t){return qp(e,t)}function Ane(e,t){return Wl(e,t)}function yne(e,t){return Wl(e,t)}function gne(e,t){return Wl(e,t)}function xne(e,t){return Ni(e,t)}function wne(e,t){return Ni(e,t)}function _ne(e,t){return Ni(e,t)}var D7={};$e(D7,{modelFromJSON:()=>Wte});var z7={};$e(z7,{l1:()=>vne,l1l2:()=>bne,l2:()=>kne});function bne(e){return new Bc(e)}function vne(e){return Xte(e)}function kne(e){return Kte(e)}var P7=class extends Ll{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof ca))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function u0(e,t){return e<t}function L7(e,t){return e>t}var W7=class extends P7{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new Me("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=u0:this.mode==="max"?this.monitorFunc=L7:this.monitor.indexOf("acc")!==-1?this.monitorFunc=L7:this.monitorFunc=u0,this.monitorFunc===u0&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===u0?Infinity:-Infinity}async onEpochEnd(e,t){await Wa(t);let n=this.getMonitorValue(t);n!=null&&(this.monitorFunc(n-this.minDelta,this.best)?(this.best=n,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=e,this.model.stopTraining=!0)))}async onTrainEnd(e){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(e){e==null&&(e={});let t=e[this.monitor];return t==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(e)}`),t}};function Ine(e){return new W7(e)}var Nne={earlyStopping:Ine},vr;(function(e){e[e.DT_INVALID=0]="DT_INVALID",e[e.DT_FLOAT=1]="DT_FLOAT",e[e.DT_DOUBLE=2]="DT_DOUBLE",e[e.DT_INT32=3]="DT_INT32",e[e.DT_UINT8=4]="DT_UINT8",e[e.DT_INT16=5]="DT_INT16",e[e.DT_INT8=6]="DT_INT8",e[e.DT_STRING=7]="DT_STRING",e[e.DT_COMPLEX64=8]="DT_COMPLEX64",e[e.DT_INT64=9]="DT_INT64",e[e.DT_BOOL=10]="DT_BOOL",e[e.DT_QINT8=11]="DT_QINT8",e[e.DT_QUINT8=12]="DT_QUINT8",e[e.DT_QINT32=13]="DT_QINT32",e[e.DT_BFLOAT16=14]="DT_BFLOAT16",e[e.DT_FLOAT_REF=101]="DT_FLOAT_REF",e[e.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",e[e.DT_INT32_REF=103]="DT_INT32_REF",e[e.DT_UINT8_REF=104]="DT_UINT8_REF",e[e.DT_INT16_REF=105]="DT_INT16_REF",e[e.DT_INT8_REF=106]="DT_INT8_REF",e[e.DT_STRING_REF=107]="DT_STRING_REF",e[e.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",e[e.DT_INT64_REF=109]="DT_INT64_REF",e[e.DT_BOOL_REF=110]="DT_BOOL_REF",e[e.DT_QINT8_REF=111]="DT_QINT8_REF",e[e.DT_QUINT8_REF=112]="DT_QUINT8_REF",e[e.DT_QINT32_REF=113]="DT_QINT32_REF",e[e.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(vr||(vr={}));var B7;(function(e){let t;(function(n){n[n.LEGACY=0]="LEGACY",n[n.V1=1]="V1",n[n.V2=2]="V2"})(t=e.CheckpointFormatVersion||(e.CheckpointFormatVersion={}))})(B7||(B7={}));var By={};function Sne(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};By[e]=n}function V7(e){return By[e]}function Tne(e){delete By[e]}function I(e,t,n,r,a){let s=t.inputParams[e];if(s&&s.inputIndexStart!==void 0){let o=s.inputIndexStart,l=s.inputIndexEnd===0?void 0:s.inputIndexEnd===void 0?o+1:s.inputIndexEnd;if(s.type==="tensor")return _n(t.inputNames[s.inputIndexStart],n,r,a);if(s.type==="tensors")return t.inputNames.slice(o,l).map(h=>_n(h,n,r,a));let c=_n(t.inputNames.slice(o)[0],n,r,a),u=c.dataSync();return s.type==="number"?u[0]:k.toNestedArray(c.shape,u)}let i=t.attrParams[e];return i&&i.value}function _n(e,t,n,r){let[a,s]=Dn(e);if(r!=null){let o=r.getHashTableHandleByName(a);if(o!=null)return o}let i=n.currentContextIds.find(o=>!!t[c0(a,o)]);return i!==void 0?t[c0(a,i)][s]:void 0}function Ene(e,t,n){return t[c0(e,n.currentContextId)]}function ha(e,t){let[n,r]=Dn(e);return[c0(n,t&&t.currentContextId),r]}function c0(e,t){return t?`${e}-${t}`:e}function Dn(e){let t=e.split(":");return t.length===1?[e,0]:[t[0],Number(t[t.length-1])]}function h0(e,t,n){let r=I("pad",e,t,n);if(r==="explicit"){r=I("explicitPaddings",e,t,n);let a=[[0,0],[0,0],[0,0],[0,0]];for(let s=0;s<4;s++)a[s][0]=r[s*2],a[s][1]=r[s*2+1];return a}return r}function da(e){return e.kept?e:tr(e)}var U7={};$e(U7,{json:()=>Cne});var Cne=[{tfOpName:"Add",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddV2",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddN",category:"arithmetic",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"BiasAdd",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"Sub",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"RealDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Div",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"DivNoNan",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mul",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Maximum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Minimum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Pow",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SquaredDifference",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorMod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],j7={};$e(j7,{json:()=>Rne});var Rne=[{tfOpName:"Abs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan2",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ceil",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ClipByValue",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"clipValueMin",type:"number"},{start:2,name:"clipValueMax",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Complex",category:"basic_math",inputs:[{start:0,name:"real",type:"tensor"},{start:1,name:"imag",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ComplexAbs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Elu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Exp",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Floor",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Imag",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Neg",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Real",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Prelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"alpha",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu6",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Selu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sigmoid",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Rsqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Square",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sign",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Round",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Expm1",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log1p",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Reciprocal",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Softplus",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Erf",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Prod",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axes",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LeakyRelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"alpha",name:"alpha",type:"number",defaultValue:.2},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],H7={};$e(H7,{json:()=>Fne});var Fne=[{tfOpName:"EmptyTensorList",category:"control",inputs:[{start:0,name:"elementShape",type:"shape"},{start:1,name:"maxNumElements",type:"number"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"LoopCond",category:"control",inputs:[{start:0,name:"pred",type:"tensor"}]},{tfOpName:"Switch",category:"control",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"pred",type:"tensor"}]},{tfOpName:"Merge",category:"control",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"Enter",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"frame_name",name:"frameName",type:"string"},{tfName:"is_constant",name:"isConstant",type:"bool"}]},{tfOpName:"Exit",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NextIteration",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"TensorArrayV3",category:"control",inputs:[{start:0,name:"size",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"element_shape",name:"elementShape",type:"shape"},{tfName:"dynamic_size",name:"dynamicSize",type:"bool"},{tfName:"clear_after_read",name:"clearAfterRead",type:"bool"},{tfName:"identical_element_shapes",name:"identicalElementShapes",type:"bool"},{tfName:"tensor_array_name",name:"name",type:"string"}]},{tfOpName:"TensorArrayWriteV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"tensor",type:"tensor"},{start:3,name:"flowIn",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"TensorArrayReadV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"flowIn",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"TensorArrayGatherV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"flowIn",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"element_shape",name:"elementShape",type:"shape"}]},{tfOpName:"TensorArrayScatterV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"tensor",type:"tensor"},{start:3,name:"flowIn",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"TensorArrayConcatV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"flowIn",type:"number"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"element_shape_except0",name:"elementShapeExcept0",type:"shape",notSupported:!0}]},{tfOpName:"TensorArraySplitV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"tensor",type:"tensor"},{start:2,name:"lengths",type:"number[]"},{start:3,name:"flowIn",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"TensorArraySizeV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"},{start:1,name:"flowIn",type:"number"}]},{tfOpName:"TensorArrayCloseV3",category:"control",inputs:[{start:0,name:"tensorArrayId",type:"tensor"}]},{tfOpName:"StatelessIf",category:"control",inputs:[{start:0,name:"cond",type:"tensor"},{start:1,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"then_branch",name:"thenBranch",type:"func"},{tfName:"else_branch",name:"elseBranch",type:"func"}]},{tfOpName:"If",category:"control",inputs:[{start:0,name:"cond",type:"tensor"},{start:1,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"then_branch",name:"thenBranch",type:"func"},{tfName:"else_branch",name:"elseBranch",type:"func"}]},{tfOpName:"StatelessWhile",category:"control",inputs:[{start:0,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"cond",name:"cond",type:"func"},{tfName:"body",name:"body",type:"func"}]},{tfOpName:"While",category:"control",inputs:[{start:0,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"cond",name:"cond",type:"func"},{tfName:"body",name:"body",type:"func"}]},{tfOpName:"TensorListScatter",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListScatterV2",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"elementShape",type:"shape"},{start:3,name:"numElements",type:"number"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListGather",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"indices",type:"number[]"},{start:2,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListGetItem",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListSetItem",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"index",type:"number"},{start:2,name:"tensor",type:"tensor"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListReserve",category:"control",inputs:[{start:0,name:"elementShape",type:"shape"},{start:1,name:"numElements",type:"number"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListFromTensor",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListStack",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"},{tfName:"num_elements",name:"numElements",type:"dtype"}]},{tfOpName:"TensorListSplit",category:"control",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"elementShape",type:"shape"},{start:2,name:"lengths",type:"number[]"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListConcat",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"}],attrs:[{tfName:"element_shape",name:"elementShape",type:"shape"},{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListPopBack",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"elementShape",type:"shape"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]},{tfOpName:"TensorListPushBack",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"tensor",type:"tensor"}],attrs:[{tfName:"element_dtype",name:"elementDType",type:"dtype"}]}],G7={};$e(G7,{json:()=>Mne});var Mne=[{tfOpName:"AvgPool",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPool",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[],notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPoolWithArgmax",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"include_batch_in_index",name:"includeBatchInIndex",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AvgPool3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPool3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Conv1D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"stride",name:"stride",type:"number"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NWC"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"dilation",name:"dilation",type:"number",defaultValue:1}]},{tfOpName:"Conv2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"useCudnnOnGpu",name:"useCudnnOnGpu",type:"bool"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"_FusedConv2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"use_cudnn_on_gpu",name:"useCudnnOnGpu",type:"bool",defaultValue:!0},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"dilations",name:"dilations",type:"number[]",defaultValue:[1,1,1,1]},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"leakyrelu_alpha",name:"leakyreluAlpha",type:"number"}]},{tfOpName:"Conv2DBackpropInput",category:"convolution",inputs:[{start:2,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:0,name:"outputShape",type:"number[]"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]",notSupported:!0}]},{tfOpName:"DepthwiseConv2d",category:"convolution",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"DepthwiseConv2dNative",category:"convolution",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"FusedDepthwiseConv2dNative",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"dilations",name:"dilations",type:"number[]",defaultValue:[1,1,1,1]},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]}]},{tfOpName:"Conv3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"Dilation2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"rates",name:"dilations",type:"number[]"},{tfName:"padding",name:"pad",type:"string"}]}],q7={};$e(q7,{json:()=>One});var One=[{tfOpName:"Fill",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"},{start:1,name:"value",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"LinSpace",category:"creation",inputs:[{start:0,name:"start",type:"number"},{start:1,name:"stop",type:"number"},{start:2,name:"num",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"OneHot",category:"creation",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"depth",type:"number"},{start:2,name:"onValue",type:"number",defaultValue:1},{start:3,name:"offValue",type:"number",defaultValue:0}],attrs:[{tfName:"axis",name:"axis",type:"number",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ones",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"OnesLike",category:"creation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"RandomUniform",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"minval",name:"minval",type:"number",defaultValue:0},{tfName:"maxval",name:"maxval",type:"number",defaultValue:1},{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"seed",name:"seed",type:"number",defaultValue:0},{tfName:"seed2",name:"seed2",type:"number",defaultValue:0,notSupported:!0},{tfName:"T",name:"T",type:"number",notSupported:!0}]},{tfOpName:"Range",category:"creation",inputs:[{start:0,name:"start",type:"number"},{start:1,name:"stop",type:"number"},{start:2,name:"step",type:"number",defaultValue:0}],attrs:[{tfName:"Tidx",name:"dtype",type:"dtype"}]},{tfOpName:"TruncatedNormal",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"means",name:"mean",type:"number",defaultValue:0},{tfName:"stddev",name:"stdDev",type:"number",defaultValue:1},{tfName:"seed",name:"seed",type:"number"},{tfName:"seed2",name:"seed2",type:"number",defaultValue:0,notSupported:!0},{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"T",name:"T",type:"number",notSupported:!0}]},{tfOpName:"Zeros",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"ZerosLike",category:"creation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"Multinomial",category:"creation",inputs:[{start:0,name:"logits",type:"tensor"},{start:1,name:"numSamples",type:"number"}],attrs:[{tfName:"seed",name:"seed",type:"number"},{tfName:"seed2",name:"seed2",type:"number"},{tfName:"T",name:"dtype",type:"dtype"},{tfName:"output_dtype",name:"output_dtype",type:"dtype"}]}],X7={};$e(X7,{json:()=>$ne});var $ne=[{tfOpName:"NonMaxSuppressionV2",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"}]},{tfOpName:"NonMaxSuppressionV3",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"}]},{tfOpName:"NonMaxSuppressionV4",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"T_threshold",name:"threshold",type:"dtype",notSupported:!0},{tfName:"pad_to_max_output_size",name:"padToMaxOutputSize",type:"bool"}]},{tfOpName:"NonMaxSuppressionV5",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"},{start:5,name:"softNmsSigma",type:"number"}]},{tfOpName:"Where",category:"dynamic",inputs:[{start:0,name:"condition",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ListDiff",category:"dynamic",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],K7={};$e(K7,{json:()=>Dne});var Dne=[{tfOpName:"TopKV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"k",type:"number"}],attrs:[{tfName:"sorted",name:"sorted",type:"bool"}]},{tfOpName:"Unique",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"UniqueV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]}],Z7={};$e(Z7,{json:()=>zne});var zne=[{tfOpName:"PlaceholderWithDefault",category:"graph",inputs:[{start:0,name:"default",type:"tensor"}],attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Placeholder",category:"graph",attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Const",category:"graph"},{tfOpName:"Identity",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IdentityN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Snapshot",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Rank",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Size",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Shape",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"ShapeN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Print",category:"graph",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"data",type:"tensors"}],attrs:[{tfName:"message",name:"message",type:"string"},{tfName:"first_n",name:"firstN",type:"number",notSupported:!0},{tfName:"summarize",name:"summarize",type:"number",defaultValue:3}]},{tfOpName:"NoOp",category:"graph",inputs:[]},{tfOpName:"StopGradient",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"FakeQuantWithMinMaxVars",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"min",name:"min",type:"number"},{tfName:"max",name:"max",type:"number"}]}],Y7={};$e(Y7,{json:()=>Pne});var Pne=[{tfOpName:"HashTable",category:"hash_table",inputs:[],attrs:[{tfName:"shared_name",name:"sharedName",type:"string"},{tfName:"use_node_name_sharing",name:"useNodeNameSharing",type:"bool"},{tfName:"key_dtype",name:"keyDType",type:"dtype"},{tfName:"value_dtype",name:"valueDType",type:"dtype"}]},{tfOpName:"HashTableV2",category:"hash_table",inputs:[],attrs:[{tfName:"shared_name",name:"sharedName",type:"string"},{tfName:"use_node_name_sharing",name:"useNodeNameSharing",type:"bool"},{tfName:"key_dtype",name:"keyDType",type:"dtype"},{tfName:"value_dtype",name:"valueDType",type:"dtype"}]},{tfOpName:"LookupTableImport",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"values",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableImportV2",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"values",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableFind",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableFindV2",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]}],J7={};$e(J7,{json:()=>Lne});var Lne=[{tfOpName:"ResizeBilinear",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"half_pixel_centers",name:"halfPixelCenters",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ResizeNearestNeighbor",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"half_pixel_centers",name:"halfPixelCenters",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"CropAndResize",category:"image",inputs:[{start:0,name:"image",type:"tensor"},{start:1,name:"boxes",type:"tensor"},{start:2,name:"boxInd",type:"tensor"},{start:3,name:"cropSize",type:"number[]"}],attrs:[{tfName:"method",name:"method",type:"string"},{tfName:"extrapolation_value",name:"extrapolationValue",type:"number"}]}],Q7={};$e(Q7,{json:()=>Wne});var Wne=[{tfOpName:"Equal",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NotEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Greater",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"GreaterEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Less",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LessEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalAnd",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalNot",category:"logical",inputs:[{start:0,name:"a",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalOr",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Select",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SelectV2",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],ev={};$e(ev,{json:()=>Bne});var Bne=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],tv={};$e(tv,{json:()=>Vne});var Vne=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}],nv={};$e(nv,{json:()=>Une});var Une=[{tfOpName:"Bincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}]},{tfOpName:"DenseBincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}],attrs:[{tfName:"binary_output",name:"binaryOutput",type:"bool"}]},{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}],rv={};$e(rv,{json:()=>jne});var jne=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}],attrs:[{tfName:"batch_dims",name:"batchDims",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool[]"}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}],av={};$e(av,{json:()=>Hne});var Hne=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}],sv={};$e(sv,{json:()=>Gne});var Gne=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}],ov=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[U7,j7,H7,G7,q7,X7,K7,Q7,J7,Z7,ev,tv,nv,rv,av,sv,Y7],t=[].concat(...e.map(n=>n.json));this.opMappers=t.reduce((n,r)=>(n[r.tfOpName]=r,n),{})}transformGraph(e,t={}){let n=e.node,r=[],a=[],s=[],i=n.reduce((f,m)=>(f[m.name]=this.mapNode(m),m.op.startsWith("Placeholder")?r.push(f[m.name]):m.op==="Const"?a.push(f[m.name]):(m.input==null||m.input.length===0)&&s.push(f[m.name]),f),{}),o=[],l=[],c={},u={};t!=null&&(c=this.mapSignatureEntries(t.inputs),u=this.mapSignatureEntries(t.outputs));let h=Object.keys(i);h.forEach(f=>{let m=i[f];m.inputNames.forEach(A=>{let[y]=ha(A);m.inputs.push(i[y]),i[y].children.push(m)})}),Object.keys(u).length===0?h.forEach(f=>{let m=i[f];m.children.length===0&&l.push(m)}):Object.keys(u).forEach(f=>{let[m]=ha(f),A=i[m];A!=null&&(A.signatureKey=u[f],l.push(A))}),Object.keys(c).length>0?Object.keys(c).forEach(f=>{let[m]=ha(f),A=i[m];A&&(A.signatureKey=c[f],o.push(A))}):o=r;let p={};e.library!=null&&e.library.function!=null&&(p=e.library.function.reduce((f,m)=>(f[m.signature.name]=this.mapFunction(m),f),{}));let d={nodes:i,inputs:o,outputs:l,weights:a,placeholders:r,signature:t,functions:p};return s.length>0&&(d.initNodes=s),d}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,n)=>(t[e[n].name]=n,t),{})}mapNode(e){let t=V7(e.op)||this.opMappers[e.op]||{};e.attr==null&&(e.attr={});let n={name:e.name,op:e.op,category:t.category,inputNames:(e.input||[]).map(r=>r.startsWith("^")?r.substr(1):r),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr};return t.inputs!=null&&(n.inputParams=t.inputs.reduce((r,a)=>(r[a.name]={type:a.type,inputIndexStart:a.start,inputIndexEnd:a.end},r),{})),t.attrs!=null&&(n.attrParams=t.attrs.reduce((r,a)=>{let s=a.type,i;switch(a.type){case"string":i=Vy(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Vy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"string[]":i=Zy(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Zy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"number":i=jy(e.attr,a.tfName,a.defaultValue||0),i===void 0&&!!a.tfDeprecatedName&&(i=jy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"number[]":i=Ky(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Ky(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"bool":i=Uy(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Uy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"bool[]":i=Jy(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Jy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"shape":i=Xy(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Xy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"shape[]":i=Yy(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Yy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"dtype":i=Gy(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Gy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"dtype[]":i=qy(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=qy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"func":i=iv(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=iv(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${a.type} for op: ${e.op}`)}return r[a.name]={value:i,type:s},r},{})),n}mapFunction(e){let t=e.nodeDef,n=[],r=[],a={};t!=null&&(a=t.reduce((c,u)=>(c[u.name]=this.mapNode(u),u.op==="Const"&&r.push(c[u.name]),c),{}));let s=[],i=[];e.signature.inputArg.forEach(c=>{let[u]=ha(c.name),h={name:u,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:Hy(c.type),type:"dtype"}},children:[]};h.signatureKey=c.name,s.push(h),a[u]=h}),Object.keys(a).forEach(c=>{let u=a[c];u.inputNames.forEach(h=>{let[p]=ha(h);u.inputs.push(a[p]),a[p].children.push(u)})});let o=e.ret;e.signature.outputArg.forEach(c=>{let[u,h]=ha(o[c.name]),p=a[u];p!=null&&(p.defaultOutput=h,i.push(p))});let l=this.mapArgsToSignature(e);return{nodes:a,inputs:s,outputs:i,weights:r,placeholders:n,signature:l}}mapArgsToSignature(e){return{methodName:e.signature.name,inputs:e.signature.inputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n),t),{}),outputs:e.signature.outputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n,e.ret),t),{})}}mapArgToTensorInfo(e,t){let n=e.name;return t!=null&&(n=t[n]),{name:n,dtype:e.type}}};function qne(e){let t=Q().global;if(typeof t.atob!="undefined")return t.atob(e);if(typeof Buffer!="undefined")return new Buffer(e,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function lv(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):qne(e);return t?n:n.toLowerCase()}function Vy(e,t,n,r=!1){let a=e[t];return a!=null?lv(a.s,r):n}function Uy(e,t,n){let r=e[t];return r?r.b:n}function jy(e,t,n){let r=e[t]||{},a=r.i!=null?r.i:r.f!=null?r.f:n;return typeof a=="number"?a:parseInt(a,10)}function Hy(e){switch(typeof e=="string"&&(e=vr[e]),e){case vr.DT_FLOAT:return"float32";case vr.DT_INT32:case vr.DT_INT64:case vr.DT_INT8:case vr.DT_UINT8:return"int32";case vr.DT_BOOL:return"bool";case vr.DT_DOUBLE:return"float32";case vr.DT_STRING:return"string";default:return null}}function iv(e,t,n){let r=e[t];return r&&r.func?r.func.name:n}function Gy(e,t,n){let r=e[t];return r&&r.type?Hy(r.type):n}function qy(e,t,n){let r=e[t];return r&&r.list&&r.list.type?r.list.type.map(a=>Hy(a)):n}function uv(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function Xy(e,t,n){let r=e[t];return r&&r.shape?uv(r.shape):n}function Ky(e,t,n){let r=e[t];return r?((r.list.f&&r.list.f.length?r.list.f:r.list.i)||[]).map(a=>typeof a=="number"?a:parseInt(a,10)):n}function Zy(e,t,n,r=!1){let a=e[t];return a&&a.list&&a.list.s?a.list.s.map(s=>lv(s,r)):n}function Yy(e,t,n){let r=e[t];return r&&r.list&&r.list.shape?r.list.shape.map(a=>uv(a)):n}function Jy(e,t,n){let r=e[t];return r&&r.list&&r.list.b?r.list.b:n}var Xne=class{constructor(e,t,n){this.node=e,this.tensorMap=t,this.context=n,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map(r=>this.getInput(r)),e.rawAttrs!=null&&(this.attrs=Object.keys(e.rawAttrs).reduce((r,a)=>(r[a]=this.getAttr(a),r),{}))}getInput(e){return _n(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return _n(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return jy(this.node.rawAttrs,e,t);if(n.s!=null)return Vy(this.node.rawAttrs,e,t);if(n.b!=null)return Uy(this.node.rawAttrs,e,t);if(n.shape!=null)return Xy(this.node.rawAttrs,e,t);if(n.type!=null)return Gy(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return Ky(this.node.rawAttrs,e,t);if(n.list.s!=null)return Zy(this.node.rawAttrs,e,t);if(n.list.shape!=null)return Yy(this.node.rawAttrs,e,t);if(n.list.b!=null)return Jy(this.node.rawAttrs,e,t);if(n.list.type!=null)return qy(this.node.rawAttrs,e,t)}return t}},Kne=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[se(I("a",e,t,n),I("b",e,t,n))];case"AddN":return[ll(I("tensors",e,t,n))];case"FloorMod":case"Mod":return[Jf(I("a",e,t,n),I("b",e,t,n))];case"Mul":return[L(I("a",e,t,n),I("b",e,t,n))];case"RealDiv":case"Div":return[be(I("a",e,t,n),I("b",e,t,n))];case"DivNoNan":return[Vf(I("a",e,t,n),I("b",e,t,n))];case"FloorDiv":return[gd(I("a",e,t,n),I("b",e,t,n))];case"Sub":return[Ae(I("a",e,t,n),I("b",e,t,n))];case"Minimum":return[Al(I("a",e,t,n),I("b",e,t,n))];case"Maximum":return[$r(I("a",e,t,n),I("b",e,t,n))];case"Pow":return[aa(I("a",e,t,n),I("b",e,t,n))];case"SquaredDifference":return[Vd(I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Zne=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[Mt(I("x",e,t,n))];case"Acos":return[Nf(I("x",e,t,n))];case"Acosh":return[Sf(I("x",e,t,n))];case"Asin":return[Ef(I("x",e,t,n))];case"Asinh":return[Cf(I("x",e,t,n))];case"Atan":return[Rf(I("x",e,t,n))];case"Atan2":return[Ff(I("x",e,t,n),I("y",e,t,n))];case"Atanh":return[Mf(I("x",e,t,n))];case"Ceil":return[zf(I("x",e,t,n))];case"Complex":return[ka(I("real",e,t,n),I("imag",e,t,n))];case"Cos":return[ec(I("x",e,t,n))];case"Cosh":return[vd(I("x",e,t,n))];case"Elu":return[dl(I("x",e,t,n))];case"Erf":return[Uf(I("x",e,t,n))];case"Exp":return[Un(I("x",e,t,n))];case"Expm1":return[jf(I("x",e,t,n))];case"Floor":return[pl(I("x",e,t,n))];case"Log":return[Sn(I("x",e,t,n))];case"Log1p":return[Sd(I("x",e,t,n))];case"Imag":return[Id(I("x",e,t,n))];case"Neg":return[_t(I("x",e,t,n))];case"Reciprocal":return[tm(I("x",e,t,n))];case"Real":return[oc(I("x",e,t,n))];case"Relu":return[zr(I("x",e,t,n))];case"Round":return[nm(I("x",e,t,n))];case"Selu":return[Dd(I("x",e,t,n))];case"Sigmoid":return[In(I("x",e,t,n))];case"Sin":return[zd(I("x",e,t,n))];case"Sign":return[am(I("x",e,t,n))];case"Sinh":return[Pd(I("x",e,t,n))];case"Softplus":return[fl(I("x",e,t,n))];case"Sqrt":return[Zt(I("x",e,t,n))];case"Square":return[lt(I("x",e,t,n))];case"Tanh":return[cl(I("x",e,t,n))];case"Tan":return[om(I("x",e,t,n))];case"ClipByValue":return[mn(I("x",e,t,n),I("clipValueMin",e,t,n),I("clipValueMax",e,t,n))];case"Relu6":return[Od(I("x",e,t,n))];case"Rsqrt":return[$d(_n(e.inputNames[0],t,n))];case"Prod":return[Fd(I("x",e,t,n),I("axes",e,t,n))];case"LeakyRelu":return[nc(I("x",e,t,n),I("alpha",e,t,n))];case"Prelu":return[ic(I("x",e,t,n),I("alpha",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function ur(e,t,n=""){k.assert(Yne(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function Yne(e,t){if(e.length!==t.length)return!1;for(let n=0;n<e.length;n++)if(e[n]!==-1&&t[n]!==-1&&e[n]!==t[n])return!1;return!0}var Jne=class{constructor(e,t,n,r,a,s,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=r,this.identicalElementShapes=a,this.dynamicSize=s,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=ke(0),Wt(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),ur(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);n.tensor=t,Wt(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,r)=>this.write(n,t[r]))}gather(e,t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let r=0;r<this.size();r++)e.push(r)}if(e.length===0)return fr([],[0].concat(this.elementShape));let n=this.readMany(e);return ur(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),Cn(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return fr([],[0].concat(this.elementShape));let t=[];for(let r=0;r<this.size();r++)t.push(r);let n=this.readMany(t);return ur(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),rt(n,0)}scatter(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);if(e.length!==t.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);let n=Math.max(...e);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(e,ar(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0,r=e.map(o=>(n+=o,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
tensor.shape[0], but sum of lengths is
${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let a=n===0?0:t.size/n,s=[];W(()=>{t=q(t,[1,n,a]);for(let o=0;o<e.length;++o){let l=o===0?0:r[o-1],c=[0,l,0],u=[1,e[o],a];s[o]=q(Te(t,c,u),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},qc=class{constructor(e,t,n,r=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(a=>{if(n!==a.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${a.dtype}`);ur(t,a.shape,"TensorList shape mismatch: "),Wt(a)}),this.idTensor=ke(0),this.maxNumElements=r,Wt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new qc([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);return ur(e,this.elementShape,"TensorList shape mismatch: "),W(()=>{let r=this.tensors.map(a=>q(a,e));return Cn(r,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=this.tensors.pop();return ur(n.shape,e,"TensorList shape mismatch: "),q(n,e)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(ur(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Wt(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);return ur(this.tensors[e].shape,t,"TensorList shape mismatch: "),this.tensors[e]}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);ur(this.elementShape,t.shape,"TensorList shape mismatch: "),Wt(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);return ur(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size()),e.length===0?fr([],[0].concat(this.elementShape)):W(()=>{let r=e.map(a=>q(this.tensors[a],n));return Cn(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);return ur(this.elementShape,t,"TensorList shape mismatch: "),this.size()===0?fr([],[0].concat(this.elementShape)):W(()=>{let n=this.tensors.map(r=>q(r,t));return rt(n,0)})}};function Qne(e,t,n){let r=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let a=e.shape.slice(1);ur(a,t,"TensorList shape mismatch: ");let s=ar(e);return new qc(s,t,r)}function ere(e,t,n){return new qc([],e,t,n)}function tre(e,t,n,r){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let a=Math.max(...t);if(r!=null&&r!==-1&&a>=r)throw new Error(`Max index must be < array size (${a} vs. ${r})`);let s=new qc([],n,e.dtype,r),i=ar(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function nre(e,t,n){let r=0,a=t.map(l=>(r+=l,r));if(r!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
tensor.shape[0], but sum of lengths is
${r}, and tensor's shape is: ${e.shape}`);let s=r===0?0:e.size/r,i=W(()=>{let l=[];e=q(e,[1,r,s]);for(let c=0;c<t.length;++c){let u=c===0?0:a[c-1],h=[0,u,0],p=[1,t[c],s];l[c]=q(Te(e,h,p),n)}return e.dispose(),l}),o=new qc([],n,e.dtype,t.length);for(let l=0;l<i.length;l++)o.setItem(l,i[l]);return o}var rre=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let r=I("thenBranch",e,t,n),a=I("elseBranch",e,t,n),s=I("cond",e,t,n),i=I("args",e,t,n);return(await s.data())[0]?n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let r=I("body",e,t,n),a=I("cond",e,t,n),s=I("args",e,t,n),i=await n.functionMap[a].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(u=>u.id),l=await i[0].data();i.forEach(u=>{!u.kept&&o.indexOf(u.id)===-1&&u.dispose()});let c=s;for(;l[0];){let u=c;c=await n.functionMap[r].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);let h=c.map(d=>d.id);u.forEach(d=>{!d.kept&&o.indexOf(d.id)===-1&&h.indexOf(d.id)===-1&&d.dispose()});let p=await n.functionMap[a].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);l=await p[0].data(),p.forEach(d=>{!d.kept&&o.indexOf(d.id)===-1&&h.indexOf(d.id)===-1&&d.dispose()})}return c}case"LoopCond":{let r=I("pred",e,t,n);return[da(r)]}case"Switch":{let r=I("pred",e,t,n),a=I("data",e,t,n);return a.kept||(a=da(a)),(await r.data())[0]?[void 0,a]:[a,void 0]}case"Merge":{let r=e.inputNames.find(a=>_n(a,t,n)!==void 0);if(r){let a=_n(r,t,n);return[da(a)]}return}case"Enter":{let r=I("frameName",e,t,n),a=I("tensor",e,t,n);return n.enterFrame(r),[da(a)]}case"Exit":{let r=I("tensor",e,t,n);return n.exitFrame(),[da(r)]}case"NextIteration":{let r=I("tensor",e,t,n);return n.nextIteration(),[da(r)]}case"TensorArrayV3":{let r=I("size",e,t,n),a=I("dtype",e,t,n),s=I("elementShape",e,t,n),i=I("dynamicSize",e,t,n),o=I("clearAfterRead",e,t,n),l=I("identicalElementShapes",e,t,n),c=I("name",e,t,n),u=new Jne(c,a,r,s,l,i,o);return n.addTensorArray(u),[u.idTensor,ke(1)]}case"TensorArrayWriteV3":{let r=I("tensorArrayId",e,t,n),a=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(r.id);return i.write(a,s),[i.idTensor]}case"TensorArrayReadV3":{let r=I("tensorArrayId",e,t,n),a=I("index",e,t,n);return[n.getTensorArray(r.id).read(a)]}case"TensorArrayGatherV3":{let r=I("tensorArrayId",e,t,n),a=I("indices",e,t,n),s=I("dtype",e,t,n);return[n.getTensorArray(r.id).gather(a,s)]}case"TensorArrayScatterV3":{let r=I("tensorArrayId",e,t,n),a=I("indices",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(r.id);return i.scatter(a,s),[i.idTensor]}case"TensorArrayConcatV3":{let r=I("tensorArrayId",e,t,n),a=n.getTensorArray(r.id),s=I("dtype",e,t,n);return[a.concat(s)]}case"TensorArraySplitV3":{let r=I("tensorArrayId",e,t,n),a=I("tensor",e,t,n),s=I("lengths",e,t,n),i=n.getTensorArray(r.id);return i.split(s,a),[i.idTensor]}case"TensorArraySizeV3":{let r=I("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return[ke(a.size(),"int32")]}case"TensorArrayCloseV3":{let r=I("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return a.clearAndClose(),[a.idTensor]}case"TensorListSetItem":{let r=I("tensorListId",e,t,n),a=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorList(r.id);return i.setItem(a,s),[i.idTensor]}case"TensorListGetItem":{let r=I("tensorListId",e,t,n),a=I("index",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(r.id).getItem(a,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let r=I("indices",e,t,n),a=I("tensor",e,t,n),s=I("elementShape",e,t,n),i=I("numElements",e,t,n),o=tre(a,r,s,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,e,t,n),o=ere(r,a,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let r=I("tensorListId",e,t,n),a=I("indices",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(r.id).gather(a,i,s)]}case"TensorListStack":{let r=I("tensorListId",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(r.id).stack(a,s,i)]}case"TensorListFromTensor":{let r=I("tensor",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=Qne(r,a,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let r=I("tensorListId",e,t,n),a=n.getTensorList(r.id),s=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[a.concat(s,i)]}case"TensorListPushBack":{let r=I("tensorListId",e,t,n),a=I("tensor",e,t,n),s=n.getTensorList(r.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{let r=I("tensorListId",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n);return[n.getTensorList(r.id).popBack(a,s)]}case"TensorListSplit":{let r=I("tensor",e,t,n),a=I("elementShape",e,t,n),s=I("lengths",e,t,n),i=nre(r,s,a);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function cv(e,t,n){let[r,a]=I("fusedOps",e,t,n),s=r==="biasadd",i=a==="prelu",o=r==="fusedbatchnorm",l=I("numArgs",e,t,n);if(s){if(i&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(o)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let c=I("strides",e,t,n),u=h0(e,t,n),h=I("dataFormat",e,t,n).toUpperCase(),p=I("dilations",e,t,n),[d,f]=I("args",e,t,n),m=I("leakyreluAlpha",e,t,n);return{stride:c,pad:u,dataFormat:h,dilations:p,biasArg:d,preluArg:f,activationFunc:a,leakyreluAlpha:m}}var are=(e,t,n)=>{switch(e.op){case"Conv1D":{let r=I("stride",e,t,n),a=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilation",e,t,n);return[_d(I("x",e,t,n),I("filter",e,t,n),r,a,s,i)]}case"Conv2D":{let r=I("strides",e,t,n),a=h0(e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[na(I("x",e,t,n),I("filter",e,t,n),[r[1],r[2]],a,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:r,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:c,leakyreluAlpha:u}=cv(e,t,n);return[Ma.conv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[r[1],r[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"FusedDepthwiseConv2dNative":{let{stride:r,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:c,leakyreluAlpha:u}=cv(e,t,n);return[Ma.depthwiseConv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[r[1],r[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let r=I("outputShape",e,t,n),a=I("strides",e,t,n),s=h0(e,t,n);return[bd(I("x",e,t,n),I("filter",e,t,n),r,[a[1],a[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let r=I("strides",e,t,n),a=h0(e,t,n),s=I("dilations",e,t,n),i=I("dataFormat",e,t,n).toUpperCase();return[hl(I("input",e,t,n),I("filter",e,t,n),[r[1],r[2]],a,i,[s[1],s[2]])]}case"Conv3D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[Lf(I("x",e,t,n),I("filter",e,t,n),[r[1],r[2],r[3]],a,s,[i[1],i[2],i[3]])]}case"AvgPool":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Yu(I("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a)]}case"MaxPool":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[ac(I("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a)]}case"MaxPoolWithArgmax":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n),i=I("includeBatchInIndex",e,t,n),{result:o,indexes:l}=z5(I("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a,i);return[o,l]}case"AvgPool3D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Df(I("x",e,t,n),[s[1],s[2],s[3]],[r[1],r[2],r[3]],a)]}case"MaxPool3D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Zf(I("x",e,t,n),[s[1],s[2],s[3]],[r[1],r[2],r[3]],a)]}case"Dilation2D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("dilations",e,t,n),i=r[1],o=r[2],l=s[1],c=s[2];return[Bf(I("x",e,t,n),I("filter",e,t,n),[i,o],a,[l,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},sre=(e,t,n)=>{switch(e.op){case"Fill":{let r=I("shape",e,t,n),a=I("dtype",e,t,n),s=I("value",e,t,n);return[tc(r,s,a)]}case"LinSpace":{let r=I("start",e,t,n),a=I("stop",e,t,n),s=I("num",e,t,n);return[C5(r,a,s)]}case"Multinomial":{let r=I("logits",e,t,n),a=I("numSamples",e,t,n),s=I("seed",e,t,n);return[P5(r,a,s)]}case"OneHot":{let r=I("indices",e,t,n),a=I("depth",e,t,n),s=I("onValue",e,t,n),i=I("offValue",e,t,n);return[al(r,a,s,i)]}case"Ones":return[Dr(I("shape",e,t,n),I("dtype",e,t,n))];case"OnesLike":return[Tn(I("x",e,t,n))];case"RandomUniform":return[yl(I("shape",e,t,n),I("minval",e,t,n),I("maxval",e,t,n),I("dtype",e,t,n))];case"Range":{let r=I("start",e,t,n),a=I("stop",e,t,n),s=I("step",e,t,n);return[Md(r,a,s,I("dtype",e,t,n))]}case"TruncatedNormal":{let r=I("shape",e,t,n),a=I("mean",e,t,n),s=I("stdDev",e,t,n),i=I("seed",e,t,n);return[Ud(r,a,s,I("dtype",e,t,n),i)]}case"Zeros":return[Tt(I("shape",e,t,n),I("dtype",e,t,n))];case"ZerosLike":return[Ve(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Qy(e,t,n){let r=I("boxes",e,t,n),a=I("scores",e,t,n),s=I("maxOutputSize",e,t,n),i=I("iouThreshold",e,t,n),o=I("scoreThreshold",e,t,n),l=I("softNmsSigma",e,t,n);return{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}}var ire=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=Qy(e,t,n),c=await at.nonMaxSuppressionWithScoreAsync(r,a,s,i,o,l);return[c.selectedIndices,c.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=Qy(e,t,n),l=I("padToMaxOutputSize",e,t,n),c=await at.nonMaxSuppressionPaddedAsync(r,a,s,i,o,l);return[c.selectedIndices,c.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=Qy(e,t,n);return[await at.nonMaxSuppressionAsync(r,a,s,i,o)]}case"Where":{let r=me(I("condition",e,t,n),"bool"),a=[await cm(r)];return r.dispose(),a}case"ListDiff":return B5(I("x",e,t,n),I("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},ore=(e,t,n)=>{switch(e.op){case"TopKV2":{let r=I("x",e,t,n),a=I("k",e,t,n),s=I("sorted",e,t,n),i=lm(r,a,s);return[i.values,i.indices]}case"Unique":{let r=I("x",e,t,n),a=jd(r);return[a.values,a.indices]}case"UniqueV2":{let r=I("x",e,t,n),a=I("axis",e,t,n),s=jd(r,a);return[s.values,s.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},lre=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let r=I("default",e,t,n);return[_n(e.name,t,n)||r];case"Placeholder":return[_n(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=I("x",e,t,n);return[da(c)]}case"IdentityN":return I("x",e,t,n).map(c=>da(c));case"Snapshot":let a=I("x",e,t,n);return[da(a)];case"Shape":return[Bt(I("x",e,t,n).shape,"int32")];case"ShapeN":return I("x",e,t,n).map(c=>Bt(c.shape));case"Size":return[ke(I("x",e,t,n).size,"int32")];case"Rank":return[ke(I("x",e,t,n).rank,"int32")];case"NoOp":return[ke(1)];case"Print":let s=I("x",e,t,n),i=I("data",e,t,n),o=I("message",e,t,n),l=I("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let c=0;c<i.length;c++)console.log(Array.prototype.slice.call(i[c].dataSync()).slice(0,l));return[s];default:throw TypeError(`Node type ${e.op} is not implemented`)}},ure=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=ke(0),this.tensorMap=new Map,Wt(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(r=>r.dispose()),this.tensorMap.clear(),W(()=>{let r=ar(t),a=n.length,s=r.length;k.assert(a===s,()=>`The number of elements doesn't match, keys has ${a} elements, the values has ${s} elements.`);for(let i=0;i<a;i++){let o=n[i],l=r[i];Wt(l),this.tensorMap.set(o,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return W(()=>{let r=[];for(let a=0;a<n.length;a++){let s=n[a],i=this.findWithDefault(s,t);r.push(i)}return Cn(r)})}findWithDefault(e,t){let n=this.tensorMap.get(e);return n!=null?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}},cre=async(e,t,n,r)=>{switch(e.op){case"HashTable":case"HashTableV2":{let a=I("keyDType",e,t,n),s=I("valueDType",e,t,n),i=new ure(a,s);return r.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{let a=I("tableHandle",e,t,n,r),s=I("keys",e,t,n),i=I("values",e,t,n);return[await r.getHashTableById(a.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let a=I("tableHandle",e,t,n,r),s=I("keys",e,t,n),i=I("defaultValue",e,t,n);return[await r.getHashTableById(a.id).find(s,i)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},hre=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{let r=I("images",e,t,n),a=I("size",e,t,n),s=I("alignCorners",e,t,n),i=I("halfPixelCenters",e,t,n);return[at.resizeBilinear(r,[a[0],a[1]],s,i)]}case"ResizeNearestNeighbor":{let r=I("images",e,t,n),a=I("size",e,t,n),s=I("alignCorners",e,t,n),i=I("halfPixelCenters",e,t,n);return[at.resizeNearestNeighbor(r,[a[0],a[1]],s,i)]}case"CropAndResize":{let r=I("image",e,t,n),a=I("boxes",e,t,n),s=I("boxInd",e,t,n),i=I("cropSize",e,t,n),o=I("method",e,t,n),l=I("extrapolationValue",e,t,n);return[at.cropAndResize(r,a,s,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},dre=(e,t,n)=>{switch(e.op){case"Equal":return[Ea(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[ci(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[nr(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[Ra(I("a",e,t,n),I("b",e,t,n))];case"Less":return[Nd(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[li(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[rr(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[rc(I("a",e,t,n))];case"LogicalOr":return[Cd(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[An(I("condition",e,t,n),I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},pre=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[He(I("a",e,t,n),I("b",e,t,n),I("transposeA",e,t,n),I("transposeB",e,t,n))];case"Transpose":return[nt(I("x",e,t,n),I("perm",e,t,n))];case"_FusedMatMul":let[r,a]=I("fusedOps",e,t,n),s=r==="biasadd",i=a==="prelu",o=I("numArgs",e,t,n),l=I("leakyreluAlpha",e,t,n);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,u]=I("args",e,t,n);return[Ma.matMul({a:I("a",e,t,n),b:I("b",e,t,n),transposeA:I("transposeA",e,t,n),transposeB:I("transposeB",e,t,n),bias:c,activation:a,preluActivationWeights:u,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},fre=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[si(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"FusedBatchNormV3":return[si(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"LRN":return[Gf(I("x",e,t,n),I("radius",e,t,n),I("bias",e,t,n),I("alpha",e,t,n),I("beta",e,t,n))];case"Softmax":return[uc(I("x",e,t,n))];case"LogSoftmax":return[Ed(I("x",e,t,n))];case"SparseToDense":return[hm(I("sparseIndices",e,t,n),I("outputShape",e,t,n),I("sparseValues",e,t,n),I("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},mre=(e,t,n)=>{switch(e.op){case"Max":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[jn(I("x",e,t,n),i,o)]}case"Mean":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[bt(I("x",e,t,n),i,o)]}case"Min":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[ml(I("x",e,t,n),i,o)]}case"Sum":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Ie(I("x",e,t,n),i,o)]}case"All":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[xd(I("x",e,t,n),i,o)]}case"Any":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Xu(I("x",e,t,n),i,o)]}case"ArgMax":{let i=I("axis",e,t,n);return[Ku(I("x",e,t,n),i)]}case"ArgMin":{let i=I("axis",e,t,n);return[Tf(I("x",e,t,n),i)]}case"Prod":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Fd(I("x",e,t,n),i,o)]}case"Cumsum":{let i=I("axis",e,t,n),o=I("exclusive",e,t,n),l=I("reverse",e,t,n);return[kd(I("x",e,t,n),i,o,l)]}case"Bincount":let r=I("x",e,t,n),a=I("weights",e,t,n),s=I("size",e,t,n);return[w5(r,a,s)];case"DenseBincount":{let i=I("x",e,t,n),o=I("weights",e,t,n),l=I("size",e,t,n),c=I("binaryOutput",e,t,n);return[I5(i,o,l,c)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Are=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=I("n",e,t,n),a=I("axis",e,t,n),s=I("tensors",e,t,n);return s=s.slice(0,r),[rt(s,a)]}case"Gather":{let r=I("x",e,t,n),a=I("indices",e,t,n);return[oi(r,me(a,"int32"),0)]}case"GatherV2":{let r=I("axis",e,t,n),a=I("batchDims",e,t,n),s=I("x",e,t,n),i=I("indices",e,t,n);return[oi(s,me(i,"int32"),r,a)]}case"Reverse":{let r=I("dims",e,t,n),a=[];for(let i=0;i<r.length;i++)r[i]&&a.push(i);let s=I("x",e,t,n);return[En(s,a)]}case"ReverseV2":{let r=I("axis",e,t,n),a=I("x",e,t,n);return[En(a,r)]}case"Slice":{let r=I("begin",e,t,n),a=I("size",e,t,n);return[Te(I("x",e,t,n),r,a)]}case"StridedSlice":{let r=I("begin",e,t,n),a=I("end",e,t,n),s=I("strides",e,t,n),i=I("beginMask",e,t,n),o=I("endMask",e,t,n),l=I("ellipsisMask",e,t,n),c=I("newAxisMask",e,t,n),u=I("shrinkAxisMask",e,t,n),h=I("x",e,t,n);return[im(h,r,a,s,i,o,l,c,u)]}case"Pack":return W(()=>{let r=I("axis",e,t,n),a=I("tensors",e,t,n),s=a[0].shape,i=Fa(a[0]).shape,o=a.map(l=>{let c=k.arraysEqual(l.shape,s);if(!c&&!k.arraysEqual(Fa(l).shape,i))throw new Error("the input tensors shape does not match");return c?l:q(l,s)});return[Cn(o,r)]});case"Unpack":{let r=I("axis",e,t,n),a=I("tensor",e,t,n);return ar(a,r)}case"Tile":{let r=I("reps",e,t,n);return[Ca(I("x",e,t,n),r)]}case"Split":case"SplitV":{let r=I("axis",e,t,n),a=I("numOrSizeSplits",e,t,n),s=I("x",e,t,n);return Kt(s,a,r)}case"ScatterNd":{let r=I("indices",e,t,n),a=I("values",e,t,n),s=I("shape",e,t,n);return[H5(r,a,s)]}case"GatherNd":{let r=I("x",e,t,n),a=I("indices",e,t,n);return[G5(r,a)]}case"SparseToDense":{let r=I("sparseIndices",e,t,n),a=I("outputShape",e,t,n),s=I("sparseValues",e,t,n),i=I("defaultValue",e,t,n);return[hm(r,s,a,s.dtype===i.dtype?i:me(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},yre=(e,t,n)=>{switch(e.op){case"FFT":return[cc(I("x",e,t,n))];case"IFFT":return[gl(I("x",e,t,n))];case"RFFT":return[hc(I("x",e,t,n))];case"IRFFT":return[Bd(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},gre=(e,t,n)=>{switch(e.op){case"Cast":return[me(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let r=I("axis",e,t,n);return[Nn(I("x",e,t,n),r)]}case"Squeeze":{let r=I("axis",e,t,n);return[Fa(I("x",e,t,n),r)]}case"Reshape":return[q(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[Yf(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[ra(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let r=I("blockShape",e,t,n),a=I("paddings",e,t,n);return[sc(I("x",e,t,n),r,a)]}case"BatchToSpaceND":{let r=I("blockShape",e,t,n),a=I("crops",e,t,n);return[Ju(I("x",e,t,n),r,a)]}case"DepthToSpace":{let r=I("blockSize",e,t,n),a=I("dataFormat",e,t,n).toUpperCase();return[Wf(I("x",e,t,n),r,a)]}case"BroadcastTo":return[Qu(I("x",e,t,n),I("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function hv(e,t,n,r){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return W(()=>Kne(s,i,o));case"basic_math":return W(()=>Zne(s,i,o));case"control":return rre(s,i,o);case"convolution":return W(()=>are(s,i,o));case"creation":return W(()=>sre(s,i,o));case"dynamic":return ire(s,i,o);case"evaluation":return W(()=>ore(s,i,o));case"image":return W(()=>hre(s,i,o));case"graph":return W(()=>lre(s,i,o));case"logical":return W(()=>dre(s,i,o));case"matrices":return W(()=>pre(s,i,o));case"normalization":return W(()=>fre(s,i,o));case"reduction":return W(()=>mre(s,i,o));case"slice_join":return W(()=>Are(s,i,o));case"spectral":return W(()=>yre(s,i,o));case"transformation":return W(()=>gre(s,i,o));case"hash_table":return cre(s,i,o,r);case"custom":let l=V7(s.op);if(l&&l.customExecutor)return l.customExecutor(new Xne(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return k.isPromise(a)?a.then(s=>[].concat(s)):[].concat(a)}var dv=class{constructor(e={},t={},n={},r={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function fv(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,c=Object.keys(e).map(p=>Dn(p)[0]),u=[];r!=null&&(u=r.map(p=>Dn(p.name)[0]));let h=[...t];for(;h.length>0;){let p=h.pop();if((pv(p)||xre(p)||wre(p))&&i==null&&(i=p,o=i.children.map(d=>d.name).filter(d=>a.has(d))),a.add(p.name),n[p.name]==null&&c.indexOf(p.name)===-1&&u.indexOf(p.name)===-1){if(p.inputs.length===0){s.push(p.name);continue}p.inputs.forEach(d=>{l.has(d.name)||(l.add(d.name),h.push(d))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function _re(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(u=>Dn(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{r.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{r.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{r.has(u.name)&&s.push(u)});let l=new Set,c=[];for(;s.length>0;){let u=s.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(p=>l.has(p.name))&&s.push(h)})}return c}var bre=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],vre=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],kre=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function pv(e){return bre.indexOf(e.op)>=0}function xre(e){return vre.indexOf(e.op)>=0}function wre(e){return kre.indexOf(e.op)>=0}var e2=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new e2(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(r=>r.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(a=>a.name).sort(),r=t.map(a=>a.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=fv(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(r.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${r}]`)}return _re(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(u=>this.graph.nodes[Dn(u)[0]]),a=t.map(u=>Dn(u)[0]),s=a.map(u=>this.graph.nodes[u]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(r,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},c={};return W(()=>{let u=new dv(this.weightMap,l,c,this.functionExecutorMap),h=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,A]=Dn(f),y=[];y[A]=e[f],h[m]=y});let p=this.getFrozenTensorIds(h),d={};for(let f=0;f<o.length;f++){let m=o[f];if(!h[m.name]){let A=hv(m,h,u,this._resourceManager);if(k.isPromise(A))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);h[m.name]=A,this.checkTensorForDisposal(m.name,m,h,u,p,a,d)}}return this.parent==null&&u.dispose(p),t.map(f=>_n(f,h,u))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=Ene(o.name,n,r);l!=null&&l.forEach(c=>{if(c&&!a.has(c.id)){let u=i[c.id];u===1?(c.dispose(),delete i[c.id]):u!=null&&i[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new dv(this.weightMap,r,a,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(h=>_n(h,i,s)),l=o.map(h=>h.id),c=Object.keys(e).map(h=>e[h].id),u=new Set([...l,...c,...this.weightIds]);return Object.keys(i).forEach(h=>{i[h].forEach(p=>{p&&!p.isDisposed&&!u.has(p.id)&&p.dispose()})}),this.parent==null&&s.dispose(u),o}async executeFunctionAsync(e,t,n){let r=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let a=Object.keys(e),s=a.map(g=>this.graph.nodes[Dn(g)[0]]),i=n.map(g=>Dn(g)[0]),o=i.map(g=>this.graph.nodes[g]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:u,syncInputs:h}=fv(e,o,this.weightMap,this._initNodes),p=[...s,...this.graph.weights,...this._initNodes||[]].map(g=>({node:g,contexts:t.currentContext})),d=Object.assign({},this.weightMap);Object.keys(e).forEach(g=>{let[_,x]=Dn(g),w=[];w[x]=e[g],d[_]=w});let f={},m=this.getFrozenTensorIds(d),A={};for(;p.length>0;){let g=this.processStack(s,p,t,d,A,m,i,f,l);await Promise.all(g)}u==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(g=>!pv(g)&&!_n(g.name,d,t)).map(g=>g.name);if(y.length>0){let g="";throw u!=null&&(g=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${c}]. ${g}`)}return d}processStack(e,t,n,r,a,s,i,o,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let h="";if(u.node.op==="Enter"&&I("isConstant",u.node,r,n)&&([h]=ha(u.node.name,n)),r[u.node.name]==null){let p=hv(u.node,r,n,this._resourceManager);h||([h]=ha(u.node.name,n));let d=n.currentContext;k.isPromise(p)?c.push(p.then(f=>(r[h]=f,n.currentContext=d,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l),f))):(r[h]=p,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l))}else this.processChildNodes(u.node,t,n,r,a,l)}return c}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=ha(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!_n(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!_n(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=Dn(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);k.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&k.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=Dn(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Dn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Ire=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Nre="?tfjs-format=file",Sre="model.json",mv=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Ire}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=fn.browserHTTPRequest(e,this.loadOptions);else{let t=fn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(fn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=fn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new e2(ov.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=ov.Instance.transformGraph(e.modelInitializer);this.initializer=new e2(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=fn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Ke)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Ht(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Sre}${Nre}`);let n=new mv(e,t);return await n.load(),n}var Tre="3.0.0",Av={};$e(Av,{CSVDataset:()=>gv,Dataset:()=>Ul,FileDataSource:()=>xv,TextLineDataset:()=>yv,URLDataSource:()=>wv,array:()=>Ere,csv:()=>Rre,func:()=>Fre,generator:()=>Mre,microphone:()=>$re,version_data:()=>Dre,webcam:()=>Ore,zip:()=>Cre});var zre=qi(rg()),Pre=qi(rg());function Lre(e,t){return d0(e,t)}function d0(e,t,n=new Map,r=new Set){if(e==null)return null;if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse)if(jl(e)){let s=Array.isArray(e)?[]:{};r.add(e);for(let i in e){let o=e[i],l=d0(o,t,n,r);s[i]=l}return r.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,a.value),a.value}function Wre(e,t=bv){return _v(e,t)}function _v(e,t,n=new Set){let r=e[0];if(n.has(r))throw new Error("Circular references are not supported.");let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse)if(jl(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let o=e.map(c=>c[i]),l=_v(o,t,n);s[i]=l}return n.delete(r),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return a.value}function bv(e){return e===null?null:jl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function vv(e,t){let n=new Map;d0(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(k.isPromise(a)){let s=await a;n.set(r,s)}}return d0(e,t,n)}function jl(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ke))}function Vre(e){return e==null||Bre(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ke||k.isTypedArray(e)}function Bre(e){return e===null||typeof e!="object"&&typeof e!="function"}function jre(e){return Lre(e,Ure)}function Ure(e){return e instanceof Ke?{value:e.clone(),recurse:!1}:jl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var kv=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},t2=class extends kv{constructor(){super(t2.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let r=0;r<n;r++)t[r]=this.get(this.wrap(this.begin+r));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};t2.INITIAL_CAPACITY=32;function Iv(e){return new Hre(e)}function n2(e){return new Gre(e)}function qre(e,t){return new Nv(e,t)}function Kre(e,t=ja.FAIL){return new Xre(e,t)}var Gt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new nae(this,e)}filter(e){return new eae(this,e)}map(e){return new tae(this,e)}mapAsync(e){return new Sv(this,e)}serialMapAsync(e){return new Sv(this,e).serial()}flatmap(e){return new rae(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new Qre(this,e,t)}columnMajorBatch(e,t=!0,n=bv){return this.rowMajorBatch(e,t).map(r=>Wre(r,n))}concatenate(e,t){return new Nv(Iv([this,e]),t)}take(e){return e<0||e==null?this:new Jre(this,e)}skip(e){return e<0||e==null?this:new Yre(this,e)}prefetch(e){return new Tv(this,e)}shuffle(e,t){return new aae(this,e,t)}serial(){return new Zre(this)}},Hre=class extends Gt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:jre(e),done:!1}}},Gre=class extends Gt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},Zre=class extends Gt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Yre=class extends Gt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Se(e.value)}return this.upstream.next()}},Jre=class extends Gt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Qre=class extends Gt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},eae=class extends Gt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Se(e.value)}}},tae=class extends Gt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=pr.getTensorsInContainer(e.value),n=this.transform(e.value),r=pr.getTensorsInContainer(n);for(let a of t)pr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},nae=class extends Gt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Sv=class extends Gt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=pr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=pr.getTensorsInContainer(n);for(let a of t)pr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},r2=class extends Gt{constructor(){super();this.outputQueue=new t2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},rae=class extends r2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=pr.getTensorsInContainer(e.value),n=this.transform(e.value),r=pr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)pr.isTensorInList(a,r)||a.dispose();return!0}},Nv=class extends Gt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},ja;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(ja||(ja={}));var Xre=class extends Gt{constructor(e,t=ja.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function r(s){return s instanceof Gt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await vv(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case ja.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ja.SHORTEST:return{value:null,done:!0};case ja.LONGEST:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Tv=class extends Gt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new kv(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},aae=class extends Tv{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Pre.alea(n||k.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Ul=class{constructor(){this.size=null}batch(e,t=!0){let n=this;k.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),zn(async()=>(await n.iterator()).columnMajorBatch(e,t,sae),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,zn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,zn(async()=>(await t.iterator()).filter(r=>W(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return zn(async()=>(await t.iterator()).map(n=>W(()=>e(n))),this.size)}mapAsync(e){let t=this;return zn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return zn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,zn(async()=>{let r=n2(async()=>({value:await t.iterator(),done:!1}));return qre(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,zn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=zre.alea(t||k.now().toString());return zn(async()=>{let s=a.int32();return n&&(s+=a.int32()),(await r.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,zn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Ul.MAX_BUFFER_SIZE=1e4;function zn(e,t=null){return new class extends Ul{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function Ere(e){return zn(async()=>Iv(e),e.length)}function Cre(e){if(!jl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return zn(async()=>{let n=await vv(e,r=>{if(r instanceof Ul)return{value:r.iterator(),recurse:!1};if(jl(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return Kre(n,ja.SHORTEST)},t)}function sae(e){if(e===null)return null;let t=e[0];return Vre(t)?{value:iae(e),recurse:!1}:{value:null,recurse:!0}}function iae(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ke?Cn(e):fr(e)}var yv=class extends Ul{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},p0='"',Xc=Symbol("out"),Ev=Symbol("field"),f0=Symbol("quote"),a2=Symbol("quoteafterquote"),Cv=Symbol("quoteinquote"),gv=class extends Ul{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new yv(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&k.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((r,a)=>(r[a]=r[a]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(k.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},r={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let c=Number(o);if(isNaN(c))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=c;else switch(i.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(o);break;default:l=c}}i&&i.isLabel?r[s]=l:n[s]=l}}return Object.keys(r).length===0?n:{xs:n,ys:r}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],r=0,a=e.length,s=Xc;for(let i=0;i<a;i++)switch(s){case Xc:switch(e.charAt(i)){case p0:r=i+1,s=f0;break;case this.delimiter:if(r=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Xc;break;default:s=Ev,r=i;break}break;case Ev:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i)),s=Xc,r=i+1;break;default:}break;case f0:switch(e.charAt(i)){case p0:s=a2;break;default:}break;case a2:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i-1)),s=Xc,r=i+1;break;case p0:s=f0;break;default:s=Cv;break}break;case Cv:switch(e.charAt(i)){case p0:s=f0;break;default:}break;default:}if(s===a2?n.push(e.substring(r,a-1)):n.push(e.substring(r)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},Rv=class extends Gt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(Q().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new Rv(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,a)=>n.set(r,a*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(k.sizeFromShape(t));return n.set(e,n.length-e.length),fr(n,t)}},Fv=class extends Gt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Bt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-n)/2,s=(1-r)/2,i=a+n,o=r+s;this.cropBox=yn([s,a,o,i],[1,4])}else this.cropBox=yn([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Q().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new Fv(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=sl.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return W(()=>{let t=Nn(me(e,"float32"),0),n;n=at.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return q(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},Mv=class{},Ov=class extends Gt{split(e){return new oae(this,e)}},oae=class extends Ov{constructor(e,t){super();this.upstream=e,this.impl=new lae(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},lae=class extends r2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},cae=class extends Gt{decodeUTF8(){return new uae(this)}},uae=class extends Ov{constructor(e){super();this.upstream=e,this.impl=new hae(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},hae=class extends r2{constructor(e){super();if(this.upstream=e,Q().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=gk();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return Q().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},$v=class extends cae{constructor(e,t={}){super();this.file=e,this.options=t,k.assert(e instanceof Uint8Array||(Q().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let r=new FileReader;r.onload=s=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},r.onabort=s=>t(new Error("Aborted")),r.onerror=s=>t(new Error(s.type));let a=this.file.slice(this.offset,n);r.readAsArrayBuffer(a)}this.offset=n}),done:!1}}};async function pae(e,t={}){let n,r;typeof e=="string"?n=e:(n=e.url,r=dae(e));let a=await k.fetch(n,r);if(a.ok){let s=new Uint8Array(await a.arrayBuffer());return new $v(s,t)}else throw new Error(a.statusText)}var dae=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function Dv(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var xv=class extends Mv{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(Dv(this.input)&&Q().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new $v(this.input,this.options)}},wv=class extends Mv{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return Dv(this.url)?new xv(this.url,this.fileOptions).iterator():pae(this.url,this.fileOptions)}};function Rre(e,t={}){return new gv(new wv(e),t)}function Fre(e){let t=n2(e);return zn(async()=>t)}function Mre(e){return zn(async()=>{let t=await e();return n2(()=>t.next())})}async function Ore(e,t){return Fv.create(e,t)}async function $re(e){return Rv.create(e)}var Dre="3.0.0",fae={tfjs:xk,"tfjs-core":wk,"tfjs-data":_k,"tfjs-layers":bk,"tfjs-converter":vk,"tfjs-backend-cpu":zx,"tfjs-backend-webgl":a_,"tfjs-backend-wasm":Kb};var hn={name:"humangl",priority:99,canvas:null,gl:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function zv(){if(!kf(hn.name)){Fe("backend registration:",hn.name);try{hn.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(hn.width,hn.height):document.createElement("canvas")}catch(e){Fe("error: cannot create canvas:",e);return}try{hn.gl=hn.canvas.getContext("webgl2",hn.webGLattr)}catch(e){Fe("error: cannot get WebGL2 context:",e);return}try{hp(2,hn.gl)}catch(e){Fe("error: cannot set WebGL2 context:",e);return}try{let e=new mp(hn.gl);ol(hn.name,()=>new yp(e),hn.priority)}catch(e){Fe("error: cannot register WebGL backend:",e);return}try{Qo("webgl").forEach(t=>{let n={...t,backendName:hn.name};Qs(n)})}catch(e){Fe("error: cannot update WebGL backend registration:",e);return}try{an.set("WEBGL_VERSION",2),an.set("WEBGL_MAX_TEXTURE_SIZE",hn.gl.getParameter(hn.gl.MAX_TEXTURE_SIZE)),an.set("WEBGL_FORCE_F16_TEXTURES",!0),an.set("WEBGL_PACK_DEPTHWISECONV",!0)}catch(e){Fe("error: cannot set WebGL backend flags:",e);return}Fe("backend registered:",hn.name)}}var W2=Pe(e6()),Qc=Pe(r6()),eh=Pe(s6()),th=Pe(l6()),Ka=Pe(c6()),B2=Pe(B6());function S0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Yc(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function V6(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return at.cropAndResize(t,s,[0],n)}function U6(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],a=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:r,palmLandmarks:a,confidence:e.confidence}}function T0(e,t=1.5){let n=Yc(e),r=S0(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function E0(e){let t=Yc(e),n=S0(e),a=Math.max(...n)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function zse(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function q6(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return zse(n)}var X6=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Xa(e,t){let n=0;for(let r=0;r<e.length;r++)n+=e[r]*t[r];return n}function Pse(e,t){let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n}function K6(e,t){let n=[],r=e.length;for(let a=0;a<r;a++){n.push([]);for(let s=0;s<r;s++)n[a].push(Xa(e[a],Pse(t,s)))}return n}function M2(e,t){let n=Math.cos(e),r=Math.sin(e),a=[[n,-r,0],[r,n,0],[0,0,1]],s=X6(t[0],t[1]),i=K6(s,a),o=X6(-t[0],-t[1]);return K6(i,o)}function Z6(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],r=[-Xa(t[0],n),-Xa(t[1],n)];return[t[0].concat(r[0]),t[1].concat(r[1]),[0,0,1]]}function O2(e,t){return[Xa(e,t[0]),Xa(e,t[1])]}var V2=Pe(o4()),Za=Pe(l4()),U2=Pe(p4()),A4=Pe(Gl());var C0={};tg(C0,{default:()=>f4});var f4={backend:"webgl",wasmPath:"../assets/",async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",inputSize:256,rotation:!1,maxFaces:10,skipFrames:11,minConfidence:.5,iouThreshold:.2,scoreThreshold:.5},mesh:{enabled:!0,modelPath:"../models/facemesh.json",inputSize:192,returnRawData:!1},iris:{enabled:!0,modelPath:"../models/iris.json",inputSize:64},age:{enabled:!0,modelPath:"../models/age-ssrnet-imdb.json",inputSize:64,skipFrames:31},gender:{enabled:!0,minConfidence:.1,modelPath:"../models/gender-ssrnet-imdb.json",inputSize:64,skipFrames:41},emotion:{enabled:!0,inputSize:64,minConfidence:.2,skipFrames:21,modelPath:"../models/emotion-large.json"},embedding:{enabled:!1,inputSize:112,modelPath:"../models/mobilefacenet.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",inputSize:257,maxDetections:10,scoreThreshold:.5,nmsRadius:20,outputStride:16,modelType:"MobileNet"},hand:{enabled:!0,rotation:!1,inputSize:256,skipFrames:12,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}}};var P2=`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==`,L2=`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`;var m4="0.11.1";var ft=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function Jl(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,r)=>(Object.keys(r||{}).forEach(a=>{let s=n[a],i=r[a];Array.isArray(s)&&Array.isArray(i)?n[a]=s.concat(...i):t(s)&&t(i)?n[a]=Jl(s,i):n[a]=i}),n),{})}var y4=class{constructor(t={}){this.tf=W1,this.version=m4,this.config=Jl(f4,t),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=W2,this.age=Qc,this.gender=eh,this.emotion=th,this.body=B2,this.hand=V2}profile(){return this.config.profile?A4.data:{}}analyze(...t){if(!this.analyzeMemoryLeaks)return;let n=Vn().state.numTensors,r=this.numTensors;this.numTensors=n;let a=n-r;a!==0&&Fe(...t,a)}sanity(t){if(!this.checkSanity)return null;if(!t)return"input is not defined";if(an.flags.IS_NODE&&!(t instanceof Ke))return"input must be a tensor";try{yd()}catch(n){return"backend not loaded"}return null}simmilarity(t,n){return this.config.face.embedding.enabled?Ka.simmilarity(t,n):0}async load(t){this.state="load";let n=ft();t&&(this.config=Jl(this.config,t)),this.firstRun&&(Fe(`version: ${this.version} TensorFlow/JS version: ${c5}`),await this.checkBackend(!0),an.flags.IS_BROWSER&&(Fe("configuration:",this.config),Fe("tf flags:",an.flags))),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?W2.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?Qc.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?eh.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?th.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?Ka.load(this.config):null),this.models.posenet||(this.config.body.enabled?B2.load(this.config):null),this.models.handpose||(this.config.hand.enabled?V2.load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await W2.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await Qc.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await eh.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await th.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await Ka.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await B2.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await V2.load(this.config))),this.firstRun&&(Fe("tf engine state:",Vn().state.numBytes,"bytes",Vn().state.numTensors,"tensors"),this.firstRun=!1);let r=Math.trunc(ft()-n);r>(this.perf.load||0)&&(this.perf.load=r)}async checkBackend(t){if(this.config.backend&&this.config.backend!==""&&t||yd()!==this.config.backend){let n=ft();this.state="backend",Fe("setting backend:",this.config.backend),this.config.backend==="wasm"&&(Fe("settings wasm path:",this.config.wasmPath),Xb(this.config.wasmPath),await Q().getAsync("WASM_HAS_SIMD_SUPPORT")||Fe("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&zv();try{await d5(this.config.backend)}catch(r){Fe("error: cannot set backend:",this.config.backend,r)}if(h5(),yd()==="webgl"){this.config.deallocate&&(Fe("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),an.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),an.set("WEBGL_FORCE_F16_TEXTURES",!0),an.set("WEBGL_PACK_DEPTHWISECONV",!0);let r=await If().getGPGPUContext().gl;Fe(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await p5(),this.perf.backend=Math.trunc(ft()-n)}}async detectFace(t){var c;let n,r,a,s,i,o=[];this.state="run:face",n=ft();let l=await((c=this.models.facemesh)==null?void 0:c.estimateFaces(t,this.config));this.perf.face=Math.trunc(ft()-n);for(let u of l){if(this.analyze("Get Face"),!u.image||u.image.isDisposedInternal){Fe("Face object is disposed:",u.image);continue}this.analyze("Start Age:"),this.config.async?r=this.config.face.age.enabled?Qc.predict(u.image,this.config):{}:(this.state="run:age",n=ft(),r=this.config.face.age.enabled?await Qc.predict(u.image,this.config):{},this.perf.age=Math.trunc(ft()-n)),this.analyze("Start Gender:"),this.config.async?a=this.config.face.gender.enabled?eh.predict(u.image,this.config):{}:(this.state="run:gender",n=ft(),a=this.config.face.gender.enabled?await eh.predict(u.image,this.config):{},this.perf.gender=Math.trunc(ft()-n)),this.analyze("Start Emotion:"),this.config.async?s=this.config.face.emotion.enabled?th.predict(u.image,this.config):{}:(this.state="run:emotion",n=ft(),s=this.config.face.emotion.enabled?await th.predict(u.image,this.config):{},this.perf.emotion=Math.trunc(ft()-n)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?i=this.config.face.embedding.enabled?Ka.predict(u.image,this.config):{}:(this.state="run:embedding",n=ft(),i=this.config.face.embedding.enabled?await Ka.predict(u.image,this.config):{},this.perf.embedding=Math.trunc(ft()-n)),this.analyze("End Emotion:"),this.config.async&&([r,a,s,i]=await Promise.all([r,a,s,i])),this.analyze("Finish Face:"),u.image.dispose(),this.config.face.iris.enabled||(delete u.annotations.leftEyeIris,delete u.annotations.rightEyeIris);let h=u.annotations.leftEyeIris&&u.annotations.rightEyeIris?11.7*Math.max(Math.abs(u.annotations.leftEyeIris[3][0]-u.annotations.leftEyeIris[1][0]),Math.abs(u.annotations.rightEyeIris[4][1]-u.annotations.rightEyeIris[2][1])):0;o.push({confidence:u.confidence,box:u.box,mesh:u.mesh,boxRaw:u.boxRaw,meshRaw:u.meshRaw,annotations:u.annotations,age:r.age,gender:a.gender,genderConfidence:a.confidence,emotion:s,embedding:i,iris:h!==0?Math.trunc(h)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),o}async image(t,n={}){this.state="image",this.config=Jl(this.config,n);let r=U2.process(t,this.config);return r.tensor.dispose(),r.canvas}async detect(t,n={}){return new Promise(async r=>{var p,d,f,m;this.state="config";let a;this.config=Jl(this.config,n),this.state="check";let s=this.sanity(t);s&&(Fe(s,t),r({error:s}));let i,o,l,c=ft();await this.checkBackend(),await this.load(),this.config.scoped&&Vn().startScope(),this.analyze("Start Scope:"),a=ft();let u=U2.process(t,this.config);if(!u||!u.tensor){Fe("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(ft()-a),this.analyze("Get Image:"),this.config.async?(l=this.config.face.enabled?this.detectFace(u.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=ft(),l=this.config.face.enabled?await this.detectFace(u.tensor):[],this.perf.face=Math.trunc(ft()-a)),this.analyze("Start Body:"),this.config.async?(i=this.config.body.enabled?(p=this.models.posenet)==null?void 0:p.estimatePoses(u.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",a=ft(),i=this.config.body.enabled?await((d=this.models.posenet)==null?void 0:d.estimatePoses(u.tensor,this.config)):[],this.perf.body=Math.trunc(ft()-a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(o=this.config.hand.enabled?(f=this.models.handpose)==null?void 0:f.estimateHands(u.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=ft(),o=this.config.hand.enabled?await((m=this.models.handpose)==null?void 0:m.estimateHands(u.tensor,this.config)):[],this.perf.hand=Math.trunc(ft()-a)),this.analyze("End Hand:"),this.config.async&&([l,i,o]=await Promise.all([l,i,o])),u.tensor.dispose(),this.config.scoped&&Vn().endScope(),this.analyze("End Scope:");let h=[];this.config.gesture.enabled&&(a=ft(),h=[...Za.face(l),...Za.body(i),...Za.hand(o),...Za.iris(l)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(ft()-a)),this.perf.total=Math.trunc(ft()-c),this.state="idle",r({face:l,body:i,hand:o,gesture:h,performance:this.perf,canvas:u.canvas})})}async warmupBitmap(){let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(P2);break;case"full":n=await t(L2);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,C0),a.close()}return r}async warmupCanvas(){return new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+P2;break;case"full":r=1200,n="data:image/jpeg;base64,"+L2;break;default:n=null}let a=new Image(r,r);a.onload=()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=r,s.height=r;let i=s.getContext("2d");i.drawImage(a,0,0);let o=i.getImageData(0,0,r,r);this.detect(o,C0).then(l=>t(l))},n?a.src=n:t(null)})}async warmup(t){let n=ft();t&&(this.config=Jl(this.config,t));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await this.warmupBitmap():a=await this.warmupCanvas(),this.config.videoOptimized=r;let s=ft();return Fe("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};export{y4 as default};
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
//# sourceMappingURL=human.esm.js.map
Reference in New Issue View Git Blame Copy Permalink