/* Face-API homepage: author: ' */ var GB=Object.create,Wh=Object.defineProperty,WB=Object.getPrototypeOf,UB=Object.prototype.hasOwnProperty,jB=Object.getOwnPropertyNames,HB=Object.getOwnPropertyDescriptor;var qB=r=>Wh(r,"__esModule",{value:!0});var Br=(r,e)=>()=>(e||(e={exports:{}},r(e.exports,e)),e.exports),et=(r,e)=>{for(var t in e)Wh(r,t,{get:e[t],enumerable:!0})},KB=(r,e,t)=>{if(e&&typeof e=="object"||typeof e=="function")for(let o of jB(e))!UB.call(r,o)&&o!=="default"&&Wh(r,o,{get:()=>e[o],enumerable:!(t=HB(e,o))||t.enumerable});return r},Tc=r=>r&&r.__esModule?r:KB(qB(Wh(r!=null?GB(WB(r)):{},"default",{value:r,enumerable:!0})),r);var nN=Br(()=>{});var MN=Br((PN,$_)=>{(function(r,e,t){function o(i){var l=this,u=a();l.next=function(){var c=2091639*l.s0+l.c*23283064365386963e-26;return l.s0=l.s1,l.s1=l.s2,l.s2=c-(l.c=c|0)},l.c=1,l.s0=u(" "),l.s1=u(" "),l.s2=u(" "),l.s0-=u(i),l.s0<0&&(l.s0+=1),l.s1-=u(i),l.s1<0&&(l.s1+=1),l.s2-=u(i),l.s2<0&&(l.s2+=1),u=null}function n(i,l){return l.c=i.c,l.s0=i.s0,l.s1=i.s1,l.s2=i.s2,l}function s(i,l){var u=new o(i),c=l&&l.state,p=u.next;return p.int32=function(){return u.next()*4294967296|0},p.double=function(){return p()+(p()*2097152|0)*11102230246251565e-32},p.quick=p,c&&(typeof c=="object"&&n(c,u),p.state=function(){return n(u,{})}),p}function a(){var i=4022871197,l=function(u){u=u.toString();for(var c=0;c>>0,p-=i,p*=i,i=p>>>0,p-=i,i+=p*4294967296}return(i>>>0)*23283064365386963e-26};return l}e&&e.exports?e.exports=s:t&&t.amd?t(function(){return s}):this.alea=s})(PN,typeof $_=="object"&&$_,typeof define=="function"&&define)});var zN=Br((LN,R_)=>{(function(r,e,t){function o(a){var i=this,l="";i.x=0,i.y=0,i.z=0,i.w=0,i.next=function(){var c=i.x^i.x<<11;return i.x=i.y,i.y=i.z,i.z=i.w,i.w^=i.w>>>19^c^c>>>8},a===(a|0)?i.x=a:l+=a;for(var u=0;u>>0)/4294967296};return c.double=function(){do var p=l.next()>>>11,m=(l.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},c.int32=l.next,c.quick=c,u&&(typeof u=="object"&&n(u,l),c.state=function(){return n(l,{})}),c}e&&e.exports?e.exports=s:t&&t.amd?t(function(){return s}):this.xor128=s})(LN,typeof R_=="object"&&R_,typeof define=="function"&&define)});var VN=Br((BN,F_)=>{(function(r,e,t){function o(a){var i=this,l="";i.next=function(){var c=i.x^i.x>>>2;return i.x=i.y,i.y=i.z,i.z=i.w,i.w=i.v,(i.d=i.d+362437|0)+(i.v=i.v^i.v<<4^(c^c<<1))|0},i.x=0,i.y=0,i.z=0,i.w=0,i.v=0,a===(a|0)?i.x=a:l+=a;for(var u=0;u>>4),i.next()}function n(a,i){return i.x=a.x,i.y=a.y,i.z=a.z,i.w=a.w,i.v=a.v,i.d=a.d,i}function s(a,i){var l=new o(a),u=i&&i.state,c=function(){return(l.next()>>>0)/4294967296};return c.double=function(){do var p=l.next()>>>11,m=(l.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},c.int32=l.next,c.quick=c,u&&(typeof u=="object"&&n(u,l),c.state=function(){return n(l,{})}),c}e&&e.exports?e.exports=s:t&&t.amd?t(function(){return s}):this.xorwow=s})(BN,typeof F_=="object"&&F_,typeof define=="function"&&define)});var WN=Br((GN,O_)=>{(function(r,e,t){function o(a){var i=this;i.next=function(){var u=i.x,c=i.i,p,m,f;return p=u[c],p^=p>>>7,m=p^p<<24,p=u[c+1&7],m^=p^p>>>10,p=u[c+3&7],m^=p^p>>>3,p=u[c+4&7],m^=p^p<<7,p=u[c+7&7],p=p^p<<13,m^=p^p<<9,u[c]=m,i.i=c+1&7,m};function l(u,c){var p,m,f=[];if(c===(c|0))m=f[0]=c;else for(c=""+c,p=0;p0;--p)u.next()}l(i,a)}function n(a,i){return i.x=a.x.slice(),i.i=a.i,i}function s(a,i){a==null&&(a=+new Date);var l=new o(a),u=i&&i.state,c=function(){return(l.next()>>>0)/4294967296};return c.double=function(){do var p=l.next()>>>11,m=(l.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},c.int32=l.next,c.quick=c,u&&(u.x&&n(u,l),c.state=function(){return n(l,{})}),c}e&&e.exports?e.exports=s:t&&t.amd?t(function(){return s}):this.xorshift7=s})(GN,typeof O_=="object"&&O_,typeof define=="function"&&define)});var jN=Br((UN,P_)=>{(function(r,e,t){function o(a){var i=this;i.next=function(){var u=i.w,c=i.X,p=i.i,m,f;return i.w=u=u+1640531527|0,f=c[p+34&127],m=c[p=p+1&127],f^=f<<13,m^=m<<17,f^=f>>>15,m^=m>>>12,f=c[p]=f^m,i.i=p,f+(u^u>>>16)|0};function l(u,c){var p,m,f,d,h,g=[],x=128;for(c===(c|0)?(m=c,c=null):(c=c+"\0",m=0,x=Math.max(x,c.length)),f=0,d=-32;d>>15,m^=m<<4,m^=m>>>13,d>=0&&(h=h+1640531527|0,p=g[d&127]^=m+h,f=p==0?f+1:0);for(f>=128&&(g[(c&&c.length||0)&127]=-1),f=127,d=4*128;d>0;--d)m=g[f+34&127],p=g[f=f+1&127],m^=m<<13,p^=p<<17,m^=m>>>15,p^=p>>>12,g[f]=m^p;u.w=h,u.X=g,u.i=f}l(i,a)}function n(a,i){return i.i=a.i,i.w=a.w,i.X=a.X.slice(),i}function s(a,i){a==null&&(a=+new Date);var l=new o(a),u=i&&i.state,c=function(){return(l.next()>>>0)/4294967296};return c.double=function(){do var p=l.next()>>>11,m=(l.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},c.int32=l.next,c.quick=c,u&&(u.X&&n(u,l),c.state=function(){return n(l,{})}),c}e&&e.exports?e.exports=s:t&&t.amd?t(function(){return s}):this.xor4096=s})(UN,typeof P_=="object"&&P_,typeof define=="function"&&define)});var qN=Br((HN,M_)=>{(function(r,e,t){function o(a){var i=this,l="";i.next=function(){var c=i.b,p=i.c,m=i.d,f=i.a;return c=c<<25^c>>>7^p,p=p-m|0,m=m<<24^m>>>8^f,f=f-c|0,i.b=c=c<<20^c>>>12^p,i.c=p=p-m|0,i.d=m<<16^p>>>16^f,i.a=f-c|0},i.a=0,i.b=0,i.c=2654435769|0,i.d=1367130551,a===Math.floor(a)?(i.a=a/4294967296|0,i.b=a|0):l+=a;for(var u=0;u>>0)/4294967296};return c.double=function(){do var p=l.next()>>>11,m=(l.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},c.int32=l.next,c.quick=c,u&&(typeof u=="object"&&n(u,l),c.state=function(){return n(l,{})}),c}e&&e.exports?e.exports=s:t&&t.amd?t(function(){return s}):this.tychei=s})(HN,typeof M_=="object"&&M_,typeof define=="function"&&define)});var KN=Br(()=>{});var XN=Br((Oxe,mg)=>{(function(r,e){var t=this,o=256,n=6,s=52,a="random",i=e.pow(o,n),l=e.pow(2,s),u=l*2,c=o-1,p;function m(_,w,v){var $=[];w=w==!0?{entropy:!0}:w||{};var A=g(h(w.entropy?[_,b(r)]:_==null?x():_,3),$),R=new f($),M=function(){for(var V=R.g(n),G=i,W=0;V=u;)V/=2,G/=2,W>>>=1;return(V+W)/G};return M.int32=function(){return R.g(4)|0},M.quick=function(){return R.g(4)/4294967296},M.double=M,g(b(R.S),r),(w.pass||v||function(V,G,W,q){return q&&(q.S&&d(q,R),V.state=function(){return d(R,{})}),W?(e[a]=V,G):V})(M,A,"global"in w?w.global:this==e,w.state)}e["seed"+a]=m;function f(_){var w,v=_.length,$=this,A=0,R=$.i=$.j=0,M=$.S=[];for(v||(_=[v++]);A{var y4=MN(),b4=zN(),_4=VN(),w4=WN(),k4=jN(),v4=qN(),Fu=XN();Fu.alea=y4;Fu.xor128=b4;Fu.xorwow=_4;Fu.xorshift7=w4;Fu.xor4096=k4;Fu.tychei=v4;YN.exports=Fu});var ME=Br(()=>{});var Jp=Br(()=>{});var _z=Br(()=>{});var wz=Br(()=>{});var kz=Br((ry,nI)=>{var sI=function(){var r=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(r=r||__filename),function(e){e=e||{};function t(){return K.buffer!=qe&&wr(K.buffer),Ft}function o(){return K.buffer!=qe&&wr(K.buffer),po}function n(){return K.buffer!=qe&&wr(K.buffer),_r}function s(){return K.buffer!=qe&&wr(K.buffer),Go}function a(){return K.buffer!=qe&&wr(K.buffer),fo}var i=typeof e!="undefined"?e:{},l={},u;for(u in i)i.hasOwnProperty(u)&&(l[u]=i[u]);var c=[],p="./this.program",m=function(I,D){throw D},f=!1,d=!1,h=!1,g=!1;f=typeof window=="object",d=typeof importScripts=="function",h=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",g=!f&&!h&&!d;var x=i.ENVIRONMENT_IS_PTHREAD||!1;x&&(qe=i.buffer,ss=i.DYNAMIC_BASE,So=i.DYNAMICTOP_PTR);var b="";function _(I){return i.locateFile?i.locateFile(I,b):b+I}var w,v,$,A,R,M;if(h){d?b=Jp().dirname(b)+"/":b=__dirname+"/",w=function(D,P){return R||(R=require("fs")),M||(M=Jp()),D=M.normalize(D),R.readFileSync(D,P?null:"utf8")},$=function(D){var P=w(D,!0);return P.buffer||(P=new Uint8Array(P)),ge(P.buffer),P},process.argv.length>1&&(p=process.argv[1].replace(/\\/g,"/")),c=process.argv.slice(2),process.on("uncaughtException",function(I){if(!(I instanceof yI))throw I}),process.on("unhandledRejection",ea),m=function(I){process.exit(I)},i.inspect=function(){return"[Emscripten Module object]"};var V;try{V=_z()}catch(I){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),I}Worker=V.Worker}else g?(typeof read!="undefined"&&(w=function(D){return read(D)}),$=function(D){var P;return typeof readbuffer=="function"?new Uint8Array(readbuffer(D)):(P=read(D,"binary"),ge(typeof P=="object"),P)},typeof scriptArgs!="undefined"?c=scriptArgs:typeof arguments!="undefined"&&(c=arguments),typeof quit=="function"&&(m=function(I){quit(I)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(f||d)&&(d?b=self.location.href:document.currentScript&&(b=document.currentScript.src),typeof r!="undefined"&&r&&(b=r),b.indexOf("blob:")!==0?b=b.substr(0,b.lastIndexOf("/")+1):b="",h?(w=function(D,P){return R||(R=require("fs")),M||(M=Jp()),D=M.normalize(D),R.readFileSync(D,P?null:"utf8")},$=function(D){var P=w(D,!0);return P.buffer||(P=new Uint8Array(P)),ge(P.buffer),P}):(w=function(D){var P=new XMLHttpRequest;return P.open("GET",D,!1),P.send(null),P.responseText},d&&($=function(D){var P=new XMLHttpRequest;return P.open("GET",D,!1),P.responseType="arraybuffer",P.send(null),new Uint8Array(P.response)}),v=function(D,P,j){var me=new XMLHttpRequest;me.open("GET",D,!0),me.responseType="arraybuffer",me.onload=function(){if(me.status==200||me.status==0&&me.response){P(me.response);return}j()},me.onerror=j,me.send(null)}),A=function(I){document.title=I});h&&typeof performance=="undefined"&&(performance=wz().performance);var G=i.print||console.log.bind(console),W=i.printErr||console.warn.bind(console);for(u in l)l.hasOwnProperty(u)&&(i[u]=l[u]);l=null,i.arguments&&(c=i.arguments),i.thisProgram&&(p=i.thisProgram),i.quit&&(m=i.quit);var q=Atomics.load,Z=Atomics.store,X=Atomics.compareExchange,Y;i.wasmBinary&&(Y=i.wasmBinary);var te;i.noExitRuntime&&(te=i.noExitRuntime),typeof WebAssembly!="object"&&W("no native wasm support detected");var K,re=new WebAssembly.Table({initial:171,maximum:171+0,element:"anyfunc"}),ie,se=0,pe=0,ae=!1,xe=0;function ge(I,D){I||ea("Assertion failed: "+D)}function _e(I){var D=i["_"+I];return ge(D,"Cannot call unknown function "+I+", make sure it is exported"),D}function ke(I,D,P,j,me){var he={string:function(go){var Xa=0;if(go!=null&&go!==0){var dm=(go.length<<2)+1;Xa=Cc(dm),ut(go,Xa,dm)}return Xa},array:function(go){var Xa=Cc(go.length);return kt(go,Xa),Xa}};function fe(go){return D==="string"?Re(go):D==="boolean"?Boolean(go):go}var Se=_e(I),mt=[],pr=0;if(j)for(var Ar=0;Ar=j);){var he=I[D++];if(!he)return me;if(!(he&128)){me+=String.fromCharCode(he);continue}var fe=I[D++]&63;if((he&224)==192){me+=String.fromCharCode((he&31)<<6|fe);continue}var Se=I[D++]&63;if((he&240)==224?he=(he&15)<<12|fe<<6|Se:he=(he&7)<<18|fe<<12|Se<<6|I[D++]&63,he<65536)me+=String.fromCharCode(he);else{var mt=he-65536;me+=String.fromCharCode(55296|mt>>10,56320|mt&1023)}}return me}function Re(I,D){return I?$e(o(),I,D):""}function He(I,D,P,j){if(!(j>0))return 0;for(var me=P,he=P+j-1,fe=0;fe=55296&&Se<=57343){var mt=I.charCodeAt(++fe);Se=65536+((Se&1023)<<10)|mt&1023}if(Se<=127){if(P>=he)break;D[P++]=Se}else if(Se<=2047){if(P+1>=he)break;D[P++]=192|Se>>6,D[P++]=128|Se&63}else if(Se<=65535){if(P+2>=he)break;D[P++]=224|Se>>12,D[P++]=128|Se>>6&63,D[P++]=128|Se&63}else{if(P+3>=he)break;D[P++]=240|Se>>18,D[P++]=128|Se>>12&63,D[P++]=128|Se>>6&63,D[P++]=128|Se&63}}return D[P]=0,P-me}function ut(I,D,P){return He(I,o(),D,P)}function wt(I){for(var D=0,P=0;P=55296&&j<=57343&&(j=65536+((j&1023)<<10)|I.charCodeAt(++P)&1023),j<=127?++D:j<=2047?D+=2:j<=65535?D+=3:D+=4}return D}function kt(I,D){t().set(I,D)}var pt=65536;function vt(I,D){return I%D>0&&(I+=D-I%D),I}var qe,Ft,po,Zt,mo,_r,Go,Jr,fo;function wr(I){qe=I,i.HEAP8=Ft=new Int8Array(I),i.HEAP16=Zt=new Int16Array(I),i.HEAP32=_r=new Int32Array(I),i.HEAPU8=po=new Uint8Array(I),i.HEAPU16=mo=new Uint16Array(I),i.HEAPU32=Go=new Uint32Array(I),i.HEAPF32=Jr=new Float32Array(I),i.HEAPF64=fo=new Float64Array(I)}var No=5256464,Wo=No,Qr=13584,ss=5256464,So=12656,xc=i.INITIAL_MEMORY||16777216;if(x)K=i.wasmMemory,qe=i.buffer;else if(i.wasmMemory)K=i.wasmMemory;else if(K=new WebAssembly.Memory({initial:xc/pt,maximum:2147483648/pt,shared:!0}),!(K.buffer instanceof SharedArrayBuffer))throw W("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),h&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");K&&(qe=K.buffer),xc=qe.byteLength,wr(qe),x||(n()[So>>2]=ss);function is(I){for(;I.length>0;){var D=I.shift();if(typeof D=="function"){D(i);continue}var P=D.func;typeof P=="number"?D.arg===void 0?i.dynCall_v(P):i.dynCall_vi(P,D.arg):P(D.arg===void 0?null:D.arg)}}var Ji=[],$l=[],iy=[],Qp=[],_h=[],em=!1;x&&(em=!0);function Uo(){if(!x){if(i.preRun)for(typeof i.preRun=="function"&&(i.preRun=[i.preRun]);i.preRun.length;)uy(i.preRun.shift());is(Ji)}}function wh(){em=!0,is($l)}function ay(){x||is(iy)}function ly(){if(!x){if(i.postRun)for(typeof i.postRun=="function"&&(i.postRun=[i.postRun]);i.postRun.length;)Rl(i.postRun.shift());is(_h)}}function uy(I){Ji.unshift(I)}function Rl(I){_h.unshift(I)}var yc=Math.ceil,cy=Math.floor,Qi=0,tm=null,Fl=null;function py(I){ge(!x,"addRunDependency cannot be used in a pthread worker"),Qi++,i.monitorRunDependencies&&i.monitorRunDependencies(Qi)}function my(I){if(Qi--,i.monitorRunDependencies&&i.monitorRunDependencies(Qi),Qi==0&&(tm!==null&&(clearInterval(tm),tm=null),Fl)){var D=Fl;Fl=null,D()}}i.preloadedImages={},i.preloadedAudios={};function ea(I){throw i.onAbort&&i.onAbort(I),x&&console.error("Pthread aborting at "+new Error().stack),I+="",G(I),W(I),ae=!0,xe=1,I="abort("+I+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(I)}function rm(I,D){return String.prototype.startsWith?I.startsWith(D):I.indexOf(D)===0}var fy="data:application/octet-stream;base64,";function kh(I){return rm(I,fy)}var dy="file://";function vh(I){return rm(I,dy)}var jo="tfjs-backend-wasm-threaded-simd.wasm";kh(jo)||(jo=_(jo));function Ch(){try{if(Y)return new Uint8Array(Y);if($)return $(jo);throw"both async and sync fetching of the wasm failed"}catch(I){ea(I)}}function hy(){return!Y&&(f||d)&&typeof fetch=="function"&&!vh(jo)?fetch(jo,{credentials:"same-origin"}).then(function(I){if(!I.ok)throw"failed to load wasm binary file at '"+jo+"'";return I.arrayBuffer()}).catch(function(){return Ch()}):new Promise(function(I,D){I(Ch())})}function gy(){var I={a:ab};function D(fe,Se){var mt=fe.exports;if(i.asm=mt,ie=Se,!x){var pr=we.unusedWorkers.length;we.unusedWorkers.forEach(function(Ar){we.loadWasmModuleToWorker(Ar,function(){--pr||my("wasm-instantiate")})})}}x||py("wasm-instantiate");function P(fe){D(fe.instance,fe.module)}function j(fe){return hy().then(function(Se){return WebAssembly.instantiate(Se,I)}).then(fe,function(Se){W("failed to asynchronously prepare wasm: "+Se),ea(Se)})}function me(){if(!Y&&typeof WebAssembly.instantiateStreaming=="function"&&!kh(jo)&&!vh(jo)&&typeof fetch=="function")fetch(jo,{credentials:"same-origin"}).then(function(fe){var Se=WebAssembly.instantiateStreaming(fe,I);return Se.then(P,function(mt){W("wasm streaming compile failed: "+mt),W("falling back to ArrayBuffer instantiation"),j(P)})});else return j(P)}if(i.instantiateWasm)try{var he=i.instantiateWasm(I,D);return he}catch(fe){return W("Module.instantiateWasm callback failed with error: "+fe),!1}return me(),{}}var xy={};function yy(){we.initRuntime()}x||$l.push({func:function(){Ph()}});var Ih=0,Nh=0,Sh=0;function bc(I,D,P){I=I|0,D=D|0,P=P|0,Ih=I,Sh=D,Nh=P}i.__register_pthread_ptr=bc;var om={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},_c=13568;function wc(I,D){if(I<=0||I>t().length||I&!0||D<0)return-28;if(D==0)return 0;D>=2147483647&&(D=Infinity);var P=Atomics.load(n(),_c>>2),j=0;if(P==I){var me=Atomics.compareExchange(n(),_c>>2,P,0);if(me==P&&(--D,j=1,D<=0))return 1}var he=Atomics.notify(n(),I>>2,D);if(he>=0)return he+j;throw"Atomics.notify returned an unexpected value "+he}i._emscripten_futex_wake=wc;function by(I){if(x)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!I)throw"Internal Error! Null pthread_ptr in _kill_thread!";n()[I+12>>2]=0;var D=we.pthreads[I];D.worker.terminate(),we.freeThreadData(D),we.runningWorkers.splice(we.runningWorkers.indexOf(D.worker),1),D.worker.pthread=void 0}function _y(I){if(x)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!I)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var D=we.pthreads[I];D.worker.postMessage({cmd:"cancel"})}function wy(I){if(x)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!I)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";n()[I+12>>2]=0;var D=we.pthreads[I];if(D){var P=D.worker;we.returnWorkerToPool(P)}}var we={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){bc(we.mainThreadBlock,!d,1),dI(we.mainThreadBlock)},initMainThreadBlock:function(){for(var I=8,D=0;D>2]=we.mainThreadBlock;var P=we.mainThreadBlock+156;n()[P>>2]=P;for(var j=13056,D=0;D<128;++D)s()[j/4+D]=0;Atomics.store(s(),we.mainThreadBlock+104>>2,j),Atomics.store(s(),we.mainThreadBlock+40>>2,we.mainThreadBlock),Atomics.store(s(),we.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(we.exitHandlers!==null){for(;we.exitHandlers.length>0;)we.exitHandlers.pop()();we.exitHandlers=null}x&&se&&fI()},threadExit:function(I){var D=Us();D&&(Atomics.store(s(),D+4>>2,I),Atomics.store(s(),D+0>>2,1),Atomics.store(s(),D+60>>2,1),Atomics.store(s(),D+64>>2,0),we.runExitHandlers(),wc(D+0,2147483647),bc(0,0,0),se=0,x&&postMessage({cmd:"exit"}))},threadCancel:function(){we.runExitHandlers(),Atomics.store(s(),se+4>>2,-1),Atomics.store(s(),se+0>>2,1),wc(se+0,2147483647),se=pe=0,bc(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var I in we.pthreads){var D=we.pthreads[I];D&&D.worker&&we.returnWorkerToPool(D.worker)}we.pthreads={};for(var P=0;P>2];n()[I.threadInfoStruct+104>>2]=0,cm(D),cm(I.threadInfoStruct)}I.threadInfoStruct=0,I.allocatedOwnStack&&I.stackBase&&cm(I.stackBase),I.stackBase=0,I.worker&&(I.worker.pthread=null)}},returnWorkerToPool:function(I){delete we.pthreads[I.pthread.thread],we.unusedWorkers.push(I),we.runningWorkers.splice(we.runningWorkers.indexOf(I),1),we.freeThreadData(I.pthread),I.pthread=void 0},receiveObjectTransfer:function(I){},loadWasmModuleToWorker:function(I,D){I.onmessage=function(P){var j=P.data,me=j.cmd;if(I.pthread&&(we.currentProxiedOperationCallerThread=I.pthread.threadInfoStruct),j.targetThread&&j.targetThread!=Us()){var he=we.pthreads[j.targetThread];he?he.worker.postMessage(P.data,j.transferList):console.error('Internal error! Worker sent a message "'+me+'" to target pthread '+j.targetThread+", but that thread no longer exists!"),we.currentProxiedOperationCallerThread=void 0;return}if(me==="processQueuedMainThreadWork")cb();else if(me==="spawnThread")Rh(P.data);else if(me==="cleanupThread")wy(j.thread);else if(me==="killThread")by(j.thread);else if(me==="cancelThread")_y(j.thread);else if(me==="loaded")I.loaded=!0,D&&D(I),I.runPthread&&(I.runPthread(),delete I.runPthread);else if(me==="print")G("Thread "+j.threadId+": "+j.text);else if(me==="printErr")W("Thread "+j.threadId+": "+j.text);else if(me==="alert")alert("Thread "+j.threadId+": "+j.text);else if(me==="exit"){var fe=I.pthread&&Atomics.load(s(),I.pthread.thread+68>>2);fe&&we.returnWorkerToPool(I)}else me==="cancelDone"?we.returnWorkerToPool(I):me==="objectTransfer"?we.receiveObjectTransfer(P.data):P.data.target==="setimmediate"?I.postMessage(P.data):W("worker sent an unknown command "+me);we.currentProxiedOperationCallerThread=void 0},I.onerror=function(P){W("pthread sent an error! "+P.filename+":"+P.lineno+": "+P.message)},h&&(I.on("message",function(P){I.onmessage({data:P})}),I.on("error",function(P){I.onerror(P)}),I.on("exit",function(P){console.log("worker exited - TODO: update the worker queue?")})),I.postMessage({cmd:"load",urlOrBlob:i.mainScriptUrlOrBlob||r,wasmMemory:K,wasmModule:ie,DYNAMIC_BASE:ss,DYNAMICTOP_PTR:So})},allocateUnusedWorker:function(){var I=_("tfjs-backend-wasm-threaded-simd.worker.js");we.unusedWorkers.push(new Worker(I))},getNewWorker:function(){return we.unusedWorkers.length==0&&(we.allocateUnusedWorker(),we.loadWasmModuleToWorker(we.unusedWorkers[0])),we.unusedWorkers.length>0?we.unusedWorkers.pop():null},busySpinWait:function(I){for(var D=performance.now()+I;performance.now()>2]=I,I}function Sy(I,D){if(x)return Ha(1,1,I,D);Qp.unshift({func:I,arg:D})}function Ty(I,D){if(I==D)postMessage({cmd:"processQueuedMainThreadWork"});else if(x)postMessage({targetThread:I,cmd:"processThreadQueue"});else{var P=we.pthreads[I],j=P&&P.worker;if(!j)return;j.postMessage({cmd:"processThreadQueue"})}return 1}function Ey(){ea()}function Ay(I,D){I=I|0,D=D|0}function Dy(I,D,P){if(I<=0||I>t().length||I&!0)return-28;if(d){var j=Atomics.wait(n(),I>>2,D,P);if(j==="timed-out")return-73;if(j==="not-equal")return-6;if(j==="ok")return 0;throw"Atomics.wait returned an unexpected value "+j}else{var me=Atomics.load(n(),I>>2);if(D!=me)return-6;var he=performance.now(),fe=he+P;Atomics.store(n(),_c>>2,I);for(var Se=I;I==Se;){if(he=performance.now(),he>fe)return-73;cb(),I=Atomics.load(n(),_c>>2)}return 0}}function $y(){return Sh|0}function Ry(){return Nh|0}function Fy(I,D,P){o().copyWithin(I,D,D+P)}function Oy(){return navigator.hardwareConcurrency}function Ha(I,D){for(var P=arguments.length-2,j=pm(),me=Cc(P*8),he=me>>3,fe=0;fe>3]),D+=8):(D=D+3&~3,P.push(n()[D>>2]),D+=4);return P}function Py(I,D,P){Pl.length=D;for(var j=P>>3,me=0;me>>16),wr(K.buffer),1}catch(D){}}function zy(I){I=I>>>0;var D=My();if(I<=D)return!1;var P=65536,j=2147483648;if(I>j)return!1;for(var me=16777216,he=1;he<=4;he*=2){var fe=D*(1+.2/he);fe=Math.min(fe,I+100663296);var Se=Math.min(j,vt(Math.max(me,I,fe),P)),mt=Ly(Se);if(mt)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 I=Ze.eventHandlers.length-1;I>=0;--I)Ze._removeHandler(I);Ze.eventHandlers=[],Ze.deferredCalls=[]},registerRemoveEventListeners:function(){Ze.removeEventListenersRegistered||(Qp.push(Ze.removeAllEventListeners),Ze.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(I,D,P){function j(fe,Se){if(fe.length!=Se.length)return!1;for(var mt in fe)if(fe[mt]!=Se[mt])return!1;return!0}for(var me in Ze.deferredCalls){var he=Ze.deferredCalls[me];if(he.targetFunction==I&&j(he.argsList,P))return}Ze.deferredCalls.push({targetFunction:I,precedence:D,argsList:P}),Ze.deferredCalls.sort(function(fe,Se){return fe.precedence>2]=P,n()[fe+4>>2]=j,n()[fe+8>>2]=me,pb(I,637534208,D,j,fe),Ic(he)},getTargetThreadForEventCallback:function(I){switch(I){case 1:return 0;case 2:return we.currentProxiedOperationCallerThread;default:return I}},getNodeNameForTarget:function(I){return I?I==window?"#window":I==screen?"#screen":I&&I.nodeName?I.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function By(I){var D=wt(I)+1,P=um(D);return ut(I,P,D),P}function Vy(I,D,P,j){var me=pm(),he=Cc(12),fe=0;D&&(fe=By(D)),n()[he>>2]=fe,n()[he+4>>2]=P,n()[he+8>>2]=j,pb(I,657457152,0,fe,he),Ic(me)}function Gy(I,D,P,j){D=D?Re(D):"",Vy(I,D,P,j)}function Wy(I){return I>2?Re(I):I}var Uy=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function jy(I){I=Wy(I);var D=Uy[I]||(typeof document!="undefined"?document.querySelector(I):void 0);return D}function nm(I){return jy(I)}function Th(I,D,P){var j=nm(I);if(!j)return-4;if(j.canvasSharedPtr&&(n()[j.canvasSharedPtr>>2]=D,n()[j.canvasSharedPtr+4>>2]=P),j.offscreenCanvas||!j.controlTransferredOffscreen){j.offscreenCanvas&&(j=j.offscreenCanvas);var me=!1;if(j.GLctxObject&&j.GLctxObject.GLctx){var he=j.GLctxObject.GLctx.getParameter(2978);me=he[0]===0&&he[1]===0&&he[2]===j.width&&he[3]===j.height}j.width=D,j.height=P,me&&j.GLctxObject.GLctx.viewport(0,0,D,P)}else if(j.canvasSharedPtr){var fe=n()[j.canvasSharedPtr+8>>2];return Gy(fe,I,D,P),1}else return-4;return 0}function Eh(I,D,P){return x?Ha(2,1,I,D,P):Th(I,D,P)}function Hy(I,D,P){var j=nm(I);return j?Th(I,D,P):Eh(I,D,P)}function qy(I){I=I|0}function Ky(I,D){I=I|0,D=D|0}function Xy(I){var D=I.getExtension("ANGLE_instanced_arrays");if(D)return I.vertexAttribDivisor=function(P,j){D.vertexAttribDivisorANGLE(P,j)},I.drawArraysInstanced=function(P,j,me,he){D.drawArraysInstancedANGLE(P,j,me,he)},I.drawElementsInstanced=function(P,j,me,he,fe){D.drawElementsInstancedANGLE(P,j,me,he,fe)},1}function Yy(I){var D=I.getExtension("OES_vertex_array_object");if(D)return I.createVertexArray=function(){return D.createVertexArrayOES()},I.deleteVertexArray=function(P){D.deleteVertexArrayOES(P)},I.bindVertexArray=function(P){D.bindVertexArrayOES(P)},I.isVertexArray=function(P){return D.isVertexArrayOES(P)},1}function Zy(I){var D=I.getExtension("WEBGL_draw_buffers");if(D)return I.drawBuffers=function(P,j){D.drawBuffersWEBGL(P,j)},1}var rt={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 I=new Float32Array(rt.MINI_TEMP_BUFFER_SIZE),D=0;D>2]:-1;me+=Re(n()[P+he*4>>2],fe<0?void 0:fe)}return me},createContext:function(I,D){var P=I.getContext("webgl",D);if(!P)return 0;var j=rt.registerContext(P,D);return j},registerContext:function(I,D){var P=um(8);n()[P+4>>2]=Us();var j={handle:P,attributes:D,version:D.majorVersion,GLctx:I};return I.canvas&&(I.canvas.GLctxObject=j),rt.contexts[P]=j,(typeof D.enableExtensionsByDefault=="undefined"||D.enableExtensionsByDefault)&&rt.initExtensions(j),P},makeContextCurrent:function(I){return rt.currentContext=rt.contexts[I],i.ctx=qa=rt.currentContext&&rt.currentContext.GLctx,!(I&&!qa)},getContext:function(I){return rt.contexts[I]},deleteContext:function(I){rt.currentContext===rt.contexts[I]&&(rt.currentContext=null),typeof Ze=="object"&&Ze.removeAllHandlersOnTarget(rt.contexts[I].GLctx.canvas),rt.contexts[I]&&rt.contexts[I].GLctx.canvas&&(rt.contexts[I].GLctx.canvas.GLctxObject=void 0),cm(rt.contexts[I].handle),rt.contexts[I]=null},initExtensions:function(I){if(I||(I=rt.currentContext),!I.initExtensionsDone){I.initExtensionsDone=!0;var D=I.GLctx;Xy(D),Yy(D),Zy(D),D.disjointTimerQueryExt=D.getExtension("EXT_disjoint_timer_query");var P=["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"],j=D.getSupportedExtensions()||[];j.forEach(function(me){P.indexOf(me)!=-1&&D.getExtension(me)})}},populateUniformTable:function(I){for(var D=rt.programs[I],P=rt.programInfos[I]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},j=P.uniforms,me=qa.getProgramParameter(D,35718),he=0;he>2;P.alpha=!!n()[j+(0>>2)],P.depth=!!n()[j+(4>>2)],P.stencil=!!n()[j+(8>>2)],P.antialias=!!n()[j+(12>>2)],P.premultipliedAlpha=!!n()[j+(16>>2)],P.preserveDrawingBuffer=!!n()[j+(20>>2)];var me=n()[j+(24>>2)];P.powerPreference=Jy[me],P.failIfMajorPerformanceCaveat=!!n()[j+(28>>2)],P.majorVersion=n()[j+(32>>2)],P.minorVersion=n()[j+(36>>2)],P.enableExtensionsByDefault=n()[j+(40>>2)],P.explicitSwapControl=n()[j+(44>>2)],P.proxyContextToMainThread=n()[j+(48>>2)],P.renderViaOffscreenBackBuffer=n()[j+(52>>2)];var he=nm(I);if(!he)return-4;if(P.explicitSwapControl)return-1;var fe=rt.createContext(he,P);return fe}function eb(I,D){return Qy(I,D)}var Ml={splitPath:function(I){var D=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return D.exec(I).slice(1)},normalizeArray:function(I,D){for(var P=0,j=I.length-1;j>=0;j--){var me=I[j];me==="."?I.splice(j,1):me===".."?(I.splice(j,1),P++):P&&(I.splice(j,1),P--)}if(D)for(;P;P--)I.unshift("..");return I},normalize:function(I){var D=I.charAt(0)==="/",P=I.substr(-1)==="/";return I=Ml.normalizeArray(I.split("/").filter(function(j){return!!j}),!D).join("/"),!I&&!D&&(I="."),I&&P&&(I+="/"),(D?"/":"")+I},dirname:function(I){var D=Ml.splitPath(I),P=D[0],j=D[1];return!P&&!j?".":(j&&(j=j.substr(0,j.length-1)),P+j)},basename:function(I){if(I==="/")return"/";var D=I.lastIndexOf("/");return D===-1?I:I.substr(D+1)},extname:function(I){return Ml.splitPath(I)[3]},join:function(){var I=Array.prototype.slice.call(arguments,0);return Ml.normalize(I.join("/"))},join2:function(I,D){return Ml.normalize(I+"/"+D)}},vc={mappings:{},buffers:[null,[],[]],printChar:function(I,D){var P=vc.buffers[I];D===0||D===10?((I===1?G:W)($e(P,0)),P.length=0):P.push(D)},varargs:void 0,get:function(){vc.varargs+=4;var I=n()[vc.varargs-4>>2];return I},getStr:function(I){var D=Re(I);return D},get64:function(I,D){return I}};function Ah(I){return x?Ha(3,1,I):0}function Dh(I,D,P,j,me){if(x)return Ha(4,1,I,D,P,j,me)}function $h(I,D,P,j){if(x)return Ha(5,1,I,D,P,j);for(var me=0,he=0;he>2],Se=n()[D+(he*8+4)>>2],mt=0;mt>2]=me,0}function tb(I){var D=we.exitHandlers.pop();I&&D()}function rb(I,D){we.exitHandlers===null&&(we.exitHandlers=[]),we.exitHandlers.push(function(){xI(I,D)})}function Rh(I){if(x)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var D=we.getNewWorker();if(D.pthread!==void 0)throw"Internal error!";if(!I.pthread_ptr)throw"Internal error, no pthread ptr!";we.runningWorkers.push(D);for(var P=um(128*4),j=0;j<128;++j)n()[P+j*4>>2]=0;var me=I.stackBase+I.stackSize,he=we.pthreads[I.pthread_ptr]={worker:D,stackBase:I.stackBase,stackSize:I.stackSize,allocatedOwnStack:I.allocatedOwnStack,thread:I.pthread_ptr,threadInfoStruct:I.pthread_ptr},fe=he.threadInfoStruct>>2;Atomics.store(s(),fe+(0>>2),0),Atomics.store(s(),fe+(4>>2),0),Atomics.store(s(),fe+(8>>2),0),Atomics.store(s(),fe+(68>>2),I.detached),Atomics.store(s(),fe+(104>>2),P),Atomics.store(s(),fe+(48>>2),0),Atomics.store(s(),fe+(40>>2),he.threadInfoStruct),Atomics.store(s(),fe+(44>>2),42),Atomics.store(s(),fe+(108>>2),I.stackSize),Atomics.store(s(),fe+(84>>2),I.stackSize),Atomics.store(s(),fe+(80>>2),me),Atomics.store(s(),fe+(108+8>>2),me),Atomics.store(s(),fe+(108+12>>2),I.detached),Atomics.store(s(),fe+(108+20>>2),I.schedPolicy),Atomics.store(s(),fe+(108+24>>2),I.schedPrio);var Se=pI(),mt=Se+40;Atomics.store(s(),fe+(176>>2),mt),D.pthread=he;var pr={cmd:"run",start_routine:I.startRoutine,arg:I.arg,threadInfoStruct:I.pthread_ptr,selfThreadId:I.pthread_ptr,parentThreadId:I.parent_pthread_ptr,stackBase:I.stackBase,stackSize:I.stackSize};D.runPthread=function(){pr.time=performance.now(),D.postMessage(pr,I.transferList)},D.loaded&&(D.runPthread(),delete D.runPthread)}function ob(I,D,P){if(!D&&!P)return om.EINVAL;if(!I)return W("pthread_getschedparam called with a null thread pointer!"),om.ESRCH;var j=n()[I+12>>2];if(j!==I)return W("pthread_getschedparam attempted on thread "+I+", which does not point to a valid thread, or does not exist anymore!"),om.ESRCH;var me=Atomics.load(s(),I+108+20>>2),he=Atomics.load(s(),I+108+24>>2);return D&&(n()[D>>2]=me),P&&(n()[P>>2]=he),0}function Us(){return Ih|0}i._pthread_self=Us;function nb(I,D,P,j){if(typeof SharedArrayBuffer=="undefined")return W("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!I)return W("pthread_create called with a null thread pointer!"),28;var me=[],he=0;if(x&&(me.length===0||he))return hI(687865856,I,D,P,j);if(he)return he;var fe=0,Se=0,mt=0,pr=0,Ar=0;if(D){fe=n()[D>>2],fe+=81920,Se=n()[D+8>>2],mt=n()[D+12>>2]!==0;var Nc=n()[D+16>>2]===0;if(Nc){var fm=n()[D+20>>2],go=n()[D+24>>2],Xa=we.currentProxiedOperationCallerThread?we.currentProxiedOperationCallerThread:Us();ob(Xa,D+20,D+24),pr=n()[D+20>>2],Ar=n()[D+24>>2],n()[D+20>>2]=fm,n()[D+24>>2]=go}else pr=n()[D+20>>2],Ar=n()[D+24>>2]}else fe=2097152;var dm=Se==0;dm?Se=mI(16,fe):(Se-=fe,ge(Se>0));for(var Sc=um(232),fb=0;fb<232>>2;++fb)s()[(Sc>>2)+fb]=0;n()[I>>2]=Sc,n()[Sc+12>>2]=Sc;var bI=Sc+156;n()[bI>>2]=bI;var db={stackBase:Se,stackSize:fe,allocatedOwnStack:dm,schedPolicy:pr,schedPrio:Ar,detached:mt,startRoutine:P,pthread_ptr:Sc,parent_pthread_ptr:Us(),arg:j,transferList:me};return x?(db.cmd="spawnThread",postMessage(db,me)):Rh(db),0}function sb(I){return I=+I,I>=0?+cy(I+.5):+yc(I-.5)}function Fh(I){if(x)return Ha(6,1,I);switch(I){case 30:return 16384;case 85:var D=2147483648;return D/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 Ny(28),-1}x?we.initWorker():we.initMainThreadBlock();var qa;rt.init();var ib=[null,Sy,Eh,Ah,Dh,$h,Fh],ab={e:Cy,r:Iy,w:Ty,a:Ey,l:Ay,d:Dy,c:wc,h:Ol,g:$y,x:Ry,q:Fy,B:Oy,t:Py,A:zy,u:Hy,k:qy,s:Ky,v:eb,m:Ah,o:Dh,i:$h,p:yy,memory:K||i.wasmMemory,y:tb,z:rb,j:nb,b:Us,f:sb,n:Fh,table:re},Oh=gy();i.asm=Oh;var Ph=i.___wasm_call_ctors=function(){return(Ph=i.___wasm_call_ctors=i.asm.C).apply(null,arguments)},Mh=i._init=function(){return(Mh=i._init=i.asm.D).apply(null,arguments)},sm=i._register_tensor=function(){return(sm=i._register_tensor=i.asm.E).apply(null,arguments)},Lh=i._dispose_data=function(){return(Lh=i._dispose_data=i.asm.F).apply(null,arguments)},Ll=i._dispose=function(){return(Ll=i._dispose=i.asm.G).apply(null,arguments)},im=i._Abs=function(){return(im=i._Abs=i.asm.H).apply(null,arguments)},lb=i._Add=function(){return(lb=i._Add=i.asm.I).apply(null,arguments)},ub=i._AddN=function(){return(ub=i._AddN=i.asm.J).apply(null,arguments)},am=i._ArgMax=function(){return(am=i._ArgMax=i.asm.K).apply(null,arguments)},zh=i._AvgPool=function(){return(zh=i._AvgPool=i.asm.L).apply(null,arguments)},Bh=i._BatchMatMul=function(){return(Bh=i._BatchMatMul=i.asm.M).apply(null,arguments)},H=i._Ceil=function(){return(H=i._Ceil=i.asm.N).apply(null,arguments)},ne=i._ClipByValue=function(){return(ne=i._ClipByValue=i.asm.O).apply(null,arguments)},be=i._Conv2D=function(){return(be=i._Conv2D=i.asm.P).apply(null,arguments)},Ke=i._Conv2DBackpropInput=function(){return(Ke=i._Conv2DBackpropInput=i.asm.Q).apply(null,arguments)},Xe=i._Cos=function(){return(Xe=i._Cos=i.asm.R).apply(null,arguments)},Ht=i._CropAndResize=function(){return(Ht=i._CropAndResize=i.asm.S).apply(null,arguments)},at=i._Cumsum=function(){return(at=i._Cumsum=i.asm.T).apply(null,arguments)},nt=i._DepthToSpace=function(){return(nt=i._DepthToSpace=i.asm.U).apply(null,arguments)},xr=i._DepthwiseConv2dNative=function(){return(xr=i._DepthwiseConv2dNative=i.asm.V).apply(null,arguments)},ta=i._Equal=function(){return(ta=i._Equal=i.asm.W).apply(null,arguments)},ra=i._Exp=function(){return(ra=i._Exp=i.asm.X).apply(null,arguments)},Vh=i._FlipLeftRight=function(){return(Vh=i._FlipLeftRight=i.asm.Y).apply(null,arguments)},lm=i._Floor=function(){return(lm=i._Floor=i.asm.Z).apply(null,arguments)},ho=i._FloorDiv=function(){return(ho=i._FloorDiv=i.asm._).apply(null,arguments)},Ka=i._FusedBatchNorm=function(){return(Ka=i._FusedBatchNorm=i.asm.$).apply(null,arguments)},Gh=i._FusedConv2D=function(){return(Gh=i._FusedConv2D=i.asm.aa).apply(null,arguments)},Tz=i._FusedDepthwiseConv2D=function(){return(Tz=i._FusedDepthwiseConv2D=i.asm.ba).apply(null,arguments)},Ez=i._Gather=function(){return(Ez=i._Gather=i.asm.ca).apply(null,arguments)},Az=i._GatherNd=function(){return(Az=i._GatherNd=i.asm.da).apply(null,arguments)},Dz=i._Greater=function(){return(Dz=i._Greater=i.asm.ea).apply(null,arguments)},$z=i._GreaterEqual=function(){return($z=i._GreaterEqual=i.asm.fa).apply(null,arguments)},Rz=i._LeakyRelu=function(){return(Rz=i._LeakyRelu=i.asm.ga).apply(null,arguments)},Fz=i._Less=function(){return(Fz=i._Less=i.asm.ha).apply(null,arguments)},Oz=i._LessEqual=function(){return(Oz=i._LessEqual=i.asm.ia).apply(null,arguments)},Pz=i._Log=function(){return(Pz=i._Log=i.asm.ja).apply(null,arguments)},Mz=i._LogicalAnd=function(){return(Mz=i._LogicalAnd=i.asm.ka).apply(null,arguments)},Lz=i._Max=function(){return(Lz=i._Max=i.asm.la).apply(null,arguments)},zz=i._MaxPool=function(){return(zz=i._MaxPool=i.asm.ma).apply(null,arguments)},Bz=i._Maximum=function(){return(Bz=i._Maximum=i.asm.na).apply(null,arguments)},Vz=i._Mean=function(){return(Vz=i._Mean=i.asm.oa).apply(null,arguments)},Gz=i._Min=function(){return(Gz=i._Min=i.asm.pa).apply(null,arguments)},Wz=i._Minimum=function(){return(Wz=i._Minimum=i.asm.qa).apply(null,arguments)},Uz=i._Multiply=function(){return(Uz=i._Multiply=i.asm.ra).apply(null,arguments)},jz=i._Neg=function(){return(jz=i._Neg=i.asm.sa).apply(null,arguments)},Hz=i._NonMaxSuppressionV3=function(){return(Hz=i._NonMaxSuppressionV3=i.asm.ta).apply(null,arguments)},qz=i._NonMaxSuppressionV4=function(){return(qz=i._NonMaxSuppressionV4=i.asm.ua).apply(null,arguments)},Kz=i._NonMaxSuppressionV5=function(){return(Kz=i._NonMaxSuppressionV5=i.asm.va).apply(null,arguments)},Xz=i._NotEqual=function(){return(Xz=i._NotEqual=i.asm.wa).apply(null,arguments)},Yz=i._OneHot=function(){return(Yz=i._OneHot=i.asm.xa).apply(null,arguments)},Zz=i._PadV2=function(){return(Zz=i._PadV2=i.asm.ya).apply(null,arguments)},Jz=i._Pow=function(){return(Jz=i._Pow=i.asm.za).apply(null,arguments)},Qz=i._Prelu=function(){return(Qz=i._Prelu=i.asm.Aa).apply(null,arguments)},eB=i._Prod=function(){return(eB=i._Prod=i.asm.Ba).apply(null,arguments)},tB=i._RealDiv=function(){return(tB=i._RealDiv=i.asm.Ca).apply(null,arguments)},rB=i._Relu=function(){return(rB=i._Relu=i.asm.Da).apply(null,arguments)},oB=i._Relu6=function(){return(oB=i._Relu6=i.asm.Ea).apply(null,arguments)},nB=i._ResizeBilinear=function(){return(nB=i._ResizeBilinear=i.asm.Fa).apply(null,arguments)},sB=i._Reverse=function(){return(sB=i._Reverse=i.asm.Ga).apply(null,arguments)},iB=i._RotateWithOffset=function(){return(iB=i._RotateWithOffset=i.asm.Ha).apply(null,arguments)},aB=i._Round=function(){return(aB=i._Round=i.asm.Ia).apply(null,arguments)},lB=i._Rsqrt=function(){return(lB=i._Rsqrt=i.asm.Ja).apply(null,arguments)},uB=i._ScatterNd=function(){return(uB=i._ScatterNd=i.asm.Ka).apply(null,arguments)},cB=i._SelectV2=function(){return(cB=i._SelectV2=i.asm.La).apply(null,arguments)},pB=i._Sigmoid=function(){return(pB=i._Sigmoid=i.asm.Ma).apply(null,arguments)},mB=i._Sin=function(){return(mB=i._Sin=i.asm.Na).apply(null,arguments)},fB=i._Softmax=function(){return(fB=i._Softmax=i.asm.Oa).apply(null,arguments)},dB=i._Sqrt=function(){return(dB=i._Sqrt=i.asm.Pa).apply(null,arguments)},hB=i._Square=function(){return(hB=i._Square=i.asm.Qa).apply(null,arguments)},gB=i._SquaredDifference=function(){return(gB=i._SquaredDifference=i.asm.Ra).apply(null,arguments)},xB=i._Step=function(){return(xB=i._Step=i.asm.Sa).apply(null,arguments)},yB=i._StridedSlice=function(){return(yB=i._StridedSlice=i.asm.Ta).apply(null,arguments)},bB=i._Sub=function(){return(bB=i._Sub=i.asm.Ua).apply(null,arguments)},_B=i._Sum=function(){return(_B=i._Sum=i.asm.Va).apply(null,arguments)},wB=i._Tanh=function(){return(wB=i._Tanh=i.asm.Wa).apply(null,arguments)},kB=i._Tile=function(){return(kB=i._Tile=i.asm.Xa).apply(null,arguments)},vB=i._TopK=function(){return(vB=i._TopK=i.asm.Ya).apply(null,arguments)},CB=i._Transpose=function(){return(CB=i._Transpose=i.asm.Za).apply(null,arguments)},IB=i.__FusedMatMul=function(){return(IB=i.__FusedMatMul=i.asm._a).apply(null,arguments)},um=i._malloc=function(){return(um=i._malloc=i.asm.$a).apply(null,arguments)},cm=i._free=function(){return(cm=i._free=i.asm.ab).apply(null,arguments)},cI=i.___errno_location=function(){return(cI=i.___errno_location=i.asm.bb).apply(null,arguments)},pI=i._emscripten_get_global_libc=function(){return(pI=i._emscripten_get_global_libc=i.asm.cb).apply(null,arguments)},NB=i.___em_js__initPthreadsJS=function(){return(NB=i.___em_js__initPthreadsJS=i.asm.db).apply(null,arguments)},mI=i._memalign=function(){return(mI=i._memalign=i.asm.eb).apply(null,arguments)},fI=i.___pthread_tsd_run_dtors=function(){return(fI=i.___pthread_tsd_run_dtors=i.asm.fb).apply(null,arguments)},cb=i._emscripten_main_thread_process_queued_calls=function(){return(cb=i._emscripten_main_thread_process_queued_calls=i.asm.gb).apply(null,arguments)},SB=i._emscripten_current_thread_process_queued_calls=function(){return(SB=i._emscripten_current_thread_process_queued_calls=i.asm.hb).apply(null,arguments)},dI=i._emscripten_register_main_browser_thread_id=function(){return(dI=i._emscripten_register_main_browser_thread_id=i.asm.ib).apply(null,arguments)},TB=i._emscripten_main_browser_thread_id=function(){return(TB=i._emscripten_main_browser_thread_id=i.asm.jb).apply(null,arguments)},EB=i._emscripten_async_run_in_main_thread=function(){return(EB=i._emscripten_async_run_in_main_thread=i.asm.kb).apply(null,arguments)},AB=i._emscripten_sync_run_in_main_thread=function(){return(AB=i._emscripten_sync_run_in_main_thread=i.asm.lb).apply(null,arguments)},DB=i._emscripten_sync_run_in_main_thread_0=function(){return(DB=i._emscripten_sync_run_in_main_thread_0=i.asm.mb).apply(null,arguments)},$B=i._emscripten_sync_run_in_main_thread_1=function(){return($B=i._emscripten_sync_run_in_main_thread_1=i.asm.nb).apply(null,arguments)},RB=i._emscripten_sync_run_in_main_thread_2=function(){return(RB=i._emscripten_sync_run_in_main_thread_2=i.asm.ob).apply(null,arguments)},FB=i._emscripten_sync_run_in_main_thread_xprintf_varargs=function(){return(FB=i._emscripten_sync_run_in_main_thread_xprintf_varargs=i.asm.pb).apply(null,arguments)},OB=i._emscripten_sync_run_in_main_thread_3=function(){return(OB=i._emscripten_sync_run_in_main_thread_3=i.asm.qb).apply(null,arguments)},hI=i._emscripten_sync_run_in_main_thread_4=function(){return(hI=i._emscripten_sync_run_in_main_thread_4=i.asm.rb).apply(null,arguments)},PB=i._emscripten_sync_run_in_main_thread_5=function(){return(PB=i._emscripten_sync_run_in_main_thread_5=i.asm.sb).apply(null,arguments)},MB=i._emscripten_sync_run_in_main_thread_6=function(){return(MB=i._emscripten_sync_run_in_main_thread_6=i.asm.tb).apply(null,arguments)},LB=i._emscripten_sync_run_in_main_thread_7=function(){return(LB=i._emscripten_sync_run_in_main_thread_7=i.asm.ub).apply(null,arguments)},gI=i._emscripten_run_in_main_runtime_thread_js=function(){return(gI=i._emscripten_run_in_main_runtime_thread_js=i.asm.vb).apply(null,arguments)},pb=i._emscripten_async_queue_on_thread_=function(){return(pb=i._emscripten_async_queue_on_thread_=i.asm.wb).apply(null,arguments)},zB=i._emscripten_tls_init=function(){return(zB=i._emscripten_tls_init=i.asm.xb).apply(null,arguments)},pm=i.stackSave=function(){return(pm=i.stackSave=i.asm.yb).apply(null,arguments)},Cc=i.stackAlloc=function(){return(Cc=i.stackAlloc=i.asm.zb).apply(null,arguments)},Ic=i.stackRestore=function(){return(Ic=i.stackRestore=i.asm.Ab).apply(null,arguments)},xI=i.dynCall_vi=function(){return(xI=i.dynCall_vi=i.asm.Bb).apply(null,arguments)},BB=i.dynCall_v=function(){return(BB=i.dynCall_v=i.asm.Cb).apply(null,arguments)},VB=i.dynCall_ii=function(){return(VB=i.dynCall_ii=i.asm.Db).apply(null,arguments)};i.asm=Oh,i.cwrap=De,i.PThread=we,i.PThread=we,i._pthread_self=Us,i.wasmMemory=K,i.ExitStatus=yI;var mm;i.then=function(I){if(mm)I(i);else{var D=i.onRuntimeInitialized;i.onRuntimeInitialized=function(){D&&D(),I(i)}}return i};function yI(I){this.name="ExitStatus",this.message="Program terminated with exit("+I+")",this.status=I}Fl=function I(){mm||mb(),mm||(Fl=I)};function mb(I){if(I=I||c,Qi>0||(Uo(),Qi>0))return;function D(){mm||(mm=!0,i.calledRun=!0,!ae&&(wh(),ay(),i.onRuntimeInitialized&&i.onRuntimeInitialized(),ly()))}i.setStatus?(i.setStatus("Running..."),setTimeout(function(){setTimeout(function(){i.setStatus("")},1),D()},1)):D()}if(i.run=mb,i.preInit)for(typeof i.preInit=="function"&&(i.preInit=[i.preInit]);i.preInit.length>0;)i.preInit.pop()();return x||(te=!0),x||mb(),e}}();typeof ry=="object"&&typeof nI=="object"?nI.exports=sI:typeof define=="function"&&define.amd?define([],function(){return sI}):typeof ry=="object"&&(ry.WasmBackendModuleThreadedSimd=sI)});var Cz=Br((oy,iI)=>{var aI=function(){var r=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(r=r||__filename),function(e){e=e||{};var t=typeof e!="undefined"?e:{},o={},n;for(n in t)t.hasOwnProperty(n)&&(o[n]=t[n]);var s=[],a="./this.program",i=function(H,ne){throw ne},l=!1,u=!1,c=!1,p=!1;l=typeof window=="object",u=typeof importScripts=="function",c=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",p=!l&&!c&&!u;var m="";function f(H){return t.locateFile?t.locateFile(H,m):m+H}var d,h,g,x,b,_;c?(u?m=Jp().dirname(m)+"/":m=__dirname+"/",d=function(ne,be){return b||(b=require("fs")),_||(_=Jp()),ne=_.normalize(ne),b.readFileSync(ne,be?null:"utf8")},g=function(ne){var be=d(ne,!0);return be.buffer||(be=new Uint8Array(be)),W(be.buffer),be},process.argv.length>1&&(a=process.argv[1].replace(/\\/g,"/")),s=process.argv.slice(2),process.on("uncaughtException",function(H){if(!(H instanceof im))throw H}),process.on("unhandledRejection",Ji),i=function(H){process.exit(H)},t.inspect=function(){return"[Emscripten Module object]"}):p?(typeof read!="undefined"&&(d=function(ne){return read(ne)}),g=function(ne){var be;return typeof readbuffer=="function"?new Uint8Array(readbuffer(ne)):(be=read(ne,"binary"),W(typeof be=="object"),be)},typeof scriptArgs!="undefined"?s=scriptArgs:typeof arguments!="undefined"&&(s=arguments),typeof quit=="function"&&(i=function(H){quit(H)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(l||u)&&(u?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="",d=function(ne){var be=new XMLHttpRequest;return be.open("GET",ne,!1),be.send(null),be.responseText},u&&(g=function(ne){var be=new XMLHttpRequest;return be.open("GET",ne,!1),be.responseType="arraybuffer",be.send(null),new Uint8Array(be.response)}),h=function(ne,be,Ke){var Xe=new XMLHttpRequest;Xe.open("GET",ne,!0),Xe.responseType="arraybuffer",Xe.onload=function(){if(Xe.status==200||Xe.status==0&&Xe.response){be(Xe.response);return}Ke()},Xe.onerror=Ke,Xe.send(null)},x=function(H){document.title=H});var w=t.print||console.log.bind(console),v=t.printErr||console.warn.bind(console);for(n in o)o.hasOwnProperty(n)&&(t[n]=o[n]);o=null,t.arguments&&(s=t.arguments),t.thisProgram&&(a=t.thisProgram),t.quit&&(i=t.quit);var $;t.wasmBinary&&($=t.wasmBinary);var A;t.noExitRuntime&&(A=t.noExitRuntime),typeof WebAssembly!="object"&&v("no native wasm support detected");var R,M=new WebAssembly.Table({initial:153,maximum:153+0,element:"anyfunc"}),V=!1,G=0;function W(H,ne){H||Ji("Assertion failed: "+ne)}function q(H){var ne=t["_"+H];return W(ne,"Cannot call unknown function "+H+", make sure it is exported"),ne}function Z(H,ne,be,Ke,Xe){var Ht={string:function(ho){var Ka=0;if(ho!=null&&ho!==0){var Gh=(ho.length<<2)+1;Ka=sm(Gh),ie(ho,Ka,Gh)}return Ka},array:function(ho){var Ka=sm(ho.length);return se(ho,Ka),Ka}};function at(ho){return ne==="string"?K(ho):ne==="boolean"?Boolean(ho):ho}var nt=q(H),xr=[],ta=0;if(Ke)for(var ra=0;ra=Ke);)++Xe;if(Xe-ne>16&&H.subarray&&Y)return Y.decode(H.subarray(ne,Xe));for(var Ht="";ne>10,56320|ta&1023)}}return Ht}function K(H,ne){return H?te(xe,H,ne):""}function re(H,ne,be,Ke){if(!(Ke>0))return 0;for(var Xe=be,Ht=be+Ke-1,at=0;at=55296&&nt<=57343){var xr=H.charCodeAt(++at);nt=65536+((nt&1023)<<10)|xr&1023}if(nt<=127){if(be>=Ht)break;ne[be++]=nt}else if(nt<=2047){if(be+1>=Ht)break;ne[be++]=192|nt>>6,ne[be++]=128|nt&63}else if(nt<=65535){if(be+2>=Ht)break;ne[be++]=224|nt>>12,ne[be++]=128|nt>>6&63,ne[be++]=128|nt&63}else{if(be+3>=Ht)break;ne[be++]=240|nt>>18,ne[be++]=128|nt>>12&63,ne[be++]=128|nt>>6&63,ne[be++]=128|nt&63}}return ne[be]=0,be-Xe}function ie(H,ne,be){return re(H,xe,ne,be)}function se(H,ne){ae.set(H,ne)}var pe,ae,xe,ge,_e,ke,De,$e,Re;function He(H){pe=H,t.HEAP8=ae=new Int8Array(H),t.HEAP16=ge=new Int16Array(H),t.HEAP32=ke=new Int32Array(H),t.HEAPU8=xe=new Uint8Array(H),t.HEAPU16=_e=new Uint16Array(H),t.HEAPU32=De=new Uint32Array(H),t.HEAPF32=$e=new Float32Array(H),t.HEAPF64=Re=new Float64Array(H)}var ut=t.INITIAL_MEMORY||16777216;function wt(H){for(;H.length>0;){var ne=H.shift();if(typeof ne=="function"){ne(t);continue}var be=ne.func;typeof be=="number"?ne.arg===void 0?t.dynCall_v(be):t.dynCall_vi(be,ne.arg):be(ne.arg===void 0?null:ne.arg)}}var kt=[],pt=[],vt=[],qe=[],Ft=!1,po=!1;function Zt(){if(t.preRun)for(typeof t.preRun=="function"&&(t.preRun=[t.preRun]);t.preRun.length;)fo(t.preRun.shift());wt(kt)}function mo(){Ft=!0,wt(pt)}function _r(){wt(vt)}function Go(){po=!0}function Jr(){if(t.postRun)for(typeof t.postRun=="function"&&(t.postRun=[t.postRun]);t.postRun.length;)wr(t.postRun.shift());wt(qe)}function fo(H){kt.unshift(H)}function wr(H){qe.unshift(H)}var No=Math.ceil,Wo=Math.floor,Qr=0,ss=null,So=null;function xc(H){Qr++,t.monitorRunDependencies&&t.monitorRunDependencies(Qr)}function is(H){if(Qr--,t.monitorRunDependencies&&t.monitorRunDependencies(Qr),Qr==0&&(ss!==null&&(clearInterval(ss),ss=null),So)){var ne=So;So=null,ne()}}t.preloadedImages={},t.preloadedAudios={};function Ji(H){throw t.onAbort&&t.onAbort(H),H+="",w(H),v(H),V=!0,G=1,H="abort("+H+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(H)}function $l(H,ne){return String.prototype.startsWith?H.startsWith(ne):H.indexOf(ne)===0}var iy="data:application/octet-stream;base64,";function Qp(H){return $l(H,iy)}var _h="file://";function em(H){return $l(H,_h)}var Uo="tfjs-backend-wasm.wasm";Qp(Uo)||(Uo=f(Uo));function wh(){try{if($)return new Uint8Array($);if(g)return g(Uo);throw"both async and sync fetching of the wasm failed"}catch(H){Ji(H)}}function ay(){return!$&&(l||u)&&typeof fetch=="function"&&!em(Uo)?fetch(Uo,{credentials:"same-origin"}).then(function(H){if(!H.ok)throw"failed to load wasm binary file at '"+Uo+"'";return H.arrayBuffer()}).catch(function(){return wh()}):new Promise(function(H,ne){H(wh())})}function ly(){var H={env:ea,wasi_snapshot_preview1:ea};function ne(at,nt){var xr=at.exports;t.asm=xr,R=xr.memory,He(R.buffer),is("wasm-instantiate")}xc("wasm-instantiate");function be(at){ne(at.instance)}function Ke(at){return ay().then(function(nt){return WebAssembly.instantiate(nt,H)}).then(at,function(nt){v("failed to asynchronously prepare wasm: "+nt),Ji(nt)})}function Xe(){if(!$&&typeof WebAssembly.instantiateStreaming=="function"&&!Qp(Uo)&&!em(Uo)&&typeof fetch=="function")fetch(Uo,{credentials:"same-origin"}).then(function(at){var nt=WebAssembly.instantiateStreaming(at,H);return nt.then(be,function(xr){v("wasm streaming compile failed: "+xr),v("falling back to ArrayBuffer instantiation"),Ke(be)})});else return Ke(be)}if(t.instantiateWasm)try{var Ht=t.instantiateWasm(H,ne);return Ht}catch(at){return v("Module.instantiateWasm callback failed with error: "+at),!1}return Xe(),{}}pt.push();function uy(H){He(R.buffer)}var Rl={splitPath:function(H){var ne=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return ne.exec(H).slice(1)},normalizeArray:function(H,ne){for(var be=0,Ke=H.length-1;Ke>=0;Ke--){var Xe=H[Ke];Xe==="."?H.splice(Ke,1):Xe===".."?(H.splice(Ke,1),be++):be&&(H.splice(Ke,1),be--)}if(ne)for(;be;be--)H.unshift("..");return H},normalize:function(H){var ne=H.charAt(0)==="/",be=H.substr(-1)==="/";return H=Rl.normalizeArray(H.split("/").filter(function(Ke){return!!Ke}),!ne).join("/"),!H&&!ne&&(H="."),H&&be&&(H+="/"),(ne?"/":"")+H},dirname:function(H){var ne=Rl.splitPath(H),be=ne[0],Ke=ne[1];return!be&&!Ke?".":(Ke&&(Ke=Ke.substr(0,Ke.length-1)),be+Ke)},basename:function(H){if(H==="/")return"/";var ne=H.lastIndexOf("/");return ne===-1?H:H.substr(ne+1)},extname:function(H){return Rl.splitPath(H)[3]},join:function(){var H=Array.prototype.slice.call(arguments,0);return Rl.normalize(H.join("/"))},join2:function(H,ne){return Rl.normalize(H+"/"+ne)}},yc={mappings:{},buffers:[null,[],[]],printChar:function(H,ne){var be=yc.buffers[H];ne===0||ne===10?((H===1?w:v)(te(be,0)),be.length=0):be.push(ne)},varargs:void 0,get:function(){yc.varargs+=4;var H=ke[yc.varargs-4>>2];return H},getStr:function(H){var ne=K(H);return ne},get64:function(H,ne){return H}};function cy(H){return 0}function Qi(H,ne,be,Ke,Xe){}function tm(H,ne,be,Ke){for(var Xe=0,Ht=0;Ht>2],nt=ke[ne+(Ht*8+4)>>2],xr=0;xr>2]=Xe,0}function Fl(H){zh(H)}function py(H){Fl(H)}function my(H){return H=+H,H>=0?+Wo(H+.5):+No(H-.5)}var ea={emscripten_notify_memory_growth:uy,fd_close:cy,fd_seek:Qi,fd_write:tm,proc_exit:py,roundf:my},rm=ly();t.asm=rm;var fy=t._init=function(){return(fy=t._init=t.asm.init).apply(null,arguments)},kh=t._register_tensor=function(){return(kh=t._register_tensor=t.asm.register_tensor).apply(null,arguments)},dy=t._dispose_data=function(){return(dy=t._dispose_data=t.asm.dispose_data).apply(null,arguments)},vh=t._dispose=function(){return(vh=t._dispose=t.asm.dispose).apply(null,arguments)},jo=t._Abs=function(){return(jo=t._Abs=t.asm.Abs).apply(null,arguments)},Ch=t._Add=function(){return(Ch=t._Add=t.asm.Add).apply(null,arguments)},hy=t._AddN=function(){return(hy=t._AddN=t.asm.AddN).apply(null,arguments)},gy=t._ArgMax=function(){return(gy=t._ArgMax=t.asm.ArgMax).apply(null,arguments)},xy=t._AvgPool=function(){return(xy=t._AvgPool=t.asm.AvgPool).apply(null,arguments)},yy=t._BatchMatMul=function(){return(yy=t._BatchMatMul=t.asm.BatchMatMul).apply(null,arguments)},Ih=t._Ceil=function(){return(Ih=t._Ceil=t.asm.Ceil).apply(null,arguments)},Nh=t._ClipByValue=function(){return(Nh=t._ClipByValue=t.asm.ClipByValue).apply(null,arguments)},Sh=t._Conv2D=function(){return(Sh=t._Conv2D=t.asm.Conv2D).apply(null,arguments)},bc=t._Conv2DBackpropInput=function(){return(bc=t._Conv2DBackpropInput=t.asm.Conv2DBackpropInput).apply(null,arguments)},om=t._Cos=function(){return(om=t._Cos=t.asm.Cos).apply(null,arguments)},_c=t._CropAndResize=function(){return(_c=t._CropAndResize=t.asm.CropAndResize).apply(null,arguments)},wc=t._Cumsum=function(){return(wc=t._Cumsum=t.asm.Cumsum).apply(null,arguments)},by=t._DepthToSpace=function(){return(by=t._DepthToSpace=t.asm.DepthToSpace).apply(null,arguments)},_y=t._DepthwiseConv2dNative=function(){return(_y=t._DepthwiseConv2dNative=t.asm.DepthwiseConv2dNative).apply(null,arguments)},wy=t._Equal=function(){return(wy=t._Equal=t.asm.Equal).apply(null,arguments)},we=t._Exp=function(){return(we=t._Exp=t.asm.Exp).apply(null,arguments)},ky=t._FlipLeftRight=function(){return(ky=t._FlipLeftRight=t.asm.FlipLeftRight).apply(null,arguments)},vy=t._Floor=function(){return(vy=t._Floor=t.asm.Floor).apply(null,arguments)},Cy=t._FloorDiv=function(){return(Cy=t._FloorDiv=t.asm.FloorDiv).apply(null,arguments)},Iy=t._FusedBatchNorm=function(){return(Iy=t._FusedBatchNorm=t.asm.FusedBatchNorm).apply(null,arguments)},Ol=t._FusedConv2D=function(){return(Ol=t._FusedConv2D=t.asm.FusedConv2D).apply(null,arguments)},Ny=t._FusedDepthwiseConv2D=function(){return(Ny=t._FusedDepthwiseConv2D=t.asm.FusedDepthwiseConv2D).apply(null,arguments)},Sy=t._Gather=function(){return(Sy=t._Gather=t.asm.Gather).apply(null,arguments)},Ty=t._GatherNd=function(){return(Ty=t._GatherNd=t.asm.GatherNd).apply(null,arguments)},Ey=t._Greater=function(){return(Ey=t._Greater=t.asm.Greater).apply(null,arguments)},Ay=t._GreaterEqual=function(){return(Ay=t._GreaterEqual=t.asm.GreaterEqual).apply(null,arguments)},Dy=t._LeakyRelu=function(){return(Dy=t._LeakyRelu=t.asm.LeakyRelu).apply(null,arguments)},$y=t._Less=function(){return($y=t._Less=t.asm.Less).apply(null,arguments)},Ry=t._LessEqual=function(){return(Ry=t._LessEqual=t.asm.LessEqual).apply(null,arguments)},Fy=t._Log=function(){return(Fy=t._Log=t.asm.Log).apply(null,arguments)},Oy=t._LogicalAnd=function(){return(Oy=t._LogicalAnd=t.asm.LogicalAnd).apply(null,arguments)},Ha=t._Max=function(){return(Ha=t._Max=t.asm.Max).apply(null,arguments)},Pl=t._MaxPool=function(){return(Pl=t._MaxPool=t.asm.MaxPool).apply(null,arguments)},kc=t._Maximum=function(){return(kc=t._Maximum=t.asm.Maximum).apply(null,arguments)},Py=t._Mean=function(){return(Py=t._Mean=t.asm.Mean).apply(null,arguments)},My=t._Min=function(){return(My=t._Min=t.asm.Min).apply(null,arguments)},Ly=t._Minimum=function(){return(Ly=t._Minimum=t.asm.Minimum).apply(null,arguments)},zy=t._Multiply=function(){return(zy=t._Multiply=t.asm.Multiply).apply(null,arguments)},Ze=t._Neg=function(){return(Ze=t._Neg=t.asm.Neg).apply(null,arguments)},By=t._NonMaxSuppressionV3=function(){return(By=t._NonMaxSuppressionV3=t.asm.NonMaxSuppressionV3).apply(null,arguments)},Vy=t._NonMaxSuppressionV4=function(){return(Vy=t._NonMaxSuppressionV4=t.asm.NonMaxSuppressionV4).apply(null,arguments)},Gy=t._NonMaxSuppressionV5=function(){return(Gy=t._NonMaxSuppressionV5=t.asm.NonMaxSuppressionV5).apply(null,arguments)},Wy=t._NotEqual=function(){return(Wy=t._NotEqual=t.asm.NotEqual).apply(null,arguments)},Uy=t._OneHot=function(){return(Uy=t._OneHot=t.asm.OneHot).apply(null,arguments)},jy=t._PadV2=function(){return(jy=t._PadV2=t.asm.PadV2).apply(null,arguments)},nm=t._Pow=function(){return(nm=t._Pow=t.asm.Pow).apply(null,arguments)},Th=t._Prelu=function(){return(Th=t._Prelu=t.asm.Prelu).apply(null,arguments)},Eh=t._Prod=function(){return(Eh=t._Prod=t.asm.Prod).apply(null,arguments)},Hy=t._RealDiv=function(){return(Hy=t._RealDiv=t.asm.RealDiv).apply(null,arguments)},qy=t._Relu=function(){return(qy=t._Relu=t.asm.Relu).apply(null,arguments)},Ky=t._Relu6=function(){return(Ky=t._Relu6=t.asm.Relu6).apply(null,arguments)},Xy=t._ResizeBilinear=function(){return(Xy=t._ResizeBilinear=t.asm.ResizeBilinear).apply(null,arguments)},Yy=t._Reverse=function(){return(Yy=t._Reverse=t.asm.Reverse).apply(null,arguments)},Zy=t._RotateWithOffset=function(){return(Zy=t._RotateWithOffset=t.asm.RotateWithOffset).apply(null,arguments)},rt=t._Round=function(){return(rt=t._Round=t.asm.Round).apply(null,arguments)},Jy=t._Rsqrt=function(){return(Jy=t._Rsqrt=t.asm.Rsqrt).apply(null,arguments)},Qy=t._ScatterNd=function(){return(Qy=t._ScatterNd=t.asm.ScatterNd).apply(null,arguments)},eb=t._SelectV2=function(){return(eb=t._SelectV2=t.asm.SelectV2).apply(null,arguments)},Ml=t._Sigmoid=function(){return(Ml=t._Sigmoid=t.asm.Sigmoid).apply(null,arguments)},vc=t._Sin=function(){return(vc=t._Sin=t.asm.Sin).apply(null,arguments)},Ah=t._Softmax=function(){return(Ah=t._Softmax=t.asm.Softmax).apply(null,arguments)},Dh=t._Sqrt=function(){return(Dh=t._Sqrt=t.asm.Sqrt).apply(null,arguments)},$h=t._Square=function(){return($h=t._Square=t.asm.Square).apply(null,arguments)},tb=t._SquaredDifference=function(){return(tb=t._SquaredDifference=t.asm.SquaredDifference).apply(null,arguments)},rb=t._Step=function(){return(rb=t._Step=t.asm.Step).apply(null,arguments)},Rh=t._StridedSlice=function(){return(Rh=t._StridedSlice=t.asm.StridedSlice).apply(null,arguments)},ob=t._Sub=function(){return(ob=t._Sub=t.asm.Sub).apply(null,arguments)},Us=t._Sum=function(){return(Us=t._Sum=t.asm.Sum).apply(null,arguments)},nb=t._Tanh=function(){return(nb=t._Tanh=t.asm.Tanh).apply(null,arguments)},sb=t._Tile=function(){return(sb=t._Tile=t.asm.Tile).apply(null,arguments)},Fh=t._TopK=function(){return(Fh=t._TopK=t.asm.TopK).apply(null,arguments)},qa=t._Transpose=function(){return(qa=t._Transpose=t.asm.Transpose).apply(null,arguments)},ib=t.__FusedMatMul=function(){return(ib=t.__FusedMatMul=t.asm._FusedMatMul).apply(null,arguments)},ab=t._malloc=function(){return(ab=t._malloc=t.asm.malloc).apply(null,arguments)},Oh=t._free=function(){return(Oh=t._free=t.asm.free).apply(null,arguments)},Ph=t.__start=function(){return(Ph=t.__start=t.asm._start).apply(null,arguments)},Mh=t.stackSave=function(){return(Mh=t.stackSave=t.asm.stackSave).apply(null,arguments)},sm=t.stackAlloc=function(){return(sm=t.stackAlloc=t.asm.stackAlloc).apply(null,arguments)},Lh=t.stackRestore=function(){return(Lh=t.stackRestore=t.asm.stackRestore).apply(null,arguments)};t.asm=rm,t.cwrap=X;var Ll;t.then=function(H){if(Ll)H(t);else{var ne=t.onRuntimeInitialized;t.onRuntimeInitialized=function(){ne&&ne(),H(t)}}return t};function im(H){this.name="ExitStatus",this.message="Program terminated with exit("+H+")",this.status=H}var lb=!1;So=function H(){Ll||am(),Ll||(So=H)};function ub(H){var ne=t.__start;try{ne();var be=0;zh(be,!0)}catch(Xe){if(Xe instanceof im)return;if(Xe=="unwind"){A=!0;return}else{var Ke=Xe;Xe&&typeof Xe=="object"&&Xe.stack&&(Ke=[Xe,Xe.stack]),v("exception thrown: "+Ke),i(1,Xe)}}finally{lb=!0}}function am(H){if(H=H||s,Qr>0||(Zt(),Qr>0))return;function ne(){Ll||(Ll=!0,t.calledRun=!0,!V&&(mo(),_r(),t.onRuntimeInitialized&&t.onRuntimeInitialized(),Bh&&ub(H),Jr()))}t.setStatus?(t.setStatus("Running..."),setTimeout(function(){setTimeout(function(){t.setStatus("")},1),ne()},1)):ne()}t.run=am;function zh(H,ne){ne&&A&&H===0||(A||(V=!0,G=H,Go(),t.onExit&&t.onExit(H)),i(H,new im(H)))}if(t.preInit)for(typeof t.preInit=="function"&&(t.preInit=[t.preInit]);t.preInit.length>0;)t.preInit.pop()();var Bh=!0;return t.noInitialRun&&(Bh=!1),A=!0,am(),e}}();typeof oy=="object"&&typeof iI=="object"?iI.exports=aI:typeof define=="function"&&define.amd?define([],function(){return aI}):typeof oy=="object"&&(oy.WasmBackendModule=aI)});var XB=1e-7,YB=1e-4,Ya=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}},js=class{refCount(e){return Ho("refCount")}incRef(e){return Ho("incRef")}timerAvailable(){return!0}time(e){return Ho("time")}read(e){return Ho("read")}readSync(e){return Ho("readSync")}numDataIds(){return Ho("numDataIds")}disposeData(e,t){return Ho("disposeData")}write(e,t,o){return Ho("write")}move(e,t,o,n,s){return Ho("move")}memory(){return Ho("memory")}floatPrecision(){return Ho("floatPrecision")}epsilon(){return this.floatPrecision()===32?XB:YB}dispose(){return Ho("dispose")}};function Ho(r){throw new Error(`'${r}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`)}function _I(r){let e=r.length,t=0,o=0;for(;e>0;)o=Math.random()*e|0,e--,t=r[e],r[e]=r[o],r[o]=t}function ZB(r,e){if(r.length!==e.length)throw Error(`Array sizes must match to be shuffled together First array length was ${r.length}Second array length was ${e.length}`);let t=r.length,o,n,s=0;for(;t>0;)s=Math.random()*t|0,t--,o=r[t],n=e[t],r[t]=r[s],e[t]=e[s],r[s]=o,e[s]=n}function Ec(r,e,t){return Math.max(r,Math.min(e,t))}function JB(r){return r%2==0?r:r+1}function QB(r){let e=0;for(let t=0;tt+` Shapes ${r} and ${e} must match`)}function To(r){T(r!=null,()=>"The input to the tensor constructor must be a non-null value.")}function as(r,e=[],t=!1){if(e==null&&(e=[]),Array.isArray(r)||rr(r)&&!t)for(let o=0;o0,t){return new Promise((o,n)=>{let s=0,a=()=>{if(r()){o();return}s++;let i=e(s);if(t!=null&&s>=t){n();return}setTimeout(a,i)};a()})}function a3(r,e){let t=1,o=-1;for(let s=0;s=0)t*=r[s];else if(r[s]===-1){if(o!==-1)throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${o} and dim ${s}`);o=s}else if(r[s]<0)throw Error(`Shapes can not be < 0. Found ${r[s]} at dim ${s}`);if(o===-1){if(e>0&&e!==t)throw Error(`Size(${e}) must match the product of shape ${r}`);return r}if(t===0)throw Error(`Cannot infer the missing size in [${r}] when there are 0 elements`);if(e%t!=0)throw Error(`The implicit shape can't be a fractional number. Got ${e} / ${t}`);let n=r.slice();return n[o]=e/t,n}function Jt(r,e){let t=e.length;return r=r==null?e.map((o,n)=>n):[].concat(r),T(r.every(o=>o>=-t&&o`All values in axis param must be in range [-${t}, ${t}) but got axis ${r}`),T(r.every(o=>st(o)),()=>`All values in axis param must be integers but got axis ${r}`),r.map(o=>o<0?t+o:o)}function hb(r,e){let t=[],o=[],n=e!=null&&Array.isArray(e)&&e.length===0,s=e==null||n?null:Jt(e,r).sort(),a=0;for(let i=0;ii)&&r[i]===1&&(t.push(r[i]),o.push(i)),s[a]<=i&&a++}r[i]!==1&&(t.push(r[i]),o.push(i))}return{newShape:t,keptDims:o}}function gb(r,e){let t=null;if(r==null||r==="float32")t=new Float32Array(e);else if(r==="int32")t=new Int32Array(e);else if(r==="bool")t=new Uint8Array(e);else throw new Error(`Unknown data type ${r}`);return t}function xb(r,e){let t=null;if(r==null||r==="float32")t=new Float32Array(e);else if(r==="int32")t=new Int32Array(e);else if(r==="bool")t=new Uint8Array(e);else if(r==="string")t=new Array(e);else throw new Error(`Unknown data type ${r}`);return t}function yb(r,e){for(let t=0;te+=t.length),e}function ls(r){return typeof r=="string"||r instanceof String}function wI(r){return typeof r=="boolean"}function kI(r){return typeof r=="number"}function Ac(r){return Array.isArray(r)?Ac(r[0]):r instanceof Float32Array?"float32":r instanceof Int32Array||r instanceof Uint8Array?"int32":kI(r)?"float32":ls(r)?"string":wI(r)?"bool":"float32"}function Hs(r){return!!(r&&r.constructor&&r.call&&r.apply)}function Dc(r,e){for(let t=e;t=0;--o)t[o]=t[o+1]*r[o+1];return t}function vI(r,e,t){let o=new Array;if(e.length===1){let n=e[0];for(let s=0;si*l);for(let i=0;io*n);if(t===0)return[];if(t!==e.length)throw new Error(`[${r}] does not match the input size ${e.length}.`);return vI(0,r,e)}function hm(r,e){let t=$c(r,e);for(let o=0;oo*n,1);if(e==null||e==="float32")return Bl(r,new Float32Array(t));if(e==="int32")return Bl(r,new Int32Array(t));if(e==="bool")return Bl(r,new Uint8Array(t));throw new Error(`Unknown data type ${e}`)}function gm(r){r.forEach(e=>{T(Number.isInteger(e)&&e>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${r}].`)})}function c3(r,e,t){if(e===0)return 0;if(e===1)return r[0];let o=r[r.length-1];for(let n=0;n{let[n,s]=o.split(":");this.urlFlags[n]=f3(n,s)})}};function m3(r){let e={};return r.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(t,...o)=>(d3(e,o[0],o[1]),o.join("="))),e}function d3(r,e,t){r[decodeURIComponent(e)]=decodeURIComponent(t||"")}function f3(r,e){if(e=e.toLowerCase(),e==="true"||e==="false")return e==="true";if(`${+e}`===e)return+e;throw new Error(`Could not parse value flag value ${e} for flag ${r}.`)}function U(){return wb}var wb=null;function II(r){wb=r}var kb;function vb(){if(kb==null){let r;if(typeof window!="undefined")r=window;else if(typeof global!="undefined")r=global;else if(typeof process!="undefined")r=process;else if(typeof self!="undefined")r=self;else throw new Error("Could not find a global object");kb=r}return kb}function h3(){let r=vb();return r._tfGlobals==null&&(r._tfGlobals=new Map),r._tfGlobals}function ym(r,e){let t=h3();if(t.has(r))return t.get(r);{let o=e();return t.set(r,o),t.get(r)}}var us="Abs",Ks="Acos",Xs="Acosh",xo="Add",qo="AddN",Vl="All",Gl="Any",Ko="ArgMax",oa="ArgMin",Ys="Asin",Zs="Asinh",Js="Atan",Qs="Atanh",ei="Atan2",Xo="AvgPool",Wl="AvgPoolGrad",na="AvgPool3D",Ul="AvgPool3DGrad",Yo="BatchMatMul",sa="BatchToSpaceND",jl="Bincount",Cb="BroadcastTo",Eo="Cast",Zo="Ceil",Ao="ClipByValue",Hl="Complex",ia="ComplexAbs",cs="Concat",Jo="Conv2D",ql="Conv2DBackpropFilter",Qo="Conv2DBackpropInput",aa="Conv3D",Kl="Conv3DBackpropFilterV2",Xl="Conv3DBackpropInputV2",en="Cos",ti="Cosh",tn="Cumsum",ri="CropAndResize",Yl="DenseBincount",oi="DepthToSpace",rn="DepthwiseConv2dNative",Zl="DepthwiseConv2dNativeBackpropFilter",Jl="DepthwiseConv2dNativeBackpropInput",Ql="Diag",la="Dilation2D",Rc="Dilation2DBackpropInput",Fc="Dilation2DBackpropFilter",on="RealDiv",ni="Elu",eu="EluGrad",si="Erf",ii="Equal",nn="Exp",ps="ExpandDims",ai="Expm1",tu="FFT",ua="Fill",li="FlipLeftRight",sn="Floor",an="FloorDiv",ln="FusedBatchNorm",ms="GatherV2",ui="GatherNd",ci="Greater",un="GreaterEqual",Do="Identity",ru="IFFT",ou="Imag",pi="IsFinite",mi="IsInf",fi="IsNan",cn="LeakyRelu",di="Less",hi="LessEqual",nu="LinSpace",pn="Log",gi="Log1p",xi="LogicalAnd",Za="LogicalNot",Ja="LogicalOr",Ib="LogSoftmax",ca="LRN",su="LRNGrad",mn="Max",fn="Maximum",dn="MaxPool",iu="MaxPoolGrad",pa="MaxPool3D",au="MaxPool3DGrad",lu="MaxPoolWithArgmax",hn="Mean",gn="Min",xn="Minimum",ma="MirrorPad",yi="Mod",uu="Multinomial",yn="Multiply",fs="Neg",bi="NotEqual",_i="NonMaxSuppressionV3",wi="NonMaxSuppressionV4",ki="NonMaxSuppressionV5",ds="OnesLike",bn="OneHot",hs="Pack",_n="PadV2",g3="Pool",wn="Pow",kn="Prelu",vi="Prod",fa="Range",cu="Real",Ci="Reciprocal",vn="Relu",gs="Reshape",da="ResizeNearestNeighbor",pu="ResizeNearestNeighborGrad",Cn="ResizeBilinear",mu="ResizeBilinearGrad",In="Relu6",Nn="Reverse",Sn="Round",Tn="Rsqrt",Ii="ScatterNd",xs="Select",Ni="Selu",ys="Slice",En="Sin",Si="Sinh",Ti="Sign",An="Sigmoid",Ei="Softplus",Dn="Sqrt",$n="Sum",ha="SpaceToBatchND",bs="SplitV",Rn="Softmax",Fn="SquaredDifference",ga="Square",On="Sub",fu="SparseToDense",Ai="StridedSlice",Di="Tan",Pn="Tanh",yo="Tile",$i="TopK",Mn="Transpose",du="Unique",_s="Unpack",xa="UnsortedSegmentSum",ws="ZerosLike",$o="Step",Oc="FromPixels",Ri="RotateWithOffset",ks="_FusedMatMul",vs="FusedConv2D",Cs="FusedDepthwiseConv2D";var Pc=ym("kernelRegistry",()=>new Map),bm=ym("gradRegistry",()=>new Map);function Mc(r,e){let t=Nb(r,e);return Pc.get(t)}function Hh(r){return bm.get(r)}function _m(r){let e=Pc.entries(),t=[];for(;;){let{done:o,value:n}=e.next();if(o)break;let[s,a]=n,[i]=s.split("_");i===r&&t.push(a)}return t}function Qa(r){let{kernelName:e,backendName:t}=r,o=Nb(e,t);Pc.has(o)&&console.warn(`The kernel '${e}' for backend '${t}' is already registered`),Pc.set(o,r)}function Sb(r){let{kernelName:e}=r;bm.has(e)&&U().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${e}'`),bm.set(e,r)}function x3(r,e){let t=Nb(r,e);if(!Pc.has(t))throw new Error(`The kernel '${r}' for backend '${e}' is not registered`);Pc.delete(t)}function y3(r){if(!bm.has(r))throw new Error(`The gradient '${r}' for backend is not registered`);bm.delete(r)}function b3(r,e){_m(r).forEach(o=>{let n=Object.assign({},o,{backendName:e});Qa(n)})}function Nb(r,e){return`${e}_${r}`}var y={};et(y,{arraysEqual:()=>Vr,assert:()=>T,assertNonNegativeIntegerDimensions:()=>gm,assertNonNull:()=>To,assertShapesMatch:()=>Nt,bytesFromStringArray:()=>_b,bytesPerElement:()=>Uh,checkConversionForErrors:()=>yb,clamp:()=>Ec,computeStrides:()=>qs,createScalarValue:()=>_3,createShuffledIndices:()=>s3,decodeString:()=>zc,distSquared:()=>t3,encodeString:()=>el,fetch:()=>k3,flatten:()=>as,getArrayFromDType:()=>xb,getTypedArrayFromDType:()=>gb,hasEncodingLoss:()=>l3,indexToLoc:()=>p3,inferDtype:()=>Ac,inferFromImplicitShape:()=>a3,isBoolean:()=>wI,isFunction:()=>Hs,isInt:()=>st,isNumber:()=>kI,isPromise:()=>xm,isScalarShape:()=>r3,isString:()=>ls,isTypedArray:()=>rr,isValidDtype:()=>bb,locToIndex:()=>c3,makeOnesTypedArray:()=>hm,makeZerosNestedTypedArray:()=>u3,makeZerosTypedArray:()=>$c,nearestDivisor:()=>Dc,nearestLargerEven:()=>JB,now:()=>hu,parseAxisParam:()=>Jt,randUniform:()=>e3,repeatedTry:()=>i3,rightPad:()=>zl,shuffle:()=>_I,shuffleCombo:()=>ZB,sizeFromShape:()=>ft,sizeToSquarishShape:()=>n3,squeezeShape:()=>hb,sum:()=>QB,tanh:()=>o3,toNestedArray:()=>Bl,toTypedArray:()=>Lc});function _3(r,e){return e==="string"?el(r):Lc([r],e)}function w3(r,e){return r instanceof Float32Array&&e==="float32"||r instanceof Int32Array&&e==="int32"||r instanceof Uint8Array&&e==="bool"}function Lc(r,e){if(e==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(r)&&(r=as(r)),U().getBool("DEBUG")&&yb(r,e),w3(r,e))return r;if(e==null||e==="float32"||e==="complex64")return new Float32Array(r);if(e==="int32")return new Int32Array(r);if(e==="bool"){let t=new Uint8Array(r.length);for(let o=0;o{n=o()},a,i=hu();if(this.backendTimer.timerAvailable()?a=this.backendTimer.time(s):(s(),n.map(u=>u.dataSync()),a=Promise.resolve({kernelMs:hu()-i})),U().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let u=0;u{v3(p,c.dtype,e)})}return{kernelName:e,outputs:n,inputs:t,timeMs:a.then(u=>u.kernelMs),extraInfo:a.then(u=>u.getExtraProfileInfo!=null?u.getExtraProfileInfo():"")}}logKernelProfile(e){let{kernelName:t,outputs:o,timeMs:n,inputs:s,extraInfo:a}=e;o.forEach(i=>{Promise.all([i.data(),n,a]).then(l=>{this.logger.logKernelProfile(t,i,l[0],l[1],s,l[2])})})}};function v3(r,e,t){if(e!=="float32")return!1;for(let o=0;o0?h:""} `}}console.log(`%c${l} %c${i} %c${u}D ${p} %c${c} %c${m} %c${a}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function SI(r,e,t){let o={},n={};for(let l=0;lo[h.id]=!0),f=!0,n[u.id]=!0;break}if(f)break}}let s={};s[t.id]=!0;let a={};for(let l=r.length-1;l>=0;l--){let u=r[l],c=u.inputs;for(let p=0;p=0;n--){let s=e[n],a=[];if(s.outputs.forEach(l=>{let u=r[l.id];u!=null?a.push(u):a.push(null)}),s.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${s.kernelName}.`);let i=s.gradient(a);for(let l in s.inputs){if(!(l in i))throw new Error(`Cannot backprop through input ${l}. Available gradients found: ${Object.keys(i)}.`);let u=t(()=>i[l]());if(u.dtype!=="float32")throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${l} must have 'float32' dtype, but has '${u.dtype}'`);let c=s.inputs[l];if(!Vr(u.shape,c.shape))throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${l}' has shape '${u.shape}', which does not match the shape of the input '${c.shape}'`);if(r[c.id]==null)r[c.id]=u;else{let p=r[c.id];r[c.id]=o(p,u),p.dispose()}}}}var EI=20,wm=3,Eb=7;function AI(r,e,t,o){let n=qs(e),s=C3(r,e,t,n),a=e.length,i=qh(r,e,t,n,s),l=["Tensor"];return o&&(l.push(` dtype: ${t}`),l.push(` rank: ${a}`),l.push(` shape: [${e}]`),l.push(" values:")),l.push(i.map(u=>" "+u).join(` `)),l.join(` `)}function C3(r,e,t,o){let n=ft(e),s=o[o.length-1],a=new Array(s).fill(0),i=e.length,l=t==="complex64"?vm(r):r;if(i>1)for(let u=0;uEI){let g=wm*a,x=Array.from(r.slice(0,g)),b=Array.from(r.slice((i-wm)*a,i*a));return t==="complex64"&&(x=vm(x),b=vm(b)),["["+x.map((_,w)=>km(_,n[w],t)).join(", ")+", ..., "+b.map((_,w)=>km(_,n[i-wm+w],t)).join(", ")+"]"]}let h=t==="complex64"?vm(r):Array.from(r);return["["+h.map((g,x)=>km(g,n[x],t)).join(", ")+"]"]}let u=e.slice(1),c=o.slice(1),p=o[0]*a,m=[];if(i>EI){for(let h=0;h`Length of values '${n}' does not match the size inferred by the shape '${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=o||xb(t,this.size),this.strides=qs(e)}set(e,...t){t.length===0&&(t=[0]),T(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let o=this.locToIndex(t);this.values[o]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let n of e){if(n<0||n>=this.shape[t]){let s=`Requested out of range element at ${e}. Buffer shape=${this.shape}`;throw new Error(s)}t++}let o=e[e.length-1];for(let n=0;nzc(o))}catch(o){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=Fi().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>zc(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 Fi().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Fi().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Bc.print(this,e)}clone(){return this.throwIfDisposed(),Bc.clone(this)}toString(e=!1){let t=this.dataSync();return AI(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Bc.cast(this,e)}variable(e=!0,t,o){return this.throwIfDisposed(),Fi().makeVariable(this,e,t,o)}};Object.defineProperty(Ve,Symbol.hasInstance,{value:r=>!!r&&r.data!=null&&r.dataSync!=null&&r.throwIfDisposed!=null});function F(){return ym("Tensor",()=>Ve)}F();var tl=class extends Ve{constructor(e,t,o,n){super(e.shape,e.dtype,e.dataId,n);this.trainable=t,this.name=o}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(!Vr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Fi().disposeTensor(this),this.dataId=e.dataId,Fi().incRef(this,null)}dispose(){Fi().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(tl,Symbol.hasInstance,{value:r=>r instanceof Ve&&r.assign!=null&&r.assign instanceof Function});var Ln={};et(Ln,{assertTypesMatch:()=>Ob,getTensorsInContainer:()=>Cm,isTensorInList:()=>S3,makeTypesMatch:()=>Ge});var Ab;(function(r){r.R0="R0",r.R1="R1",r.R2="R2",r.R3="R3",r.R4="R4",r.R5="R5",r.R6="R6"})(Ab||(Ab={}));var Db;(function(r){r.float32="float32",r.int32="int32",r.bool="int32",r.complex64="complex64"})(Db||(Db={}));var $b;(function(r){r.float32="float32",r.int32="int32",r.bool="bool",r.complex64="complex64"})($b||($b={}));var Rb;(function(r){r.float32="float32",r.int32="float32",r.bool="float32",r.complex64="complex64"})(Rb||(Rb={}));var Fb;(function(r){r.float32="complex64",r.int32="complex64",r.bool="complex64",r.complex64="complex64"})(Fb||(Fb={}));var N3={float32:Rb,int32:Db,bool:$b,complex64:Fb};function mr(r,e){if(r==="string"||e==="string"){if(r==="string"&&e==="string")return"string";throw new Error(`Can not upcast ${r} with ${e}`)}return N3[r][e]}function gu(r){return mr(r,"int32")}function Ge(r,e){if(r.dtype===e.dtype)return[r,e];let t=mr(r.dtype,e.dtype);return[r.cast(t),e.cast(t)]}function Ob(r,e){T(r.dtype===e.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`)}function S3(r,e){return e.some(t=>t.id===r.id)}function Cm(r){let e=[],t=new Set;return OI(r,e,t),e}function OI(r,e,t){if(r==null)return;if(r instanceof Ve){e.push(r);return}if(!T3(r))return;let o=r;for(let n in o){let s=o[n];t.has(s)||(t.add(s),OI(s,e,t))}}function T3(r){return Array.isArray(r)||typeof r=="object"}function Pb(r){return r.kernelName!=null}var Mb=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()}},xu=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Mb}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){_m(e).forEach(o=>{o.disposeFunc!=null&&o.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let o=t.factory();if(o&&!(o instanceof js)&&typeof o.then=="function"){let n=++this.pendingBackendInitId,s=o.then(a=>n(nthis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;tthis.startScope(o),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n))}scopedRun(e,t,o){e();try{let n=o();return t(),n}catch(n){throw t(),n}}nextTensorId(){return xu.nextTensorId++}nextVariableId(){return xu.nextVariableId++}clone(e){let t=E.runKernel(Do,{x:e}),o={x:e},n=a=>({x:()=>{let i="float32",l={x:a},u={dtype:i};return E.runKernel(Eo,l,u)}}),s=[];return this.addTapeNode(this.state.activeScope.name,o,[t],n,s,{}),t}runKernel(e,t,o){if(!(Mc(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,o){let n=this.backend.numDataIds(),s=0;o.forEach(l=>{s+=l.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let l,u=Pb(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Pb(e)){let{kernelName:d,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=Mc(d,this.backendName);T(x!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();l=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let _=Array.isArray(l)?l:[l];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,b,_);let w=_.map(v=>{if(v.rank!=null)return v;let{dataId:$,shape:A,dtype:R}=v;return this.makeTensorFromDataId($,A,R)});if(n){let v=this.getTensorsForGradient(d,h,w);o=this.saveTensorsForBackwardMode(v)}return w}}else{let{forwardFunc:d}=e,h=g=>{!n||(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();l=this.tidy(()=>d(this.backend,h));let x=Array.isArray(l)?l:[l];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:p}=e,m=Pb(e)?null:e.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(f=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),t=f.outputs)}),n&&this.addTapeNode(u,c,t,m,o,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:t.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(l)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=Hh(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?(T(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let l=o.filter((u,c)=>a[c]);return i.concat(l)}return[]}makeTensor(e,t,o,n){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");o=o||"float32",n=n||this.backend;let s=e;o==="string"&&ls(e[0])&&(s=e.map(l=>el(l)));let a=n.write(s,t,o),i=new Ve(t,o,a,this.nextTensorId());if(this.trackTensor(i,n),o==="string"){let l=this.state.tensorInfo.get(a),u=_b(s);this.state.numBytes+=u-l.bytes,l.bytes=u}return i}makeTensorFromDataId(e,t,o,n){o=o||"float32";let s=new Ve(t,o,e,this.nextTensorId());return this.trackTensor(s,n),s}makeVariable(e,t=!0,o,n){o=o||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let s=new tl(e,t,o,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let o=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(o=e.size*Uh(e.dtype)),this.state.numBytes+=o,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:o})),e instanceof tl||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let o=e.size*Uh(e.dtype);this.state.numBytes-=o}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}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,o=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(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-o;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,o,n,s,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:o,saved:s},l=Hh(e);l!=null&&(n=l.gradFunc),n!=null&&(i.gradient=u=>(u=u.map((c,p)=>{if(c==null){let m=o[p],f=$c(m.size,m.dtype);return this.makeTensor(f,m.shape,m.dtype)}return c}),n(u.length>1?u:u[0],s,a))),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=Cm(e),o=new Set(t.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===n.id&&this.track(s)})}gradients(e,t,o,n=!1){if(T(t.length>0,()=>"gradients() received an empty list of xs."),o!=null&&o.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));T(s instanceof Ve,()=>"The result y returned by f() must be a tensor.");let a=SI(this.state.activeTape,t,s);if(!n&&a.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[s.id]=o==null?E3(s.shape):o,TI(i,a,u=>this.tidy(u),A3);let l=t.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let c of u.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:l}})}customGrad(e){return T(Hs(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{T(t.every(i=>i instanceof Ve),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let o,n={};t.forEach((i,l)=>{n[l]=i});let s=(i,l)=>(o=e(...t,l),T(o.value instanceof Ve,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),T(Hs(o.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),o.value),a=(i,l)=>{let u=o.gradFunc(i,l),c=Array.isArray(u)?u:[u];T(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(...)."),T(c.every(m=>m instanceof Ve),()=>"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 p={};return c.forEach((m,f)=>{p[f]=()=>m}),p};return this.runKernelFunc({forwardFunc:s,backwardsFunc:a,inputs:n})}}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=hu(),o=await this.backend.time(e);return o.wallMs=hu()-t,o}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 Mb;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}};xu.nextTensorId=0;xu.nextVariableId=0;function E3(r){let e=hm(ft(r),"float32");return E.makeTensor(e,r,"float32")}function Lb(){let r=vb();if(r._tfengine==null){let e=new jh(r);r._tfengine=new xu(e)}return II(r._tfengine.ENV),$I(()=>r._tfengine),r._tfengine}var E=Lb();function A3(r,e){let t={a:r,b:e};return E.runKernel(xo,t)}var Vc={};et(Vc,{isBrowser:()=>zb,isMobile:()=>$3});function D3(){return typeof navigator!="undefined"&&navigator!=null}function $3(){if(D3()){let r=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(r)||/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(r.substr(0,4))}return!1}function zb(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var Oi=U();Oi.registerFlag("DEBUG",()=>!1,r=>{r&&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.")});Oi.registerFlag("IS_BROWSER",()=>zb());Oi.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");Oi.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));Oi.registerFlag("PROD",()=>!1);Oi.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>Oi.getBool("DEBUG"));Oi.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);Oi.registerFlag("IS_TEST",()=>!1);Oi.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);function kr(r,e){let t=r;if(rr(r))return e==="string"?[]:[r.length];if(!Array.isArray(r))return[];let o=[];for(;Array.isArray(t)||rr(t)&&e!=="string";)o.push(t.length),t=t[0];return Array.isArray(r)&&U().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&PI(r,o,[]),o}function PI(r,e,t){if(t=t||[],!Array.isArray(r)&&!rr(r)){T(e.length===0,()=>`Element arr[${t.join("][")}] is a primitive, but should be an array/TypedArray of ${e[0]} elements`);return}T(e.length>0,()=>`Element arr[${t.join("][")}] should be a primitive, but is an array of ${r.length} elements`),T(r.length===e[0],()=>`Element arr[${t.join("][")}] should have ${e[0]} elements, but has ${r.length} elements`);let o=e.slice(1);for(let n=0;n=0&&(n=o),MI(o,n,e,t),r==null||!rr(r)&&!Array.isArray(r)&&typeof r!="number"&&typeof r!="boolean"&&typeof r!="string"){let l=r==null?"null":r.constructor.name;throw new Error(`Argument '${e}' passed to '${t}' must be a Tensor or TensorLike, but got '${l}'`)}let s=kr(r,n);!rr(r)&&!Array.isArray(r)&&(r=[r]);let i=n!=="string"?Lc(r,n):as(r,[],!0);return E.makeTensor(i,s,n)}function ya(r,e,t,o="numeric"){if(!Array.isArray(r))throw new Error(`Argument ${e} passed to ${t} must be a \`Tensor[]\` or \`TensorLike[]\``);return r.map((s,a)=>k(s,`${e}[${a}]`,t,o))}var LI="__op";function S(r){let e=Object.keys(r);if(e.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${e.length} keys.`);let t=e[0],o=r[t];t.endsWith("_")&&(t=t.substring(0,t.length-1)),t=t+LI;let n=(...s)=>{E.startScope(t);try{let a=o(...s);return xm(a)&&console.error("Cannot return a Promise inside of tidy."),E.endScope(a),a}catch(a){throw E.endScope(null),a}};return Object.defineProperty(n,"name",{value:t,configurable:!0}),n}function R3(r,e){let t=k(r,"real","complex"),o=k(e,"imag","complex");Nt(t.shape,o.shape,`real and imag shapes, ${t.shape} and ${o.shape}, must match in call to tf.complex().`);let n={real:t,imag:o};return E.runKernel(Hl,n)}var bo=S({complex_:R3});function Gr(r,e,t,o){if(o==null&&(o=Ac(r)),o==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!rr(r)&&!Array.isArray(r)&&typeof r!="number"&&typeof r!="boolean"&&typeof r!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(e!=null){gm(e);let n=ft(e),s=ft(t);T(n===s,()=>`Based on the provided shape, [${e}], the tensor should have ${n} values but has ${s}`);for(let a=0;a`Error creating a new Tensor. Inferred shape (${t}) does not match the provided shape (${e}). `)}}return!rr(r)&&!Array.isArray(r)&&(r=[r]),e=e||t,r=o!=="string"?Lc(r,o):as(r,[],!0),E.makeTensor(r,e,o)}function Dr(r,e,t){let o=kr(r,t);return Gr(r,e,o,t)}var Im={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8};var Kh=4;async function zI(r,e){let t=[],o=[],n=Array.isArray(r)?r.map(a=>a.name):Object.keys(r);for(let a=0;a{let m=await l.bytes(),f=m.reduce((g,x)=>g+x.length,0)+Kh*m.length,d=new Uint8Array(f),h=0;for(let g=0;g{if(e+=s.byteLength,t.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 o=new Uint8Array(e),n=0;return t.forEach(s=>{o.set(new Uint8Array(s.buffer),n),n+=s.byteLength}),o.buffer}var Bb=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function BI(r){return Bb?Buffer.byteLength(r):new Blob([r]).size}function VI(r){if(Bb)return Buffer.from(r).toString("base64");let e=new Uint8Array(r),t="";for(let o=0,n=e.length;o{e+=n.byteLength});let t=new Uint8Array(e),o=0;return r.forEach(n=>{t.set(new Uint8Array(n),o),o+=n.byteLength}),t.buffer}function Vb(r){let e="/";for(r=r.trim();r.endsWith(e);)r=r.slice(0,r.length-1);let t=r.split(e);return t[t.length-1]}function Pi(r){if(r.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:r.modelTopology==null?0:BI(JSON.stringify(r.modelTopology)),weightSpecsBytes:r.weightSpecs==null?0:BI(JSON.stringify(r.weightSpecs)),weightDataBytes:r.weightData==null?0:r.weightData.byteLength}}function P3(){let r=t=>{let o=t<<13,n=0;for(;(o&8388608)==0;)n-=8388608,o<<=1;return o&=~8388608,n+=947912704,o|n},e=new Uint32Array(2048);e[0]=0;for(let t=1;t<1024;t++)e[t]=r(t);for(let t=1024;t<2048;t++)e[t]=939524096+(t-1024<<13);return e}function M3(){let r=new Uint32Array(64);r[0]=0,r[31]=1199570944,r[32]=2147483648,r[63]=3347054592;for(let e=1;e<31;e++)r[e]=e<<23;for(let e=33;e<63;e++)r[e]=2147483648+(e-32<<23);return r}function L3(){let r=new Uint32Array(64);for(let e=0;e<64;e++)r[e]=1024;return r[0]=r[32]=0,r}function O3(){let r=P3(),e=M3(),t=L3();return o=>{let n=new ArrayBuffer(4*o.length),s=new Uint32Array(n);for(let a=0;a>10]+(i&1023)]+e[i>>10];s[a]=l}return new Float32Array(n)}}var St=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return St.instance==null&&(St.instance=new St),St.instance}static registerSaveRouter(e){St.getInstance().saveRouters.push(e)}static registerLoadRouter(e){St.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return St.getHandlers(e,"save")}static getLoadHandlers(e,t){return St.getHandlers(e,"load",t)}static getHandlers(e,t,o){let n=[];return(t==="load"?St.getInstance().loadRouters:St.getInstance().saveRouters).forEach(a=>{let i=a(e,o);i!==null&&n.push(i)}),n}},WI=r=>St.registerSaveRouter(r),UI=r=>St.registerLoadRouter(r),jI=r=>St.getSaveHandlers(r),HI=(r,e)=>St.getLoadHandlers(r,e);var Gb="tensorflowjs",Wb=1,yu="models_store",rl="model_info_store";function qI(){if(!U().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let r=typeof window=="undefined"?self:window,e=r.indexedDB||r.mozIndexedDB||r.webkitIndexedDB||r.msIndexedDB||r.shimIndexedDB;if(e==null)throw new Error("The current browser does not appear to support IndexedDB.");return e}function Ub(r){let e=r.result;e.createObjectStore(yu,{keyPath:"modelPath"}),e.createObjectStore(rl,{keyPath:"modelPath"})}var ba=class{constructor(e){if(this.indexedDB=qI(),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((o,n)=>{let s=this.indexedDB.open(Gb,Wb);s.onupgradeneeded=()=>Ub(s),s.onsuccess=()=>{let a=s.result;if(t==null){let i=a.transaction(yu,"readonly"),u=i.objectStore(yu).get(this.modelPath);u.onsuccess=()=>{if(u.result==null)return a.close(),n(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));o(u.result.modelArtifacts)},u.onerror=c=>(a.close(),n(u.error)),i.oncomplete=()=>a.close()}else{let i=Pi(t),l=a.transaction(rl,"readwrite"),u=l.objectStore(rl),c=u.put({modelPath:this.modelPath,modelArtifactsInfo:i}),p;c.onsuccess=()=>{p=a.transaction(yu,"readwrite");let f=p.objectStore(yu).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:i});f.onsuccess=()=>o({modelArtifactsInfo:i}),f.onerror=d=>{u=l.objectStore(rl);let h=u.delete(this.modelPath);h.onsuccess=()=>(a.close(),n(f.error)),h.onerror=g=>(a.close(),n(f.error))}},c.onerror=m=>(a.close(),n(c.error)),l.oncomplete=()=>{p==null?a.close():p.oncomplete=()=>a.close()}}},s.onerror=a=>n(s.error)})}};ba.URL_SCHEME="indexeddb://";var KI=r=>U().getBool("IS_BROWSER")&&!Array.isArray(r)&&r.startsWith(ba.URL_SCHEME)?z3(r.slice(ba.URL_SCHEME.length)):null;St.registerSaveRouter(KI);St.registerLoadRouter(KI);function z3(r){return new ba(r)}function B3(r){return r.startsWith(ba.URL_SCHEME)?r.slice(ba.URL_SCHEME.length):r}var jb=class{constructor(){this.indexedDB=qI()}async listModels(){return new Promise((e,t)=>{let o=this.indexedDB.open(Gb,Wb);o.onupgradeneeded=()=>Ub(o),o.onsuccess=()=>{let n=o.result,s=n.transaction(rl,"readonly"),i=s.objectStore(rl).getAll();i.onsuccess=()=>{let l={};for(let u of i.result)l[u.modelPath]=u.modelArtifactsInfo;e(l)},i.onerror=l=>(n.close(),t(i.error)),s.oncomplete=()=>n.close()},o.onerror=n=>t(o.error)})}async removeModel(e){return e=B3(e),new Promise((t,o)=>{let n=this.indexedDB.open(Gb,Wb);n.onupgradeneeded=()=>Ub(n),n.onsuccess=()=>{let s=n.result,a=s.transaction(rl,"readwrite"),i=a.objectStore(rl),l=i.get(e),u;l.onsuccess=()=>{if(l.result==null)return s.close(),o(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{let c=i.delete(e),p=()=>{u=s.transaction(yu,"readwrite");let f=u.objectStore(yu).delete(e);f.onsuccess=()=>t(l.result.modelArtifactsInfo),f.onerror=d=>o(l.error)};c.onsuccess=p,c.onerror=m=>(p(),s.close(),o(l.error))}},l.onerror=c=>(s.close(),o(l.error)),a.oncomplete=()=>{u==null?s.close():u.oncomplete=()=>s.close()}},n.onerror=s=>o(n.error)})}};var _a="/",Wc="tensorflowjs_models",XI="info",V3="model_topology",G3="weight_specs",W3="weight_data",U3="model_metadata";function YI(r){return{info:[Wc,r,XI].join(_a),topology:[Wc,r,V3].join(_a),weightSpecs:[Wc,r,G3].join(_a),weightData:[Wc,r,W3].join(_a),modelMetadata:[Wc,r,U3].join(_a)}}function j3(r){let e=r.split(_a);if(e.length<3)throw new Error(`Invalid key format: ${r}`);return e.slice(1,e.length-1).join(_a)}function H3(r){return r.startsWith(wa.URL_SCHEME)?r.slice(wa.URL_SCHEME.length):r}var wa=class{constructor(e){if(!U().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=YI(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),o=JSON.stringify(e.weightSpecs),n=Pi(e);try{this.LS.setItem(this.keys.info,JSON.stringify(n)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,o),this.LS.setItem(this.keys.weightData,VI(e.weightData));let s={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};return e.signature!=null&&(s.signature=e.signature),e.userDefinedMetadata!=null&&(s.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(s.modelInitializer=e.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(s)),{modelArtifactsInfo:n}}catch(s){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${n.modelTopologyBytes}, weightSpecsBytes=${n.weightSpecsBytes}, weightDataBytes=${n.weightDataBytes}.`)}}}async load(){let e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(e.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let t={},o=JSON.parse(this.LS.getItem(this.keys.topology));if(o==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);t.modelTopology=o;let n=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(n==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=n;let s=this.LS.getItem(this.keys.modelMetadata);if(s!=null){let i=JSON.parse(s);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 a=this.LS.getItem(this.keys.weightData);if(a==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=GI(a),t}};wa.URL_SCHEME="localstorage://";var ZI=r=>U().getBool("IS_BROWSER")&&!Array.isArray(r)&&r.startsWith(wa.URL_SCHEME)?q3(r.slice(wa.URL_SCHEME.length)):null;St.registerSaveRouter(ZI);St.registerLoadRouter(ZI);function q3(r){return new wa(r)}var Hb=class{constructor(){T(U().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),T(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=Wc+_a,o=_a+XI;for(let n=0;n"scheme must not be undefined or null."),e.endsWith(Uc)&&(e=e.slice(0,e.indexOf(Uc))),T(e.length>0,()=>"scheme must not be an empty string.");let o=Wr.getInstance();T(o.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),o.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 Yh(r){if(r.indexOf(Uc)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Wr.getSchemes().join(",")}`);return{scheme:r.split(Uc)[0],path:r.split(Uc)[1]}}async function JI(r,e,t=!1){T(r!==e,()=>`Old path and new path are the same: '${r}'`);let o=St.getLoadHandlers(r);T(o.length>0,()=>`Copying failed because no load handler is found for source URL ${r}.`),T(o.length<2,()=>`Copying failed because more than one (${o.length}) load handlers for source URL ${r}.`);let n=o[0],s=St.getSaveHandlers(e);T(s.length>0,()=>`Copying failed because no save handler is found for destination URL ${e}.`),T(s.length<2,()=>`Copying failed because more than one (${o.length}) save handlers for destination URL ${e}.`);let a=s[0],i=Yh(r).scheme,l=Yh(r).path,u=i===Yh(r).scheme,c=await n.load();t&&u&&await Wr.getManager(i).removeModel(l);let p=await a.save(c);return t&&!u&&await Wr.getManager(i).removeModel(l),p.modelArtifactsInfo}async function QI(){let r=Wr.getSchemes(),e={};for(let t of r){let o=await Wr.getManager(t).listModels();for(let n in o){let s=t+Uc+n;e[s]=o[n]}}return e}async function eN(r){let e=Yh(r);return Wr.getManager(e.scheme).removeModel(e.path)}async function tN(r,e){return JI(r,e,!1)}async function rN(r,e){return JI(r,e,!0)}var oN=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(U().get("IS_BROWSER")){U().setPlatform("browser",new oN);try{Wr.registerManager(wa.URL_SCHEME,new Hb)}catch(r){}try{Wr.registerManager(ba.URL_SCHEME,new jb)}catch(r){}}var K3={importFetch:()=>nN()},qb;var sN=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return U().global.fetch!=null?U().global.fetch(e,t):(qb==null&&(qb=K3.importFetch()),qb(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)}};U().get("IS_NODE")&&U().setPlatform("node",new sN);function Ce(r,e="float32",t){return e=e||"float32",gm(r),new ct(r,e,t)}function X3(r,e){let t=k(r,"x","cast");if(!bb(e))throw new Error(`Failed to cast to unknown dtype ${e}`);if(e==="string"&&t.dtype!=="string"||e!=="string"&&t.dtype==="string")throw new Error("Only strings can be casted to strings");let o={x:t},n={dtype:e};return E.runKernel(Eo,o,n)}var oe=S({cast_:X3});function Y3(r){let t={x:k(r,"x","clone","string_or_numeric")};return E.runKernel(Do,t)}var Ro=S({clone_:Y3});function Kb(r,e=!1){console.log(r.toString(e))}Lb();var Z3={buffer:Ce,cast:oe,clone:Ro,print:Kb};RI(Z3);var vr={};et(vr,{browserFiles:()=>lN,browserHTTPRequest:()=>pN,concatenateArrayBuffers:()=>Gc,copyModel:()=>tN,decodeWeights:()=>Xh,encodeWeights:()=>zI,fromMemory:()=>fN,getLoadHandlers:()=>HI,getModelArtifactsInfoForJSON:()=>Pi,getSaveHandlers:()=>jI,http:()=>Qh,isHTTPScheme:()=>Jh,listModels:()=>QI,loadWeights:()=>uN,moveModel:()=>rN,registerLoadRouter:()=>UI,registerSaveRouter:()=>WI,removeModel:()=>eN,weightsLoaderFactory:()=>Zb,withSaveHandler:()=>dN});var J3="model",Q3=".json",eV=".weights.bin";function iN(r){return new Promise(e=>setTimeout(e)).then(r)}var ol=class{constructor(e){if(!U().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(ol.URL_SCHEME)&&(e=e.slice(ol.URL_SCHEME.length)),(e==null||e.length===0)&&(e=J3),this.modelTopologyFileName=e+Q3,this.weightDataFileName=e+eV}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 o=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],n={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:o};e.signature!=null&&(n.signature=e.signature),e.userDefinedMetadata!=null&&(n.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(n.modelInitializer=e.modelInitializer);let s=window.URL.createObjectURL(new Blob([JSON.stringify(n)],{type:"application/json"})),a=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(a.download=this.modelTopologyFileName,a.href=s,await iN(()=>a.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 iN(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Pi(e)}}}};ol.URL_SCHEME="downloads://";var aN=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((o,n)=>{let s=new FileReader;s.onload=a=>{let i=JSON.parse(a.target.result),l=i.modelTopology;if(l==null){n(new Error(`modelTopology field is missing from file ${e.name}`));return}t.length===0&&o({modelTopology:l});let u=i.weightsManifest;if(u==null){n(new Error(`weightManifest field is missing from file ${e.name}`));return}let c;try{c=this.checkManifestAndWeightFiles(u,t)}catch(d){n(d);return}let p=[],m=[],f=[];u.forEach(d=>{d.paths.forEach(h=>{m.push(h),f.push(null)}),p.push(...d.weights)}),u.forEach(d=>{d.paths.forEach(h=>{let g=new FileReader;g.onload=x=>{let b=x.target.result,_=m.indexOf(h);if(f[_]=b,f.indexOf(null)===-1){let w={modelTopology:l,weightSpecs:p,weightData:Gc(f),format:i.format,generatedBy:i.generatedBy,convertedBy:i.convertedBy};i.signature!=null&&(w.signature=i.signature),i.userDefinedMetadata!=null&&(w.userDefinedMetadata=i.userDefinedMetadata),i.modelInitializer!=null&&(w.modelInitializer=i.modelInitializer),o(w)}},g.onerror=x=>n(`Failed to weights data from file of path '${h}'.`),g.readAsArrayBuffer(c[h])})})},s.onerror=a=>n(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),s.readAsText(e)})}checkManifestAndWeightFiles(e,t){let o=[],n=t.map(a=>Vb(a.name)),s={};for(let a of e)a.paths.forEach(i=>{let l=Vb(i);if(o.indexOf(l)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${l}'`);if(o.push(l),n.indexOf(l)===-1)throw new Error(`Weight file with basename '${l}' is not provided.`);s[i]=t[n.indexOf(l)]});if(o.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${o.length}) and the number of weight files provided (${t.length}).`);return s}},rV=r=>U().getBool("IS_BROWSER")&&!Array.isArray(r)&&r.startsWith(ol.URL_SCHEME)?tV(r.slice(ol.URL_SCHEME.length)):null;St.registerSaveRouter(rV);function tV(r="model"){return new ol(r)}function lN(r){return new aN(r)}function Xb(r,e,t,o){a(r),t=t==null?0:t,o=o==null?1:o,i(t,o);let n=0,s=l=>(l.then(u=>{let c=t+ ++n/r.length*(o-t);return e(c),u}),l);function a(l){T(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function i(l,u){T(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),T(u>=0&&u<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${u}`),T(u>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${u}`)}return Promise.all(r.map(s))}async function Yb(r,e){e==null&&(e={});let t=e.fetchFunc==null?U().platform.fetch:e.fetchFunc,o=r.map(p=>t(p,e.requestInit,{isBinary:!0})),n=0,s=.5,i=(e.onProgress==null?await Promise.all(o):await Xb(o,e.onProgress,n,s)).map(p=>p.arrayBuffer()),l=.5,u=1;return e.onProgress==null?await Promise.all(i):await Xb(i,e.onProgress,l,u)}async function uN(r,e="",t,o){return Zb(a=>Yb(a,{requestInit:o}))(r,e,t)}function Zb(r){return async(e,t="",o)=>{let n=e.map(()=>!1),s={},a=o!=null?o.map(()=>!1):[],i=[];if(e.forEach((f,d)=>{let h=0;f.weights.forEach(g=>{let x="quantization"in g?g.quantization.dtype:g.dtype,b=Im[x]*ft(g.shape),_=()=>{n[d]=!0,s[d]==null&&(s[d]=[]),s[d].push({manifestEntry:g,groupOffset:h,sizeBytes:b})};o!=null?o.forEach((w,v)=>{w===g.name&&(_(),a[v]=!0)}):_(),i.push(g.name),h+=b})}),!a.every(f=>f)){let f=o.filter((d,h)=>!a[h]);throw new Error(`Could not find weights in manifest with names: ${f.join(", ")}. Manifest JSON has weights with names: ${i.join(", ")}.`)}let l=n.reduce((f,d,h)=>(d&&f.push(h),f),[]),u=[];l.forEach(f=>{e[f].paths.forEach(d=>{let h=t+(t.endsWith("/")?"":"/")+d;u.push(h)})});let c=await r(u),p={},m=0;return l.forEach(f=>{let d=e[f].paths.length,h=0;for(let w=0;w{let v=g.slice(w.groupOffset,w.groupOffset+w.sizeBytes),$=Xh(v,[w.manifestEntry]);for(let A in $)p[A]=$[A]}),m+=d}),p}}var oV="application/octet-stream",nV="application/json",Zh=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?(T(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=U().platform.fetch,T(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&T(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 o=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],n={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:o};e.signature!=null&&(n.signature=e.signature),e.userDefinedMetadata!=null&&(n.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(n.modelInitializer=e.modelInitializer),t.body.append("model.json",new Blob([JSON.stringify(n)],{type:nV}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:oV}),"model.weights.bin");let s=await this.fetch(this.path,t);if(s.ok)return{modelArtifactsInfo:Pi(e),responses:[s]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${s.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 h=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?h+=" 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.":h+=" Please make sure the server is serving valid JSON for this request.",new Error(h)}let o=t.modelTopology,n=t.weightsManifest,s=t.generatedBy,a=t.convertedBy,i=t.format,l=t.signature,u=t.userDefinedMetadata;if(o==null&&n==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let c,p;n!=null&&([c,p]=await this.loadWeights(n));let m={modelTopology:o,weightSpecs:c,weightData:p,generatedBy:s,convertedBy:a,format:i};l!=null&&(m.signature=l),u!=null&&(m.userDefinedMetadata=u);let f=t.modelInitializer;return f&&(m.modelInitializer=f),m}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[o,n]=sV(t),s=this.weightPathPrefix||o,a=[];for(let c of e)a.push(...c.weights);let i=[],l=[];for(let c of e)for(let p of c.paths)this.weightUrlConverter!=null?l.push(this.weightUrlConverter(p)):i.push(s+p+n);this.weightUrlConverter&&i.push(...await Promise.all(l));let u=await Yb(i,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[a,Gc(u)]}};Zh.URL_SCHEME_REGEX=/^https?:\/\//;function sV(r){let e=r.lastIndexOf("/"),t=r.lastIndexOf("?"),o=r.substring(0,e),n=t>e?r.substring(t):"";return[o+"/",n]}function Jh(r){return r.match(Zh.URL_SCHEME_REGEX)!=null}var cN=(r,e)=>{if(typeof fetch=="undefined"&&(e==null||e.fetchFunc==null))return null;{let t=!0;if(Array.isArray(r)?t=r.every(o=>Jh(o)):t=Jh(r),t)return Qh(r,e)}return null};St.registerSaveRouter(cN);St.registerLoadRouter(cN);function Qh(r,e){return new Zh(r,e)}function pN(r,e){return Qh(r,e)}var eg=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},mN=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function fN(r,e,t,o){return arguments.length===1?r.modelTopology!=null||r.weightSpecs!=null?new eg(r):(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 eg({modelTopology:r})):(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 eg({modelTopology:r,weightSpecs:e,weightData:t,trainingConfig:o}))}function dN(r){return new mN(r)}var hN={};et(hN,{confusionMatrix:()=>cV});function iV(r,e,t=!1,o=!1){let n=k(r,"a","matMul"),s=k(e,"b","matMul");[n,s]=Ge(n,s);let a={a:n,b:s},i={transposeA:t,transposeB:o};return E.runKernel(Yo,a,i)}var We=S({matMul_:iV});function aV(r,e,t=1,o=0){if(e<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${e}`);let s={indices:k(r,"indices","oneHot","int32")},a={depth:e,onValue:t,offValue:o};return E.runKernel(bn,s,a)}var Is=S({oneHot_:aV});function lV(r,e){let t=k(r,"x","transpose");if(e==null&&(e=t.shape.map((s,a)=>a).reverse()),T(t.rank===e.length,()=>`Error in transpose: rank of input ${t.rank} must match length of perm ${e}.`),e.forEach(s=>{T(s>=0&&s`All entries in 'perm' must be between 0 and ${t.rank-1} but got ${e}`)}),t.rank<=1)return t.clone();let o={x:t},n={perm:e};return E.runKernel(Mn,o,n)}var Ue=S({transpose_:lV});function uV(r,e,t){let o=k(r,"labels","confusionMatrix"),n=k(e,"predictions","confusionMatrix");T(t==null||t>0&&Number.isInteger(t),()=>`If provided, numClasses must be a positive integer, but got ${t}`),T(o.rank===1,()=>`Expected the rank of labels to be 1, but got ${o.rank}`),T(n.rank===1,()=>`Expected the rank of predictions to be 1, but got ${n.rank}`),T(o.shape[0]===n.shape[0],()=>`Mismatch in the number of examples: ${o.shape[0]} vs. ${n.shape[0]}. Labels and predictions should have the same number of elements.`),T(t>0&&Number.isInteger(t),()=>`numClasses is required to be a positive integer, but got ${t}`);let s=Is(oe(o,"int32"),t),a=Is(oe(n,"int32"),t),i=Ue(s),l=We(i,a);return oe(l,"int32")}var cV=S({confusionMatrix_:uV});var tg={};et(tg,{fromPixels:()=>fV,toPixels:()=>mV});function Jb(r,e,t){if(To(r),e!=null&&e.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let o=kr(r,t);if(o.length!==3&&o.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(o.length===1&&e==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return Gr(r,e,o,t)}var jc;function pV(r,e=3){if(e>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let t=!1,o=!1,n=!1,s=!1,a=!1,i=!1;if(r.data instanceof Uint8Array)t=!0;else if(typeof ImageData!="undefined"&&r instanceof ImageData)o=!0;else if(typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement)n=!0;else if(typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement)s=!0;else if(r.getContext!=null)a=!0;else if(typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap)i=!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 ${r.constructor.name}`);if(n){let d=2;if(n&&r.readyState element.")}if(Mc(Oc,E.backendName)!=null){let d={pixels:r},h={numChannels:e};return E.runKernel(Oc,d,h)}let[u,c]=n?[r.videoWidth,r.videoHeight]:[r.width,r.height],p;a?p=r.getContext("2d").getImageData(0,0,u,c).data:o||t?p=r.data:(s||n||i)&&(jc==null&&(jc=document.createElement("canvas").getContext("2d")),jc.canvas.width=u,jc.canvas.height=c,jc.drawImage(r,0,0,u,c),p=jc.getImageData(0,0,u,c).data);let m;if(e===4)m=new Int32Array(p);else{let d=u*c;m=new Int32Array(d*e);for(let h=0;h4||s===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${s}`);if(t.dtype!=="float32"&&t.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${t.dtype}. Please use float32 or int32 tensors.`);let a=await t.data(),i=t.dtype==="float32"?255:1,l=new Uint8ClampedArray(n*o*4);for(let u=0;u1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${f}.`)}else if(t.dtype==="int32"&&(f<0||f>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${f}.`);s===1?(c[0]=f*i,c[1]=f*i,c[2]=f*i):c[m]=f*i}let p=u*4;l[p+0]=Math.round(c[0]),l[p+1]=Math.round(c[1]),l[p+2]=Math.round(c[2]),l[p+3]=Math.round(c[3])}if(e!=null){e.width=n,e.height=o;let u=e.getContext("2d"),c=new ImageData(l,n,o);u.putImageData(c,0,0)}return t!==r&&t.dispose(),l}var fV=S({fromPixels_:pV});var rg={};et(rg,{prepareAndValidate:()=>gN});function gN(r,e){let t=r.shape.length,o=e.shape.length;if(t<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${t}.`);if(o<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${o}.`);if(e.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${e.dtype}.`);if(e.shape[o-1]>t)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${e.shape[o-1]} vs. ${t}`);if(ft(r.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${r.shape}.`);let n=e.shape,s=n[n.length-1],a=1;for(let p=0;pp/u),1].slice(0,s);return[l,a,u,c]}var og={};et(og,{calculateShapes:()=>xN,validateInput:()=>ng,validateUpdateShape:()=>Qb});function Qb(r,e,t){let o=e.rank>1?e.shape[e.rank-1]:1,n=e.rank>1?e.rank-1:1,s=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${t.shape}, indices.shape: ${e.shape}, shape: ${r}, sliceDim: ${o}, and batchDim: ${n}.`;if(t.rank1?e.shape[o-1]:1,s=t.length,a=1;for(let p=n;pdV,computeFlatOffset:()=>gV,computeOutShape:()=>yN,getNormalizedAxes:()=>SN,isSliceContinous:()=>hV,maskToAxes:()=>sg,parseSliceParams:()=>e_,sliceInfo:()=>xV,startForAxis:()=>IN,startIndicesWithElidedDims:()=>kN,stopForAxis:()=>NN,stopIndicesWithElidedDims:()=>vN,stridesForAxis:()=>CN,stridesWithElidedDims:()=>bN});function dV(r,e,t){let o=r.shape.length;T(o===e.length,()=>`Error in slice${o}D: Length of begin ${e} must match the rank of the array (${o}).`),T(o===t.length,()=>`Error in slice${o}D: Length of size ${t} must match the rank of the array (${o}).`);for(let n=0;n`Error in slice${o}D: begin[${n}] + size[${n}] (${e[n]+t[n]}) would overflow input.shape[${n}] (${r.shape[n]})`)}function sg(r){let e=[],t=0;for(;r>0;)r&1&&e.push(t),r/=2,t++;return e}function yN(r,e,t){let o=[];for(let n=0;n0){let f=e[0],d=t+1;c=kN(a,f,d,o,r),p=vN(i,f,d,n,r),m=bN(s,f,d,r)}else for(let f=0;f-1)s[i]=0;else{let l=_N(e,t,i),u=o[l];r&1<-1)s[i]=Number.MAX_SAFE_INTEGER;else{let l=_N(e,t,i),u=o[l];r&1<0?a=Number.MIN_SAFE_INTEGER:a=Number.MAX_SAFE_INTEGER);let l=o[n];return a<0&&(a+=l),a=Ec(0,a,l-1),a}function NN(r,e,t,o,n,s){let a=e[n],i=t[n]||1;(r&1<0?a=Number.MAX_SAFE_INTEGER:a=Number.MIN_SAFE_INTEGER);let l=o[n];return a<0&&(a+=l),i>0?a=Ec(0,a,l):a=Ec(-1,a,l-1),a}function hV(r,e,t){let o=t.length;for(let n=0;n1){o=n;break}for(let n=o+1;n0||t[n]!==r[n])return!1;return!0}function gV(r,e){let t=r.length>0?r[r.length-1]:1;for(let o=0;o{T(a!==-1,()=>"slice() does not support negative begin indexing.")});let s;return t==null?s=new Array(n).fill(-1):typeof t=="number"?s=[t,...new Array(n-1).fill(-1)]:t.lengtha>=0?a:(T(a===-1,()=>`Negative size values should be exactly -1 but got ${a} for the slice() size at index ${i}.`),r.shape[i]-o[i])),[o,s]}function xV(r,e,t,o,n,s,a,i,l){let u=e.slice(),c=t.slice(),p=o;o==null&&(p=new Array(u.length));let m=sg(a);if(m.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(a!==0&&i!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(a!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let f=r.length-u.length,d=sg(i),h=r.slice();d.forEach(A=>{u[A]=0,c[A]=1,h.splice(A,0,1)});let{begin:g,end:x,strides:b}=SN(h,m,f,u,c,p,n,s,a);u=g,c=x,p=b;let _=sg(l);_.forEach(A=>{c[A]=u[A]+1,p[A]=1});let w=yN(u,c,p),v=w.filter((A,R)=>_.indexOf(R)===-1);return{nonStrided:p.every(A=>A===1),$begin:u,$end:c,$strides:p,size:w,newShape:h,outShape:v}}var J={};et(J,{Serializable:()=>ig,SerializationMap:()=>ka,registerClass:()=>eo});var ig=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},ka=class{constructor(){this.classNameMap={}}static getMap(){return ka.instance==null&&(ka.instance=new ka),ka.instance}static register(e){ka.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function eo(r){T(r.className!=null,()=>"Class being registered does not have the static className property defined."),T(typeof r.className=="string",()=>"className is required to be a string, but got type "+typeof r.className),T(r.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),ka.register(r)}var TN={};et(TN,{TEST_EPSILON_FLOAT16:()=>EN,encodeStrings:()=>AN,expectArrayBuffersEqual:()=>CV,expectArraysClose:()=>bV,expectArraysEqual:()=>wV,expectNumbersClose:()=>kV,expectPromiseToFail:()=>_V,expectValuesInRange:()=>vV,testEpsilon:()=>t_});var yV=.001,EN=.1;function bV(r,e,t){return t==null&&(t=t_()),r_(r,e,(o,n)=>o_(o,n,t))}function t_(){return E.backend.floatPrecision()===32?yV:EN}function r_(r,e,t){let o=!0;if((rr(r)||rr(e))&&(o=!1),rr(r)&&rr(e)&&(o=!0),o){let a=r.constructor.name,i=e.constructor.name;if(a!==i)throw new Error(`Arrays are of different type. Actual: ${a}. Expected: ${i}`)}if(Array.isArray(r)&&Array.isArray(e)){let a=kr(r),i=kr(e);if(!Vr(a,i))throw new Error(`Arrays have different shapes. Actual: [${a}]. Expected: [${i}]`)}let n=rr(r)?r:as(r),s=rr(e)?e:as(e);if(n.length!==s.length)throw new Error(`Arrays have different lengths actual: ${n.length} vs expected: ${s.length}. Actual: ${n}. Expected: ${s}.`);for(let a=0;ae.fail(),()=>e())}function wV(r,e){let t=typeof e=="string"||typeof e=="number"||typeof e=="boolean"?[e]:e;return ls(r)||ls(r[0])||ls(e)||ls(e[0])?r_(r,t,(o,n)=>o==n):r_(r,e,(o,n)=>o_(o,n,0))}function kV(r,e,t){if(t==null&&(t=t_()),!o_(r,e,t))throw new Error(`Numbers differ: actual === ${r}, expected === ${e}`)}function o_(r,e,t){return!isFinite(r)&&!isFinite(e)?!0:!(isNaN(r)||isNaN(e)||Math.abs(r-e)>t)}function vV(r,e,t){for(let o=0;ot)throw new Error(`Value out of range:${r[o]} low: ${e}, high: ${t}`)}function CV(r,e){expect(new Float32Array(r)).toEqual(new Float32Array(e))}function AN(r){for(let e=0;et.dispose())}function Dt(r){return E.keep(r)}function AV(r){return E.time(r)}function DN(r){return E.setBackend(r)}function DV(){return E.ready()}function $V(){return E.backendName}function RV(r){E.removeBackend(r)}function FV(r){return E.findBackend(r)}function OV(r){return E.findBackendFactory(r)}function bu(r,e,t=1){return E.registerBackend(r,e,t)}function s_(){return E.backend}function PV(r,e){U().setPlatform(r,e)}function MV(r,e){let t=k(r,"a","add"),o=k(e,"b","add");[t,o]=Ge(t,o);let n={a:t,b:o};return E.runKernel(xo,n)}var Q=S({add_:MV});function LV(r,e){let t=k(r,"a","floorDiv"),o=k(e,"b","floorDiv");[t,o]=Ge(t,o);let n={a:t,b:o};return E.runKernel(an,n)}var _u=S({floorDiv_:LV});function zV(r,e){let t=k(r,"a","div"),o=k(e,"b","div");if([t,o]=Ge(t,o),t.dtype==="int32"&&o.dtype==="int32")return _u(t,o);let n={a:t,b:o},s={};return E.runKernel(on,n,s)}var de=S({div_:zV});function BV(r,e){let t=k(r,"a","mul"),o=k(e,"b","mul");[t,o]=Ge(t,o);let n={a:t,b:o};return E.runKernel(yn,n)}var O=S({mul_:BV});function VV(r){let e=k(r,"x","abs");if(e.dtype==="complex64"){let t={x:e};return E.runKernel(ia,t)}else{let t={x:e};return E.runKernel(us,t)}}var Tt=S({abs_:VV});function GV(r){let t={x:k(r,"x","acos")};return E.runKernel(Ks,t)}var Nm=S({acos_:GV});function WV(r){let t={x:k(r,"x","acosh")};return E.runKernel(Xs,t)}var Sm=S({acosh_:WV});function UV(r){T(Array.isArray(r),()=>"The argument passed to tf.addN() must be a list of tensors"),T(r.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${r.length}`);let e=r.map((n,s)=>k(n,`tensors${s}`,"addN")),t=e[0];e.forEach(n=>{if(n.dtype!==t.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),e.forEach(n=>{if(!Vr(n.shape,t.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let o=e;return E.runKernel(qo,o)}var i_=S({addN_:UV});function jV(r,e=null,t=!1){let n={x:k(r,"x","all","bool")},s={axis:e,keepDims:t};return E.runKernel(Vl,n,s)}var wu=S({all_:jV});function HV(r,e=null,t=!1){let n={x:k(r,"x","any","bool")},s={axis:e,keepDims:t};return E.runKernel(Gl,n,s)}var nl=S({any_:HV});function qV(r,e=0){let o={x:k(r,"x","argMax")},n={axis:e};return E.runKernel(Ko,o,n)}var sl=S({argMax_:qV});function KV(r,e=0){let o={x:k(r,"x","argMin")},n={axis:e};return E.runKernel(oa,o,n)}var Tm=S({argMin_:KV});function XV(r){let t={x:k(r,"x","asin")};return E.runKernel(Ys,t)}var Em=S({asin_:XV});function YV(r){let t={x:k(r,"x","asinh")};return E.runKernel(Zs,t)}var Am=S({asinh_:YV});function ZV(r){let t={x:k(r,"x","atan")};return E.runKernel(Js,t)}var Dm=S({atan_:ZV});function JV(r,e){let t=k(r,"a","atan2"),o=k(e,"b","atan2");[t,o]=Ge(t,o);let n={a:t,b:o};return E.runKernel(ei,n)}var $m=S({atan2_:JV});function QV(r){let t={x:k(r,"x","atanh")};return E.runKernel(Qs,t)}var Rm=S({atanh_:QV});function eG(r,e,t,o,n="NHWC",s){let a=r[3],i=[...e,a],l=$N(n);return ku(r,i,t,s,o,null,null,l)}function a_(r,e,t,o,n,s,a="channelsLast"){let[i,l]=lg(e),u;if(a==="channelsLast")u=[i,l,r[3],r[3]];else if(a==="channelsFirst")u=[i,l,r[1],r[1]];else throw new Error(`Unknown dataFormat ${a}`);return ku(r,u,t,o,n,s,!1,a)}function tG(r,e,t,o,n,s,a="NDHWC"){let[i,l,u]=l_(e),c,p;if(a==="NDHWC")p="channelsLast",c=[i,l,u,r[4],r[4]];else if(a==="NCDHW")p="channelsFirst",c=[i,l,u,r[1],r[1]];else throw new Error(`Unknown dataFormat ${a}`);return RN(r,c,t,o,n,!1,p,s)}function ku(r,e,t,o,n,s,a=!1,i="channelsLast"){let[l,u,c,p]=[-1,-1,-1,-1];if(i==="channelsLast")[l,u,c,p]=r;else if(i==="channelsFirst")[l,p,u,c]=r;else throw new Error(`Unknown dataFormat ${i}`);let[m,f,,d]=e,[h,g]=lg(t),[x,b]=lg(o),_=qc(m,x),w=qc(f,b),{padInfo:v,outHeight:$,outWidth:A}=rG(n,u,c,h,g,_,w,s,i),R=a?d*p:d,M;return i==="channelsFirst"?M=[l,R,$,A]:i==="channelsLast"&&(M=[l,$,A,R]),{batchSize:l,dataFormat:i,inHeight:u,inWidth:c,inChannels:p,outHeight:$,outWidth:A,outChannels:R,padInfo:v,strideHeight:h,strideWidth:g,filterHeight:m,filterWidth:f,effectiveFilterHeight:_,effectiveFilterWidth:w,dilationHeight:x,dilationWidth:b,inShape:r,outShape:M,filterShape:e}}function RN(r,e,t,o,n,s=!1,a="channelsLast",i){let[l,u,c,p,m]=[-1,-1,-1,-1,-1];if(a==="channelsLast")[l,u,c,p,m]=r;else if(a==="channelsFirst")[l,m,u,c,p]=r;else throw new Error(`Unknown dataFormat ${a}`);let[f,d,h,,g]=e,[x,b,_]=l_(t),[w,v,$]=l_(o),A=qc(f,w),R=qc(d,v),M=qc(h,$),{padInfo:V,outDepth:G,outHeight:W,outWidth:q}=oG(n,u,c,p,x,b,_,A,R,M,i),Z=s?g*m:g,X;return a==="channelsFirst"?X=[l,Z,G,W,q]:a==="channelsLast"&&(X=[l,G,W,q,Z]),{batchSize:l,dataFormat:a,inDepth:u,inHeight:c,inWidth:p,inChannels:m,outDepth:G,outHeight:W,outWidth:q,outChannels:Z,padInfo:V,strideDepth:x,strideHeight:b,strideWidth:_,filterDepth:f,filterHeight:d,filterWidth:h,effectiveFilterDepth:A,effectiveFilterHeight:R,effectiveFilterWidth:M,dilationDepth:w,dilationHeight:v,dilationWidth:$,inShape:r,outShape:X,filterShape:e}}function nG(r,e,t,o,n){o==null&&(o=u_(r,e,t));let s=r[0],a=r[1],i=vu((s-e+2*o)/t+1,n),l=vu((a-e+2*o)/t+1,n);return[i,l]}function sG(r,e,t,o,n,s){n==null&&(n=u_(r,e,o));let a=r[0],i=r[1],l=r[2],u=vu((a-e+2*n)/o+1,s),c=vu((i-e+2*n)/o+1,s),p=vu((l-e+2*n)/o+1,s);return[u,c,p,t]}function u_(r,e,t,o=1){let n=qc(e,o);return Math.floor((r[0]*(t-1)-t+n)/2)}function lg(r){return typeof r=="number"?[r,r,r]:r.length===2?[r[0],r[1],1]:r}function l_(r){return typeof r=="number"?[r,r,r]:r}function qc(r,e){return e<=1?r:r+(r-1)*(e-1)}function rG(r,e,t,o,n,s,a,i,l){let u,c,p;if(typeof r=="number"){u={top:r,bottom:r,left:r,right:r,type:r===0?"VALID":"NUMBER"};let f=nG([e,t],s,o,r,i);c=f[0],p=f[1]}else if(r==="same"){c=Math.ceil(e/o),p=Math.ceil(t/n);let m=Math.max(0,(c-1)*o+s-e),f=Math.max(0,(p-1)*n+a-t),d=Math.floor(m/2),h=m-d,g=Math.floor(f/2),x=f-g;u={top:d,bottom:h,left:g,right:x,type:"SAME"}}else if(r==="valid")u={top:0,bottom:0,left:0,right:0,type:"VALID"},c=Math.ceil((e-s+1)/o),p=Math.ceil((t-a+1)/n);else if(typeof r=="object"){let m=l==="channelsLast"?r[1][0]:r[2][0],f=l==="channelsLast"?r[1][1]:r[2][1],d=l==="channelsLast"?r[2][0]:r[3][0],h=l==="channelsLast"?r[2][1]:r[3][1];u={top:m,bottom:f,left:d,right:h,type:m===0&&f===0&&d===0&&h===0?"VALID":"EXPLICIT"},c=vu((e-s+m+f)/o+1,i),p=vu((t-a+d+h)/n+1,i)}else throw Error(`Unknown padding parameter: ${r}`);return{padInfo:u,outHeight:c,outWidth:p}}function oG(r,e,t,o,n,s,a,i,l,u,c){let p,m,f,d;if(typeof r=="number"){p={top:r,bottom:r,left:r,right:r,front:r,back:r,type:r===0?"VALID":"NUMBER"};let g=sG([e,t,o,1],i,1,n,r,c);m=g[0],f=g[1],d=g[2]}else if(r==="same"){m=Math.ceil(e/n),f=Math.ceil(t/s),d=Math.ceil(o/a);let h=(m-1)*n+i-e,g=(f-1)*s+l-t,x=(d-1)*a+u-o,b=Math.floor(h/2),_=h-b,w=Math.floor(g/2),v=g-w,$=Math.floor(x/2),A=x-$;p={top:w,bottom:v,left:$,right:A,front:b,back:_,type:"SAME"}}else if(r==="valid")p={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},m=Math.ceil((e-i+1)/n),f=Math.ceil((t-l+1)/s),d=Math.ceil((o-u+1)/a);else throw Error(`Unknown padding parameter: ${r}`);return{padInfo:p,outDepth:m,outHeight:f,outWidth:d}}function vu(r,e){if(!e)return Math.trunc(r);switch(e){case"round":return Math.round(r);case"ceil":return Math.ceil(r);case"floor":return Math.floor(r);default:throw new Error(`Unknown roundingMode ${e}`)}}function Oo(r){let[e,t,o]=lg(r);return e===1&&t===1&&o===1}function yr(r,e){return Oo(r)||Oo(e)}function $N(r){if(r==="NHWC")return"channelsLast";if(r==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${r}`)}function iG(r,e){let o={x:k(r,"x","reshape","string_or_numeric")},n={shape:e};return E.runKernel(gs,o,n)}var L=S({reshape_:iG});function aG(r,e,t,o,n){let s=k(r,"x","avgPool","float32"),a=1;T(yr(t,a),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${t} and dilations '${a}'`);let i=s,l=!1;s.rank===3&&(l=!0,i=L(s,[1,s.shape[0],s.shape[1],s.shape[2]])),T(i.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${i.rank}.`),n!=null&&T(st(o),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${n} but got pad ${o}.`);let u={x:i},c={filterSize:e,strides:t,pad:o,dimRoundingMode:n},p=E.runKernel(Xo,u,c);return p=oe(p,s.dtype),l?L(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var va=S({avgPool_:aG});function lG(r,e,t,o,n,s="NDHWC"){let a=k(r,"x","avgPool3d","float32"),i=a,l=!1;a.rank===4&&(l=!0,i=L(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),T(i.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`),T(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),n!=null&&T(st(o),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${n} but got pad ${o}.`);let u={x:i},c={filterSize:e,strides:t,pad:o,dimRoundingMode:n,dataFormat:s},p=E.runKernel(na,u,c);return p=oe(p,i.dtype),l?L(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Fm=S({avgPool3d_:lG});function uG(r,e=0){T(r.length>=1,()=>"Pass at least one tensor to concat");let t=ya(r,"tensors","concat","string_or_numeric");if(t[0].dtype==="complex64"&&t.forEach(s=>{if(s.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor with dtype ${s.dtype}. `)}),t.length===1)return Ro(t[0]);let o=t,n={axis:e};return E.runKernel(cs,o,n)}var Je=S({concat_:uG});function cG(r){let t={x:k(r,"x","sigmoid")};return E.runKernel(An,t)}var Ur=S({sigmoid_:cG});function pG(r,e,t){let o=k(r,"x","slice","string_or_numeric");if(o.rank===0)throw new Error("Slicing scalar is not possible");let n={x:o},s={begin:e,size:t};return E.runKernel(ys,n,s)}var Fe=S({slice_:pG});function mG(r){let t={x:k(r,"x","tanh")};return E.runKernel(Pn,t)}var Mi=S({tanh_:mG});function fG(r,e,t,o,n,s){let a=k(r,"forgetBias","basicLSTMCell"),i=k(e,"lstmKernel","basicLSTMCell"),l=k(t,"lstmBias","basicLSTMCell"),u=k(o,"data","basicLSTMCell"),c=k(n,"c","basicLSTMCell"),p=k(s,"h","basicLSTMCell"),m=Je([u,p],1),f=We(m,i),d=Q(f,l),h=d.shape[0],g=d.shape[1]/4,x=[h,g],b=Fe(d,[0,0],x),_=Fe(d,[0,g],x),w=Fe(d,[0,g*2],x),v=Fe(d,[0,g*3],x),$=Q(O(Ur(b),Mi(_)),O(c,Ur(Q(a,w)))),A=O(Mi($),Ur(v));return[$,A]}var dG=S({basicLSTMCell_:fG});function hG(r,e,t){let o=k(r,"x","batchToSpaceND"),n=e.reduce((i,l)=>i*l);T(o.rank>=1+e.length,()=>`input rank is ${o.rank} but should be > than blockShape.length ${e.length}`),T(t.length===e.length,()=>`crops.length is ${t.length} but should be equal to blockShape.length ${e.length}`),T(o.shape[0]%n==0,()=>`input tensor batch is ${o.shape[0]} but is not divisible by the product of the elements of blockShape ${e.join(" * ")} === ${n}`);let s={x:o},a={blockShape:e,crops:t};return E.runKernel(sa,s,a)}var Ca=S({batchToSpaceND_:hG});function FN(r){let e;return r.rank===0||r.rank===1?e=L(r,[1,1,1,r.size]):r.rank===2?e=L(r,[1,1,r.shape[0],r.shape[1]]):r.rank===3?e=L(r,[1,r.shape[0],r.shape[1],r.shape[2]]):e=r,e}function gG(r,e,t,o,n,s){s==null&&(s=.001);let a=k(r,"x","batchNorm"),i=k(e,"mean","batchNorm"),l=k(t,"variance","batchNorm"),u;n!=null&&(u=k(n,"scale","batchNorm"));let c;o!=null&&(c=k(o,"offset","batchNorm")),T(i.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),T(c==null||i.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),T(u==null||i.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let m={x:FN(a),scale:u,offset:c,mean:i,variance:l},f={varianceEpsilon:s},d=E.runKernel(ln,m,f);return L(d,a.shape)}var zn=S({batchNorm_:gG});function xG(r,e,t,o,n,s){let a=k(r,"x","batchNorm"),i=k(e,"mean","batchNorm"),l=k(t,"variance","batchNorm"),u;n!=null&&(u=k(n,"scale","batchNorm"));let c;return o!=null&&(c=k(o,"offset","batchNorm")),T(a.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`),T(i.rank===2||i.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`),T(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&T(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),c!=null&&T(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),zn(a,i,l,c,u,s)}var c_=S({batchNorm2d_:xG});function yG(r,e,t,o,n,s){let a=k(r,"x","batchNorm"),i=k(e,"mean","batchNorm"),l=k(t,"variance","batchNorm"),u;n!=null&&(u=k(n,"scale","batchNorm"));let c;return o!=null&&(c=k(o,"offset","batchNorm")),T(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),T(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),T(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&T(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&T(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),zn(a,i,l,c,u,s)}var p_=S({batchNorm3d_:yG});function bG(r,e,t,o,n,s){let a=k(r,"x","batchNorm"),i=k(e,"mean","batchNorm"),l=k(t,"variance","batchNorm"),u;n!=null&&(u=k(n,"scale","batchNorm"));let c;return o!=null&&(c=k(o,"offset","batchNorm")),T(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),T(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),T(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&T(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&T(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),zn(a,i,l,c,u,s)}var m_=S({batchNorm4d_:bG});function _G(r,e,t){let o=k(r,"x","bincount"),n=k(e,"weights","bincount");T(o.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${o.dtype}`),T(t>=0,()=>`size must be non-negative, but got ${t}.`),T(n.size===o.size||n.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`);let s={x:o,weights:n},a={size:t};return E.runKernel(jl,s,a)}var f_=S({bincount_:_G});function wG(r,e){let t=k(r,"broadcastTo","x"),o=t.shape;if(e.some(u=>!(u>0)||u%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${e}].`);if(e.lengtht.rank){let u=t.shape.slice();for(;u.length=0;u--)if(n[u]===e[u])s[u]=1;else if(t.shape[u]!==1)throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${e}].`);if(s.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return Ro(t);let i={x:t},l={reps:s};return E.runKernel(yo,i,l)}var il=S({broadcastTo_:wG});function kG(r){let t={x:k(r,"x","ceil")};return E.runKernel(Zo,t)}var Om=S({ceil_:kG});function vG(r,e,t){let o=k(r,"x","clipByValue");T(e<=t,()=>`Error in clip: min (${e}) must be less than or equal to max (${t}).`);let n={x:o},s={clipValueMin:e,clipValueMax:t};return E.runKernel(Ao,n,s)}var nr=S({clipByValue_:vG});function CG(r){return Je(r,0)}var d_=S({concat1d_:CG});function IG(r,e){return Je(r,e)}var h_=S({concat2d_:IG});function NG(r,e){return Je(r,e)}var g_=S({concat3d_:NG});function SG(r,e){return Je(r,e)}var x_=S({concat4d_:SG});function TG(r,e,t,o,n="NHWC",s=[1,1],a){let i=k(r,"x","conv2d"),l=k(e,"filter","conv2d"),u=i,c=!1;i.rank===3&&(c=!0,u=L(i,[1,i.shape[0],i.shape[1],i.shape[2]])),T(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),T(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),a!=null&&T(st(o),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${o}.`);let p=n==="NHWC"?u.shape[3]:u.shape[1];T(p===l.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${l.shape[2]}.`),T(yr(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`);let m={x:u,filter:l},f={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},d=E.runKernel(Jo,m,f);return c?L(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var jr=S({conv2d_:TG});function EG(r,e,t,o,n="NWC",s=1,a){let i=k(r,"x","conv1d"),l=k(e,"filter","conv1d"),u=i,c=!1;i.rank===2&&(c=!0,u=L(i,[1,i.shape[0],i.shape[1]])),T(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),T(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),a!=null&&T(st(o),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${o}.`),T(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),T(yr(t,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${t} and dilation '${s}'`),T(n==="NWC",()=>`Error in conv1d: got dataFormat of ${n} but only NWC is currently supported.`);let p=L(l,[1,l.shape[0],l.shape[1],l.shape[2]]),m=L(u,[u.shape[0],1,u.shape[1],u.shape[2]]),g=jr(m,p,[1,t],o,"NHWC",[1,s],a);return c?L(g,[g.shape[2],g.shape[3]]):L(g,[g.shape[0],g.shape[2],g.shape[3]])}var Cu=S({conv1d_:EG});function AG(r,e,t,o,n,s="NHWC",a){T(r.length===e.rank,()=>`Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);let i=r,l=e,u=!1;e.rank===3&&(u=!0,l=L(e,[1,e.shape[0],e.shape[1],e.shape[2]]),i=[1,r[0],r[1],r[2]]),T(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),T(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),T(t.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${t.rank}`);let c=s==="NHWC"?i[3]:i[1],p=s==="NHWC"?l.shape[3]:l.shape[1];T(c===t.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${t.shape[2]}.`),T(p===t.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${t.shape[3]}.`),a!=null&&T(st(n),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${a} but got pad ${n}.`);let m={dy:l,filter:t},f={strides:o,pad:n,dataFormat:s,dimRoundingMode:a,inputShape:i},d=E.runKernel(Qo,m,f);return u?L(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Kc=S({conv2DBackpropInput_:AG});function DG(r,e,t,o,n,s){let a=k(r,"x","conv2dTranspose"),i=k(e,"filter","conv2dTranspose");return Kc(t,a,i,o,n,"NHWC",s)}var Iu=S({conv2dTranspose_:DG});function $G(r,e,t,o,n="NDHWC",s=[1,1,1]){let a=k(r,"x","conv3d"),i=k(e,"filter","conv3d"),l=a,u=!1;a.rank===4&&(u=!0,l=L(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),T(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),T(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),T(l.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${i.shape[3]}.`),T(yr(t,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`),T(n==="NDHWC",()=>`Error in conv3d: got dataFormat of ${n} but only NDHWC is currently supported.`);let c={x:l,filter:i},p={strides:t,pad:o,dataFormat:n,dilations:s},m=E.runKernel(aa,c,p);return u?L(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var Pm=S({conv3d_:$G});function RG(r,e,t,o,n){T(r.length===e.rank,()=>`Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);let s=r,a=e,i=!1;e.rank===4&&(i=!0,a=L(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]),s=[1,r[0],r[1],r[2],r[3]]);let l=s[4],u=a.shape[4];T(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),T(a.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${a.rank}`),T(t.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${t.rank}`),T(l===t.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${t.shape[3]}.`),T(u===t.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${t.shape[4]}.`);let c={dy:a,filter:t},p={pad:n,strides:o,inputShape:s},m=E.runKernel(Xl,c,p);return i?L(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var ug=S({conv3DBackpropInput_:RG});function FG(r,e,t,o,n){let s=k(r,"x","conv3dTranspose"),a=k(e,"filter","conv3dTranspose");return ug(t,s,a,o,n)}var OG=S({conv3dTranspose_:FG});function PG(r){let t={x:k(r,"x","cos")};return E.runKernel(en,t)}var Ia=S({cos_:PG});function MG(r){let t={x:k(r,"x","cosh")};return E.runKernel(ti,t)}var Nu=S({cosh_:MG});function LG(r,e=0,t=!1,o=!1){let s={x:k(r,"x","cumsum")},a={axis:e,exclusive:t,reverse:o};return E.runKernel(tn,s,a)}var Su=S({cumsum_:LG});function zG(r,e,t,o=!1){let n=k(r,"x","denseBincount"),s=k(e,"weights","denseBincount");T(n.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${n.dtype}`),T(n.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`),T(t>=0,()=>`size must be non-negative, but got ${t}.`),T(s.size===n.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${n.shape}, weights shape: ${s.shape}.`);let a={x:n,weights:s},i={size:t,binaryOutput:o};return E.runKernel(Yl,a,i)}var y_=S({denseBincount_:zG});function BG(r,e,t="NHWC"){let o=k(r,"x","depthToSpace"),n=t==="NHWC"?o.shape[1]:o.shape[2],s=t==="NHWC"?o.shape[2]:o.shape[3],a=t==="NHWC"?o.shape[3]:o.shape[1];T(n*e>=0,()=>`Negative dimension size caused by overflow when multiplying ${n} and ${e} for depthToSpace with input shape ${o.shape}`),T(s*e>=0,()=>`Negative dimension size caused by overflow when multiplying ${s} and ${e} for depthToSpace with input shape ${o.shape}`),T(a%(e*e)==0,()=>`Dimension size must be evenly divisible by ${e*e} but is ${a} for depthToSpace with input shape ${o.shape}`);let i={x:o},l={blockSize:e,dataFormat:t};return E.runKernel(oi,i,l)}var Mm=S({depthToSpace_:BG});function VG(r,e,t,o,n="NHWC",s=[1,1],a){let i=k(r,"x","depthwiseConv2d"),l=k(e,"filter","depthwiseConv2d"),u=i,c=!1;i.rank===3&&(c=!0,u=L(i,[1,i.shape[0],i.shape[1],i.shape[2]])),T(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),T(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),T(u.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),a!=null&&T(st(o),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${o}.`);let p={x:u,filter:l},m={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},f=E.runKernel(rn,p,m);return c?L(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Ns=S({depthwiseConv2d_:VG});function GG(r){let t={x:k(r,"x","diag")};return E.runKernel(Ql,t)}var WG=S({diag_:GG});function UG(r,e,t,o,n=[1,1],s="NHWC"){let a=k(r,"x","dilation2d"),i=k(e,"filter","dilation2d");T(a.rank===3||a.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`),T(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),T(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=a,u=!1;a.rank===3&&(l=L(a,[1,a.shape[0],a.shape[1],a.shape[2]]),u=!0);let c={x:l,filter:i},p={strides:t,pad:o,dilations:n},m=E.runKernel(la,c,p);return u?L(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Lm=S({dilation2d_:UG});function jG(r,e){let t=r.length,o=[];for(let n=0;n1&&a===1&&o.unshift(s)}return o}function Ct(r,e){let t=[];for(let o=0;o1)&&t.unshift(s)}return t}function ze(r,e){let t=[],o=Math.max(r.length,e.length);for(let n=0;n"The first dimension of `a` must match the size of `condition`."),s.rank!==1&&Nt(s.shape,l.shape,"Error in where: ");let u={condition:s,t:i,e:l};return E.runKernel(xs,u)}var $t=S({where_:qG});function KG(r){let t={x:k(r,"x","zerosLike")};return E.runKernel(ws,t)}var Ie=S({zerosLike_:KG});function XG(r,e){let t=k(r,"a","div"),o=k(e,"b","div");[t,o]=Ge(t,o);let n=de(t,o),s=Ie(n),a=_o(o,s);return $t(a,s,n)}var zm=S({divNoNan_:XG});function YG(r,e){let t=k(r,"t1","dot"),o=k(e,"t2","dot");T((t.rank===1||t.rank===2)&&(o.rank===1||o.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${t.rank} and ${o.rank}.`);let n=t.rank===1?t.size:t.shape[1],s=o.rank===1?o.size:o.shape[0];if(T(n===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${n} and ${s}.`),t.rank===1&&o.rank===1){let a=L(t,[1,-1]),i=L(o,[-1,1]),l=We(a,i);return L(l,[])}else if(t.rank===1&&o.rank===2){let a=L(t,[1,-1]),i=L(o,[o.shape[0],o.shape[1]]),l=We(a,i);return L(l,[l.size])}else if(t.rank===2&&o.rank===1){let a=L(o,[-1,1]),i=We(t,a);return L(i,[i.size])}else{let a=L(o,[o.shape[0],o.shape[1]]);return We(t,a)}}var b_=S({dot_:YG});function ZG(r){let t={x:k(r,"x","elu")};return E.runKernel(ni,t)}var Ss=S({elu_:ZG});function JG(r){let e=k(r,"x","erf");T(e.dtype==="int32"||e.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),e.dtype==="int32"&&(e=oe(e,"float32"));let t={x:e};return E.runKernel(si,t)}var Bm=S({erf_:JG});function QG(r){let t={x:k(r,"x","exp")};return E.runKernel(nn,t)}var Xt=S({exp_:QG});function eW(r,e=0){let t=k(r,"x","expandDims","string_or_numeric");T(e<=t.rank,()=>"Axis must be <= rank of the tensor");let o={input:t},n={dim:e};return E.runKernel(ps,o,n)}var sr=S({expandDims_:eW});function tW(r){let t={x:k(r,"x","expm1")};return E.runKernel(ai,t)}var Vm=S({expm1_:tW});function rW(r,e){let t=k(r,"x","tile","string_or_numeric");T(t.rank===e.length,()=>`Error in transpose: rank of input ${t.rank} must match length of reps ${e}.`);let o={x:t},n={reps:e};return E.runKernel(yo,o,n)}var Po=S({tile_:rW});function oW(r,e,t,o="float32"){e==null&&(e=r);let n=Ce([r,e],o),s=r<=e?r:e;for(let i=0;i`Error in localResponseNormalization: x must be rank 3 or 4 but got rank ${s.rank}.`),T(st(e),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${e}.`);let a=s,i=!1;s.rank===3&&(i=!0,a=L(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let l={x:a},u={depthRadius:e,bias:t,alpha:o,beta:n},c=E.runKernel(ca,l,u);return i?L(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var Gm=S({localResponseNormalization_:hW});function gW(r){let t={x:k(r,"x","log")};return E.runKernel(pn,t)}var ir=S({log_:gW});function xW(r){let t={x:k(r,"x","log1p")};return E.runKernel(gi,t)}var Au=S({log1p_:xW});function yW(r){return T(Hs(r),()=>"The f passed in grad(f) must be a function"),(e,t)=>{let o=k(e,"x","tf.grad","string_or_numeric"),n=t!=null?k(t,"dy","tf.grad"):null;return E.tidy(()=>{let{value:s,grads:a}=E.gradients(()=>r(o),[o],n);return n!=null&&Nt(s.shape,n.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),cg(a),a[0]})}}function bW(r){return T(Hs(r),()=>"The f passed in grads(f) must be a function"),(e,t)=>{T(Array.isArray(e),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let o=ya(e,"args","tf.grads","string_or_numeric"),n=t!=null?k(t,"dy","tf.grads"):null;return E.tidy(()=>{let{value:s,grads:a}=E.gradients(()=>r(...o),o,n);return n!=null&&Nt(s.shape,n.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),cg(a),a})}}function _W(r){return T(Hs(r),()=>"The f passed in valueAndGrad(f) must be a function"),(e,t)=>{T(e instanceof Ve,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),T(t==null||t instanceof Ve,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:o,value:n}=E.gradients(()=>r(e),[e],t);return cg(o),{grad:o[0],value:n}}}function wW(r){return T(Hs(r),()=>"The f passed in valueAndGrads(f) must be a function"),(e,t)=>{T(Array.isArray(e)&&e.every(n=>n instanceof Ve),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),T(t==null||t instanceof Ve,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let o=E.gradients(()=>r(...e),e,t);return t!=null&&Nt(o.value.shape,t.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),cg(o.grads),o}}function pg(r,e){T(Hs(r),()=>"The f passed in variableGrads(f) must be a function"),T(e==null||Array.isArray(e)&&e.every(u=>u instanceof tl),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let t=e!=null;if(!t){e=[];for(let u in E.registeredVariables)e.push(E.registeredVariables[u])}let o=t?e.filter(u=>!u.trainable):null,n=e.length;e=e.filter(u=>u.trainable),T(e.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${n} variables is trainable.`);let s=!0,{value:a,grads:i}=E.gradients(r,e,null,s);T(i.some(u=>u!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),T(a.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${a.rank} tensor`);let l={};return e.forEach((u,c)=>{i[c]!=null&&(l[u.name]=i[c])}),o!=null&&o.forEach(u=>l[u.name]=null),{value:a,grads:l}}function Hr(r){return E.customGrad(r)}function cg(r){if(r.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 kW(r){let t={x:k(r,"x","neg")};return E.runKernel(fs,t)}var je=S({neg_:kW});function vW(r){let t={x:k(r,"x","softplus")};return E.runKernel(Ei,t)}var Es=S({softplus_:vW});function CW(r){let e=k(r,"x","logSigmoid");return Hr(o=>({value:je(Es(je(o))),gradFunc:a=>O(a,Ur(je(o)))}))(e)}var C_=S({logSigmoid_:CW});function IW(r,e=null,t=!1){let n={x:k(r,"x","max")},s={reductionIndices:e,keepDims:t};return E.runKernel(mn,n,s)}var ar=S({max_:IW});function NW(r,e){let t=k(r,"a","sub"),o=k(e,"b","sub");[t,o]=Ge(t,o);let n={a:t,b:o};return E.runKernel(On,n)}var ue=S({sub_:NW});function SW(r,e=null,t=!1){let o=k(r,"x","sum");o.dtype==="bool"&&(o=oe(o,"int32"));let n={x:o},s={axis:e,keepDims:t};return E.runKernel($n,n,s)}var ye=S({sum_:SW});function TW(r,e=-1){let t=k(r,"logits","logSoftmax");if(e===-1&&(e=t.rank-1),e!==t.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${t.rank} and axis was ${e}`);return Hr((n,s)=>{let a=!0,i=ar(n,e,!0),l=ue(n,i),u=ue(oe(l,"float32"),ir(ye(Xt(l),e,a)));return s([u]),{value:u,gradFunc:(p,m)=>{let[f]=m,d=!0,h=Xt(f);return ue(p,O(ye(p,e,d),h))}}})(t)}var Du=S({logSoftmax_:TW});function I_(r,e){for(let t=0;tr[s]);return[t,n]}function Vn(r,e){let t=e.map(o=>1);return ON(r,t,e)}function EW(r,e,t){T(I_(e,t),()=>`${r} supports only inner-most axes for now. Got axes ${e} and rank-${t} input.`)}function S_(r,e){if(I_(r,e))return null;let t=[];for(let o=0;ot.push(o)),t}function Wm(r){return r.map((e,t)=>[t,e]).sort((e,t)=>e[1]-t[1]).map(e=>e[0])}function AW(r,e){let t=[];for(let o=e-r;o`Error in maxPool: input must be rank 4 but got rank ${i.rank}.`),T(yr(t,a),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${t} and dilations '${a}'`),n!=null&&T(st(o),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${n} but got pad ${o}.`);let u={x:i},c={filterSize:e,strides:t,pad:o,dimRoundingMode:n},p=E.runKernel(dn,u,c);return l?L(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var Ea=S({maxPool_:PW});function MW(r,e=[1,1,1],t,o,n,s="NDHWC"){let a=k(r,"x","maxPool3d"),i=a,l=!1;a.rank===4&&(l=!0,i=L(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),T(i.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${i.rank}.`),T(s==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),n!=null&&T(st(o),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${n} but got pad ${o}.`);let u={x:i},c={filterSize:e,strides:t,pad:o,dimRoundingMode:n,dataFormat:s},p=E.runKernel(pa,u,c);return l?L(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var jm=S({maxPool3d_:MW});function LW(r,e,t,o,n=!1){let a={x:k(r,"x","maxPoolWithArgmax")},i={filterSize:e,strides:t,pad:o,includeBatchInIndex:n},l=E.runKernel(lu,a,i);return{result:l[0],indexes:l[1]}}var E_=S({maxPoolWithArgmax_:LW});function zW(r,e){let t=k(r,"a","maximum"),o=k(e,"b","maximum");[t,o]=Ge(t,o),t.dtype==="bool"&&(t=oe(t,"int32"),o=oe(o,"int32")),ze(t.shape,o.shape);let n={a:t,b:o};return E.runKernel(fn,n)}var qr=S({maximum_:zW});function BW(r,e=null,t=!1){let n={x:k(r,"x","mean")},s={axis:e,keepDims:t};return E.runKernel(hn,n,s)}var ht=S({mean_:BW});function VW(r,e=null,t=!1){let n={x:k(r,"x","min")},s={axis:e,keepDims:t};return E.runKernel(gn,n,s)}var Li=S({min_:VW});function GW(r,e){let t=k(r,"a","minimum"),o=k(e,"b","minimum");[t,o]=Ge(t,o),t.dtype==="bool"&&(t=oe(t,"int32"),o=oe(o,"int32")),ze(t.shape,o.shape);let n={a:t,b:o};return E.runKernel(xn,n)}var As=S({minimum_:GW});function WW(r,e,t){T(t==="reflect"||t==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${t}.`);let o=k(r,"x","mirrorPad");if(o.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");T(e.length===o.rank,()=>`Padding doesn't match input. Must be ${o.rank}. Got ${e.length}.`);let n=t==="reflect"?1:0;for(let i=0;i"Invalid number of paddings. Must be length of 2 each."),T(e[i][0]>=0&&e[i][0]<=o.shape[i]-n&&e[i][1]>=0&&e[i][1]<=o.shape[i]-n,()=>`Padding in dimension ${i} cannot be greater than or equal to ${o.shape[i]-n} or less than 0 for input of shape ${o.shape}`);let s={paddings:e,mode:t},a={x:o};return E.runKernel(ma,a,s)}var Hm=S({mirrorPad_:WW});function UW(r,e){let t=k(r,"a","mod"),o=k(e,"b","mod");[t,o]=Ge(t,o);let n={a:t,b:o};return E.runKernel(yi,n)}var qm=S({mod_:UW});function jW(r){let e=k(r,"x","square"),t={};return E.runKernel("Square",{x:e},t)}var Pe=S({square_:jW});function HW(r,e=null,t=!1){r=k(r,"x","moments");let o=Jt(e,r.shape),n=ht(r,o,t),s=n.shape;t||(s=Vn(n.shape,o));let a=Pe(ue(oe(r,"float32"),L(n,s))),i=ht(a,o,t);return{mean:n,variance:i}}var Yc=S({moments_:HW});function qW(r,e,t,o){let n=k(e,"data","multiRNNCell"),s=ya(t,"c","multiRNNCell"),a=ya(o,"h","multiRNNCell"),i=n,l=[];for(let p=0;p2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${a}`);t=t||Math.random();let l={logits:a===1?L(n,[1,-1]):n},u={numSamples:e,seed:t,normalized:o},c=E.runKernel(uu,l,u);return a===1?L(c,[c.size]):c}var A_=S({multinomial_:XW});function YW(r,e){let t=k(r,"a","notEqual"),o=k(e,"b","notEqual");[t,o]=Ge(t,o),ze(t.shape,o.shape);let n={a:t,b:o};return E.runKernel(bi,n)}var Gn=S({notEqual_:YW});function gt(r,e="float32"){if(e==="complex64"){let o=gt(r,"float32"),n=gt(r,"float32");return bo(o,n)}let t=$c(ft(r),e);return E.makeTensor(t,r,e)}function Cr(r,e="float32"){if(e==="complex64"){let o=Cr(r,"float32"),n=gt(r,"float32");return bo(o,n)}let t=hm(ft(r),e);return E.makeTensor(t,r,e)}function ZW(r){let t={x:k(r,"x","onesLike")};return E.runKernel(ds,t)}var er=S({onesLike_:ZW});function JW(r,e){let t=k(r,"v1","outerProduct"),o=k(e,"v2","outerProduct");T(t.rank===1&&o.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${t.rank} and ${o.rank}.`);let n=L(t,[-1,1]),s=L(o,[1,-1]);return We(n,s)}var QW=S({outerProduct_:JW});function e4(r,e,t=0){let o=k(r,"x","pad");if(o.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let n={paddings:e,constantValue:t},s={x:o};return E.runKernel(_n,s,n)}var $r=S({pad_:e4});function t4(r,e,t=0){return T(e.length===2,()=>"Invalid number of paddings. Must be length of 2."),$r(r,[e],t)}var r4=S({pad1d_:t4});function o4(r,e,t=0){return T(e.length===2&&e[0].length===2&&e[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),$r(r,e,t)}var n4=S({pad2d_:o4});function s4(r,e,t=0){return T(e.length===3&&e[0].length===2&&e[1].length===2&&e[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),$r(r,e,t)}var i4=S({pad3d_:s4});function a4(r,e,t=0){return T(e.length===4&&e[0].length===2&&e[1].length===2&&e[2].length===2&&e[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),$r(r,e,t)}var l4=S({pad4d_:a4});function u4(r,e,t){let o=k(r,"x","spaceToBatchND");T(o.rank>=1+e.length,()=>`input rank ${o.rank} should be > than [blockShape] ${e.length}`),T(t.length===e.length,()=>`paddings.shape[0] ${t.length} must be equal to [blockShape] ${e.length}`),T(o.shape.reduce((a,i,l)=>l>0&&l<=e.length?a&&(i+t[l-1][0]+t[l-1][1])%e[l-1]==0:a,!0),()=>`input spatial dimensions ${o.shape.slice(1)} with paddings ${t.toString()} must be divisible by blockShapes ${e.toString()}`);let n={x:o},s={blockShape:e,paddings:t};return E.runKernel(ha,n,s)}var Aa=S({spaceToBatchND_:u4});function m4(r,e,t,o,n,s){n==null&&(n=[1,1]),s==null&&(s=1),o===0&&(o="valid");let a=k(r,"x","maxPool"),i=a,l=!1;a.rank===3&&(l=!0,i=L(a,[1,a.shape[0],a.shape[1],a.shape[2]])),T(yr(s,n),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${n}'`);let u=a_(i.shape,e,s,n,o),c=[u.dilationHeight,u.dilationWidth],p;o==="same"?p=p4([u.filterHeight,u.filterWidth],c):p=[[0,0],[0,0]];let m=c[0]===1&&c[1]===1,[f,d]=c4([u.inHeight,u.inWidth],c,p),h=m?o:"valid",g=m?i:Aa(i,c,f),b=(t==="avg"?()=>va(g,e,s,h):()=>Ea(g,e,s,h))(),_=m?b:Ca(b,c,d);return l?L(_,[_.shape[1],_.shape[2],_.shape[3]]):_}function c4(r,e,t){let o=t.map(c=>c[0]),n=t.map(c=>c[1]),s=r.concat(o,n),a=e.map((c,p)=>(c-s[p]%c)%c),i=n.map((c,p)=>c+a[p]),l=e.map((c,p)=>[o[p],i[p]]),u=e.map((c,p)=>[0,a[p]]);return[l,u]}function p4(r,e){let o=r.map((a,i)=>a+(a-1)*(e[i]-1)).map(a=>a-1),n=o.map(a=>Math.floor(a/2)),s=o.map((a,i)=>a-n[i]);return o.map((a,i)=>[n[i],s[i]])}var D_=S({pool_:m4});function f4(r,e){let t=k(r,"base","pow"),o=k(e,"exp","pow");[t,o]=Ge(t,o);let n={a:t,b:o};return E.runKernel(wn,n)}var Rr=S({pow_:f4});function d4(r,e){let t=k(r,"x","prelu"),o=k(e,"alpha","prelu"),n={x:t,alpha:o};return E.runKernel(kn,n)}var Da=S({prelu_:d4});function h4(r,e=null,t=!1){let o=k(r,"x","prod");o.dtype==="bool"&&(o=oe(o,"int32"));let n={x:o},s={axis:e,keepDims:t};return E.runKernel(vi,n,s)}var Ru=S({prod_:h4});function g4(r,e,t){let o=ft(r),n=null;if(t==null||t==="float32")n=new Float32Array(o);else if(t==="int32")n=new Int32Array(o);else if(t==="bool")n=new Uint8Array(o);else throw new Error(`Unknown data type ${t}`);for(let s=0;s=1||a===0);let i=Math.sqrt(-2*Math.log(a)/a);e=this.mean+this.stdDev*n*i,t=this.mean+this.stdDev*s*i,(!this.truncated||this.isValidTruncated(e))&&(o=!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}},L_=class{constructor(e,t,o,n){this.alpha=e,this.beta=1/t,this.dtype=o;let s=n||Math.random();this.randu=fg.alea(s.toString()),this.randn=new Zc(0,1,o,!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,o,n,s,a;for(;;){do n=this.randn.nextValue(),a=1+this.c*n;while(a<=0);if(a*=a*a,e=n*n,t=1-.331*e*e,o=.5*e+this.d*(1-a+Math.log(a)),s=this.randu(),sthis.dtype==null||this.dtype==="float32",this.min=e,this.range=t-e,this.dtype=o,n==null&&(n=Math.random()),typeof n=="number"&&(n=n.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);this.random=fg.alea(n)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function C4(r,e,t=1,o="float32",n){if(t==null&&(t=1),o==null&&(o="float32"),o!=="float32"&&o!=="int32")throw new Error(`Unsupported data type ${o}`);let s=new L_(e,t,o,n),a=Ce(r,o);for(let i=0;i`Error in reverse1D: x must be rank 1 but got rank ${e.rank}.`),qt(e,0)}var F4=S({reverse1d_:R4});function O4(r,e){let t=k(r,"x","reverse");return T(t.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${t.rank}.`),qt(t,e)}var P4=S({reverse2d_:O4});function M4(r,e){let t=k(r,"x","reverse");return T(t.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${t.rank}.`),qt(t,e)}var L4=S({reverse3d_:M4});function z4(r,e){let t=k(r,"x","reverse");return T(t.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${t.rank}.`),qt(t,e)}var B4=S({reverse4d_:z4});function V4(r){let t={x:k(r,"x","round")};return E.runKernel(Sn,t)}var Ym=S({round_:V4});function G4(r){let t={x:k(r,"x","rsqrt")};return E.runKernel(Tn,t)}var Pu=S({rsqrt_:G4});function le(r,e){if((rr(r)&&e!=="string"||Array.isArray(r))&&e!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(e==="string"&&rr(r)&&!(r instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return Gr(r,[],[],e)}function W4(r){let t={x:k(r,"x","selu")};return E.runKernel(Ni,t)}var Mu=S({selu_:W4});function U4(r,e,t,o,n,s=[1,1],a="NHWC"){let i=k(r,"x","separableConv2d"),l=k(e,"depthwiseFilter","separableConv2d"),u=k(t,"pointwiseFilter","separableConv2d"),c=i,p=!1;if(i.rank===3&&(p=!0,c=L(i,[1,i.shape[0],i.shape[1],i.shape[2]])),a==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");T(c.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`),T(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),T(u.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),T(u.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${u.shape[0]}.`),T(u.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${u.shape[1]}.`);let m=l.shape[2],f=l.shape[3];T(u.shape[2]===m*f,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${m*f}, but got ${u.shape[2]}.`);let d=Ns(c,l,o,n,a,s),g=jr(d,u,1,"valid",a);return p?L(g,[g.shape[1],g.shape[2],g.shape[3]]):g}var Zm=S({separableConv2d_:U4});async function j4(r,e){let t=k(r,"x","setdiff1d"),o=k(e,"y","setdiff1d");T(t.dtype===o.dtype,()=>`x and y should have the same dtype, but got x (${t.dtype}) and y (${o.dtype}).`),T(t.rank===1,()=>`x should be 1D tensor, but got x (${t.shape}).`),T(o.rank===1,()=>`y should be 1D tensor, but got y (${o.shape}).`);let n=await t.data(),s=await o.data(),a=new Set(s),i=0;for(let c=0;c`slice1d expects a rank-1 tensor, but got a rank-${o.rank} tensor`),Fe(o,[e],[t])}var Qm=S({slice1d_:X4});function Y4(r,e,t){let o=k(r,"x","slice2d");return T(o.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${o.rank} tensor`),Fe(o,e,t)}var hg=S({slice2d_:Y4});function Z4(r,e,t){let o=k(r,"x","slice3d");return T(o.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${o.rank} tensor`),Fe(o,e,t)}var ef=S({slice3d_:Z4});function J4(r,e,t){let o=k(r,"x","slice4d");return T(o.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${o.rank} tensor`),Fe(o,e,t)}var Qc=S({slice4d_:J4});function Q4(r,e=-1){let t=k(r,"logits","softmax","float32");if(e===-1&&(e=t.rank-1),e!==t.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${t.rank} and dim was ${e}`);let o={logits:t},n={dim:e};return E.runKernel(Rn,o,n)}var $a=S({softmax_:Q4});function eU(r){T(r.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${r.dtype}.`);let e={input:r};return E.runKernel(tu,e)}var Ra=S({fft_:eU});function tU(r){T(r.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${r.dtype}.`);let e={input:r};return E.runKernel(ru,e)}var zi=S({ifft_:tU});function rU(r){let e=r.shape[r.shape.length-1],t=r.size/e,o;if(e<=2){let n=L(r,[t,e]);o=zi(n)}else{let n=[t,2*(e-1)],s=L(al(r),[t,e]),a=L(Tu(r),[t,e]),i=qt(Fe(s,[0,1],[t,e-2]),1),l=O(qt(Fe(a,[0,1],[t,e-2]),1),le(-1)),u=Je([s,i],1),c=Je([a,l],1),p=L(bo(u,c),[n[0],n[1]]);o=zi(p)}if(o=al(o),r.rank===3&&r.shape[0]!==0){let n=o,s=r.shape[0];o=L(o,[s,o.shape[0]/s,o.shape[1]]),n.dispose()}return o}var Bu=S({irfft_:rU});function oU(r,e,t=0){let n={x:k(r,"x","split")},s={numOrSizeSplits:e,axis:t};return E.runKernel(bs,n,s)}var lr=S({split_:oU});function nU(r,e){T(r.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${r.dtype}`);let t=r.shape[r.shape.length-1],o=r.size/t,n;if(e!=null&&e0),h=r.shape.map(g=>g);h[r.shape.length-1]=e,n=Fe(r,d,h),t=e}else if(e!=null&&e>t){let d=r.shape.map(h=>h);d[r.shape.length-1]=e-t,n=Je([r,gt(d)],r.shape.length-1),t=e}else n=r;let s=Ie(n),a=L(bo(n,s),[o,t]),i=Ra(a),l=Math.floor(t/2)+1,u=al(i),c=Tu(i),p=lr(u,[l,t-l],u.shape.length-1),m=lr(c,[l,t-l],c.shape.length-1),f=n.shape.slice();return f[n.shape.length-1]=l,L(bo(p[0],m[0]),f)}var Fa=S({rfft_:nU});function sU(r){let t={x:k(r,"x","sqrt")};return E.runKernel(Dn,t)}var xt=S({sqrt_:sU});function iU(r,e){let t=k(r,"a","squaredDifference"),o=k(e,"b","squaredDifference");[t,o]=Ge(t,o),ze(t.shape,o.shape);let n={a:t,b:o},s={};return E.runKernel(Fn,n,s)}var Vu=S({squaredDifference_:iU});function aU(r,e){let t=k(r,"x","squeeze");return L(t,hb(t.shape,e).newShape)}var wo=S({squeeze_:aU});function lU(r,e=0){let t=ya(r,"tensors","stack","string_or_numeric");T(t.length>=1,()=>"Pass at least one tensor to tf.stack"),t.length>0&&T(e<=t[0].rank,()=>"Axis must be <= rank of the tensor");let o=t,n={axis:e};return E.runKernel(hs,o,n)}var Bt=S({stack_:lU});function uU(r,e=0){let o={x:k(r,"x","step")},n={alpha:e};return E.runKernel($o,o,n)}var $s=S({step_:uU});function cU(r,e,t,o,n=0,s=0,a=0,i=0,l=0){let c={x:k(r,"x","stridedSlice")},p={begin:e,end:t,strides:o,beginMask:n,endMask:s,ellipsisMask:a,newAxisMask:i,shrinkAxisMask:l};return E.runKernel(Ai,c,p)}var tf=S({stridedSlice_:cU});function pU(r){let t={x:k(r,"x","tan")};return E.runKernel(Di,t)}var rf=S({tan_:pU});function Vt(r,e){To(r);let t=kr(r,e);if(t.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return Gr(r,null,t,e)}function Bi(r,e,t){if(To(r),e!=null&&e.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let o=kr(r,t);if(o.length!==2&&o.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(o.length===1&&e==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return Gr(r,e,o,t)}function mU(r,e,t){if(To(r),e!=null&&e.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let o=kr(r,t);if(o.length!==4&&o.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(o.length===1&&e==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return Gr(r,e,o,t)}function fU(r,e,t){if(To(r),e!=null&&e.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let o=kr(r,t);if(o.length!==5&&o.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(o.length===1&&e==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return Gr(r,e,o,t)}function dU(r,e,t){if(To(r),e!=null&&e.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let o=kr(r,t);if(o.length!==6&&o.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(o.length===1&&e==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return e=e||o,Gr(r,e,o,t)}function hU(r,e=1,t=!0){let o=k(r,"x","topk");if(o.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let n=o.shape[o.shape.length-1];if(e>n)throw new Error(`'k' passed to topk() must be <= the last dimension (${n}) but got ${e}`);let s={x:o},a={k:e,sorted:t},[i,l]=E.runKernel($i,s,a);return{values:i,indices:l}}var of=S({topk_:hU});function gU(r,e=0,t=1,o,n){if(o!=null&&o==="bool")throw new Error("Unsupported data type $ { dtype }");let s=new Zc(e,t,o,!0,n),a=Ce(r,o);for(let i=0;i0,()=>"The input tensor must be at least 1D");let o={x:t},n={axis:e},[s,a]=E.runKernel(du,o,n);return{values:s,indices:a}}var ep=S({unique_:xU});function yU(r,e,t){let o=k(r,"x","unsortedSegmentSum"),n=k(e,"segmentIds","unsortedSegmentSum","int32");T(st(t),()=>"numSegments must be of dtype int");let s={x:o,segmentIds:n},a={numSegments:t};return E.runKernel(xa,s,a)}var nf=S({unsortedSegmentSum_:yU});function bU(r,e=0){let t=k(r,"x","unstack","string_or_numeric");T(e>=-t.shape.length&&e`Axis = ${e} is not in [-${t.shape.length}, ${t.shape.length})`);let o={value:t},n={axis:e};return E.runKernel(_s,o,n)}var ur=S({unstack_:bU});function V_(r,e=!0,t,o){return E.makeVariable(r,e,t,o)}function G_(r,e){let t=[];for(let s=0;s0,()=>"mask cannot be scalar"),Nt(i.slice(s,s+a),n.shape,"mask's shape must match the first K dimensions of tensor's shape,");let l=1;for(let h=s;h"Shape mismatch in v and x");let l=le(1),u=ue(l,i),c=O(ue(a,s),u);if(n){T(o!=null,()=>"When using zeroDebias: true, step is required.");let p=k(o,"step","movingAverage");c=de(c,ue(l,Rr(i,p)))}return Q(s,c)}var IU=S({movingAverage_:CU});function NU(r,e,t){let o=k(r,"indices","scatterND","int32"),n=k(e,"updates","scatterND");ng(n,o,t);let s={indices:o,updates:n},a={shape:t};return E.runKernel(Ii,s,a)}var W_=S({scatterND_:NU});function JN(r,e,t,o){if(r.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${r.dtype}.`);if(r.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${r.shape}.`);let n=r.rank>0?r.shape[0]:1,s=r.rank>1?r.shape[1]:1;if(t.length!==s)throw new Error(`outputShape has incorrect number of elements:, ${t.length}, should be: ${s}.`);let a=e.size;if(!(e.rank===0||e.rank===1&&a===n))throw new Error(`sparseValues has incorrect shape ${e.shape}, should be [] or [${n}]`);if(e.dtype!==o.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function SU(r,e,t,o=0){let n=k(r,"sparseIndices","sparseToDense","int32"),s=k(e,"sparseValues","sparseToDense"),a=k(o,"defaultValue","sparseToDense",s.dtype);JN(n,s,t,a);let i={sparseIndices:n,sparseValues:s,defaultValue:a},l={outputShape:t};return E.runKernel(fu,i,l)}var af=S({sparseToDense_:SU});function TU(r,e){let t=k(e,"indices","gatherND","int32"),n={params:k(r,"x","gatherND"),indices:t};return E.runKernel(ui,n)}var U_=S({gatherND_:TU});function QN(r,e){if(e==null)return r.shape.slice();if(Vr(r.shape,e))return e;if(r.shape.length===e.length){let t=[];for(let o=0;o`x has to be a floating point tensor since it's going to be scaled, but got a ${n.dtype} tensor instead.`),T(e>=0&&e<1,()=>`rate must be a float in the range [0, 1), but got ${e}.`),e===0)return r instanceof Ve?n.clone():n;let s=QN(n,t),a=1-e,i=de(Ts(Q(Ds(s,0,1,"float32",o),a)),a);return O(n,i)}var j_=S({dropout_:EU});function H_(r){return Math.floor(Math.pow(2,Math.ceil(Math.log(r)/Math.log(2))))}function lf(r,e,t){let o=1-r%2,n=new Float32Array(r);for(let s=0;s1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${o.rank}`),T(o.rank-1===n.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${o.rank} and targets rank ${n.rank}`),Nt(o.shape.slice(0,o.shape.length-1),n.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let s=o.shape[o.shape.length-1];T(t>0&&t<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${t}`);let a=await o.data(),i=await n.data(),[l,u]=[a.length/s,s],c=gb("bool",l);for(let p=0;pg.value-h.value),c[p]=0;for(let h=0;hFU,depthwiseConv2d:()=>LU,matMul:()=>BU});function $U(r,e,t,o,n,s="NHWC",a){let i=r;r.rank===3&&(i=L(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let l=e;l.rank===3&&(l=L(e,[1,e.shape[0],e.shape[1],e.shape[2]])),T(i.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`),T(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),T(t.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${t}.`);let u=s==="NHWC"?i.shape[3]:i.shape[1],c=s==="NHWC"?l.shape[3]:l.shape[1];T(u===t[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${t[2]}.`),T(c===t[3],()=>`Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${t[3]}).`),a!=null&&T(st(n),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${a} but got pad ${n}.`);let p={x:i,dy:l},m={strides:o,pad:n,dataFormat:s,dimRoundingMode:a,filterShape:t};return E.runKernel(ql,p,m)}var tp=S({conv2DBackpropFilter_:$U});function Uu(r,e,t){if(t==null||t==="linear")return r;if(t==="relu")return O(r,$s(e));throw new Error(`Cannot compute gradient for fused activation ${t}.`)}function ju(r,e){let t=e,o=Ct(r.shape,e.shape);return o.length>0&&(t=ye(t,o)),L(t,r.shape)}function Hu(r,e,t,o){if(e==="linear")return r;if(e==="relu")return Ir(r);if(e==="elu")return Ss(r);if(e==="relu6")return Ou(r);if(e==="prelu")return Da(r,t);if(e==="leakyrelu")return Sa(r,o);throw new Error(`Unknown fused activation ${e}.`)}var qu=(r,e)=>!(r>0)||e==="linear";function RU({x:r,filter:e,strides:t,pad:o,dataFormat:n="NHWC",dilations:s=[1,1],dimRoundingMode:a,bias:i,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(l=l||"linear",qu(E.state.gradientDepth,l)===!1){let v=jr(r,e,t,o,n,s,a);return i!=null&&(v=Q(v,i)),Hu(v,l,u,c)}let p=k(r,"x","conv2d"),m=k(e,"filter","conv2d"),f=p,d=!1;p.rank===3&&(d=!0,f=L(p,[1,p.shape[0],p.shape[1],p.shape[2]])),T(f.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${f.rank}.`),T(m.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`),a!=null&&T(st(o),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${o}.`),T(f.shape[3]===m.shape[2],()=>`Error in conv2d: depth of input (${f.shape[3]}) must match input depth for filter ${m.shape[2]}.`),T(yr(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`),T(n==="NHWC",()=>`Error in conv2d: got dataFormat of ${n} but only NHWC is currently supported.`);let h=ku(f.shape,m.shape,t,s,o,a),g;i!=null&&(g=k(i,"bias","fused conv2d"),[g]=Ge(g,p),ze(h.outShape,g.shape));let x;u!=null&&(x=k(u,"prelu weights","fused conv2d"));let b=(v,$)=>{let[A,R,M,V]=$,G=Uu(v,M,l);T(Oo(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let W=Kc(R.shape,G,A,t,o),q=tp(R,G,A.shape,t,o),Z=[W,q];if(V!=null){let X=ju(V,G);Z.push(X)}return Z},_={x:f,filter:m,bias:g,preluActivationWeights:x},w={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a,activation:l,leakyreluAlpha:c};return i==null?Hr(($,A,R)=>{let M=E.runKernel(vs,_,w);return R([A,$,M]),d&&(M=L(M,[M.shape[1],M.shape[2],M.shape[3]])),{value:M,gradFunc:b}})(f,m):Hr(($,A,R,M)=>{let V=E.runKernel(vs,_,w);return M([A,$,V,R]),d&&(V=L(V,[V.shape[1],V.shape[2],V.shape[3]])),{value:V,gradFunc:b}})(f,m,g)}var FU=S({fusedConv2d_:RU});function OU(r,e,t,o,n,s=[1,1],a){let i=r;r.rank===3&&(i=L(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let l=e;l.rank===3&&(l=L(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let u={x:i,dy:l},c={strides:o,pad:n,dimRoundingMode:a,dilations:s,filterShape:t};return E.runKernel(Zl,u,c)}var gg=S({depthwiseConv2dNativeBackpropFilter_:OU});function PU(r,e,t,o,n,s=[1,1],a){let i=e,l=!1;e.rank===3&&(l=!0,i=L(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let u={dy:i,filter:t},c={strides:o,pad:n,dimRoundingMode:a,dilations:s,inputShape:r},p=E.runKernel(Jl,u,c);return l?L(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var xg=S({depthwiseConv2dNativeBackpropInput_:PU});function MU({x:r,filter:e,strides:t,pad:o,dataFormat:n="NHWC",dilations:s=[1,1],dimRoundingMode:a,bias:i,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(qu(E.state.gradientDepth,l)===!1){let v=Ns(r,e,t,o,n,s,a);return i!=null&&(v=Q(v,i)),Hu(v,l,u,c)}let p=k(r,"x","depthwiseConv2d"),m=k(e,"filter","depthwiseConv2d"),f=p,d=!1;p.rank===3&&(d=!0,f=L(p,[1,p.shape[0],p.shape[1],p.shape[2]])),T(f.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${f.rank}.`),T(m.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${m.rank}.`),T(f.shape[3]===m.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${f.shape[3]}) must match the inChannels dimension in filter ${m.shape[2]}.`),s==null&&(s=[1,1]),T(yr(t,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`),a!=null&&T(st(o),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${a} but got pad ${o}.`);let h=ku(f.shape,m.shape,t,s,o,a,!0),g;i!=null&&(g=k(i,"bias","fused conv2d"),[g]=Ge(g,p),ze(h.outShape,g.shape));let x;u!=null&&(x=k(u,"prelu weights","fused depthwiseConv2d"));let b=(v,$)=>{T(Oo(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[A,R,M,V]=$,G=Uu(v,M,l),W=xg(R.shape,G,A,t,o,s,a),q=gg(R,G,A.shape,t,o,s,a);if(V!=null){let Z=ju(g,G);return[W,q,Z]}return[W,q]},_={x:f,filter:m,bias:g,preluActivationWeights:x},w={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a,activation:l,leakyreluAlpha:c};return i==null?Hr(($,A,R)=>{let M=E.runKernel(Cs,_,w);return R([A,$,M]),d&&(M=L(M,[M.shape[1],M.shape[2],M.shape[3]])),{value:M,gradFunc:b}})(f,m):Hr(($,A,R,M)=>{let V=E.runKernel(Cs,_,w);return M([A,$,V,R]),d&&(V=L(V,[V.shape[1],V.shape[2],V.shape[3]])),{value:V,gradFunc:b}})(f,m,g)}var LU=S({fusedDepthwiseConv2d_:MU});function zU({a:r,b:e,transposeA:t=!1,transposeB:o=!1,bias:n,activation:s="linear",preluActivationWeights:a,leakyreluAlpha:i}){if(qu(E.state.gradientDepth,s)===!1){let V=We(r,e,t,o);return n!=null&&(V=Q(V,n)),Hu(V,s,a,i)}let l=k(r,"a","fused matMul"),u=k(e,"b","fused matMul");[l,u]=Ge(l,u);let c=t?l.shape[l.rank-2]:l.shape[l.rank-1],p=o?u.shape[u.rank-1]:u.shape[u.rank-2],m=t?l.shape[l.rank-1]:l.shape[l.rank-2],f=o?u.shape[u.rank-2]:u.shape[u.rank-1],d=l.shape.slice(0,-2),h=u.shape.slice(0,-2),g=ft(d),x=ft(h);T(l.rank>=2&&u.rank>=2&&l.rank===u.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${l.rank} and ${u.rank}.`),T(Vr(d,h),()=>`Error in fused matMul: outer dimensions (${d}) and (${h}) of Tensors with shapes ${l.shape} and ${u.shape} must match.`),T(c===p,()=>`Error in fused matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${l.shape} and ${u.shape} and transposeA=${t} and transposeB=${o} must match.`);let b=l.shape.slice(0,-2).concat([m,f]),_=t?L(l,[g,c,m]):L(l,[g,m,c]),w=o?L(u,[x,f,p]):L(u,[x,p,f]),v;n!=null&&(v=k(n,"bias","fused matMul"),[v]=Ge(v,l),ze(b,v.shape));let $;a!=null&&($=k(a,"prelu weights","fused matMul"));let A=(V,G)=>{let[W,q,Z,X]=G,Y=Uu(L(V,Z.shape),Z,s),te,K;if(!t&&!o?(te=We(Y,q,!1,!0),K=We(W,Y,!0,!1)):!t&&o?(te=We(Y,q,!1,!1),K=We(Y,W,!0,!1)):t&&!o?(te=We(q,Y,!1,!0),K=We(W,Y,!1,!1)):(te=We(q,Y,!0,!0),K=We(Y,W,!0,!0)),n!=null){let re=ju(X,Y);return[te,K,re]}else return[te,K]},R={a:_,b:w,bias:v,preluActivationWeights:$},M={transposeA:t,transposeB:o,activation:s,leakyreluAlpha:i};return n==null?Hr((G,W,q)=>{let Z=E.runKernel(ks,R,M);return q([G,W,Z]),{value:L(Z,b),gradFunc:A}})(_,w):Hr((G,W,q,Z)=>{let X=E.runKernel(ks,R,M);return Z([G,W,X,q]),{value:L(X,b),gradFunc:A}})(_,w,v)}var BU=S({fusedMatMul_:zU});function VU(r){return lf(r,.54,.46)}var e0=S({hammingWindow_:VU});function GU(r){return lf(r,.5,.5)}var yg=S({hannWindow_:GU});function WU(r,e,t,o=!1,n=0){let s=0,a=[];for(;s+e<=r.size;)a.push(Fe(r,s,e)),s+=t;if(o)for(;s`Error in cropAndResize: image must be rank 4,but got rank ${a.rank}.`),T(i.rank===2&&i.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${u},4] but had shape ${i.shape}.`),T(l.rank===1&&l.shape[0]===u,()=>`Error in cropAndResize: boxInd must be have size [${u}] but had shape ${i.shape}.`),T(o.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${o.length}.`),T(o[0]>=1&&o[1]>=1,()=>`cropSize must be atleast [1,1], but was ${o}`),T(n==="bilinear"||n==="nearest",()=>`method must be bilinear or nearest, but was ${n}`);let c={image:a,boxes:i,boxInd:l},p={method:n,extrapolationValue:s,cropSize:o};return E.runKernel(ri,c,p)}var r0=S({cropAndResize_:jU});function HU(r){let e=k(r,"image","flipLeftRight","float32");T(e.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${e.rank}.`);let t={image:e};return E.runKernel(li,t,{})}var o0=S({flipLeftRight_:HU});function qU(r,e,t=0,o=.5){let n=k(r,"image","rotateWithOffset","float32");T(n.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${n.rank}.`);let s={image:n},a={radians:e,fillValue:t,center:o};return E.runKernel(Ri,s,a)}var n0=S({rotateWithOffset_:qU});function Un(r,e,t,o,n,s){o==null&&(o=.5),n==null&&(n=Number.NEGATIVE_INFINITY),s==null&&(s=0);let a=r.shape[0];return t=Math.min(t,a),T(0<=o&&o<=1,()=>`iouThreshold must be in [0, 1], but was '${o}'`),T(r.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${r.rank}'`),T(r.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`),T(e.rank===1,()=>"scores must be a 1D tensor"),T(e.shape[0]===a,()=>`scores has incompatible shape with boxes. Expected ${a}, but was ${e.shape[0]}`),T(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:t,iouThreshold:o,scoreThreshold:n,softNmsSigma:s}}function KU(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY){let s=k(r,"boxes","nonMaxSuppression"),a=k(e,"scores","nonMaxSuppression"),i=Un(s,a,t,o,n);t=i.maxOutputSize,o=i.iouThreshold,n=i.scoreThreshold;let l={maxOutputSize:t,iouThreshold:o,scoreThreshold:n};return E.runKernel(_i,{boxes:s,scores:a},l)}var s0=S({nonMaxSuppression_:KU});function i0(r,e,t){let o=XU(r,e,t),n=o<0?-(o+1):o;r.splice(n,0,e)}function XU(r,e,t){return ZU(r,e,t||YU)}function YU(r,e){return r>e?1:r>>1);let i=t(e,r[s]);i>0?o=s+1:(n=s,a=!i)}return a?o:-o-1}function K_(r,e,t,o,n){return q_(r,e,t,o,n,0)}function X_(r,e,t,o,n,s){return q_(r,e,t,o,n,0,!1,s,!0)}function Y_(r,e,t,o,n,s){return q_(r,e,t,o,n,s,!0)}function q_(r,e,t,o,n,s,a=!1,i=!1,l=!1){let u=[];for(let g=0;gn&&u.push({score:e[g],boxIndex:g,suppressBeginIndex:0});u.sort(a0);let c=s>0?-.5/s:0,p=[],m=[];for(;p.length0;){let g=u.pop(),{score:x,boxIndex:b,suppressBeginIndex:_}=g;if(x=_;--v){let $=JU(r,b,p[v]);if($>=o){w=!0;break}if(g.score=g.score*QU(o,c,$),g.score<=n)break}g.suppressBeginIndex=p.length,w||(g.score===x?(p.push(b),m.push(g.score)):g.score>n&&i0(u,g,a0))}let f=p.length,d=t-f;i&&d>0&&(p.push(...new Array(d).fill(0)),m.push(...new Array(d).fill(0)));let h={selectedIndices:p};return a&&(h.selectedScores=m),l&&(h.validOutputs=f),h}function JU(r,e,t){let o=r.subarray(e*4,e*4+4),n=r.subarray(t*4,t*4+4),s=Math.min(o[0],o[2]),a=Math.min(o[1],o[3]),i=Math.max(o[0],o[2]),l=Math.max(o[1],o[3]),u=Math.min(n[0],n[2]),c=Math.min(n[1],n[3]),p=Math.max(n[0],n[2]),m=Math.max(n[1],n[3]),f=(i-s)*(l-a),d=(p-u)*(m-c);if(f<=0||d<=0)return 0;let h=Math.max(s,u),g=Math.max(a,c),x=Math.min(i,p),b=Math.min(l,m),_=Math.max(x-h,0)*Math.max(b-g,0);return _/(f+d-_)}function QU(r,e,t){let o=Math.exp(e*t*t);return t<=r?o:0}function a0(r,e){return r.score-e.score||r.score===e.score&&e.boxIndex-r.boxIndex}async function ej(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY){let s=k(r,"boxes","nonMaxSuppressionAsync"),a=k(e,"scores","nonMaxSuppressionAsync"),i=Un(s,a,t,o,n);t=i.maxOutputSize,o=i.iouThreshold,n=i.scoreThreshold;let l=await Promise.all([s.data(),a.data()]),u=l[0],c=l[1],{selectedIndices:p}=K_(u,c,t,o,n);return s!==r&&s.dispose(),a!==e&&a.dispose(),Vt(p,"int32")}var l0=ej;function tj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=0){let a=k(r,"boxes","nonMaxSuppression"),i=k(e,"scores","nonMaxSuppression"),l=Un(a,i,t,o,n,s);t=l.maxOutputSize,o=l.iouThreshold,n=l.scoreThreshold,s=l.softNmsSigma;let u={boxes:a,scores:i},c={maxOutputSize:t,iouThreshold:o,scoreThreshold:n,softNmsSigma:s},p=E.runKernel(ki,u,c);return{selectedIndices:p[0],selectedScores:p[1]}}var u0=S({nonMaxSuppressionWithScore_:tj});async function rj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=0){let a=k(r,"boxes","nonMaxSuppressionAsync"),i=k(e,"scores","nonMaxSuppressionAsync"),l=Un(a,i,t,o,n,s);t=l.maxOutputSize,o=l.iouThreshold,n=l.scoreThreshold,s=l.softNmsSigma;let u=await Promise.all([a.data(),i.data()]),c=u[0],p=u[1],{selectedIndices:m,selectedScores:f}=Y_(c,p,t,o,n,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:Vt(m,"int32"),selectedScores:Vt(f)}}var c0=rj;function oj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=!1){let a=k(r,"boxes","nonMaxSuppression"),i=k(e,"scores","nonMaxSuppression"),l=Un(a,i,t,o,n,null),u=l.maxOutputSize,c=l.iouThreshold,p=l.scoreThreshold,m={boxes:a,scores:i},f={maxOutputSize:u,iouThreshold:c,scoreThreshold:p,padToMaxOutputSize:s},d=E.runKernel(wi,m,f);return{selectedIndices:d[0],validOutputs:d[1]}}var p0=S({nonMaxSuppressionPadded_:oj});async function nj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=!1){let a=k(r,"boxes","nonMaxSuppressionAsync"),i=k(e,"scores","nonMaxSuppressionAsync"),l=Un(a,i,t,o,n,null),u=l.maxOutputSize,c=l.iouThreshold,p=l.scoreThreshold,[m,f]=await Promise.all([a.data(),i.data()]),{selectedIndices:d,validOutputs:h}=X_(m,f,u,c,p,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:Vt(d,"int32"),validOutputs:le(h,"int32")}}var m0=nj;function sj(r,e,t=!1,o=!1){let n=k(r,"images","resizeBilinear");T(n.rank===3||n.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${n.rank}.`),T(e.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${e}.`),T(o===!1||t===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=n,a=!1;n.rank===3&&(a=!0,s=L(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let[]=e,i={images:s},l={alignCorners:t,halfPixelCenters:o,size:e},u=E.runKernel(Cn,i,l);return a?L(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var _g=S({resizeBilinear_:sj});function ij(r,e,t=!1,o=!1){let n=k(r,"images","resizeNearestNeighbor");T(n.rank===3||n.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${n.rank}.`),T(e.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${e}.`),T(n.dtype==="float32"||n.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),T(o===!1||t===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=n,a=!1;n.rank===3&&(a=!0,s=L(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let[]=e,i={images:s},l={alignCorners:t,halfPixelCenters:o,size:e},u=E.runKernel(da,i,l);return a?L(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var wg=S({resizeNearestNeighbor_:ij});function aj(r,e,t){T(e%1==0,()=>`bandPart(): numLower must be an integer, got ${e}.`),T(t%1==0,()=>`bandPart(): numUpper must be an integer, got ${t}.`);let o=k(r,"a","bandPart");T(o.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${o.rank}.`);let n=o.shape,[s,a]=o.shape.slice(-2);if(!(e<=s))throw new Error(`bandPart(): numLower (${e}) must not be greater than the number of rows (${s}).`);if(!(t<=a))throw new Error(`bandPart(): numUpper (${t}) must not be greater than the number of columns (${a}).`);e<0&&(e=s),t<0&&(t=a);let i=L(Jc(0,s,1,"int32"),[-1,1]),l=Jc(0,a,1,"int32"),u=ue(i,l),c=fr(Mo(u,le(+e,"int32")),to(u,le(-t,"int32"))),p=gt([s,a],o.dtype);return L(Bt(ur(L(o,[-1,s,a])).map(m=>$t(c,m,p))),n)}var f0=S({bandPart_:aj});function lj(r){let e;if(Array.isArray(r)){e=!1,T(r!=null&&r.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let n=r[0].shape[0];for(let s=1;s`Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${n})`)}else e=!0,r=lr(r,r.shape[0],0).map(n=>wo(n,[0]));T(r.length<=r[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`);let t=[],o=r;for(let n=0;n{let s=o[n];if(n>0)for(let a=0;a=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`),r.rank===2)return h0(r,e);{let t=r.shape.slice(0,r.shape.length-2).reduce((l,u)=>l*u),o=ur(L(r,[t,r.shape[r.shape.length-2],r.shape[r.shape.length-1]]),0),n=[],s=[];o.forEach(l=>{let[u,c]=h0(l,e);n.push(u),s.push(c)});let a=L(Bt(n,0),r.shape),i=L(Bt(s,0),r.shape);return[a,i]}}function h0(r,e=!1){return E.tidy(()=>{T(r.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);let t=r.shape[0],o=r.shape[1],n=Xc(t),s=Ro(r),a=Bi([[1]],[1,1]),i=Ro(a),l=t>=o?o:t;for(let u=0;u{let f=Fe(s,[u,u],[t-u,1]),d=Wu(f),h=Fe(s,[u,u],[1,1]),g=$t(Qt(h,0),Bi([[-1]]),Bi([[1]])),x=ue(h,O(g,d)),b=de(f,x);b.shape[0]===1?i=Ro(a):i=Je([a,Fe(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let _=je(de(We(g,x),d)),w=Fe(s,[u,0],[t-u,o]),v=O(_,i),$=Ue(i);if(u===0)s=ue(w,We(v,We($,w)));else{let M=ue(w,We(v,We($,w)));s=Je([Fe(s,[0,0],[u,o]),M],0)}let A=Ue(v),R=Fe(n,[0,u],[t,n.shape[1]-u]);if(u===0)n=ue(R,We(We(R,i),A));else{let M=ue(R,We(We(R,i),A));n=Je([Fe(n,[0,0],[t,u]),M],1)}return[i,s,n]}),Te([c,p,m])}return!e&&t>o&&(n=Fe(n,[0,0],[t,o]),s=Fe(s,[0,0],[o,o])),[n,s]})}var g0=S({qr_:uj});var Gt;(function(r){r[r.NONE=0]="NONE",r[r.MEAN=1]="MEAN",r[r.SUM=2]="SUM",r[r.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Gt||(Gt={}));function cj(r,e,t=Gt.SUM_BY_NONZERO_WEIGHTS){let o=k(r,"losses","computeWeightedLoss"),n=null;e!=null&&(n=k(e,"weights","computeWeightedLoss"));let s=n==null?o:O(o,n);if(t===Gt.NONE)return s;if(t===Gt.SUM)return ye(s);if(t===Gt.MEAN){if(n==null)return ht(s);{let a=o.size/n.size,i=de(ye(s),ye(n));return a>1?de(i,le(a)):i}}if(t===Gt.SUM_BY_NONZERO_WEIGHTS){if(n==null)return de(ye(s),le(o.size));{let a=O(n,Cr(o.shape)),i=oe(ye(Gn(a,le(0))),"float32");return de(ye(s),i)}}throw Error(`Unknown reduction: ${t}`)}var Nr=S({computeWeightedLoss_:cj});function pj(r,e,t,o=Gt.SUM_BY_NONZERO_WEIGHTS){let n=k(r,"labels","absoluteDifference"),s=k(e,"predictions","absoluteDifference"),a=null;t!=null&&(a=k(t,"weights","absoluteDifference")),Nt(n.shape,s.shape,"Error in absoluteDifference: ");let i=Tt(ue(n,s));return Nr(i,a,o)}var x0=S({absoluteDifference_:pj});function mj(r,e,t,o,n=Gt.SUM_BY_NONZERO_WEIGHTS){let s=k(r,"labels","cosineDistance"),a=k(e,"predictions","cosineDistance"),i=null;o!=null&&(i=k(o,"weights","cosineDistance")),Nt(s.shape,a.shape,"Error in cosineDistance: ");let l=le(1),u=ue(l,ye(O(s,a),t,!0));return Nr(u,i,n)}var y0=S({cosineDistance_:mj});function fj(r,e,t,o=Gt.SUM_BY_NONZERO_WEIGHTS){let n=k(r,"labels","hingeLoss"),s=k(e,"predictions","hingeLoss"),a=null;t!=null&&(a=k(t,"weights","hingeLoss")),Nt(n.shape,s.shape,"Error in hingeLoss: ");let i=le(1);n=ue(O(le(2),n),i);let l=Ir(ue(i,O(n,s)));return Nr(l,a,o)}var b0=S({hingeLoss_:fj});function dj(r,e,t,o=1,n=Gt.SUM_BY_NONZERO_WEIGHTS){let s=k(r,"labels","huberLoss"),a=k(e,"predictions","huberLoss"),i=null;t!=null&&(i=k(t,"weights","huberLoss")),Nt(s.shape,a.shape,"Error in huberLoss: ");let l=le(o),u=Tt(ue(a,s)),c=As(u,l),p=ue(u,c),m=Q(O(le(.5),Pe(c)),O(l,p));return Nr(m,i,n)}var _0=S({huberLoss_:dj});function hj(r,e,t,o=1e-7,n=Gt.SUM_BY_NONZERO_WEIGHTS){let s=k(r,"labels","logLoss"),a=k(e,"predictions","logLoss"),i=null;t!=null&&(i=k(t,"weights","logLoss")),Nt(s.shape,a.shape,"Error in logLoss: ");let l=le(1),u=le(o),c=je(O(s,ir(Q(a,u)))),p=O(ue(l,s),ir(Q(ue(l,a),u))),m=ue(c,p);return Nr(m,i,n)}var w0=S({logLoss_:hj});function gj(r,e,t,o=Gt.SUM_BY_NONZERO_WEIGHTS){let n=k(r,"labels","meanSquaredError"),s=k(e,"predictions","meanSquaredError"),a=null;t!=null&&(a=k(t,"weights","meanSquaredError")),Nt(n.shape,s.shape,"Error in meanSquaredError: ");let i=Vu(n,s);return Nr(i,a,o)}var k0=S({meanSquaredError_:gj});function xj(r,e){let t=k(r,"labels","sigmoidCrossEntropyWithLogits"),o=k(e,"logits","sigmoidCrossEntropyWithLogits");Nt(t.shape,o.shape,"Error in sigmoidCrossEntropyWithLogits: ");let n=Ir(o),s=O(o,t),a=Au(Xt(je(Tt(o))));return Q(ue(n,s),a)}function yj(r,e,t,o=0,n=Gt.SUM_BY_NONZERO_WEIGHTS){let s=k(r,"multiClassLabels","sigmoidCrossEntropy"),a=k(e,"logits","sigmoidCrossEntropy"),i=null;if(t!=null&&(i=k(t,"weights","sigmoidCrossEntropy")),Nt(s.shape,a.shape,"Error in sigmoidCrossEntropy: "),o>0){let u=le(o),c=le(1),p=le(.5);s=Q(O(s,ue(c,u)),O(p,u))}let l=xj(s,a);return Nr(l,i,n)}var v0=S({sigmoidCrossEntropy_:yj});function bj(r,e,t=-1){if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${e.rank} and dim was ${t}`);return Hr((n,s,a)=>{let l=Um(s,[t],!0),u=ue(oe(s,"float32"),l);a([n,u]);let c=je(O(u,n));return{value:ye(c,[t]),gradFunc:(f,d)=>{let[h,g]=d,x=Vn(f.shape,[t]);return[O(L(f,x),ue(oe(h,"float32"),Xt(g))),O(L(f,x),ue(Xt(g),oe(h,"float32")))]}}})(r,e)}function _j(r,e,t,o=0,n=Gt.SUM_BY_NONZERO_WEIGHTS){let s=k(r,"onehotLabels","softmaxCrossEntropy"),a=k(e,"logits","softmaxCrossEntropy"),i=null;if(t!=null&&(i=k(t,"weights","softmaxCrossEntropy")),Nt(s.shape,a.shape,"Error in softmaxCrossEntropy: "),o>0){let u=le(o),c=le(1),p=le(s.shape[1]);s=Q(O(s,ue(c,u)),de(u,p))}let l=bj(s,a);return Nr(l,i,n)}var C0=S({softmaxCrossEntropy_:_j});var wj={fft:Ra,ifft:zi,rfft:Fa,irfft:Bu},kj={hammingWindow:e0,hannWindow:yg,frame:bg,stft:t0},Rs={flipLeftRight:o0,resizeNearestNeighbor:wg,resizeBilinear:_g,rotateWithOffset:n0,cropAndResize:r0,nonMaxSuppression:s0,nonMaxSuppressionAsync:l0,nonMaxSuppressionWithScore:u0,nonMaxSuppressionWithScoreAsync:c0,nonMaxSuppressionPadded:p0,nonMaxSuppressionPaddedAsync:m0},Z_={bandPart:f0,gramSchmidt:d0,qr:g0},vj={absoluteDifference:x0,computeWeightedLoss:Nr,cosineDistance:y0,hingeLoss:b0,huberLoss:_0,logLoss:w0,meanSquaredError:k0,sigmoidCrossEntropy:v0,softmaxCrossEntropy:C0};var Fr=class extends ig{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Te(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return pg(e,t)}dispose(){this.iterations_!=null&&Te(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:le(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Fr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var rp=class extends Fr{constructor(e,t,o=null){super();this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=E.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=E.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:B(()=>Ie(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:B(()=>Ie(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let l=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;B(()=>{let c=Q(O(l,this.rho),O(Pe(i),1-this.rho)),p=O(de(xt(Q(u,this.epsilon)),xt(Q(l,this.epsilon))),i),m=Q(O(u,this.rho),O(Pe(p),1-this.rho));l.assign(c),u.assign(m);let f=Q(O(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Te(this.accumulatedGrads.map(e=>e.variable)),Te(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};rp.className="Adadelta";eo(rp);var op=class extends Fr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=E.registeredVariables[o];if(this.accumulatedGrads[n]==null){let l=!1;this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:B(()=>Na(s.shape,this.initialAccumulatorValue).variable(l))}}let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;B(()=>{let l=Q(i,Pe(a));i.assign(l);let u=Q(O(de(a,xt(Q(l,E.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Te(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(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};op.className="Adagrad";eo(op);var np=class extends Fr{constructor(e,t,o,n=null){super();this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=le(t).variable(),this.accBeta2=le(o).variable()}),n==null&&(this.epsilon=E.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);B(()=>{let o=ue(1,this.accBeta1),n=ue(1,this.accBeta2);t.forEach((s,a)=>{let i=E.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:B(()=>Ie(i).variable(l))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:B(()=>Ie(i).variable(l))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,p=this.accumulatedSecondMoment[a].variable,m=Q(O(c,this.beta1),O(u,1-this.beta1)),f=Q(O(p,this.beta2),O(Pe(u),1-this.beta2)),d=de(m,o),h=de(f,n);c.assign(m),p.assign(f);let g=Q(O(de(d,Q(xt(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(O(this.accBeta1,this.beta1)),this.accBeta2.assign(O(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Te(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Te(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),B(()=>{this.accBeta1.assign(Rr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Rr(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}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)}};np.className="Adam";eo(np);var sp=class extends Fr{constructor(e,t,o,n=null,s=0){super();this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],B(()=>{this.iteration=le(0).variable(),this.accBeta1=le(t).variable()}),n==null&&(this.epsilon=E.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);B(()=>{let o=ue(1,this.accBeta1),n=de(-this.learningRate,Q(O(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=E.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ie(i).variable(l)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ie(i).variable(l)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,p=this.accumulatedWeightedInfNorm[a].variable,m=Q(O(c,this.beta1),O(u,1-this.beta1)),f=O(p,this.beta2),d=Tt(u),h=qr(f,d);c.assign(m),p.assign(h);let g=Q(O(de(n,o),de(m,Q(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(Q(this.iteration,1)),this.accBeta1.assign(O(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Te(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Te(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)}};sp.className="Adamax";eo(sp);var ll=class extends Fr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=E.registeredVariables[o];B(()=>{let i=Q(O(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Dt(le(-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)}};ll.className="SGD";eo(ll);var ip=class extends ll{constructor(e,t,o=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=le(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=E.registeredVariables[o];if(this.accumulations[n]==null){let l=!1;this.accumulations[n]={originalName:`${o}/momentum`,variable:B(()=>Ie(s).variable(l))}}let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&B(()=>{let l,u=Q(O(this.m,a),i);this.useNesterov?l=Q(O(this.c,Q(i,O(u,this.m))),s):l=Q(O(this.c,u),s),a.assign(u),s.assign(l)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Te(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};ip.className="Momentum";eo(ip);var ap=class extends Fr{constructor(e,t=.9,o=0,n=null,s=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=E.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=E.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:B(()=>Ie(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:B(()=>Ie(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:B(()=>Ie(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let l=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;B(()=>{let c=Q(O(l,this.decay),O(Pe(i),1-this.decay));if(this.centered){let p=this.accumulatedMeanGrads[n].variable,m=Q(O(p,this.decay),O(i,1-this.decay)),f=de(O(i,this.learningRate),xt(ue(c,Q(Pe(m),this.epsilon)))),d=Q(O(u,this.momentum),f);l.assign(c),p.assign(m),u.assign(d);let h=ue(s,d);s.assign(h)}else{let p=Q(O(l,this.decay),O(Pe(i),1-this.decay)),m=Q(O(u,this.momentum),de(O(i,this.learningRate),xt(Q(p,this.epsilon))));l.assign(p),u.assign(m);let f=ue(s,m);s.assign(f)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Te(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Te(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Te(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,o=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}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)}};ap.className="RMSProp";eo(ap);var Oa=class{static sgd(e){return new ll(e)}static momentum(e,t,o=!1){return new ip(e,t,o)}static rmsprop(e,t=.9,o=0,n=null,s=!1){return new ap(e,t,o,n,s)}static adam(e=.001,t=.9,o=.999,n=null){return new np(e,t,o,n)}static adadelta(e=.001,t=.95,o=null){return new rp(e,t,o)}static adamax(e=.002,t=.9,o=.999,n=null,s=0){return new sp(e,t,o,n,s)}static adagrad(e,t=.1){return new op(e,t)}};var ul={sgd:Oa.sgd,momentum:Oa.momentum,adadelta:Oa.adadelta,adagrad:Oa.adagrad,rmsprop:Oa.rmsprop,adamax:Oa.adamax,adam:Oa.adam};var Cj=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:r=>r())();function uf(){return new Promise(r=>Cj(()=>r()))}var N={};et(N,{ERF_A1:()=>Oj,ERF_A2:()=>Pj,ERF_A3:()=>Mj,ERF_A4:()=>Lj,ERF_A5:()=>zj,ERF_P:()=>Fj,PARALLELIZE_THRESHOLD:()=>kg,SELU_SCALE:()=>Q_,SELU_SCALEALPHA:()=>J_,applyActivation:()=>Hu,assertAndGetBroadcastShape:()=>ze,assertAxesAreInnerMostDims:()=>EW,assertParamsConsistent:()=>Ij,assignToTypedArray:()=>qj,axesAreInnerMostDims:()=>I_,calculateShapes:()=>xN,combineLocations:()=>ON,complexWithEvenIndex:()=>Uj,complexWithOddIndex:()=>jj,computeConv2DInfo:()=>ku,computeConv3DInfo:()=>RN,computeDefaultPad:()=>u_,computeDilation2DInfo:()=>eG,computeOptimalWindowSize:()=>Sj,computeOutAndReduceShapes:()=>N_,computeOutShape:()=>Nj,computePool2DInfo:()=>a_,computePool3DInfo:()=>tG,convertConv2DDataFormat:()=>$N,eitherStridesOrDilationsAreOne:()=>yr,expandShapeToKeepDim:()=>Vn,exponent:()=>Xj,exponents:()=>Kj,fromStringArrayToUint8:()=>tH,fromUint8ToStringArray:()=>eH,getAxesPermutation:()=>S_,getBroadcastDims:()=>jG,getComplexWithIndex:()=>Hj,getFusedBiasGradient:()=>ju,getFusedDyActivation:()=>Uu,getImageCenter:()=>Tj,getInnerMostAxes:()=>AW,getPermuted:()=>Aj,getReductionAxes:()=>Ct,getReshaped:()=>Ej,getReshapedPermuted:()=>Dj,getSliceBeginCoords:()=>$j,getSliceSize:()=>Rj,getUndoAxesPermutation:()=>Wm,log:()=>Vj,mergeRealAndImagArrays:()=>Gj,prepareAndValidate:()=>gN,prepareSplitSize:()=>Yj,segment_util:()=>I0,shouldFuse:()=>qu,slice_util:()=>or,splitRealAndImagArrays:()=>Wj,tupleValuesAreOne:()=>Oo,upcastType:()=>mr,validateInput:()=>ng,validateUpdateShape:()=>Qb,warn:()=>Bj});function Ij(r,e){let t=r[0].length;r.forEach((n,s)=>{T(n.length===t,()=>`Error in concat${t}D: rank of tensors[${s}] must be the same as the rank of the rest (${t})`)}),T(e>=0&&e`Error in concat${t}D: axis must be between 0 and ${t-1}.`);let o=r[0];r.forEach((n,s)=>{for(let a=0;a`Error in concat${t}D: Shape of tensors[${s}] (${n}) does not match the shape of the rest (${o}) along the non-concatenated axis ${s}.`)})}function Nj(r,e){let t=r[0].slice();for(let o=1;o=e*2+1||a%2==1?s.push(a):n.push(a);o.push(...n),o.push(0),o.push(...s)}return o}function Dj(r,e,t,o=!0){let n=[];o?n.push(r[0]/t):n.push(r[0]*t);for(let s=1;s"Number of splits must evenly divide the axis."),o=new Array(e).fill(r.shape[t]/e);else{let n=e.reduce((a,i)=>(i===-1&&(a+=1),a),0);T(n<=1,()=>"There should be only one negative value in split array.");let s=e.indexOf(-1);if(s!==-1){let a=e.reduce((i,l)=>l>0?i+l:i);e[s]=r.shape[t]-a}T(r.shape[t]===e.reduce((a,i)=>a+i),()=>"The sum of sizes must match the size of the axis dimension."),o=e}return o}var I0={};et(I0,{collectGatherOpShapeInfo:()=>Qj,computeOutShape:()=>Jj,segOpComputeOptimalWindowSize:()=>Zj});function Zj(r,e){let t=!1,o;for(r<=kg?(o=r,t=!0):o=Dc(r,Math.floor(Math.sqrt(r)));!t;)o>e||o===r?t=!0:o=Dc(r,o+1);return o}function Jj(r,e,t){let o=[],n=r.length;for(let s=0;sn))throw new Error(`Expect batchDims in the range of [-${n}, ${n}], but got ${o}`);if(o<0&&(o+=n),o>s)throw new Error(`batchDims (${o}) must be less than rank(x) ( ${s}).`);if(tzc(e))}catch(e){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${e}`)}}function tH(r){return r.map(e=>el(e))}var Sr={};et(Sr,{nonMaxSuppressionV3Impl:()=>K_,nonMaxSuppressionV4Impl:()=>X_,nonMaxSuppressionV5Impl:()=>Y_,whereImpl:()=>G_});var vg={kernelName:us,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>O(r,$s(oe(t,"float32"),-1))}}};var N0={kernelName:Ks,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>{let o=Pe(oe(t,"float32")),n=xt(ue(le(1),o));return je(de(r,n))}}}};var S0={kernelName:Xs,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>{let o=xt(ue(Pe(oe(t,"float32")),1));return de(r,o)}}}};var T0={kernelName:xo,inputsToSave:["a","b"],gradFunc:(r,e)=>{let[t,o]=e,n=ze(t.shape,o.shape);return{a:()=>{let i=r,l=Ct(t.shape,n);return l.length>0&&(i=ye(i,l)),L(i,t.shape)},b:()=>{let i=r,l=Ct(o.shape,n);return l.length>0&&(i=ye(i,l)),L(i,o.shape)}}}};var E0={kernelName:qo,saveAllInputs:!0,gradFunc:(r,e)=>{let t={};return e.forEach((o,n)=>{t[n]=()=>r.clone()}),t}};var A0={kernelName:Ko,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>Ie(t)}}};var D0={kernelName:oa,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>Ie(t)}}};var $0={kernelName:Ys,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>de(r,xt(ue(le(1),Pe(oe(t,"float32")))))}}};var R0={kernelName:Zs,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>{let o=xt(Q(le(1),Pe(oe(t,"float32"))));return de(r,o)}}}};var F0={kernelName:ei,inputsToSave:["a","b"],gradFunc:(r,e)=>{let[t,o]=e,n=ze(t.shape,o.shape);return{a:()=>{let i=Q(Pe(t),Pe(o)),l=O(r,de(o,i)),u=Ct(t.shape,n);return u.length>0&&(l=ye(l,u)),L(l,t.shape)},b:()=>{let i=Q(Pe(t),Pe(o)),l=je(O(r,de(t,i))),u=Ct(o.shape,n);return u.length>0&&(l=ye(l,u)),L(l,o.shape)}}}};var O0={kernelName:Js,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>de(r,Q(Pe(oe(t,"float32")),1))}}};var P0={kernelName:Qs,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>de(r,ue(le(1),Pe(oe(t,"float32"))))}}};function rH(r,e,t,o,n,s){let a=k(r,"dy","avgPool3dGrad"),i=k(e,"input","avgPool3dGrad"),l=a,u=i,c=!1;i.rank===4&&(c=!0,l=L(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]]),u=L(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),T(l.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${l.rank}.`),T(u.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${u.rank}.`),s!=null&&T(st(n),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${s} but got pad ${n}.`);let p={dy:l,input:u},m={filterSize:t,strides:o,pad:n,dimRoundingMode:s},f=E.runKernel(Ul,p,m);return c?L(f,[f.shape[1],f.shape[2],f.shape[3],f.shape[4]]):f}var M0=S({avgPool3dGrad_:rH});var L0={kernelName:na,inputsToSave:["x"],gradFunc:(r,e,t)=>{let[o]=e,{filterSize:n,strides:s,pad:a,dimRoundingMode:i}=t;return{x:()=>M0(r,o,n,s,a,i)}}};function oH(r,e,t,o,n){let s=k(r,"dy","avgPoolGrad"),a=k(e,"input","avgPoolGrad");T(a.rank===s.rank,()=>`Rank of input (${a.rank}) does not match rank of dy (${s.rank})`);let i=a,l=s,u=!1;a.rank===3&&(u=!0,i=L(a,[1,a.shape[0],a.shape[1],a.shape[2]]),l=L(s,[1,s.shape[0],s.shape[1],s.shape[2]])),T(l.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${l.rank}.`),T(i.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${i.rank}.`);let c={dy:l,input:i},p={filterSize:t,strides:o,pad:n},m=E.runKernel(Wl,c,p);return u?L(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var z0=S({avgPoolGrad_:oH});var B0={kernelName:Xo,inputsToSave:["x"],gradFunc:(r,e,t)=>{let[o]=e,{filterSize:n,strides:s,pad:a}=t;return{x:()=>z0(r,o,n,s,a)}}};var V0={kernelName:Yo,inputsToSave:["a","b"],gradFunc:(r,e,t)=>{let[o,n]=e,{transposeA:s,transposeB:a}=t;return!s&&!a?{a:()=>We(r,n,!1,!0),b:()=>We(o,r,!0,!1)}:!s&&a?{a:()=>We(r,n,!1,!1),b:()=>We(r,o,!0,!1)}:s&&!a?{a:()=>We(n,r,!1,!0),b:()=>We(o,r,!1,!1)}:{a:()=>We(n,r,!0,!0),b:()=>We(r,o,!0,!0)}}};var G0={kernelName:sa,gradFunc:(r,e,t)=>{let{blockShape:o,crops:n}=t;return{x:()=>Aa(r,o,n)}}};var W0={kernelName:Cb,gradFunc:(r,e,t)=>{let o=t,n=o.inputShape,s=o.shape,a=Array.from(s);for(let l=n.length-1;l>=0;l--)if(n[l]===s[l])a[l]=1;else if(n[l]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${s}].`);let i=[];for(let l=0;l1&&i.push(l);return{x:()=>ye(r,i,!0)}}};var U0={kernelName:Eo,gradFunc:r=>({x:()=>r.clone()})};var j0={kernelName:Zo,gradFunc:r=>({x:()=>Ie(r)})};var H0={kernelName:Ao,inputsToSave:["x"],gradFunc:(r,e,t)=>{let[o]=e,{clipValueMin:n,clipValueMax:s}=t;return{x:()=>$t(fr(to(o,n),Mo(o,s)),r,Ie(r))}}};var q0={kernelName:ia,inputsToSave:["x"],gradFunc:vg.gradFunc};var K0={kernelName:cs,saveAllInputs:!0,gradFunc:(r,e,t)=>{let o=e.map(l=>l.shape),{axis:n}=t,s=Jt(n,e[0].shape)[0],a=o.map(l=>l[s]);return lr(r,a,s).map(l=>()=>l)}};var X0={kernelName:Jo,inputsToSave:["x","filter"],gradFunc:(r,e,t)=>{let[o,n]=e,{dilations:s,strides:a,pad:i,dataFormat:l}=t;return T(Oo(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`),{x:()=>Kc(o.shape,r,n,a,i,l),filter:()=>tp(o,r,n.shape,a,i,l)}}};var Y0={kernelName:Qo,inputsToSave:["dy","filter"],gradFunc:(r,e,t)=>{let[o,n]=e,{strides:s,pad:a,dataFormat:i,dimRoundingMode:l}=t;return{dy:()=>jr(r,n,s,a,i,1,l),filter:()=>tp(r,o,n.shape,s,a,i,l)}}};function nH(r,e,t,o,n){let s=r;r.rank===4&&(s=L(r,[1,r.shape[0],r.shape[1],r.shape[2],r.shape[3]]));let a=e;a.rank===4&&(a=L(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]])),T(s.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${s.shape}.`),T(a.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${a.shape}.`),T(t.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${t}.`),T(s.shape[4]===t[3],()=>`Error in conv3dDerFilter: depth of input ${s.shape[4]}) must match input depth in filter (${t[3]}.`),T(a.shape[4]===t[4],()=>`Error in conv3dDerFilter: depth of dy (${a.shape[4]}) must match output depth for filter (${t[4]}).`);let i={x:s,dy:a},l={strides:o,pad:n,filterShape:t};return E.runKernel(Kl,i,l)}var Z0=S({conv3DBackpropFilter_:nH});var J0={kernelName:aa,inputsToSave:["x","filter"],gradFunc:(r,e,t)=>{let{dilations:o,strides:n,pad:s}=t;T(Oo(o),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${o}'`);let[a,i]=e;return{x:()=>ug(a.shape,r,i,n,s),filter:()=>Z0(a,r,i.shape,n,s)}}};var Q0={kernelName:en,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>O(je(Lu(oe(t,"float32"))),r)}}};var eS={kernelName:ti,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>O(zu(oe(t,"float32")),r)}}};var tS={kernelName:tn,inputsToSave:["x"],gradFunc:(r,e,t)=>{let[o]=e,{axis:n,exclusive:s,reverse:a}=t;return{x:()=>{let i=S_([n],o.rank),l=Su(r,n,s,!a);return i!=null&&(l=Ue(l,i)),l}}}};var rS={kernelName:rn,inputsToSave:["x","filter"],gradFunc:(r,e,t)=>{let{dilations:o,strides:n,pad:s,dimRoundingMode:a}=t,i=o==null?[1,1]:o;T(Oo(i),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${i}'`);let[l,u]=e;return T(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${l.rank}.`),T(u.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${u.rank}.`),T(l.shape[3]===u.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${u.shape[2]}.`),T(yr(n,i),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'.`),a!=null&&T(st(s),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${s}.`),{x:()=>xg(l.shape,r,u,n,s,o,a),filter:()=>gg(l,r,u.shape,n,s,o,a)}}};var oS={kernelName:la,inputsToSave:["x","filter"],gradFunc:(r,e,t)=>{let[o,n]=e,s={x:o,filter:n,dy:r},a={x:o,filter:n,dy:r};return{x:()=>E.runKernel(Rc,s,t),filter:()=>E.runKernel(Fc,a,t)}}};var nS={kernelName:ni,outputsToSave:[!0],gradFunc:(r,e)=>{let[t]=e,o={dy:r,y:t};return{x:()=>E.runKernel(eu,o)}}};var sS={kernelName:si,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e,o=O(Xt(je(Pe(t))),2/Math.sqrt(Math.PI));return{x:()=>O(r,o)}}};var iS={kernelName:nn,outputsToSave:[!0],gradFunc:(r,e)=>{let[t]=e;return{x:()=>O(r,t)}}};var aS={kernelName:ps,inputsToSave:["input"],gradFunc:(r,e)=>{let[t]=e;return{input:()=>L(r,t.shape)}}};var lS={kernelName:ai,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>O(r,Xt(t))}}};var uS={kernelName:sn,gradFunc:r=>({x:()=>Ie(r)})};var cS={kernelName:an,inputsToSave:["a","b"],gradFunc:(r,e)=>{let[t,o]=e,n=ze(t.shape,o.shape);return{a:()=>{let i=de(r,oe(o,"float32")),l=Ct(t.shape,n);return l.length>0?L(ye(i,l),t.shape):i},b:()=>{let i=O(r,oe(t,"float32")),l=Ct(o.shape,n);l.length>0&&(i=L(ye(i,l),o.shape));let u=Pe(o);return je(de(i,oe(u,"float32")))}}}};var pS={kernelName:ln,inputsToSave:["x","mean","variance","scale"],gradFunc:(r,e,t)=>{let{varianceEpsilon:o}=t,[n,s,a,i]=e,l=i==null?le(1):i,u=Ct(s.shape,n.shape),c=[];if(s.rank===1){for(let w=0;ws.rank===1?L(O(O(r,Po(L(f,[1,1,1,s.shape[0]]),c)),l),n.shape):L(O(O(r,f),l),n.shape),mean:()=>{let w=O(O(f,le(-1)),m);return s.rank===1&&(w=ye(w,u)),L(w,s.shape)},variance:()=>{let w=O(O(d,p),m);return s.rank===1&&(w=ye(w,u)),L(w,s.shape)},scale:()=>{let w=O(p,f),v=O(r,w);return s.rank===1&&(v=ye(v,u)),L(v,s.shape)},offset:()=>{let w=r;return s.rank===1&&(w=ye(w,u)),L(w,s.shape)}}}};var dS={kernelName:ms,inputsToSave:["x","indices"],gradFunc:(r,e,t)=>{let[o,n]=e,{axis:s}=t,a=Jt(s,o.shape)[0];return{x:()=>{let l=o.shape,u=n.size,c=l.slice(0,a),p=c.length,m=l.slice(s,l.length).slice(1),f=m.length,d=mS(0,p),h=mS(p+1,p+1+f),g=fS([c,[u],m]),x=L(r,g),b=L(n,[u]),_=fS([[p],d,h]),w=Ue(x,_),v=nf(w,b,o.shape[a]),$=Wm(_);return v=Ue(v,$),v},indices:()=>n}}};function mS(r,e){let t=[];for(let o=r;o{let[t,o]=e;return{a:()=>Ie(t),b:()=>Ie(o)}}};var gS={kernelName:Do,gradFunc:r=>({x:()=>oe(r,"float32")})};var xS={kernelName:pi,gradFunc:r=>({x:()=>Ie(r)})};var yS={kernelName:mi,gradFunc:r=>({x:()=>Ie(r)})};var bS={kernelName:fi,gradFunc:r=>({x:()=>Ie(r)})};var _S={kernelName:cn,inputsToSave:["x"],gradFunc:(r,e,t)=>{let[o]=e,{alpha:n}=t,s=Qt(o,0);return{x:()=>$t(s,r,O(r,n))}}};var wS={kernelName:gi,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>de(r,Q(t,1))}}};var kS={kernelName:pn,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>de(r,oe(t,"float32"))}}};var vS={kernelName:Ib,inputsToSave:[],outputsToSave:[!0],gradFunc:(r,e,t)=>{let[o]=e,{axis:n}=t;return{logits:()=>{let s=!0,a=Xt(o);return ue(r,O(ye(r,n,s),a))}}}};function sH(r,e,t,o=5,n=1,s=1,a=.5){let i={x:r,y:e,dy:t},l={depthRadius:o,bias:n,alpha:s,beta:a};return E.runKernel(su,i,l)}var CS=S({localResponseNormalizationBackprop_:sH});var IS={kernelName:ca,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(r,e,t)=>{let[o,n]=e,{depthRadius:s,bias:a,alpha:i,beta:l}=t;return{x:()=>CS(o,n,r,s,a,i,l)}}};function Cg(r,e,t,o){return e.rankO(r,oe(_o(t,e),r.dtype))}}var ew={kernelName:mn,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(r,e,t)=>{let o=t,{reductionIndices:n}=o,s=e[0],a=e[1],i=Jt(n,s.shape),l=Cg(r,a,s,i);return{x:()=>l.x()}}};var NS={kernelName:fn,inputsToSave:["a","b"],gradFunc:(r,e)=>{let[t,o]=e;return{a:()=>O(r,oe(to(t,o),"float32")),b:()=>O(r,oe(Eu(t,o),"float32"))}}};function iH(r,e,t,o,n,s,a){let i=k(r,"dy","maxPool3dGrad"),l=k(e,"input","maxPool3dGrad"),u=k(t,"output","maxPool3dGrad"),c=i,p=l,m=u,f=!1;l.rank===4&&(f=!0,c=L(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]]),p=L(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]]),m=L(u,[1,u.shape[0],u.shape[1],u.shape[2],u.shape[3]])),T(c.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${c.rank}.`),T(p.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${p.rank}.`),T(m.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${m.rank}.`),a!=null&&T(st(s),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${a} but got pad ${s}.`);let d={dy:c,input:p,output:m},h={filterSize:o,strides:n,pad:s,dimRoundingMode:a},g=E.runKernel(au,d,h);return f?L(g,[g.shape[1],g.shape[2],g.shape[3],g.shape[4]]):g}var SS=S({maxPool3dGrad_:iH});var TS={kernelName:pa,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(r,e,t)=>{let[o,n]=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=t;return{x:()=>SS(r,o,n,s,a,i,l)}}};function aH(r,e,t,o,n,s,a){let i=k(r,"dy","maxPoolGrad"),l=k(e,"input","maxPoolGrad"),u=k(t,"output","maxPoolGrad");T(l.rank===i.rank,()=>`Rank of input (${l.rank}) does not match rank of dy (${i.rank})`),T(i.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${i.rank}.`),T(l.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${l.rank}.`),a!=null&&T(st(s),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${a} but got pad ${s}.`);let c={dy:i,input:l,output:u},p={filterSize:o,strides:n,pad:s,dimRoundingMode:a};return E.runKernel(iu,c,p)}var ES=S({maxPoolGrad_:aH});var AS={kernelName:dn,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(r,e,t)=>{let[o,n]=e,{filterSize:s,strides:a,pad:i}=t;return{x:()=>ES(r,o,n,s,a,i)}}};var DS={kernelName:hn,inputsToSave:["x"],gradFunc:(r,e,t)=>{let[o]=e,{axis:n}=t,s=Jt(n,o.shape),i=N_(o.shape,s)[1],l=ft(i);return{x:()=>{let c=o.shape.slice();s.forEach(f=>{c[f]=1});let p=L(r,c);return de(O(p,Cr(o.shape,"float32")),l)}}}};var $S={kernelName:gn,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(r,e,t)=>{let o=t,{axis:n}=o,[s,a]=e,i=Jt(n,s.shape),l=Cg(r,a,s,i);return{x:()=>l.x()}}};var RS={kernelName:xn,inputsToSave:["a","b"],gradFunc:(r,e)=>{let[t,o]=e;return{a:()=>O(r,oe(Mo(t,o),"float32")),b:()=>O(r,oe(Qt(t,o),"float32"))}}};var FS={kernelName:ma,inputsToSave:["x"],gradFunc:(r,e,t)=>{let o=e[0],{paddings:n}=t,s=n.map(a=>a[0]);return{x:()=>Fe(r,s,o.shape)}}};var OS={kernelName:yi,inputsToSave:["a","b"],gradFunc:(r,e)=>{let[t,o]=e,n=ze(t.shape,o.shape);return{a:()=>{let i=Ct(t.shape,n);return i.length>0?L(ye(r,i),t.shape):r},b:()=>{let i=O(r,je(Ts(de(t,o)))),l=Ct(o.shape,n);return l.length>0?L(ye(i,l),o.shape):i}}}};var PS={kernelName:yn,inputsToSave:["a","b"],gradFunc:(r,e)=>{let[t,o]=e,n=ze(t.shape,o.shape);return{a:()=>{let i=O(r,oe(o,"float32")),l=Ct(t.shape,n);return l.length>0?L(ye(i,l),t.shape):i},b:()=>{let i=O(r,oe(t,"float32")),l=Ct(o.shape,n);return l.length>0?L(ye(i,l),o.shape):i}}}};var MS={kernelName:fs,gradFunc:r=>({x:()=>je(r)})};var LS={kernelName:bn,inputsToSave:["indices"],gradFunc:(r,e)=>{let t=e[0];return{indices:()=>gt(t.shape,"float32")}}};var zS={kernelName:ds,gradFunc:r=>({x:()=>Ie(r)})};var BS={kernelName:hs,saveAllInputs:!0,gradFunc:(r,e,t)=>{let{axis:o}=t;return ur(r,o).map(s=>()=>s)}};var tw={kernelName:_n,inputsToSave:["x"],gradFunc:(r,e,t)=>{let o=e[0],{paddings:n}=t,s=n.map(a=>a[0]);return{x:()=>Fe(r,s,o.shape)}}};var VS={kernelName:wn,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(r,e)=>{let[t,o,n]=e,s=t,a=o,i=ze(s.shape,a.shape);return{a:()=>{let c=oe(a,"float32"),p=O(r,O(c,Rr(s,ue(c,le(1))))),m=Ct(s.shape,i);return m.length>0&&(p=ye(p,m)),L(p,s.shape)},b:()=>{let c=Qt(s,0),p=$t(c,ir(s),Ie(s)),m=O(r,O(n,p)),f=Ct(a.shape,i);return f.length>0&&(m=ye(m,f)),L(m,a.shape)}}}};var GS={kernelName:kn,inputsToSave:["x","alpha"],gradFunc:(r,e)=>{let[t,o]=e,n=Qt(t,0);return{x:()=>$t(n,r,O(r,o)),alpha:()=>{let s=$t(n,Ie(r),O(r,t)),a=Ct(o.shape,r.shape);return a.length>0&&(s=ye(s,a)),L(s,o.shape)}}}};var WS={kernelName:on,inputsToSave:["a","b"],gradFunc:(r,e)=>{let[t,o]=e,n=ze(t.shape,o.shape);return{a:()=>{let i=de(r,oe(o,"float32")),l=Ct(t.shape,n);return l.length>0?L(ye(i,l),t.shape):i},b:()=>{let i=O(r,oe(t,"float32")),l=Ct(o.shape,n);l.length>0&&(i=L(ye(i,l),o.shape));let u=Pe(o);return je(de(i,oe(u,"float32")))}}}};var US={kernelName:Ci,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>de(r,je(Pe(t)))}}};var jS={kernelName:In,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e,o=O(Mo(t,6),$s(t));return{x:()=>O(r,oe(o,"float32"))}}};var HS={kernelName:vn,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>O(r,oe($s(t),"float32"))}}};var qS={kernelName:gs,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>L(r,t.shape)}}};var KS={kernelName:Cn,inputsToSave:["images"],gradFunc:(r,e,t)=>{let[o]=e,n={dy:r,images:o};return{images:()=>E.runKernel(mu,n,t)}}};var XS={kernelName:da,inputsToSave:["images"],gradFunc:(r,e,t)=>{let[o]=e,n={dy:r,images:o};return{images:()=>E.runKernel(pu,n,t)}}};var YS={kernelName:Nn,gradFunc:(r,e,t)=>{let{dims:o}=t,n=Jt(o,r.shape);return{x:()=>qt(r,n)}}};var ZS={kernelName:Sn,gradFunc:r=>({x:()=>Ie(r)})};var JS={kernelName:Tn,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>je(de(r,O(Rr(t,1.5),2)))}}};var QS={kernelName:xs,inputsToSave:["condition"],gradFunc:(r,e)=>{let[t]=e;return{condition:()=>oe(Ie(t),"float32"),t:()=>O(r,oe(t,r.dtype)),e:()=>O(r,oe(Ta(t),r.dtype))}}};var eT={kernelName:Ni,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>{let o=Qt(t,le(0)),n=le(J_),s=le(Q_),a=O(r,s),i=O(O(r,n),Xt(oe(t,"float32")));return $t(o,a,i)}}}};var tT={kernelName:An,outputsToSave:[!0],gradFunc:(r,e)=>{let[t]=e;return{x:()=>O(r,O(t,ue(le(1),t)))}}};var rT={kernelName:Ti,gradFunc:r=>({x:()=>Ie(r)})};var oT={kernelName:En,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>O(Ia(oe(t,"float32")),r)}}};var nT={kernelName:Si,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>O(Nu(oe(t,"float32")),r)}}};var sT={kernelName:ys,inputsToSave:["x"],gradFunc:(r,e,t)=>{let[o]=e,{begin:n,size:s}=t,a=o.shape,[i,l]=e_(o,n,s),u=[];for(let c=0;c$r(r,u)}}};var iT={kernelName:Rn,outputsToSave:[!0],gradFunc:(r,e,t)=>{let[o]=e,{dim:n}=t,s=!0,a=O(r,o);return{logits:()=>ue(a,O(ye(a,[n],s),o))}}};var aT={kernelName:Ei,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>O(r,Ur(t))}}};var rw={kernelName:ha,gradFunc:(r,e,t)=>{let{blockShape:o,paddings:n}=t;return{x:()=>Ca(r,o,n)}}};var ow={kernelName:bs,gradFunc:(r,e,t)=>{let{axis:o}=t;return{x:()=>Je(r,o)}}};var lT={kernelName:Dn,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>de(r,O(xt(oe(t,"float32")),2))}}};var uT={kernelName:ga,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>O(r,O(oe(t,"float32"),2))}}};var cT={kernelName:Fn,inputsToSave:["a","b"],gradFunc:(r,e)=>{let[t,o]=e,n=le(2);return{a:()=>O(r,O(n,ue(t,o))),b:()=>O(r,O(n,ue(o,t)))}}};var pT={kernelName:$o,gradFunc:r=>({x:()=>Ie(r)})};var mT={kernelName:On,inputsToSave:["a","b"],gradFunc:(r,e)=>{let[t,o]=e,n=ze(t.shape,o.shape);return{a:()=>{let i=r,l=Ct(t.shape,n);return l.length>0&&(i=ye(i,l)),L(i,t.shape)},b:()=>{let i=r,l=Ct(o.shape,n);return l.length>0&&(i=ye(i,l)),L(je(i),o.shape)}}}};var fT={kernelName:$n,inputsToSave:["x"],gradFunc:(r,e,t)=>{let[o]=e,n=o.shape.slice(),{axis:s}=t;Jt(s,o.shape).forEach(u=>{n[u]=1});let i=L(r,n),l=O(i,Cr(o.shape,"float32"));return{x:()=>l}}};var dT={kernelName:Di,inputsToSave:["x"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>de(r,Pe(Ia(t)))}}};var hT={kernelName:Pn,outputsToSave:[!0],gradFunc:(r,e)=>{let[t]=e;return{x:()=>O(ue(le(1),Pe(t)),r)}}};var gT={kernelName:yo,inputsToSave:["x"],gradFunc:(r,e,t)=>{let[o]=e,{reps:n}=t;return{x:()=>{let a=Ie(o);if(o.rank===1)for(let i=0;i{let o=t,{perm:n}=o,s=Wm(n);return{x:()=>Ue(r,s)}}};var yT={kernelName:_s,gradFunc:(r,e,t)=>{let o=t,{axis:n}=o;return{value:()=>Bt(r,n)}}};var bT={kernelName:xa,inputsToSave:["segmentIds"],gradFunc:(r,e)=>{let[t]=e;return{x:()=>lH(r,t)}}};function lH(r,e){let t=qr(e,Ie(e)),o=Bn(r,t),n=to(e,le(0,"int32")),s=o.rank-n.rank;for(let i=0;i({x:()=>Ie(r)})};var uH=[vg,N0,S0,T0,E0,A0,D0,$0,R0,F0,O0,P0,L0,B0,V0,G0,W0,U0,j0,H0,q0,K0,Y0,X0,J0,Q0,eS,tS,rS,oS,WS,nS,sS,iS,aS,lS,cS,uS,pS,dS,hS,gS,xS,yS,bS,_S,wS,kS,vS,IS,ew,ew,NS,TS,AS,DS,$S,RS,FS,OS,PS,MS,LS,zS,BS,tw,tw,VS,GS,US,jS,HS,qS,KS,XS,YS,ZS,JS,QS,eT,tT,rT,oT,nT,sT,iT,aT,rw,rw,ow,ow,lT,cT,uT,pT,mT,fT,dT,hT,gT,xT,yT,bT,_T];for(let r of uH)Sb(r);F().prototype.abs=function(){return this.throwIfDisposed(),Tt(this)};F().prototype.acos=function(){return this.throwIfDisposed(),Nm(this)};F().prototype.acosh=function(){return this.throwIfDisposed(),Sm(this)};F().prototype.add=function(r){return this.throwIfDisposed(),Q(this,r)};F().prototype.all=function(r,e){return this.throwIfDisposed(),wu(this,r,e)};F().prototype.any=function(r,e){return this.throwIfDisposed(),nl(this,r,e)};F().prototype.argMax=function(r){return this.throwIfDisposed(),sl(this,r)};F().prototype.argMin=function(r){return this.throwIfDisposed(),Tm(this,r)};F().prototype.asScalar=function(){return this.throwIfDisposed(),T(this.size===1,()=>"The array must have only 1 element."),L(this,[])};F().prototype.asType=function(r){return this.throwIfDisposed(),oe(this,r)};F().prototype.as1D=function(){return this.throwIfDisposed(),L(this,[this.size])};F().prototype.as2D=function(r,e){return this.throwIfDisposed(),L(this,[r,e])};F().prototype.as3D=function(r,e,t){return this.throwIfDisposed(),L(this,[r,e,t])};F().prototype.as4D=function(r,e,t,o){return this.throwIfDisposed(),L(this,[r,e,t,o])};F().prototype.as5D=function(r,e,t,o,n){return this.throwIfDisposed(),L(this,[r,e,t,o,n])};F().prototype.asin=function(){return this.throwIfDisposed(),Em(this)};F().prototype.asinh=function(){return this.throwIfDisposed(),Am(this)};F().prototype.atan=function(){return this.throwIfDisposed(),Dm(this)};F().prototype.atan2=function(r){return this.throwIfDisposed(),$m(this,r)};F().prototype.atanh=function(){return this.throwIfDisposed(),Rm(this)};F().prototype.avgPool=function(r,e,t,o){return this.throwIfDisposed(),va(this,r,e,t,o)};F().prototype.batchToSpaceND=function(r,e){return this.throwIfDisposed(),Ca(this,r,e)};F().prototype.batchNorm=function(r,e,t,o,n){return this.throwIfDisposed(),zn(this,r,e,t,o,n)};F().prototype.broadcastTo=function(r){return this.throwIfDisposed(),il(this,r)};F().prototype.cast=function(r){return this.throwIfDisposed(),oe(this,r)};F().prototype.ceil=function(){return this.throwIfDisposed(),Om(this)};F().prototype.clipByValue=function(r,e){return this.throwIfDisposed(),nr(this,r,e)};F().prototype.concat=function(r,e){return this.throwIfDisposed(),r instanceof Ve&&(r=[r]),Je([this,...r],e)};F().prototype.conv1d=function(r,e,t,o,n,s){return this.throwIfDisposed(),Cu(this,r,e,t,o,n,s)};F().prototype.conv2dTranspose=function(r,e,t,o,n){return this.throwIfDisposed(),Iu(this,r,e,t,o,n)};F().prototype.conv2d=function(r,e,t,o,n,s){return this.throwIfDisposed(),jr(this,r,e,t,o,n,s)};F().prototype.cos=function(){return this.throwIfDisposed(),Ia(this)};F().prototype.cosh=function(){return this.throwIfDisposed(),Nu(this)};F().prototype.cumsum=function(r,e,t){return this.throwIfDisposed(),Su(this,r,e,t)};F().prototype.depthToSpace=function(r,e){return this.throwIfDisposed(),Mm(this,r,e)};F().prototype.depthwiseConv2d=function(r,e,t,o,n,s){return this.throwIfDisposed(),Ns(this,r,e,t,o,n,s)};F().prototype.dilation2d=function(r,e,t,o,n){return this.throwIfDisposed(),Lm(this,r,e,t,o,n)};F().prototype.divNoNan=function(r){return this.throwIfDisposed(),zm(this,r)};F().prototype.div=function(r){return this.throwIfDisposed(),de(this,r)};F().prototype.dot=function(r){return this.throwIfDisposed(),b_(this,r)};F().prototype.elu=function(){return this.throwIfDisposed(),Ss(this)};F().prototype.equal=function(r){return this.throwIfDisposed(),_o(this,r)};F().prototype.erf=function(){return this.throwIfDisposed(),Bm(this)};F().prototype.exp=function(){return this.throwIfDisposed(),Xt(this)};F().prototype.expandDims=function(r){return this.throwIfDisposed(),sr(this,r)};F().prototype.expm1=function(){return this.throwIfDisposed(),Vm(this)};F().prototype.fft=function(){return this.throwIfDisposed(),Ra(this)};F().prototype.flatten=function(){return this.throwIfDisposed(),L(this,[this.size])};F().prototype.floor=function(){return this.throwIfDisposed(),Ts(this)};F().prototype.floorDiv=function(r){return this.throwIfDisposed(),_u(this,r)};F().prototype.gather=function(r,e){return this.throwIfDisposed(),Bn(this,r,e)};F().prototype.greaterEqual=function(r){return this.throwIfDisposed(),to(this,r)};F().prototype.greater=function(r){return this.throwIfDisposed(),Qt(this,r)};F().prototype.ifft=function(){return this.throwIfDisposed(),zi(this)};F().prototype.irfft=function(){return this.throwIfDisposed(),Bu(this)};F().prototype.isFinite=function(){return this.throwIfDisposed(),__(this)};F().prototype.isInf=function(){return this.throwIfDisposed(),w_(this)};F().prototype.isNaN=function(){return this.throwIfDisposed(),k_(this)};F().prototype.leakyRelu=function(r){return this.throwIfDisposed(),Sa(this,r)};F().prototype.lessEqual=function(r){return this.throwIfDisposed(),Mo(this,r)};F().prototype.less=function(r){return this.throwIfDisposed(),Eu(this,r)};F().prototype.localResponseNormalization=function(r,e,t,o){return this.throwIfDisposed(),Gm(this,r,e,t,o)};F().prototype.logSigmoid=function(){return this.throwIfDisposed(),C_(this)};F().prototype.logSoftmax=function(r){return this.throwIfDisposed(),Du(this,r)};F().prototype.logSumExp=function(r,e){return this.throwIfDisposed(),Um(this,r,e)};F().prototype.log=function(){return this.throwIfDisposed(),ir(this)};F().prototype.log1p=function(){return this.throwIfDisposed(),Au(this)};F().prototype.logicalAnd=function(r){return this.throwIfDisposed(),fr(this,r)};F().prototype.logicalNot=function(){return this.throwIfDisposed(),Ta(this)};F().prototype.logicalOr=function(r){return this.throwIfDisposed(),$u(this,r)};F().prototype.logicalXor=function(r){return this.throwIfDisposed(),T_(this,r)};F().prototype.matMul=function(r,e,t){return this.throwIfDisposed(),We(this,r,e,t)};F().prototype.maxPool=function(r,e,t,o){return this.throwIfDisposed(),Ea(this,r,e,t,o)};F().prototype.max=function(r,e){return this.throwIfDisposed(),ar(this,r,e)};F().prototype.maximum=function(r){return this.throwIfDisposed(),qr(this,r)};F().prototype.mean=function(r,e){return this.throwIfDisposed(),ht(this,r,e)};F().prototype.min=function(r,e){return this.throwIfDisposed(),Li(this,r,e)};F().prototype.minimum=function(r){return this.throwIfDisposed(),As(this,r)};F().prototype.mirrorPad=function(r,e){return this.throwIfDisposed(),Hm(this,r,e)};F().prototype.mod=function(r){return this.throwIfDisposed(),qm(this,r)};F().prototype.mul=function(r){return this.throwIfDisposed(),O(this,r)};F().prototype.neg=function(){return this.throwIfDisposed(),je(this)};F().prototype.norm=function(r,e,t){return this.throwIfDisposed(),Wu(this,r,e,t)};F().prototype.notEqual=function(r){return this.throwIfDisposed(),Gn(this,r)};F().prototype.oneHot=function(r,e=1,t=0){return this.throwIfDisposed(),Is(this,r,e,t)};F().prototype.onesLike=function(){return this.throwIfDisposed(),er(this)};F().prototype.pad=function(r,e){return this.throwIfDisposed(),$r(this,r,e)};F().prototype.pool=function(r,e,t,o,n){return this.throwIfDisposed(),D_(this,r,e,t,o,n)};F().prototype.pow=function(r){return this.throwIfDisposed(),Rr(this,r)};F().prototype.prelu=function(r){return this.throwIfDisposed(),Da(this,r)};F().prototype.prod=function(r,e){return this.throwIfDisposed(),Ru(this,r,e)};F().prototype.reciprocal=function(){return this.throwIfDisposed(),Xm(this)};F().prototype.relu=function(){return this.throwIfDisposed(),Ir(this)};F().prototype.relu6=function(){return this.throwIfDisposed(),Ou(this)};F().prototype.reshapeAs=function(r){return this.throwIfDisposed(),L(this,r.shape)};F().prototype.reshape=function(r){return this.throwIfDisposed(),L(this,r)};F().prototype.resizeBilinear=function(r,e,t){return this.throwIfDisposed(),_g(this,r,e,t)};F().prototype.resizeNearestNeighbor=function(r,e,t){return this.throwIfDisposed(),wg(this,r,e,t)};F().prototype.reverse=function(r){return this.throwIfDisposed(),qt(this,r)};F().prototype.rfft=function(){return this.throwIfDisposed(),Fa(this)};F().prototype.round=function(){return this.throwIfDisposed(),Ym(this)};F().prototype.rsqrt=function(){return this.throwIfDisposed(),Pu(this)};F().prototype.selu=function(){return this.throwIfDisposed(),Mu(this)};F().prototype.separableConv2d=function(r,e,t,o,n,s){return this.throwIfDisposed(),Zm(this,r,e,t,o,n,s)};F().prototype.sigmoid=function(){return this.throwIfDisposed(),Ur(this)};F().prototype.sign=function(){return this.throwIfDisposed(),Jm(this)};F().prototype.sin=function(){return this.throwIfDisposed(),Lu(this)};F().prototype.sinh=function(){return this.throwIfDisposed(),zu(this)};F().prototype.slice=function(r,e){return this.throwIfDisposed(),Fe(this,r,e)};F().prototype.softmax=function(r){return this.throwIfDisposed(),$a(this,r)};F().prototype.softplus=function(){return this.throwIfDisposed(),Es(this)};F().prototype.spaceToBatchND=function(r,e){return this.throwIfDisposed(),Aa(this,r,e)};F().prototype.split=function(r,e){return this.throwIfDisposed(),lr(this,r,e)};F().prototype.sqrt=function(){return this.throwIfDisposed(),xt(this)};F().prototype.square=function(){return this.throwIfDisposed(),Pe(this)};F().prototype.squaredDifference=function(r){return this.throwIfDisposed(),Vu(this,r)};F().prototype.squeeze=function(r){return this.throwIfDisposed(),wo(this,r)};F().prototype.stack=function(r,e){this.throwIfDisposed();let t=r instanceof Ve?[this,r]:[this,...r];return Bt(t,e)};F().prototype.step=function(r){return this.throwIfDisposed(),$s(this,r)};F().prototype.stridedSlice=function(r,e,t,o,n,s,a,i){return this.throwIfDisposed(),tf(this,r,e,t,o,n,s,a,i)};F().prototype.sub=function(r){return this.throwIfDisposed(),ue(this,r)};F().prototype.sum=function(r,e){return this.throwIfDisposed(),ye(this,r,e)};F().prototype.tan=function(){return this.throwIfDisposed(),rf(this)};F().prototype.tanh=function(){return this.throwIfDisposed(),Mi(this)};F().prototype.tile=function(r){return this.throwIfDisposed(),Po(this,r)};F().prototype.toBool=function(){return this.throwIfDisposed(),oe(this,"bool")};F().prototype.toFloat=function(){return this.throwIfDisposed(),oe(this,"float32")};F().prototype.toInt=function(){return this.throwIfDisposed(),oe(this,"int32")};F().prototype.topk=function(r,e){return this.throwIfDisposed(),of(this,r,e)};F().prototype.transpose=function(r){return this.throwIfDisposed(),Ue(this,r)};F().prototype.unique=function(r){return this.throwIfDisposed(),ep(this,r)};F().prototype.unsortedSegmentSum=function(r,e){return this.throwIfDisposed(),nf(this,r,e)};F().prototype.unstack=function(r){return this.throwIfDisposed(),ur(this,r)};F().prototype.where=function(r,e){return this.throwIfDisposed(),$t(r,this,e)};F().prototype.zerosLike=function(){return this.throwIfDisposed(),Ie(this)};var uw={};et(uw,{maxNorm:()=>pH,minMaxNorm:()=>dH,nonNeg:()=>fH,unitNorm:()=>mH});var nw;function Yt(){return nw==null&&(nw=s_().epsilon()),nw}function Kr(){return"channelsLast"}var ko=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,ko.prototype)}},Or=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Or.prototype)}},z=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,z.prototype)}},Ne=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Ne.prototype)}},cf=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,cf.prototype)}},sw=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,sw.prototype)}};function jn(r,e){if(Array.isArray(r)){let t=[];for(let o=0;ot.toUpperCase())}var qn={};function lp(r){if(r==null)return null;let e={};return e.className=r.getClassName(),e.config=r.getConfig(),e}function aw(r){if(!(r==null||typeof r!="object"))if(Array.isArray(r))r.forEach(e=>aw(e));else{let e=Object.keys(r);for(let t of e){let o=r[t];o!=null&&typeof o=="object"&&(!Array.isArray(o)&&o.type==="ndarray"&&typeof o.value=="number"?r[t]=o.value:aw(o))}}}function Vi(r,e={},t={},o="object",n=!1){if(typeof r=="string"){let s=r,a;if(s in t)a=t[s];else if(s in qn)a=qn[s];else if(a=e[s],a==null)throw new z(`Unknown ${o}: ${r}. This may be due to one of the following reasons: 1. The ${o} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code. 2. The custom ${o} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return a}else{let s=r;if(s.className==null||s.config==null)throw new z(`${o}: Improper config format: ${JSON.stringify(s)}. 'className' and 'config' must set.`);let a=s.className,i,l;if(a in t?[i,l]=t[a]:a in qn?[i,l]=qn.className:a in e&&([i,l]=e[a]),i==null)throw new z(`Unknown ${o}: ${a}. This may be due to one of the following reasons: 1. The ${o} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code. 2. The custom ${o} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(l!=null){let u={};for(let f of Object.keys(qn))u[f]=qn[f];for(let f of Object.keys(t))u[f]=t[f];let c=s.config;c.customObjects=u;let p=Object.assign({},qn);for(let f of Object.keys(t))qn[f]=t[f];aw(s.config);let m=l(i,s.config,t,n);return qn=Object.assign({},p),m}else{let u=Object.assign({},qn);for(let p of Object.keys(t))qn[p]=t[p];let c=new i(s.config);return qn=Object.assign({},u),c}}}function cH(r,e){return re?1:0}function pf(r,e){return-1*cH(r,e)}function Kn(r){if(r==null)return r;let e=[];for(let t of r)e.indexOf(t)===-1&&e.push(t);return e}function wT(r){if(r==null)throw new z(`Invalid value in obj: ${JSON.stringify(r)}`);for(let e in r)if(r.hasOwnProperty(e))return!1;return!0}function Gi(r,e,t){if(t!=null&&r.indexOf(t)<0)throw new z(`${t} is not a valid ${e}. Valid values are ${r} or null/undefined.`)}function Ig(r,e,t=0,o=Infinity){return Lo(t>=0),Lo(o>=t),Array.isArray(r)&&r.length>=t&&r.length<=o&&r.every(n=>typeof n===e)}function Wt(r,e){Array.isArray(r)?(y.assert(r.length>0,()=>`${e} is unexpectedly an empty array.`),r.forEach((t,o)=>Wt(t,`element ${o+1} of ${e}`))):y.assert(Number.isInteger(r)&&r>0,()=>`Expected ${e} to be a positive integer, but got ${kT(r)}.`)}function kT(r){return r===null?"null":Array.isArray(r)?"["+r.map(e=>kT(e)).join(",")+"]":typeof r=="string"?`"${r}"`:`${r}`}function vT(r,e){let t=y.now(),o;return(...s)=>{let a=y.now();return a-txt(ye(O(r,r),e,!0)))}var up=class extends J.Serializable{getConfig(){return{}}},mf=class extends up{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 B(()=>{let t=lw(e,this.axis),o=nr(t,0,this.maxValue);return O(e,de(o,Q(Yt(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};mf.className="MaxNorm";J.registerClass(mf);var ff=class extends up{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return B(()=>de(e,Q(Yt(),lw(e,this.axis))))}getConfig(){return{axis:this.axis}}};ff.className="UnitNorm";J.registerClass(ff);var df=class extends up{apply(e){return Ir(e)}};df.className="NonNeg";J.registerClass(df);var hf=class extends up{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 B(()=>{let t=lw(e,this.axis),o=Q(O(this.rate,nr(t,this.minValue,this.maxValue)),O(1-this.rate,t));return O(e,de(o,Q(Yt(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};hf.className="MinMaxNorm";J.registerClass(hf);var CT={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Ot(r){return lp(r)}function IT(r,e={}){return Vi(r,J.SerializationMap.getMap().classNameMap,e,"constraint")}function Pt(r){if(r==null)return null;if(typeof r=="string"){let t={className:r in CT?CT[r]:r,config:{}};return IT(t)}else return r instanceof up?r:IT(r)}function pH(r){return new mf(r)}function mH(r){return new ff(r)}function fH(){return new df}function dH(r){return new hf(r)}var dw={};et(dw,{constant:()=>kH,glorotNormal:()=>EH,glorotUniform:()=>TH,heNormal:()=>AH,heUniform:()=>DH,identity:()=>NH,leCunNormal:()=>$H,leCunUniform:()=>RH,ones:()=>wH,orthogonal:()=>FH,randomNormal:()=>CH,randomUniform:()=>vH,truncatedNormal:()=>IH,varianceScaling:()=>SH,zeros:()=>_H});var NT=["channelsFirst","channelsLast"],ST=["nearest","bilinear"],TT=["valid","same","causal"],ET=["max","avg"],AT=["sum","mul","concat","ave"];var cp=new Map;function Rt(r){Gi(NT,"DataFormat",r)}function DT(r){Gi(ST,"InterpolationFormat",r)}function Xr(r){Gi(TT,"PaddingMode",r)}function cw(r){Gi(ET,"PoolMode",r)}var gf=[],$T="/";function Fs(r,e){gf.push(r);try{let t=e();return gf.pop(),t}catch(t){throw gf.pop(),t}}function hH(){return gf.length===0?"":gf.join($T)+$T}function Sg(r){if(!RT(r))throw new Error("Not a valid tensor name: '"+r+"'");return hH()+r}function Tg(r){if(!RT(r))throw new Error("Not a valid tensor name: '"+r+"'");cp.has(r)||cp.set(r,0);let e=cp.get(r);if(cp.set(r,cp.get(r)+1),e>0){let t=`${r}_${e}`;return cp.set(t,1),t}else return r}var gH=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function RT(r){return!!r.match(gH)}function FT(r){return r===parseInt(r.toString(),10)}function Xn(r,e,t){e==null&&(e=0),t==null&&(t=r.length);let o=1;for(let n=e;n{if(r.shape.length!==2)throw new z(`repeat() expects a rank-2 tensor, but received a rank-${r.shape.length} tensor.`);let t=La(r,1);return Eg(t,[1,e,1])})}function MT(r){let e=[Xn(r.shape)];return r.reshape(e)}function LT(r){if(r.rank<=1)throw new z(`batchFlatten requires a minimum rank of 2. Got rank: ${r.rank}.`);let e=[r.shape[0],Xn(r.shape,1)];return r.reshape(e)}function za(r,e,t){return B(()=>{switch(r.rank){case 1:return Qm(r,e,t);case 2:return hg(r,[e,0],[t,r.shape[1]]);case 3:return ef(r,[e,0,0],[t,r.shape[1],r.shape[2]]);case 4:return Qc(r,[e,0,0,0],[t,r.shape[1],r.shape[2],r.shape[3]]);case 5:return Fe(r,[e,0,0,0,0],[t,r.shape[1],r.shape[2],r.shape[3],r.shape[4]]);case 6:return Fe(r,[e,0,0,0,0,0],[t,r.shape[1],r.shape[2],r.shape[3],r.shape[4],r.shape[5]]);default:throw new z(`sliceAlongFirstAxis() received an unsupported tensor rank: ${r.rank}`)}})}function pw(r,e,t){return B(()=>{switch(r.rank){case 1:return Qm(r,e,t);case 2:return hg(r,[0,e],[r.shape[0],t]);case 3:return ef(r,[0,0,e],[r.shape[0],r.shape[1],t]);case 4:return Qc(r,[0,0,0,e],[r.shape[0],r.shape[1],r.shape[2],t]);default:throw new z(`sliceAlongLastAxis() received an unsupported tensor rank: ${r.rank}`)}})}function xf(r,e,t,o){return B(()=>{switch(r.rank){case 1:return Qm(r,e,t);case 2:switch(o){case 1:return za(r,e,t);case 2:return pw(r,e,t);default:throw new z(`The axis is not within the rank of the tensor ${o}`)}case 3:switch(o){case 1:return za(r,e,t);case 2:return ef(r,[0,e,0],[r.shape[0],t,r.shape[2]]);case 3:return pw(r,e,t);default:throw new z(`The axis is not within the rank of the tensor ${o}`)}case 4:switch(o){case 1:return za(r,e,t);case 2:return Qc(r,[0,e,0,0],[r.shape[0],t,r.shape[2],r.shape[3]]);case 3:return Qc(r,[0,0,e,0],[r.shape[0],r.shape[1],t,r.shape[3]]);case 4:return pw(r,e,t);default:throw new z(`The axis is not within the rank of the tensor ${o}`)}default:throw new z(`sliceAlongLastAxis() received an unsupported tensor rank: ${r.rank}`)}})}function pp(r,e=-1){let t;return e<0&&(t=r[0].rank,t!==0?e=t:e=0),e===r[0].rank&&(e=-1),Je(r,e)}function mw(r,e){switch(r.rank){case 1:return d_([r,e]);case 2:return h_([r,e],0);case 3:return g_([r,e],0);case 4:return x_([r,e],0);default:throw new z(`concatAlongFirstAxis() received an unsupported tensor rank: ${r.rank}`)}}function Eg(r,e){if(Array.isArray(e)||(e=[e]),r.rank!==e.length)throw new z(`The length of input n (${e.length}) does not match the number of dimensions in input x (${r.rank})`);return Po(r,e)}function mp(r,e=0,t=1,o,n){return dg(r,e,t,o,n)}function Yn(r,e,t,o){if(r.rank<2||e.rank<2)throw new Ne(`dot requires both inputs to be rank >= 2 but got x shape = ${r.shape} and y shape = ${e.shape}`);if(e.rank>=3){let n=r.shape.slice(-1)[0],s=e.shape.slice(-2)[0];if(n!==s)throw new Ne(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${r.shape} and y shape = ${e.shape}`)}if(r.rank===2&&e.rank===2){let n=!1,s=!1;return Wn.matMul({a:r,b:e,transposeA:n,transposeB:s,bias:o?fw(r.rank,o,Kr()):null,activation:t})}else{let n=r.shape.slice(),s=n.pop();r=r.reshape([-1,s]);let a=e.shape.slice(),i=a.pop(),l=a.pop(),u=[...a,i],c=Array.from({length:e.rank},(d,h)=>h===0?e.rank-2:h<=e.rank-2?h-1:h);e=e.transpose(c).reshape([l,-1]);let p=[...n,...u],m=!1,f=!1;return Wn.matMul({a:r,b:e,transposeA:m,transposeB:f,bias:o?fw(r.rank,o,Kr()):null,activation:t}).reshape(p)}}function Ag(r,e,t){return B(()=>(Array.isArray(e)?e=Vt(e,"int32"):e=e.toInt(),Bn(r,e,t)))}function Xu(r){return O(r,r)}function fw(r,e,t){let o=e.shape;if(e.rank!==1&&e.rank!==r)throw new z(`Unexpected bias dimensions: ${e.rank}; expected it to be 1 or ${r}`);if(r===5){if(t==="channelsFirst")return o.length===1?e.reshape([1,o[0],1,1,1]):e.reshape([1,o[3],o[0],o[1],o[2]]);if(t==="channelsLast")return o.length===1?e.reshape([1,1,1,1,o[0]]):e.reshape([1].concat(o))}else if(r===4){if(t==="channelsFirst")return o.length===1?e.reshape([1,o[0],1,1]):e.reshape([1,o[2],o[0],o[1]]);if(t==="channelsLast")return o.length===1?e.reshape([1,1,1,o[0]]):e.reshape([1].concat(o))}else if(r===3){if(t==="channelsFirst")return o.length===1?e.reshape([1,o[0],1]):e.reshape([1,o[1],o[0]]);if(t==="channelsLast")return o.length===1?e.reshape([1,1,o[0]]):e.reshape([1].concat(o))}else if(r<3)return e;throw new z(`Unsupported input rank by biasAdd: ${e.rank}`)}function ro(r,e,t){return B(()=>(t==null&&(t=Kr()),Rt(t),r.add(fw(r.rank,e,t))))}function zT(r,e=1){if(e!==1)throw new Ne(`Support for alpha values other than 1 (${e}) is not implemented yet.`);return Ss(r)}function BT(r){return B(()=>de(r,Tt(r).add(1)))}function Dg(r,e,t,o){return B(()=>j_(r,e,t,o))}function VT(r){return B(()=>{let e=Q(.5,O(.2,r));return nr(e,0,1)})}function cl(r,e,t=!1){return t?r():e()}var GT=["fanIn","fanOut","fanAvg"],WT=["normal","uniform","truncatedNormal"];function xH(r){Gi(GT,"FanMode",r)}function yH(r){Gi(WT,"Distribution",r)}var oo=class extends J.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},yf=class extends oo{apply(e,t){return gt(e,t)}};yf.className="Zeros";J.registerClass(yf);var Yu=class extends oo{apply(e,t){return Cr(e,t)}};Yu.className="Ones";J.registerClass(Yu);var bf=class extends oo{constructor(e){super();if(typeof e!="object")throw new z(`Expected argument of type ConstantConfig but got ${e}`);if(e.value===void 0)throw new z(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return B(()=>O(le(this.value),Cr(e,t)))}getConfig(){return{value:this.value}}};bf.className="Constant";J.registerClass(bf);var _f=class extends oo{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 Ds(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};_f.className="RandomUniform";J.registerClass(_f);var wf=class extends oo{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 Ne(`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}}};wf.className="RandomNormal";J.registerClass(wf);var kf=class extends oo{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 Ne(`truncatedNormal does not support dType ${t}.`);return Gu(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};kf.className="TruncatedNormal";J.registerClass(kf);var vf=class extends oo{constructor(e){super();this.gain=e.gain!=null?e.gain:1}apply(e,t){return B(()=>{if(e.length!==2||e[0]!==e[1])throw new z("Identity matrix initializer can only be used for 2D square matrices.");return O(this.gain,Xc(e[0]))})}getConfig(){return{gain:this.gain}}};vf.className="Identity";J.registerClass(vf);function bH(r,e="channelsLast"){let t,o;if(Rt(e),r.length===2)t=r[0],o=r[1];else if([3,4,5].indexOf(r.length)!==-1){if(e==="channelsFirst"){let n=Xn(r,2);t=r[1]*n,o=r[0]*n}else if(e==="channelsLast"){let n=Xn(r,0,r.length-2);t=r[r.length-2]*n,o=r[r.length-1]*n}}else{let n=Xn(r);t=Math.sqrt(n),o=Math.sqrt(n)}return[t,o]}var Mr=class extends oo{constructor(e){super();if(e.scale<0)throw new z(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,xH(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,yH(this.distribution),this.seed=e.seed}apply(e,t){let o=bH(e),n=o[0],s=o[1],a=this.scale;if(this.mode==="fanIn"?a/=Math.max(1,n):this.mode==="fanOut"?a/=Math.max(1,s):a/=Math.max(1,(n+s)/2),this.distribution==="normal"){let i=Math.sqrt(a);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Ne(`${this.getClassName()} does not support dType ${t}.`);return Gu(e,0,i,t,this.seed)}else{let i=Math.sqrt(3*a);return Ds(e,-i,i,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};Mr.className="VarianceScaling";J.registerClass(Mr);var fp=class extends Mr{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Mr.className}};fp.className="GlorotUniform";J.registerClass(fp);var dp=class extends Mr{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Mr.className}};dp.className="GlorotNormal";J.registerClass(dp);var hp=class extends Mr{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Mr.className}};hp.className="HeNormal";J.registerClass(hp);var gp=class extends Mr{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Mr.className}};gp.className="HeUniform";J.registerClass(gp);var xp=class extends Mr{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Mr.className}};xp.className="LeCunNormal";J.registerClass(xp);var yp=class extends Mr{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Mr.className}};yp.className="LeCunNormal";J.registerClass(yp);var Cf=class extends oo{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new Ne("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return B(()=>{if(e.length<2)throw new Ne("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let o=e[0]>e[1]?[e[1],e[0]]:e,n=mp(o,0,1,"float32"),s=Z_.gramSchmidt(n);return e[0]>e[1]&&(s=s.transpose()),O(this.gain,s)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Cf.className="Orthogonal";J.registerClass(Cf);var UT={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 jT(r,e={}){return Vi(r,J.SerializationMap.getMap().classNameMap,e,"initializer")}function It(r){return lp(r)}function dt(r){if(typeof r=="string"){let e=r in UT?UT[r]:r;if(e==="GlorotNormal")return new dp;if(e==="GlorotUniform")return new fp;if(e==="HeNormal")return new hp;if(e==="HeUniform")return new gp;if(e==="LeCunNormal")return new xp;if(e==="LeCunUniform")return new yp;{let t={};return t.className=e,t.config={},jT(t)}}else return r instanceof oo?r:jT(r)}function _H(){return new yf}function wH(){return new Yu}function kH(r){return new bf(r)}function vH(r){return new _f(r)}function CH(r){return new wf(r)}function IH(r){return new kf(r)}function NH(r){return new vf(r)}function SH(r){return new Mr(r)}function TH(r){return new fp(r)}function EH(r){return new dp(r)}function AH(r){return new hp(r)}function DH(r){return new gp(r)}function $H(r){return new xp(r)}function RH(r){return new yp(r)}function FH(r){return new Cf(r)}var Yw={};et(Yw,{Layer:()=>Me,RNN:()=>ao,RNNCell:()=>bl,activation:()=>Jq,add:()=>aK,alphaDropout:()=>UK,average:()=>lK,averagePooling1d:()=>Zw,averagePooling2d:()=>Jw,averagePooling3d:()=>Qw,avgPool1d:()=>xK,avgPool2d:()=>bK,avgPool3d:()=>wK,avgPooling1d:()=>yK,avgPooling2d:()=>_K,avgPooling3d:()=>kK,batchNormalization:()=>dK,bidirectional:()=>PK,concatenate:()=>uK,conv1d:()=>Uq,conv2d:()=>jq,conv2dTranspose:()=>Hq,conv3d:()=>qq,convLstm2d:()=>$K,convLstm2dCell:()=>RK,cropping2D:()=>Xq,dense:()=>Qq,depthwiseConv2d:()=>Zq,dot:()=>fK,dropout:()=>eK,elu:()=>Lq,embedding:()=>iK,flatten:()=>rK,gaussianDropout:()=>WK,gaussianNoise:()=>GK,globalAveragePooling1d:()=>vK,globalAveragePooling2d:()=>CK,globalMaxPool1d:()=>LK,globalMaxPool2d:()=>zK,globalMaxPooling1d:()=>N1,globalMaxPooling2d:()=>S1,gru:()=>NK,gruCell:()=>SK,input:()=>Qg,inputLayer:()=>Mq,layerNormalization:()=>hK,leakyReLU:()=>Bq,lstm:()=>TK,lstmCell:()=>EK,masking:()=>jK,maxPool1d:()=>BK,maxPool2d:()=>VK,maxPooling1d:()=>T1,maxPooling2d:()=>E1,maxPooling3d:()=>IK,maximum:()=>cK,minimum:()=>pK,multiply:()=>mK,permute:()=>sK,prelu:()=>Vq,reLU:()=>zq,repeatVector:()=>oK,reshape:()=>nK,rnn:()=>FK,separableConv2d:()=>Kq,simpleRNN:()=>AK,simpleRNNCell:()=>DK,softmax:()=>Gq,spatialDropout1d:()=>tK,stackedRNNCells:()=>OK,thresholdedReLU:()=>Wq,timeDistributed:()=>MK,upSampling2d:()=>Yq,zeroPadding2d:()=>gK});var OH=0;function $g(){return OH++}var Rg={};function pl(r=""){return r in Rg||(Rg[r]=0),Rg[r]+=1,r+Rg[r].toString()}function Fg(r){return Array.isArray(r)&&Array.isArray(r[0])}function bp(r){return r.length===0?[]:Array.isArray(r[0])?r:[r]}function Oe(r){let e;if(Array.isArray(r)){if(r.length!==1)throw new z(`Expected Tensor length to be 1; got ${r.length}`);e=r[0]}else e=r;return e}function Qe(r){if(Array.isArray(r)&&Array.isArray(r[0])){if(r.length===1)return r=r,r[0];throw new z(`Expected exactly 1 Shape; got ${r.length}`)}else return r}function _p(r){let e=0;for(let t of r)t.shape.length===0?e+=1:e+=t.shape.reduce((o,n)=>o*n);return e}var HT="Variable",If=class{constructor(e,t="float32",o=HT,n=!0,s=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=$g(),o=o==null?HT:o,this.originalName=Sg(o),this.name=Tg(this.originalName),this.trainable_=n,this.constraint=s,this.val=V_(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),PH(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 ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}};function PH(r,e){if(r.shape.toString()!==e.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(r.shape)+" vs. "+JSON.stringify(e.shape))}function Nf(r){return r.map(e=>e.read())}function wp(r){r.forEach(e=>{e[0].write(e[1])})}var Et=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},Lr=class{constructor(e,t,o,n,s,a,i){this.dtype=e,this.shape=t,this.sourceLayer=o,this.inputs=n,this.callArgs=s,this.outputTensorIndex=i,this.id=$g(),a!=null&&(this.originalName=Sg(a),this.name=Tg(this.originalName)),this.rank=t.length}},MH=0,ml=class{constructor(e,t){this.callArgs=t,this.id=MH++,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 o of e.inboundLayers)o!=null&&o.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}}},LH=0,Me=class extends J.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=LH++,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 o=this.getClassName();t=Hn(o)+"_"+pl(o)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let o;if(e.batchInputShape!=null)o=e.batchInputShape;else if(e.inputShape!=null){let s=null;e.batchSize!=null&&(s=e.batchSize),o=[s].concat(e.inputShape)}this.batchInputShape=o;let n=e.dtype;n==null&&(n=e.inputDType),n==null&&(n="float32"),this.dtype=n}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 Or(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new z(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return dr(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return dr(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new ko(`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 ko(`Layer ${this.name} is not connected, no input to return.`);return dr(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new ko(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new ko(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return dr(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=yt(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=yt(this.inputSpec);if(e.length!==t.length)throw new z(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let o=0;os.maxNDim)throw new z(`Input ${o} is incompatible with layer ${this.name}: expected max_ndim=${s.maxNDim}, found ndim=${a}`);if(s.minNDim!=null&&a=0?i[u]:i[i.length+u];if(c!=null&&[c,null].indexOf(p)===-1)throw new z(`Input ${o} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${c} but got shape ${i}.`)}}if(s.shape!=null)for(let i=0;i{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let i of yt(e))a.push(i.shape);this.build(dr(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&s&&(this._refCount=1)}if(this.assertInputCompatibility(e),s){let a=this.call(e,t),i=yt(a),l=[];for(let u of i)o.indexOf(u)!==-1&&(u=u.clone()),l.push(u);if(a=dr(l),this.activityRegularizer!=null)throw new Ne("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=zH(e),i=this.computeOutputShape(a),l,u=BH(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),i!=null&&i.length>0&&Array.isArray(i[0])?l=i.map((c,p)=>new Lr(u,c,this,yt(e),t,this.name,p)):l=new Lr(u,i,this,yt(e),t,this.name),this.addInboundNode(e,l,null,null,a,i,t),this._refCount++,this.activityRegularizer!=null)throw new Ne("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return l}})}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 ${this.name}`);else{let t=!1;this.batchInputShape.forEach((o,n)=>{o!=null&&e[n]!=null&&e[n]!==o&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${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 ko(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let o=JSON.stringify(t.outputShapes);e.indexOf(o)===-1&&e.push(o)}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 ko(`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 Or(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return _p(this.weights)}build(e){this.built=!0}getWeights(e=!1){return Nf(e?this.trainableWeights:this.weights)}setWeights(e){B(()=>{let t=this.weights;if(t.length!==e.length)throw new z(`You called setWeights(weights) on layer "${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 o=[],n=Nf(t);for(let s=0;ss.apply(u.read())),a==null&&(a=!0),a?this._trainableWeights.push(u):this._nonTrainableWeights.push(u),u}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=yt(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(o=>{if(o!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,o,n,s,a,i=null){let l=yt(e);t=yt(t),o=yt(o),n=yt(n),s=bp(s),a=bp(a);let u=[],c=[],p=[];for(let m of l)u.push(m.sourceLayer),c.push(m.nodeIndex),p.push(m.tensorIndex);new ml({outboundLayer:this,inboundLayers:u,nodeIndices:c,tensorIndices:p,inputTensors:l,outputTensors:t,inputMasks:o,outputMasks:n,inputShapes:s,outputShapes:a},i);for(let m=0;me.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 zH(r){r=yt(r);let e=[];for(let t of r)e.push(t.shape);return dr(e)}function BH(r){return"float32"}function hw(r,e,t){if((e==null||t!=null&&t>0)&&(e=r.sourceLayer,t=r.nodeIndex),e.inboundNodes.length===0)return[r];{let o=e.inboundNodes[t];if(o.inboundLayers.length===0)return o.inputTensors;{let n=[];for(let s=0;s0){let n=await Promise.all(e);for(let s=0;sQ(this.totals[n],O(s,o)));this.totals[n]=i,a!=null&&a.dispose()}}}async onEpochEnd(e,t){if(t!=null)for(let o of this.params.metrics)this.totals[o]!=null&&(typeof this.totals[o]=="number"?t[o]=this.totals[o]/this.seen:B(()=>{let n=O(de(1,this.seen),this.totals[o]);t[o]=n,this.totals[o].dispose(),Dt(t[o])}))}},Lg=class extends fl{async onTrainBegin(e){this.epoch=[],this.history={}}async onEpochEnd(e,t){t==null&&(t={}),this.epoch.push(e);for(let o in t)this.history[o]==null&&(this.history[o]=[]),this.history[o].push(t[o])}async syncData(){let e=[],t=[],o=[];for(let s in this.history){let a=this.history[s];for(let i=0;inew zg(o,e))}var no=class{constructor(){}static registerCallbackConstructor(e,t){y.assert(e>=0&&Number.isInteger(e),()=>`Verbosity level is expected to be an integer >= 0, but got ${e}`),no.checkForDuplicate(t),no.constructors[e]==null&&(no.constructors[e]=[]),no.constructors[e].push(t)}static checkForDuplicate(e){for(let t in no.constructors)no.constructors[+t].forEach(n=>{if(n===e)throw new z("Duplicate callback constructor.")})}static clear(){no.constructors={}}static createCallbacks(e){let t=[];for(let o in no.constructors){let n=+o;e>=n&&t.push(...no.constructors[n])}return t.map(o=>new o)}};no.constructors={};function Vg(r,e,t,o,n,s,a,i,l){let u=new Lg,c=[new KT,...no.createCallbacks(e)];r!=null&&c.push(...r),c.push(u);let p=new Mg(c);return p.setParams({epochs:t,initialEpoch:o,samples:n,steps:s,batchSize:a,verbose:e,doValidation:i,metrics:l}),{callbackList:p,history:u}}function Yr(r,e={},t=!1){return Vi(r,J.SerializationMap.getMap().classNameMap,e,"layer",t)}function Sf(r,e){return B(()=>{r.dtype!=="float32"&&(r=r.asType("float32"));let t=ye(Xu(r),e,!0),o=Na(t.shape,Yt()),n=xt(qr(t,o));return de(r,n)})}function ji(r,e){return B(()=>ht(Xu(ue(e,r)),-1))}function kp(r,e){return B(()=>ht(Tt(ue(e,r)),-1))}function dl(r,e){return B(()=>{let t=ue(r,e),o=nr(Tt(r),Yt(),Number.MAX_VALUE),n=Tt(de(t,o));return O(100,ht(n,-1))})}function GH(r,e){return B(()=>{let t=nr(e,Yt(),Number.MAX_VALUE),o=ir(Q(1,t)),n=nr(r,Yt(),Number.MAX_VALUE),s=ir(Q(1,n));return ht(Xu(ue(o,s)),-1)})}function WH(r,e){return B(()=>{let t=qr(0,ue(1,O(r,e)));return ht(Xu(t),-1)})}function UH(r,e){return B(()=>{let t=qr(0,ue(1,O(r,e)));return ht(t,-1)})}function jH(r,e){return B(()=>{let t=ye(O(r,e),-1),o=ar(O(ue(1,r),e),-1);return qr(0,Q(1,ue(o,t)))})}function HH(r,e){return B(()=>{let t=Math.log(2),o=ue(e,r),n=ue(Q(o,Es(O(-2,o))),t);return ht(n,-1)})}function Zu(r,e,t=!1){return B(()=>{if(t)e=$a(e);else{let o=ye(e,e.shape.length-1,!0);e=de(e,o)}return e=nr(e,Yt(),1-Yt()),je(ye(O(r.toFloat(),ir(e)),e.shape.length-1))})}function vp(r,e,t=!1){return B(()=>{let o=Ts(MT(r)).toInt();e=nr(e,Yt(),1-Yt());let n=e.shape,s=Is(o,n[n.length-1]).reshape(n);return Zu(s,e,t)})}function qH(r,e){if(!y.arraysEqual(r.shape,e.shape))throw new z(`logits and labels must have the same shape, but got shapes ${JSON.stringify(r.shape)} and ${JSON.stringify(e.shape)}`);return B(()=>{let t=e.relu(),o=e.abs().neg();return t.sub(e.mul(r)).add(o.exp().log1p())})}function Cp(r,e){return B(()=>{let t;return t=nr(e,Yt(),1-Yt()),t=ir(de(t,ue(1,t))),ht(qH(r,t),-1)})}function KH(r,e){return B(()=>{let t=nr(r,Yt(),1),o=nr(e,Yt(),1);return ye(O(r,ir(de(t,o))),-1)})}function XH(r,e){return B(()=>{let t=ir(Q(Yt(),e));return ht(ue(e,O(r,t)),-1)})}function Tf(r,e){return B(()=>{let t=Sf(r,-1),o=Sf(e,-1),n=O(t,o);return je(ye(n,-1))})}var Ef={meanSquaredError:ji,meanAbsoluteError:kp,meanAbsolutePercentageError:dl,meanSquaredLogarithmicError:GH,squaredHinge:WH,hinge:UH,categoricalHinge:jH,logcosh:HH,categoricalCrossentropy:Zu,sparseCategoricalCrossentropy:vp,binaryCrossentropy:Cp,kullbackLeiblerDivergence:KH,poisson:XH,cosineProximity:Tf};function Gg(r){if(typeof r=="string"){if(r in Ef)return Ef[r];let e=`Unknown loss ${r}`;throw r.toLowerCase().includes("softmaxcrossentropy")&&(e=`Unknown loss ${r}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new z(e)}else return r}function Af(r,e){return B(()=>{let t=O(.5,er(e)),o=Ma(Qt(e,t),r.dtype);return ht(_o(r,o),-1)})}function Df(r,e){return B(()=>Ma(_o(sl(r,-1),sl(e,-1)),"float32"))}function XT(r,e){return B(()=>fr(r.equal(1),e.equal(1)).sum().cast("float32"))}function YH(r,e){return B(()=>fr(r.equal(1),e.equal(0)).sum().cast("float32"))}function ZH(r,e){return B(()=>fr(r.equal(0),e.equal(1)).sum().cast("float32"))}function gw(r,e){return B(()=>{let t=XT(r,e),o=ZH(r,e),n=t.add(o);return $t(Qt(n,0),t.div(n),0).cast("float32")})}function YT(r,e){return B(()=>{let t=XT(r,e),o=YH(r,e),n=t.add(o);return $t(Qt(n,0),t.div(n),0).cast("float32")})}function Wg(r,e){return Cp(r,e)}function Ug(r,e){return r.rank===e.rank&&(r=r.squeeze([r.rank-1])),e=e.argMax(-1),e.dtype!==r.dtype&&(e=e.asType(r.dtype)),_o(r,e).asType("float32")}var JH=ji,QH=ji,eq=kp,tq=kp,rq=dl,oq=dl,$f=Zu,nq=Tf,xw=vp,jg={binaryAccuracy:Af,categoricalAccuracy:Df,precision:gw,categoricalCrossentropy:$f,sparseCategoricalCrossentropy:xw,mse:JH,MSE:QH,mae:eq,MAE:tq,mape:rq,MAPE:oq,cosine:nq};function ZT(r){if(typeof r=="string"&&r in jg)return jg[r];if(typeof r!="string"&&r!=null)return r;throw new z(`Unknown metric ${r}`)}function Rf(r){if(Lo(r!==null,`Unknown LossOrMetricFn ${r}`),typeof r=="string")return r;{let e;for(let t of Object.keys(Ef))if(Ef[t]===r){e=t;break}if(e!==void 0)return e;for(let t of Object.keys(jg))if(jg[t]===r){e=t;break}return e!==void 0?e:r.name}}function JT(r){let e={Adagrad:()=>ul.adagrad(.01),Adadelta:()=>ul.adadelta(1,.95,Yt()),Adam:()=>ul.adam(.001,.9,.999,Yt()),Adamax:()=>ul.adamax(.002,.9,.999,Yt(),0),RMSProp:()=>ul.rmsprop(.001,.9,0,Yt()),SGD:()=>ul.sgd(.01)};if(e.adagrad=e.Adagrad,e.adadelta=e.Adadelta,e.adam=e.Adam,e.adamax=e.Adamax,e.rmsprop=e.RMSProp,e.sgd=e.SGD,r in e)return e[r]();throw new z(`Unknown Optimizer ${r}`)}var QT=1*1024*1024;function bw(r,e,t=!1){if(r==null||typeof r!="object"||Object.getPrototypeOf(r)!==Object.prototype||!yw(r))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(t){let o=JSON.stringify(r);o.length>QT&&console.warn(`User-defined metadata of model "${e}" is too large in size (length=${o.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${QT}.`)}}function yw(r){if(r===null)return!0;if(typeof r=="object")if(Object.getPrototypeOf(r)===Object.prototype){let e=Object.keys(r);for(let t of e)if(typeof t!="string"||!yw(r[t]))return!1;return!0}else if(Array.isArray(r)){for(let e of r)if(!yw(e))return!1;return!0}else return!1;else{let e=typeof r;return e==="string"||e==="number"||e==="boolean"}}function e1(r,e,t,o=console.log){let n=iq(r),s=["Layer (type)","Output shape","Param #"];n?(e=e||65,t=t||[.45,.85,1]):(e=e||98,t=t||[.33,.55,.67,1]),t[t.length-1]<=1&&(t=t.map(c=>Math.floor(e*c)));let a;if(!n){s.push("Receives inputs"),a=[];for(let c in r.nodesByDepth)a.push(...r.nodesByDepth[c])}o("_".repeat(e)),Hg(s,t,o),o("=".repeat(e));let i=r.layers;for(let c=0;c1||n.length===1&&n[0].inboundLayers.length>1){e=!1;break}o.push(...n)}if(e)for(let n of r.layers){let s=!1;for(let a of n.inboundNodes)if(o.indexOf(a)!==-1)if(s){e=!1;break}else s=!0;if(!e)break}return e}function Hg(r,e,t=console.log){let o="";for(let n=0;n0&&(o=o.slice(0,o.length-1)+" "),o+=r[n],o=o.slice(0,e[n]),o+=" ".repeat(e[n]-o.length);t(o)}function aq(r,e,t){let o;try{o=JSON.stringify(r.outputShape)}catch(i){o="multiple"}let n=r.name,s=r.getClassName(),a=[`${n} (${s})`,o,r.countParams().toString()];Hg(a,e,t)}function lq(r,e,t,o){let n;try{n=JSON.stringify(r.outputShape)}catch(c){n="multiple"}let s=[];for(let c of r.inboundNodes)if(!(t!=null&&t.length>0&&t.indexOf(c)===-1))for(let p=0;pd.name),l=[],u=e.names();for(let d of i)u.indexOf(d)!==-1?l.push(e.getValue(d)):l.push(null);o!=null&&(o.maxNumTensors=-Infinity,o.minNumTensors=Infinity);let c=i.join(",")+"|"+e.names().join(","),p,m;if(_w[c]==null){let d=cq(a,e);p=d.sorted,m=d.recipientCounts,_w[c]=p,r1[c]=m}p=_w[c],m={},n||Object.assign(m,r1[c]);let f=new Ps(e);for(let d=0;do.maxNumTensors&&(o.maxNumTensors=M),M0,()=>"Expected at least one fetch, got none");let t=[],o={};if(r.length===1){let n=o1(r[0],e);t=n.sorted,o=n.recipientMap}else{let n=new Set;for(let s of r){let{sorted:a,recipientMap:i}=o1(s,e);for(let l of a)n.has(l.name)||(t.push(l),n.add(l.name));for(let l in i)o[l]==null&&(o[l]=new Set),i[l].forEach(u=>o[l].add(u))}}return{sorted:t,recipientCounts:mq(o)}}function mq(r){let e={};for(let t in r)e[t]=r[t].size;return e}function o1(r,e){let t=new Set,o=[],n={};for(let i of e.names())t.add(i);let s=[],a=[];for(s.push(r);s.length>0;){let i=s[s.length-1];if(t.has(i.name)){s.pop();continue}let l=a[a.length-1]===s.length-1;if(i.inputs.length===0||l)s.pop(),o.push(i),t.add(i.name),l&&a.pop();else{a.push(s.length-1);for(let u of i.inputs)n[u.name]==null&&(n[u.name]=new Set),n[u.name].add(i.name),!t.has(u.name)&&s.push(u)}}return{sorted:o,recipientMap:n}}function pq(r){let e;if(r.sourceLayer.inboundNodes.length===1)e=r.sourceLayer.output;else{let t=null;for(let o=0;ob.name)}`);Kn(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: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let _=b.sourceLayer,w=b.nodeIndex,v=b.tensorIndex;this.outputLayers.push(_),this.outputLayersNodeIndices.push(w),this.outputLayersTensorIndices.push(v)}for(let b of this.inputs){let _=b.sourceLayer,w=b.nodeIndex,v=b.tensorIndex;Lo(w===0,"input layer has >1 nodes"),Lo(v===0,"input layer has >1 tensors"),this.inputLayers.push(_),this.inputLayersNodeIndices.push(w),this.inputLayersTensorIndices.push(v)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;bb.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},o={},n={},s={},a={},i=[],l=(b,_,w,v,$,A)=>{(v==null||$==null||A==null)&&(v=b.sourceLayer,$=b.nodeIndex,A=b.tensorIndex);let R=v.inboundNodes[$];if(w.indexOf(R)!==-1)throw new Or(`The tensor ${b.name} at layer "${v.name}" is part of a cycle.`);if(_.indexOf(R)!==-1)return;this.containerNodes.add(zo.nodeKey(v,$)),v.id in a||(a[v.id]=Object.keys(a).length),w.indexOf(R)===-1&&w.push(R);let M=R.inboundLayers.length;for(let V=0;V=0;)w.splice(w.indexOf(R),1);i.push(R)},u=[],c=[];for(let b of this.outputs)l(b,u,c);let p=i.slice().reverse();for(let b of p){o[b.id]=b,b.id in t||(t[b.id]=0);let _=t[b.id],w=n[b.outboundLayer.id]==null?0:n[b.outboundLayer.id];_=Math.max(_,w),n[b.outboundLayer.id]=_,s[b.outboundLayer.id]=b.outboundLayer,t[b.id]=_;for(let v=0;vparseInt(b,10)).sort(pf);this.layers=[];for(let b of d){let _=f[b];_.sort((w,v)=>{let $=a[w.id],A=a[v.id];return $A?1:0});for(let w of _)w instanceof zo&&this.internalContainerRefs.push(w),this.layers.push(w)}this.layersByDepth=f,d=Object.keys(m).map(b=>parseInt(b,10)).sort(pf);let h=this.inputs.slice(),g=[];for(let b of d)for(let _ of m[b]){let w=_.outboundLayer;if(w!=null){for(let v of _.inputTensors)if(h.indexOf(v)===-1)throw new Or(`Graph disconnected: cannot obtain value for tensor ${v} at layer "${w.name}". The following previous layers were accessed without issue: ${g}`);for(let v of _.outputTensors)h.push(v);g.push(w.name)}}this.nodesByDepth=m;let x=this.layers.map(b=>b.name);for(let b of x){let _=x.filter(w=>w===b).length;if(_!==1)throw new Or(`The name "${b}" is used ${_} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(x))}this.outboundNodes=[],this.inboundNodes=[],new ml({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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(o=>o.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new z("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 o of this.layers)t.push(...o.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let o={},n=0;for(let a of this.layers)for(let i of a.weights){if(o[i.originalName]!=null)throw new z(`Duplicate weight name: ${i.originalName}`);o[i.originalName]=i,n++}let s=[];for(let a in e){let i=a;if(o[a]==null){let l=a.split("/");i=l.slice(0,-2).concat([l[l.length-1]]).join("/")}if(o[i]!=null)s.push([o[i],e[a]]);else if(t)throw new z(`Provided weight data has no target variable: ${a}`);delete o[i]}if(t){let a=[];for(let i in o)a.push(i);if(a.length>0)throw new z(`${a.length} of ${n} weights are not set: ${a}`)}wp(s)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${hl}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let o=qg(this.updatedConfig());return t?JSON.stringify(o):o}call(e,t){return B(()=>{e=yt(e);let o=new Ps;for(let n=0;n{e=yt(e);let o;return t==null?o=jn(null,e.length):o=yt(t),this.runInternalGraph(e,o)[1]})}computeOutputShape(e){let t=bp(e);if(t.length!==this.inputLayers.length)throw new z(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let o={};for(let i=0;iparseInt(i,10)).sort(pf);if(n.length>1)for(let i of n){let l=this.nodesByDepth[i];for(let u of l){let c=u.outboundLayer;if(this.inputLayers.map(h=>h.id).indexOf(c.id)!==-1)continue;let p=[];for(let h=0;hparseInt(l,10)).sort(pf);for(let l of n){let u=this.nodesByDepth[l];for(let c of u){let p=c.outboundLayer,m=c.inputTensors,f=c.outputTensors,d=new Array;for(let h of m)h.id in o&&d.push(o[h.id]);if(d.length===m.length){let h={},g,x,b,_;if(c.callArgs!=null&&(h=c.callArgs),d.length===1){let[w,v]=d[0];h.mask==null&&(h.mask=v),b=yt(p.call(w,h)),_=yt(p.computeMask(w,v)),g=[w],x=[v]}else g=d.map(w=>w[0]),x=d.map(w=>w[1]),h.mask==null&&(h.mask=x),b=yt(p.call(g,h)),_=yt(p.computeMask(g,x));if(p.activityRegularizer)throw new Ne("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let w=0;w{let e=[];for(let t of this.layers)for(let o=0;o0){let h=[];for(let g=0;g0&&g.apply(dr(b),_)}function u(g){let x=g.name,b=Yr(g,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(n),s[x]=b,g.inboundNodes.forEach(w=>{if(!(w instanceof Array))throw new z(`Corrupted configuration, expected array for nodeData: ${w}`);i(b,w)})}let c=t.name,p=t.layers;for(let g of p)u(g);for(;!wT(a);)for(let g of p){let x=s[g.name];if(x.name in a){let b=a[x.name];delete a[x.name];for(let _ of b)l(x,_)}}let m=[],f=[],d=t.inputLayers;for(let g of d){let x=g[0],b=g[1],_=g[2];Lo(x in s);let v=s[x].inboundNodes[b].outputTensors;m.push(v[_])}let h=t.outputLayers;for(let g of h){let x=g[0],b=g[1],_=g[2];Lo(x in s);let v=s[x].inboundNodes[b].outputTensors;f.push(v[_])}return new e({inputs:m,outputs:f,name:c})}get stateful(){if(this._stateful)throw new z("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(){B(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function fq(r,e,t){let o=e.length;if(r==null||Array.isArray(r)&&r.length===0)return e.map(n=>null);if(o===1)return Array.isArray(r)&&r.length===1?r:typeof r=="object"&&e[0]in r?[r[e[0]]]:[r];if(Array.isArray(r)){if(r.length!==o)throw new Error(`Provided ${t} is an array of ${r.length} element(s), but the model has ${o} outputs. Make sure a set of weights is provided for each model output.`);return r}else if(typeof r=="object"&&Object.keys(r).length>0&&typeof r[Object.keys(r)[0]]=="object"){let n=[];return e.forEach(s=>{s in r?n.push(r[s]):n.push(null)}),n}else throw new Error(`The model has multiple (${o}) outputs, so ${t} must be either an array with ${o} elements or an object with ${e} keys. Provided ${t} not understood: ${JSON.stringify(r)}`)}function Kg(r,e){return fq(r,e,"classWeight")}async function Xg(r,e,t,o){if(e!=null||o!=null)throw new Error("Support sampleWeight is not implemented yet");if(t!=null){let n=B(()=>{if(r.shape.length===1)return r.clone();if(r.shape.length===2)if(r.shape[1]>1){let i=1;return r.argMax(i)}else{if(r.shape[1]===1)return r.reshape([r.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${r.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${r.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await n.data());Te(n);let a=[];return s.forEach(i=>{if(t[i]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${i} exists in the data but not in classWeight`);a.push(t[i])}),Vt(a,"float32")}else return null}function n1(r,e){return O(r,e)}var dq=32;function i1(r,e){let t,o,n=e;t=n.xs,o=n.ys,y.assert(t!=null&&o!=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 ${e}`);let s=s1("input",r.inputNames,t),a=s1("output",r.outputNames,o),i=s[0].shape[0];y.assert(s.length===r.inputs.length,()=>`LayersModel has ${r.inputs.length} inputs, but the dataset provides ${s.length} inputs. (Expected input keys: ${JSON.stringify(r.inputNames)})`),y.assert(a.length===r.outputs.length,()=>`LayersModel has ${r.outputs.length} outputs, but the dataset provides ${a.length} outputs. (Expected output keys: ${JSON.stringify(r.outputNames)})`);for(let l=0;l`Batch size mismatch: input ${r.inputNames[l]} has ${s[l].shape[0]}; expected ${i} based on input ${r.inputNames[0]}.`);for(let l=0;l`Batch size mismatch: output ${r.outputNames[l]} has ${a[l].shape[0]}; expected ${i} based on input ${r.inputNames[0]}.`);return{xs:s,ys:a}}function s1(r,e,t){if(t instanceof Ve)return[t];if(Array.isArray(t))return y.assert(t.length===e.length,()=>`Received an array of ${t.length} Tensors, but expected ${e.length} to match the ${r} keys ${e}.`),t;{let o=[];for(let n of e){if(t[n]==null)throw new z(`The feature data generated by the dataset lacks the required ${r} key '${n}'.`);o.push(t[n])}return o}}function hq(r){if(r.length===3)throw new Ne("Validation with sample weights is not implemented yet.");return{xs:r[0],ys:r[1]}}async function l1(r,e,t){let o=t.batchesPerEpoch!=null;if(y.assert(r.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),y.assert(t!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),y.assert(t.epochs!=null&&t.epochs>0&&Number.isInteger(t.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${t.epochs}`),y.assert(!o||t.batchesPerEpoch>0&&Number.isInteger(t.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${t.batchesPerEpoch}`),y.assert(t.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),r.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");r.isTraining=!0;try{let n=t.validationData!=null,s,a;if(n)if(a1(t.validationData))y.assert(t.validationBatches==null||t.validationBatches>0&&Number.isInteger(t.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${t.validationBatches}`);else{let g=hq(t.validationData);s=g.xs,a=g.ys}let i=r.makeTrainFunction(),l=r.getDedupedMetricsNames(),u;n?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let c=Bg(t.callbacks,t.yieldEvery),p=t.verbose==null?1:t.verbose,{callbackList:m,history:f}=Vg(c,p,t.epochs,null,null,gq(e,t),null,n,u);m.setModel(r),r.history=f,await m.onTrainBegin(),r.stopTraining_=!1;let d=t.initialEpoch==null?0:t.initialEpoch,h=await e.iterator();for(;d=t.batchesPerEpoch:_.done){if(n){let w;a1(t.validationData)?w=yt(await r.evaluateDataset(t.validationData,{batches:t.validationBatches})):w=yt(r.evaluate(s,a,{batchSize:t.validationBatchSize==null?dq:t.validationBatchSize,verbose:0}));for(let v=0;v0)throw new Ne("Verbose mode is not implemented yet.");y.assert(!o||t.batches>0&&Number.isInteger(t.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(t.batches)}`);let a=xq(e)?e:await e.iterator(),i=0,l=0;for(;o?l{if(u.value){let{xs:c,ys:p}=i1(r,u.value),m=c.concat(p),f=B(()=>n(m));if(Te(m),l===0)for(let h=0;hQ(s[h],O(d,g))),l>0&&Te(x)}Te(f),i+=d,++l}return s}),u.done){o&&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, ${t.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let u=0;u0&&Number.isInteger(r),()=>`batchSize is required to be a positive integer, but got ${r}`)}function Ip(r,e,t){return r==null?[null]:Array.isArray(r)?r.map(o=>za(o,e,t-e)):za(r,e,t-e)}function Zg(r,e){return B(()=>r==null?null:Array.isArray(r)?r.map(t=>Zg(t,e)):Ag(r,e.dtype==="int32"?e:e.toInt()))}function Jg(r,e){let t=[],o=0,n=null;for(;o=r&&(n=r),t.push([o,n]),o=n;return t}async function yq(r,e,t,o,n,s,a,i,l,u,c,p,m,f,d){n==null&&(n=32),s==null&&(s=1),c==null&&(c=!0),m==null&&(m=0);let h=!1;if(l!=null&&u!=null&&(h=!0),d!=null&&(h=!0,f==null))throw new z("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let g=r.checkNumSamples(t,n,f,"steps_per_epoch"),x;g!=null&&(x=Pr(0,g)),a==null&&(a=1);let{callbackList:b,history:_}=Vg(i,a,s,m,g,f,n,h,p);b.setModel(r),r.history=_,await b.onTrainBegin(),r.stopTraining_=!1;for(let w=m;w{let V=A[R][0],G=A[R][1],W=za($,V,G-V);M.batch=R,M.size=G-V;let q=Zg(t,W),Z=e(q);for(let X=0;X0){if(d=!0,o.validationData.length===2)a=o.validationData[0],i=o.validationData[1];else throw o.validationData.length===3?new Ne("validationData including sample weights is not supported yet."):new z(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${o.validationData} is invalid.`);let A=!0,R=await r.standardizeUserData(a,i,null,null,A,p);l=R[0],u=R[1],h=l.concat(u)}else if(o.validationSplit!=null&&o.validationSplit>0&&o.validationSplit<1){d=!0;let A=Math.floor(n[0].shape[0]*(1-o.validationSplit)),R=n[0].shape[0];l=Ip(n,A,R),n=Ip(n,0,A),u=Ip(s,A,R),s=Ip(s,0,A),h=l.concat(u)}else o.validationSteps!=null&&(d=!0);let g=n.concat(s).concat(c);r.checkTrainableWeightsConsistency();let x=r.makeTrainFunction(),b=r.getDedupedMetricsNames(),_,w;d?(r.makeTestFunction(),_=r.testFunction,w=b.slice().concat(b.map(A=>"val_"+A))):(_=null,h=[],w=b.slice());let v=Bg(o.callbacks,o.yieldEvery);return await yq(r,x,g,b,p,o.epochs,o.verbose,v,_,h,o.shuffle,w,o.initialEpoch,null,null)}finally{r.isTraining=!1,gl(n,e),gl(s,t),gl(l,a),gl(u,i),c!=null&&Te(c)}}function ww(r){let e=[];r instanceof Ve&&(r=[r]);for(let t=0;tt.push(n.id));else if(e!=null)for(let n in e){let s=e[n];t.push(s.id)}let o=[];if(r instanceof Ve)t.indexOf(r.id)===-1&&o.push(r);else if(Array.isArray(r))r.forEach(n=>{t.indexOf(n.id)===-1&&o.push(n)});else if(r!=null)for(let n in r){let s=r[n];t.indexOf(s.id)===-1&&o.push(s)}o.forEach(n=>{n.isDisposed||n.dispose()})}function bq(r){return r instanceof Ve}function kw(r){return Array.isArray(r)}function p1(r){return!bq(r)&&!kw(r)}function m1(r,e,t,o=!0,n=""){if(e==null||e.length===0){if(r!=null){let a=!1;if(kw(r)&&r.length>0)a=!0;else if(p1(r)){for(let i in r)if(r.hasOwnProperty(i)){a=!0;break}}else a=!0;if(a)throw new z(`Error when checking model ${n} expected no data, but got ${r}`)}return[]}if(r==null)return e.map(a=>null);let s;if(p1(r)){r=r,s=[];for(let a of e){if(r[a]==null)throw new z(`No data provided for "${a}". Need data for each key in: ${e}`);s.push(r[a])}}else if(kw(r)){if(r=r,r.length!==e.length)throw new z(`Error when checking model ${n}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${e.length} Tensor(s), but instead got the following list of Tensor(s): ${r}`);s=r}else{if(r=r,e.length>1)throw new z(`The model ${n} expects ${e.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${r.shape}`);s=[r]}if(s=ww(s),t!=null)for(let a=0;a=0&&u!==c)throw new z(`Error when checking ${n}: expected ${e[a]} to have shape [${t[a]}], but got array with shape [${i.shape}].`)}}return s}function _q(r,e,t){let o=Kn(r.map(s=>s.shape[0]));o.sort();let n=Kn(e.map(s=>s.shape[0]));if(n.sort(),o.length>1)throw new z(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(r.map(s=>s.shape))}`);if(n.length>1)throw new z(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map(s=>s.shape))}`);if(o.length>0&&n.length>0&&!y.arraysEqual(o,n))throw new z(`Input Tensors should have the same number of samples as target Tensors. Found ${o[0]} input sample(s) and ${n[0]} target sample(s).`)}function wq(r,e,t){let o=[ji,Cp,Zu];for(let n=0;n1)throw new z(`The model expects ${e.length} ${n} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(r.shape)}.`);s=[r]}if(t!=null)for(let a=0;a[]);let t;if(typeof r=="string"||typeof r=="function")t=[r];else if(Array.isArray(r)||typeof r=="object")t=r;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${r}`);if(Array.isArray(t))return e.map(o=>t);{let o=[];for(let n of e){let s=t.hasOwnProperty(n)?t[n]:[];Array.isArray(s)||(s=[s]),o.push(s)}return o}}var vq="layers-model",vo=class extends zo{constructor(e){super(e);this.isTraining=!1}summary(e,t,o=console.log){if(!this.built)throw new z("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");e1(this,e,t,o)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=JT(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof Fr))throw new z("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let a in e.loss)if(this.outputNames.indexOf(a)===-1)throw new z(`Unknown entry in loss dictionary: "${a}". Only expected the following keys: ${this.outputNames}`);for(let a of this.outputNames)e.loss[a]==null&&console.warn(`Output "${a}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${a} during training`),t.push(Gg(e.loss[a]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new z(`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(i=>Gg(i))}else{let a=Gg(e.loss);this.outputs.forEach(i=>{t.push(a)})}this.lossFunctions=t,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let a=0;a{for(let a=0;a1&&(this.metricsTensors.push([i,a]),this.metricsNames.push(this.outputNames[a]+"_loss"))}});let n=kq(e.metrics,this.outputNames),s=(a,i,l)=>{this.outputNames.length>1&&(i=this.outputNames[a]+"_"+i),this.metricsNames.push(i),this.metricsTensors.push([l,a])};Fs("metric",()=>{for(let a=0;a{let c="",p,m,f;for(let d of u){if(typeof d=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(d)!==-1){let g=this.internalOutputShapes[a];g[g.length-1]===1||this.lossFunctions[a]===Cp?["accuracy","acc"].indexOf(d)!==-1?m=Af:["crossentropy","ce"].indexOf(d)!==-1&&(m=Wg):this.lossFunctions[a]===vp?["accuracy","acc"].indexOf(d)!==-1?m=Ug:["crossentropy","ce"].indexOf(d)!==-1&&(m=xw):["accuracy","acc"].indexOf(d)!==-1?m=Df:["crossentropy","ce"].indexOf(d)!==-1&&(m=$f);let x;["accuracy","acc"].indexOf(d)!==-1?x="acc":["crossentropy","ce"].indexOf(d)!==-1&&(x="ce"),f=m,p=c+x}else f=ZT(d),p=c+Rf(d);let h;Fs(p,()=>{h=f}),s(a,p,h)}})(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,o={}){let n=o.batchSize==null?32:o.batchSize;Yg(n);let s=!0,a=this.standardizeUserDataXY(e,t,s,n);try{let i=a[0].concat(a[1]);this.makeTestFunction();let l=this.testFunction,u=this.testLoop(l,i,n,o.verbose,o.steps);return dr(u)}finally{gl(a[0],e),gl(a[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),u1(this,e,t)}checkNumSamples(e,t,o,n="steps"){let s;if(o!=null){if(s=null,t!=null)throw new z(`If ${n} is set, batchSize must be null or undefined.Got batchSize = ${t}`)}else if(e!=null)Array.isArray(e)?s=e[0].shape[0]:s=e.shape[0];else throw new z(`Either the input data should have a defined shape, or ${n} shoud be specified.`);return s}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new z("`outputs` is an empty Array, which is not allowed.");let o=Array.isArray(t),n=o?t:[t],s=this.retrieveSymbolicTensors(n),a=new Ps;if(e instanceof Ve&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new z(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let l=0;li.name);for(let i=0;i0){let n=[];throw t.forEach((s,a)=>{s==null&&n.push(e[a])}),new z(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(n)}`)}return t}predictLoop(e,t=32,o=!1){return B(()=>{let n=this.checkNumSamples(e);if(o)throw new Ne("Verbose predictLoop() is not implemented yet.");let s=Jg(n,t),a=this.outputs.map(i=>[]);for(let i=0;i{let u=s[i][0],c=s[i][1],p=Ip(e,u,c),m=[];if(Array.isArray(p))for(let d=0;da[c].push(u));return dr(a.map(i=>Je(i,0)))})}predict(e,t={}){let o=ww(e);f1(o,this.inputNames,this.feedInputShapes,!1);try{let n=t.batchSize==null?32:t.batchSize;return Yg(n),this.predictLoop(o,n)}finally{gl(o,e)}}predictOnBatch(e){f1(e,this.inputNames,this.feedInputShapes,!0);let t=(Array.isArray(e)?e[0]:e).shape[0];return this.predictLoop(e,t)}standardizeUserDataXY(e,t,o=!0,n){if(this.optimizer_==null)throw new Or("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let s=[];for(let a=0;a0&&e[0].shape[0]%n!=0)throw new z(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${n}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,o,n,s=!0,a){let[i,l]=this.standardizeUserDataXY(e,t,s,a);if(o!=null)throw new Error("sample weight is not supported yet.");let u=null;if(n!=null){let c=Kg(n,this.outputNames);u=[];for(let p=0;p{let a=this.checkNumSamples(t,o,s,"steps"),i=[];if(n>0)throw new Ne("Verbose mode is not implemented yet.");if(s!=null)throw new Ne("steps mode in testLoop() is not implemented yet");{let l=Jg(a,o),u=Vt(Pr(0,a));for(let c=0;c1&&(s+=`_${iw(e.slice(0,o),n)}`),t.push(s)}return t}makeTrainFunction(){return e=>{let t=[],o=e.slice(0,this.inputs.length),n=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],i=()=>{let p=[];for(let h=0;h1&&h{d=Q(d,h)}),d},l=this.collectedTrainableWeights.map(p=>p.read()),u=!0;return[this.optimizer_.minimize(i,u,l)].concat(a)}}makeTestFunction(){this.testFunction=e=>B(()=>{let t=[],o,n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let u=0;uHn(t))}else{let t=Object.keys(this.loss);e={};let o=this.loss;for(let n of t)if(typeof o[n]=="string")e[n]=Hn(o[n]);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[Hn(Rf(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Hn(Rf(e)));{let e={};for(let t in this.metrics)e[t]=Hn(Rf(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=Ju(e.optimizer_config),o=Yr(t),n;if(typeof e.loss=="string")n=Pa(e.loss);else if(Array.isArray(e.loss))n=e.loss.map(a=>Pa(a));else if(e.loss!=null){n={};for(let a in e.loss)n[a]=Pa(e.loss[a])}let s;if(Array.isArray(e.metrics))s=e.metrics.map(a=>Pa(a));else if(e.metrics!=null){s={};for(let a in e.metrics)s[a]=Pa(e.metrics[a])}this.compile({loss:n,metrics:s,optimizer:o})}async save(e,t){if(typeof e=="string"){let u=vr.getSaveHandlers(e);if(u.length===0)throw new z(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new z(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new z("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let o=await vr.encodeWeights(this.getNamedWeights(t)),n=!1,s=null,i={modelTopology:this.toJSON(s,n),format:vq,generatedBy:`TensorFlow.js tfjs-layers v${hl}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u="optimizer",{data:c,specs:p}=await vr.encodeWeights(await this.optimizer.getWeights(),u);o.specs.push(...p),o.data=vr.concatenateArrayBuffers([o.data,c])}if(this.userDefinedMetadata!=null){let u=!0;bw(this.userDefinedMetadata,this.name,u),i.userDefinedMetadata=this.userDefinedMetadata}return i.weightData=o.data,i.weightSpecs=o.specs,e.save(i)}setUserDefinedMetadata(e){bw(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};vo.className="Model";J.registerClass(vo);var vw=class extends vo{};vw.className="Functional";J.registerClass(vw);async function d1(r,e){"modelTopology"in r||(r={modelTopology:r}),r=r;let t=r.modelTopology;t.model_config!=null&&(t=t.model_config);let o=Ju(t),n=Yr(o,e);if(r.weightsManifest!=null){let s=await vr.loadWeights(r.weightsManifest,r.pathPrefix,n.weights.map(i=>i.originalName)),a={};for(let i of n.weights)a[i.originalName]=s[i.originalName];n.loadWeights(a),Te(s)}return n}async function h1(r,e){if(e==null&&(e={}),typeof r=="string"){let t=vr.getLoadHandlers(r,e);if(t.length===0)t.push(vr.browserHTTPRequest(r,e));else if(t.length>1)throw new z(`Found more than one (${t.length}) load handlers for URL '${r}'`);r=t[0]}return Cq(r,void 0,e)}async function Cq(r,e,t){if(t==null&&(t={}),r.load==null)throw new z("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let o=await r.load(),n=o.modelTopology;n.model_config!=null&&(n=n.model_config);let s=t.strict==null?!0:t.strict,a=o.weightData!=null&&o.weightSpecs!=null&&s,i=Yr(Ju(n),e,a),l=o.trainingConfig;if(l!=null&&i.loadTrainingConfig(l),o.userDefinedMetadata!=null&&i.setUserDefinedMetadata(o.userDefinedMetadata),o.weightData!=null){if(o.weightSpecs==null)throw new z("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:c}=Iq(o.weightData,o.weightSpecs);i.loadWeights(u,s),i.optimizer!=null&&c.length>0&&await i.optimizer.setWeights(c),Te(u),Te(c.map(p=>p.tensor))}return i}function Iq(r,e){let t=vr.decodeWeights(r,e),o={},n=[];return e.forEach(s=>{s.group==="optimizer"?n.push({name:s.name,tensor:t[s.name]}):o[s.name]=t[s.name]}),{modelWeights:o,optimizerWeights:n}}var Hi=class extends vo{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:pl("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(o=>o<0))throw new z(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Hi||e instanceof vo,o;if(t){if(o=e,o.outputs.length!==1)throw new z("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(o.inputs.length!==1)throw new z("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 z("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let n=Og({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(n)}if(t)this.outputs=o.outputs,this.inputs=o.inputs;else{if(e.inboundNodes.length!==1)throw new z(`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 z("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=hw(this.outputs[0])}this.inboundNodes=[],new ml({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:jn(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(n=>n.shape),outputShapes:this.outputs[0].shape})}else{let n=e.apply(this.outputs[0]);if(Array.isArray(n))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=[n],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(Qe(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 vo({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,o=console.log){this.built||this.build(),super.summary(e,t,o)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,o={}){if(!this.built)throw new Or("The model needs to be compiled before being used.");return this.model.evaluate(e,t,o)}async evaluateDataset(e,t){if(!this.built)throw new Or("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,o={}){if(!this.built)throw new Or("The model needs to be compiled before being used.");return this.model.fit(e,t,o)}async fitDataset(e,t){if(!this.built)throw new Or("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,o={},n=!1){let s,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new z("Legacy serialization format not supported yet.");s=t}else y.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."),s=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof Hi))throw new Ne(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let l of s){let c=Yr(l,void 0,n);n&&c.setFastWeightInitDuringBuild(!0),i.add(c)}return i}set stopTraining(e){if(this.model==null)throw new z("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 z("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 o={};o.className=t.getClassName(),o.config=t.getConfig(),e.push(o)}return{name:this.name,layers:e}}};Hi.className="Sequential";J.registerClass(Hi);function g1(r){return new vo(r)}function x1(r){return new Hi(r)}function y1(r,e){return e==null&&(e={}),h1(r,e)}function Qg(r){return Og(r)}function b1(r,e){no.registerCallbackConstructor(r,e)}var so=class extends J.Serializable{getConfig(){return{}}},Cw=class extends so{apply(e,t=1){return zT(e,t)}};Cw.className="elu";J.registerClass(Cw);var Iw=class extends so{apply(e){return Mu(e)}};Iw.className="selu";J.registerClass(Iw);var Nw=class extends so{apply(e){return Ir(e)}};Nw.className="relu";J.registerClass(Nw);var Sw=class extends so{apply(e){return B(()=>As(6,Ir(e)))}};Sw.className="relu6";J.registerClass(Sw);var Tw=class extends so{apply(e){return e}};Tw.className="linear";J.registerClass(Tw);var Ew=class extends so{apply(e){return Ur(e)}};Ew.className="sigmoid";J.registerClass(Ew);var Aw=class extends so{apply(e){return VT(e)}};Aw.className="hardSigmoid";J.registerClass(Aw);var Dw=class extends so{apply(e){return Es(e)}};Dw.className="softplus";J.registerClass(Dw);var $w=class extends so{apply(e){return BT(e)}};$w.className="softsign";J.registerClass($w);var Rw=class extends so{apply(e){return Mi(e)}};Rw.className="tanh";J.registerClass(Rw);var Ff=class extends so{apply(e,t=-1){return $a(e,t)}};Ff.className="softmax";J.registerClass(Ff);var Fw=class extends so{apply(e,t=-1){return Du(e,t)}};Fw.className="logSoftmax";J.registerClass(Fw);var Ow=class extends so{apply(e,t=1){return B(()=>Ur(e.mul(t)).mul(e))}};Ow.className="swish";J.registerClass(Ow);function Ms(r){return r.getClassName()}function Pw(r,e={}){return Vi(r,J.SerializationMap.getMap().classNameMap,e,"activation")}function Ls(r){if(r==null){let e={};return e.className="linear",e.config={},Pw(e)}if(typeof r=="string"){let e={};return e.className=r,e.config={},Pw(e)}else return r instanceof so?r:Pw(r)}function Mw(r){if(r!=null&&typeof r!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var Lw=class extends J.Serializable{},ec=class extends Lw{constructor(e){super();Mw(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 B(()=>{let t=gt([1]);return this.hasL1&&(t=Q(t,ye(O(this.l1,Tt(e))))),this.hasL2&&(t=Q(t,ye(O(this.l2,Xu(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};ec.className="L1L2";J.registerClass(ec);function _1(r){return Mw(r),new ec({l1:r!=null?r.l1:null,l2:0})}function w1(r){return Mw(r),new ec({l2:r!=null?r.l2:null,l1:0})}var k1={l1l2:"L1L2"};function lt(r){return lp(r)}function v1(r,e={}){return Vi(r,J.SerializationMap.getMap().classNameMap,e,"regularizer")}function bt(r){if(r==null)return null;if(typeof r=="string"){let t={className:r in k1?k1[r]:r,config:{}};return v1(t)}else return r instanceof Lw?r:v1(r)}var Of=class extends Me{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Oe(e);let o=Ir(e);return this.maxValue!=null&&(o=nr(o,0,this.maxValue)),o}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Of.className="ReLU";J.registerClass(Of);var Pf=class extends Me{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 o=Oe(e);return Sa(o,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Pf.className="LeakyReLU";J.registerClass(Pf);var Mf=class extends Me{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=dt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=bt(e.alphaRegularizer),this.alphaConstraint=Pt(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 z(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Qe(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let n of this.sharedAxes)t[n-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let o={};if(this.sharedAxes!=null)for(let n=1;n(Rt(e),e==="channelsFirst"?Ue(r,[0,2,3,1]):r))}function zw(r,e){return B(()=>(Rt(e),e==="channelsFirst"?Ue(r,[0,2,3,4,1]):r))}function Nq(r,e,t,o=1,n="valid",s,a=1){return B(()=>{if(s==null&&(s=Kr()),Rt(s),r.shape.length!==3)throw new z(`The input of a conv1dWithBias operation should be 3, but is ${r.shape.length} instead.`);if(e.shape.length!==3)throw new z(`The kernel for a conv1dWithBias operation should be 3, but is ${e.shape.length} instead`);if(t!=null&&t.shape.length!==1)throw new z(`The bias for a conv1dWithBias operation should be 1, but is ${e.shape.length} instead`);if(s==="channelsFirst"&&(r=Ue(r,[0,2,1])),n==="causal")throw new Ne("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=Cu(r,e,o,n==="same"?"same":"valid","NWC",a);return t!=null&&(i=ro(i,t)),i})}function C1(r,e,t,o=[1,1],n="valid",s,a,i=null){return B(()=>{if(s==null&&(s=Kr()),Rt(s),r.rank!==3&&r.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(e.rank!==3&&e.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let l=Gf(r,s);if(n==="causal")throw new Ne("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Wn.conv2d({x:l,filter:e,strides:o,pad:n==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:t,activation:i}),s==="channelsFirst"&&(l=Ue(l,[0,3,1,2])),l})}function Sq(r,e,t,o=[1,1,1],n="valid",s,a){return B(()=>{if(s==null&&(s=Kr()),Rt(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(e.rank!==4&&e.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let i=zw(r,s);if(n==="causal")throw new Ne("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=Pm(i,e,o,n==="same"?"same":"valid","NDHWC",a),t!=null&&(i=ro(i,t)),s==="channelsFirst"&&(i=Ue(i,[0,4,1,2,3])),i})}var Np=class extends Me{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Np.verifyArgs(t),this.rank=e,Wt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ne(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=xl(t.kernelSize,e,"kernelSize"),this.strides=xl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Xr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Rt(this.dataFormat),this.activation=Ls(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=dt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=bt(t.biasRegularizer),this.activityRegularizer=bt(t.activityRegularizer),this.dilationRate=xl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`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 z(`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 z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Lo("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Ig(e.kernelSize,"number",1,3))throw new z(`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:Ms(this.activation),useBias:this.useBias,biasInitializer:It(this.biasInitializer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},tc=class extends Np{constructor(e,t){super(e,t);this.kernel=null,tc.verifyArgs(t),this.filters=t.filters,Wt(this.filters,"filters"),this.kernelInitializer=dt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=bt(t.kernelRegularizer)}build(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z(`The channel dimension of the input should be defined. Found ${e[t]}`);let o=e[t],n=this.kernelSize.concat([o,this.filters]);this.kernel=this.addWeight("kernel",n,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]:o}}],this.built=!0}call(e,t){return B(()=>{e=Oe(e);let o,n=this.bias==null?null:this.bias.read(),s=Ng(this.activation.getClassName());if(s!=null&&this.rank===2)o=C1(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)o=Nq(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)o=C1(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)o=Sq(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ne("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(o=this.activation.apply(o))}return o})}computeOutputShape(e){e=Qe(e);let t=[],o=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s 0 but got ${JSON.stringify(e.filters)}`)}},yl=class extends tc{constructor(e){super(2,e);yl.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Ig(e.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};yl.className="Conv2D";J.registerClass(yl);var rc=class extends tc{constructor(e){super(3,e);rc.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 z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};rc.className="Conv3D";J.registerClass(rc);var Wf=class extends yl{constructor(e){super(e);if(this.inputSpec=[new Et({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Qe(e),e.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let o=e[t],n=this.kernelSize.concat([this.filters,o]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Et({ndim:4,axes:{[t]:o}})],this.built=!0}call(e,t){return B(()=>{let o=Oe(e);if(o.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${o.shape.length}`);let n=o.shape,s=n[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let l=n[a],u=n[i],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=Vf(l,m,c,this.padding),h=Vf(u,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(o=Ue(o,[0,2,3,1]));let x=Iu(o,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Ue(x,[0,3,1,2])),this.bias!=null&&(x=ro(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(e){e=Qe(e);let t=e.slice(),o,n,s;this.dataFormat==="channelsFirst"?(o=1,n=2,s=3):(o=3,n=1,s=2);let a=this.kernelSize[0],i=this.kernelSize[1],l=this.strides[0],u=this.strides[1];return t[o]=this.filters,t[n]=Vf(t[n],l,a,this.padding),t[s]=Vf(t[s],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Wf.className="Conv2DTranspose";J.registerClass(Wf);var Bw=class extends tc{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 z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new z("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 z(`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=dt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=bt(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=dt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=bt(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=Qe(e),e.length{e=Oe(e);let o;if(this.rank===1)throw new Ne("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ue(e,[0,2,3,1])),o=Zm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(o=ro(o,this.bias.read(),this.dataFormat)),this.activation!=null&&(o=this.activation.apply(o)),this.dataFormat==="channelsFirst"&&(o=Ue(o,[0,3,1,2])),o})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=It(this.depthwiseInitializer),e.pointwiseInitializer=It(this.pointwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.pointwiseRegularizer=lt(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};Bw.className="SeparableConv";var Uf=class extends Bw{constructor(e){super(2,e)}};Uf.className="SeparableConv2D";J.registerClass(Uf);var oc=class extends tc{constructor(e){super(1,e);oc.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"&&!Ig(e.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};oc.className="Conv1D";J.registerClass(oc);var jf=class extends Me{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 B(()=>{if(e=Oe(e),this.dataFormat==="channelsLast"){let o=xf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return xf(o,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let o=xf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return xf(o,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}};jf.className="Cropping2D";J.registerClass(jf);var Hf=class extends Me{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,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,DT(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],o=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,o]}else{let t=e[1]==null?null:this.size[0]*e[1],o=e[2]==null?null:this.size[1]*e[2];return[e[0],t,o,e[3]]}}call(e,t){return B(()=>{let o=Oe(e),n=o.shape;if(this.dataFormat==="channelsFirst"){o=Ue(o,[0,2,3,1]);let s=this.size[0]*n[2],a=this.size[1]*n[3],i=this.interpolation==="nearest"?o.resizeNearestNeighbor([s,a]):o.resizeBilinear([s,a]);return Ue(i,[0,3,1,2])}else{let s=this.size[0]*n[1],a=this.size[1]*n[2];return this.interpolation==="nearest"?o.resizeNearestNeighbor([s,a]):o.resizeBilinear([s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Hf.className="UpSampling2D";J.registerClass(Hf);function Tq(r,e,t=[1,1],o="valid",n,s){return B(()=>{n==null&&(n=Kr()),Rt(n);let a=Gf(r,n);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(e.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${e.rank}-D`);return a=Ns(a,e,t,o==="same"?"same":"valid","NHWC",s),n==="channelsFirst"&&(a=Ue(a,[0,3,1,2])),a})}var qf=class extends Np{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=dt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=bt(e.depthwiseRegularizer)}build(e){if(e=Qe(e),e.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let o=e[t],n=[this.kernelSize[0],this.kernelSize[1],o,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[o*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{e=Oe(e);let o=Tq(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(o=ro(o,this.bias.read(),this.dataFormat)),this.activation!=null&&(o=this.activation.apply(o)),o})}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=io(t,this.kernelSize[0],this.padding,this.strides[0]),a=io(o,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,s,a]:[e[0],s,a,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=It(this.depthwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};qf.className="DepthwiseConv2D";J.registerClass(qf);function Vw(r,e,t,o){if(Array.isArray(r)){if(e!=null||t!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");o!=null&&(t=r.slice(r.length-o,r.length),r=r.slice(0,r.length-o)),r.length>1&&(e=r.slice(1,r.length)),r=r[0]}function n(s){return s==null||Array.isArray(s)?s:[s]}return e=n(e),t=n(t),{inputs:r,initialState:e,constants:t}}function Gw(r,e,t,o=!1,n,s,a=!1,i=!1){return B(()=>{let l=e.shape.length;if(l<3)throw new z(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Pr(2,l));if(e=Ue(e,u),s!=null)throw new Ne("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),n!=null&&(n=n.asType("bool").asType("float32"),n.rank===l-1&&(n=sr(n,-1)),n=Ue(n,u)),o&&(e=qt(e,0),n!=null&&(n=qt(n,0)));let c=[],p,m=t,f=e.shape[0],d=ur(e),h;n!=null&&(h=ur(n));for(let x=0;xr(b,m));if(n==null)p=_[0],m=_[1];else{let w=B(()=>{let v=h[x],$=er(v).sub(v),A=_[0].mul(v).add(m[0].mul($)),R=m.map((M,V)=>_[1][V].mul(v).add(M.mul($)));return{output:A,newStates:R}});p=w.output,m=w.newStates}i&&c.push(p)}let g;return i&&(g=Bt(c,1)),[p,g,m]})}var ao=class extends Me{constructor(e){super(e);let t;if(e.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Sp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new z("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 Et({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 Pr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Fg(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let o=t[0],n;if(this.returnSequences?n=[e[0],e[1],o]:n=[e[0],o],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[n].concat(s)}else return n}computeMask(e,t){return B(()=>{Array.isArray(t)&&(t=t[0]);let o=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(s=>null);return[o].concat(n)}else return o})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let o=0;oi.shape[i.shape.length-1]),a))throw new z(`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=a.map(i=>new Et({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new ko("Cannot call resetStates() on an RNN Layer that is not stateful.");let o=this.inputSpec[0].shape[0];if(o==null)throw new z("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(n=>gt([o,n])):this.states_=[gt([o,this.cell.stateSize])];else if(e==null)Te(this.states_),this.keptStates!=null&&(Te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>gt([o,n])):this.states_[0]=gt([o,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`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()):Te(this.states_);for(let n=0;nDt(n.clone()))})}apply(e,t){let o=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let s=Vw(e,o,n,this.numConstants);e=s.inputs,o=s.initialState,n=s.constants;let a=[],i=[];if(o!=null){t.initialState=o,a=a.concat(o),this.stateSpec=[];for(let u of o)this.stateSpec.push(new Et({shape:u.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,a=a.concat(n),this.numConstants=n.length),a[0]instanceof Lr){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return B(()=>{let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;e=Oe(e),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==a)throw new z(`RNN Layer has ${a} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},u=Gw((d,h)=>{let g=this.cell.call([d].concat(h),i);return[g[0],g.slice(1)]},e,s,this.goBackwards,o,null,this.unroll,this.returnSequences),c=u[0],p=u[1],m=u[2];this.stateful&&this.resetStates(m,n);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(e){return B(()=>{let t=gt(e.shape);return t=ye(t,[1,2]),t=La(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(o=>o>1?Eg(t,[1,o]):t):this.cell.stateSize>1?[Eg(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 o=this.cell.getConfig();return this.getClassName()===ao.className&&(t.cell={className:this.cell.getClassName(),config:o}),Object.assign({},o,e,t)}static fromConfig(e,t,o={}){let n=t.cell,s=Yr(n,o);return new e(Object.assign(t,{cell:s}))}};ao.className="RNN";J.registerClass(ao);var bl=class extends Me{},Tp=class extends bl{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,Wt(this.units,"units"),this.activation=Ls(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Ku([1,Os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ku([1,Os([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Qe(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 B(()=>{if(e=e,e.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let o=e[1];e=e[0];let n=t.training==null?!1:t.training;0er(e),rate:this.dropout,training:n})),0er(o),rate:this.recurrentDropout,training:n}));let s,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?s=Yn(O(e,a),this.kernel.read()):s=Yn(e,this.kernel.read()),this.bias!=null&&(s=ro(s,this.bias.read())),i!=null&&(o=O(o,i));let l=Q(s,Yn(o,this.recurrentKernel.read()));return this.activation!=null&&(l=this.activation.apply(l)),[l,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ms(this.activation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),recurrentInitializer:It(this.recurrentInitializer),biasInitializer:It(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),recurrentRegularizer:lt(this.recurrentRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Tp.className="SimpleRNNCell";J.registerClass(Tp);var Kf=class extends ao{constructor(e){e.cell=new Tp(e),super(e)}call(e,t){return B(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return new e(t)}};Kf.className="SimpleRNN";J.registerClass(Kf);var Ep=class extends bl{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 z("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Wt(this.units,"units"),this.activation=Ls(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ls(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Ku([1,Os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ku([1,Os([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Qe(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 B(()=>{if(e=e,e.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let o=t.training==null?!1:t.training,n=e[1];e=e[0],0er(e),rate:this.dropout,training:o,count:3})),0er(n),rate:this.recurrentDropout,training:o,count:3}));let s=this.dropoutMask,a=this.recurrentDropoutMask,i,l,u;0{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Xf.className="GRU";J.registerClass(Xf);var _l=class extends bl{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,Wt(this.units,"units"),this.activation=Ls(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ls(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Ku([1,Os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ku([1,Os([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=Qe(e);let o=e[e.length-1];this.kernel=this.addWeight("kernel",[o,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 n;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;n=new(t=class extends oo{apply(l,u){let c=s.apply([a]),p=new Yu().apply([a]),m=s.apply([a*2]);return mw(mw(c,p),m)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return B(()=>{let o=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1],s=e[2];e=e[0],0er(e),rate:this.dropout,training:o,count:4})),0er(n),rate:this.recurrentDropout,training:o,count:4}));let a=this.dropoutMask,i=this.recurrentDropoutMask,l,u,c,p;0{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Yf.className="LSTM";J.registerClass(Yf);var Sp=class extends bl{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return B(()=>{e=e;let o=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(o.splice(0,i.stateSize.length)):n.push(o.splice(0,1));n.reverse();let s=[],a;for(let i=0;i{Fs(`RNNCell_${n}`,()=>{o.build(e),Array.isArray(o.stateSize)?t=o.stateSize[0]:t=o.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,o={}){let n=[];for(let s of t.cells)n.push(Yr(s,o));return new e({cells:n})}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 o of this.cells)t.push(...o.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Nf(e)}setWeights(e){let t=[];for(let o of this.cells){let n=o.weights.length,s=e.splice(n);for(let a=0;aDg(e(),t),a=()=>cl(s,e,o);return!n||n<=1?Dt(a().clone()):Array(n).fill(void 0).map(a).map(l=>Dt(l.clone()))}var Eq=function(r,e){var t={};for(var o in r)Object.prototype.hasOwnProperty.call(r,o)&&e.indexOf(o)<0&&(t[o]=r[o]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var n=0,o=Object.getOwnPropertySymbols(r);n{if(this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new z("ConvRNN2D cell does not support constants");let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}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 B(()=>{let{stateSize:t}=this.cell,o=e.shape,n=this.computeSingleOutputShape(o),s=[n[0],...n.slice(2)],a=gt(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new ko("Cannot call resetStates() on an RNN Layer that is not stateful.");let o=this.inputSpec[0].shape,n=this.computeSingleOutputShape(o),s=[n[0],...n.slice(2)];if(o[0]==null)throw new z("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(()=>gt(s)):this.states_=[gt(s)];else if(e==null)Te(this.states_),this.keptStates!=null&&(Te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>gt(s)):this.states_[0]=gt(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`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()):Te(this.states_);for(let i=0;iDt(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:o,kernelSize:n,padding:s,strides:a,dilationRate:i}=this.cell,l=t==="channelsFirst",u=e[l?3:2],c=e[l?4:3],p=io(u,n[0],s,a[0],i[0]),m=io(c,n[1],s,a[1],i[1]);return[...e.slice(0,2),...l?[o,p,m]:[p,m,o]]}};Ww.className="ConvRNN2D";var Ap=class extends _l{constructor(e){let{filters:t,kernelSize:o,strides:n,padding:s,dataFormat:a,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Wt(this.filters,"filters"),this.kernelSize=xl(o,2,"kernelSize"),this.kernelSize.forEach(l=>Wt(l,"kernelSize")),this.strides=xl(n||1,2,"strides"),this.strides.forEach(l=>Wt(l,"strides")),this.padding=s||"valid",Xr(this.padding),this.dataFormat=a||"channelsLast",Rt(this.dataFormat),this.dilationRate=xl(i||1,2,"dilationRate"),this.dilationRate.forEach(l=>Wt(l,"dilationRate"))}build(e){var t;e=Qe(e);let o=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[o]==null)throw new z(`The channel dimension of the input should be defined. Found ${e[o]}`);let n=e[o],s=4,a=this.kernelSize.concat([n,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let l;if(this.unitForgetBias){let u=this.biasInitializer,c=this.filters;l=new(t=class extends oo{apply(m,f){let d=u.apply([c]),h=Cr([c]),g=u.apply([c*2]);return pp([d,h,g])}},t.className="CustomInit",t)}else l=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,l,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return B(()=>{if(e.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let o=t.training||!1,n=e[0],s=e[1],a=e[2],i=4;0er(n),rate:this.dropout,training:o,count:i}));let l=this.dropoutMask,u=(ie,se,pe)=>!se||!se[pe]?ie:O(se[pe],ie),c=u(n,l,0),p=u(n,l,1),m=u(n,l,2),f=u(n,l,3);0er(s),rate:this.recurrentDropout,training:o,count:i}));let d=this.recurrentDropoutMask,h=u(s,d,0),g=u(s,d,1),x=u(s,d,2),b=u(s,d,3),_=3,[w,v,$,A]=lr(this.kernel.read(),i,_),[R,M,V,G]=this.useBias?lr(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,w,R,this.padding),p=this.inputConv(p,v,M,this.padding),m=this.inputConv(m,$,V,this.padding),f=this.inputConv(f,A,G,this.padding);let[W,q,Z,X]=lr(this.recurrentKernel.read(),i,_);h=this.recurrentConv(h,W),g=this.recurrentConv(g,q),x=this.recurrentConv(x,Z),b=this.recurrentConv(b,X);let Y=this.recurrentActivation.apply(Q(c,h)),te=this.recurrentActivation.apply(Q(p,g)),K=Q(O(te,a),O(Y,this.activation.apply(Q(m,x)))),re=O(this.recurrentActivation.apply(Q(f,b)),this.activation.apply(K));return[re,re,K]})}getConfig(){let e=super.getConfig(),{units:t}=e,o=Eq(e,["units"]),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},o,n)}inputConv(e,t,o,n){let s=jr(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return o?ro(s,o,this.dataFormat):s}recurrentConv(e,t){return jr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Ap.className="ConvLSTM2DCell";J.registerClass(Ap);var Zf=class extends Ww{constructor(e){let t=new Ap(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Zf.className="ConvLSTM2D";J.registerClass(Zf);var Dp=class extends Me{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,o=[];for(let n=0;n{this.invokeCallHook(e,t);let o=Oe(e);if(0Dg(o,this.rate,s,this.seed),()=>o,n)}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()}};Dp.className="Dropout";J.registerClass(Dp);var Jf=class extends Dp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Jf.className="SpatialDropout1D";J.registerClass(Jf);var Qf=class extends Me{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,Wt(this.units,"units"),this.activation=Ls(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=bt(e.kernelRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.activityRegularizer=bt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Qe(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=Qe(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return B(()=>{this.invokeCallHook(e,t);let o=Oe(e),n=Ng(this.activation.getClassName()),s;return n!=null?s=Yn(o,this.kernel.read(),n,this.bias?this.bias.read():null):(s=Yn(o,this.kernel.read()),this.bias!=null&&(s=ro(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:Ms(this.activation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),biasInitializer:It(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Qf.className="Dense";J.registerClass(Qf);var ed=class extends Me{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Qe(e);for(let t of e.slice(1))if(t==null)throw new z(`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],Xn(e,1)]}call(e,t){return B(()=>{this.invokeCallHook(e,t);let o=Oe(e);if(this.dataFormat==="channelsFirst"&&o.rank>1){let n=[0];for(let s=2;s{this.invokeCallHook(e,t);let o=Oe(e);return this.activation.apply(o)})}getConfig(){let e={activation:Ms(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};td.className="Activation";J.registerClass(td);var rd=class extends Me{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 B(()=>(e=Oe(e),PT(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};rd.className="RepeatVector";J.registerClass(rd);var od=class extends Me{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t{this.invokeCallHook(e,t);let o=Oe(e),n=o.shape,s=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return o.reshape(s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};od.className="Reshape";J.registerClass(od);var nd=class extends Me{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=Pr(1,e.dims.length+1);if(!y.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 Et({ndim:this.dims.length+1})]}computeOutputShape(e){e=Qe(e);let t=e.slice();return this.dims.forEach((o,n)=>{t[n+1]=e[o]}),t}call(e,t){return Ue(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};nd.className="Permute";J.registerClass(nd);var sd=class extends Me{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 o=Oe(e),n=-1;return nl(Gn(o,this.maskValue),n)}call(e,t){return B(()=>{this.invokeCallHook(e,t);let o=Oe(e),n=-1,s=!0,a=nl(Gn(o,this.maskValue),n,s);return o.mul(a.asType(o.dtype))})}};sd.className="Masking";J.registerClass(sd);var id=class extends Me{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(yt(e.inputLength))}this.inputDim=e.inputDim,Wt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Wt(this.outputDim,"outputDim"),this.embeddingsInitializer=dt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=bt(e.embeddingsRegularizer),this.activityRegularizer=bt(e.activityRegularizer),this.embeddingsConstraint=Pt(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 B(()=>this.maskZero?(e=Oe(e),Gn(e,Ie(e))):null)}computeOutputShape(e){if(e=Qe(e),this.inputLength==null)return[...e,this.outputDim];let t=yt(this.inputLength);if(t.length!==e.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let o=0;for(let n=0;n{this.invokeCallHook(e,t);let o=Oe(e);return o.dtype!=="int32"&&(o=Ma(o,"int32")),Ag(this.embeddings.read(),o.as1D()).reshape(Qe(this.computeOutputShape(o.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:It(this.embeddingsInitializer),embeddingsRegularizer:lt(this.embeddingsRegularizer),activityRegularizer:lt(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};id.className="Embedding";J.registerClass(id);var wl=class extends Me{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Ne}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length1)throw new z(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let o=e[0]==null?null:e[0].slice(1);for(let s=1;ss.length);e.indexOf(null)===-1&&Kn(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return B(()=>{if(e=e,this.reshapeRequired){let o=[],n=e.map(s=>s.rank);if(n.indexOf(null)===-1){let s=Os(n);for(let a of e){let i=a.rank;for(let l=0;l1){let c=Pr(1,u).concat([0]);o.push(Ue(l,c)),s=!0}else o.push(l)}let a=this.mergeFunction(o),i=a.rank;if(s){if(i==null){let l=a.shape,u=l.length,c=l[u-1],p=[c].concat(l.slice(0,l.length-1));a=Ue(a.reshape([-1,c]),[1,0]).reshape(p)}else if(i>1){let l=[i-1].concat(Pr(0,i-1));a=Ue(a,l)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n{if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an Array");if(!Array.isArray(e))throw new z("`inputs` should be an Array");if(t.length!==e.length)throw new z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:sr(n,0));let o=t[0];for(let n=1;n{let t=e[0].clone();for(let o=1;o{let t=e[0].clone();for(let o=1;o{let t=e[0].clone();for(let o=1;o{let t=e[0];for(let o=1;o{let t=e[0];for(let o=1;o1)throw new z("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return B(()=>pp(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new z("A `Concatenate` layer should be called on a list of inputs.");let t=e,o=t[0].slice(),n=this.axis<0?o.length+this.axis:this.axis;for(let s of t.slice(1)){if(o[n]==null||s[n]==null){o[n]=null;break}o[n]+=s[n]}return o}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new z("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new z(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return B(()=>{let o=!0;if(t.forEach(a=>{if(a!=null){o=!1;return}}),o)return null;let n=[];for(let a=0;a3||e.shape.length>3)throw new Ne("batchDot is not implemented for tensors of 4D or higher rank yet");if(y.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),y.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${e.shape.length}`),typeof t=="number"&&(t=[t,t]),r.dtype==="complex64"||e.dtype==="complex64")throw new Ne("batchDot is not implemented for complex64-type Tensors yet.");let o=r.shape.length,n=e.shape.length;t==null&&(t=[o-1,n-2]);let s=t;return B(()=>{let a;if(o>n){a=o-n;let l=[];for(let u=0;uo){a=n-o;let l=[];for(let u=0;u0){let l;o>n?l=o+n-3:l=o-1;let u=[];for(let c=l;c"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],o=e[1];if(t.length>3||o.length>3)throw new Ne("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,o);if(t[n[0]]!==o[n[1]])throw new z(`Dimension incompatibility: ${t[n[0]]} !== ${o[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],o=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((s,a)=>fd(s,e[a].shape.length)):n=[fd(this.axes,t.shape.length),fd(this.axes,o.shape.length)],this.normalize&&(t=Sf(t,n[0]),o=Sf(o,n[1])),Aq(t,o,n)}interpretAxes(e,t){let o;return Array.isArray(this.axes)?o=this.axes:o=[fd(this.axes,e.length),fd(this.axes,t.length)],o}computeOutputShape(e){y.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(),o=e[1].slice();if(t.length>3||o.length>3)throw new Ne("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,o);t.splice(n[0],1),o.splice(n[1],1),o.splice(0,1);let s=t.concat(o);return s.length===1&&s.push(1),s}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};dd.className="Dot";J.registerClass(dd);var hd=class extends Me{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 B(()=>{this.invokeCallHook(e,t);let o=Oe(e);return cl(()=>mp(o.shape,0,this.stddev).add(o),()=>o,t.training||!1)})}};hd.className="GaussianNoise";J.registerClass(hd);var gd=class extends Me{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 B(()=>{this.invokeCallHook(e,t);let o=Oe(e);return this.rate>0&&this.rate<1?cl(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return o.mul(mp(o.shape,1,s))},()=>o,t.training||!1):o})}};gd.className="GaussianDropout";J.registerClass(gd);var xd=class extends Me{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(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 B(()=>{if(this.rate<1&&this.rate>0){let o=this._getNoiseShape(e);return cl(()=>{let s=Oe(e),a=1.6732632423543772,i=1.0507009873554805,l=-a*i,u=to(Ds(o),this.rate);u=Ma(u,"float32");let c=((1-this.rate)*(1+this.rate*l**2))**-.5,p=-c*l*this.rate;return s.mul(u).add(u.add(-1).mul(l)).mul(c).add(p)},()=>Oe(e),t.training||!1)}return e})}};xd.className="AlphaDropout";J.registerClass(xd);function yd(r,e,t,o,n,s=.001){let a;if(r.rank===2)a=c_(r,e,t,o,n,s);else if(r.rank===3)a=p_(r,e,t,o,n,s);else if(r.rank===4)a=m_(r,e,t,o,n,s);else throw new Ne(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return a}function Dq(r,e,t,o,n=.001){return B(()=>{let s=Yc(r,o),a=s.mean,i=s.variance;return[yd(r,a,i,t,e,n),a,i]})}function $q(r,e,t,o,n=.001){return B(()=>{let s=Yc(r,o),a=s.mean,i=s.variance,l=[];for(let d of Pr(0,r.rank))o.indexOf(d)!==-1?l.push(1):l.push(r.shape[d]);let u=a.reshape(l),c=i.reshape(l),p=e==null?null:e.reshape(l),m=t==null?null:t.reshape(l);return[yd(r,u,c,m,p,n),a,i]})}function Rq(r,e,t,o,n=.001){return y.arraysEqual(o.slice().sort(),Pr(0,r.rank-1))?Dq(r,e,t,o,n):$q(r,e,t,o,n)}var bd=class extends Me{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=dt(e.betaInitializer||"zeros"),this.gammaInitializer=dt(e.gammaInitializer||"ones"),this.movingMeanInitializer=dt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=dt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=bt(e.betaRegularizer),this.gammaRegularizer=bt(e.gammaRegularizer)}build(e){e=Qe(e);let t=this.axis>=0?this.axis:this.axis+e.length,o=e[t];if(o==null)throw new z(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Et({ndim:e.length,axes:{[t]:o}})];let n=[o];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return B(()=>{let o=t.training==null?!1:t.training,n=Oe(e),s=n.shape,a=s.length,i=Pr(0,a),l=this.axis>=0?this.axis:this.axis+a;i.splice(l,1);let u=jn(1,a);u[l]=s[l];let c=i.slice();c.sort();let p=!y.arraysEqual(c,Pr(0,a).slice(0,a-1)),m=()=>{if(p){let b=this.movingMean.read().reshape(u),_=this.movingVariance.read().reshape(u),w=this.center?this.beta.read().reshape(u):null,v=this.scale?this.gamma.read().reshape(u):null;return yd(n,b,_,w,v,this.epsilon)}else return yd(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!o)return m();let[f,d,h]=Rq(n,this.gamma.read(),this.beta.read(),i,this.epsilon),g=(b,_,w)=>{B(()=>{let v=1-w,$=b.read(),A=$.sub(_).mul(v);b.write($.sub(A))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:It(this.betaInitializer),gammaInitializer:It(this.gammaInitializer),movingMeanInitializer:It(this.movingMeanInitializer),movingVarianceInitializer:It(this.movingVarianceInitializer),betaRegularizer:lt(this.betaRegularizer),gammaRegularizer:lt(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};bd.className="BatchNormalization";J.registerClass(bd);var _d=class extends Me{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=dt(e.betaInitializer||"zeros"),this.gammaInitializer=dt(e.gammaInitializer||"ones"),this.betaRegularizer=bt(e.betaRegularizer),this.gammaRegularizer=bt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Qe(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s=t)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==Kn(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let o=this.axis.map(s=>e[s]),n=!0;this.scale?this.gamma=this.addWeight("gamma",o,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",o,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let o=Oe(e),n=o.shape,s=n.length;return B(()=>{let a=!0,{mean:i,variance:l}=Yc(o,this.axis,a),u=jn(1,s);for(let h of this.axis)u[h]=n[h];let c=h=>h!=null&&h.shape.length!==s&&this.axis!==[s-1]?h.reshape(u):h,p=c(this.gamma.read()),m=c(this.beta.read()),f=[],d=[];for(let h=0;h{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(e==null&&(e=[[1,1],[1,1]]),e.length!==2||e[0].length!==2||e[1].length!==2)throw new z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(t==null&&(t=Kr()),t!=="channelsLast"&&t!=="channelsFirst")throw new z(`Unknown data format: ${t}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let o;return t==="channelsFirst"?o=[[0,0],[0,0],e[0],e[1]]:o=[[0,0],e[0],e[1],[0,0]],$r(r,o)})}var wd=class extends Me{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Kr():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 z(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,o;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],o=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new z(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new z(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);o=e.padding[1]}this.padding=[t,o]}this.inputSpec=[new Et({ndim:4})]}computeOutputShape(e){e=Qe(e);let t,o;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?o=e[3]+this.padding[1][0]+this.padding[1][1]:o=null,[e[0],e[1],t,o]):(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?o=e[2]+this.padding[1][0]+this.padding[1][1]:o=null,[e[0],t,o,e[3]])}call(e,t){return B(()=>Fq(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};wd.className="ZeroPadding2D";J.registerClass(wd);function ex(r,e,t,o,n,s){return B(()=>{Rt(n),cw(s),Xr(o),t==null&&(t=[1,1]),o==null&&(o="valid"),n==null&&(n=Kr()),s==null&&(s="max"),r=Gf(r,n);let a,i=o==="same"?"same":"valid";return s==="max"?a=Ea(r,e,t,i):a=va(r,e,t,i),n==="channelsFirst"&&(a=Ue(a,[0,3,1,2])),a})}function I1(r,e,t,o,n,s){return B(()=>{Rt(n),cw(s),Xr(o),t==null&&(t=[1,1,1]),o==null&&(o="valid"),n==null&&(n=Kr()),s==null&&(s="max"),r=zw(r,n);let a,i=o==="same"?"same":"valid";return s==="max"?a=jm(r,e,t,i):a=Fm(r,e,t,i),n==="channelsFirst"&&(a=Ue(a,[0,4,1,2,3])),a})}var Uw=class extends Me{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 z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Wt(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 z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Wt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Xr(this.padding),this.inputSpec=[new Et({ndim:3})]}computeOutputShape(e){e=Qe(e);let t=io(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return B(()=>{this.invokeCallHook(e,t),e=La(Oe(e),2);let o=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return wo(o,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},kd=class extends Uw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Xr(n),ex(e,t,o,n,s,"max")}};kd.className="MaxPooling1D";J.registerClass(kd);var vd=class extends Uw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Xr(n),ex(e,t,o,n,s,"avg")}};vd.className="AveragePooling1D";J.registerClass(vd);var jw=class extends Me{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 z(`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];Wt(this.poolSize,"poolSize"),Wt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),Xr(this.padding),this.inputSpec=[new Et({ndim:4})]}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=io(t,this.poolSize[0],this.padding,this.strides[0]),o=io(o,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,o]:[e[0],t,o,e[3]]}call(e,t){return B(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(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}},Cd=class extends jw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Xr(n),ex(e,t,o,n,s,"max")}};Cd.className="MaxPooling2D";J.registerClass(Cd);var Id=class extends jw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Xr(n),ex(e,t,o,n,s,"avg")}};Id.className="AveragePooling2D";J.registerClass(Id);var Hw=class extends Me{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 z(`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];Wt(this.poolSize,"poolSize"),Wt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),Xr(this.padding),this.inputSpec=[new Et({ndim:5})]}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=io(t,this.poolSize[0],this.padding,this.strides[0]),o=io(o,this.poolSize[1],this.padding,this.strides[1]),n=io(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,o,n]:[e[0],t,o,n,e[4]]}call(e,t){return B(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(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}},Nd=class extends Hw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Xr(n),I1(e,t,o,n,s,"max")}};Nd.className="MaxPooling3D";J.registerClass(Nd);var Sd=class extends Hw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Xr(n),I1(e,t,o,n,s,"avg")}};Sd.className="AveragePooling3D";J.registerClass(Sd);var qw=class extends Me{constructor(e){super(e);this.inputSpec=[new Et({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ne}},Td=class extends qw{constructor(e){super(e||{})}call(e,t){return B(()=>{let o=Oe(e);return ht(o,1)})}};Td.className="GlobalAveragePooling1D";J.registerClass(Td);var Ed=class extends qw{constructor(e){super(e||{})}call(e,t){return B(()=>{let o=Oe(e);return ar(o,1)})}};Ed.className="GlobalMaxPooling1D";J.registerClass(Ed);var Kw=class extends Me{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.inputSpec=[new Et({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ne}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ad=class extends Kw{call(e,t){return B(()=>{let o=Oe(e);return this.dataFormat==="channelsLast"?ht(o,[1,2]):ht(o,[2,3])})}};Ad.className="GlobalAveragePooling2D";J.registerClass(Ad);var Dd=class extends Kw{call(e,t){return B(()=>{let o=Oe(e);return this.dataFormat==="channelsLast"?ar(o,[1,2]):ar(o,[2,3])})}};Dd.className="GlobalMaxPooling2D";J.registerClass(Dd);var Xw=class extends Me{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,o={}){let n=t.layer,s=Yr(n,o);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},$d=class extends Xw{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=Qe(e),e.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=Qe(e);let t=[e[0]].concat(e.slice(2)),o=this.layer.computeOutputShape(t),n=e[1];return[o[0],n].concat(o.slice(1))}call(e,t){return B(()=>(e=Oe(e),Gw((a,i)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};$d.className="TimeDistributed";J.registerClass($d);function Oq(r){Gi(AT,"BidirectionalMergeMode",r)}var Pq="concat",Rd=class extends Xw{constructor(e){super(e);let t=e.layer.getConfig(),o={};o.className=e.layer.getClassName(),o.config=t,this.forwardLayer=Yr(o),t.goBackwards=t.goBackwards!==!0;let n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=Yr(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Pq:e.mergeMode,Oq(this.mergeMode),e.weights)throw new Ne("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,o=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,o)),this.backwardLayer.setWeights(e.slice(o))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let o,n,s;return this.returnState&&(s=t.slice(1)),o=t[0],o=o,this.mergeMode==="concat"?(o[o.length-1]*=2,n=[o]):this.mergeMode==null?n=[o,o.slice()]:n=[o],this.returnState?this.mergeMode==null?n.concat(s).concat(s.slice()):[o].concat(s).concat(s.slice()):dr(n)}apply(e,t){let o=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let s=Vw(e,o,n,this.numConstants);if(e=s.inputs,o=s.initialState,n=s.constants,Array.isArray(e)&&(o=e.slice(1),e=e[0]),(o==null||o.length===0)&&n==null)return super.apply(e,t);let a=[],i=[];if(o!=null){let u=o.length;if(u%2>0)throw new z("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=o,a.push(...o);let c=o.map(p=>new Et({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,u/2),this.backwardLayer.stateSpec=c.slice(u/2),i.push(...c)}if(n!=null)throw new Ne("Support for constants in Bidirectional layers is not implemented yet.");let l=a[0]instanceof Lr;for(let u of a)if(u instanceof Lr!==l)throw new z("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(l){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return B(()=>{let o=t.initialState,n,s;if(o==null)n=this.forwardLayer.call(e,t),s=this.backwardLayer.call(e,t);else{let l=o.slice(0,o.length/2),u=o.slice(o.length/2);n=this.forwardLayer.call(e,Object.assign(t,{initialState:l})),s=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(n)&&(a=n.slice(1).concat(s.slice(1))),n=n[0],s=s[0]),this.returnSequences&&(s=qt(s,1));let i;return this.mergeMode==="concat"?i=pp([n,s]):this.mergeMode==="sum"?i=Q(n,s):this.mergeMode==="ave"?i=O(.5,Q(n,s)):this.mergeMode==="mul"?i=O(n,s):this.mergeMode==null&&(i=[n,s]),this.returnState?this.mergeMode==null?i.concat(a):[i].concat(a):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Fs(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Fs(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let o;if(this.returnSequences?this.mergeMode==null?o=[t,t]:o=t:this.mergeMode==null?o=[null,null]:o=null,this.returnState){let s=this.forwardLayer.states.map(a=>null);return Array.isArray(o)?o.concat(s).concat(s):[o].concat(s).concat(s)}else return o}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 o=Yr(t.layer);if(delete t.layer,t.numConstants!=null)throw new Ne("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let n=t;return n.layer=o,new e(n)}};Rd.className="Bidirectional";J.registerClass(Rd);function Mq(r){return new Wi(r)}function Lq(r){return new Lf(r)}function zq(r){return new Of(r)}function Bq(r){return new Pf(r)}function Vq(r){return new Mf(r)}function Gq(r){return new Bf(r)}function Wq(r){return new zf(r)}function Uq(r){return new oc(r)}function jq(r){return new yl(r)}function Hq(r){return new Wf(r)}function qq(r){return new rc(r)}function Kq(r){return new Uf(r)}function Xq(r){return new jf(r)}function Yq(r){return new Hf(r)}function Zq(r){return new qf(r)}function Jq(r){return new td(r)}function Qq(r){return new Qf(r)}function eK(r){return new Dp(r)}function tK(r){return new Jf(r)}function rK(r){return new ed(r)}function oK(r){return new rd(r)}function nK(r){return new od(r)}function sK(r){return new nd(r)}function iK(r){return new id(r)}function aK(r){return new ad(r)}function lK(r){return new ud(r)}function uK(r){return new md(r)}function cK(r){return new cd(r)}function pK(r){return new pd(r)}function mK(r){return new ld(r)}function fK(r){return new dd(r)}function dK(r){return new bd(r)}function hK(r){return new _d(r)}function gK(r){return new wd(r)}function Zw(r){return new vd(r)}function xK(r){return Zw(r)}function yK(r){return Zw(r)}function Jw(r){return new Id(r)}function bK(r){return Jw(r)}function _K(r){return Jw(r)}function Qw(r){return new Sd(r)}function wK(r){return Qw(r)}function kK(r){return Qw(r)}function vK(r){return new Td(r)}function CK(r){return new Ad(r)}function N1(r){return new Ed(r)}function S1(r){return new Dd(r)}function T1(r){return new kd(r)}function E1(r){return new Cd(r)}function IK(r){return new Nd(r)}function NK(r){return new Xf(r)}function SK(r){return new Ep(r)}function TK(r){return new Yf(r)}function EK(r){return new _l(r)}function AK(r){return new Kf(r)}function DK(r){return new Tp(r)}function $K(r){return new Zf(r)}function RK(r){return new Ap(r)}function FK(r){return new ao(r)}function OK(r){return new Sp(r)}function PK(r){return new Rd(r)}function MK(r){return new $d(r)}var LK=N1,zK=S1,BK=T1,VK=E1;function GK(r){return new hd(r)}function WK(r){return new gd(r)}function UK(r){return new xd(r)}function jK(r){return new sd(r)}var ek={};et(ek,{MAPE:()=>r6,MSE:()=>s6,binaryAccuracy:()=>HK,binaryCrossentropy:()=>qK,categoricalAccuracy:()=>XK,categoricalCrossentropy:()=>YK,cosineProximity:()=>QK,mape:()=>o6,meanAbsoluteError:()=>e6,meanAbsolutePercentageError:()=>t6,meanSquaredError:()=>n6,mse:()=>i6,precision:()=>ZK,recall:()=>JK,sparseCategoricalAccuracy:()=>KK});function HK(r,e){return Af(r,e)}function qK(r,e){return Wg(r,e)}function KK(r,e){return Ug(r,e)}function XK(r,e){return Df(r,e)}function YK(r,e){return $f(r,e)}function ZK(r,e){return gw(r,e)}function JK(r,e){return YT(r,e)}function QK(r,e){return Tf(r,e)}function e6(r,e){return kp(r,e)}function t6(r,e){return dl(r,e)}function r6(r,e){return dl(r,e)}function o6(r,e){return dl(r,e)}function n6(r,e){return ji(r,e)}function s6(r,e){return ji(r,e)}function i6(r,e){return ji(r,e)}var tk={};et(tk,{modelFromJSON:()=>d1});var rk={};et(rk,{l1:()=>l6,l1l2:()=>a6,l2:()=>u6});function a6(r){return new ec(r)}function l6(r){return _1(r)}function u6(r){return w1(r)}var tx=class extends fl{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof vo))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function rx(r,e){return re}var ox=class extends tx{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new Ne("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=rx:this.mode==="max"?this.monitorFunc=A1:this.monitor.indexOf("acc")!==-1?this.monitorFunc=A1:this.monitorFunc=rx,this.monitorFunc===rx&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===rx?Infinity:-Infinity}async onEpochEnd(e,t){await Ui(t);let o=this.getMonitorValue(t);o!=null&&(this.monitorFunc(o-this.minDelta,this.best)?(this.best=o,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 c6(r){return new ox(r)}var D1={earlyStopping:c6};var Zn;(function(r){r[r.DT_INVALID=0]="DT_INVALID",r[r.DT_FLOAT=1]="DT_FLOAT",r[r.DT_DOUBLE=2]="DT_DOUBLE",r[r.DT_INT32=3]="DT_INT32",r[r.DT_UINT8=4]="DT_UINT8",r[r.DT_INT16=5]="DT_INT16",r[r.DT_INT8=6]="DT_INT8",r[r.DT_STRING=7]="DT_STRING",r[r.DT_COMPLEX64=8]="DT_COMPLEX64",r[r.DT_INT64=9]="DT_INT64",r[r.DT_BOOL=10]="DT_BOOL",r[r.DT_QINT8=11]="DT_QINT8",r[r.DT_QUINT8=12]="DT_QUINT8",r[r.DT_QINT32=13]="DT_QINT32",r[r.DT_BFLOAT16=14]="DT_BFLOAT16",r[r.DT_FLOAT_REF=101]="DT_FLOAT_REF",r[r.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",r[r.DT_INT32_REF=103]="DT_INT32_REF",r[r.DT_UINT8_REF=104]="DT_UINT8_REF",r[r.DT_INT16_REF=105]="DT_INT16_REF",r[r.DT_INT8_REF=106]="DT_INT8_REF",r[r.DT_STRING_REF=107]="DT_STRING_REF",r[r.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",r[r.DT_INT64_REF=109]="DT_INT64_REF",r[r.DT_BOOL_REF=110]="DT_BOOL_REF",r[r.DT_QINT8_REF=111]="DT_QINT8_REF",r[r.DT_QUINT8_REF=112]="DT_QUINT8_REF",r[r.DT_QINT32_REF=113]="DT_QINT32_REF",r[r.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(Zn||(Zn={}));var $1;(function(r){let e;(function(t){t[t.LEGACY=0]="LEGACY",t[t.V1=1]="V1",t[t.V2=2]="V2"})(e=r.CheckpointFormatVersion||(r.CheckpointFormatVersion={}))})($1||($1={}));var ok={};function R1(r,e){let t={tfOpName:r,category:"custom",inputs:[],attrs:[],customExecutor:e};ok[r]=t}function nx(r){return ok[r]}function F1(r){delete ok[r]}function C(r,e,t,o,n){let s=e.inputParams[r];if(s&&s.inputIndexStart!==void 0){let i=s.inputIndexStart,l=s.inputIndexEnd===0?void 0:s.inputIndexEnd===void 0?i+1:s.inputIndexEnd;if(s.type==="tensor")return hr(e.inputNames[s.inputIndexStart],t,o,n);if(s.type==="tensors")return e.inputNames.slice(i,l).map(m=>hr(m,t,o,n));let u=hr(e.inputNames.slice(i)[0],t,o,n),c=u.dataSync();return s.type==="number"?c[0]:y.toNestedArray(u.shape,c)}let a=e.attrParams[r];return a&&a.value}function hr(r,e,t,o){let[n,s]=Zr(r);if(o!=null){let i=o.getHashTableHandleByName(n);if(i!=null)return i}let a=t.currentContextIds.find(i=>!!e[sx(n,i)]);return a!==void 0?e[sx(n,a)][s]:void 0}function O1(r,e,t){return e[sx(r,t.currentContextId)]}function zs(r,e){let[t,o]=Zr(r);return[sx(t,e&&e.currentContextId),o]}function sx(r,e){return e?`${r}-${e}`:r}function Zr(r){let e=r.split(":");return e.length===1?[r,0]:[e[0],Number(e[e.length-1])]}function Fd(r,e,t){let o=C("pad",r,e,t);if(o==="explicit"){o=C("explicitPaddings",r,e,t);let n=[[0,0],[0,0],[0,0],[0,0]];for(let s=0;s<4;s++)n[s][0]=o[s*2],n[s][1]=o[s*2+1];return n}return o}function Bs(r){return r.kept?r:Ro(r)}var nk={};et(nk,{json:()=>p6});var p6=[{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}]}];var sk={};et(sk,{json:()=>m6});var m6=[{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}]}];var ik={};et(ik,{json:()=>f6});var f6=[{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"}]}];var ak={};et(ak,{json:()=>d6});var d6=[{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"}]}];var lk={};et(lk,{json:()=>h6});var h6=[{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"}]}];var uk={};et(uk,{json:()=>g6});var g6=[{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}]}];var ck={};et(ck,{json:()=>x6});var x6=[{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"}]}];var pk={};et(pk,{json:()=>y6});var y6=[{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"}]}];var mk={};et(mk,{json:()=>b6});var b6=[{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}]}];var fk={};et(fk,{json:()=>_6});var _6=[{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"}]}];var dk={};et(dk,{json:()=>w6});var w6=[{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}]}];var hk={};et(hk,{json:()=>k6});var k6=[{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}]}];var gk={};et(gk,{json:()=>v6});var v6=[{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}]}];var xk={};et(xk,{json:()=>C6});var C6=[{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"}]}];var yk={};et(yk,{json:()=>I6});var I6=[{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}]}];var bk={};et(bk,{json:()=>N6});var N6=[{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}]}];var _k={};et(_k,{json:()=>S6});var S6=[{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:[]}];var ix=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[nk,sk,ik,ak,lk,uk,ck,dk,fk,pk,hk,gk,xk,yk,bk,_k,mk],t=[].concat(...e.map(o=>o.json));this.opMappers=t.reduce((o,n)=>(o[n.tfOpName]=n,o),{})}transformGraph(e,t={}){let o=e.node,n=[],s=[],a=[],i=o.reduce((h,g)=>(h[g.name]=this.mapNode(g),g.op.startsWith("Placeholder")?n.push(h[g.name]):g.op==="Const"?s.push(h[g.name]):(g.input==null||g.input.length===0)&&a.push(h[g.name]),h),{}),l=[],u=[],c={},p={};t!=null&&(c=this.mapSignatureEntries(t.inputs),p=this.mapSignatureEntries(t.outputs));let m=Object.keys(i);m.forEach(h=>{let g=i[h];g.inputNames.forEach(x=>{let[b]=zs(x);g.inputs.push(i[b]),i[b].children.push(g)})}),Object.keys(p).length===0?m.forEach(h=>{let g=i[h];g.children.length===0&&u.push(g)}):Object.keys(p).forEach(h=>{let[g]=zs(h),x=i[g];x!=null&&(x.signatureKey=p[h],u.push(x))}),Object.keys(c).length>0?Object.keys(c).forEach(h=>{let[g]=zs(h),x=i[g];x&&(x.signatureKey=c[h],l.push(x))}):l=n;let f={};e.library!=null&&e.library.function!=null&&(f=e.library.function.reduce((h,g)=>(h[g.signature.name]=this.mapFunction(g),h),{}));let d={nodes:i,inputs:l,outputs:u,weights:s,placeholders:n,signature:t,functions:f};return a.length>0&&(d.initNodes=a),d}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,o)=>(t[e[o].name]=o,t),{})}mapNode(e){let t=nx(e.op)||this.opMappers[e.op]||{};e.attr==null&&(e.attr={});let o={name:e.name,op:e.op,category:t.category,inputNames:(e.input||[]).map(n=>n.startsWith("^")?n.substr(1):n),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr};return t.inputs!=null&&(o.inputParams=t.inputs.reduce((n,s)=>(n[s.name]={type:s.type,inputIndexStart:s.start,inputIndexEnd:s.end},n),{})),t.attrs!=null&&(o.attrParams=t.attrs.reduce((n,s)=>{let a=s.type,i;switch(s.type){case"string":i=ax(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=ax(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"string[]":i=dx(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=dx(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"number":i=ux(e.attr,s.tfName,s.defaultValue||0),i===void 0&&!!s.tfDeprecatedName&&(i=ux(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"number[]":i=fx(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=fx(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"bool":i=lx(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=lx(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"bool[]":i=gx(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=gx(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"shape":i=mx(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=mx(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"shape[]":i=hx(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=hx(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"dtype":i=cx(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=cx(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"dtype[]":i=px(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=px(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"func":i=P1(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=P1(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${s.type} for op: ${e.op}`)}return n[s.name]={value:i,type:a},n},{})),o}mapFunction(e){let t=e.nodeDef,o=[],n=[],s={};t!=null&&(s=t.reduce((p,m)=>(p[m.name]=this.mapNode(m),m.op==="Const"&&n.push(p[m.name]),p),{}));let a=[],i=[];e.signature.inputArg.forEach(p=>{let[m]=zs(p.name),f={name:m,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:wk(p.type),type:"dtype"}},children:[]};f.signatureKey=p.name,a.push(f),s[m]=f}),Object.keys(s).forEach(p=>{let m=s[p];m.inputNames.forEach(f=>{let[d]=zs(f);m.inputs.push(s[d]),s[d].children.push(m)})});let u=e.ret;e.signature.outputArg.forEach(p=>{let[m,f]=zs(u[p.name]),d=s[m];d!=null&&(d.defaultOutput=f,i.push(d))});let c=this.mapArgsToSignature(e);return{nodes:s,inputs:a,outputs:i,weights:n,placeholders:o,signature:c}}mapArgsToSignature(e){return{methodName:e.signature.name,inputs:e.signature.inputArg.reduce((t,o)=>(t[o.name]=this.mapArgToTensorInfo(o),t),{}),outputs:e.signature.outputArg.reduce((t,o)=>(t[o.name]=this.mapArgToTensorInfo(o,e.ret),t),{})}}mapArgToTensorInfo(e,t){let o=e.name;return t!=null&&(o=t[o]),{name:o,dtype:e.type}}};function T6(r){let e=U().global;if(typeof e.atob!="undefined")return e.atob(r);if(typeof Buffer!="undefined")return new Buffer(r,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function M1(r,e){let t=Array.isArray(r)?String.fromCharCode.apply(null,r):T6(r);return e?t:t.toLowerCase()}function ax(r,e,t,o=!1){let n=r[e];return n!=null?M1(n.s,o):t}function lx(r,e,t){let o=r[e];return o?o.b:t}function ux(r,e,t){let o=r[e]||{},n=o.i!=null?o.i:o.f!=null?o.f:t;return typeof n=="number"?n:parseInt(n,10)}function wk(r){switch(typeof r=="string"&&(r=Zn[r]),r){case Zn.DT_FLOAT:return"float32";case Zn.DT_INT32:case Zn.DT_INT64:case Zn.DT_INT8:case Zn.DT_UINT8:return"int32";case Zn.DT_BOOL:return"bool";case Zn.DT_DOUBLE:return"float32";case Zn.DT_STRING:return"string";default:return null}}function P1(r,e,t){let o=r[e];return o&&o.func?o.func.name:t}function cx(r,e,t){let o=r[e];return o&&o.type?wk(o.type):t}function px(r,e,t){let o=r[e];return o&&o.list&&o.list.type?o.list.type.map(n=>wk(n)):t}function L1(r){if(!r.unknownRank)return r.dim!=null?r.dim.map(e=>typeof e.size=="number"?e.size:parseInt(e.size,10)):[]}function mx(r,e,t){let o=r[e];return o&&o.shape?L1(o.shape):t}function fx(r,e,t){let o=r[e];return o?((o.list.f&&o.list.f.length?o.list.f:o.list.i)||[]).map(n=>typeof n=="number"?n:parseInt(n,10)):t}function dx(r,e,t,o=!1){let n=r[e];return n&&n.list&&n.list.s?n.list.s.map(s=>M1(s,o)):t}function hx(r,e,t){let o=r[e];return o&&o.list&&o.list.shape?o.list.shape.map(n=>L1(n)):t}function gx(r,e,t){let o=r[e];return o&&o.list&&o.list.b?o.list.b:t}var kk=class{constructor(e,t,o){this.node=e,this.tensorMap=t,this.context=o,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map(n=>this.getInput(n)),e.rawAttrs!=null&&(this.attrs=Object.keys(e.rawAttrs).reduce((n,s)=>(n[s]=this.getAttr(s),n),{}))}getInput(e){return hr(e,this.tensorMap,this.context)}getAttr(e,t){let o=this.node.rawAttrs[e];if(o.tensor!=null)return hr(e,this.tensorMap,this.context);if(o.i!=null||o.f!=null)return ux(this.node.rawAttrs,e,t);if(o.s!=null)return ax(this.node.rawAttrs,e,t);if(o.b!=null)return lx(this.node.rawAttrs,e,t);if(o.shape!=null)return mx(this.node.rawAttrs,e,t);if(o.type!=null)return cx(this.node.rawAttrs,e,t);if(o.list!=null){if(o.list.i!=null||o.list.f!=null)return fx(this.node.rawAttrs,e,t);if(o.list.s!=null)return dx(this.node.rawAttrs,e,t);if(o.list.shape!=null)return hx(this.node.rawAttrs,e,t);if(o.list.b!=null)return gx(this.node.rawAttrs,e,t);if(o.list.type!=null)return px(this.node.rawAttrs,e,t)}return t}};var z1=(r,e,t)=>{switch(r.op){case"BiasAdd":case"AddV2":case"Add":return[Q(C("a",r,e,t),C("b",r,e,t))];case"AddN":return[i_(C("tensors",r,e,t))];case"FloorMod":case"Mod":return[qm(C("a",r,e,t),C("b",r,e,t))];case"Mul":return[O(C("a",r,e,t),C("b",r,e,t))];case"RealDiv":case"Div":return[de(C("a",r,e,t),C("b",r,e,t))];case"DivNoNan":return[zm(C("a",r,e,t),C("b",r,e,t))];case"FloorDiv":return[_u(C("a",r,e,t),C("b",r,e,t))];case"Sub":return[ue(C("a",r,e,t),C("b",r,e,t))];case"Minimum":return[As(C("a",r,e,t),C("b",r,e,t))];case"Maximum":return[qr(C("a",r,e,t),C("b",r,e,t))];case"Pow":return[Rr(C("a",r,e,t),C("b",r,e,t))];case"SquaredDifference":return[Vu(C("a",r,e,t),C("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var B1=(r,e,t)=>{switch(r.op){case"Abs":case"ComplexAbs":return[Tt(C("x",r,e,t))];case"Acos":return[Nm(C("x",r,e,t))];case"Acosh":return[Sm(C("x",r,e,t))];case"Asin":return[Em(C("x",r,e,t))];case"Asinh":return[Am(C("x",r,e,t))];case"Atan":return[Dm(C("x",r,e,t))];case"Atan2":return[$m(C("x",r,e,t),C("y",r,e,t))];case"Atanh":return[Rm(C("x",r,e,t))];case"Ceil":return[Om(C("x",r,e,t))];case"Complex":return[bo(C("real",r,e,t),C("imag",r,e,t))];case"Cos":return[Ia(C("x",r,e,t))];case"Cosh":return[Nu(C("x",r,e,t))];case"Elu":return[Ss(C("x",r,e,t))];case"Erf":return[Bm(C("x",r,e,t))];case"Exp":return[Xt(C("x",r,e,t))];case"Expm1":return[Vm(C("x",r,e,t))];case"Floor":return[Ts(C("x",r,e,t))];case"Log":return[ir(C("x",r,e,t))];case"Log1p":return[Au(C("x",r,e,t))];case"Imag":return[Tu(C("x",r,e,t))];case"Neg":return[je(C("x",r,e,t))];case"Reciprocal":return[Xm(C("x",r,e,t))];case"Real":return[al(C("x",r,e,t))];case"Relu":return[Ir(C("x",r,e,t))];case"Round":return[Ym(C("x",r,e,t))];case"Selu":return[Mu(C("x",r,e,t))];case"Sigmoid":return[Ur(C("x",r,e,t))];case"Sin":return[Lu(C("x",r,e,t))];case"Sign":return[Jm(C("x",r,e,t))];case"Sinh":return[zu(C("x",r,e,t))];case"Softplus":return[Es(C("x",r,e,t))];case"Sqrt":return[xt(C("x",r,e,t))];case"Square":return[Pe(C("x",r,e,t))];case"Tanh":return[Mi(C("x",r,e,t))];case"Tan":return[rf(C("x",r,e,t))];case"ClipByValue":return[nr(C("x",r,e,t),C("clipValueMin",r,e,t),C("clipValueMax",r,e,t))];case"Relu6":return[Ou(C("x",r,e,t))];case"Rsqrt":return[Pu(hr(r.inputNames[0],e,t))];case"Prod":return[Ru(C("x",r,e,t),C("axes",r,e,t))];case"LeakyRelu":return[Sa(C("x",r,e,t),C("alpha",r,e,t))];case"Prelu":return[Da(C("x",r,e,t),C("alpha",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Co(r,e,t=""){if(!(typeof r=="number"||typeof e=="number")){y.assert(r.length===e.length,()=>t+` Shapes ${r} and ${e} must match`);for(let o=0;ot+` Shapes ${r} and ${e} must match`)}}}function V1(r){return!(typeof r=="number"||r.some(e=>e<0))}function $p(r,e,t){let o=xx(r,t),n=!V1(o);if(n&&e.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${o}`);if(n&&e.forEach(s=>{o=xx(s.shape,o)}),!V1(o))throw new Error(`Non-fully-defined elementShape: ${o}`);return o}function xx(r,e){if(typeof r=="number")return e;if(typeof e=="number")return r;if(r.length!==e.length)throw new Error(`Incompatible ranks during merge: ${r} vs. ${e}`);let t=[];for(let o=0;o=0&&s>=0&&n!==s)throw new Error(`Incompatible shape during merge: ${r} vs. ${e}`);t[o]=n>=0?n:s}return t}var vk=class{constructor(e,t,o,n,s,a,i){this.name=e,this.dtype=t,this.maxSize=o,this.elementShape=n,this.identicalElementShapes=s,this.dynamicSize=a,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=le(0),Dt(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 o=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),Co(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),o.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(o.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);o.tensor=t,Dt(t),o.written=!0,this.tensors[e]=o}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((o,n)=>this.write(o,t[n]))}gather(e,t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let n=0;n=this.maxSize)throw new Error(`Max index must be < array size (${o} vs. ${this.maxSize})`);this.writeMany(e,ur(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 o=0,n=e.map(l=>(o+=l,o));if(o!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is ${o}, 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 s=o===0?0:t.size/o,a=[];B(()=>{t=L(t,[1,o,s]);for(let l=0;l{if(o!==s.dtype)throw new Error(`Invalid data types; op elements ${o}, but list elements ${s.dtype}`);Co(t,s.shape,"TensorList shape mismatch: "),Dt(s)}),this.idTensor=le(0),this.maxNumElements=n,Dt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new nc([...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,o=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(o!==-1&&this.tensors.length!==o)throw new Error(`Operation expected a list with ${o} elements but got a list with ${this.tensors.length} elements.`);Co(e,this.elementShape,"TensorList shape mismatch: ");let n=$p(this.elementShape,this.tensors,e);return B(()=>{let s=this.tensors.map(a=>L(a,n));return Bt(s,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let o=$p(this.elementShape,this.tensors,e),n=this.tensors.pop();return Co(n.shape,e,"TensorList shape mismatch: "),L(n,o)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Co(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Dt(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,o){if(o!==this.elementDtype)throw new Error(`Invalid data types; op elements ${o}, 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 ${e} is null.`);Co(this.tensors[e].shape,t,"TensorList shape mismatch: ");let n=$p(this.elementShape,this.tensors,t);return L(this.tensors[e],n)}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.`);Co(this.elementShape,t.shape,"TensorList shape mismatch: "),Dt(t),this.tensors[e]=t}gather(e,t,o){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);Co(this.elementShape,o,"TensorList shape mismatch: "),e=e.slice(0,this.size());let n=$p(this.elementShape,this.tensors,o);return e.length===0?Dr([],[0].concat(n)):B(()=>{let s=e.map(a=>L(this.tensors[a],n));return Bt(s,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);Co(this.elementShape,t,"TensorList shape mismatch: ");let o=$p(this.elementShape,this.tensors,t);return this.size()===0?Dr([],[0].concat(o)):B(()=>{let n=this.tensors.map(s=>L(s,o));return Je(n,0)})}};function G1(r,e,t){let o=r.dtype;if(r.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${r.shape}`);if(r.dtype!==t)throw new Error(`Invalid data types; op elements ${r.dtype}, but list elements ${t}`);let n=r.shape.slice(1);Co(n,e,"TensorList shape mismatch: ");let s=ur(r);return new nc(s,e,o)}function W1(r,e,t){return new nc([],r,e,t)}function U1(r,e,t,o){if(e.length!==r.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${r.shape[0]}`);let n=Math.max(...e);if(o!=null&&o!==-1&&n>=o)throw new Error(`Max index must be < array size (${n} vs. ${o})`);let s=new nc([],t,r.dtype,o),a=ur(r,0);return e.forEach((i,l)=>{s.setItem(i,a[l])}),s}function j1(r,e,t){let o=0,n=e.map(c=>(o+=c,o));if(o!==r.shape[0])throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is ${o}, and tensor's shape is: ${r.shape}`);let s=r.shape.slice(1),a=xx(s,t),i=o===0?0:r.size/o,l=B(()=>{let c=[];r=L(r,[1,o,i]);for(let p=0;p{switch(r.op){case"If":case"StatelessIf":{let o=C("thenBranch",r,e,t),n=C("elseBranch",r,e,t),s=C("cond",r,e,t),a=C("args",r,e,t);return(await s.data())[0]?t.functionMap[o].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap):t.functionMap[n].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap)}case"While":case"StatelessWhile":{let o=C("body",r,e,t),n=C("cond",r,e,t),s=C("args",r,e,t),a=await t.functionMap[n].executeFunctionAsync(s,t.tensorArrayMap,t.tensorListMap),i=s.map(c=>c.id),l=await a[0].data();a.forEach(c=>{!c.kept&&i.indexOf(c.id)===-1&&c.dispose()});let u=s;for(;l[0];){let c=u;u=await t.functionMap[o].executeFunctionAsync(u,t.tensorArrayMap,t.tensorListMap);let p=u.map(f=>f.id);c.forEach(f=>{!f.kept&&i.indexOf(f.id)===-1&&p.indexOf(f.id)===-1&&f.dispose()});let m=await t.functionMap[n].executeFunctionAsync(u,t.tensorArrayMap,t.tensorListMap);l=await m[0].data(),m.forEach(f=>{!f.kept&&i.indexOf(f.id)===-1&&p.indexOf(f.id)===-1&&f.dispose()})}return u}case"LoopCond":{let o=C("pred",r,e,t);return[Bs(o)]}case"Switch":{let o=C("pred",r,e,t),n=C("data",r,e,t);return n.kept||(n=Bs(n)),(await o.data())[0]?[void 0,n]:[n,void 0]}case"Merge":{let o=r.inputNames.find(n=>hr(n,e,t)!==void 0);if(o){let n=hr(o,e,t);return[Bs(n)]}return}case"Enter":{let o=C("frameName",r,e,t),n=C("tensor",r,e,t);return t.enterFrame(o),[Bs(n)]}case"Exit":{let o=C("tensor",r,e,t);return t.exitFrame(),[Bs(o)]}case"NextIteration":{let o=C("tensor",r,e,t);return t.nextIteration(),[Bs(o)]}case"TensorArrayV3":{let o=C("size",r,e,t),n=C("dtype",r,e,t),s=C("elementShape",r,e,t),a=C("dynamicSize",r,e,t),i=C("clearAfterRead",r,e,t),l=C("identicalElementShapes",r,e,t),u=C("name",r,e,t),c=new vk(u,n,o,s,l,a,i);return t.addTensorArray(c),[c.idTensor,le(1)]}case"TensorArrayWriteV3":{let o=C("tensorArrayId",r,e,t),n=C("index",r,e,t),s=C("tensor",r,e,t),a=t.getTensorArray(o.id);return a.write(n,s),[a.idTensor]}case"TensorArrayReadV3":{let o=C("tensorArrayId",r,e,t),n=C("index",r,e,t);return[t.getTensorArray(o.id).read(n)]}case"TensorArrayGatherV3":{let o=C("tensorArrayId",r,e,t),n=C("indices",r,e,t),s=C("dtype",r,e,t);return[t.getTensorArray(o.id).gather(n,s)]}case"TensorArrayScatterV3":{let o=C("tensorArrayId",r,e,t),n=C("indices",r,e,t),s=C("tensor",r,e,t),a=t.getTensorArray(o.id);return a.scatter(n,s),[a.idTensor]}case"TensorArrayConcatV3":{let o=C("tensorArrayId",r,e,t),n=t.getTensorArray(o.id),s=C("dtype",r,e,t);return[n.concat(s)]}case"TensorArraySplitV3":{let o=C("tensorArrayId",r,e,t),n=C("tensor",r,e,t),s=C("lengths",r,e,t),a=t.getTensorArray(o.id);return a.split(s,n),[a.idTensor]}case"TensorArraySizeV3":{let o=C("tensorArrayId",r,e,t),n=t.getTensorArray(o.id);return[le(n.size(),"int32")]}case"TensorArrayCloseV3":{let o=C("tensorArrayId",r,e,t),n=t.getTensorArray(o.id);return n.clearAndClose(),[n.idTensor]}case"TensorListSetItem":{let o=C("tensorListId",r,e,t),n=C("index",r,e,t),s=C("tensor",r,e,t),a=t.getTensorList(o.id);return a.setItem(n,s),[a.idTensor]}case"TensorListGetItem":{let o=C("tensorListId",r,e,t),n=C("index",r,e,t),s=C("elementShape",r,e,t),a=C("elementDType",r,e,t);return[t.getTensorList(o.id).getItem(n,s,a)]}case"TensorListScatterV2":case"TensorListScatter":{let o=C("indices",r,e,t),n=C("tensor",r,e,t),s=C("elementShape",r,e,t),a=C("numElements",r,e,t),i=U1(n,o,s,a);return t.addTensorList(i),[i.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let o=C("elementShape",r,e,t),n=C("elementDType",r,e,t),s;r.op==="TensorListReserve"?s="numElements":s="maxNumElements";let a=C(s,r,e,t),i=W1(o,n,a);return t.addTensorList(i),[i.idTensor]}case"TensorListGather":{let o=C("tensorListId",r,e,t),n=C("indices",r,e,t),s=C("elementShape",r,e,t),a=C("elementDType",r,e,t);return[t.getTensorList(o.id).gather(n,a,s)]}case"TensorListStack":{let o=C("tensorListId",r,e,t),n=C("elementShape",r,e,t),s=C("elementDType",r,e,t),a=C("numElements",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case"TensorListFromTensor":{let o=C("tensor",r,e,t),n=C("elementShape",r,e,t),s=C("elementDType",r,e,t),a=G1(o,n,s);return t.addTensorList(a),[a.idTensor]}case"TensorListConcat":{let o=C("tensorListId",r,e,t),n=t.getTensorList(o.id),s=C("dtype",r,e,t),a=C("elementShape",r,e,t);return[n.concat(s,a)]}case"TensorListPushBack":{let o=C("tensorListId",r,e,t),n=C("tensor",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case"TensorListPopBack":{let o=C("tensorListId",r,e,t),n=C("elementShape",r,e,t),s=C("elementDType",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case"TensorListSplit":{let o=C("tensor",r,e,t),n=C("elementShape",r,e,t),s=C("lengths",r,e,t),a=j1(o,s,n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function q1(r,e,t){let[o,n]=C("fusedOps",r,e,t),s=o==="biasadd",a=n==="prelu",i=o==="fusedbatchnorm",l=C("numArgs",r,e,t);if(s){if(a&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(i)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let u=C("strides",r,e,t),c=Fd(r,e,t),p=C("dataFormat",r,e,t).toUpperCase(),m=C("dilations",r,e,t),[f,d]=C("args",r,e,t),h=C("leakyreluAlpha",r,e,t);return{stride:u,pad:c,dataFormat:p,dilations:m,biasArg:f,preluArg:d,activationFunc:n,leakyreluAlpha:h}}var K1=(r,e,t)=>{switch(r.op){case"Conv1D":{let o=C("stride",r,e,t),n=C("pad",r,e,t),s=C("dataFormat",r,e,t).toUpperCase(),a=C("dilation",r,e,t);return[Cu(C("x",r,e,t),C("filter",r,e,t),o,n,s,a)]}case"Conv2D":{let o=C("strides",r,e,t),n=Fd(r,e,t),s=C("dataFormat",r,e,t).toUpperCase(),a=C("dilations",r,e,t);return[jr(C("x",r,e,t),C("filter",r,e,t),[o[1],o[2]],n,s,[a[1],a[2]])]}case"_FusedConv2D":{let{stride:o,pad:n,dataFormat:s,dilations:a,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=q1(r,e,t);return[Wn.conv2d({x:C("x",r,e,t),filter:C("filter",r,e,t),strides:[o[1],o[2]],pad:n,dataFormat:s,dilations:[a[1],a[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"FusedDepthwiseConv2dNative":{let{stride:o,pad:n,dataFormat:s,dilations:a,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=q1(r,e,t);return[Wn.depthwiseConv2d({x:C("x",r,e,t),filter:C("filter",r,e,t),strides:[o[1],o[2]],pad:n,dataFormat:s,dilations:[a[1],a[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let o=C("outputShape",r,e,t),n=C("strides",r,e,t),s=Fd(r,e,t);return[Iu(C("x",r,e,t),C("filter",r,e,t),o,[n[1],n[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let o=C("strides",r,e,t),n=Fd(r,e,t),s=C("dilations",r,e,t),a=C("dataFormat",r,e,t).toUpperCase();return[Ns(C("input",r,e,t),C("filter",r,e,t),[o[1],o[2]],n,a,[s[1],s[2]])]}case"Conv3D":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("dataFormat",r,e,t).toUpperCase(),a=C("dilations",r,e,t);return[Pm(C("x",r,e,t),C("filter",r,e,t),[o[1],o[2],o[3]],n,s,[a[1],a[2],a[3]])]}case"AvgPool":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("kernelSize",r,e,t);return[va(C("x",r,e,t),[s[1],s[2]],[o[1],o[2]],n)]}case"MaxPool":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("kernelSize",r,e,t);return[Ea(C("x",r,e,t),[s[1],s[2]],[o[1],o[2]],n)]}case"MaxPoolWithArgmax":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("kernelSize",r,e,t),a=C("includeBatchInIndex",r,e,t),{result:i,indexes:l}=E_(C("x",r,e,t),[s[1],s[2]],[o[1],o[2]],n,a);return[i,l]}case"AvgPool3D":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("kernelSize",r,e,t);return[Fm(C("x",r,e,t),[s[1],s[2],s[3]],[o[1],o[2],o[3]],n)]}case"MaxPool3D":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("kernelSize",r,e,t);return[jm(C("x",r,e,t),[s[1],s[2],s[3]],[o[1],o[2],o[3]],n)]}case"Dilation2D":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("dilations",r,e,t),a=o[1],i=o[2],l=s[1],u=s[2];return[Lm(C("x",r,e,t),C("filter",r,e,t),[a,i],n,[l,u],"NHWC")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var X1=(r,e,t)=>{switch(r.op){case"Fill":{let o=C("shape",r,e,t),n=C("dtype",r,e,t),s=C("value",r,e,t);return[Na(o,s,n)]}case"LinSpace":{let o=C("start",r,e,t),n=C("stop",r,e,t),s=C("num",r,e,t);return[v_(o,n,s)]}case"Multinomial":{let o=C("logits",r,e,t),n=C("numSamples",r,e,t),s=C("seed",r,e,t);return[A_(o,n,s)]}case"OneHot":{let o=C("indices",r,e,t),n=C("depth",r,e,t),s=C("onValue",r,e,t),a=C("offValue",r,e,t);return[Is(o,n,s,a)]}case"Ones":return[Cr(C("shape",r,e,t),C("dtype",r,e,t))];case"OnesLike":return[er(C("x",r,e,t))];case"RandomUniform":return[Ds(C("shape",r,e,t),C("minval",r,e,t),C("maxval",r,e,t),C("dtype",r,e,t))];case"Range":{let o=C("start",r,e,t),n=C("stop",r,e,t),s=C("step",r,e,t);return[Jc(o,n,s,C("dtype",r,e,t))]}case"TruncatedNormal":{let o=C("shape",r,e,t),n=C("mean",r,e,t),s=C("stdDev",r,e,t),a=C("seed",r,e,t);return[Gu(o,n,s,C("dtype",r,e,t),a)]}case"Zeros":return[gt(C("shape",r,e,t),C("dtype",r,e,t))];case"ZerosLike":return[Ie(C("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Ck(r,e,t){let o=C("boxes",r,e,t),n=C("scores",r,e,t),s=C("maxOutputSize",r,e,t),a=C("iouThreshold",r,e,t),i=C("scoreThreshold",r,e,t),l=C("softNmsSigma",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:l}}var Y1=async(r,e,t)=>{switch(r.op){case"NonMaxSuppressionV5":{let{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:l}=Ck(r,e,t),u=await Rs.nonMaxSuppressionWithScoreAsync(o,n,s,a,i,l);return[u.selectedIndices,u.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i}=Ck(r,e,t),l=C("padToMaxOutputSize",r,e,t),u=await Rs.nonMaxSuppressionPaddedAsync(o,n,s,a,i,l);return[u.selectedIndices,u.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i}=Ck(r,e,t);return[await Rs.nonMaxSuppressionAsync(o,n,s,a,i)]}case"Where":{let o=oe(C("condition",r,e,t),"bool"),n=[await sf(o)];return o.dispose(),n}case"ListDiff":return B_(C("x",r,e,t),C("y",r,e,t));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var Z1=(r,e,t)=>{switch(r.op){case"TopKV2":{let o=C("x",r,e,t),n=C("k",r,e,t),s=C("sorted",r,e,t),a=of(o,n,s);return[a.values,a.indices]}case"Unique":{let o=C("x",r,e,t),n=ep(o);return[n.values,n.indices]}case"UniqueV2":{let o=C("x",r,e,t),n=C("axis",r,e,t),s=ep(o,n);return[s.values,s.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var J1=(r,e,t)=>{switch(r.op){case"Const":return e[r.name];case"PlaceholderWithDefault":let o=C("default",r,e,t);return[hr(r.name,e,t)||o];case"Placeholder":return[hr(r.name,e,t)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let u=C("x",r,e,t);return[Bs(u)]}case"IdentityN":return C("x",r,e,t).map(u=>Bs(u));case"Snapshot":let n=C("x",r,e,t);return[Bs(n)];case"Shape":return[Vt(C("x",r,e,t).shape,"int32")];case"ShapeN":return C("x",r,e,t).map(u=>Vt(u.shape));case"Size":return[le(C("x",r,e,t).size,"int32")];case"Rank":return[le(C("x",r,e,t).rank,"int32")];case"NoOp":return[le(1)];case"Print":let s=C("x",r,e,t),a=C("data",r,e,t),i=C("message",r,e,t),l=C("summarize",r,e,t);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(i);for(let u=0;ue.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),B(()=>{let n=ur(t),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let n=[];for(let s=0;s{switch(r.op){case"HashTable":case"HashTableV2":{let n=C("keyDType",r,e,t),s=C("valueDType",r,e,t),a=new Ik(n,s);return o.addHashTable(r.name,a),[a.handle]}case"LookupTableImport":case"LookupTableImportV2":{let n=C("tableHandle",r,e,t,o),s=C("keys",r,e,t),a=C("values",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case"LookupTableFind":case"LookupTableFindV2":{let n=C("tableHandle",r,e,t,o),s=C("keys",r,e,t),a=C("defaultValue",r,e,t);return[await o.getHashTableById(n.id).find(s,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var eE=(r,e,t)=>{switch(r.op){case"ResizeBilinear":{let o=C("images",r,e,t),n=C("size",r,e,t),s=C("alignCorners",r,e,t),a=C("halfPixelCenters",r,e,t);return[Rs.resizeBilinear(o,[n[0],n[1]],s,a)]}case"ResizeNearestNeighbor":{let o=C("images",r,e,t),n=C("size",r,e,t),s=C("alignCorners",r,e,t),a=C("halfPixelCenters",r,e,t);return[Rs.resizeNearestNeighbor(o,[n[0],n[1]],s,a)]}case"CropAndResize":{let o=C("image",r,e,t),n=C("boxes",r,e,t),s=C("boxInd",r,e,t),a=C("cropSize",r,e,t),i=C("method",r,e,t),l=C("extrapolationValue",r,e,t);return[Rs.cropAndResize(o,n,s,a,i,l)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var tE=(r,e,t)=>{switch(r.op){case"Equal":return[_o(C("a",r,e,t),C("b",r,e,t))];case"NotEqual":return[Gn(C("a",r,e,t),C("b",r,e,t))];case"Greater":return[Qt(C("a",r,e,t),C("b",r,e,t))];case"GreaterEqual":return[to(C("a",r,e,t),C("b",r,e,t))];case"Less":return[Eu(C("a",r,e,t),C("b",r,e,t))];case"LessEqual":return[Mo(C("a",r,e,t),C("b",r,e,t))];case"LogicalAnd":return[fr(C("a",r,e,t),C("b",r,e,t))];case"LogicalNot":return[Ta(C("a",r,e,t))];case"LogicalOr":return[$u(C("a",r,e,t),C("b",r,e,t))];case"Select":case"SelectV2":return[$t(C("condition",r,e,t),C("a",r,e,t),C("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var rE=(r,e,t)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[We(C("a",r,e,t),C("b",r,e,t),C("transposeA",r,e,t),C("transposeB",r,e,t))];case"Transpose":return[Ue(C("x",r,e,t),C("perm",r,e,t))];case"_FusedMatMul":let[o,n]=C("fusedOps",r,e,t),s=o==="biasadd",a=n==="prelu",i=C("numArgs",r,e,t),l=C("leakyreluAlpha",r,e,t);if(s){if(a&&i!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&i!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,c]=C("args",r,e,t);return[Wn.matMul({a:C("a",r,e,t),b:C("b",r,e,t),transposeA:C("transposeA",r,e,t),transposeB:C("transposeB",r,e,t),bias:u,activation:n,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var oE=(r,e,t)=>{switch(r.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[zn(C("x",r,e,t),C("mean",r,e,t),C("variance",r,e,t),C("offset",r,e,t),C("scale",r,e,t),C("epsilon",r,e,t))];case"FusedBatchNormV3":return[zn(C("x",r,e,t),C("mean",r,e,t),C("variance",r,e,t),C("offset",r,e,t),C("scale",r,e,t),C("epsilon",r,e,t))];case"LRN":return[Gm(C("x",r,e,t),C("radius",r,e,t),C("bias",r,e,t),C("alpha",r,e,t),C("beta",r,e,t))];case"Softmax":return[$a(C("x",r,e,t))];case"LogSoftmax":return[Du(C("x",r,e,t))];case"SparseToDense":return[af(C("sparseIndices",r,e,t),C("outputShape",r,e,t),C("sparseValues",r,e,t),C("defaultValue",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var nE=(r,e,t)=>{switch(r.op){case"Max":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[ar(C("x",r,e,t),a,i)]}case"Mean":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[ht(C("x",r,e,t),a,i)]}case"Min":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[Li(C("x",r,e,t),a,i)]}case"Sum":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[ye(C("x",r,e,t),a,i)]}case"All":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[wu(C("x",r,e,t),a,i)]}case"Any":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[nl(C("x",r,e,t),a,i)]}case"ArgMax":{let a=C("axis",r,e,t);return[sl(C("x",r,e,t),a)]}case"ArgMin":{let a=C("axis",r,e,t);return[Tm(C("x",r,e,t),a)]}case"Prod":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[Ru(C("x",r,e,t),a,i)]}case"Cumsum":{let a=C("axis",r,e,t),i=C("exclusive",r,e,t),l=C("reverse",r,e,t);return[Su(C("x",r,e,t),a,i,l)]}case"Bincount":let o=C("x",r,e,t),n=C("weights",r,e,t),s=C("size",r,e,t);return[f_(o,n,s)];case"DenseBincount":{let a=C("x",r,e,t),i=C("weights",r,e,t),l=C("size",r,e,t),u=C("binaryOutput",r,e,t);return[y_(a,i,l,u)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var sE=(r,e,t)=>{switch(r.op){case"ConcatV2":case"Concat":{let o=C("n",r,e,t),n=C("axis",r,e,t),s=C("tensors",r,e,t);return s=s.slice(0,o),[Je(s,n)]}case"Gather":{let o=C("x",r,e,t),n=C("indices",r,e,t);return[Bn(o,oe(n,"int32"),0)]}case"GatherV2":{let o=C("axis",r,e,t),n=C("batchDims",r,e,t),s=C("x",r,e,t),a=C("indices",r,e,t);return[Bn(s,oe(a,"int32"),o,n)]}case"Reverse":{let o=C("dims",r,e,t),n=[];for(let a=0;a{let o=C("axis",r,e,t),n=C("tensors",r,e,t),s=n[0].shape,a=wo(n[0]).shape,i=n.map(l=>{let u=y.arraysEqual(l.shape,s);if(!u&&!y.arraysEqual(wo(l).shape,a))throw new Error("the input tensors shape does not match");return u?l:L(l,s)});return[Bt(i,o)]});case"Unpack":{let o=C("axis",r,e,t),n=C("tensor",r,e,t);return ur(n,o)}case"Tile":{let o=C("reps",r,e,t);return[Po(C("x",r,e,t),o)]}case"Split":case"SplitV":{let o=C("axis",r,e,t),n=C("numOrSizeSplits",r,e,t),s=C("x",r,e,t);return lr(s,n,o)}case"ScatterNd":{let o=C("indices",r,e,t),n=C("values",r,e,t),s=C("shape",r,e,t);return[W_(o,n,s)]}case"GatherNd":{let o=C("x",r,e,t),n=C("indices",r,e,t);return[U_(o,n)]}case"SparseToDense":{let o=C("sparseIndices",r,e,t),n=C("outputShape",r,e,t),s=C("sparseValues",r,e,t),a=C("defaultValue",r,e,t);return[af(o,s,n,s.dtype===a.dtype?a:oe(a,s.dtype))]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var iE=(r,e,t)=>{switch(r.op){case"FFT":return[Ra(C("x",r,e,t))];case"IFFT":return[zi(C("x",r,e,t))];case"RFFT":return[Fa(C("x",r,e,t))];case"IRFFT":return[Bu(C("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var aE=(r,e,t)=>{switch(r.op){case"Cast":return[oe(C("x",r,e,t),C("dtype",r,e,t))];case"ExpandDims":{let o=C("axis",r,e,t);return[sr(C("x",r,e,t),o)]}case"Squeeze":{let o=C("axis",r,e,t);return[wo(C("x",r,e,t),o)]}case"Reshape":return[L(C("x",r,e,t),C("shape",r,e,t))];case"MirrorPad":return[Hm(C("x",r,e,t),C("padding",r,e,t),C("mode",r,e,t))];case"PadV2":case"Pad":return[$r(C("x",r,e,t),C("padding",r,e,t),C("constantValue",r,e,t))];case"SpaceToBatchND":{let o=C("blockShape",r,e,t),n=C("paddings",r,e,t);return[Aa(C("x",r,e,t),o,n)]}case"BatchToSpaceND":{let o=C("blockShape",r,e,t),n=C("crops",r,e,t);return[Ca(C("x",r,e,t),o,n)]}case"DepthToSpace":{let o=C("blockSize",r,e,t),n=C("dataFormat",r,e,t).toUpperCase();return[Mm(C("x",r,e,t),o,n)]}case"BroadcastTo":return[il(C("x",r,e,t),C("shape",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Nk(r,e,t,o){let n=((s,a,i)=>{switch(s.category){case"arithmetic":return B(()=>z1(s,a,i));case"basic_math":return B(()=>B1(s,a,i));case"control":return H1(s,a,i);case"convolution":return B(()=>K1(s,a,i));case"creation":return B(()=>X1(s,a,i));case"dynamic":return Y1(s,a,i);case"evaluation":return B(()=>Z1(s,a,i));case"image":return B(()=>eE(s,a,i));case"graph":return B(()=>J1(s,a,i));case"logical":return B(()=>tE(s,a,i));case"matrices":return B(()=>rE(s,a,i));case"normalization":return B(()=>oE(s,a,i));case"reduction":return B(()=>nE(s,a,i));case"slice_join":return B(()=>sE(s,a,i));case"spectral":return B(()=>iE(s,a,i));case"transformation":return B(()=>aE(s,a,i));case"hash_table":return Q1(s,a,i,o);case"custom":let l=nx(s.op);if(l&&l.customExecutor)return l.customExecutor(new kk(s,a,i));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()`)}})(r,e,t);return y.isPromise(n)?n.then(s=>[].concat(s)):[].concat(n)}var yx=class{constructor(e={},t={},o={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,this.functionMap=n,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;tt.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 Tk(r,e,t,o){let n=new Set,s=[],a=null,i=null,l=new Set,u=Object.keys(r).map(m=>Zr(m)[0]),c=[];o!=null&&(c=o.map(m=>Zr(m.name)[0]));let p=[...e];for(;p.length>0;){let m=p.pop();if((Sk(m)||E6(m)||A6(m))&&a==null&&(a=m,i=a.children.map(f=>f.name).filter(f=>n.has(f))),n.add(m.name),t[m.name]==null&&u.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{l.has(f.name)||(l.add(f.name),p.push(f))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function lE(r,e,t){let{usedNodes:o,inputs:n}=t,s=[],a=Object.keys(n).map(c=>Zr(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{o.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{o.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{o.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),e[c.name]||u.push(c),c.children.forEach(p=>{!l.has(p.name)&&o.has(p.name)&&p.inputs.every(m=>l.has(m.name))&&s.push(p)})}return u}var D6=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],$6=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],R6=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function Sk(r){return D6.indexOf(r.op)>=0}function E6(r){return $6.indexOf(r.op)>=0}function A6(r){return R6.indexOf(r.op)>=0}var Rp=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new Rp(e.functions[o],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(o=>e[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let o=Tk(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let i=t.map(u=>u.name),l=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${l}]. Missing the following inputs: [${n}]`)}return lE(this.graph,this.weightMap,o)}execute(e,t){e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(p=>this.graph.nodes[Zr(p)[0]]),s=t.map(p=>Zr(p)[0]),a=s.map(p=>this.graph.nodes[p]);a.length===0&&(a=this._outputs);let i=this.getCompilationKey(n,a),l=this.compiledMap.get(i);l==null&&(l=this.compile(e,a),this.compiledMap.set(i,l));let u={},c={};return B(()=>{let p=new yx(this.weightMap,u,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);Object.keys(e).forEach(h=>{let[g,x]=Zr(h),b=[];b[x]=e[h],m[g]=b});let f=this.getFrozenTensorIds(m),d={};for(let h=0;hhr(h,m,p))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){t.category==="control"||a.indexOf(e)!==-1||(o[e].forEach(l=>{l!=null&&(i[l.id]=(i[l.id]||0)+t.children.length)}),t.inputs.forEach(l=>{if(l.category!=="control"){let u=O1(l.name,o,n);u!=null&&u.forEach(c=>{if(c&&!s.has(c.id)){let p=i[c.id];p===1?(c.dispose(),delete i[c.id]):p!=null&&i[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,o=!1,n={},s={}){o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let a=new yx(this.weightMap,n,s,this.functionExecutorMap),i=await this.executeWithControlFlow(e,a,t,o),l=t.map(m=>hr(m,i,a)),u=l.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),p=new Set([...u,...c,...this.weightIds]);return Object.keys(i).forEach(m=>{i[m].forEach(d=>{d&&!d.isDisposed&&!p.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(p),l}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(_=>this.graph.nodes[Zr(_)[0]]),i=o.map(_=>Zr(_)[0]),l=i.map(_=>this.graph.nodes[_]);l.length===0&&(l=this._outputs);let{usedNodes:u,missingInputs:c,dynamicNode:p,syncInputs:m}=Tk(e,l,this.weightMap,this._initNodes),f=[...a,...this.graph.weights,...this._initNodes||[]].map(_=>({node:_,contexts:t.currentContext})),d=Object.assign({},this.weightMap);Object.keys(e).forEach(_=>{let[w,v]=Zr(_),$=[];$[v]=e[_],d[w]=$});let h={},g=this.getFrozenTensorIds(d),x={};for(;f.length>0;){let _=this.processStack(a,f,t,d,x,g,i,h,u);await Promise.all(_)}p==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let b=l.filter(_=>!Sk(_)&&!hr(_.name,d,t)).map(_=>_.name);if(b.length>0){let _="";throw p!=null&&(_=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${_}`)}return d}processStack(e,t,o,n,s,a,i,l,u){let c=[];for(;t.length>0;){let p=t.pop();o.currentContext=p.contexts;let m="";if(p.node.op==="Enter"&&C("isConstant",p.node,n,o)&&([m]=zs(p.node.name,o)),n[p.node.name]==null){let f=Nk(p.node,n,o,this._resourceManager);m||([m]=zs(p.node.name,o));let d=o.currentContext;y.isPromise(f)?c.push(f.then(h=>(n[m]=h,o.currentContext=d,this.checkTensorForDisposal(m,p.node,n,o,a,i,l),this.processChildNodes(p.node,t,o,n,s,u),h))):(n[m]=f,this.checkTensorForDisposal(m,p.node,n,o,a,i,l),this.processChildNodes(p.node,t,o,n,s,u))}else this.processChildNodes(p.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[l]=zs(i.name,o);s[l]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!hr(u,n,o))&&(s[l]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!hr(u,n,o))&&(s[l]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Zr(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((l,u)=>a[u]===-1||a[u]===l);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){let t={};for(let o in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[o]!=null){let n=this._signature.inputs[o];t[n.name]=e[o]}else t[o]=e[o];return t}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Zr(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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[o]=Zr(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var Ek=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}};var F6="?tfjs-format=file",O6="model.json",bx=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Ek}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=vr.browserHTTPRequest(e,this.loadOptions);else{let t=vr.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(vr.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,o;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?o=this.artifacts.userDefinedMetadata.signature:o=this.artifacts.signature,this.signature=o,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=vr.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Rp(ix.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=ix.Instance.transformGraph(e.modelInitializer);this.initializer=new Rp(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let o=vr.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);e=o[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 Ve)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,o,n)=>(t[o]=e[n],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=this.executor.execute(e,t);return o.length>1?o:o[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=await this.executor.executeAsync(e,t);return o.length>1?o:o[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,o)=>(t[o]=[e[o]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function uE(r,e={}){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");e==null&&(e={}),e.fromTFHub&&r.load==null&&(r.endsWith("/")||(r=r+"/"),r=`${r}${O6}${F6}`);let t=new bx(r,e);return await t.load(),t}var _x="3.1.0";var Mk={};et(Mk,{CSVDataset:()=>zd,Dataset:()=>qi,FileDataSource:()=>Ud,TextLineDataset:()=>Md,URLDataSource:()=>jd,array:()=>DE,csv:()=>VE,func:()=>GE,generator:()=>WE,microphone:()=>jE,version_data:()=>Sx,webcam:()=>UE,zip:()=>$E});var AE=Tc(Km());var hE=Tc(Km());function cE(r,e){return wx(r,e)}function wx(r,e,t=new Map,o=new Set){if(r==null)return null;if(o.has(r))throw new Error("Circular references are not supported.");if(t.has(r))return t.get(r);let n=e(r);if(n.recurse&&n.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(n.recurse)if(kl(r)){let s=Array.isArray(r)?[]:{};o.add(r);for(let a in r){let i=r[a],l=wx(i,e,t,o);s[a]=l}return o.delete(r),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return t.set(r,n.value),n.value}function mE(r,e=Ak){return pE(r,e)}function pE(r,e,t=new Set){let o=r[0];if(t.has(o))throw new Error("Circular references are not supported.");let n=e(r);if(n.recurse&&n.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(n.recurse)if(kl(o)){let s=Array.isArray(o)?[]:{};t.add(o);for(let a in o){let i=r.map(u=>u[a]),l=pE(i,e,t);s[a]=l}return t.delete(o),s}else throw new Error(`Can't recurse into non-iterable type: ${o}`);else return n.value}function Ak(r){return r===null?null:kl(r[0])?{value:null,recurse:!0}:{value:r,recurse:!1}}async function kx(r,e){let t=new Map;wx(r,e,t);for(let n of Array.from(t.keys())){let s=t.get(n);if(y.isPromise(s)){let a=await s;t.set(n,a)}}return wx(r,e,t)}function kl(r){return r!=null&&!ArrayBuffer.isView(r)&&(Array.isArray(r)||typeof r=="object"&&!(r instanceof Ve))}function fE(r){return r==null||P6(r)||Array.isArray(r)||typeof r=="object"&&r instanceof Ve||y.isTypedArray(r)}function P6(r){return r===null||typeof r!="object"&&typeof r!="function"}function dE(r){return cE(r,M6)}function M6(r){return r instanceof Ve?{value:r.clone(),recurse:!1}:kl(r)?{value:null,recurse:!0}:{value:r,recurse:!1}}var Od=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),o=this.get(t);return this.set(t,this.pop()),o}};var Fp=class extends Od{constructor(){super(Fp.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),o=this.length();for(let n=0;nt===!0)}rowMajorBatch(e,t=!0){return new CE(this,e,t)}columnMajorBatch(e,t=!0,o=Ak){return this.rowMajorBatch(e,t).map(s=>mE(s,o))}concatenate(e,t){return new $k(Dk([this,e]),t)}take(e){return e<0||e==null?this:new vE(this,e)}skip(e){return e<0||e==null?this:new kE(this,e)}prefetch(e){return new Fk(this,e)}shuffle(e,t){return new EE(this,e,t)}serial(){return new wE(this)}},gE=class extends Kt{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:dE(e),done:!1}}},xE=class extends Kt{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: ${e.message}`,e}}},wE=class extends Kt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},kE=class extends Kt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},CE=class extends Kt{constructor(e,t,o=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=o,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},IE=class extends Kt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Te(e.value)}}},NE=class extends Kt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ln.getTensorsInContainer(e.value),o=this.transform(e.value),n=Ln.getTensorsInContainer(o);for(let s of t)Ln.isTensorInList(s,n)||s.dispose();return{value:o,done:!1}}},SE=class extends Kt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Rk=class extends Kt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ln.getTensorsInContainer(e.value),o=await this.transform(e.value),n=Ln.getTensorsInContainer(o);for(let s of t)Ln.isTensorInList(s,n)||s.dispose();return{value:o,done:!1}}},Op=class extends Kt{constructor(){super();this.outputQueue=new Fp,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}}},TE=class extends Op{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Ln.getTensorsInContainer(e.value),o=this.transform(e.value),n=Ln.getTensorsInContainer(o);this.outputQueue.pushAll(o);for(let s of t)Ln.isTensorInList(s,n)||s.dispose();return!0}},$k=class extends Kt{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 o=await this.moreIterators.next();if(o.done)return{value:null,done:!0};this.iterator=o.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}},Va;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(Va||(Va={}));var bE=class extends Kt{constructor(e,t=Va.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,o=0;function n(a){return a instanceof Kt?{value:a.next().then(l=>(t++,l.done&&o++,l.value)),recurse:!1}:{value:null,recurse:!0}}let s=await kx(this.iterators,n);if(t===o)return{value:null,done:!0};if(o>0)switch(this.mismatchMode){case Va.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Va.SHORTEST:return{value:null,done:!0};case Va.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Fk=class extends Kt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new Od(t)}summary(){return`${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()}},EE=class extends Fk{constructor(e,t,o){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=hE.alea(o||y.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}}};var qi=class{constructor(){this.size=null}batch(e,t=!0){let o=this;y.assert(e>0,()=>`batchSize needs to be positive, but it is ${e}`);let n;return this.size===Infinity||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),lo(async()=>(await o.iterator()).columnMajorBatch(e,t,L6),n)}concatenate(e){let t=this,o;return this.size===Infinity||e.size===Infinity?o=Infinity:this.size!=null&&e.size!=null?o=this.size+e.size:o=null,lo(async()=>(await t.iterator()).concatenate(await e.iterator()),o)}filter(e){let t=this,o;return this.size===Infinity?o=Infinity:o=null,lo(async()=>(await t.iterator()).filter(n=>B(()=>e(n))),o)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return lo(async()=>(await t.iterator()).map(o=>B(()=>e(o))),this.size)}mapAsync(e){let t=this;return lo(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return lo(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,o;return this.size!=null&&e>0?o=this.size*e:e===0?o=0:this.size!=null&&(e===void 0||e<0)?o=Infinity:o=null,lo(async()=>{let n=Pd(async()=>({value:await t.iterator(),done:!1}));return yE(n.take(e))},o)}skip(e){let t=this,o;return this.size!=null&&e>=0&&this.size>=e?o=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),o)}shuffle(e,t,o=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let n=this,s=AE.alea(t||y.now().toString());return lo(async()=>{let a=s.int32();return o&&(a+=s.int32()),(await n.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,o;return this.size!=null&&this.size>e?o=e:this.size!=null&&this.size<=e?o=this.size:o=null,lo(async()=>(await t.iterator()).take(e),o)}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()}};qi.MAX_BUFFER_SIZE=1e4;function lo(r,e=null){return new class extends qi{constructor(){super(...arguments);this.size=e}async iterator(){return r()}}}function DE(r){return lo(async()=>Dk(r),r.length)}function $E(r){if(!kl(r))throw new Error("The argument to zip() must be an object or array.");let e;if(Array.isArray(r))for(let t=0;t{let t=await kx(r,o=>{if(o instanceof qi)return{value:o.iterator(),recurse:!1};if(kl(o))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return _E(t,Va.SHORTEST)},e)}function L6(r){if(r===null)return null;let e=r[0];return fE(e)?{value:z6(r),recurse:!1}:{value:null,recurse:!0}}function z6(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof Ve?Bt(r):Dr(r)}var Md=class extends qi{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` `).map(n=>(n.endsWith("\r")&&(n=n.slice(0,-1)),n))}};var vx='"',Ld=Symbol("out"),RE=Symbol("field"),Cx=Symbol("quote"),Ok=Symbol("quoteafterquote"),FE=Symbol("quoteinquote"),zd=class extends qi{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 Md(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(y.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&&y.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((n,s)=>(n[s]=n[s]+1||1,n),{}),o=Object.keys(t).filter(n=>t[n]>1);if(y.assert(o.length===0,()=>"Duplicate column names found: "+o.toString()),this.columnConfigs){for(let n of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(n)===-1)throw new Error('The key "'+n+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let o=t.value;return this.parseRow(o,!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),o={},n={};for(let s=0;s14||!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(U().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new Bd(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(o){throw new Error(`Error thrown while initializing video stream: ${o.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,o=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(o.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(o.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[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=[],o=0;return new Promise(n=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++o===this.numFrames&&(clearInterval(s),n({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,o=new Float32Array(e.length*t);return e.forEach((n,s)=>o.set(n,s*t)),o}getTensorFromAudioDataArray(e,t){let o=new Float32Array(y.sizeFromShape(t));return o.set(e,o.length-e.length),Dr(o,t)}};var Vd=class extends Kt{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=Vt([0],"int32"),this.webcamConfig.centerCrop){let o=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-o)/2,a=(1-n)/2,i=s+o,l=n+a;this.cropBox=Bi([a,s,l,i],[1,4])}else this.cropBox=Bi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(U().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 o=new Vd(e,t);return await o.start(),o}async start(){this.webcamConfig.facingMode&&y.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=tg.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return B(()=>{let t=sr(oe(e,"float32"),0),o;o=Rs.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=o.shape;return L(o,n.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var Gd=class{};var Ix=class extends Kt{split(e){return new OE(this,e)}},OE=class extends Ix{constructor(e,t){super();this.upstream=e,this.impl=new PE(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},PE=class extends Op{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let o of t.slice(0,-1))this.outputQueue.push(o);return this.carryover=t[t.length-1],!0}};var Pk=class extends Kt{decodeUTF8(){return new LE(this)}},LE=class extends Ix{constructor(e){super();this.upstream=e,this.impl=new zE(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},zE=class extends Op{constructor(e){super();if(this.upstream=e,U().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=ME();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let o;return U().get("IS_BROWSER")?o=this.decoder.decode(t,{stream:!0}):o=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(o),!0}};var Wd=class extends Pk{constructor(e,t={}){super();this.file=e,this.options=t,y.assert(e instanceof Uint8Array||(U().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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,o)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,n)));else{let s=new FileReader;s.onload=i=>{let l=s.result;if(l instanceof ArrayBuffer&&(l=new Uint8Array(l)),!(l instanceof Uint8Array))return o(new TypeError("FileReader returned unknown type."));t(l)},s.onabort=i=>o(new Error("Aborted")),s.onerror=i=>o(new Error(i.type));let a=this.file.slice(this.offset,n);s.readAsArrayBuffer(a)}this.offset=n}),done:!1}}};async function BE(r,e={}){let t,o;typeof r=="string"?t=r:(t=r.url,o=B6(r));let n=await y.fetch(t,o);if(n.ok){let s=new Uint8Array(await n.arrayBuffer());return new Wd(s,e)}else throw new Error(n.statusText)}var B6=r=>({method:r.method,headers:r.headers,body:r.body,mode:r.mode,credentials:r.credentials,cache:r.cache,redirect:r.redirect,referrer:r.referrer,integrity:r.integrity});function Nx(r){return typeof r=="string"&&r.substr(0,7)==="file://"}var Ud=class extends Gd{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(Nx(this.input)&&U().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new Wd(this.input,this.options)}};var jd=class extends Gd{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return Nx(this.url)?new Ud(this.url,this.fileOptions).iterator():BE(this.url,this.fileOptions)}};function VE(r,e={}){return new zd(new jd(r),e)}function GE(r){let e=Pd(r);return lo(async()=>e)}function WE(r){return lo(async()=>{let e=await r();return Pd(()=>e.next())})}async function UE(r,e){return Vd.create(r,e)}async function jE(r){return Bd.create(r)}var Sx="3.1.0";function ee(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the CPU backend.`)})}var V6=Sr.whereImpl,Pp=class extends js{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Ya(this,Fo())}nextDataId(){return Pp.nextDataId++}write(e,t,o){this.firstUse&&(this.firstUse=!1,U().get("IS_NODE")&&N.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 n={id:this.nextDataId()};return this.data.set(n,{values:e,dtype:o,refCount:1}),n}makeTensorInfo(e,t,o){let n;if(t==="string"&&o!=null&&o.length>0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return{dataId:n,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}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,o,n,s){this.data.set(e,{values:t,dtype:n,refCount:s})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:o}=this.data.get(e);if(t==="complex64"){let n=this.readSync(o.real.dataId),s=this.readSync(o.imag.dataId);return N.mergeRealAndImagArrays(n,s)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),o=t;if(e.dtype==="string")try{o=t.map(n=>y.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ce(e.shape,e.dtype,o)}makeOutput(e,t,o){let n=this.write(e,t,o);return Fo().makeTensorFromDataId(n,t,o,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:o}=this.data.get(e);o!=null&&(this.disposeData(o.real.dataId,!0),this.disposeData(o.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=y.now();return e(),{kernelMs:y.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){ee([e],"where");let t=this.readSync(e.dataId);return V6(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};Pp.nextDataId=0;var qk={};et(qk,{addImpl:()=>JE,bincountImpl:()=>Hd,bincountReduceImpl:()=>Lk,ceilImpl:()=>eA,concatImpl:()=>qd,expImpl:()=>rA,expm1Impl:()=>nA,floorImpl:()=>iA,gatherV2Impl:()=>Bk,greaterImpl:()=>lA,lessImpl:()=>cA,linSpaceImpl:()=>Vk,logImpl:()=>mA,maxImpl:()=>Gk,maximumImpl:()=>dA,minimumImpl:()=>gA,multiplyImpl:()=>Tx,negImpl:()=>bA,notEqualImpl:()=>wA,prodImpl:()=>CA,rangeImpl:()=>Yd,rsqrtImpl:()=>NA,simpleAbsImpl:()=>HE,sliceImpl:()=>Zd,squaredDifferenceImpl:()=>EA,stridedSliceImpl:()=>Wk,subImpl:()=>DA,tileImpl:()=>Uk,topKImpl:()=>jk,transposeImpl:()=>Xd,uniqueImpl:()=>Hk});function HE(r){let e=new Float32Array(r.length);for(let t=0;t{let{x:e}=r.inputs,t=r.backend;ee(e,"abs");let o=new Float32Array(y.sizeFromShape(e.shape)),n=t.data.get(e.dataId).values;return o=HE(n),t.makeOutput(o,e.shape,"float32")},qE={kernelName:us,backendName:"cpu",kernelFunc:G6};function Ye(r){return(e,t,o,n,s)=>{let a=N.assertAndGetBroadcastShape(e,t),i=a.length,l=y.computeStrides(a),u=y.sizeFromShape(a),c=y.getTypedArrayFromDType(s,u),p=e.length,m=t.length,f=y.computeStrides(e),d=y.computeStrides(t),h=N.getBroadcastDims(e,a),g=N.getBroadcastDims(t,a);if(h.length+g.length===0)for(let x=0;x_[A]=0);let w=y.locToIndex(_,p,f),v=b.slice(-m);g.forEach(A=>v[A]=0);let $=y.locToIndex(v,m,d);c[x]=r(o[w],n[$])}return[c,a]}}function cr(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.data.get(o.dataId).values,a=t.data.get(n.dataId).values,i=t.makeTensorInfo(o.shape,"complex64"),l=t.data.get(i.dataId);return l.complexTensorInfos={real:t.makeTensorInfo(o.shape,"float32",s),imag:t.makeTensorInfo(n.shape,"float32",a)},i}var KE={kernelName:Hl,backendName:"cpu",kernelFunc:cr};function Mp(r,e,t="float32"){if(t==="complex64"){let n=Mp(r,e,"float32"),s=Mp(r,e,"float32");return cr({inputs:{real:n,imag:s},backend:r})}let o=y.makeZerosTypedArray(y.sizeFromShape(e),t);return r.makeTensorInfo(e,t,o)}function Tr(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var XE={kernelName:Do,backendName:"cpu",kernelFunc:Tr};function Jn(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.data.get(o.dataId).complexTensorInfos.real,s=t.data.get(n.dataId).values;return t.makeTensorInfo(n.shape,n.dtype,s)}var YE={kernelName:cu,backendName:"cpu",kernelFunc:Jn};function Qn(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return Tr({inputs:{x:n},backend:t});let a=Mp(t,n.shape,n.dtype),i=Qn({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),l=cr({inputs:{real:i,imag:a},backend:t});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}if(n.dtype==="complex64"){let a=Jn({inputs:{input:n},backend:t}),i=Qn({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Tr({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(s==="int32"){let a=t.data.get(n.dataId).values,i=Int32Array.from(a);return t.makeTensorInfo(n.shape,"int32",i)}if(s==="bool"){let a=t.data.get(n.dataId).values,i=y.toTypedArray([0],n.dtype),[l,u]=Ye((c,p)=>c!==p?1:0)(n.shape,[],a,i,"bool");return t.makeTensorInfo(u,"bool",l)}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var ZE={kernelName:Eo,backendName:"cpu",kernelFunc:Qn};function ot(r,e,t,o){return t==null?({inputs:n,backend:s})=>{let{a,b:i}=n,l=s;ee([a,i],r);let u=l.data.get(a.dataId).values,c=l.data.get(i.dataId).values,p=o||a.dtype,[m,f]=e(a.shape,i.shape,u,c,p);return l.makeTensorInfo(f,p,m)}:({inputs:n,backend:s})=>{let{a,b:i}=n,l=s;if(a.dtype==="complex64"||i.dtype==="complex64"){let u=Qn({inputs:{x:a},backend:l,attrs:{dtype:"complex64"}}),c=l.data.get(u.dataId),p=c.complexTensorInfos.real,m=c.complexTensorInfos.imag,f=l.data.get(p.dataId).values,d=l.data.get(m.dataId).values,h=Qn({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),g=l.data.get(h.dataId),x=g.complexTensorInfos.real,b=g.complexTensorInfos.imag,_=l.data.get(x.dataId).values,w=l.data.get(b.dataId).values,[v,$,A]=t(a.shape,i.shape,f,d,_,w),R=l.makeTensorInfo(A,"float32",v),M=l.makeTensorInfo(A,"float32",$),V=cr({inputs:{real:R,imag:M},backend:l});return l.disposeIntermediateTensorInfo(u),l.disposeIntermediateTensorInfo(h),l.disposeIntermediateTensorInfo(R),l.disposeIntermediateTensorInfo(M),V}else{let u=l.data.get(a.dataId).values,c=l.data.get(i.dataId).values,p=o||a.dtype,[m,f]=e(a.shape,i.shape,u,c,p);return l.makeTensorInfo(f,p,m)}}}function Lp(r){return(e,t,o,n,s,a)=>{let i=N.assertAndGetBroadcastShape(e,t),l=y.sizeFromShape(i),u=i.length,c=y.computeStrides(i),p=y.getTypedArrayFromDType("float32",l),m=y.getTypedArrayFromDType("float32",l),f=N.getBroadcastDims(e,i),d=N.getBroadcastDims(t,i),h=N.mergeRealAndImagArrays(o,n),g=N.mergeRealAndImagArrays(s,a),x=e.length,b=y.computeStrides(e),_=t.length,w=y.computeStrides(t);if(f.length+d.length===0)for(let v=0;vA[W]=0);let R=y.locToIndex(A,x,b),M=$.slice(-_);d.forEach(W=>M[W]=0);let V=y.locToIndex(M,_,w),G=r(h[R*2],h[R*2+1],g[V*2],g[V*2+1]);p[v]=G.real,m[v]=G.imag}return[p,m,i]}}var JE=Ye((r,e)=>r+e),W6=Lp((r,e,t,o)=>({real:r+t,imag:e+o})),Ga=ot(xo,JE,W6),QE={kernelName:xo,backendName:"cpu",kernelFunc:Ga};function Hd(r,e,t,o,n){let s=y.sizeFromShape(o),a=y.makeZerosTypedArray(n,t);for(let i=0;i=n||(s>0?a[l]+=e[i]:a[l]+=1)}return a}function Lk(r,e,t,o=!1){let n=r.shape[0],s=r.shape[1],a=Ce([n,t],e.dtype);for(let i=0;i=t||(o?a.set(1,i,u):e.size>0?a.set(a.get(i,u)+e.get(i,l),i,u):a.set(a.get(i,u)+1,i,u))}return a}function es(r){return(e,t,o)=>{let n=y.getTypedArrayFromDType(t,e.length);for(let s=0;s{let{x:a}=o;if(ee(a,r),a.dtype==="string"||t==="string")throw new Error("unaryKernelFunc does not support string input/output");let i=s,l=i.data.get(a.dataId).values,u=y.sizeFromShape(a.shape),c=t||a.dtype,p=y.getArrayFromDType(c,u);for(let m=0;m{let{x:a}=o;if(ee(a,r),a.dtype==="string"||t==="string")throw new Error("unaryKernelFunc does not support string input/output");let i=s,l=i.data.get(a.dataId).values,u=t||a.dtype,c=e(l,u,n);return i.makeTensorInfo(a.shape,u,c)}}var eA=es(r=>Math.ceil(r)),U6=ts(Zo,eA),tA={kernelName:Zo,backendName:"cpu",kernelFunc:U6};function qd(r,e,t,o){let n=y.getArrayFromDType(t,y.sizeFromShape(e));if(o&&t!=="string"){let s=0;r.forEach(a=>{let i=y.sizeFromShape(a.shape);n.set(a.vals,s),s+=i})}else{let s=0;r.forEach(a=>{let i=t==="string"?N.fromUint8ToStringArray(a.vals):a.vals,l=0;for(let u=0;uMath.exp(r)),zk=ts(nn,rA),oA={kernelName:nn,backendName:"cpu",kernelFunc:zk};var nA=es(r=>Math.expm1(r)),j6=ts(ai,nA),sA={kernelName:ai,backendName:"cpu",kernelFunc:j6};var iA=es(r=>Math.floor(r)),H6=ts(sn,iA),aA={kernelName:sn,backendName:"cpu",kernelFunc:H6};function Bk(r,e,t){let o=Ce(t,r.dtype);for(let n=0;nr>e?1:0),q6=ot(ci,lA,null,"bool"),uA={kernelName:ci,backendName:"cpu",kernelFunc:q6};var cA=Ye((r,e)=>rMath.log(r)),X6=ts(pn,mA),fA={kernelName:pn,backendName:"cpu",kernelFunc:X6};function Gk(r,e,t,o){let n=y.getTypedArrayFromDType(o,y.sizeFromShape(t));for(let s=0;si&&(i=u)}n[s]=i}return n}var dA=Ye((r,e)=>Math.max(r,e)),Y6=ot(fn,dA),hA={kernelName:fn,backendName:"cpu",kernelFunc:Y6};var gA=Ye((r,e)=>Math.min(r,e)),Z6=ot(xn,gA),xA={kernelName:xn,backendName:"cpu",kernelFunc:Z6};var Tx=Ye((r,e)=>r*e),J6=Lp((r,e,t,o)=>({real:r*t-e*o,imag:r*o+e*t})),Kd=ot(yn,Tx,J6),yA={kernelName:yn,backendName:"cpu",kernelFunc:Kd};function bA(r,e,t){let o=y.createScalarValue(-1,t);return Tx([],e,o,r,t)}function Q6(r){let{inputs:e,backend:t}=r,{x:o}=e;ee(o,"neg");let n=t.data.get(o.dataId).values,[s,a]=bA(n,o.shape,o.dtype);return t.makeTensorInfo(a,o.dtype,s)}var _A={kernelName:fs,backendName:"cpu",kernelFunc:Q6};var wA=Ye((r,e)=>r!==e?1:0),e5=ot(bi,wA,null,"bool"),kA={kernelName:bi,backendName:"cpu",kernelFunc:e5};function Xd(r,e,t,o,n){let s=e.length,a=y.sizeFromShape(e),i=y.computeStrides(e),l=y.computeStrides(n),u=y.getTypedArrayFromDType(t,y.sizeFromShape(n));for(let c=0;ct.disposeIntermediateTensorInfo(b)),t.makeTensorInfo(x,g,d)}var IA={kernelName:vi,backendName:"cpu",kernelFunc:t5};function Yd(r,e,t,o){let n=r===e,s=r1;if(n||s||a)return y.makeZerosTypedArray(0,o);let i=Math.abs(Math.ceil((e-r)/t)),l=y.makeZerosTypedArray(i,o);e1/Math.sqrt(r)),r5=ts(Tn,NA),SA={kernelName:Tn,backendName:"cpu",kernelFunc:r5};function Zd(r,e,t,o,n){let s=or.isSliceContinous(o,e,t),a=y.sizeFromShape(t),i=y.computeStrides(o);if(s){let p=or.computeFlatOffset(e,i);return n==="string"?r.slice(p,p+a):r.subarray(p,p+a)}let l=n==="string"?N.fromUint8ToStringArray(r):r,u=Ce(o,n,l),c=Ce(t,n);for(let p=0;pd+e[h]);c.set(u.get(...f),...m)}return n==="string"?N.fromStringArrayToUint8(c.values):c.values}function rs(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o;ee(n,"slice");let[i,l]=or.parseSliceParams(n,s,a);or.assertParamsValid(n,i,l);let u=t.data.get(n.dataId).values,c=Zd(u,i,l,n.shape,n.dtype);return t.makeTensorInfo(l,n.dtype,c)}var TA={kernelName:ys,backendName:"cpu",kernelFunc:rs};var EA=Ye((r,e)=>{let t=r-e;return t*t}),o5=ot(Fn,EA),AA={kernelName:Fn,backendName:"cpu",kernelFunc:o5};function Wk(r,e,t,o){let n=Ce(r,e.dtype);for(let s=0;sr-e),n5=Lp((r,e,t,o)=>({real:r-t,imag:e-o})),Jd=ot(On,DA,n5),$A={kernelName:On,backendName:"cpu",kernelFunc:Jd};function Uk(r,e){let t=new Array(r.rank);for(let n=0;n_.value-b.value);let h=p*o,g=l.subarray(h,h+o),x=u.subarray(h,h+o);for(let b=0;b{for(let g=0;gnew Pp,1);var Kk=Ae(ni,r=>r>=0?r:Math.exp(r)-1),FA={kernelName:ni,backendName:"cpu",kernelFunc:Kk};function Xk(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o;ee([n],"leakyRelu");let a=y.sizeFromShape(n.shape),i=t.data.get(n.dataId).values,l=y.getTypedArrayFromDType("float32",a);for(let u=0;ur<0?e*r:r);function Yk(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e;ee([o,n],"prelu");let s=t.data.get(o.dataId).values,a=t.data.get(n.dataId).values,[i,l]=s5(o.shape,n.shape,s,a,o.dtype);return t.makeTensorInfo(l,o.dtype,i)}var PA={kernelName:kn,backendName:"cpu",kernelFunc:Yk};var Zk=Ae(vn,r=>Math.max(0,r)),MA={kernelName:vn,backendName:"cpu",kernelFunc:Zk};var Jk=Ae(In,r=>Math.min(Math.max(0,r),6)),LA={kernelName:In,backendName:"cpu",kernelFunc:Jk};function zp(r,e,t,o,n){if(t==="linear")return Tr({inputs:{x:e},backend:r});if(t==="relu")return Zk({inputs:{x:e},backend:r});if(t==="elu")return Kk({inputs:{x:e},backend:r});if(t==="relu6")return Jk({inputs:{x:e},backend:r});if(t==="prelu")return Yk({inputs:{x:e,alpha:o},backend:r});if(t==="leakyrelu")return Xk({inputs:{x:e},backend:r,attrs:{alpha:n}});throw new Error(`Activation ${t} has not been implemented for the CPU backend.`)}function tt(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{shape:s}=o,a=y.sizeFromShape(n.shape),i=y.inferFromImplicitShape(s,a),l=y.sizeFromShape(i);y.assert(a===l,()=>`The new shape (${i}) has ${l} elements and the old shape (${n.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),t.incRef(n.dataId);let u=t.data.get(n.dataId);if(u.complexTensorInfos!=null){let c=u.complexTensorInfos.real,p=u.complexTensorInfos.imag;c.shape=i,p.shape=i}return{dataId:n.dataId,shape:i,dtype:n.dtype}}var zA={kernelName:gs,backendName:"cpu",kernelFunc:tt};function Qk(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;ee([n,s],"matMul");let l=n.shape.length,u=s.shape.length,c=a?n.shape[l-2]:n.shape[l-1],p=i?s.shape[u-1]:s.shape[u-2],m=a?n.shape[l-1]:n.shape[l-2],f=i?s.shape[u-2]:s.shape[u-1],d=n.shape.slice(0,-2),h=s.shape.slice(0,-2),g=y.sizeFromShape(d),x=y.sizeFromShape(h),b=g===x||g===1||x===1;y.assert(l>=2&&u>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${d}) and (${h}).`);let w=(g>x?n.shape.slice(0,-2):s.shape.slice(0,-2)).concat([m,f]);y.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${n.shape} and ${s.shape} and transposeA=${a} and transposeB=${i} must match.`);let v=a?[g,c,m]:[g,m,c],$=i?[x,f,p]:[x,p,f],A=tt({inputs:{x:n},backend:t,attrs:{shape:v}}),R=tt({inputs:{x:s},backend:t,attrs:{shape:$}}),M=a?A.shape[1]:A.shape[2],V=a?A.shape[2]:A.shape[1],G=i?R.shape[1]:R.shape[2],W=Math.max(g,x),q=t.data.get(A.dataId).values,Z=t.data.get(R.dataId).values,X=y.computeStrides(A.shape),Y=y.computeStrides(R.shape),[te,K,re]=a?[X[0],1,X[1]]:[X[0],X[1],1],[ie,se,pe]=i?[1,Y[1],Y[0]]:[Y[1],1,Y[0]],ae=V*G,xe=Ce([W,V,G],A.dtype),ge=xe.values,_e=t.blockSize;for(let ke=0;keMath.acos(r)),GA={kernelName:Ks,backendName:"cpu",kernelFunc:a5};var l5=Ae(Xs,r=>Math.acosh(r)),WA={kernelName:Xs,backendName:"cpu",kernelFunc:l5};function u5(r){let{inputs:e,backend:t}=r,o=e;ee(e,"addN");let n=o.map(i=>t.data.get(i.dataId).values),s=Ce(o[0].shape,o[0].dtype),a=s.values;for(let i=0;ib&&(b=v,_=w)}f[g]=_}return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),t.makeTensorInfo(c,"int32",f)}var qA={kernelName:Ko,backendName:"cpu",kernelFunc:m5};function f5(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o;ee(n,"argMin");let a=y.parseAxisParam(s,n.shape),i=N.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=tr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),a=[a[0]],N.assertAxesAreInnerMostDims("argMin",a,l.shape.length);let[c,p]=N.computeOutAndReduceShapes(l.shape,a),m=y.sizeFromShape(c),f=y.makeZerosTypedArray(m,"int32"),d=y.sizeFromShape(p),h=t.data.get(l.dataId).values;for(let g=0;gt.disposeIntermediateTensorInfo(g)),t.makeTensorInfo(c,"int32",f)}var KA={kernelName:oa,backendName:"cpu",kernelFunc:f5};var d5=Ae(Ys,r=>Math.asin(r)),XA={kernelName:Ys,backendName:"cpu",kernelFunc:d5};var h5=Ae(Zs,r=>Math.asinh(r)),YA={kernelName:Zs,backendName:"cpu",kernelFunc:h5};var g5=Ae(Js,r=>Math.atan(r)),ZA={kernelName:Js,backendName:"cpu",kernelFunc:g5};var x5=Ye((r,e)=>Math.atan2(r,e)),y5=ot(ei,x5),JA={kernelName:ei,backendName:"cpu",kernelFunc:y5};var b5=Ae(Qs,r=>Math.atanh(r)),QA={kernelName:Qs,backendName:"cpu",kernelFunc:b5};function Bp(r,e,t,o,n,s){let a=n.strideHeight,i=n.strideWidth,l=n.dilationHeight,u=n.dilationWidth,c=n.effectiveFilterHeight,p=n.effectiveFilterWidth,m=n.padInfo.top,f=n.padInfo.left,d=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,h=Ce(n.outShape,t),g=h.values,x=n.outShape[1]*n.outShape[2]*n.outShape[3],b=n.outShape[2]*n.outShape[3],_=n.outShape[3];for(let w=0;wte?te=ge:s==="avg"&&(K+=ge,re++)}if(isNaN(te))break}let ie=W+q*_+A;g[ie]=s==="avg"?K/re:te}}}return h}function Ex(r,e,t,o,n=!1,s=!1){let a=Ce(o.outShape,"int32"),i=o.strideHeight,l=o.strideWidth,u=o.dilationHeight,c=o.dilationWidth,p=o.effectiveFilterHeight,m=o.effectiveFilterWidth,f=o.padInfo.top,d=o.padInfo.left,h=Ce(e,t,r);for(let g=0;gV&&(V=Y,n?G=s?((g*o.inHeight+W)*o.inWidth+Z)*o.inChannels+x:(W*o.inWidth+Z)*o.inChannels+x:G=q*m+X)}}a.set(G,g,b,$,x)}}return a}function Ax(r,e,t,o,n,s){let a=n.strideDepth,i=n.strideHeight,l=n.strideWidth,u=n.dilationDepth,c=n.dilationHeight,p=n.dilationWidth,m=n.effectiveFilterDepth,f=n.effectiveFilterHeight,d=n.effectiveFilterWidth,h=n.padInfo.front,g=n.padInfo.top,x=n.padInfo.left,b=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,_=Ce(n.outShape,t),w=_.values,v=n.outShape[1]*n.outShape[2]*n.outShape[3]*n.outShape[4],$=n.outShape[2]*n.outShape[3]*n.outShape[4],A=n.outShape[3]*n.outShape[4],R=n.outShape[4];for(let M=0;MDe?De=Ft:s==="avg"&&($e+=Ft,Re++),isNaN(De))break}if(isNaN(De))break}if(isNaN(De))break}let He=ke+W;w[He]=s==="avg"?$e/Re:De}}}}return _}function e2(r,e){let t=Ce(e.outShape,"int32"),o=e.strideDepth,n=e.strideHeight,s=e.strideWidth,a=e.dilationDepth,i=e.dilationHeight,l=e.dilationWidth,u=e.effectiveFilterDepth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,m=e.padInfo.front,f=e.padInfo.top,d=e.padInfo.left;for(let h=0;h=q&&(q=se,Z=Y*c*p+K*c+ie)}}}t.set(Z,h,x,v,M,g)}}}return t}function _5(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;ee(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=o,u=1;y.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(n.shape,s,a,u,i,l),p;if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))p=Tr({inputs:{x:n},backend:t});else{let m=t.data.get(n.dataId).values,f=y.computeStrides(n.shape),d=Bp(m,n.shape,n.dtype,f,c,"avg");p=t.makeTensorInfo(c.outShape,n.dtype,d.values)}return p}var t2={kernelName:Xo,backendName:"cpu",kernelFunc:_5};function w5(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u}=o;ee(n,"avgPool3d");let c=N.computePool3DInfo(n.shape,s,a,1,i,l,u),p=t.data.get(n.dataId).values,m=Ax(p,n.shape,n.dtype,y.computeStrides(n.shape),c,"avg");return t.makeTensorInfo(m.shape,"float32",m.values)}var r2={kernelName:na,backendName:"cpu",kernelFunc:w5};function k5(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:l,dimRoundingMode:u}=o;ee([n,s],"avgPool3DGrad");let c=N.computePool3DInfo(s.shape,a,i,1,l,u),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,d=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,_=c.dilationWidth,w=c.effectiveFilterDepth,v=c.effectiveFilterHeight,$=c.effectiveFilterWidth,A=w-1-c.padInfo.front,R=$-1-c.padInfo.left,M=v-1-c.padInfo.top,V=Ce(s.shape,"float32"),G=1/(d*h*g),W=t.bufferSync(n);for(let q=0;q=c.outDepth||Math.floor(ae)!==ae))for(let xe=0;xe=c.outHeight||Math.floor(ge)!==ge))for(let _e=0;_e<$;_e+=_){let ke=(ie+_e)/f;if(ke<0||ke>=c.outWidth||Math.floor(ke)!==ke)continue;se+=W.get(q,ae,ge,ke,Z)}}}V.set(se*G,q,X,Y,te,Z)}return t.makeTensorInfo(V.shape,V.dtype,V.values)}var o2={kernelName:Ul,backendName:"cpu",kernelFunc:k5};function v5(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;ee([n,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=o,c=N.computePool2DInfo(a.shape,i,l,1,u),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,_=b-1-c.padInfo.left,w=x-1-c.padInfo.top,v=Ce(a.shape,"float32"),$=1/(f*d),A=t.data.get(n.dataId).values,R=Ce(n.shape,"float32",A);for(let M=0;M=c.outHeight||Math.floor(te)!==te))for(let K=0;K=c.outWidth||Math.floor(re)!==re)continue;X+=R.get(M,te,re,V)}}v.set(X*$,M,G,W,V)}return t.makeTensorInfo(v.shape,v.dtype,v.values)}var n2={kernelName:Wl,backendName:"cpu",kernelFunc:v5};function C5(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,scale:s,offset:a,mean:i,variance:l}=e;y.assert(i.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(s==null||i.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),ee([n,i,l,s,a],"batchNorm");let{varianceEpsilon:u}=o;u==null&&(u=.001);let c=t.data.get(n.dataId).values,p=t.data.get(i.dataId).values,m=t.data.get(l.dataId).values,f=s?t.data.get(s.dataId).values:new Float32Array([1]),d=a?t.data.get(a.dataId).values:new Float32Array([0]),h=new Float32Array(c.length),g=d.length,x=f.length,b=m.length,_=p.length,w=0,v=0,$=0,A=0;for(let R=0;R=g&&(w=0),v>=_&&(v=0),$>=x&&($=0),A>=b&&(A=0);return t.makeTensorInfo(n.shape,n.dtype,h)}var s2={kernelName:ln,backendName:"cpu",kernelFunc:C5};function I5(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;ee([n],"batchToSpaceND");let i=s.reduce((x,b)=>x*b),l=N.getReshaped(n.shape,s,i),u=N.getPermuted(l.length,s.length),c=N.getReshapedPermuted(n.shape,s,i),p=N.getSliceBeginCoords(a,s.length),m=N.getSliceSize(c,a,s.length),f=tt({inputs:{x:n},backend:t,attrs:{shape:l}}),d=tr({inputs:{x:f},backend:t,attrs:{perm:u}}),h=tt({inputs:{x:d},backend:t,attrs:{shape:c}}),g=rs({inputs:{x:h},backend:t,attrs:{begin:p,size:m}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var i2={kernelName:sa,backendName:"cpu",kernelFunc:I5};function N5(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.data.get(n.dataId).values,l=t.data.get(s.dataId).values,u=Hd(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var a2={kernelName:jl,backendName:"cpu",kernelFunc:N5};var S5=Ae(Ao,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r{let{x:e}=r.inputs,t=r.backend,o=new Float32Array(y.sizeFromShape(e.shape)),n=t.data.get(e.dataId),s=n.complexTensorInfos.real,a=n.complexTensorInfos.imag,i=t.data.get(s.dataId).values,l=t.data.get(a.dataId).values;for(let u=0;uh.shape),s);if(y.sizeFromShape(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);let i=e.filter(h=>y.sizeFromShape(h.shape)>0);if(i.length===1)return Tr({inputs:{x:i[0]},backend:t});let l=i.map(h=>h.shape);if(N.assertParamsConsistent(l,s),i[0].dtype==="complex64"){let h=i.map(w=>Jn({inputs:{input:w},backend:t})),g=i.map(w=>Ki({inputs:{input:w},backend:t})),x=vl({inputs:h,backend:t,attrs:{axis:s}}),b=vl({inputs:g,backend:t,attrs:{axis:s}}),_=cr({inputs:{real:x,imag:b},backend:t});return h.forEach(w=>t.disposeIntermediateTensorInfo(w)),g.forEach(w=>t.disposeIntermediateTensorInfo(w)),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(b),_}let u=i.map(h=>{let g=y.sizeFromShape(h.shape.slice(s));return tt({inputs:{x:h},backend:t,attrs:{shape:[-1,g]}})}),c=u.map(h=>({vals:t.data.get(h.dataId).values,shape:h.shape}));a=N.computeOutShape(u.map(h=>h.shape),1);let p=u[0].shape[0]===1,m=qd(c,a,e[0].dtype,p),f=N.computeOutShape(i.map(h=>h.shape),s),d=t.makeTensorInfo(f,e[0].dtype,m);return u.forEach(h=>t.disposeIntermediateTensorInfo(h)),d}var p2={kernelName:cs,backendName:"cpu",kernelFunc:vl};function ev(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=o;ee([n,s],"conv2d");let p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,_=m.dataFormat==="channelsLast",w=new ct(m.outShape,n.dtype),v=y.computeStrides(n.shape),$=y.computeStrides(s.shape),A=v[0],R=_?v[1]:v[2],M=_?v[2]:1,V=_?1:v[1],G=w.strides[0],W=_?w.strides[1]:w.strides[2],q=_?w.strides[2]:1,Z=_?1:w.strides[1],X=t.data.get(n.dataId).values,Y=t.data.get(s.dataId).values,te=w.values;for(let K=0;K=m.inHeight)continue;let _e=xe*$[0],ke=re+ge*R;for(let De=0;De=m.inWidth)continue;let wt=_e+He*$[1],kt=ke+ut*M,pt=wt;for(let vt=0;vt=u.inDepth)continue;let K=Y*M[0],re=G+te*R[1];for(let ie=0;ie=u.inHeight)continue;let ge=K+ae*M[1],_e=re+xe*R[2];for(let ke=0;ke=u.inWidth)continue;let ut=ge+Re*M[2],wt=_e+He*u.inChannels,kt=ut;for(let pt=0;ptMath.cos(r)),y2={kernelName:en,backendName:"cpu",kernelFunc:F5};var O5=Ae(ti,r=>Math.cosh(r)),b2={kernelName:ti,backendName:"cpu",kernelFunc:O5};function P5(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=o,[c,p,m,f]=n.shape,d=s.shape[0],[h,g]=i,x=Ce([d,h,g,f],"float32"),b=t.data.get(s.dataId).values,_=t.data.get(a.dataId).values,w=t.data.get(n.dataId).values,v=y.computeStrides(n.shape),$=y.computeStrides(x.shape);for(let A=0;A=c)continue;let Z=h>1?(G-M)*(p-1)/(h-1):0,X=g>1?(W-V)*(m-1)/(g-1):0;for(let Y=0;Y1?M*(p-1)+Y*Z:.5*(M+G)*(p-1);if(te<0||te>p-1){for(let K=0;K1?V*(m-1)+se*X:.5*(V+W)*(m-1);if(pe<0||pe>m-1){for(let _e=0;_e1?V*(m-1)+K*X:.5*(V+W)*(m-1);if(re<0||re>m-1){for(let pe=0;pex+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`),y.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let i=n.shape[0],l=n.shape[1],u=n.shape[2],c=n.shape[3],p=l*s,m=u*s,f=c/(s*s),d=t.data.get(n.dataId).values,h=new Float32Array(i*p*m*f),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`);let f=N.computeConv2DInfo(n.shape,s.shape,a,m,i,u,!0),{filterHeight:d,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=f,_=b.left,w=b.top,v=f.outChannels/f.inChannels,$=new ct(f.outShape,n.dtype),A=t.data.get(n.dataId).values,R=t.data.get(s.dataId).values,M=$.values;for(let V=0;V=f.inHeight)continue;let K=Y*p[0],re=G+te*c[1];for(let ie=0;ie=f.inWidth)continue;let ge=K+ae*p[1],_e=re+xe*f.inChannels,ke=se,De=ge;for(let $e=0;$e{let{x:o,filter:n}=r,{strides:s,pad:a,dilations:i}=t,l=e,u=l.data.get(o.dataId).values,c=o.shape.length,p=l.data.get(n.dataId).values,m=n.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:_,strideHeight:w,strideWidth:v,filterHeight:$,filterWidth:A,dilationHeight:R,dilationWidth:M,outShape:V}=N.computeDilation2DInfo(o.shape,n.shape,s,a,"NHWC",i),G=y.sizeFromShape(V),W=V.length,q=y.getArrayFromDType(o.dtype,G);for(let X=0;X=0&&xe=0&&_ese&&(se=$e)}}}let pe=y.locToIndex([X,Y,K,ie],W,y.computeStrides(V));q[pe]=se}}}return{dataId:l.write(y.toTypedArray(q,o.dtype),V,o.dtype),shape:V,dtype:o.dtype}}};var E2={kernelName:Fc,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:l}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),p=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:_,strideWidth:w,filterHeight:v,filterWidth:$,dilationHeight:A,dilationWidth:R,outShape:M}=N.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",l);y.assert(s.rank===M.length,()=>`Error in ${Fc}, dy must have the same rank as output ${M.length}, but got ${s.rank}`);let V=y.toNestedArray(M,u.data.get(s.dataId).values),G=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let q=0;q=0&&ae=0&&gere&&(re=_e,ie=pe,se=xe)}}}G[ie][se][K]+=V[q][Z][Y][K]}}}return{dataId:u.write(y.toTypedArray(G,o.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var A2={kernelName:Rc,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:l}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),p=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:_,strideWidth:w,filterHeight:v,filterWidth:$,dilationHeight:A,dilationWidth:R,outShape:M}=N.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",l);y.assert(s.rank===M.length,()=>`Error in ${Rc}, dy must have the same rank as output ${M.length}, but got ${s.rank}`);let V=y.toNestedArray(M,u.data.get(s.dataId).values),G=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let q=0;q=0&&ae=0&&gere&&(re=_e,ie=ae,se=ge)}}}G[q][ie][se][K]+=V[q][Z][Y][K]}}}return{dataId:u.write(y.toTypedArray(G,o.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};function W5(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e;ee([o,n],"eluGrad");let s=new Float32Array(y.sizeFromShape(n.shape)),a=t.data.get(n.dataId).values,i=t.data.get(o.dataId).values;for(let l=0;l=1?s[l]=i[l]:s[l]=i[l]*(u+1)}return t.makeTensorInfo(n.shape,"float32",s)}var D2={kernelName:eu,backendName:"cpu",kernelFunc:W5};var U5=Ye((r,e)=>r===e?1:0),rv=ot(ii,U5,null,"bool"),$2={kernelName:ii,backendName:"cpu",kernelFunc:rv};var j5=N.ERF_P,H5=N.ERF_A1,q5=N.ERF_A2,K5=N.ERF_A3,X5=N.ERF_A4,Y5=N.ERF_A5,Z5=Ae(si,r=>{let e=Math.sign(r),t=Math.abs(r),o=1/(1+j5*t);return e*(1-((((Y5*o+X5)*o+K5)*o+q5)*o+H5)*o*Math.exp(-t*t))}),R2={kernelName:si,backendName:"cpu",kernelFunc:Z5};function Vp(r){let{inputs:e,backend:t,attrs:o}=r,{input:n}=e,{dim:s}=o,a=n.shape.length,i=n.shape.slice(),l=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+s+1),i.splice(l,0,1),tt({inputs:{x:n},backend:t,attrs:{shape:i}})}var F2={kernelName:ps,backendName:"cpu",kernelFunc:Vp};var J5=Ye((r,e)=>r/e),Qd=ot(on,J5),eh={kernelName:on,backendName:"cpu",kernelFunc:Qd};function Dx(r,e,t){let o=r.shape,n=o[0],s=o[1],a=t.data.get(r.dataId),i=a.complexTensorInfos.real,l=a.complexTensorInfos.imag,u=[n,s],c=y.sizeFromShape(u),p=y.getTypedArrayFromDType("float32",c),m=y.getTypedArrayFromDType("float32",c);for(let g=0;g{let{image:o}=r,n=t,s=y.getTypedArrayFromDType(o.dtype,y.sizeFromShape(o.shape)),[a,i,l,u]=o.shape,c=n.data.get(o.dataId).values;for(let m=0;m=0&&vMath.floor(r/e)),sX=ot(an,nX,null,"int32"),L2={kernelName:an,backendName:"cpu",kernelFunc:sX};function iX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=o,h=ev({inputs:{x:n,filter:s},backend:t,attrs:{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m}});if(a){let g=h;h=Ga({inputs:{a:h,b:a},backend:t}),t.disposeIntermediateTensorInfo(g)}if(f){let g=h;h=zp(t,h,f,i,d),t.disposeIntermediateTensorInfo(g)}return h}var z2={kernelName:vs,backendName:"cpu",kernelFunc:iX};function aX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=o,h=tv({inputs:{x:n,filter:s},backend:t,attrs:{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m}});if(a){let g=h;h=Ga({inputs:{a:h,b:a},backend:t}),t.disposeIntermediateTensorInfo(g)}if(f){let g=h;h=zp(t,h,f,i,d),t.disposeIntermediateTensorInfo(g)}return h}var B2={kernelName:Cs,backendName:"cpu",kernelFunc:aX};function lX(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=y.sizeFromShape(o.shape),a=n.shape,i=a[a.length-1],[l,u,c,p]=N.prepareAndValidate(o,n);if(u===0)return t.makeTensorInfo(l,o.dtype,[]);let m=Ce([u,c],o.dtype),f=t.data.get(n.dataId).values,d=t.data.get(o.dataId).values;for(let h=0;h=s/c)throw new Error(`Invalid indices: ${g} does not index into ${o.shape}`);for(let b=0;br>=e?1:0),pX=ot(un,cX,null,"bool"),W2={kernelName:un,backendName:"cpu",kernelFunc:pX};function mX(r){let{inputs:e,backend:t}=r,{input:o}=e,n=y.sizeFromShape(o.shape),s=o.shape[o.shape.length-1],a=n/s,i=tt({inputs:{x:o},backend:t,attrs:{shape:[a,s]}}),l=Dx(i,!0,t),u=tt({inputs:{x:l},backend:t,attrs:{shape:o.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(l),u}var U2={kernelName:ru,backendName:"cpu",kernelFunc:mX};var fX=Ae(pi,r=>Number.isFinite(r)?1:0,"bool"),j2={kernelName:pi,backendName:"cpu",kernelFunc:fX};var dX=Ae(mi,r=>Math.abs(r)===Infinity?1:0,"bool"),H2={kernelName:mi,backendName:"cpu",kernelFunc:dX};var hX=Ae(fi,r=>Number.isNaN(r)?1:0,"bool"),q2={kernelName:fi,backendName:"cpu",kernelFunc:hX};var gX=Ye((r,e)=>r<=e?1:0),xX=ot(hi,gX,null,"bool"),K2={kernelName:hi,backendName:"cpu",kernelFunc:xX};function yX(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=Vk(o,n,s);return e.makeTensorInfo([a.length],"float32",a)}var X2={kernelName:nu,backendName:"cpu",kernelFunc:yX};var bX=Ae(gi,r=>Math.log1p(r)),Y2={kernelName:gi,backendName:"cpu",kernelFunc:bX};var _X=Ye((r,e)=>r&&e),wX=ot(xi,_X,null,"bool"),Z2={kernelName:xi,backendName:"cpu",kernelFunc:wX};var kX=Ae(Za,r=>r?0:1,"bool"),J2={kernelName:Za,backendName:"cpu",kernelFunc:kX};var vX=Ye((r,e)=>r||e),CX=ot(Ja,vX,null,"bool"),Q2={kernelName:Ja,backendName:"cpu",kernelFunc:CX};function IX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:l}=o;ee(n,"LRN");let u=n.shape[3],c=u-1,p=t.data.get(n.dataId).values,m=y.sizeFromShape(n.shape),f=new Float32Array(m);function d(h){let g=h%u,x=h-g+Math.max(0,g-s),b=h-g+Math.min(g+s,c),_=0;for(;x<=b;x++){let w=p[x];_+=w*w}return _}for(let h=0;h`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(n.shape,s,a,u,i,l),p;if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))p=Tr({inputs:{x:n},backend:t});else{let m=t.data.get(n.dataId).values,f=y.computeStrides(n.shape),d=Bp(m,n.shape,n.dtype,f,c,"max");p=t.makeTensorInfo(c.outShape,n.dtype,d.values)}return p}var oD={kernelName:dn,backendName:"cpu",kernelFunc:SX};function TX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u}=o;ee(n,"maxPool3d");let c=N.computePool3DInfo(n.shape,s,a,1,i,l,u),p=t.data.get(n.dataId).values,m=Ax(p,n.shape,n.dtype,y.computeStrides(n.shape),c,"max");return t.makeTensorInfo(m.shape,"float32",m.values)}var nD={kernelName:pa,backendName:"cpu",kernelFunc:TX};function EX(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:l,dimRoundingMode:u}=o;ee([n,s],"maxPool3DGrad");let c=N.computePool3DInfo(s.shape,a,i,1,l,u),p=t.bufferSync(s),m=e2(p,c),f=c.strideDepth,d=c.strideHeight,h=c.strideWidth,g=c.dilationDepth,x=c.dilationHeight,b=c.dilationWidth,_=c.effectiveFilterDepth,w=c.effectiveFilterHeight,v=c.effectiveFilterWidth,$=_-1-c.padInfo.front,A=v-1-c.padInfo.left,R=w-1-c.padInfo.top,M=Ce(s.shape,"float32"),V=t.bufferSync(n);for(let G=0;G=c.outDepth||Math.floor(se)!==se))for(let pe=0;pe=c.outHeight||Math.floor(ae)!==ae))for(let xe=0;xe=c.outWidth||Math.floor(ge)!==ge)continue;let _e=_*w*v-1-m.get(G,se,ae,ge,W),ke=ie*w*v+pe*v+xe,De=_e===ke?1:0;if(De===0)continue;re+=V.get(G,se,ae,ge,W)*De}}}M.set(re,G,q,Z,X,W)}return t.makeTensorInfo(M.shape,M.dtype,M.values)}var sD={kernelName:au,backendName:"cpu",kernelFunc:EX};function AX(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;ee([s,a],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=o,m=N.computePool2DInfo(i.shape,l,u,1,c,p),f=t.data.get(i.dataId).values,d=Ce(m.outShape,i.dtype,Ex(f,i.shape,i.dtype,m).values),h=m.strideHeight,g=m.strideWidth,x=m.dilationHeight,b=m.dilationWidth,_=m.effectiveFilterHeight,w=m.effectiveFilterWidth,v=w-1-m.padInfo.left,$=_-1-m.padInfo.top,A=Ce(i.shape,"float32"),R=t.data.get(n.dataId).values,M=Ce(n.shape,"float32",R);for(let V=0;V=m.outHeight||Math.floor(K)!==K))for(let re=0;re=m.outWidth||Math.floor(ie)!==ie)continue;let se=_*w-1-d.get(V,K,ie,G),pe=te*w+re,ae=se===pe?1:0;if(ae===0)continue;Y+=M.get(V,K,ie,G)*ae}}A.set(Y,V,W,q,G)}return t.makeTensorInfo(A.shape,A.dtype,A.values)}var iD={kernelName:iu,backendName:"cpu",kernelFunc:AX};function aD(r,e,t,o,n){let s=y.computeStrides(e),a=Bp(r,e,t,s,n,"max"),i=Ex(r,e,t,n,!0,o);return[a.values,i.values]}var lD={kernelName:lu,backendName:"cpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,l=t;ee(o,"MaxPoolWithArgmax");let u=l.data.get(o.dataId).values,c=N.computePool2DInfo(o.shape,n,s,[1,1],a),[p,m]=aD(u,o.shape,o.dtype,i,c),f=l.write(p,c.outShape,o.dtype),d=l.write(m,c.outShape,o.dtype);return[{dataId:f,shape:c.outShape,dtype:o.dtype},{dataId:d,shape:c.outShape,dtype:"int32"}]}};function sc(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;ee(n,"sum");let i;n.dtype==="bool"?i=Qn({inputs:{x:n},backend:t,attrs:{dtype:"int32"}}):i=Tr({inputs:{x:n},backend:t});let l=i.shape.length,u=y.parseAxisParam(s,i.shape),c=N.getAxesPermutation(u,l),p=u,m=i;c!=null&&(m=tr({inputs:{x:i},backend:t,attrs:{perm:c}}),p=N.getInnerMostAxes(p.length,l)),N.assertAxesAreInnerMostDims("sum",p,m.shape.length);let[f,d]=N.computeOutAndReduceShapes(m.shape,p),h=N.upcastType(m.dtype,"int32"),g=Mp(t,f,h),x=y.sizeFromShape(d),b=t.data.get(g.dataId).values,_=t.data.get(m.dataId).values;for(let w=0;wt.disposeIntermediateTensorInfo(g)),h}var cD={kernelName:hn,backendName:"cpu",kernelFunc:DX};function $X(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;ee(n,"min");let i=y.parseAxisParam(s,n.shape),l=i,u=N.getAxesPermutation(l,n.shape.length),c=n;u!=null&&(c=tr({inputs:{x:n},backend:t,attrs:{perm:u}}),l=N.getInnerMostAxes(l.length,n.shape.length)),N.assertAxesAreInnerMostDims("min",l,c.shape.length);let[p,m]=N.computeOutAndReduceShapes(c.shape,l),f=y.sizeFromShape(m),d=y.makeZerosTypedArray(y.sizeFromShape(p),c.dtype),h=t.data.get(c.dataId).values;for(let x=0;x_[0]+n.shape[w]+_[1]),l=s.map(_=>_[0]),u=s.map((_,w)=>_[0]+n.shape[w]),c=a==="reflect"?0:1,p=t.data.get(n.dataId).values,m=n.shape.length,f=y.computeStrides(n.shape),d=y.sizeFromShape(i),h=i.length,g=y.computeStrides(i),x=y.getTypedArrayFromDType(n.dtype,d);for(let _=0;_=u[$]&&(w[$]=(u[$]-1)*2-w[$]+c);w=w.map(($,A)=>$-l[A]);let v=y.locToIndex(w,m,f);x[_]=p[v]}return{dataId:t.write(x,i,n.dtype),shape:i,dtype:n.dtype}}var mD={kernelName:ma,backendName:"cpu",kernelFunc:RX};var FX=Ye((r,e)=>{let t=r%e;return r<0&&e<0||r>=0&&e>=0?t:(t+e)%e}),OX=ot(yi,FX),fD={kernelName:yi,backendName:"cpu",kernelFunc:OX};var hD=Tc(Km());function sv(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=n.shape.length,i=s;if(i===-1&&(i=a-1),i!==a-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${a} and dim was ${i}`);let l=y.parseAxisParam([i],n.shape),u=nv({inputs:{x:n},backend:t,attrs:{reductionIndices:l,keepDims:!1}}),c=N.expandShapeToKeepDim(u.shape,l),p=tt({inputs:{x:u},backend:t,attrs:{shape:c}}),m=Jd({inputs:{a:n,b:p},backend:t}),f=zk({inputs:{x:m},backend:t}),d=sc({inputs:{x:f},backend:t,attrs:{axis:l,keepDims:!1}}),h=tt({inputs:{x:d},backend:t,attrs:{shape:c}}),g=Qd({inputs:{a:f,b:h},backend:t});return t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var dD={kernelName:Rn,backendName:"cpu",kernelFunc:sv};function PX(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o;ee(n,"multinomial");let l=i?n:sv({inputs:{logits:n},backend:t,attrs:{dim:-1}}),u=l.shape[0],c=l.shape[1],p=t.data.get(l.dataId).values,m=[u,s],f=y.makeZerosTypedArray(y.sizeFromShape(m),"int32");for(let d=0;d=0&&c[p]{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=e.map(c=>{let p=Vp({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(p),p}),u=vl({inputs:l,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var CD={kernelName:hs,backendName:"cpu",kernelFunc:iv};function UX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;ee(n,"pad");let i=s.map((b,_)=>b[0]+n.shape[_]+b[1]),l=s.map(b=>b[0]),u=t.data.get(n.dataId).values,c=y.sizeFromShape(n.shape),p=n.shape.length,m=y.computeStrides(n.shape),f=y.sizeFromShape(i),d=i.length,h=y.computeStrides(i),g=y.getTypedArrayFromDType(n.dtype,f);a!==0&&g.fill(a);for(let b=0;b$+l[A]),v=y.locToIndex(w,d,h);g[v]=u[b]}return{dataId:t.write(g,i,n.dtype),shape:i,dtype:n.dtype}}var $x={kernelName:_n,backendName:"cpu",kernelFunc:UX};var jX=Ye((r,e)=>Math.pow(r,e)),HX=ot(wn,jX),ID={kernelName:wn,backendName:"cpu",kernelFunc:HX};function qX(r){let{backend:e,attrs:t}=r,{start:o,stop:n,dtype:s,step:a}=t,i=Yd(o,n,a,s);return e.makeTensorInfo([i.length],s,i)}var ND={kernelName:fa,backendName:"cpu",kernelFunc:qX};var KX=Ae(Ci,r=>1/r),SD={kernelName:Ci,backendName:"cpu",kernelFunc:KX};function XX(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o;ee(n,"resizeBilinear");let l=y.computeStrides(n.shape),[u,c]=i,[p,m,f,d]=n.shape,h=t.data.get(n.dataId).values,g=new Float32Array(y.sizeFromShape([p,u,c,d])),x=[s&&u>1?m-1:m,s&&c>1?f-1:f],b=[s&&u>1?u-1:u,s&&c>1?c-1:c],_=0,w=x[0]/b[0],v=x[1]/b[1];for(let $=0;$1?u-1:u,a&&f>1?c-1:c],g=[a&&m>1?m-1:m,a&&f>1?f-1:f],x=h[0]/g[0],b=h[1]/g[1],_=t.data.get(s.dataId).values,w=0;for(let v=0;v1?m-1:m,s&&c>1?f-1:f],b=[s&&u>1?u-1:u,s&&c>1?c-1:c],_=x[0]/b[0],w=x[1]/b[1],v=0;for(let $=0;$1?c-1:c,a&&d>1?p-1:p],b=[a&&f>1?f-1:f,a&&d>1?d-1:d],_=x[0]/b[0],w=x[1]/b[1],v=1/_,$=1/w,A=Math.ceil(v)*2+2,R=Math.ceil($)*2+2;for(let M=0;M=f)continue;let ae=V+pe*l[1],xe=pe*_,ge=Math.min(c-1,a?Math.round(xe):Math.floor(xe));if(G===ge)for(let _e=0;_e=d)continue;let De=ae+ke*l[2],$e=ke*w,Re=Math.min(p-1,a?Math.round($e):Math.floor($e));X===Re&&(ie+=g[De+re])}}h[Y+re]=ie}}}}return t.makeTensorInfo(n.shape,n.dtype,h)}var DD={kernelName:pu,backendName:"cpu",kernelFunc:JX};function QX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o;ee(n,"reverse");let a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return Tr({inputs:{x:n},backend:t});let l=new ct(n.shape,n.dtype),u=t.bufferSync(n);for(let c=0;cm[f]=n.shape[f]-1-m[f]),l.set(u.get(...m),...p)}return t.makeTensorInfo(l.shape,l.dtype,l.values)}var $D={kernelName:Nn,backendName:"cpu",kernelFunc:QX};var RD={kernelName:Ri,backendName:"cpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,l=y.getTypedArrayFromDType(o.dtype,y.sizeFromShape(o.shape)),[u,c,p,m]=o.shape,[f,d]=N.getImageCenter(a,c,p),h=255,g=Math.sin(n),x=Math.cos(n),b=i.data.get(o.dataId).values;for(let w=0;w=0&&Z=0&&X{let e=Math.floor(r);return r-e<.5?Math.floor(r):r-e>.5?Math.ceil(r):e%2==0?e:e+1}),FD={kernelName:Sn,backendName:"cpu",kernelFunc:e8};function Rx(r,e,t,o,n,s,a,i,l,u){let c=[o/n,n],p=r.values,m=e.values;if(o===0)return Ce(t,e.dtype);let f=Ce(c,e.dtype);f.values.fill(l);for(let d=0;d=o/n)throw new Error(`Invalid indices: ${h} does not index into ${t}`);for(let x=0;x1||n.shape.length===1?1:y.sizeFromShape(n.shape.slice(1));for(let d=0;dr>=0?n8*r:o8*(Math.exp(r)-1)),MD={kernelName:Ni,backendName:"cpu",kernelFunc:s8};var i8=Ae(An,r=>1/(1+Math.exp(-r))),LD={kernelName:An,backendName:"cpu",kernelFunc:i8};var a8=Ae(Ti,r=>r<0?-1:r>0?1:0),zD={kernelName:Ti,backendName:"cpu",kernelFunc:a8};var l8=Ae(En,r=>Math.sin(r)),BD={kernelName:En,backendName:"cpu",kernelFunc:l8};var u8=Ae(Si,r=>Math.sinh(r)),VD={kernelName:Si,backendName:"cpu",kernelFunc:u8};var c8=11920928955078125e-23,GD=Math.log(c8)+2,p8=Ae(Ei,r=>{let e=r>-GD,t=r{let m=[...c];m[i]=p;let f=rs({inputs:{x:n},backend:t,attrs:{begin:u,size:m}});return u[i]+=p,f})}var HD={kernelName:bs,backendName:"cpu",kernelFunc:d8};var h8=Ae(Dn,r=>Math.sqrt(r)),qD={kernelName:Dn,backendName:"cpu",kernelFunc:h8};var KD={kernelName:ga,backendName:"cpu",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e;ee(t,"square");let n=o.data.get(t.dataId).values,s=new Float32Array(n.length);for(let i=0;i{let t=e;return isNaN(r)?NaN:r>0?1:t.alpha}),XD={kernelName:$o,backendName:"cpu",kernelFunc:g8};function x8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=o;ee(n,"stridedSlice");let{nonStrided:f,$begin:d,$strides:h,size:g,newShape:x,outShape:b}=or.sliceInfo(n.shape,s,a,i,l,u,c,p,m),_=tt({inputs:{x:n},backend:t,attrs:{shape:x}}),w;if(f){let $=rs({inputs:{x:_},backend:t,attrs:{begin:d,size:g}});w=tt({inputs:{x:$},backend:t,attrs:{shape:b}}),t.disposeIntermediateTensorInfo($)}else if(b.some($=>$===0))w=t.makeTensorInfo(b,n.dtype,[]);else{let $=t.bufferSync(_),A=Wk(b,$,h,d);w=t.makeTensorInfo(A.shape,A.dtype,A.values)}let v=tt({inputs:{x:w},backend:t,attrs:{shape:b}});return t.disposeIntermediateTensorInfo(_),t.disposeIntermediateTensorInfo(w),v}var YD={kernelName:Ai,backendName:"cpu",kernelFunc:x8};var y8=Ae(Di,r=>Math.tan(r)),ZD={kernelName:Di,backendName:"cpu",kernelFunc:y8};var b8=Ae(Pn,r=>Math.tanh(r)),JD={kernelName:Pn,backendName:"cpu",kernelFunc:b8};function _8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reps:s}=o;ee(n,"tile");let a=Uk(t.bufferSync(n),s);return t.makeTensorInfo(a.shape,a.dtype,a.values)}var QD={kernelName:yo,backendName:"cpu",kernelFunc:_8};function w8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{k:s,sorted:a}=o;ee(n,"topk");let i=t.data.get(n.dataId).values,[l,u]=jk(i,n.shape,n.dtype,s,a);return[t.makeTensorInfo(l.shape,l.dtype,l.values),t.makeTensorInfo(u.shape,u.dtype,u.values)]}var e$={kernelName:$i,backendName:"cpu",kernelFunc:w8};function k8(r){let{inputs:e,attrs:t,backend:o}=r,{axis:n}=t,{x:s}=e;ee(s,"unique");let a=o.data.get(s.dataId).values,{outputValues:i,outputShape:l,indices:u}=Hk(a,n,s.shape,s.dtype);return[o.makeTensorInfo(l,s.dtype,i),o.makeTensorInfo([u.length],"int32",u)]}var t$={kernelName:du,backendName:"cpu",kernelFunc:k8};function v8(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n.shape.length,i=n.shape[s],l=new Array(a-1),u=0;for(let f=0;ft.disposeIntermediateTensorInfo(d)),f}var o$={kernelName:xa,backendName:"cpu",kernelFunc:C8};var I8=[VA,qE,GA,WA,QE,UA,jA,HA,qA,KA,XA,YA,ZA,JA,QA,t2,r2,o2,n2,BA,s2,i2,a2,ZE,tA,l2,KE,u2,p2,f2,d2,m2,g2,x2,h2,y2,b2,_2,w2,k2,v2,C2,I2,N2,S2,T2,A2,E2,eh,FA,D2,$2,R2,oA,F2,sA,O2,P2,M2,aA,L2,z2,B2,V2,G2,uA,W2,XE,U2,c2,j2,H2,q2,OA,pA,K2,X2,fA,Y2,Z2,J2,Q2,eD,tD,hA,oD,nD,sD,iD,lD,rD,cD,pD,xA,mD,fD,gD,yA,_A,xD,yD,bD,kA,_D,vD,CD,$x,ID,PA,IA,ND,YE,SD,MA,LA,zA,TD,ED,AD,DD,$D,RD,FD,SA,OD,PD,MD,LD,zD,BD,VD,TA,dD,WD,UD,jD,HD,qD,KD,AA,XD,YD,$A,uD,ZD,JD,QD,e$,vA,t$,r$,o$,wD];for(let r of I8)Qa(r);var ic={},av={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function n$(r,e){ic[r]=e}function Bo(r){if(!(r in ic)){let t=N8(r);if(t!==null)ic[r]=t;else return console.log("Could not get context for WebGL version",r),null}let e=ic[r];return e.isContextLost()?(delete ic[r],Bo(r)):(e.disable(e.DEPTH_TEST),e.disable(e.STENCIL_TEST),e.disable(e.BLEND),e.disable(e.DITHER),e.disable(e.POLYGON_OFFSET_FILL),e.disable(e.SAMPLE_COVERAGE),e.enable(e.SCISSOR_TEST),e.enable(e.CULL_FACE),e.cullFace(e.BACK),ic[r])}function S8(r){if(typeof OffscreenCanvas!="undefined"&&r===2)return new OffscreenCanvas(300,150);if(typeof document!="undefined")return document.createElement("canvas");throw new Error("Cannot create a canvas in this context")}function N8(r){if(r!==1&&r!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let e=S8(r);return e.addEventListener("webglcontextlost",t=>{t.preventDefault(),delete ic[r]},!1),r===1?e.getContext("webgl",av)||e.getContext("experimental-webgl",av):e.getContext("webgl2",av)}var Cl;(function(r){r[r.DENSE=0]="DENSE",r[r.SHARED_BATCH=1]="SHARED_BATCH"})(Cl||(Cl={}));var Er;(function(r){r[r.RENDER=0]="RENDER",r[r.UPLOAD=1]="UPLOAD",r[r.PIXELS=2]="PIXELS",r[r.DOWNLOAD=3]="DOWNLOAD"})(Er||(Er={}));var br;(function(r){r[r.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",r[r.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",r[r.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",r[r.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",r[r.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(br||(br={}));function ac(r,e){return[e,r]}function s$(r,e){return r*e}function Il(r){let e=y.sizeFromShape(r),t=Math.ceil(e/4);return y.sizeToSquarishShape(t)}function Xi(r,e){return[Math.max(1,Math.ceil(e/2)),Math.max(1,Math.ceil(r/2))]}function i$(r,e){let[t,o]=Xi(r,e);return t*o*4}function oh(r,e){let t=r,o,n,s,a,i,l,u,c,p,m;return U().getNumber("WEBGL_VERSION")===2?(o=t.R32F,n=t.R16F,s=t.RGBA16F,a=t.RGBA32F,i=t.RED,u=4,c=1,p=t.HALF_FLOAT,m=t.FLOAT):(o=r.RGBA,n=r.RGBA,s=r.RGBA,a=t.RGBA,i=r.RGBA,u=4,c=4,p=e!=null?e.HALF_FLOAT_OES:null,m=r.FLOAT),l=r.RGBA,{internalFormatFloat:o,internalFormatHalfFloat:n,internalFormatPackedHalfFloat:s,internalFormatPackedFloat:a,textureFormatFloat:i,downloadTextureFormat:l,downloadUnpackNumChannels:u,defaultNumChannels:c,textureTypeHalfFloat:p,textureTypeFloat:m}}function Ee(r,e){let t=e();return U().getBool("DEBUG")&&T8(r),t}function T8(r){let e=r.getError();if(e!==r.NO_ERROR)throw new Error("WebGL Error: "+E8(r,e))}var A8=596e-10,D8=65504;function a$(r){return!!(U().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||r===0||A8r.getExtension(e),'Extension "'+e+'" not supported on this browser.')}function l$(r,e){let t=Wa(r,()=>r.createShader(r.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(Ee(r,()=>r.shaderSource(t,e)),Ee(r,()=>r.compileShader(t)),r.getShaderParameter(t,r.COMPILE_STATUS)===!1)throw console.log(r.getShaderInfoLog(t)),new Error("Failed to compile vertex shader.");return t}function u$(r,e){let t=Wa(r,()=>r.createShader(r.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(Ee(r,()=>r.shaderSource(t,e)),Ee(r,()=>r.compileShader(t)),r.getShaderParameter(t,r.COMPILE_STATUS)===!1)throw $8(e,r.getShaderInfoLog(t)),new Error("Failed to compile fragment shader.");return t}var R8=/ERROR: [0-9]+:([0-9]+):/g;function $8(r,e){let t=R8.exec(e);if(t==null){console.log(`Couldn't parse line number in error: ${e}`),console.log(r);return}let o=+t[1],n=r.split(` `),s=n.length.toString().length+2,a=n.map((p,m)=>y.rightPad((m+1).toString(),s)+p),i=0;for(let p=0;pr.createProgram(),"Unable to create WebGLProgram.")}function p$(r,e){if(Ee(r,()=>r.linkProgram(e)),r.getProgramParameter(e,r.LINK_STATUS)===!1)throw console.log(r.getProgramInfoLog(e)),new Error("Failed to link vertex and fragment shaders.")}function Fx(r,e){if(Ee(r,()=>r.validateProgram(e)),r.getProgramParameter(e,r.VALIDATE_STATUS)===!1)throw console.log(r.getProgramInfoLog(e)),new Error("Shader program validation failed.")}function m$(r,e){let t=Wa(r,()=>r.createBuffer(),"Unable to create WebGLBuffer");return Ee(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),Ee(r,()=>r.bufferData(r.ARRAY_BUFFER,e,r.STATIC_DRAW)),t}function f$(r,e){let t=Wa(r,()=>r.createBuffer(),"Unable to create WebGLBuffer");return Ee(r,()=>r.bindBuffer(r.ELEMENT_ARRAY_BUFFER,t)),Ee(r,()=>r.bufferData(r.ELEMENT_ARRAY_BUFFER,e,r.STATIC_DRAW)),t}function d$(r){return Wa(r,()=>r.createTexture(),"Unable to create WebGLTexture.")}function h$(r,e){let t=U().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(r<=0||e<=0){let o=`[${r}x${e}]`;throw new Error("Requested texture size "+o+" is invalid.")}if(r>t||e>t){let o=`[${r}x${e}]`,n=`[${t}x${t}]`;throw new Error("Requested texture size "+o+" greater than WebGL maximum on this browser / GPU "+n+".")}}function g$(r){return Wa(r,()=>r.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function lv(r,e,t,o,n,s,a){let i=r.getAttribLocation(e,t);return i===-1?!1:(Ee(r,()=>r.bindBuffer(r.ARRAY_BUFFER,o)),Ee(r,()=>r.vertexAttribPointer(i,n,r.FLOAT,!1,s,a)),Ee(r,()=>r.enableVertexAttribArray(i)),!0)}function O8(r,e,t){F8(r,t),Ee(r,()=>r.activeTexture(r.TEXTURE0+t)),Ee(r,()=>r.bindTexture(r.TEXTURE_2D,e))}function x$(r,e,t){return Wa(r,()=>r.getUniformLocation(e,t),'uniform "'+t+'" not present in program.')}function y$(r,e,t){return r.getUniformLocation(e,t)}function b$(r,e,t,o){Ee(r,()=>O8(r,e,o)),Ee(r,()=>r.uniform1i(t,o))}function Ox(r,e,t){Ee(r,()=>r.bindFramebuffer(r.FRAMEBUFFER,t)),Ee(r,()=>r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,e,0))}function uv(r,e){Ee(r,()=>r.bindFramebuffer(r.FRAMEBUFFER,e)),Ee(r,()=>r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,null,0))}function sh(r){let e=r.checkFramebufferStatus(r.FRAMEBUFFER);if(e!==r.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+P8(r,e))}function P8(r,e){switch(e){case r.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case r.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case r.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case r.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${e}`}}function Wa(r,e,t){let o=Ee(r,()=>e());if(o==null)throw new Error(t);return o}function F8(r,e){let t=r.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,o=e+r.TEXTURE0;if(ot){let n=`[gl.TEXTURE0, gl.TEXTURE${t}]`;throw new Error(`textureUnit must be in ${n}.`)}}function Nl(r,e=2){return y.sizeFromShape(r.slice(0,r.length-e))}function Sl(r){if(r.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[r.length>1?r[r.length-2]:1,r[r.length-1]]}function Px(r){let e=[1,1,1];return r.length===0||r.length===1&&r[0]===1||(e=[Nl(r),...Sl(r)]),e}function _$(r,e=!1){let t=U().getNumber("WEBGL_MAX_TEXTURE_SIZE");e&&(t=t*2,r=r.map((n,s)=>s>=r.length-2?y.nearestLargerEven(r[s]):r[s]),r.length===1&&(r=[2,r[0]])),r.length!==2&&(r=y.squeezeShape(r).newShape);let o=y.sizeFromShape(r);if(r.length<=1&&o<=t)return[1,o];if(r.length===2&&r[0]<=t&&r[1]<=t)return r;if(r.length===3&&r[0]*r[1]<=t&&r[2]<=t)return[r[0]*r[1],r[2]];if(r.length===3&&r[0]<=t&&r[1]*r[2]<=t)return[r[0],r[1]*r[2]];if(r.length===4&&r[0]*r[1]*r[2]<=t&&r[3]<=t)return[r[0]*r[1]*r[2],r[3]];if(r.length===4&&r[0]<=t&&r[1]*r[2]*r[3]<=t)return[r[0],r[1]*r[2]*r[3]];if(e){let n=Nl(r),s=2,a=2;return r.length&&([s,a]=Sl(r)),o=n*(s/2)*(a/2),y.sizeToSquarishShape(o).map(i=>i*2)}return y.sizeToSquarishShape(o)}function Mx(r){return r%2==0}function lc(r,e){if(r=r.slice(-2),e=e.slice(-2),y.arraysEqual(r,e)||!r.length||!e.length||r[0]===0||r[1]===0||e[0]===0||e[1]===0)return!0;if(r.length!==e.length){let t=r.slice(-1)[0],o=e.slice(-1)[0];if(t===o||Mx(t)&&Mx(o)&&(r[0]===1||e[0]===1))return!0}return r[1]===e[1]&&Mx(r[0])&&Mx(e[0])}var cv,pv;function w$(r){if(cv==null){let e=Bo(r);cv=e.getParameter(e.MAX_TEXTURE_SIZE)}return cv}function k$(r){if(pv==null){let e=Bo(r);pv=e.getParameter(e.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,pv)}function v$(r){if(r===0)return 0;let e,t=Bo(r);return Vo(t,"EXT_disjoint_timer_query_webgl2")&&r===2?e=2:Vo(t,"EXT_disjoint_timer_query")?e=1:e=0,e}function Vo(r,e){return r.getExtension(e)!=null}function mv(r){try{if(Bo(r)!=null)return!0}catch(e){return console.log("Error when getting WebGL context: ",e),!1}return!1}function C$(r){if(r===0)return!1;let e=Bo(r);if(r===1){if(!Vo(e,"OES_texture_float"))return!1}else if(!Vo(e,"EXT_color_buffer_float"))return!1;return fv(e)}function I$(r){if(r===0)return!1;let e=Bo(r);if(r===1){if(!Vo(e,"OES_texture_float")||!Vo(e,"WEBGL_color_buffer_float"))return!1}else{if(Vo(e,"EXT_color_buffer_float"))return fv(e);let o="EXT_color_buffer_half_float";if(Vo(e,o)){let n=e.getExtension(o);return M8(e,n)}return!1}return fv(e)}function fv(r){let e=oh(r),t=r.createTexture();r.bindTexture(r.TEXTURE_2D,t);let o=1,n=1;r.texImage2D(r.TEXTURE_2D,0,e.internalFormatFloat,o,n,0,e.textureFormatFloat,e.textureTypeFloat,null);let s=r.createFramebuffer();r.bindFramebuffer(r.FRAMEBUFFER,s),r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,t,0);let a=r.checkFramebufferStatus(r.FRAMEBUFFER)===r.FRAMEBUFFER_COMPLETE;return r.bindTexture(r.TEXTURE_2D,null),r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteTexture(t),r.deleteFramebuffer(s),a}function M8(r,e){let t=oh(r,e),o=r.createTexture();r.bindTexture(r.TEXTURE_2D,o);let n=1,s=1;r.texImage2D(r.TEXTURE_2D,0,t.internalFormatHalfFloat,n,s,0,t.textureFormatFloat,t.textureTypeHalfFloat,null);let a=r.createFramebuffer();r.bindFramebuffer(r.FRAMEBUFFER,a),r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,o,0);let i=r.checkFramebufferStatus(r.FRAMEBUFFER)===r.FRAMEBUFFER_COMPLETE;return r.bindTexture(r.TEXTURE_2D,null),r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteTexture(o),r.deleteFramebuffer(a),i}function N$(r){return r!==2?!1:Bo(r).fenceSync!=null}function Yi(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the WebGL backend.`)})}var Be=U();Be.registerFlag("HAS_WEBGL",()=>Be.getNumber("WEBGL_VERSION")>0);Be.registerFlag("WEBGL_VERSION",()=>mv(2)?2:mv(1)?1:0);Be.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Be.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Be.get("WEBGL_VERSION")===2);Be.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Be.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Be.registerFlag("WEBGL_PACK",()=>Be.getBool("HAS_WEBGL"));Be.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Be.getBool("WEBGL_PACK"));Be.registerFlag("WEBGL_PACK_CLIP",()=>Be.getBool("WEBGL_PACK"));Be.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);Be.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Be.getBool("WEBGL_PACK"));Be.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Be.getBool("WEBGL_PACK"));Be.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Be.getBool("WEBGL_PACK"));Be.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Be.getBool("WEBGL_PACK"));Be.registerFlag("WEBGL_PACK_REDUCE",()=>Be.getBool("WEBGL_PACK"));Be.registerFlag("WEBGL_LAZILY_UNPACK",()=>Be.getBool("WEBGL_PACK"));Be.registerFlag("WEBGL_CONV_IM2COL",()=>Be.getBool("WEBGL_PACK"));Be.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>w$(Be.getNumber("WEBGL_VERSION")));Be.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>k$(Be.getNumber("WEBGL_VERSION")));Be.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let r=Be.getNumber("WEBGL_VERSION");return r===0?0:v$(r)});Be.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Be.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Vc.isMobile());Be.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>C$(Be.getNumber("WEBGL_VERSION")));Be.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Be.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Be.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Be.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>I$(Be.getNumber("WEBGL_VERSION")));Be.registerFlag("WEBGL_FENCE_API_ENABLED",()=>N$(Be.getNumber("WEBGL_VERSION")));Be.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Be.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Be.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});function Mt(){let r,e,t,o,n,s,a,i,l,u;return U().getNumber("WEBGL_VERSION")===2?(r="#version 300 es",e="in",t="out",o="in",n="texture",s="outputColor",a="out vec4 outputColor;",i=` 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="",u=` #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))); } `):(r="",e="attribute",t="varying",o="varying",n="texture2D",s="gl_FragColor",a="",i=` #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)); } `,u=` int round(float value) { return int(floor(value + 0.5)); } ivec4 round(vec4 value) { return ivec4(floor(value + vec4(0.5))); } `),{version:r,attribute:e,varyingVs:t,varyingFs:o,texture2D:n,output:s,defineOutput:a,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function Vs(r,e,t="index"){let o=y.computeStrides(e);return o.map((n,s)=>{let a=`int ${r[s]} = ${t} / ${n}`,i=s===o.length-1?`int ${r[s+1]} = ${t} - ${r[s]} * ${n}`:`index -= ${r[s]} * ${n}`;return`${a}; ${i};`}).join("")}function Gp(r){let e=y.computeStrides(r).map(t=>t.toString());return` int getFlatIndex(ivec3 coords) { return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z; } `}var Lx=` 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; } `;var dv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Cl.DENSE;let t=Il(e),o=Mt();this.outputShape=e,this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { ${Vs(["r","c","d"],e)} return ivec3(r, c, d); } void main() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${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); } ${o.output} = result; } `}};var hv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Cl.DENSE;let t=Il(e),o=Mt();this.outputShape=e,this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { ${Vs(["r","c","d"],e)} return ivec3(r, c, d); } void main() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${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)); } ${o.output} = result; } `}};var gv=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Er.DOWNLOAD;let t=Mt();this.outputShape=e,this.userCode=` ${Lx} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } `}};var xv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Er.DOWNLOAD;let t=Mt();this.outputShape=e,this.userCode=` ${Lx} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${t.output} = encode_float(x); } `}};var yv=class{constructor(e,t,o=!1){this.variableNames=["A"];let n=Mt(),[s,a]=t;this.outputShape=e;let i="result";o&&(i="floor(result * 255. + 0.5)"),this.userCode=` ${Gp(e)} void main() { ivec3 coords = getOutputCoords(); int flatIndex = getFlatIndex(coords); int offset = imod(flatIndex, 4); flatIndex = idiv(flatIndex, 4, 1.); int r = flatIndex / ${a}; int c = imod(flatIndex, ${a}); vec2 uv = (vec2(c, r) + halfCR) / vec2(${a}.0, ${s}.0); vec4 values = ${n.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]; } ${n.output} = vec4(${i}, 0., 0., 0.); } `}};var bv=class{constructor(e,t,o=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let n=Mt(),[s,a]=t;this.outputShape=e;let i="",l="result";o&&(l="floor(result * 255. + 0.5)");for(let u=0;u<=1;u++)for(let c=0;c<=1;c++){let p=u*2+c;i+=` localCoords = coords; if(localCoords[2] + ${c} < ${e[2]}) { localCoords[2] += ${c}; if(localCoords[1] + ${u} < ${e[1]}) { localCoords[1] += ${u}; flatIndex = getFlatIndex(localCoords); offset = imod(flatIndex, 4); flatIndex = idiv(flatIndex, 4, 1.); r = flatIndex / ${a}; c = imod(flatIndex, ${a}); uv = (vec2(c, r) + halfCR) / vec2(${a}.0, ${s}.0); values = ${n.texture2D}(A, uv); if(offset == 0) { result[${p}] = values[0]; } else if(offset == 1) { result[${p}] = values[1]; } else if(offset == 2) { result[${p}] = values[2]; } else { result[${p}] = values[3]; } } } `}this.userCode=` ${Gp(e)} void main() { ivec3 coords = getOutputCoords(); vec4 result = vec4(0.); int flatIndex, r, c, offset; ivec3 localCoords; vec2 uv; vec4 values; ${i} ${n.output} = ${l}; } `}};function S$(r){let e=Mt(),t=`${e.version} precision highp float; ${e.attribute} vec3 clipSpacePos; ${e.attribute} vec2 uv; ${e.varyingVs} vec2 resultUV; void main() { gl_Position = vec4(clipSpacePos, 1); resultUV = uv; }`;return l$(r,t)}function T$(r){let e=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return m$(r,e)}function E$(r){let e=new Uint16Array([0,1,2,2,1,3]);return f$(r,e)}function ih(r,e,t,o,n,s){h$(e,t);let a=d$(r),i=r.TEXTURE_2D;return Ee(r,()=>r.bindTexture(i,a)),Ee(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),Ee(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),Ee(r,()=>r.texParameteri(i,r.TEXTURE_MIN_FILTER,r.NEAREST)),Ee(r,()=>r.texParameteri(i,r.TEXTURE_MAG_FILTER,r.NEAREST)),Ee(r,()=>r.texImage2D(i,0,o,e,t,0,n,s,null)),Ee(r,()=>r.bindTexture(r.TEXTURE_2D,null)),a}function _v(r){return r.internalFormatFloat}function A$(r,e,t,o){let[n,s]=ac(e,t);return ih(r,n,s,_v(o),o.textureFormatFloat,r.FLOAT)}function wv(r){return r.internalFormatHalfFloat}function D$(r,e,t,o){let[n,s]=ac(e,t);return ih(r,n,s,wv(o),o.textureFormatFloat,o.textureTypeHalfFloat)}function kv(r){return r.downloadTextureFormat}function $$(r,e,t,o){let[n,s]=ac(e,t);return ih(r,n,s,kv(o),r.RGBA,r.UNSIGNED_BYTE)}function vv(r){return r.internalFormatPackedFloat}function R$(r,e,t,o){let[n,s]=Xi(e,t);return ih(r,n,s,vv(o),r.RGBA,r.FLOAT)}function Cv(r){return r.internalFormatPackedHalfFloat}function F$(r,e,t,o){let[n,s]=Xi(e,t);return ih(r,n,s,Cv(o),r.RGBA,o.textureTypeHalfFloat)}function O$(r,e,t){let o=0,n=3*4,s=3*4+2*4;return Ee(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),lv(r,e,"clipSpacePos",t,3,s,o)&&lv(r,e,"uv",t,2,s,n)}function P$(r,e,t,o,n,s){Ee(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,l;n instanceof Uint8Array?(a=new Uint8Array(t*o*4),i=r.UNSIGNED_BYTE,l=r.RGBA):(a=new Float32Array(t*o*4),i=r.FLOAT,l=s.internalFormatPackedFloat),a.set(n),Ee(r,()=>r.texImage2D(r.TEXTURE_2D,0,l,t,o,0,r.RGBA,i,a)),Ee(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function M$(r,e,t){Ee(r,()=>r.bindTexture(r.TEXTURE_2D,e)),t.data instanceof Uint8Array?Ee(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,t.width,t.height,0,r.RGBA,r.UNSIGNED_BYTE,t.data)):Ee(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,t)),Ee(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function L$(r,e,t,o){let n=r.createBuffer();Ee(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,n));let i=4*4*e*t;return Ee(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),Ee(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),Ee(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),n}function z$(r,e,t){let o=r,n=new Float32Array(t);return o.bindBuffer(o.PIXEL_PACK_BUFFER,e),o.getBufferSubData(o.PIXEL_PACK_BUFFER,0,n),o.bindBuffer(o.PIXEL_PACK_BUFFER,null),n}function B$(r,e,t,o){let[n,s]=ac(e,t),a=4,i=new Uint8Array(s$(e*t,a));return Ee(r,()=>r.readPixels(0,0,n,s,o.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function V$(r,e,t,o,n,s,a,i){let l=r,u=new Float32Array(i$(s,a));return l.bindBuffer(l.PIXEL_PACK_BUFFER,e),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function G$(r,e,t){let o=new Float32Array(e*t*4);return Ee(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,o)),o}var Iv=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=U().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,n$(t,e)):this.gl=Bo(t);let o="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(U().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=nh(this.gl,s),Vo(this.gl,a))this.textureHalfFloatExtension=nh(this.gl,a);else if(U().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(o),Vo(this.gl,n))this.colorBufferHalfFloatExtension=nh(this.gl,n);else if(U().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(o="EXT_color_buffer_float",Vo(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(Vo(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=T$(this.gl),this.indexBuffer=E$(this.gl),this.framebuffer=g$(this.gl),this.textureConfig=oh(this.gl,this.textureHalfFloatExtension)}get debug(){return U().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;Ee(e,()=>e.finish()),Ee(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ee(e,()=>e.deleteFramebuffer(this.framebuffer)),Ee(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Ee(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Ee(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),A$(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),D$(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),$$(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),M$(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,o,n){this.throwIfDisposed(),P$(this.gl,e,t,o,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),F$(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),R$(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(uv(this.gl,this.framebuffer),this.outputTexture=null),Ee(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,o){return this.downloadMatrixDriver(e,()=>B$(this.gl,t,o,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,o,n,s,a){return V$(this.gl,e,t,o,n,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return z$(this.gl,e,t)}createBufferFromTexture(e,t,o){this.bindTextureToFrameBuffer(e);let n=L$(this.gl,t,o,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,o;if(U().getBool("WEBGL_FENCE_API_ENABLED")){let n=e,s=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),o=()=>{let a=n.clientWaitSync(s,0,0);return a===n.ALREADY_SIGNALED||a===n.CONDITION_SATISFIED},t=s}else U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),o=()=>this.isQueryAvailable(t,U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):o=()=>!0;return{query:t,isFencePassed:o}}downloadMatrixFromPackedTexture(e,t,o){return this.downloadMatrixDriver(e,()=>G$(this.gl,t,o))}createProgram(e){this.throwIfDisposed();let t=this.gl,o=u$(t,e),n=S$(t),s=c$(t);return Ee(t,()=>t.attachShader(s,n)),Ee(t,()=>t.attachShader(s,o)),p$(t,s),this.debug&&Fx(t,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=O$(t,this.program,this.vertexBuffer)),s}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ee(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Fx(this.gl,this.program),Ee(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?x$(this.gl,e,t):y$(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ee(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),b$(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Xi(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Fx(this.gl,this.program),sh(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ee(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ee(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=nh(this.gl,U().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(U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.createQuery();return o.beginQuery(n.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,o=this.getQueryTimerExtensionWebGL2();t.endQuery(o.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let o=this.gl;return o.getQueryParameter(e,o.QUERY_RESULT)/1e6}else{let o=this.getQueryTimerExtensionWebGL1();return o.getQueryObjectEXT(e,o.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.getQueryParameter(e,o.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(e,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=L8(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:o}=this.itemsToPoll[t];o()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Ox(this.gl,e,this.framebuffer),this.debug&&sh(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Ox(this.gl,this.outputTexture,this.framebuffer),this.debug&&sh(this.gl)):uv(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let o=t();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;Ox(n,e,this.framebuffer),this.debug&&sh(n),this.outputTexture=e,Ee(n,()=>n.viewport(0,0,t,o)),Ee(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),Ee(this.gl,()=>this.gl.scissor(e,t,o,n))}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 L8(r){let e=0;for(;e{let h=y.sizeFromShape(d.shapeInfo.logicalShape);d.shapeInfo.isUniform?n.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(n.push(`uniform sampler2D ${d.name};`),n.push(`uniform int offset${d.name};`))});let s=n.join(` `),a=r.map(d=>z8(d,e,o)).join(` `),i=e.texShape,l=Mt(),u=G8(l),c,p,m=j8(l);return e.isPacked?(c=B8(e.logicalShape,i),p=U8(l)):(c=V8(e.logicalShape,i),p=W8(l)),o&&(m+=H8),[m,u,p,s,c,a,t].join(` `)}function Wp(r){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return q8(r);case 1:return K8(r);case 2:return X8(r);case 3:return Y8(r);case 4:return Z8(r);case 5:return J8(r);case 6:return Q8(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function j$(r){switch(r.shapeInfo.logicalShape.length){case 0:return eY(r);case 1:return tY(r);case 2:return rY(r);case 3:return oY(r);default:return nY(r)}}function z8(r,e,t=!1){let o="";t?o+=j$(r):o+=Wp(r);let n=r.shapeInfo.logicalShape,s=e.logicalShape;return n.length<=s.length&&(t?o+=sY(r,e):o+=iY(r,e)),o}function B8(r,e){switch(r.length){case 0:return H$();case 1:return aY(r,e);case 2:return cY(r,e);case 3:return lY(r,e);default:return uY(r,e)}}function V8(r,e){switch(r.length){case 0:return H$();case 1:return pY(r,e);case 2:return gY(r,e);case 3:return mY(r,e);case 4:return fY(r,e);case 5:return dY(r,e);case 6:return hY(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function G8(r){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${r.texture2D}(textureSampler, uv).r; } `}function W8(r){return` void setOutput(float val) { ${r.output} = vec4(val, 0, 0, 0); } `}function U8(r){return` void setOutput(vec4 val) { ${r.output} = val; } `}function j8(r){return`${r.version} precision highp float; precision highp int; precision highp sampler2D; ${r.varyingFs} vec2 resultUV; ${r.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; ${r.defineSpecialNaN} ${r.defineSpecialInf} ${r.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); } ${xY} ${yY} ${bY} `}var xY=` vec2 uvFromFlat(int texNumR, int texNumC, int index) { int texR = index / texNumC; int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } vec2 packedUVfrom1D(int texNumR, int texNumC, int index) { int texelIndex = index / 2; int texR = texelIndex / texNumC; int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } `,yY=` vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR, int texNumC, int row, int col) { int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2); int texR = texelIndex / texNumC; int texC = texelIndex - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } `,bY=` vec2 packedUVfrom3D(int texNumR, int texNumC, int texelsInBatch, int texelsInLogicalRow, int b, int row, int col) { int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2); int texR = index / texNumC; int texC = index - texR * texNumC; return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR); } `,H8=` float getChannel(vec4 frag, vec2 innerDims) { vec2 modCoord = mod(innerDims, 2.); return modCoord.x == 0. ? (modCoord.y == 0. ? frag.r : frag.g) : (modCoord.y == 0. ? frag.b : frag.a); } float getChannel(vec4 frag, int dim) { float modCoord = mod(float(dim), 2.); return modCoord == 0. ? frag.r : frag.g; } `;function H$(){return` int getOutputCoords() { return 0; } `}function aY(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return t[0]===1?` int getOutputCoords() { return 2 * int(resultUV.x * ${t[1]}.0); } `:t[1]===1?` int getOutputCoords() { return 2 * int(resultUV.y * ${t[0]}.0); } `:` int getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); return 2 * (resTexRC.x * ${t[1]} + resTexRC.y); } `}function pY(r,e){return e[0]===1?` int getOutputCoords() { return int(resultUV.x * ${e[1]}.0); } `:e[1]===1?` int getOutputCoords() { return int(resultUV.y * ${e[0]}.0); } `:` int getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]})); return resTexRC.x * ${e[1]} + resTexRC.y; } `}function lY(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],o=Math.ceil(r[2]/2),n=o*Math.ceil(r[1]/2);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]})); int index = resTexRC.x * ${t[1]} + resTexRC.y; int b = index / ${n}; index -= b * ${n}; int r = 2 * (index / ${o}); int c = imod(index, ${o}) * 2; return ivec3(b, r, c); } `}function mY(r,e){let t=Vs(["r","c","d"],r);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]})); int index = resTexRC.x * ${e[1]} + resTexRC.y; ${t} return ivec3(r, c, d); } `}function uY(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],o=Math.ceil(r[r.length-1]/2),n=o*Math.ceil(r[r.length-2]/2),s=n,a="",i="b, r, c";for(let l=2;l=1?c="coords = 0;":c=i.map(b=>`coords.${p[b+u]} = 0;`).join(` `);let m="";a<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,_)=>`coords.${p[_+u]}`).join(", ");let f="return outputValue;",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(e.logicalShape)===1;if(s===1&&!h&&!x)f=` return vec4(outputValue.xy, outputValue.xy); `;else if(h&&!x)a===1?f=` return vec4(outputValue.x, outputValue.x, 0., 0.); `:f=` return vec4(outputValue.x); `;else if(i.length){let b=s-2,_=s-1;i.indexOf(b)>-1&&i.indexOf(_)>-1?f="return vec4(outputValue.x);":i.indexOf(b)>-1?f="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(_)>-1&&(f="return vec4(outputValue.xx, outputValue.zz);")}return` vec4 ${n}() { ${l} coords = getOutputCoords(); ${c} vec4 outputValue = get${o}(${m}); ${f} } `}function iY(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,l=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===l&&r.shapeInfo.flatOffset==null&&y.arraysEqual(a,s))return` float ${n}() { return sampleTexture(${t}, resultUV); } `;let u=Le(l),c=W$(r.shapeInfo.logicalShape,e.logicalShape),p=l-i,m,f=["x","y","z","w","u","v"];i===0?m="":l<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(` `);let d="";return l<2&&i>0?d="coords":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(", "),` float ${n}() { ${u} coords = getOutputCoords(); ${m} return get${o}(${d}); } `}function Le(r){if(r<=1)return"int";if(r===2)return"ivec2";if(r===3)return"ivec3";if(r===4)return"ivec4";if(r===5)return"ivec5";if(r===6)return"ivec6";throw Error(`GPU for rank ${r} is not yet supported`)}function jp(r,e){let t=JSON.parse(JSON.stringify(r));return t.shapeInfo.logicalShape=e,t}function Hp(r,e){return e.map(t=>r[t]).join(", ")}function q$(r,e,t,o){let n=e.userCode,s=t.map((f,d)=>{let h={logicalShape:f.shape,texShape:f.isUniform?null:f.texData.texShape,isUniform:f.isUniform,isPacked:f.isUniform?!1:f.texData.isPacked,flatOffset:null};return f.texData!=null&&f.texData.slice!=null&&f.texData.slice.flatOffset>0&&(h.flatOffset=f.texData.slice.flatOffset),{name:e.variableNames[d],shapeInfo:h}}),a=s.map(f=>f.shapeInfo),i={logicalShape:o.shape,texShape:o.texData.texShape,isUniform:!1,isPacked:o.texData.isPacked,flatOffset:null},l=U$(s,i,n,e.packedInputs),u=r.createProgram(l),c=null,p=r.getUniformLocation(u,"NAN",!1);U().getNumber("WEBGL_VERSION")===1&&(c=r.getUniformLocation(u,"INFINITY",!1));let m={};for(let f=0;f{let n=t.logicalShape,s=e[o],a=s.shape;if(!y.arraysEqual(n,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${n} and ${a} must match`);if(t.isUniform&&s.isUniform)return;let i=t.texShape,l=s.isUniform?null:s.texData.texShape;if(!y.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function X$(r,e,t,o,n){K$(e.inShapeInfos,t),K$([e.outShapeInfo],[o]);let s=o.texData.texture,a=o.texData.texShape;o.texData.isPacked?r.setOutputPackedMatrixTexture(s,a[0],a[1]):r.setOutputMatrixTexture(s,a[0],a[1]),r.setProgram(e.webGLProgram),U().getNumber("WEBGL_VERSION")===1&&e.infLoc!==null&&r.gl.uniform1f(e.infLoc,Infinity),e.nanLoc!==null&&r.gl.uniform1f(e.nanLoc,NaN),t.forEach((i,l)=>{let u=e.program.variableNames[l],c=e.uniformLocations[u],p=e.uniformLocations[`offset${u}`];if(c!=null){if(i.isUniform){if(y.sizeFromShape(i.shape)<2)r.gl.uniform1f(c,i.uniformValues[0]);else{let m=i.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),r.gl.uniform1fv(c,m)}return}i.texData.slice!=null&&p!=null&&r.gl.uniform1i(p,i.texData.slice.flatOffset),r.setInputMatrixTexture(i.texData.texture,c,l)}}),n!=null&&n(r,e.webGLProgram),r.executeProgram()}function Y$(r,e,t){let o="";e.concat(t).forEach(a=>{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0,l=a.isUniform?"uniform":a.texData.texShape;o+=`${a.shape}_${l}_${i}`});let n=r.userCode,s=r.constructor.name;return s+="_"+o+"_"+n,s}var{addImpl:Z$,bincountImpl:zx,bincountReduceImpl:J$,ceilImpl:Q$,concatImpl:eR,expImpl:tR,expm1Impl:rR,floorImpl:oR,gatherV2Impl:nR,greaterImpl:sR,lessImpl:iR,linSpaceImpl:aR,logImpl:lR,maxImpl:uR,maximumImpl:cR,minimumImpl:pR,multiplyImpl:mR,negImpl:fR,prodImpl:dR,rangeImpl:hR,rsqrtImpl:gR,simpleAbsImpl:Bx,sliceImpl:xR,stridedSliceImpl:yR,subImpl:bR,tileImpl:_R,topKImpl:wR,transposeImpl:qp,uniqueImpl:kR}=qk;function Nv(r,e){return["x","y","z","w","u","v"].slice(0,e).map(t=>`${r}.${t}`)}function Ut(r,e){return e===1?[r]:Nv(r,e)}function vR(r,e){if(r===1)return"rc";let t="";for(let o=0;o ${e[0]}`;let o="";for(let n=r-2;n= ${e[n]}`,n= ${e}; bool rEdge = rp1 >= ${t}; `}function kY(r,e){let t=r.length,o=vY(t,e);return t===1?`getA(rc), rc + 1 >= ${r[0]} ? 0. : getA(rc + 1), 0, 0`:`getA(${o[0]}), cEdge ? 0. : getA(${o[1]}), rEdge ? 0. : getA(${o[2]}), rEdge || cEdge ? 0. : getA(${o[3]})`}var ah=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let o="";for(let n=0;n<4;n++){let s="thisRC = rc;";n%2==1&&(s+="thisRC.z += 1;"),n>1&&(s+="thisRC.y += 1;"),o+=` ${s} ${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex); vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z)); result[${n}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); ${n>0?"}":""} `}this.userCode=` ${CY(t)} ${Gp(e)} void main() { ivec3 rc = getOutputCoords(); vec4 result = vec4(0.); ivec3 thisRC; int rows = ${e[1]}; int cols = ${e[2]}; ${o} setOutput(result); } `}};function CY(r){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { ${Vs(["r","c","d"],r)} return ivec3(r, c, d); } `}var Tv=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,o){let n=IR(t,o),s=NR(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=CR(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let l=this.freeTextures[s].shift();return this.usedTextures[s].push(l),l}let i;return n===br.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===br.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===br.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===br.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===br.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=IR(o,n),a=NR(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=CR(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),l=U().get("WEBGL_DELETE_TEXTURE_THRESHOLD");l!==-1&&this._numBytesAllocated>l?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 IY(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;throw new Error(`Unknown internal format ${e}`)}function CR(r,e,t,o,n){let s=NY(e,o),a;if(n){let[l,u]=Xi(r[0],r[1]);a=l*u}else{let[l,u]=ac(r[0],r[1]);a=l*u}let i=IY(t,s);return a*i}function NY(r,e){switch(r){case br.PACKED_2X2_FLOAT32:return vv(e);case br.PACKED_2X2_FLOAT16:return Cv(e);case br.UNPACKED_FLOAT32:return _v(e);case br.UNPACKED_FLOAT16:return wv(e);case br.PACKED_4X1_UNSIGNED_BYTE:return kv(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function SY(r){return U().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?br.PACKED_2X2_FLOAT32:br.UNPACKED_FLOAT32:r?br.PACKED_2X2_FLOAT16:br.UNPACKED_FLOAT16}function IR(r,e){if(r===Er.UPLOAD)return br.PACKED_2X2_FLOAT32;if(r===Er.RENDER||r==null)return SY(e);if(r===Er.DOWNLOAD||r===Er.PIXELS)return br.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function NR(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var uo=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=` float unaryOperation(float x) { ${t} } void main() { float x = getAAtOutCoords(); float y = unaryOperation(x); setOutput(y); } `}},gr="if (isnan(x)) return x;",SR="return x;",Ev="return abs(x);";var TR="return (x >= 0.0) ? x : (exp(x) - 1.0);",ER=gr+` return (x < 0.0) ? 0.0 : x; `,AR=gr+` return (x < 0.0) ? 0.0 : min(6.0, x); `,lh="return x;";var DR="return x;",$R=` 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; `,RR=` 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; `,FR=` 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; `,Gs=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } void main() { vec4 x = getAAtOutCoords(); vec4 y = unaryOperation(x); setOutput(y); } `}};var Av=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,o=Ut("rc",t),n=Le(t),s=vR(t,o),a=o.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=` void main() { ${n} rc = getOutputCoords(); vec4 packedInput = getA(${s}); setOutput(getChannel(packedInput, ${i})); } `}};var TY=Sr.whereImpl,EY=1e-7,AY=1e-4,Vx={};function DY(r){return r in Vx||(Vx[r]={}),Vx[r]}var $Y=128,RY=600;function FY(){return U().global.screen==null?1024:U().global.screen.height*U().global.screen.width*window.devicePixelRatio*RY/1024/1024}var Kp=class extends js{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,!U().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Bo(U().getNumber("WEBGL_VERSION"));this.binaryCache=DY(U().getNumber("WEBGL_VERSION")),this.gpgpu=new Iv(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 Tv(this.gpgpu),this.numMBBeforeWarning=FY(),this.texData=new Ya(this,Fo())}nextDataId(){return Kp.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,o){if((U().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||U().getBool("DEBUG"))&&this.checkNumericalProblems(e),o==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:Er.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}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--}}move(e,t,o,n,s){if(U().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:o,dtype:n,values:t,usage:Er.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:l}=t;if(a!=null){let m;l?m=new Gs(i,lh):m=new uo(i,lh);let f=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(o!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let p;if(n==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=N.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let d=this.pendingRead.get(e);return new Promise(h=>d.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:l}=t;if(s!=null){let d;l?d=new Gs(n,lh):d=new uo(n,lh);let h=this.runWebGLProgram(d,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(!U().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&U().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&U().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let d=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(d.texture,...Il(n))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(a==="complex64"){let d=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=d[0],g=d[1];p=N.mergeRealAndImagArrays(h,g)}else if(u==null)p=this.getValuesFromTexture(e);else{let d=y.sizeFromShape(n);p=this.gpgpu.downloadFloat32MatrixFromBuffer(u,d)}c!=null&&this.disposeIntermediateTensorInfo(c);let m=this.convertAndCacheOnCPU(e,p),f=this.pendingRead.get(e);return this.pendingRead.delete(e),f.forEach(d=>d(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Fo().removeDataId(e,this),this.pendingDeletes--),m}bufferSync(e){let t=this.readSync(e.dataId),o=t;if(e.dtype==="string")try{o=t.map(n=>y.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ce(e.shape,e.dtype,o)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}async time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=y.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);i.kernelMs=y.sum(l),i.getExtraProfileInfo=()=>l.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),l=i&&i.origDataId||e,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return U().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Fo().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=$Y){let o=this.getCPUBackend();return!U().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&o==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),o!=null&&e.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){let{dataId:n}=this.makeTensorInfo(e,t,o);return Fo().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){let t=new Av(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Sv(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[Nl(e.shape),...Sl(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[Nl(t),...Sl(t)],a=new ah(s,o),i=!0,l=this.runWebGLProgram(a,[n],e.dtype,null,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:o,shape:n,dtype:s}=t,a=Px(n),i;o?i=new hv(a):i=new dv(a);let l=!0,u=this.runWebGLProgram(i,[{shape:a,dtype:s,dataId:e}],s,null,l);return{dtype:s,shape:n,dataId:u.dataId}}runWebGLProgram(e,t,o,n,s=!1){let a=this.makeTensorInfo(e.outputShape,o),i=this.texData.get(a.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Cl.DENSE){let h=Il(e.outputShape);i.texShape=h.map(g=>g*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),y.sizeFromShape(a.shape)===0)return i.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],u=t.map(h=>{if(h.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let g=this.texData.get(h.dataId);if(g.texture==null){if(!e.packedInputs&&y.sizeFromShape(h.shape)<=U().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:h.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=h.shape)}else if(!!g.isPacked!=!!e.packedInputs)h=g.isPacked?this.unpackTensor(h):this.packTensor(h),l.push(h),g=this.texData.get(h.dataId);else if(g.isPacked&&!lc(g.shape,h.shape)){let x=h,b=h.shape;h.shape=g.shape,h=this.packedReshape(h,b),l.push(h),g=this.texData.get(h.dataId),x.shape=b}return this.uploadToGPU(h.dataId),{shape:h.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:i,isUniform:!1},p=Y$(e,u,c),m=this.getAndSaveBinary(p,()=>q$(this.gpgpu,e,u,c)),f=this.activeTimers!=null,d;if(f&&(d=this.startTimer()),X$(this.gpgpu,m,u,c,n),l.forEach(h=>this.disposeIntermediateTensorInfo(h)),f&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)})),!U().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&s===!1){let h=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),h}return a}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(U().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!U().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=U().getBool("DEBUG");U().set("DEBUG",!1);let t=this.abs(le(1e-8)).dataSync()[0];if(U().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?EY:AY}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:l}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let p=t.texShape;if(p==null&&(p=_$(o,l),t.texShape=p),s!=null){let m=Px(o),f,d=p[1],h=p[0],g=s instanceof Uint8Array;l?([d,h]=Xi(p[0],p[1]),f=new bv(m,[h,d],g)):f=new yv(m,[h,d],g);let x=this.makeTensorInfo([h,d],n);g?this.texData.get(x.dataId).usage=Er.PIXELS:this.texData.get(x.dataId).usage=Er.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(x.dataId),d,h,s);let b=!0,_=this.runWebGLProgram(f,[x],n,null,b),w=this.texData.get(_.dataId);t.texture=w.texture,t.texShape=w.texShape,t.isPacked=w.isPacked,t.usage=w.usage,this.disposeIntermediateTensorInfo(x),this.texData.delete(_.dataId),t.values=null,u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,i,n,l);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return this.releaseGPUData(e),t!=null&&(o.values=OY(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}};Kp.nextDataId=0;function OY(r,e){if(e==="float32"||e==="complex64")return r;if(e==="int32"||e==="bool"){let t=e==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew Kp,2);var Gx=` if (isnan(a)) return a; if (isnan(b)) return b; `;var os=class{constructor(e,t,o){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,o),this.userCode=` float binaryOperation(float a, float b) { ${e} } void main() { float a = getAAtOutCoords(); float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } `}};var Tl=` 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; `;var Ws=class{constructor(e,t,o,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length,a="";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=` result.y = 0.; result.z = 0.; result.w = 0.; `;else if(a=` ${Le(s)} coords = getOutputCoords(); `,s===1)a+=` result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; `;else{let l=Ut("coords",s);a+=` bool nextRowOutOfBounds = (${l[s-2]} + 1) >= ${this.outputShape[s-2]}; bool nextColOutOfBounds = (${l[s-1]} + 1) >= ${this.outputShape[s-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); ${a} setOutput(result); } `}};function jt(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var PR={kernelName:Do,backendName:"webgl",kernelFunc:jt};function co(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.texData.get(s.dataId),i=jt({inputs:{x:o},backend:t}),l=jt({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:l},s}var MR={kernelName:Hl,backendName:"webgl",kernelFunc:co};var Dv="return (a < 0.) ? b * a : a;",$v=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function PY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),i=U().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ws($v,n.shape,a.shape):new os(Dv,n.shape,a.shape),l=t.runWebGLProgram(i,[n,a],n.dtype);return t.disposeIntermediateTensorInfo(a),l}var LR={kernelName:cn,backendName:"webgl",kernelFunc:PY};var Rv="return (a < 0.) ? b * a : a;",Fv=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function MY(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=U().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ws(Fv,o.shape,n.shape):new os(Rv,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)}var zR={kernelName:kn,backendName:"webgl",kernelFunc:MY};var Wx="if (isnan(x)) return x;",BR=` if (isnan(a)) return a; if (isnan(b)) return b; `,VR=` result.r = isNaN.r > 0. ? NAN : result.r; result.g = isNaN.g > 0. ? NAN : result.g; result.b = isNaN.b > 0. ? NAN : result.b; result.a = isNaN.a > 0. ? NAN : result.a; `;function ve({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,l=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let p=i.texData.get(a.dataId),m=t(p.values,l);return i.makeTensorInfo(a.shape,l,m)}let u=U().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new Gs(a.shape,e):c=new uo(a.shape,r),i.runWebGLProgram(c,[a],l)}}function it({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:l,b:u}=a,c=i;if(o&&l.dtype==="complex64"){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(_=>{let[w,v]=_,$={dataId:w.dataId,dtype:w.dtype,shape:l.shape},A={dataId:v.dataId,dtype:v.dtype,shape:u.shape},R=new os(r,l.shape,u.shape);return c.runWebGLProgram(R,[$,A],mr(w.dtype,v.dtype))}),b=co({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||mr(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&n!=null){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,x]=n(l.shape,u.shape,d.values,h.values,p),b=c.makeTensorInfo(x,p),_=c.texData.get(b.dataId);return _.values=g,b}let m=U().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,f;return m?f=new Ws(e,l.shape,u.shape,t):f=new os(r,l.shape,u.shape),c.runWebGLProgram(f,[l,u],p)}}function El(r,e=!1){if(r==="linear")return e?DR:SR;if(r==="relu")return e?RR:ER;if(r==="elu")return e?$R:TR;if(r==="relu6")return e?FR:AR;if(r==="prelu")return e?Fv:Rv;if(r==="leakyrelu")return e?$v:Dv;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var uh=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,l=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o;let c=n?e[1]:e[2],p=Math.ceil(c/2),m=n?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";i&&(l?g=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:u?g=`vec4 activation(vec4 a) { vec4 b = getLeakyreluAlphaAtOutCoords(); ${i} }`:g=`vec4 activation(vec4 x) { ${i} }`,x="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let _="rc.x",w="rc.x";e[0]`The new shape (${l}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!lc(n.shape,l)&&!(c.texture!==null&&lc(c.shape,l))?UR(n,l,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:l,dtype:n.dtype})}var jR={kernelName:gs,backendName:"webgl",kernelFunc:ce};var jx=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,l=o%4,u="sumValue += dot(values, ones);";if(t!=null){let p=1/t;u=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%o>0&&(c=` if (inIdx < 0 || inIdx >= ${s}) { 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 * ${o}; 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) ); ${u} } int inIdx = inOffset + ${i}; if (${l===1}) { vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0); ${u} } else if (${l===2}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), 0.0, 0.0); ${u} } else if (${l===3}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), 0.0); ${u} } setOutput(sumValue); } `}};var Mv=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i="0.0",l="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",l="min"):t==="max"&&(i="-1.0 / 1e-20",l="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let c=Math.floor(o/4)*4,p=o%4,m=` 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 = ${l}(values, minMaxValue); } `,f="vec4";t==="all"?(i="1.0",m=` bool reducedAllValue = all(values); float floatedReducedAllValue = float(reducedAllValue); allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0); `,f="bvec4"):t==="any"&&(i="0.0",m=` bool reducedAnyValue = any(values); float floatedReducedAnyValue = float(reducedAnyValue); anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0); `,f="bvec4");let d="";s%o>0&&(d=` if (inIdx < 0 || inIdx >= ${s}) { 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 * ${o}; vec4 minMaxValue = vec4(${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; ${f} values = ${f}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); ${m} } int inIdx = inOffset + ${c}; if (${p===1}) { ${f} values = ${f}( getValue(batch, inIdx), initializationValue, initializationValue, initializationValue ); ${m} } else if (${p===2}) { ${f} values = ${f}( getValue(batch, inIdx), getValue(batch, inIdx + 1), initializationValue, initializationValue ); ${m} } else if (${p===3}) { ${f} values = ${f}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), initializationValue ); ${m} } setOutput(${u}); } `}};function LY(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=N.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function Io(r,e,t,o){let n=LY(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],o=new Array(e);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Le(this.rank),s=Nv("rc",this.rank),a=new Array(this.rank);for(let c=0;c=2&&c>=2&&_,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${h}) and (${g}).`);let v=(x>b?r.shape.slice(0,-2):e.shape.slice(0,-2)).concat([f,d]);y.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let $=t?[x,p,f]:[x,f,p],A=o?[b,d,m]:[b,m,d],R=ce({inputs:{x:r},backend:n,attrs:{shape:$}}),M=ce({inputs:{x:e},backend:n,attrs:{shape:A}}),V=[R,M],G=Math.max(x,b),W=t?R.shape[1]:R.shape[2],q=s!=null,Z=a!=null,X=l==="leakyrelu",Y=l!=null?El(l,!0):null,te=q||Z||X||Y!=null,K;if((f===1||d===1)&&W>Bv&&te===!1){let ie=R,se=M;t&&(ie=Lt({inputs:{x:R},backend:n,attrs:{perm:[0,2,1]}}),V.push(ie)),o&&(se=Lt({inputs:{x:M},backend:n,attrs:{perm:[0,2,1]}}),V.push(se));let pe=d!==1,ae=d===1,xe=ie;pe&&(xe=ce({inputs:{x:ie},backend:n,attrs:{shape:[G,W,1]}}),V.push(xe));let ge=d===1?2:1,_e=se;ae&&(_e=ce({inputs:{x:se},backend:n,attrs:{shape:[G,1,W]}}),V.push(_e));let ke=Pv({inputs:{a:xe,b:_e},backend:n});K=ch({inputs:{x:ke},backend:n,attrs:{axis:ge,keepDims:!0}}),V.push(ke)}else{let ie=mr(r.dtype,e.dtype),se=new uh($,A,[G,f,d],t,o,q,Y,Z,X),pe=[R,M];if(s!=null&&pe.push(s),Z&&pe.push(a),X){let ae=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));pe.push(ae),V.push(ae)}K=n.runWebGLProgram(se,pe,ie)}let re=ce({inputs:{x:K},backend:n,attrs:{shape:v}});V.push(K);for(let ie of V)n.disposeIntermediateTensorInfo(ie);return re}function BY(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=o;return cc({a:n,b:s,transposeA:l,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var XR={kernelName:ks,backendName:"webgl",kernelFunc:BY};var YR="return abs(x);";function VY(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!=="complex64"){let s=t.texData.get(o.dataId),a=Bx(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return U().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Gs(o.shape,YR):n=new uo(o.shape,YR),t.runWebGLProgram(n,[o],o.dtype)}var ZR={kernelName:us,backendName:"webgl",kernelFunc:VY};var GY=gr+` if (abs(x) > 1.) { return NAN; } return acos(x); `,WY=ve({opSnippet:GY}),JR={kernelName:Ks,backendName:"webgl",kernelFunc:WY};var UY=gr+` if (x < 1.0) return NAN; return log(x + sqrt(x * x - 1.0));`,jY=ve({opSnippet:UY}),QR={kernelName:Xs,backendName:"webgl",kernelFunc:jY};var eF="return a + b;",HY=it({opSnippet:eF,packedOpSnippet:eF,supportsComplex:!0,cpuKernelImpl:Z$}),tF={kernelName:xo,backendName:"webgl",kernelFunc:HY};var Vv=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` void main() { ${o.join(` `)} float result = ${n}; setOutput(result); } `}};var Gv=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=` void main() { ${o.join(` `)} vec4 result = ${n}; setOutput(result); } `}};function Hx(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return jt({inputs:{x:o[0]},backend:t});if(o.length>U().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(o.length/2),u=Hx({inputs:o.slice(0,l),backend:t}),c=Hx({inputs:o.slice(l),backend:t});return Hx({inputs:[u,c],backend:t})}let n=o.map(l=>l.dtype).reduce((l,u)=>mr(l,u)),s=o.map(l=>l.shape),i=U().getBool("WEBGL_PACK")?new Gv(o[0].shape,s):new Vv(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var rF={kernelName:qo,backendName:"webgl",kernelFunc:Hx};function qY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Lt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("all",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=ce({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Io(h,h.dtype,"all",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=ce({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=ce({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var oF={kernelName:Vl,backendName:"webgl",kernelFunc:qY};function KY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Lt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("any",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=ce({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Io(h,h.dtype,"any",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=ce({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=ce({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var nF={kernelName:Gl,backendName:"webgl",kernelFunc:KY};var Wv=class{constructor(e,t,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",l=o?"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 * ${n}; int bestIndex = inOffset; float bestValue = getA(batch, bestIndex); for (int i = 0; i < ${n}; i++) { int inIdx = ${l}; float candidate = getA(batch, inIdx); if (candidate ${i} bestValue) { bestValue = candidate; bestIndex = inIdx; } } setOutput(float(bestIndex)); } `}};var Uv=class{constructor(e,t,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,l=i.length,u=Le(l),c=Ut("coords",l),p,m;if(a===1){m=l+1;let R=Le(m);p=` ${R} sourceLocR = ${R}(${c.join()}, 0); ++${c[l-1]}; ${R} sourceLocG = ${R}(${c.join()}, 0); ++${c[l-2]}; ${R} sourceLocA = ${R}(${c.join()}, 0); --${c[l-1]}; ${R} sourceLocB = ${R}(${c.join()}, 0); --${c[l-2]};`}else m=l,p=` ${u} sourceLocR = coords; ++${c[l-1]}; ${u} sourceLocG = coords; ++${c[l-2]}; ${u} sourceLocA = coords; --${c[l-1]}; ${u} sourceLocB = coords; --${c[l-2]};`;let f=["x","y","z","w","u","v"].slice(0,m),d="."+f[m-1],h=f.map(R=>"int "+R),g=Ut("sourceLocR",m-1).concat("inIdx.r"),x=Ut("sourceLocG",m-1).concat("inIdx.g"),b=Ut("sourceLocB",m-1).concat("inIdx.b"),_=Ut("sourceLocA",m-1).concat("inIdx.a"),w=o==="max"?"greaterThan":"lessThan",v=n?"":` inIdx = round(vec4(getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${x.join()}), getBestIndicesAChannel(${b.join()}), getBestIndicesAChannel(${_.join()})));`,$=`vec4( getAChannel(${g.join()}), hasNextCol ? getAChannel(${x.join()}) : 0., hasNextRow ? getAChannel(${b.join()}) : 0., hasNextRow && hasNextCol ? getAChannel(${_.join()}) : 0.)`,A=n?"":` float getBestIndicesAChannel(${h.join()}) { return getChannel(getBestIndicesA(${f.join()}), vec2(${f.slice(-2).join()})); }`;this.userCode=` float getAChannel(${h.join()}) { return getChannel(getA(${f.join()}), vec2(${f.slice(-2).join()})); } ${A} void main() { ${u} coords = getOutputCoords(); bool hasNextCol = ${c[l-1]} < ${i[l-1]-1}; bool hasNextRow = ${c[l-2]} < ${i[l-2]-1}; ${p} ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d}, sourceLocB${d}, sourceLocA${d}) * ${t}; ivec4 inIdx = srcIdx; vec4 bestIndex = vec4(inIdx); vec4 bestValue = ${$}; for (int i = 0; i < ${t}; i++) { inIdx = srcIdx; ${v} vec4 candidate = ${$}; bvec4 nan = isnan(candidate); bvec4 replace = bvec4( vec4(${w}(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 sF(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=N.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},l=new Wv(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=sF(r,e,t,c);return r.disposeIntermediateTensorInfo(c),p}function iF(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=N.computeOptimalWindowSize(s),i=new Uv(n,a,t,o==null),l=o==null?[e]:[e,o],u=r.runWebGLProgram(i,l,"int32");if(u.shape.length===e.shape.length){let c=iF(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function qx(r,e,t,o){let n=[t];if(N.assertAxesAreInnerMostDims("arg"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!U().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],[a,i]=N.computeOutAndReduceShapes(e.shape,n),l=y.sizeFromShape(i),u=ce({inputs:{x:e},backend:r,attrs:{shape:[-1,l]}});s.push(u);let c=sF(r,u,o);s.push(c);let p=ce({inputs:{x:c},backend:r,attrs:{shape:a}});return s.forEach(m=>r.disposeIntermediateTensorInfo(m)),p}return iF(r,e,o)}function XY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=N.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=Lt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[a[0]],l.shape.length);let c=qx(t,l,a[0],"max");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var aF={kernelName:Ko,backendName:"webgl",kernelFunc:XY};function YY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=N.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=Lt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[a[0]],l.shape.length);let c=qx(t,l,a[0],"min");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var lF={kernelName:oa,backendName:"webgl",kernelFunc:YY};var ZY=gr+` if (abs(x) > 1.) { return NAN; } return asin(x); `,JY=ve({opSnippet:ZY}),uF={kernelName:Ys,backendName:"webgl",kernelFunc:JY};var QY=gr+"return log(x + sqrt(x * x + 1.0));",e7=ve({opSnippet:QY}),cF={kernelName:Zs,backendName:"webgl",kernelFunc:e7};var t7=gr+` return atan(x); `,r7=ve({opSnippet:t7}),pF={kernelName:Js,backendName:"webgl",kernelFunc:r7};var o7=BR+` return atan(a, b); `,n7=` vec4 result = atan(a, b); vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0)); `+VR+` return result; `,s7=it({opSnippet:o7,packedOpSnippet:n7}),mF={kernelName:ei,backendName:"webgl",kernelFunc:s7};var i7=gr+` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,a7=ve({opSnippet:i7}),fF={kernelName:Qs,backendName:"webgl",kernelFunc:a7};var Zi=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),o){let R=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${l}); const ivec2 pads = ivec2(${f}, ${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 < ${p}; wR += ${u}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${m}; 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 ${R} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`}; } } } setOutput(float(minMaxPosition)); } `;return}let _="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let v=Math.floor(a/4)*4,$=a%4,A=` if (${h}) { avgValue += dot(values, ones); } else { minMaxValue = ${_}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${i}, ${l}); const ivec2 pads = ivec2(${f}, ${d}); const float initializationValue = ${b}; 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 >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xR, xC, d); } void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined vec4 minMaxValue = vec4(${b}); float avgValue = 0.0; count = 0.0; for (int wR = 0; wR < ${p}; wR += ${u}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${v}; 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) ); ${A} } int xC = xCCorner + ${v}; if (${$===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, initializationValue, initializationValue ); ${A} } else if (${$===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), initializationValue, initializationValue ); ${A} } else if (${$===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), getValue(batch, xR, xC + 2 * ${c}, d), initializationValue ); ${A} } } setOutput(${w}); } `}},pc=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,l=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,p=e.dilationHeight,m=e.dilationWidth,f=e.effectiveFilterDepth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let _=t==="avg",w="0.0";if(_||(w="-1.0 / 1e-20"),o){let V=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${l}, ${u}); const ivec3 pads = ivec3(${g}, ${x}, ${b}); 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 < ${f}; wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${d}; wR += ${p}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${h}; wC += ${m}) { 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 ${V} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${n?s?`(((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} * ${h} + wR * ${h} + wC`}; } } } } setOutput(float(minMaxPosition)); } `;return}let v="max",$=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&($="avgValue / count");let A=Math.floor(a/4)*4,R=a%4,M=` if (${_}) { avgValue += dot(values, ones); } else { minMaxValue = ${v}(values, minMaxValue); } `;this.userCode=` const ivec3 strides = ivec3(${i}, ${l}, ${u}); const ivec3 pads = ivec3(${g}, ${x}, ${b}); const float initializationValue = ${w}; 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 >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xD, xR, xC, ch); } void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined vec4 minMaxValue = vec4(${w}); float avgValue = 0.0; count = 0.0; for (int wD = 0; wD < ${f}; wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${d}; wR += ${p}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${A}; wC += 4) { int xC = xCCorner + wC * ${m}; vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${m}, ch), getValue(batch, xD, xR, xC + 2 * ${m}, ch), getValue(batch, xD, xR, xC + 3 * ${m}, ch) ); ${M} } int xC = xCCorner + ${A}; if (${R===1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), initializationValue, initializationValue, initializationValue ); ${M} } else if (${R===2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${m}, ch), initializationValue, initializationValue ); ${M} } else if (${R===3}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${m}, ch), getValue(batch, xD, xR, xC + 2 * ${m}, ch), initializationValue ); ${M} } } setOutput(${$}); } } `}};function l7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Yi(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=o,u=1;y.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(n.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return jt({inputs:{x:n},backend:t});let p=new Zi(c,"avg",!1);return t.runWebGLProgram(p,[n],"float32")}var dF={kernelName:Xo,backendName:"webgl",kernelFunc:l7};function u7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u}=o,c=[1,1,1],p=N.computePool3DInfo(n.shape,s,a,c,i,l,u),m=new pc(p,"avg",!1);return t.runWebGLProgram(m,[n],"float32")}var hF={kernelName:na,backendName:"webgl",kernelFunc:u7};var jv=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=l-1-e.padInfo.top,p=u-1-e.padInfo.left,m=1/(t*o);this.userCode=` const ivec2 pads = ivec2(${c}, ${p}); const float avgMultiplier = float(${m}); 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 < ${l}; wR += ${a}) { 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 < ${u}; wC+= ${i}) { float dyC = float(dyCCorner + wC) / ${s}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); dotProd += dyValue * avgMultiplier; } } setOutput(dotProd); } `}},Hv=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterDepth,m=e.effectiveFilterHeight,f=e.effectiveFilterWidth,d=p-1-e.padInfo.front,h=m-1-e.padInfo.top,g=f-1-e.padInfo.left,x=1/(t*o*n);this.userCode=` const ivec3 pads = ivec3(${d}, ${h}, ${g}); const float avgMultiplier = float(${x}); 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 < ${p}; wD += ${l}) { float dyD = float(dyDCorner + wD) / ${s}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${m}; wR += ${u}) { float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${f}; 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 c7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=o,p=[1,1,1],m=N.computePool3DInfo(a.shape,i,l,p,u,c),f=new Hv(m);return t.runWebGLProgram(f,[n],a.dtype)}var gF={kernelName:Ul,backendName:"webgl",kernelFunc:c7};function p7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Yi([n,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=o,c=N.computePool2DInfo(a.shape,i,l,1,u),p=new jv(c);return t.runWebGLProgram(p,[n],a.dtype)}var xF={kernelName:Wl,backendName:"webgl",kernelFunc:p7};function m7(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return cc({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var yF={kernelName:Yo,backendName:"webgl",kernelFunc:m7};var qv=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,o);let i="0.0";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="1.0";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); float variance = getVarianceAtOutCoords(); float offset = ${i}; float scale = ${l}; float inv = scale * inversesqrt(variance + float(${a})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } `}};var Kv=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,o);let i="vec4(0.0)";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="vec4(1.0)";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${l}; vec4 x = getXAtOutCoords(); vec4 mean = getMeanAtOutCoords(); vec4 variance = getVarianceAtOutCoords(); vec4 inv = scale * inversesqrt(variance + vec4(${a})); setOutput((x - mean) * inv + offset); } `}};var f7=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||n.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(i==null||n.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=t;l==null&&(l=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let p=null;i!=null&&(p=i.shape,u.push(i));let m=U().getBool("WEBGL_PACK_NORMALIZATION")?new Kv(o.shape,n.shape,s.shape,c,p,l):new qv(o.shape,n.shape,s.shape,c,p,l);return e.runWebGLProgram(m,u,u[0].dtype)},bF={kernelName:ln,backendName:"webgl",kernelFunc:f7};var Xv=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=Le(this.rank),o=`uniform int start[${this.rank}];`,n=d7(this.rank),s,a=e.map((i,l)=>`sourceLoc.${Yv[l]} = start[${l}] + coords.${Yv[l]};`);s=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${a.join(` `)} `,this.userCode=` ${o} void main() { ${s} setOutput(getSource(${n})); } `}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,o)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(o,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},Yv=["x","y","z","w","u","v"];function d7(r){if(r===1)return"sourceLoc";if(r<=6)return Yv.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var Zv=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=Le(this.rank),o=Ut("coords",this.rank),n=Ut("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=` result.x = ${a}; if (++${o[this.rank-1]} < ${e[this.rank-1]}) { ++${n[this.rank-1]}; result.y = ${a}; --${n[this.rank-1]}; } `,l=this.rank===1?"":` --${o[this.rank-1]}; if (++${o[this.rank-2]} < ${e[this.rank-2]}) { ++${n[this.rank-2]}; result.z = ${a}; if (++${o[this.rank-1]} < ${e[this.rank-1]}) { ++${n[this.rank-1]}; result.w = ${a}; } } `,u=this.rank<=4?`sourceLoc = coords + ${t}(${e.map((c,p)=>`start[${p}]`).join()});`:e.map((c,p)=>`${n[p]} = ${o[p]} + start[${p}];`).join(` `);this.userCode=` uniform int start[${this.rank}]; void main() { ${t} coords = getOutputCoords(); ${t} sourceLoc; ${u} vec4 result = vec4(0.); ${i} ${l} setOutput(result); } `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,o)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(o,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function h7(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=or.computeFlatOffset(e,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let l=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,l+1),s}function Ua(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,l]=or.parseSliceParams(n,s,a);if(or.assertParamsValid(n,i,l),y.sizeFromShape(l)===0)return t.makeTensorInfo(l,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype==="string"){let p=t.texData.get(n.dataId),m=xR(p.values,i,l,n.shape,n.dtype);return t.makeTensorInfo(l,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=or.isSliceContinous(n.shape,i,l);if(u||!c){let p=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Zv(l):new Xv(l),m=p.getCustomSetupFunc(i);return t.runWebGLProgram(p,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),h7(n,i,l,t)}var _F={kernelName:ys,backendName:"webgl",kernelFunc:Ua};var g7=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((b,_)=>b*_),l=N.getReshaped(n.shape,s,i),u=N.getPermuted(l.length,s.length),c=N.getReshapedPermuted(n.shape,s,i),p=N.getSliceBeginCoords(a,s.length),m=N.getSliceSize(c,a,s.length),f=[],d=ce({inputs:{x:n},backend:t,attrs:{shape:l}}),h=Lt({inputs:{x:d},backend:t,attrs:{perm:u}}),g=ce({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Ua({inputs:{x:g},backend:t,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},wF={kernelName:sa,backendName:"webgl",kernelFunc:g7};function x7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),l=t.readSync(s.dataId),u=zx(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var kF={kernelName:jl,backendName:"webgl",kernelFunc:x7};var y7="return float(a != b);",Jv=it({opSnippet:y7,dtype:"bool"}),vF={kernelName:bi,backendName:"webgl",kernelFunc:Jv};function ja(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return jt({inputs:{x:n.complexTensorInfos.real},backend:t})}var CF={kernelName:cu,backendName:"webgl",kernelFunc:ja};var b7="return float(int(x));";function IF(r,e){let t=new uo(r.shape,b7),o=e.runWebGLProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function Qv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return jt({inputs:{x:n},backend:t});let a=gt(n.shape),i=Qv({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),l=co({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),l}if(n.dtype==="complex64"){let a=ja({inputs:{input:n},backend:t}),i=Qv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=jt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(s==="int32")return IF(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),l=Jv({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),l}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var NF={kernelName:Eo,backendName:"webgl",kernelFunc:Qv};var SF="return ceil(x);",_7=ve({opSnippet:SF,packedOpSnippet:SF,cpuKernelImpl:Q$}),TF={kernelName:Zo,backendName:"webgl",kernelFunc:_7};var eC=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(o,n)=>{this.minLoc==null&&(this.minLoc=o.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=o.getUniformLocationNoThrow(n,"maxVal")),o.gl.uniform1f(this.minLoc,e),o.gl.uniform1f(this.maxLoc,t)}}};var tC=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(o,n)=>{this.minLoc==null&&(this.minLoc=o.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=o.getUniformLocationNoThrow(n,"maxVal")),o.gl.uniform1f(this.minLoc,e),o.gl.uniform1f(this.maxLoc,t)}}};function w7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;U().getBool("WEBGL_PACK_CLIP")?i=new tC(n.shape):i=new eC(n.shape);let l=i.getCustomSetupFunc(s,a);return t.runWebGLProgram(i,[n],n.dtype,l)}var EF={kernelName:Ao,backendName:"webgl",kernelFunc:w7};var rC=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 AF(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function k7(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new rC(o.shape),a=[AF(o,n.complexTensorInfos.real),AF(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var DF={kernelName:ia,backendName:"webgl",kernelFunc:k7};var oC=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a`T${g}`);let l=new Array(e.length-1);l[0]=e[0][t];for(let h=1;h= ${l[h-1]}) { return getChannel( getT${h}(${Kx(i,u,g)}), vec2(${Kx(c,u,g)})); }`}let f=l.length,d=l[l.length-1];m+=` return getChannel( getT${f}(${Kx(i,u,d)}), vec2(${Kx(c,u,d)}));`,this.userCode=` float getValue(${i.map(h=>"int "+h)}) { ${m} } void main() { ${s} coords = getOutputCoords(); vec4 result = vec4(getValue(${a}), 0., 0., 0.); ${a[n-1]} = ${a[n-1]} + 1; if (${a[n-1]} < ${o[n-1]}) { result.g = getValue(${a}); } ${a[n-2]} = ${a[n-2]} + 1; if (${a[n-2]} < ${o[n-2]}) { result.a = getValue(${a}); } ${a[n-1]} = ${a[n-1]} - 1; if (${a[n-2]} < ${o[n-2]} && ${a[n-1]} < ${o[n-1]}) { result.b = getValue(${a}); } setOutput(result); } `}};function Kx(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function mc(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return jt({inputs:{x:n.complexTensorInfos.imag},backend:t})}var $F={kernelName:ou,backendName:"webgl",kernelFunc:mc};function fc(r,e,t){let o=r[0].dtype;if(o==="complex64"){let u=r.map(d=>ja({inputs:{input:d},backend:t})),c=r.map(d=>mc({inputs:{input:d},backend:t})),p=fc(u,e,t),m=fc(c,e,t),f=co({inputs:{real:p,imag:m},backend:t});return u.forEach(d=>t.disposeIntermediateTensorInfo(d)),c.forEach(d=>t.disposeIntermediateTensorInfo(d)),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),f}if(o==="string"){let{tensors2D:u,outShape:c}=RF(r,e,t),p=u.map(g=>({vals:t.readSync(g.dataId),shape:g.shape})),m=u[0].shape[0]===1,f=eR(p,c,o,m),d=N.computeOutShape(r.map(g=>g.shape),e),h=t.makeTensorInfo(d,o,f);return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}if(r.length>U().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(r.length/2),c=fc(r.slice(0,u),e,t),p=fc(r.slice(u),e,t),m=fc([c,p],e,t);return t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),m}if(U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&r[0].shape.length>1){let u=new nC(r.map(c=>c.shape),e);return t.runWebGLProgram(u,r,o)}let{tensors2D:n,outShape:s}=RF(r,e,t),a=new oC(n.map(u=>u.shape)),i=t.runWebGLProgram(a,n,o);n.forEach(u=>t.disposeIntermediateTensorInfo(u));let l=ce({inputs:{x:i},attrs:{shape:s},backend:t});return t.disposeIntermediateTensorInfo(i),l}function RF(r,e,t){let o=N.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>ce({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function sC(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=N.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);let i=e.filter(u=>y.sizeFromShape(u.shape)>0);if(i.length===1)return jt({inputs:{x:i[0]},backend:t});let l=i.map(u=>u.shape);return N.assertParamsConsistent(l,s),fc(i,s,t)}var FF={kernelName:cs,backendName:"webgl",kernelFunc:sC};var ph=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,l=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,p=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,_=g?3:1,w="",v="";o&&(n?w=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${o} }`:s?w=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${o} }`:w=` float activation(float x) { ${o} } `,v="result = activation(result);");let $=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${w} const ivec2 strides = ivec2(${l}, ${u}); const ivec2 pads = ivec2(${a}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d2 = coords[${_}]; ivec2 xRCCorner = ivec2(coords[${x}], coords[${b}]) * 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 < ${m}; wR++) { int xR = xRCorner + wR * ${c}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${f}; wC++) { int xC = xCCorner + wC * ${p}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${d}; d1 += 4) { vec4 wValues = vec4( getW(wR, wC, d1, d2), getW(wR, wC, d1 + 1, d2), getW(wR, wC, d1 + 2, d2), getW(wR, wC, d1 + 3, d2) ); if (${g}) { 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 (${h===1}) { if (${g}) { dotProd += getX(batch, xR, xC, ${d}) * getW(wR, wC, ${d}, d2); } else { dotProd += getX(batch, ${d}, xR, xC) * getW(wR, wC, ${d}, d2); } } else if (${h===2}) { vec2 wValues = vec2( getW(wR, wC, ${d}, d2), getW(wR, wC, ${d} + 1, d2) ); if (${g}) { 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, ${d} + 1, xR, xC) ); dotProd += dot(xValues, wValues); } } else if (${h===3}) { vec3 wValues = vec3( getW(wR, wC, ${d}, d2), getW(wR, wC, ${d} + 1, d2), getW(wR, wC, ${d} + 2, d2) ); if (${g}) { 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; ${$} ${v} setOutput(result); } `}},iC=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterDepth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${s}, ${a}, ${i}); const ivec3 pads = ivec3(${t}, ${o}, ${n}); 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 < ${p}; wF++) { int xF = xFCorner + wF * ${l}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${m}; wR++) { int xR = xRCorner + wR * ${u}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${f}; wC++) { int xC = xCCorner + wC * ${c}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${d}; d1 += 4) { vec4 xValues = vec4( getX(batch, xF, xR, xC, d1), getX(batch, xF, xR, xC, d1 + 1), getX(batch, xF, xR, xC, d1 + 2), getX(batch, xF, xR, xC, d1 + 3) ); vec4 wValues = vec4( getW(wF, wR, wC, d1, d2), getW(wF, wR, wC, d1 + 1, d2), getW(wF, wR, wC, d1 + 2, d2), getW(wF, wR, wC, d1 + 3, d2) ); dotProd += dot(xValues, wValues); } if (${h===1}) { dotProd += getX(batch, xF, xR, xC, ${d}) * getW(wF, wR, wC, ${d}, d2); } else if (${h===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, ${d} + 1, d2) ); dotProd += dot(xValues, wValues); } else if (${h===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); } `}};var aC=class{constructor(e,t,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:n,inChannels:s,strideWidth:a,strideHeight:i,padInfo:l,outWidth:u,dilationWidth:c,dilationHeight:p,dataFormat:m}=o,{left:f,top:d}=l,h=s*n,g=Mt(),x=m==="channelsLast",b=x?0:1,_=x?1:2,w="";for(let v=0;v<=1;v++)for(let $=0;$<=1;$++)w+=` blockIndex = rc.y + ${$}; pos = rc.x + ${v}; if(blockIndex < ${e[1]} && pos < ${e[0]}) { offsetY = int(blockIndex / (${u})) * ${i} - ${d}; d0 = offsetY + ${p} * (pos / ${h}); if(d0 < ${t[b]} && d0 >= 0) { offsetX = int(mod(float(blockIndex), ${u}.) * ${a}. - ${f}.); d1 = offsetX + ${c} * (int(mod(float(pos), ${h}.) / ${s}.)); if(d1 < ${t[_]} && d1 >= 0) { ch = int(mod(float(pos), ${s}.)); if (${x}) { innerDims = vec2(d1, ch); result[${v*2+$}] = getChannel( getA(d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); result[${v*2+$}] = 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; ${w} ${g.output} = result; } `}};function Xx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let l=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,p=l[0]*l[1]*l[2],m=t.outChannels,f=t.dataFormat==="channelsLast",d=!1,h=!1,g,x=[],b=(p===1||m===1)&&c>Bv,_=l[2]%2!=0&&!!u.isPacked;if(b||!U().getBool("WEBGL_LAZILY_UNPACK")||!U().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!_){let w=f?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],v=ce({inputs:{x:r},backend:o,attrs:{shape:[1,w,t.inChannels]}}),$=ce({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),A=cc({a:v,b:$,transposeA:d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=ce({inputs:{x:A},backend:o,attrs:{shape:t.outShape}}),x.push(v),x.push($),x.push(A)}else{let w=f?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),v={dataId:r.dataId,shape:[1,w,t.inChannels],dtype:r.dtype},$=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(lc(u.shape,v.shape),()=>`packed reshape ${u.shape} to ${v.shape} isn't free`);let A=ce({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(A);let R=cc({a:v,b:A,backend:o,transposeA:d,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),M=o.texData.get(R.dataId);y.assert(M.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=$,M.shape=t.outShape,g=jt({inputs:{x:R},backend:o}),g.shape=t.outShape,x.push(R)}for(let w of x)o.disposeIntermediateTensorInfo(w);return g}function Yx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=t,d=f==="channelsLast",h=l*u*c,g=m*p,x=[h,g],b=!0,_=!1,w=[],v=ce({inputs:{x:r},backend:o,attrs:{shape:r.shape.slice(1)}}),$=ce({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});w.push(v),w.push($);let A=new aC(x,v.shape,t),R=o.runWebGLProgram(A,[v],"float32"),M=ce({inputs:{x:R},backend:o,attrs:{shape:[1,x[0],x[1]]}});w.push(R),w.push(M);let V=n!=null,G=s!=null,W=i==="leakyrelu",q=i?El(i,!0):null,Z=new uh(M.shape,$.shape,[1,g,t.outChannels],b,_,V,q,G,W),X=[M,$];if(n&&X.push(n),G&&X.push(s),W){let re=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));X.push(re),w.push(re)}let Y=o.runWebGLProgram(Z,X,"float32"),te=d?[1,m,p,t.outChannels]:[1,t.outChannels,m,p],K=ce({inputs:{x:Y},backend:o,attrs:{shape:te}});w.push(Y);for(let re of w)o.disposeIntermediateTensorInfo(re);return K}function v7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=o,p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=Xx({x:n,filter:s,convInfo:m,backend:t});else if(U().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)f=Yx({x:n,filter:s,convInfo:m,backend:t});else{let h=new ph(m);f=t.runWebGLProgram(h,[n,s],"float32")}let d=ce({inputs:{x:f},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(f),d}var OF={kernelName:Jo,backendName:"webgl",kernelFunc:v7};var lC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=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} - ${n}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${o} - ${s}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } if (${a}) { 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); } `}},uC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,l=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,p=a?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${l}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[${p}]; ivec2 dyCorner = ivec2(coords[${u}], coords[${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) / ${n}.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 < ${o}; wC++) { float dyC = float(dyCCorner + wC) / ${s}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${o} - 1 - wC; for (int d2 = 0; d2 < ${e.outChannels}; d2++) { if (${a}) { 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); } `}},cC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=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} - ${s}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int yR = 0; yR < ${e.outHeight}; yR++) { int xR = wR + yR * ${o} - ${a}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${n} - ${i}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yF, yR, yC, d2); float xValue = getX(b, xF, xR, xC, d1); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},pC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${l}, ${u}, ${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) / ${s}.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 < ${o}; wR++) { float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${o} - 1 - wR; for (int wC = 0; wC < ${n}; 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 = ${n} - 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 C7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=o,p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(n.shape,c,a,1,i,u,!1,p),f=new lC(m);return t.runWebGLProgram(f,[n,s],"float32")}var PF={kernelName:ql,backendName:"webgl",kernelFunc:C7};function I7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=o,p=N.convertConv2DDataFormat(u),m=N.computeConv2DInfo(a,s.shape,i,1,l,c,!1,p),f=new uC(m);return t.runWebGLProgram(f,[n,s],"float32")}var MF={kernelName:Qo,backendName:"webgl",kernelFunc:I7};function N7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o,u=N.computeConv3DInfo(n.shape,s.shape,a,l,i),c=new iC(u);return t.runWebGLProgram(c,[n,s],"float32")}var LF={kernelName:aa,backendName:"webgl",kernelFunc:N7};function S7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:l}=o,u=N.computeConv3DInfo(n.shape,l,a,1,i),c=new cC(u);return t.runWebGLProgram(c,[n,s],"float32")}var zF={kernelName:Kl,backendName:"webgl",kernelFunc:S7};function T7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:l}=o,u=N.computeConv3DInfo(l,s.shape,i,1,a),c=new pC(u);return t.runWebGLProgram(c,[n,s],"float32")}var BF={kernelName:Xl,backendName:"webgl",kernelFunc:T7};var E7=Wx+` return cos(x); `,A7=ve({opSnippet:E7}),VF={kernelName:en,backendName:"webgl",kernelFunc:A7};var D7=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,$7=ve({opSnippet:D7}),GF={kernelName:ti,backendName:"webgl",kernelFunc:$7};var mC=class{constructor(e,t,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,l,u]=e,[c]=t,[p,m]=o;this.outputShape=[c,p,m,u];let f=n==="bilinear"?1:0,[d,h]=[`${i-1}.0`,`${l-1}.0`],[g,x,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[_,w,v]=m>1?[`${(l-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=` const float height_ratio = float(${g}); const float width_ratio = float(${_}); 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 >= ${a}) { return; } float height_scale = ${x}; float width_scale = ${w}; float in_y = ${b}; if( in_y < 0.0 || in_y > ${d} ) { setOutput(float(${s})); return; } float in_x = ${v}; if( in_x < 0.0 || in_x > ${h} ) { setOutput(float(${s})); return; } vec2 sourceFracIndexCR = vec2(in_x,in_y); if(${f} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d); float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d); float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d); float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d); vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR); float top = topLeft + (topRight - topLeft) * fracCR.x; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; float newValue = top + (bottom - top) * fracCR.y; setOutput(newValue); } else { // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestCR = ivec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d); setOutput(newValue); } } `}};var R7=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=o,c=new mC(n.shape,s.shape,i,l,u);return t.runWebGLProgram(c,[n,s,a],"float32")},WF={kernelName:ri,backendName:"webgl",kernelFunc:R7};var Zx=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=e;let n=e.length,s=t?"0.0":`getX(${UF(n,"coords")})`,a=e[e.length-1],i="",l="";t?(i=o?`end != ${a-1}`:"end != 0",l=o?"end + 1":"end - 1"):(i=o?`end + pow2 < ${a}`:"end >= pow2",l=o?"end + pow2":"end - pow2"),this.userCode=` uniform float index; void main() { ${Le(n)} coords = getOutputCoords(); int end = ${jF(n,"coords")}; float val = ${s}; int pow2 = int(pow(2.0, index)); if (${i}) { int idx = ${l}; ${jF(n,"coords")} = idx; val += getX(${UF(n,"coords")}); } setOutput(val); } `}getCustomSetupFunc(e){return(t,o)=>{this.index==null&&(this.index=t.getUniformLocation(o,"index")),t.gl.uniform1f(this.index,e)}}};function UF(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function jF(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function F7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o,l=n.shape.length,u=N.getAxesPermutation([s],l),c=n;u!=null&&(c=Lt({inputs:{x:n},backend:t,attrs:{perm:u}}));let p=N.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${n.shape.length-1} but got axis=${s}`);let m=c.shape[p],f=jt({inputs:{x:c},backend:t});for(let d=0;d<=Math.ceil(Math.log2(m))-1;d++){let h=new Zx(c.shape,!1,i),g=h.getCustomSetupFunc(d),x=f;f=t.runWebGLProgram(h,[f],f.dtype,g),t.disposeIntermediateTensorInfo(x)}if(a){let d=new Zx(c.shape,a,i),h=f;f=t.runWebGLProgram(d,[f],f.dtype),t.disposeIntermediateTensorInfo(h)}if(u!=null){let d=N.getUndoAxesPermutation(u),h=Lt({inputs:{x:f},backend:t,attrs:{perm:d}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(c),h}return f}var HF={kernelName:tn,backendName:"webgl",kernelFunc:F7};function O7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let l=t.readSync(n.dataId),u=t.readSync(s.dataId),c=zx(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let l=t.bufferSync(n),u=t.bufferSync(s),c=J$(l,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var qF={kernelName:Yl,backendName:"webgl",kernelFunc:O7};var fC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,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 P7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o;y.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let i=n.shape[0],l=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],p=l*s,m=u*s,f=c/(s*s),d=a==="NHWC"?[i,p,m,f]:[i,f,p,m],h=new fC(d,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var KF={kernelName:oi,backendName:"webgl",kernelFunc:P7};var mh=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=e.outChannels/e.inChannels,x="",b="";o&&(n?x=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${o} }`:s?x=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${o} }`:x=` float activation(float x) { ${o} } `,b="result = activation(result);");let _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${x} const ivec2 strides = ivec2(${c}, ${p}); const ivec2 pads = ivec2(${l}, ${u}); void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; ivec2 xRCCorner = coords.yz * strides - pads; int d2 = coords.w; int d1 = d2 / ${g}; int q = d2 - d1 * ${g}; 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 * ${m}; if (xR < 0 || xR >= ${a}) { continue; } for (int wC = 0; wC < ${h}; wC++) { int xC = xCCorner + wC * ${f}; 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; ${_} ${b} setOutput(result); } `}};var fh=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=h,x="int xR; int xC; int xCOffset;";for(let v=0;v= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) { xTexelR${v}C${A} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if(xCOffset + 1 >= ${i}) { xTexelR${v}C${A}.zw = vec2(0.); } } else { xTexelR${v}C${A} = vec4(0.); } xCOffset = xC + 1 - 2; if(xR >= 0 && xR < ${a} && 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 >= ${i}) { previous.zw = vec2(0.); } xR${v}C${A} = vec4(previous.zw, xTexelR${v}C${A}.xy); } else { xR${v}C${A} = vec4(0, 0, xTexelR${v}C${A}.xy); } `:x+=` if(xR >= 0 && xR < ${a} && xC >= 0 && xC < ${i}) { xTexelR${v}C${A} = getX(batch, xR, xC, d1); } else { xTexelR${v}C${A} = vec4(0.); } xR${v}C${A} = xTexelR${v}C${A}; `,A+1= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) { xTexelR${v}C${A+2} = getX(batch, xR, xCOffset, d1); } `,f>1&&(x+=` xCOffset -= 2; if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) { xTexelR${v}C${A} = getX(batch, xR, xCOffset, d1); } else { xTexelR${v}C${A} = vec4(0.); } `),x+=` xR${v}C${A+1} = vec4( xTexelR${v}C${A}.zw, xTexelR${v}C${A+2}.xy); `):x+=` xCOffset = xC + ${R}; if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) { xTexelR${v}C${A+2} = getX(batch, xR, xCOffset, d1); } xR${v}C${A+1} = xTexelR${v}C${A+2}; `}}else A= 0 && xR < ${a}) { `,u%2==1?(x+=` xCOffset = xC + 1 - ${p}; if(xCOffset >= 0 && xCOffset < ${i}) { xTexelR${v}C${A} = getX(batch, xR, xCOffset, d1); } else { xTexelR${v}C${A} = vec4(0.); } if(xC + 1 >= 0 && xC + 1 < ${i}) { xTexelR${v}C${A+2} = getX(batch, xR, xC + 1, d1); } else { xTexelR${v}C${A+2} = vec4(0.); } xR${v}C${A} = vec4( xTexelR${v}C${A}.zw, xTexelR${v}C${A+2}.zw); `,A+1= 0 && xCOffset < ${i}) { final = getX(batch, xR, xCOffset, d1); } xR${v}C${A+1} = vec4(xTexelR${v}C${A+2}.xy, final.xy); `)):(x+=` if(xC >= 0 && xC < ${i}) { xTexelR${v}C${A} = getX(batch, xR, xC, d1); } else { xTexelR${v}C${A} = vec4(0.); } xCOffset = xC + ${p}; if(xCOffset >= 0 && xCOffset < ${i}) { xTexelR${v}C${A+2} = getX(batch, xR, xCOffset, d1); } else { xTexelR${v}C${A+2} = vec4(0.); } xR${v}C${A} = vec4( xTexelR${v}C${A}.xy, xTexelR${v}C${A+2}.xy); `,A+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let p=N.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;return U().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?m=new fh(p):m=new mh(p),t.runWebGLProgram(m,[n,s],"float32")}var XF={kernelName:rn,backendName:"webgl",kernelFunc:M7};var dC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=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 * ${a} + 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} - ${n}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${o} - ${s}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } } } setOutput(dotProd); } `}},hC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,l=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${a}, ${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) / ${n}.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 < ${o}; wC++) { float dyC = float(dyCCorner + wC) / ${s}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${o} - 1 - wC; // TO DO: Vec4 over the channelMul for (int dm = 0; dm < ${l}; dm++) { int d2 = d1 * ${l} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; } } } setOutput(dotProd); } `}};function L7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=o,p=N.computeConv2DInfo(n.shape,c,a,i,l,u,!0),m=new dC(p);return t.runWebGLProgram(m,[n,s],"float32")}var YF={kernelName:Zl,backendName:"webgl",kernelFunc:L7};function z7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=o,p=N.computeConv2DInfo(c,s.shape,a,i,l,u,!0),m=new hC(p);return t.runWebGLProgram(m,[n,s],"float32")}var ZF={kernelName:Jl,backendName:"webgl",kernelFunc:z7};var gC=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } `}};function B7(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=ce({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new gC(s),l=t.runWebGLProgram(i,[a],a.dtype),u=ce({inputs:{x:l},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(l),u}var JF={kernelName:Ql,backendName:"webgl",kernelFunc:B7};var xC=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:l,dilationHeight:u,dilationWidth:c}=e,{top:p,left:m}=n;this.userCode=` const ivec2 strides = ivec2(${s}, ${a}); const ivec2 pads = ivec2(${p}, ${m}); 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 * ${u}; if (hIn >= 0 && hIn < ${t}) { for (int w = 0; w < ${l}; w++) { int wIn = wBeg + w * ${c}; if (wIn >= 0 && wIn < ${o}) { 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 V7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o,u=N.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",l),c,p=new xC(u);c=t.runWebGLProgram(p,[n,s],"float32");let m=ce({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var QF={kernelName:la,backendName:"webgl",kernelFunc:V7};var G7="return (x >= 0.0) ? x : (exp(x) - 1.0);",W7=` 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; `,U7=ve({opSnippet:G7,packedOpSnippet:W7}),eO={kernelName:ni,backendName:"webgl",kernelFunc:U7};var j7="return (b >= 1.0) ? a : a * (b + 1.0);",H7=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `,q7=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=U().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ws(H7,o.shape,n.shape):new os(j7,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},tO={kernelName:eu,backendName:"webgl",kernelFunc:q7};var K7=` return vec4(equal(a, b)); `,X7="return float(a == b);",Y7=it({opSnippet:X7,packedOpSnippet:K7,dtype:"bool"}),rO={kernelName:ii,backendName:"webgl",kernelFunc:Y7};var Z7=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. float p = ${N.ERF_P}; float a1 = ${N.ERF_A1}; float a2 = ${N.ERF_A2}; float a3 = ${N.ERF_A3}; float a4 = ${N.ERF_A4}; float a5 = ${N.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); `,J7=ve({opSnippet:Z7}),oO={kernelName:si,backendName:"webgl",kernelFunc:J7};var nO="return exp(x);",yC=ve({opSnippet:nO,packedOpSnippet:nO,cpuKernelImpl:tR}),sO={kernelName:nn,backendName:"webgl",kernelFunc:yC};function Jx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),l=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+n+1),i.splice(l,0,1),ce({inputs:{x:s},backend:o,attrs:{shape:i}})}var iO={kernelName:ps,backendName:"webgl",kernelFunc:Jx};var aO="return exp(x) - 1.0;",Q7=ve({opSnippet:aO,packedOpSnippet:aO,cpuKernelImpl:rR}),lO={kernelName:ai,backendName:"webgl",kernelFunc:Q7};var Qx=class{constructor(e,t,o){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.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 = ${s}; float unaryOpComplex(float real, float expR, float imag, float expI) { ${i} } float mulMatDFT(int batch, int index) { float indexRatio = float(index) / float(${n}); float exponentMultiplierTimesIndexRatio = exponentMultiplier * indexRatio; float result = 0.0; for (int i = 0; i < ${n}; 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) / ${a}; } return result; } void main() { ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } `}};function ey(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=ce({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),l=i.shape,u=new Qx("real",l,e),c=new Qx("imag",l,e),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:l},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:l}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=co({inputs:{real:m,imag:f},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f);let h=ce({inputs:{x:d},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(d),h}function eZ(r){let{inputs:e,backend:t}=r,{input:o}=e;return ey(o,!1,t)}var uO={kernelName:tu,backendName:"webgl",kernelFunc:eZ};var bC=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,o)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(o,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function dh(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new bC(o,n),i=a.getCustomSetupFunc(n);return e.runWebGLProgram(a,[],s,i)}}var cO={kernelName:ua,backendName:"webgl",kernelFunc:dh};var _C=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 < ${t}) { outputValue = getImage(coords[0], coords[1], coordX, coords[3]); } else { outputValue = getImage(coords[0], coords[1], coords[2], coords[3]); } setOutput(outputValue); } `}};var pO={kernelName:li,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new _C(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var mO="return floor(x);",tZ=ve({opSnippet:mO,packedOpSnippet:mO,cpuKernelImpl:oR}),fO={kernelName:sn,backendName:"webgl",kernelFunc:tZ};var rZ=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); if (ib != 0) { // Windows (D3D) wants guaranteed non-zero int division at compile-time. return float(idiv(ia, ib, s)); } else { return NAN; } `,oZ=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); ivec4 result = ivec4(0); vec4 s = sign(a) * sign(b); // Windows (D3D) wants guaranteed non-zero int division at compile-time. if (cond[0]) { result[0] = idiv(ia[0], ib[0], s[0]); } if (cond[1]) { result[1] = idiv(ia[1], ib[1], s[1]); } if (cond[2]) { result[2] = idiv(ia[2], ib[2], s[2]); } if (cond[3]) { result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); `,nZ=it({opSnippet:rZ,packedOpSnippet:oZ,dtype:"int32"}),dO={kernelName:an,backendName:"webgl",kernelFunc:nZ};var wC=class{constructor(e){this.variableNames=["A"];let t=Mt(),[o,n]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0); vec4 values = ${t.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } setOutput(floor(value * 255.0 + 0.5)); } `}};var kC=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Mt(),[o,n]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec4 result = vec4(0.); for(int row=0; row<=1; row++) { for(int col=0; col<=1; col++) { texC = coords[1] + row; depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.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); } } ${t.output} = result; } `}};var hO={kernelName:Oc,backendName:"webgl",kernelFunc:sZ},Xp;function sZ(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!="undefined"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&n instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&n instanceof ImageBitmap,[u,c]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],p=[c,u],m=[c,u,s];(i||a||l)&&(Xp==null&&(Xp=document.createElement("canvas").getContext("2d")),Xp.canvas.width=u,Xp.canvas.height=c,Xp.drawImage(n,0,0,u,c),n=Xp.canvas);let f=t.makeTensorInfo(p,"int32");t.texData.get(f.dataId).usage=Er.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(f.dataId),n);let d=U().getBool("WEBGL_PACK")?new kC(m):new wC(m),h=t.runWebGLProgram(d,[f],"int32");return t.disposeData(f.dataId),h}function iZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=o,h=N.convertConv2DDataFormat(c),g=N.computeConv2DInfo(n.shape,s.shape,l,p,u,m,!1,h),x,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))x=Xx({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else if(U().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)x=Yx({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else{let w=a!=null,v=i!=null,$=f==="leakyrelu",A=f?El(f,!1):null,R=new ph(g,w,A,v,$),M=[n,s];if(a&&M.push(a),i&&M.push(i),$){let V=t.makeTensorInfo([],"float32",y.createScalarValue(d,"float32"));M.push(V),b.push(V)}x=t.runWebGLProgram(R,M,"float32")}let _=ce({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(w=>t.disposeIntermediateTensorInfo(w)),_}var gO={kernelName:vs,backendName:"webgl",kernelFunc:iZ};function aZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=o,d=[],h=c;h==null&&(h=[1,1]),y.assert(N.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let g=N.computeConv2DInfo(n.shape,s.shape,l,h,u,p,!0),x=U().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=m?El(m,x):null,_=[n,s],w=a!=null,v=i!=null,$=m==="leakyrelu";if(w&&_.push(a),v&&_.push(i),$){let M=t.makeTensorInfo([],"float32",y.createScalarValue(f,"float32"));_.push(M),d.push(M)}let A;x?A=new fh(g,w,b,v,$):A=new mh(g,w,b,v,$);let R=t.runWebGLProgram(A,_,"float32");return d.forEach(M=>t.disposeIntermediateTensorInfo(M)),R}var xO={kernelName:Cs,backendName:"webgl",kernelFunc:aZ};var vC=class{constructor(e,t,o){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=o;let n=Le(t.length),s=Le(o.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=` ${n} strides = ${n}(${this.strides}); void main() { ${s} coords = getOutputCoords(); int flattenIndex = 0; for (int j = 0; j < ${this.sliceDim}; j++) { int index = round(getIndices(coords[0], j)); flattenIndex += index * ${a}; } setOutput(getX(flattenIndex, coords[1])); } `}};function lZ(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],[i,l,u,c]=N.prepareAndValidate(o,n),p=ce({inputs:{x:n},backend:t,attrs:{shape:[l,a]}}),m=ce({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/u,u]}}),f=new vC(a,c,[l,u]),d=t.runWebGLProgram(f,[m,p],m.dtype),h=ce({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),h}var yO={kernelName:ui,backendName:"webgl",kernelFunc:lZ};var CC=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let o=Le(this.rank),n=uZ(e,2);this.userCode=` void main() { ${o} resRC = getOutputCoords(); setOutput(getA(${n})); } `}};function uZ(r,e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let n=0;nt.disposeIntermediateTensorInfo(v)),t.makeTensorInfo(u.outputShape,w.dtype,w.values)}let h=new CC(m.shape,d),g=t.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=ce({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return p.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var bO={kernelName:ms,backendName:"webgl",kernelFunc:cZ};var pZ="return float(a > b);",mZ=` return vec4(greaterThan(a, b)); `,fZ=it({opSnippet:pZ,packedOpSnippet:mZ,cpuKernelImpl:sR,dtype:"bool"}),_O={kernelName:ci,backendName:"webgl",kernelFunc:fZ};var dZ="return float(a >= b);",hZ=` return vec4(greaterThanEqual(a, b)); `,gZ=it({opSnippet:dZ,packedOpSnippet:hZ,dtype:"bool"}),wO={kernelName:un,backendName:"webgl",kernelFunc:gZ};function xZ(r){let{inputs:e,backend:t}=r,{input:o}=e;return ey(o,!0,t)}var kO={kernelName:ru,backendName:"webgl",kernelFunc:xZ};var yZ="return float(!isnan(x) && !isinf(x));",bZ=ve({opSnippet:yZ,dtype:"bool"}),vO={kernelName:pi,backendName:"webgl",kernelFunc:bZ};var _Z="return float(isinf(x));",wZ=ve({opSnippet:_Z,dtype:"bool"}),CO={kernelName:mi,backendName:"webgl",kernelFunc:wZ};var kZ="return float(isnan(x));",vZ=ve({opSnippet:kZ,dtype:"bool"}),IO={kernelName:fi,backendName:"webgl",kernelFunc:vZ};var CZ="return float(a < b);",IZ=` return vec4(lessThan(a, b)); `,NZ=it({opSnippet:CZ,packedOpSnippet:IZ,cpuKernelImpl:iR,dtype:"bool"}),NO={kernelName:di,backendName:"webgl",kernelFunc:NZ};var SZ="return float(a <= b);",TZ=` return vec4(lessThanEqual(a, b)); `,EZ=it({opSnippet:SZ,packedOpSnippet:TZ,dtype:"bool"}),SO={kernelName:hi,backendName:"webgl",kernelFunc:EZ};function AZ(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=aR(o,n,s);return e.makeTensorInfo([a.length],"float32",a)}var TO={kernelName:nu,backendName:"webgl",kernelFunc:AZ};var DZ=`if (x < 0.0) return NAN; return log(x);`,$Z=` vec4 result = log(x); vec4 isNaN = vec4(lessThan(x, vec4(0.0))); result.r = isNaN.r == 1.0 ? NAN : result.r; result.g = isNaN.g == 1.0 ? NAN : result.g; result.b = isNaN.b == 1.0 ? NAN : result.b; result.a = isNaN.a == 1.0 ? NAN : result.a; return result; `,RZ=ve({opSnippet:DZ,packedOpSnippet:$Z,cpuKernelImpl:lR}),EO={kernelName:pn,backendName:"webgl",kernelFunc:RZ};var FZ="return log(1.0 + x);",OZ=ve({opSnippet:FZ}),AO={kernelName:gi,backendName:"webgl",kernelFunc:OZ};var PZ="return float(a >= 1.0 && b >= 1.0);",MZ=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); `,LZ=it({opSnippet:PZ,packedOpSnippet:MZ,dtype:"bool"}),DO={kernelName:xi,backendName:"webgl",kernelFunc:LZ};var zZ="return float(!(x >= 1.0));",BZ=ve({opSnippet:zZ}),$O={kernelName:Za,backendName:"webgl",kernelFunc:BZ};var VZ="return float(a >= 1.0 || b >= 1.0);",GZ=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); `,WZ=it({opSnippet:VZ,packedOpSnippet:GZ,dtype:"bool"}),RO={kernelName:Ja,backendName:"webgl",kernelFunc:WZ};var IC=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${o}) + float(${n}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int r = coords[1]; int c = coords[2]; int d = coords[3]; float x = getX(b, r, c, d); float sum = 0.0; for (int j = -${a}; j <= ${a}; j++) { int idx = d + j; if (idx >= 0 && idx <= ${i}) { float z = getX(b, r, c, idx); sum += z * z; } } float val = x * ${l}; setOutput(val); } `}};var NC=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${o}) + float(${n}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,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 - ${a}; 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 = - ${a}; j <= ${a}; j++) { ivec2 idx = depth + j; bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0)); bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${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 * ${l}; setOutput(result); } `}};var UZ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:l}=o,u=U().getBool("WEBGL_PACK_NORMALIZATION")?new NC(n.shape,s,a,i,l):new IC(n.shape,s,a,i,l);return t.runWebGLProgram(u,[n],n.dtype)},FO={kernelName:ca,backendName:"webgl",kernelFunc:UZ};var SC=class{constructor(e,t,o,n,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,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 = ${this.depth}; float norm = 0.0; for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) { if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd) { norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k); } else { break; } } norm = float(${n}) * norm + float(${o}); for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){ if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd){ float dyi = -2.0 * float(${n}) * float(${s}) * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { dyi += pow(norm, -1.0 * ${s}); } if (k == coords[3]) { dyi *= getDy(b, r, c, d); result += dyi; } } else { break; } } } setOutput(result); } `}};var jZ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:l,alpha:u,beta:c}=o,p=new SC(n.shape,i,l,u,c);return t.runWebGLProgram(p,[n,s,a],n.dtype)},OO={kernelName:su,backendName:"webgl",kernelFunc:jZ};function PO(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=ce({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),l=Io(i,r.dtype,"max",o),u=ce({inputs:{x:l},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(l),u}function TC(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=c!=null,m=t.shouldExecuteOnCPU([n]),f=n;if(p){if(m){let _=t.texData.get(f.dataId).values,w=new Array(i);for(let A=0;A`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(n.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return jt({inputs:{x:n},backend:t});let p=new Zi(c,"max",!1);return t.runWebGLProgram(p,[n],n.dtype)}var zO={kernelName:dn,backendName:"webgl",kernelFunc:XZ};function YZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:l,dimRoundingMode:u}=o,c=[1,1,1],p=N.computePool3DInfo(n.shape,s,a,c,i,u,l),m=new pc(p,"max",!1);return t.runWebGLProgram(m,[n],n.dtype)}var BO={kernelName:pa,backendName:"webgl",kernelFunc:YZ};var EC=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,l=a-1-e.padInfo.left,u=s*a-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${l}); 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 < ${s}; wR += ${n}) { 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 < ${a}; wC++) { float dyC = float(dyCCorner + wC) / ${o}.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 = ${u} - int(getMaxPos(b, idyR, idyC, d)); // Get the current value, check it against the value from the // position matrix. int curPosValue = wR * ${a} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } setOutput(dotProd); } `}},AC=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,p=l-1-e.padInfo.front,m=u-1-e.padInfo.top,f=c-1-e.padInfo.left,d=l*u*c-1;this.userCode=` const ivec3 pads = ivec3(${p}, ${m}, ${f}); 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 < ${l}; wD += ${s}) { 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 < ${u}; wR += ${a}) { float dyR = float(dyRCorner + wR) / ${o}.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) / ${n}.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 * ${u} * ${c} + wR * ${c} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } } setOutput(dotProd); } `}};function ZZ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=o,p=[1,1,1],m=N.computePool3DInfo(a.shape,i,l,p,u,c),f=new pc(m,"max",!0),d=t.runWebGLProgram(f,[a],a.dtype),h=new AC(m),g=t.runWebGLProgram(h,[n,d],a.dtype);return t.disposeIntermediateTensorInfo(d),g}var VO={kernelName:au,backendName:"webgl",kernelFunc:ZZ};function JZ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;Yi([s,a],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=o,m=N.computePool2DInfo(i.shape,l,u,1,c,p),f=!0,d=new Zi(m,"max",f),h=t.runWebGLProgram(d,[i],i.dtype),g=new EC(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var GO={kernelName:iu,backendName:"webgl",kernelFunc:JZ};function WO(r,e,t,o){let n=new Zi(t,"max",!1),s=o.runWebGLProgram(n,[r],"float32");n=new Zi(t,"max",!0,!0,e);let a=o.runWebGLProgram(n,[r],"float32");return[s,a]}var UO={kernelName:lu,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,l=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=N.computePool2DInfo(o.shape,n,s,u,a),[p,m]=WO(o,i,c,l);return[p,m]}};function jO(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=ce({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),l=Io(i,"float32","mean",o),u=ce({inputs:{x:l},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(l),u}var HO={kernelName:hn,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{keepDims:n,axis:s}=e,a=t,i=o.shape.length,l=y.parseAxisParam(s,o.shape),u=l,c=N.getAxesPermutation(u,i),p=c!=null,m=a.shouldExecuteOnCPU([o]),f=[],d=o;if(p){if(m){let w=a.texData.get(d.dataId).values,v=new Array(i);for(let R=0;Rc[0]+e[p]+c[1]);let n=e.length,s=Le(n),a=t.map(c=>c[0]).join(","),i=t.map((c,p)=>c[0]+e[p]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),u=o==="reflect"?0:1;if(n===1){this.userCode=` int start = ${a}; int end = ${i}; void main() { int outC = getOutputCoords(); if (outC < start) { outC = start * 2 - outC - ${u}; } else if(outC >= end) { outC = (end - 1) * 2 - outC + ${u}; } setOutput(getX(outC - start)); } `;return}this.userCode=` ${s} start = ${s}(${a}); ${s} end = ${s}(${i}); void main() { ${s} outC = getOutputCoords(); for (int i = 0; i < ${n}; i++) { if (outC[i] < start[i]) { outC[i] = start[i] * 2 - outC[i] - ${u}; } else if(outC[i] >= end[i]) { outC[i] = (end[i] - 1) * 2 - outC[i] + ${u}; } } ${s} coords = outC - start; setOutput(getX(${l})); } `}};var $C=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((d,h)=>d[0]+e[h]+d[1]);let n=e.length,s=Le(n),a=t.map(d=>d[0]).join(","),i=t.map((d,h)=>d[0]+e[h]).join(","),l=Ut("rc",n),u=Ut("source",n),c=`${l[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${u.slice(-2).join()})`,m=o==="reflect"?0:1,f="";if(n===1){let d=` ${s} source = rc; if (source < start) { source = start * 2 - source - ${m}; } else if (source >= end) { source = (end - 1) * 2 - source + ${m}; } source -= start; `;f=` ${s} rc = outputLoc; ${d} result[0] = getChannel(getX(${u.join()}), ${p}); ${l[n-1]} += 1; if(${c}) { ${d} result[1] = getChannel(getX(${u.join()}), ${p}); } `}else{let d=` ${s} source = rc; ${s} lt = ${s}(lessThan(source, start)); ${s} gte = ${s}(greaterThanEqual(source, end)); ${s} orig = 1 - (lt + gte); source = orig * source + lt * (start * 2 - source - ${m}) + gte * ((end - 1) * 2 - source + ${m}); source -= start; `;f=` ${s} rc = outputLoc; ${d} result[0] = getChannel(getX(${u.join()}), ${p}); ${l[n-1]} += 1; if(${c}) { ${d} result[1] = getChannel(getX(${u.join()}), ${p}); } rc = outputLoc; ${l[n-2]} += 1; if(${l[n-2]} < ${this.outputShape[n-2]}) { ${d} result[2] = getChannel(getX(${u.join()}), ${p}); ${l[n-1]} += 1; if(${c}) { ${d} result[3] = getChannel(getX(${u.join()}), ${p}); } } `}this.userCode=` const ${s} start = ${s}(${a}); const ${s} end = ${s}(${i}); void main() { ${s} outputLoc = getOutputCoords(); vec4 result = vec4(0.); ${f} setOutput(result); } `}};var oJ=({inputs:r,backend:e,attrs:t})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $C(o.shape,n,s):new DC(o.shape,n,s);return e.runWebGLProgram(a,[o],o.dtype)},XO={kernelName:ma,backendName:"webgl",kernelFunc:oJ};var nJ=`if (b == 0.0) return NAN; return mod(a, b);`,sJ=` vec4 result = mod(a, b); vec4 isNaN = vec4(equal(b, vec4(0.0))); `+Tl+` return result; `,iJ=it({opSnippet:nJ,packedOpSnippet:sJ}),YO={kernelName:yi,backendName:"webgl",kernelFunc:iJ};var RC=class{constructor(e,t,o){this.variableNames=["probs"],this.outputShape=[e,o],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,o)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(o,"seed")),t.gl.uniform1f(this.seedLoc,e)}}};var aJ=` if (a == b) { return 1.0; }; return a / b;`,lJ=` // 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; `,FC=it({opSnippet:aJ,packedOpSnippet:lJ,checkOutOfBounds:!0}),ZO={kernelName:on,backendName:"webgl",kernelFunc:FC};var JO="return a - b;",OC=it({opSnippet:JO,packedOpSnippet:JO,supportsComplex:!0,cpuKernelImpl:bR}),QO={kernelName:On,backendName:"webgl",kernelFunc:OC};function PC(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=TC({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),l=N.expandShapeToKeepDim(i.shape,a),u=ce({inputs:{x:i},backend:t,attrs:{shape:l}}),c=OC({inputs:{a:n,b:u},backend:t}),p=yC({inputs:{x:c},backend:t}),m=ch({inputs:{x:p},backend:t,attrs:{axis:a,keepDims:!1}}),f=ce({inputs:{x:m},backend:t,attrs:{shape:l}}),d=FC({inputs:{a:p,b:f},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}var eP={kernelName:Rn,backendName:"webgl",kernelFunc:PC};function uJ(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,l=i?n:PC({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new RC(u,c,s),m=p.getCustomSetupFunc(a),f=t.runWebGLProgram(p,[l],"int32",m);return i||t.disposeIntermediateTensorInfo(l),f}var tP={kernelName:uu,backendName:"webgl",kernelFunc:uJ};var rP="return -x;";function cJ(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.texData.get(o.dataId),[a,i]=fR(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n;return U().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Gs(o.shape,rP):n=new uo(o.shape,rP),t.runWebGLProgram(n,[o],o.dtype)}var oP={kernelName:fs,backendName:"webgl",kernelFunc:cJ};var pJ=Sr.nonMaxSuppressionV3Impl;function mJ(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:p}=pJ(u,c,a,i,l);return t.makeTensorInfo([p.length],"int32",new Int32Array(p))}var nP={kernelName:_i,backendName:"webgl",kernelFunc:mJ};var fJ=Sr.nonMaxSuppressionV4Impl;function dJ(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=o,c=t.readSync(n.dataId),p=t.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=fJ(c,p,a,i,l,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([f]))]}var sP={kernelName:wi,backendName:"webgl",kernelFunc:dJ};var hJ=Sr.nonMaxSuppressionV5Impl;function gJ(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=o,c=t.readSync(n.dataId),p=t.readSync(s.dataId),m=a,f=i,d=l,h=u,{selectedIndices:g,selectedScores:x}=hJ(c,p,m,f,d,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var iP={kernelName:ki,backendName:"webgl",kernelFunc:gJ};var MC=class{constructor(e,t,o,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${n}), float(${o}), float(index == coords.y))); } `}};var xJ=r=>{let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{depth:s,onValue:a,offValue:i}=o,l=y.sizeFromShape(n.shape),u=new MC(l,s,a,i),c=ce({inputs:{x:n},backend:t,attrs:{shape:[l]}}),p=t.runWebGLProgram(u,[c],n.dtype);t.disposeIntermediateTensorInfo(c);let m=[...n.shape,s],f=ce({inputs:{x:p},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(p),f},aP={kernelName:bn,backendName:"webgl",kernelFunc:xJ};function hh(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=ja({inputs:{input:o},backend:t}),s=hh({inputs:{x:n},backend:t}),a=mc({inputs:{input:o},backend:t}),i=hh({inputs:{x:a},backend:t}),l=co({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return dh({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var lP={kernelName:ws,backendName:"webgl",kernelFunc:hh};function uP(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=ja({inputs:{input:o},backend:t}),s=uP({inputs:{x:n},backend:t}),a=mc({inputs:{input:o},backend:t}),i=hh({inputs:{x:a},backend:t}),l=co({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return dh({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var cP={kernelName:ds,backendName:"webgl",kernelFunc:uP};function yJ(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Jx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=e.map(c=>{let p=Jx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(p),p}),u=sC({inputs:l,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var pP={kernelName:hs,backendName:"webgl",kernelFunc:yJ};var LC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=Le(n),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=` int start = ${a}; int end = ${i}; void main() { int outC = getOutputCoords(); if (outC < start || outC >= end) { setOutput(float(${o})); } else { setOutput(getX(outC - start)); } } `;return}this.userCode=` ${s} start = ${s}(${a}); ${s} end = ${s}(${i}); void main() { ${s} outC = getOutputCoords(); if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) { setOutput(float(${o})); } else { ${s} coords = outC - start; setOutput(getX(${l})); } } `}};var zC=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=Le(n),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),l=Ut("rc",n),u=Ut("source",n),c=`${l[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${l[n-1]} += 1; if(${c}) { `,n===1?"":`} rc = outputLoc; ${l[n-2]} += 1; if(${l[n-2]} < ${this.outputShape[n-2]}) {`,n===1?"":` ${l[n-1]} += 1; if(${c}) {`],f=n===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let h=0,g=n===1?2:4;h{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o,i=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new zC(n.shape,s,a):new LC(n.shape,s,a);return t.runWebGLProgram(i,[n],n.dtype)},mP={kernelName:_n,backendName:"webgl",kernelFunc:BC};var bJ=` 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); `,_J=` // 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)); `+Tl+` return result; `,wJ=it({opSnippet:bJ,packedOpSnippet:_J}),fP={kernelName:wn,backendName:"webgl",kernelFunc:wJ};function kJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=[],u=y.parseAxisParam(s,n.shape),c=u,p=N.getAxesPermutation(c,i),m=n;p!=null&&(m=Lt({inputs:{x:n},backend:t,attrs:{perm:p}}),c=N.getInnerMostAxes(c.length,i),l.push(m)),N.assertAxesAreInnerMostDims("prod",c,i);let f;if(t.shouldExecuteOnCPU([m])){let d=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=dR(m.shape,m.dtype,d,c);f=t.makeTensorInfo(g,x,h)}else{let[d,h]=N.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=ce({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=gu(n.dtype),_=Io(x,b,"prod",t);f=ce({inputs:{x:_},backend:t,attrs:{shape:d}}),l.push(x),l.push(_)}if(a){l.push(f);let d=N.expandShapeToKeepDim(f.shape,u);f=ce({inputs:{x:f},backend:t,attrs:{shape:d}})}return l.forEach(d=>t.disposeIntermediateTensorInfo(d)),f}var dP={kernelName:vi,backendName:"webgl",kernelFunc:kJ};var VC=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=hR(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},hP={kernelName:fa,backendName:"webgl",kernelFunc:VC};var vJ="return 1.0 / x;",CJ=ve({opSnippet:vJ}),gP={kernelName:Ci,backendName:"webgl",kernelFunc:CJ};var IJ=gr+` return (x < 0.0) ? 0.0 : x; `,NJ=` 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; `,SJ=ve({opSnippet:IJ,packedOpSnippet:NJ}),xP={kernelName:vn,backendName:"webgl",kernelFunc:SJ};var TJ=gr+` return (x < 0.0) ? 0.0 : min(6.0, x); `,EJ=` 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; `,AJ=ve({opSnippet:TJ,packedOpSnippet:EJ}),yP={kernelName:In,backendName:"webgl",kernelFunc:AJ};var GC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/p[0]}, ${c[1]/p[1]}); const vec2 inputShapeRC = vec2(${i}.0, ${l}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${m}; // 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); } `}};var WC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/p[0]}, ${c[1]/p[1]}, ${c[1]/p[1]}); const vec3 inputShapeRC = vec3(${i}.0, ${l}.0, ${l}.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 = ${m}; // 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 < ${u-1}; bool hasNextRow = coords.z < ${o-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 DJ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,c=U().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new WC(n.shape,l,u,s,a):new GC(n.shape,l,u,s,a);return t.runWebGLProgram(c,[n],"float32")}var bP={kernelName:Cn,backendName:"webgl",kernelFunc:DJ};var UC=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,l=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*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(${p}); const float invHeightScale = float(${m}); const float invWidthScale = float(${f}); const int winHeight = int(${d}); const int winWidth = int(${h}); // 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 >= ${a}) { 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), ${n-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), ${s-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 $J(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new UC(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var _P={kernelName:mu,backendName:"webgl",kernelFunc:$J};var jC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/p[0]}, ${c[1]/p[1]}); const vec2 inputShapeRC = vec2(${i}.0, ${l}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${f}; // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestRC = ivec2( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m}))); float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutput(newValue); } `}};function RJ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,c=new jC(n.shape,l,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var wP={kernelName:da,backendName:"webgl",kernelFunc:RJ};var HC=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,l=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${c}); const float widthScale = float(${p}); const float invHeightScale = float(${m}); const float invWidthScale = float(${f}); const int winHeight = int(${d}); const int winWidth = int(${h}); // 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 >= ${a}) { 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 >= ${i}) { continue; } float sourceFracRow = float(${l[0]}) * (float(dyR) / float(${u[0]})); float sourceFracCol = float(${l[1]}) * (float(dyC) / float(${u[1]})); int sourceNearestRow = int(min( float(int(${n}) - 1), ${o} ? float(round(sourceFracRow)) : float(floor(sourceFracRow)))); int sourceNearestCol = int(min( float(int(${s}) - 1), ${o} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { accumulator += getDy(b, dyR, dyC, d); } } } // End loop over dy setOutput(accumulator); } `}};function FJ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new HC(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var kP={kernelName:pu,backendName:"webgl",kernelFunc:FJ};var qC=class{constructor(e,t){this.variableNames=["x"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); } `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,l)=>n(l)).join(","),a=Le(o);this.userCode=` void main() { ${a} coords = getOutputCoords(); setOutput(getX(${s})); } `}};var KC=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Ut("rc",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Le(o);o===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); result.r = getChannel(getX(${e[0]} - rc - 1), ${e[0]} - rc - 1); if(${s}){ 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 = ${l(n.slice())}; if(${s}){ result.g = ${u(n.slice())}; } if(${a}) { result.b = ${c(n.slice())}; if(${s}) { result.a = ${p(n.slice())}; } } setOutput(result); } `;function l(d){return m(d)}function u(d){return d[o-1]="("+d[o-1]+" + 1)",m(d)}function c(d){return d[o-2]="("+d[o-2]+" + 1)",m(d)}function p(d){return d[o-1]="("+d[o-1]+" + 1)",d[o-2]="("+d[o-2]+" + 1)",m(d)}function m(d){let h=e.map((b,_)=>f(_,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${h[d]} - 1`:`${h[d]}`}}};function OJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return jt({inputs:{x:n},backend:t});let l=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new KC(n.shape,i):new qC(n.shape,i);return t.runWebGLProgram(l,[n],n.dtype)}var vP={kernelName:Nn,backendName:"webgl",kernelFunc:OJ};var XC=class{constructor(e,t,o,n){this.variableNames=["Image"],this.outputShape=[];let s=e[1],a=e[2],i=Math.sin(t).toFixed(3),l=Math.cos(t).toFixed(3);this.outputShape=e;let[u,c]=N.getImageCenter(n,s,a),p=u.toFixed(3),m=c.toFixed(3),f="";typeof o=="number"?f=`float outputValue = ${o.toFixed(2)};`:f=` vec3 fill = vec3(${o.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) - ${p}) * ${l} - (float(y) - ${m}) * ${i}; float coordYFloat = (float(x) - ${p}) * ${i} + (float(y) - ${m}) * ${l}; int coordX = int(round(coordXFloat + ${p})); int coordY = int(round(coordYFloat + ${m})); ${f} if(coordX >= 0 && coordX < ${a} && coordY >= 0 && coordY < ${s}) { outputValue = getImage(coords[0], coordY, coordX, coords[3]); } setOutput(outputValue); } `}};var CP={kernelName:Ri,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,l=new XC(o.shape,n,s,a);return i.runWebGLProgram(l,[o],o.dtype)}};var PJ=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); if ((x - base) < 0.5) { return floor(x); } else if ((x - base) > 0.5) { return ceil(x); } else { if (mod(base, 2.0) == 0.0) { return base; } else { return base + 1.0; } } `,MJ=ve({opSnippet:PJ}),IP={kernelName:Sn,backendName:"webgl",kernelFunc:MJ};var LJ="return inversesqrt(x);",zJ=ve({opSnippet:LJ,cpuKernelImpl:gR}),NP={kernelName:Tn,backendName:"webgl",kernelFunc:zJ};var gh=class{constructor(e,t,o,n,s,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let l=Le(s.length),u=Le(a.length),c="";o===1?c="i":o===2&&(c="i, j");let p=`getIndices(${c})`,m="";n===1?m="i":n===2&&(m="i, coords[1]");let f=`getUpdates(${m})`,d=t>1?"strides[j]":"strides";this.userCode=` ${l} strides = ${l}(${s}); void main() { ${u} coords = getOutputCoords(); float sum = 0.0; bool found = false; for (int i = 0; i < ${e}; i++) { int flattenedIndex = 0; for (int j = 0; j < ${t}; j++) { int index = round(${p}); flattenedIndex += index * ${d}; } if (flattenedIndex == coords[0]) { sum += ${f}; found = true; } } setOutput(mix(getDefaultValue(), sum, float(found))); } `}};function BJ(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=N.calculateShapes(s,n,a),m=[p/u,u];if(p===0)return t.makeTensorInfo(a,n.dtype);let f=ce({inputs:{x:n},backend:t,attrs:{shape:[l,i]}}),d=ce({inputs:{x:s},backend:t,attrs:{shape:[l,u]}}),h=t.makeTensorInfo([],"float32",new Float32Array([0])),g=new gh(l,i,f.shape.length,d.shape.length,c,m),x=t.runWebGLProgram(g,[d,f,h],d.dtype),b=ce({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var SP={kernelName:Ii,backendName:"webgl",kernelFunc:BJ};var YC=class{constructor(e,t,o){this.variableNames=["c","a","b"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],l=[],u=[];for(let c=0;c= 1.0) { setOutput(getA(${s})); } else { setOutput(getB(${s})); } } `}};function VJ(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new YC(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],mr(n.dtype,s.dtype))}var TP={kernelName:xs,backendName:"webgl",kernelFunc:VJ};var GJ=` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 float scaleAlpha = ${N.SELU_SCALEALPHA}; float scale = ${N.SELU_SCALE}; return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); `,WJ=ve({opSnippet:GJ}),EP={kernelName:Ni,backendName:"webgl",kernelFunc:WJ};var UJ="return 1.0 / (1.0 + exp(-1.0 * x));",jJ=ve({opSnippet:UJ}),AP={kernelName:An,backendName:"webgl",kernelFunc:jJ};var HJ=` if (isnan(x)) { return 0.0; } return sign(x); `,qJ=ve({opSnippet:HJ}),DP={kernelName:Ti,backendName:"webgl",kernelFunc:qJ};var KJ=Wx+` return sin(x); `,XJ=ve({opSnippet:KJ}),$P={kernelName:En,backendName:"webgl",kernelFunc:XJ};var YJ=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; `,ZJ=ve({opSnippet:YJ}),RP={kernelName:Si,backendName:"webgl",kernelFunc:ZJ};var JJ=` 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; `,QJ=ve({opSnippet:JJ}),FP={kernelName:Ei,backendName:"webgl",kernelFunc:QJ};var e9=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((x,b)=>x*b),l=[[0,0]];l.push(...a);for(let x=1+s.length;xt.disposeIntermediateTensorInfo(x)),g},OP={kernelName:ha,backendName:"webgl",kernelFunc:e9};function t9(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:l,numUpdates:u,strides:c,outputSize:p}=N.calculateShapes(s,n,i),m=!1,f=new gh(u,l,n.shape.length,s.shape.length,c,[p,1],m),d=t.runWebGLProgram(f,[s,n,a],s.dtype),h=ce({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(d),h}var PP={kernelName:fu,backendName:"webgl",kernelFunc:t9};function r9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],l=N.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),p=n.shape.slice();return l.map(m=>{let f=[...p];f[i]=m;let d=Ua({inputs:{x:n},backend:t,attrs:{begin:c,size:f}});return c[i]+=m,d})}var MP={kernelName:bs,backendName:"webgl",kernelFunc:r9};var o9="return sqrt(x);",n9=ve({opSnippet:o9}),LP={kernelName:Dn,backendName:"webgl",kernelFunc:n9};var s9="return x * x;",i9=ve({opSnippet:s9}),zP={kernelName:ga,backendName:"webgl",kernelFunc:i9};var BP="return (a - b) * (a - b);",a9=it({opSnippet:BP,packedOpSnippet:BP}),VP={kernelName:Fn,backendName:"webgl",kernelFunc:a9};function l9({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=gr+` return x > 0.0 ? 1.0 : float(${e.alpha}); `,s=new uo(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var GP={kernelName:$o,backendName:"webgl",kernelFunc:l9};var ZC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=o;let n=o.length,s=Le(o.length),a=Le(o.length),i="";if(n===1)i="coords * strides + begin";else{let l=0;i=o.map((u,c)=>(l++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${l-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=` ${s} begin = ${s}(${e}); ${s} strides = ${s}(${t}); void main() { ${a} coords = getOutputCoords(); setOutput(getX(${i})); } `}};function u9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=o,{nonStrided:f,$begin:d,$strides:h,size:g,newShape:x,outShape:b}=or.sliceInfo(n.shape,s,a,i,l,u,c,p,m),_=ce({inputs:{x:n},backend:t,attrs:{shape:x}}),w;if(f){let $=Ua({inputs:{x:_},backend:t,attrs:{begin:d,size:g}});w=ce({inputs:{x:$},backend:t,attrs:{shape:b}}),t.disposeIntermediateTensorInfo($)}else if(b.some($=>$===0))w=t.makeTensorInfo(b,n.dtype,[]);else if(t.shouldExecuteOnCPU([_])){let R=t.texData.get(_.dataId).values,M=Ce(_.shape,_.dtype,R),V=yR(b,M,h,d);w=t.makeTensorInfo(b,_.dtype,V.values)}else{let A=new ZC(d,h,b);w=t.runWebGLProgram(A,[_],_.dtype)}let v=ce({inputs:{x:w},backend:t,attrs:{shape:b}});return t.disposeIntermediateTensorInfo(_),t.disposeIntermediateTensorInfo(w),v}var WP={kernelName:Ai,backendName:"webgl",kernelFunc:u9};var c9="return tan(x);",p9=ve({opSnippet:c9}),UP={kernelName:Di,backendName:"webgl",kernelFunc:p9};var m9=` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); `,f9=ve({opSnippet:m9}),jP={kernelName:Pn,backendName:"webgl",kernelFunc:f9};var JC=class{constructor(e,t){this.variableNames=["A"];let o=new Array(e.length);for(let a=0;a5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],o=[];for(let n=0;ny.decodeString(m)),c=Ce(n.shape,n.dtype,u),p=_R(c,s);return t.makeTensorInfo(p.shape,p.dtype,p.values)}let a=new JC(n.shape,s);return t.runWebGLProgram(a,[n],n.dtype)}var HP={kernelName:yo,backendName:"webgl",kernelFunc:QC};function h9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{k:s,sorted:a}=o,i=t.readSync(n.dataId),[l,u]=wR(i,n.shape,n.dtype,s,a);return[t.makeTensorInfo(l.shape,l.dtype,l.values),t.makeTensorInfo(u.shape,u.dtype,u.values)]}var qP={kernelName:$i,backendName:"webgl",kernelFunc:h9};function g9(r){let{inputs:e,attrs:t,backend:o}=r,{axis:n}=t,{x:s}=e;Yi(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let a=o.readSync(s.dataId),{outputValues:i,outputShape:l,indices:u}=kR(a,n,s.shape,s.dtype);return[o.makeTensorInfo(l,s.dtype,i),o.makeTensorInfo([u.length],"int32",u)]}var KP={kernelName:du,backendName:"webgl",kernelFunc:g9};function x9(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,l=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeIntermediateTensorInfo(h)),d}var XP={kernelName:_s,backendName:"webgl",kernelFunc:x9};var eI=class{constructor(e,t){this.variableNames=["x","segmentIds"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let l="0.0",u="sumValue",c=Math.floor(o/4)*4,p=o%4,m=` sumValue += dot(values, segFilter); `,f="";s%o>0&&(f=` if (inIdx < 0 || inIdx >= ${s}) { return initializationValue; } `);let d="";s%o>0&&(d=` if (inIdx < 0 || inIdx >= ${s}) { return -1.0; } `),this.userCode=` const float initializationValue = ${l}; float getValue(int batch, int inIdx) { ${f} 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( ${a})) * float(${o})); int currentSeg = int(mod(float(outIdx), float(${a}))); 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 ); ${m} } int inIdx = inOffset + ${c}; if (${p===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 ); ${m} } else if (${p===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 ); ${m} } else if (${p===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 ); ${m} } setOutput(${u}); } `}};function y9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,l=[],u=0,c=N.getAxesPermutation([u],i),p=n;c!=null&&(p=Lt({inputs:{x:n},backend:t,attrs:{perm:c}}),l.push(p),u=N.getInnerMostAxes(1,i)[0]);let m=N.segment_util.computeOutShape(p.shape,u,a),f=y.sizeFromShape([p.shape[u]]),d=ce({inputs:{x:p},backend:t,attrs:{shape:[-1,f]}});l.push(d);let h=gu(n.dtype),g=(w,v,$,A,R)=>{let M=w.shape[0],V=w.shape[1],G=N.segment_util.segOpComputeOptimalWindowSize(V,R),W={windowSize:G,inSize:V,batchSize:M,numSegments:R},q=new eI(W,v),Z=t.compileAndRun(q,[w,$],A);if(l.push(Z),Z.shape[1]===R)return Z;let X=VC({backend:t,attrs:{start:0,stop:R,step:1,dtype:"float32"}}),Y=QC({inputs:{x:X},backend:t,attrs:{reps:[V/G]}});return l.push(X),l.push(Y),g(Z,v,Y,A,R)},x=g(d,"unsortedSegmentSum",s,h,a),b=ce({inputs:{x},backend:t,attrs:{shape:m}}),_=b;if(c!=null){l.push(b);let w=N.getUndoAxesPermutation(c);_=Lt({inputs:{x:_},backend:t,attrs:{perm:w}})}return l.forEach(w=>t.disposeIntermediateTensorInfo(w)),_}var YP={kernelName:xa,backendName:"webgl",kernelFunc:y9};var b9=[FO,OO,XR,ZR,JR,QR,tF,rF,oF,nF,aF,lF,uF,cF,mF,pF,fF,hF,dF,gF,xF,yF,bF,wF,kF,NF,TF,EF,DF,MR,FF,PF,MF,OF,zF,BF,LF,VF,GF,WF,HF,qF,KF,YF,ZF,XF,JF,QF,eO,tO,rO,oO,sO,iO,lO,uO,cO,pO,fO,dO,hO,gO,xO,yO,bO,_O,wO,PR,kO,$F,vO,CO,IO,LR,NO,SO,TO,AO,EO,DO,$O,RO,MO,BO,zO,VO,GO,UO,LO,HO,qO,KO,XO,YO,tP,WR,oP,nP,sP,iP,vF,aP,cP,pP,mP,fP,zR,dP,hP,CF,ZO,gP,yP,xP,jR,bP,_P,wP,kP,vP,CP,IP,NP,SP,TP,EP,AP,DP,$P,RP,_F,eP,FP,OP,PP,MP,LP,zP,VP,GP,WP,QO,qR,UP,jP,HP,qP,KR,KP,XP,YP,lP];for(let r of b9)Qa(r);var ZP="3.1.0";var _9={"tfjs-core":n_,"tfjs-backend-cpu":RA,"tfjs-backend-webgl":OR,"tfjs-data":Sx,"tfjs-layers":hl,"tfjs-converter":_x,tfjs:ZP};var zt;(function(r){r[r.float32=0]="float32",r[r.int32=1]="int32",r[r.bool=2]="bool",r[r.string=3]="string",r[r.complex64=4]="complex64"})(zt||(zt={}));var Dl;(function(r){r[r.linear=0]="linear",r[r.relu=1]="relu",r[r.relu6=2]="relu6",r[r.prelu=3]="prelu",r[r.leakyrelu=4]="leakyrelu"})(Dl||(Dl={}));var JP;function w9(r){JP=r.wasm.cwrap(ks,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function k9(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e;if(n.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=o,m=t.dataIdMap.get(n.dataId).id,f=t.dataIdMap.get(s.dataId).id,d=0;if(a!=null){let R=t.dataIdMap.get(a.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);d=R.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=Dl[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=l?n.shape[2]:n.shape[1],b=u?s.shape[1]:s.shape[2],_=n.shape[0],w=t.makeOutput([_,x,b],n.dtype),v=t.dataIdMap.get(w.dataId).id,$=new Uint8Array(new Int32Array(n.shape).buffer),A=new Uint8Array(new Int32Array(s.shape).buffer);return JP(m,$,n.shape.length,f,A,s.shape.length,l,u,g,d,h,p||0,v),w}var QP={kernelName:ks,backendName:"wasm",setupFunc:w9,kernelFunc:k9};function At(r){let e;function t(n){e=n.wasm.cwrap(r,null,["number","number"])}function o(n){let{backend:s,inputs:{x:a}}=n,i=s.dataIdMap.get(a.dataId).id,l=s.makeOutput(a.shape,a.dtype),u=s.dataIdMap.get(l.dataId).id;return y.sizeFromShape(l.shape)===0||e(i,u),l}return{kernelName:r,backendName:"wasm",setupFunc:t,kernelFunc:o}}var eM=At(us);function _t(r,e,t){let o;function n(a){o=a.wasm.cwrap(r,null,["number","array","number","number","array","number","number","number"])}function s(a){let{backend:i,inputs:l}=a,{a:u,b:c}=l,p=i.dataIdMap.get(u.dataId).id,m=i.dataIdMap.get(c.dataId).id,f=t!=null?t:u.dtype,d=N.assertAndGetBroadcastShape(u.shape,c.shape),h=i.makeOutput(d,f);if(y.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(u.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=i.dataIdMap.get(h.dataId).id,_=()=>o(p,g,u.shape.length,m,x,c.shape.length,zt[u.dtype],b);if(e&&u.dtype==="float32")return _(),h;let w=N.getBroadcastDims(u.shape,d),v=N.getBroadcastDims(c.shape,d),$=w.every((R,M)=>R===M),A=v.every((R,M)=>R===M);if($&&A)return _(),h;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${r}.`)}return{kernelName:r,backendName:"wasm",setupFunc:n,kernelFunc:s}}var v9=!0,tM=_t(xo,v9);var rM;function C9(r){rM=r.wasm.cwrap(qo,null,["array","number","number","number"])}function I9(r){let{inputs:e,backend:t}=r,o=t.makeOutput(e[0].shape,e[0].dtype);if(y.sizeFromShape(o.shape)===0)return o;let n=e.map(i=>t.dataIdMap.get(i.dataId).id),s=new Uint8Array(new Int32Array(n).buffer),a=t.dataIdMap.get(o.dataId).id;return rM(s,n.length,zt[o.dtype],a),o}var oM={kernelName:qo,backendName:"wasm",setupFunc:C9,kernelFunc:I9};function dc(r){let{inputs:{x:e},backend:t}=r,o=t.makeOutput(e.shape,e.dtype),n=t.typedArrayFromHeap(e);return t.typedArrayFromHeap(o).set(n),o}var nM={kernelName:Do,backendName:"wasm",kernelFunc:dc};var sM;function N9(r){sM=r.wasm.cwrap(Mn,null,["number","array","number","number","number","array","number"])}function Yp(r){let{inputs:e,backend:t,attrs:o}=r,[n,s]=T9(e.x.shape,o.perm),a=!0;for(let d=0;d=n&&(s===-1||o[s]>o[a])&&(s=a);o[s]=n}return[t,o]}var iM={kernelName:Mn,backendName:"wasm",kernelFunc:Yp,setupFunc:N9};function ns(r,e,t){let o=r.shape,n=r.shape.length,s=y.parseAxisParam(e,o),a=s,i=N.getAxesPermutation(a,n),l=null,u=!1;if(i!=null){let c=new Array(n);for(let f=0;f`new shape: ${a}, old shape: ${o.shape}. New shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var pM={kernelName:gs,backendName:"wasm",kernelFunc:zr};var mM;function R9(r){mM=r.wasm.cwrap(Yo,null,["number","array","number","number","array","number","number","number","number"])}function F9(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;if(n.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=n.shape.length,u=s.shape.length,c=a?n.shape[l-2]:n.shape[l-1],p=i?s.shape[u-1]:s.shape[u-2],m=a?n.shape[l-1]:n.shape[l-2],f=i?s.shape[u-2]:s.shape[u-1],d=n.shape.slice(0,-2),h=s.shape.slice(0,-2),g=y.sizeFromShape(d),x=y.sizeFromShape(h),b=g===x||g===1||x===1;y.assert(l>=2&&u>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${d}) and (${h}).`);let w=(g>x?n.shape.slice(0,-2):s.shape.slice(0,-2)).concat([m,f]);y.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${n.shape} and ${s.shape} and transposeA=${a} and transposeB=${i} must match.`);let v=a?[g,c,m]:[g,m,c],$=i?[x,f,p]:[x,p,f],A=zr({inputs:{x:n},backend:t,attrs:{shape:v}}),R=zr({inputs:{x:s},backend:t,attrs:{shape:$}}),M=t.dataIdMap.get(A.dataId).id,V=t.dataIdMap.get(R.dataId).id,G=a?A.shape[2]:A.shape[1],W=i?R.shape[1]:R.shape[2],q=Math.max(g,x),Z=t.makeOutput([q,G,W],A.dtype),X=t.dataIdMap.get(Z.dataId).id,Y=new Uint8Array(new Int32Array(A.shape).buffer),te=new Uint8Array(new Int32Array(R.shape).buffer);return mM(M,Y,A.shape.length,V,te,R.shape.length,a,i,X),t.disposeData(A.dataId),t.disposeData(R.dataId),Z.shape=w,Z}var fM={kernelName:Yo,backendName:"wasm",setupFunc:R9,kernelFunc:F9};function hc(r){let{inputs:{x:e},attrs:{dtype:t},backend:o}=r,n=o.makeOutput(e.shape,t),s=o.typedArrayFromHeap(e);return o.typedArrayFromHeap(n).set(s),n}var dM={kernelName:Eo,backendName:"wasm",kernelFunc:hc};var hM=At(Zo);var gM;function O9(r){gM=r.wasm.cwrap(Ao,null,["number","number","number","number"])}function P9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i=t.dataIdMap.get(n.dataId).id,l=t.makeOutput(n.shape,n.dtype),u=t.dataIdMap.get(l.dataId).id;return gM(i,s,a,u),l}var xM={kernelName:Ao,backendName:"wasm",setupFunc:O9,kernelFunc:P9};function tI(r){let{inputs:e,backend:t}=r,o=y.parseAxisParam(r.attrs.axis,e[0].shape)[0],n=N.computeOutShape(e.map(f=>f.shape),o),s=e.filter(f=>y.sizeFromShape(f.shape)>0);if(s.length===1)return dc({inputs:{x:s[0]},backend:t});let a=t.makeOutput(n,e[0].dtype);if(y.sizeFromShape(n)===0)return a;let i=s.map(f=>f.shape);if(N.assertParamsConsistent(i,o),s[0].dtype==="string"){let f=s.map(_=>{let w=y.sizeFromShape(_.shape.slice(o));return zr({inputs:{x:_},backend:t,attrs:{shape:[-1,w]}})}),d=f.map(_=>({vals:t.readSync(_.dataId),shape:_.shape}));n=N.computeOutShape(f.map(_=>_.shape),1);let h=f[0].shape[0]===1,g=qd(d,n,e[0].dtype,h),x=N.computeOutShape(s.map(_=>_.shape),o);a.shape=x;let b=t.dataIdMap.get(a.dataId);return b.stringBytes=N.fromStringArrayToUint8(g),a}let l=y.sizeFromShape(s[0].shape.slice(0,o)),u=0,c=s.map(f=>{let d=y.sizeFromShape(f.shape.slice(o));return u+=d,d}),p=s.map(f=>t.typedArrayFromHeap(f)),m=t.typedArrayFromHeap(a);for(let f=0;f`cumsum does not support ${n.dtype} tensors in the WASM backend`);let u=N.getAxesPermutation([s],l),c=n;u!==null&&(c=Yp({inputs:{x:n},attrs:{perm:u},backend:t}));let p=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumsum",[p],l);let m=t.makeOutput(c.shape,c.dtype),f=c.shape[p],d=t.dataIdMap.get(c.dataId).id,h=t.dataIdMap.get(m.dataId).id;NM(d,a?1:0,i?1:0,f,h,zt[n.dtype]);let g=m;if(u!==null){let x=N.getUndoAxesPermutation(u);g=Yp({inputs:{x:m},attrs:{perm:x},backend:t}),t.disposeData(c.dataId),t.disposeData(m.dataId)}return g}var SM={kernelName:tn,backendName:"wasm",setupFunc:W9,kernelFunc:U9};var TM;function j9(r){TM=r.wasm.cwrap(oi,null,["number","number","number","array","number","array","array","number","number"])}function H9(r){let{backend:e,inputs:t,attrs:o}=r,{x:n}=t,{blockSize:s,dataFormat:a}=o;y.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let i=n.shape[0],l=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],p=l*s,m=u*s,f=c/(s*s),d=a==="NHWC"?[i,p,m,f]:[i,f,p,m],h=e.makeOutput(d,"float32"),x=e.dataIdMap.get(n.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer),_=new Uint8Array(new Int32Array(d).buffer),w=new Uint8Array(new Int32Array(y.computeStrides(d)).buffer),v=e.dataIdMap.get(h.dataId).id;return TM(x,s,a==="NHWC"?1:0,b,n.shape.length-1,_,w,d.length,v),h}var EM={kernelName:oi,backendName:"wasm",setupFunc:j9,kernelFunc:H9};var AM;function q9(r){AM=r.wasm.cwrap(rn,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function K9(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,a=o.dataIdMap.get(n.dataId).id,i=o.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:p}=t,m=u==null?[1,1]:u,f=N.computeConv2DInfo(n.shape,s.shape,l,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,x=f.padInfo.right,b=f.padInfo.bottom,_=f.padInfo.left,w=f.dilationHeight,v=f.dilationWidth,$=f.strideHeight,A=f.strideWidth,R=f.inChannels,M=f.outChannels,V=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let G=o.makeOutput(f.outShape,"float32"),W=o.dataIdMap.get(G.dataId).id;return AM(a,n.shape[0],n.shape[1],n.shape[2],i,d,h,g,x,b,_,V,w,v,$,A,R,M,W),G}var DM={kernelName:rn,backendName:"wasm",setupFunc:q9,kernelFunc:K9};var X9=!1,$M=_t(ii,X9,"bool");var RM=At(nn);function ty(r){let{inputs:e,attrs:t,backend:o}=r,{input:n}=e,{dim:s}=t,a=n.shape.length,i=n.shape.slice(),l=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+s+1),i.splice(l,0,1),zr({inputs:{x:n},backend:o,attrs:{shape:i}})}var FM={kernelName:ps,backendName:"wasm",kernelFunc:ty};function Y9(r){let{attrs:{shape:e,value:t,dtype:o},backend:n}=r,s=n.makeOutput(e,o);return n.typedArrayFromHeap(s).fill(t),s}var OM={kernelName:ua,backendName:"wasm",kernelFunc:Y9};var PM;function Z9(r){PM=r.wasm.cwrap(li,null,["number","number","number","number","number","number"])}function J9(r){let{inputs:e,backend:t}=r,{image:o}=e,n=t.makeOutput(o.shape,o.dtype),s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,[i,l,u,c]=o.shape;return PM(s,i,l,u,c,a),n}var MM={kernelName:li,backendName:"wasm",kernelFunc:J9,setupFunc:Z9};var LM=At(sn);var Q9=!1,zM=_t(an,Q9);var BM;function eQ(r){BM=r.wasm.cwrap(ln,null,["number","number","number","number","number","number","number"])}function tQ(r){let{backend:e,inputs:t,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:l,scale:u}=t,c=e.dataIdMap.get(s.dataId).id,p=e.dataIdMap.get(a.dataId).id,m=e.dataIdMap.get(i.dataId).id,f=l!=null?e.dataIdMap.get(l.dataId).id:0,d=u!=null?e.dataIdMap.get(u.dataId).id:0,h=e.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=e.dataIdMap.get(h.dataId).id;return BM(c,p,m,f,d,n,g),h}var VM={kernelName:ln,backendName:"wasm",setupFunc:eQ,kernelFunc:tQ};var GM;function rQ(r){GM=r.wasm.cwrap(vs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function oQ(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=t,h=N.computeConv2DInfo(n.shape,s.shape,l,c,u,m),g=Dl[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,_=h.outChannels,w=0;if(a!=null){let ae=o.dataIdMap.get(a.dataId);if(ae.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==_)throw new Error(`FusedConv2D bias shape (${ae.shape}) does not match the number of output channels (${_})`);w=ae.id}let v=h.filterHeight,$=h.filterWidth,A=h.padInfo.top,R=h.padInfo.right,M=h.padInfo.bottom,V=h.padInfo.left,G=h.dilationHeight,W=h.dilationWidth,q=h.strideHeight,Z=h.strideWidth,X=h.inChannels,Y=h.padInfo.type==="SAME"?1:0,te=h.batchSize,K=h.inHeight,re=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ie=o.makeOutput(h.outShape,"float32"),se=o.dataIdMap.get(ie.dataId).id,pe=i==null?0:o.dataIdMap.get(i.dataId).id;return GM(x,te,K,re,b,v,$,w,A,R,M,V,Y,G,W,q,Z,X,_,g,pe,d||0,se),ie}var WM={kernelName:vs,backendName:"wasm",setupFunc:rQ,kernelFunc:oQ};var UM;function nQ(r){UM=r.wasm.cwrap(Cs,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 sQ(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=t,h=N.computeConv2DInfo(n.shape,s.shape,l,c,u,m,!0),g=Dl[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,_=h.outChannels,w=0;if(a!=null){let ae=o.dataIdMap.get(a.dataId);if(ae.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==_)throw new Error(`FusedDepthwiseConv2D bias shape (${ae.shape}) does not match the number of output channels (${_})`);w=ae.id}let v=h.filterHeight,$=h.filterWidth,A=h.padInfo.top,R=h.padInfo.right,M=h.padInfo.bottom,V=h.padInfo.left,G=h.dilationHeight,W=h.dilationWidth,q=h.strideHeight,Z=h.strideWidth,X=h.inChannels,Y=h.padInfo.type==="SAME"?1:0,te=h.batchSize,K=h.inHeight,re=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ie=o.makeOutput(h.outShape,"float32"),se=o.dataIdMap.get(ie.dataId).id,pe=i==null?0:o.dataIdMap.get(i.dataId).id;return UM(x,te,K,re,b,v,$,w,A,R,M,V,Y,G,W,q,Z,X,_,g,pe,d||0,se),ie}var jM={kernelName:Cs,backendName:"wasm",setupFunc:nQ,kernelFunc:sQ};var HM;function iQ(r){HM=r.wasm.cwrap(ui,null,["number","number","number","number","number","number","array","number"])}function aQ(r){let{backend:e,inputs:t}=r,{params:o,indices:n}=t,[s,a,i,l]=rg.prepareAndValidate(o,n),u=e.makeOutput(s,o.dtype);if(a===0)return u;let c=n.shape,p=c[c.length-1],f=e.dataIdMap.get(o.dataId).id,h=e.dataIdMap.get(n.dataId).id,g=new Uint8Array(new Int32Array(l).buffer),x=e.dataIdMap.get(u.dataId).id;return HM(f,zt[o.dtype],h,a,p,i,g,x),u}var qM={kernelName:ui,backendName:"wasm",setupFunc:iQ,kernelFunc:aQ};var KM;function lQ(r){KM=r.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function uQ(r){let{backend:e,inputs:t,attrs:o}=r,{x:n,indices:s}=t,{axis:a,batchDims:i}=o,l=y.parseAxisParam(a,n.shape)[0],u=N.segment_util.collectGatherOpShapeInfo(n,s,l,i),c=zr({inputs:{x:n},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:e}),p=y.sizeFromShape(s.shape),m=zr({inputs:{x:s},attrs:{shape:[u.batchSize,p/u.batchSize]},backend:e}),f=[u.batchSize,u.outerSize,p/u.batchSize,u.sliceSize],d=e.makeOutput(f,n.dtype);if(y.sizeFromShape(n.shape)===0)return d;let h=c.shape.length-1,x=e.dataIdMap.get(c.dataId).id,_=e.dataIdMap.get(m.dataId).id,w=e.dataIdMap.get(d.dataId).id,v=new Uint8Array(new Int32Array(y.computeStrides(c.shape)).buffer),$=new Uint8Array(new Int32Array(y.computeStrides(f)).buffer);return KM(x,zt[n.dtype],v,h,_,u.batchSize,$,w),e.disposeData(c.dataId),e.disposeData(m.dataId),d.shape=u.outputShape,d}var XM={kernelName:ms,backendName:"wasm",setupFunc:lQ,kernelFunc:uQ};var cQ=!1,YM=_t(ci,cQ,"bool");var pQ=!1,ZM=_t(un,pQ,"bool");var JM;function mQ(r){JM=r.wasm.cwrap(cn,null,["number","number","number"])}function fQ(r){let{inputs:{x:e},attrs:{alpha:t},backend:o}=r,n=o.dataIdMap.get(e.dataId).id,s=o.makeOutput(e.shape,e.dtype);if(y.sizeFromShape(e.shape)!==0){let a=o.dataIdMap.get(s.dataId).id;JM(n,t,a)}return s}var QM={kernelName:cn,backendName:"wasm",setupFunc:mQ,kernelFunc:fQ};var dQ=!1,eL=_t(di,dQ,"bool");var hQ=!1,tL=_t(hi,hQ,"bool");var rL=At(pn);var gQ=!1,oL=_t(xi,gQ,"bool");var nL;function xQ(r){nL=r.wasm.cwrap(mn,null,["number, number, number"])}function yQ(r){let{backend:e,inputs:t,attrs:o}=r,{reductionIndices:n,keepDims:s}=o,{x:a}=t,l=e.dataIdMap.get(a.dataId).id,u=a,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=ns(a,n,e);if(f){let _=e.dataIdMap.get(c.dataId).id;u=c,l=_}let d=u.shape.length;N.assertAxesAreInnerMostDims("max",p,d);let[h,g]=N.computeOutAndReduceShapes(u.shape,p),x=y.sizeFromShape(g),b=e.makeOutput(h,a.dtype);if(y.sizeFromShape(u.shape)!==0){let _=e.dataIdMap.get(b.dataId).id;nL(l,x,_)}if(f&&e.disposeData(c.dataId),s){let _=N.expandShapeToKeepDim(b.shape,m);b.shape=_}return b}var sL={kernelName:mn,backendName:"wasm",setupFunc:xQ,kernelFunc:yQ};var bQ=!1,iL=_t(fn,bQ);var aL;function _Q(r){aL=r.wasm.cwrap(dn,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function wQ(r){let{inputs:e,attrs:t,backend:o}=r,n=e.x,s=o.dataIdMap.get(n.dataId).id,{filterSize:a,strides:i,pad:l,dimRoundingMode:u}=t,c=N.computePool2DInfo(n.shape,a,i,1,l,u),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,_=c.strideHeight,w=c.strideWidth,v=c.inChannels,$=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let A=o.makeOutput(c.outShape,"float32"),R=o.dataIdMap.get(A.dataId).id;return aL(s,n.shape[0],n.shape[1],n.shape[2],p,m,f,d,h,g,x,b,_,w,v,$,R),A}var lL={kernelName:dn,backendName:"wasm",setupFunc:_Q,kernelFunc:wQ};var uL;function kQ(r){uL=r.wasm.cwrap(hn,null,["number, number, number"])}function vQ(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,l=i,u=a,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=ns(a,n,e),d=p;if(f){let w=e.dataIdMap.get(c.dataId).id;w!==i&&(u=c,l=w,d=N.getInnerMostAxes(d.length,u.shape.length))}N.assertAxesAreInnerMostDims("mean",d,u.shape.length);let[h,g]=N.computeOutAndReduceShapes(u.shape,d),x=y.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=hc({backend:e,inputs:{x:u},attrs:{dtype:"float32"}}),l=e.dataIdMap.get(b.dataId).id);let _=e.makeOutput(h,"float32");if(y.sizeFromShape(u.shape)!==0){let w=e.dataIdMap.get(_.dataId).id;uL(l,x,w)}if(f&&e.disposeData(c.dataId),s){let w=N.expandShapeToKeepDim(_.shape,m);_.shape=w}return u.dtype!=="float32"&&e.disposeData(b.dataId),_}var cL={kernelName:hn,backendName:"wasm",setupFunc:kQ,kernelFunc:vQ};var pL;function CQ(r){pL=r.wasm.cwrap(gn,null,["number, number, number"])}function IQ(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,l=i,u=a,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=ns(a,n,e);if(f){let _=e.dataIdMap.get(c.dataId).id;_!==i&&(u=c,l=_)}let d=u.shape.length;N.assertAxesAreInnerMostDims("min",p,d);let[h,g]=N.computeOutAndReduceShapes(u.shape,p),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let _=e.dataIdMap.get(b.dataId).id;pL(l,x,_)}if(f&&e.disposeData(c.dataId),s){let _=N.expandShapeToKeepDim(b.shape,m);b.shape=_}return b}var mL={kernelName:gn,backendName:"wasm",setupFunc:CQ,kernelFunc:IQ};var NQ=!1,fL=_t(xn,NQ);var SQ=!0,dL=_t(yn,SQ);var hL=At(fs);function Zp(r,e){let t=new Int32Array(r.wasm.HEAPU8.buffer,e,4),o=t[0],n=t[1],s=t[2],a=t[3];return r.wasm._free(e),{pSelectedIndices:o,selectedSize:n,pSelectedScores:s,pValidOutputs:a}}var gL;function TQ(r){gL=r.wasm.cwrap(_i,"number",["number","number","number","number","number"])}function EQ(r){let{backend:e,inputs:t,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a}=o,{boxes:i,scores:l}=t,u=e.dataIdMap.get(i.dataId).id,c=e.dataIdMap.get(l.dataId).id,p=gL(u,c,s,n,a),{pSelectedIndices:m,selectedSize:f,pSelectedScores:d,pValidOutputs:h}=Zp(e,p);return e.wasm._free(d),e.wasm._free(h),e.makeOutput([f],"int32",m)}var xL={kernelName:_i,backendName:"wasm",setupFunc:TQ,kernelFunc:EQ};var yL;function AQ(r){yL=r.wasm.cwrap(wi,"number",["number","number","number","number","number","bool"])}function DQ(r){let{backend:e,inputs:t,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a,padToMaxOutputSize:i}=o,{boxes:l,scores:u}=t,c=e.dataIdMap.get(l.dataId).id,p=e.dataIdMap.get(u.dataId).id,m=yL(c,p,s,n,a,i),{pSelectedIndices:f,selectedSize:d,pSelectedScores:h,pValidOutputs:g}=Zp(e,m);e.wasm._free(h);let x=e.makeOutput([d],"int32",f),b=e.makeOutput([],"int32",g);return[x,b]}var bL={kernelName:wi,backendName:"wasm",setupFunc:AQ,kernelFunc:DQ};var _L;function $Q(r){_L=r.wasm.cwrap(ki,"number",["number","number","number","number","number","number"])}function RQ(r){let{backend:e,inputs:t,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a,softNmsSigma:i}=o,{boxes:l,scores:u}=t,c=e.dataIdMap.get(l.dataId).id,p=e.dataIdMap.get(u.dataId).id,m=_L(c,p,s,n,a,i),{pSelectedIndices:f,selectedSize:d,pSelectedScores:h,pValidOutputs:g}=Zp(e,m);e.wasm._free(g);let x=e.makeOutput([d],"int32",f),b=e.makeOutput([d],"float32",h);return[x,b]}var wL={kernelName:ki,backendName:"wasm",setupFunc:$Q,kernelFunc:RQ};var FQ=!1,kL=_t(bi,FQ,"bool");var vL;function OQ(r){vL=r.wasm.cwrap(bn,null,["number","number","number","number","number"])}function PQ(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{depth:s,onValue:a,offValue:i}=o,l=t.makeOutput([...n.shape,s],"int32"),u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(n.dataId).id;return vL(p,s,a,i,u),l}var CL={kernelName:bn,backendName:"wasm",setupFunc:OQ,kernelFunc:PQ};function MQ(r){let{inputs:{x:e},backend:t}=r,o=t.makeOutput(e.shape,e.dtype);return t.typedArrayFromHeap(o).fill(1),o}var IL={kernelName:ds,backendName:"wasm",kernelFunc:MQ};function LQ(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return ty({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(l=>{y.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=e.map(l=>ty({inputs:{input:l},backend:t,attrs:{dim:n}}));return tI({inputs:i,backend:t,attrs:{axis:n}})}var NL={kernelName:hs,backendName:"wasm",kernelFunc:LQ};var SL;function zQ(r){SL=r.wasm.cwrap(_n,null,["number","array","number","number","array","array","number","number"])}function BQ(r){let{inputs:{x:e},backend:t,attrs:{paddings:o,constantValue:n}}=r,s=o.map((d,h)=>d[0]+e.shape[h]+d[1]),a=t.dataIdMap.get(e.dataId).id,i=t.makeOutput(s,e.dtype),l=t.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(e.shape).buffer),c=o.map(d=>d[0]),p=o.map(d=>d[1]),m=new Uint8Array(new Int32Array(c).buffer),f=new Uint8Array(new Int32Array(p).buffer);return SL(a,u,e.shape.length,zt[e.dtype],m,f,n,l),i}var TL={kernelName:_n,backendName:"wasm",kernelFunc:BQ,setupFunc:zQ};var VQ=!1,EL=_t(wn,VQ);var AL;function GQ(r){AL=r.wasm.cwrap(kn,null,["number","number","number"])}function WQ(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,i=t.makeOutput(o.shape,"float32"),l=t.dataIdMap.get(i.dataId).id;return AL(s,a,l),i}var DL={kernelName:kn,backendName:"wasm",setupFunc:GQ,kernelFunc:WQ};var $L;function UQ(r){$L=r.wasm.cwrap(vi,null,["number","number","number","number"])}function jQ(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,l=i,u=a,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=ns(a,n,e),d=p;if(f){let _=e.dataIdMap.get(c.dataId).id;_!==i&&(u=c,l=_,d=N.getInnerMostAxes(d.length,u.shape.length))}N.assertAxesAreInnerMostDims("prod",d,u.shape.length);let[h,g]=N.computeOutAndReduceShapes(u.shape,d),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let _=e.dataIdMap.get(b.dataId).id;$L(l,x,zt[b.dtype],_)}if(f&&e.disposeData(c.dataId),s){let _=N.expandShapeToKeepDim(b.shape,m);b.shape=_}return b}var RL={kernelName:vi,backendName:"wasm",setupFunc:UQ,kernelFunc:jQ};var HQ=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=Yd(o,n,s,a),l=e.makeOutput([i.length],a);return e.typedArrayFromHeap(l).set(i),l},FL={kernelName:fa,backendName:"wasm",kernelFunc:HQ};var qQ=!0,OL=_t(on,qQ);var PL=At(vn);var ML=At(In);var LL;function KQ(r){LL=r.wasm.cwrap(Cn,null,["number","number","number","number","number","number","number","number","number","number"])}function XQ(r){let{backend:e,inputs:t,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,[c,p,m,f]=n.shape,d=[c,l,u,f],h=e.dataIdMap.get(n.dataId),g;h.dtype!=="float32"&&(g=hc({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),h=e.dataIdMap.get(g.dataId));let x=h.id,b=e.makeOutput(d,"float32");if(y.sizeFromShape(n.shape)===0)return b;let _=e.dataIdMap.get(b.dataId).id;return LL(x,c,p,m,f,l,u,s?1:0,a?1:0,_),g!=null&&e.disposeData(g.dataId),b}var zL={kernelName:Cn,backendName:"wasm",setupFunc:KQ,kernelFunc:XQ};var BL;function YQ(r){BL=r.wasm.cwrap(Nn,null,["number","array","number","array","number","number"])}function ZQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=y.parseAxisParam(s,n.shape);if(n.shape.length===0)return dc({inputs:{x:n},backend:t});let i=t.makeOutput(n.shape,n.dtype),l=t.dataIdMap.get(n.dataId).id,u=t.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),p=new Uint8Array(new Int32Array(n.shape).buffer);BL(l,c,a.length,p,n.shape.length,u);let m=zr({inputs:{x:i},attrs:{shape:n.shape},backend:t});return t.disposeData(i.dataId),m}var VL={kernelName:Nn,backendName:"wasm",kernelFunc:ZQ,setupFunc:YQ};var GL;function JQ(r){GL=r.wasm.cwrap(Ri,null,["number","number","number","number","number","number","number","number","array","number","number"])}function QQ(r){let{inputs:e,backend:t,attrs:o}=r,{image:n}=e,{radians:s,fillValue:a,center:i}=o,l=t.makeOutput(n.shape,n.dtype),u=t.dataIdMap.get(n.dataId).id,c=t.dataIdMap.get(l.dataId).id,[p,m,f,d]=n.shape,[h,g]=N.getImageCenter(i,m,f),x=a===0,b=255,_=typeof a=="number"?[a,a,a,x?0:b]:[...a,b],w=new Uint8Array(new Int32Array(_).buffer);return GL(u,p,m,f,d,s,h,g,w,_.length,c),l}var WL={kernelName:Ri,backendName:"wasm",kernelFunc:QQ,setupFunc:JQ};var UL=At(Sn);var jL=At(Tn);var HL;function eee(r){HL=r.wasm.cwrap(Ii,null,["number","number","number","number","number","number","array","number","number"])}function tee(r){let{backend:e,inputs:t,attrs:o}=r,{indices:n,updates:s}=t,{shape:a}=o,i=e.makeOutput(a,s.dtype);if(y.sizeFromShape(a)===0)return i;let{sliceRank:l,numUpdates:u,sliceSize:c,strides:p,outputSize:m}=og.calculateShapes(s,n,a),d=e.dataIdMap.get(n.dataId).id,g=e.dataIdMap.get(s.dataId).id,x=new Uint8Array(new Int32Array(p).buffer),b=e.dataIdMap.get(i.dataId).id;return HL(d,g,zt[s.dtype],l,u,c,x,m,b),i}var qL={kernelName:Ii,backendName:"wasm",setupFunc:eee,kernelFunc:tee};var KL;function ree(r){KL=r.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function oee(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=t.dataIdMap.get(o.dataId).id,i=t.dataIdMap.get(n.dataId).id,l=t.dataIdMap.get(s.dataId).id,u=t.makeOutput(n.shape,n.dtype),c=t.dataIdMap.get(u.dataId).id,p=o.shape.length,m=n.shape.length,f=p===0||p>1||m===1?1:y.sizeFromShape(n.shape.slice(1));return KL(a,i,l,f,c),u}var XL={kernelName:xs,backendName:"wasm",kernelFunc:oee,setupFunc:ree};var YL;function nee(r){YL=r.wasm.cwrap(An,null,["number","number"])}function see(r){let{backend:e,inputs:{x:t}}=r,o=e.dataIdMap.get(t.dataId).id,n=e.makeOutput(t.shape,t.dtype),s=e.dataIdMap.get(n.dataId).id;return y.sizeFromShape(n.shape)===0||YL(o,s),n}var ZL={kernelName:"Sigmoid",backendName:"wasm",setupFunc:nee,kernelFunc:see};var JL=At(En);function gc(r){let{inputs:{x:e},attrs:{begin:t,size:o},backend:n}=r,[s,a]=or.parseSliceParams(e,t,o),i=or.isSliceContinous(e.shape,s,a),l=n.readSync(e.dataId),u=n.makeOutput(a,e.dtype),c=y.computeStrides(e.shape),p=n.dataIdMap.get(u.dataId);if(i){let d=or.computeFlatOffset(s,c);return e.dtype==="string"?p.stringBytes=l.slice(d,d+y.sizeFromShape(a)):n.typedArrayFromHeap(u).set(l.subarray(d,d+y.sizeFromShape(a))),u}if(e.dtype==="string"){let d=Zd(l,s,a,e.shape,e.dtype);return p.stringBytes=d,u}let m=n.typedArrayFromHeap(u),f=e.shape.length;if(f===2)iee(l,c[0],m,s,a);else if(f===3)aee(l,c[0],c[1],m,s,a);else if(f===4)lee(l,c[0],c[1],c[2],m,s,a);else{let d=Zd(l,s,a,e.shape,e.dtype);m.set(d)}return u}function iee(r,e,t,o,n){let s=0,a=o[0],i=o[1],l=a+n[0];for(let u=a;u{let m=[...c];m[i]=p;let f=gc({inputs:{x:n},attrs:{begin:u,size:m},backend:o});return u[i]+=p,f})}var rz={kernelName:bs,backendName:"wasm",kernelFunc:pee};var oz=At(Dn);var nz=At(ga);var mee=!0,sz=_t(Fn,mee);var iz;function fee(r){iz=r.wasm.cwrap($o,null,["number","number","number"])}function dee(r){let{backend:e,inputs:t,attrs:o}=r,{alpha:n}=o,{x:s}=t,a=e.dataIdMap.get(s.dataId).id,i=e.makeOutput(s.shape,s.dtype),l=e.dataIdMap.get(i.dataId).id;return iz(a,n,l),i}var az={kernelName:$o,backendName:"wasm",setupFunc:fee,kernelFunc:dee};var lz;function hee(r){lz=r.wasm.cwrap(Ai,null,["number","array","number","array","array","array","array","array","number","number"])}function gee(r){let{backend:e,inputs:t,attrs:o}=r,{x:n}=t,{begin:s,end:a,strides:i}=o;i==null&&(i=new Array(s.length));let{beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=o,f=N.slice_util.maskToAxes(c);if(f.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(c!==0&&p!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(c!==0&&m!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let d=n.shape.length-s.length,h=N.slice_util.maskToAxes(p),g=n.shape.slice();h.forEach(G=>{s[G]=0,a[G]=1,g.splice(G,0,1)});let x=zr({inputs:{x:n},attrs:{shape:g},backend:e}),{begin:b,end:_,strides:w}=N.slice_util.getNormalizedAxes(x.shape,f,d,s,a,i,l,u,c);s=b,a=_,i=w;let v=N.slice_util.maskToAxes(m);v.forEach(G=>{a[G]=s[G]+1,i[G]=1});let $=N.slice_util.computeOutShape(s,a,i),A=$.filter((G,W)=>v.indexOf(W)===-1);if(i.every(G=>G===1)){let G=gc({inputs:{x:n},attrs:{begin:s,size:$},backend:e});e.disposeData(x.dataId);let W=zr({inputs:{x:G},attrs:{shape:A},backend:e});return e.disposeData(G.dataId),W}let M=e.makeOutput(A,"float32");if(!A.some(G=>G===0)){let G=e.dataIdMap.get(x.dataId).id,W=new Uint8Array(new Int32Array(y.computeStrides(x.shape)).buffer),q=new Uint8Array(new Int32Array(s).buffer),Z=new Uint8Array(new Int32Array(a).buffer),X=new Uint8Array(new Int32Array(i).buffer),Y=new Uint8Array(new Int32Array(A).buffer),te=new Uint8Array(new Int32Array(y.computeStrides(A)).buffer),K=e.dataIdMap.get(M.dataId).id;lz(G,W,x.shape.length,q,Z,X,Y,te,A.length,K)}e.disposeData(x.dataId);let V=zr({inputs:{x:M},attrs:{shape:A},backend:e});return e.disposeData(M.dataId),V}var uz={kernelName:Ai,backendName:"wasm",setupFunc:hee,kernelFunc:gee};var xee=!0,cz=_t(On,xee);var pz;function yee(r){pz=r.wasm.cwrap($n,null,["number, number, number"])}function bee(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,l=i,u=a,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=ns(a,n,e),d=p;if(f){let _=e.dataIdMap.get(c.dataId).id;_!==i&&(u=c,l=_,d=N.getInnerMostAxes(d.length,u.shape.length))}N.assertAxesAreInnerMostDims("sum",d,u.shape.length);let[h,g]=N.computeOutAndReduceShapes(u.shape,d),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let _=e.dataIdMap.get(b.dataId).id;pz(l,x,_)}if(f&&e.disposeData(c.dataId),s){let _=N.expandShapeToKeepDim(b.shape,m);b.shape=_}return b}var mz={kernelName:$n,backendName:"wasm",setupFunc:yee,kernelFunc:bee};var fz=At(Pn);var dz;function _ee(r){dz=r.wasm.cwrap(yo,null,["number","array","number","array","number","number"])}function wee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,s=t.dataIdMap.get(n.dataId).id,{reps:a}=o,i=new Array(n.shape.length);for(let m=0;m{let{x:o}=r,{k:n,sorted:s}=t,a=e.dataIdMap.get(o.dataId).id,i=new Uint8Array(new Int32Array(o.shape).buffer),l=o.shape.slice();l[l.length-1]=n;let u=e.makeOutput(l,o.dtype),c=e.dataIdMap.get(u.dataId).id,p=e.makeOutput(l,"int32"),m=e.dataIdMap.get(p.dataId).id;return gz(a,i,o.shape.length,zt[o.dtype],n,s,c,m),[u,p]},xz={kernelName:$i,backendName:"wasm",setupFunc:kee,kernelFunc:vee};function Cee(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n.shape[s],i=n.shape.length,l=new Array(i-1),u=0;for(let f=0;f({dataId:f,dtype:d,shape:l}))}var yz={kernelName:_s,backendName:"wasm",kernelFunc:Cee};function Iee(r){let{inputs:{x:e},backend:t}=r,o=t.makeOutput(e.shape,e.dtype);return t.typedArrayFromHeap(o).fill(0),o}var bz={kernelName:ws,backendName:"wasm",kernelFunc:Iee};var Nee=[eM,tM,oM,lM,cM,fM,dM,hM,xM,yM,_M,kM,vM,IM,SM,EM,DM,$M,RM,FM,OM,MM,LM,zM,QP,VM,WM,jM,qM,XM,YM,ZM,nM,QM,eL,tL,rL,oL,sL,iL,lL,cL,mL,fL,dL,hL,xL,bL,wL,kL,CL,IL,NL,TL,EL,DL,RL,FL,OL,PL,ML,pM,zL,VL,WL,jL,UL,qL,XL,ZL,JL,QL,tz,rz,oz,nz,sz,az,uz,cz,mz,fz,hz,xz,iM,yz,bz];for(let r of Nee)Qa(r);var oI=U();oI.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])));oI.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(oI.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(r){return!1}});var lI=Tc(kz());var vz='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()}}}}';var Iz=Tc(Cz());var ny=class extends js{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new Ya(this,Fo())}write(e,t,o){let n={id:this.dataIdNextNumber++};return this.move(n,e,t,o,1),n}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=y.now();return e(),{kernelMs:y.now()-t}}move(e,t,o,n,s){let a=this.dataIdNextNumber++;if(n==="string"){let c=t;this.dataIdMap.set(e,{id:a,stringBytes:c,shape:o,dtype:n,memoryOffset:null,refCount:s});return}let i=y.sizeFromShape(o),l=i*y.bytesPerElement(n),u=this.wasm._malloc(l);this.dataIdMap.set(e,{id:a,memoryOffset:u,shape:o,dtype:n,refCount:s}),this.wasm.tfjs.registerTensor(a,i,u),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,l),u)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:o,shape:n,stringBytes:s}=this.dataIdMap.get(e);if(o==="string")return s;let a=this.wasm.HEAPU8.slice(t,t+y.sizeFromShape(n)*y.bytesPerElement(o));return See(a.buffer,o)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let o=this.dataIdMap.get(e);if(o.refCount--,!t&&o.refCount>0)return!1;this.wasm._free(o.memoryOffset),this.wasm.tfjs.disposeData(o.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}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,o){let n;if(o==null)n=this.write(null,e,t);else{let s=this.dataIdNextNumber++;n={id:s},this.dataIdMap.set(n,{id:s,memoryOffset:o,shape:e,dtype:t,refCount:1});let a=y.sizeFromShape(e);this.wasm.tfjs.registerTensor(s,a,o)}return{dataId:n,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:o}){let n=this.wasm.HEAPU8.buffer,{memoryOffset:s}=this.dataIdMap.get(o),a=y.sizeFromShape(e);switch(t){case"float32":return new Float32Array(n,s,a);case"int32":return new Int32Array(n,s,a);case"bool":return new Uint8Array(n,s,a);default:throw new Error(`Unknown dtype ${t}`)}}};function Tee(r){return(e,t)=>(y.fetch(r,{credentials:"same-origin"}).then(o=>{o.ok||e.env.a(`failed to load wasm binary file at '${r}'`),o.arrayBuffer().then(n=>{WebAssembly.instantiate(n,e).then(s=>{t(s.instance)})})}),{})}function Nz(r,e,t){if(sy!=null)return sy;let o="tfjs-backend-wasm.wasm";return r&&e?o="tfjs-backend-wasm-threaded-simd.wasm":r&&(o="tfjs-backend-wasm-simd.wasm"),xh!=null&&xh[o]!=null?xh[o]:t+o}async function Sz(){let[r,e]=await Promise.all([U().getAsync("WASM_HAS_SIMD_SUPPORT"),U().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((t,o)=>{let n={};n.locateFile=(l,u)=>{if(l.endsWith(".worker.js")){let c=vz,p=new Blob([c],{type:"application/javascript"});return URL.createObjectURL(p)}return l.endsWith(".wasm")?Nz(r,e,yh!=null?yh:u):u+l},uI&&(n.instantiateWasm=Tee(Nz(r,e,yh!=null?yh:"")));let s;e&&r&&sy==null?(s=lI.default(n),s.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+lI.default.toString()],{type:"text/javascript"})):s=Iz.default(n);let a=null;s.tfjs={init:s.cwrap("init",null,[]),registerTensor:s.cwrap("register_tensor",null,["number","number","number"]),disposeData:s.cwrap("dispose_data",a,["number"]),dispose:s.cwrap("dispose",a,[])};let i=!1;s.onRuntimeInitialized=()=>{i=!0,bh=!1,t({wasm:s})},s.onAbort=()=>{if(i||bh)return;bh=!0,o({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 See(r,e){switch(e){case"float32":return new Float32Array(r);case"int32":return new Int32Array(r);case"bool":return new Uint8Array(r);default:throw new Error(`Unknown dtype ${e}`)}}var Eee=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],sy=null,yh=null,xh={},bh=!1,uI=!1;function Aee(r,e=!1){if(ag("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),bh)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");sy=r,uI=e}function Dee(r,e=!1){if(bh)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof r=="string")yh=r;else{xh=r;let t=Eee.filter(o=>xh[o]==null);if(t.length>0)throw new Error(`There were no entries found for the following binaries: ${t.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.`)}uI=e}var $ee="3.1.0";var Ree=2;bu("wasm",async()=>{let{wasm:r}=await Sz();return new ny(r)},Ree);export{us as Abs,Ks as Acos,Xs as Acosh,rp as AdadeltaOptimizer,op as AdagradOptimizer,np as AdamOptimizer,sp as AdamaxOptimizer,xo as Add,qo as AddN,Vl as All,Gl as Any,Ko as ArgMax,oa as ArgMin,Ys as Asin,Zs as Asinh,Js as Atan,ei as Atan2,Qs as Atanh,Xo as AvgPool,na as AvgPool3D,Ul as AvgPool3DGrad,Wl as AvgPoolGrad,ny as BackendWasm,Yo as BatchMatMul,sa as BatchToSpaceND,jl as Bincount,Cb as BroadcastTo,tx as Callback,Mg as CallbackList,Eo as Cast,Zo as Ceil,Ao as ClipByValue,Hl as Complex,ia as ComplexAbs,cs as Concat,Jo as Conv2D,ql as Conv2DBackpropFilter,Qo as Conv2DBackpropInput,aa as Conv3D,Kl as Conv3DBackpropFilterV2,Xl as Conv3DBackpropInputV2,en as Cos,ti as Cosh,ri as CropAndResize,tn as Cumsum,zg as CustomCallback,Ya as DataStorage,Yl as DenseBincount,oi as DepthToSpace,rn as DepthwiseConv2dNative,Zl as DepthwiseConv2dNativeBackpropFilter,Jl as DepthwiseConv2dNativeBackpropInput,Ql as Diag,la as Dilation2D,Fc as Dilation2DBackpropFilter,Rc as Dilation2DBackpropInput,wb as ENV,ox as EarlyStopping,ni as Elu,eu as EluGrad,jh as Environment,ii as Equal,si as Erf,nn as Exp,ps as ExpandDims,ai as Expm1,tu as FFT,ua as Fill,li as FlipLeftRight,sn as Floor,an as FloorDiv,Oc as FromPixels,ln as FusedBatchNorm,vs as FusedConv2D,Cs as FusedDepthwiseConv2D,ui as GatherNd,ms as GatherV2,bx as GraphModel,ci as Greater,un as GreaterEqual,Lg as History,ru as IFFT,Do as Identity,ou as Imag,Et as InputSpec,pi as IsFinite,mi as IsInf,fi as IsNan,js as KernelBackend,ca as LRN,su as LRNGrad,If as LayerVariable,vo as LayersModel,cn as LeakyRelu,di as Less,hi as LessEqual,nu as LinSpace,pn as Log,gi as Log1p,Ib as LogSoftmax,xi as LogicalAnd,Za as LogicalNot,Ja as LogicalOr,mn as Max,dn as MaxPool,pa as MaxPool3D,au as MaxPool3DGrad,iu as MaxPoolGrad,lu as MaxPoolWithArgmax,fn as Maximum,hn as Mean,gn as Min,xn as Minimum,ma as MirrorPad,yi as Mod,ip as MomentumOptimizer,uu as Multinomial,yn as Multiply,fs as Neg,_i as NonMaxSuppressionV3,wi as NonMaxSuppressionV4,ki as NonMaxSuppressionV5,bi as NotEqual,LI as OP_SCOPE_SUFFIX,bn as OneHot,ds as OnesLike,Fr as Optimizer,hs as Pack,_n as PadV2,g3 as Pool,wn as Pow,kn as Prelu,vi as Prod,ap as RMSPropOptimizer,ao as RNN,fa as Range,Ab as Rank,cu as Real,on as RealDiv,Ci as Reciprocal,Gt as Reduction,vn as Relu,In as Relu6,gs as Reshape,Cn as ResizeBilinear,mu as ResizeBilinearGrad,da as ResizeNearestNeighbor,pu as ResizeNearestNeighborGrad,Nn as Reverse,Ri as RotateWithOffset,Sn as Round,Tn as Rsqrt,ll as SGDOptimizer,Ii as ScatterNd,xs as Select,Ni as Selu,Hi as Sequential,An as Sigmoid,Ti as Sign,En as Sin,Si as Sinh,ys as Slice,Rn as Softmax,Ei as Softplus,ha as SpaceToBatchND,fu as SparseToDense,bs as SplitV,Dn as Sqrt,ga as Square,Fn as SquaredDifference,$o as Step,Ai as StridedSlice,On as Sub,$n as Sum,Lr as SymbolicTensor,Di as Tan,Pn as Tanh,Ve as Tensor,ct as TensorBuffer,yo as Tile,$i as TopK,Mn as Transpose,du as Unique,_s as Unpack,xa as UnsortedSegmentSum,tl as Variable,ws as ZerosLike,ks as _FusedMatMul,Tt as abs,Nm as acos,Sm as acosh,Q as add,i_ as addN,wu as all,nl as any,sl as argMax,Tm as argMin,Em as asin,Am as asinh,Dm as atan,$m as atan2,Rm as atanh,va as avgPool,Fm as avgPool3d,s_ as backend,N as backend_util,dG as basicLSTMCell,zn as batchNorm,c_ as batchNorm2d,p_ as batchNorm3d,m_ as batchNorm4d,Ca as batchToSpaceND,f_ as bincount,kU as booleanMaskAsync,il as broadcastTo,tg as browser,Ce as buffer,D1 as callbacks,oe as cast,Om as ceil,nr as clipByValue,Ro as clone,bo as complex,Je as concat,d_ as concat1d,h_ as concat2d,g_ as concat3d,x_ as concat4d,uw as constraints,Cu as conv1d,jr as conv2d,Iu as conv2dTranspose,Pm as conv3d,OG as conv3dTranspose,b3 as copyRegisteredKernels,Ia as cos,Nu as cosh,lf as cosineWindow,Su as cumsum,Hr as customGrad,Mk as data,y_ as denseBincount,ag as deprecationWarn,Mm as depthToSpace,Ns as depthwiseConv2d,F1 as deregisterOp,Vc as device_util,WG as diag,Lm as dilation2d,SV as disableDeprecationWarnings,Te as dispose,TV as disposeVariables,de as div,zm as divNoNan,b_ as dot,j_ as dropout,Ss as elu,NV as enableDebugMode,IV as enableProdMode,H_ as enclosingPowerOfTwo,Fo as engine,U as env,_o as equal,Bm as erf,Xt as exp,sr as expandDims,Vm as expm1,Xc as eye,Ra as fft,Na as fill,FV as findBackend,OV as findBackendFactory,Ts as floor,_u as floorDiv,Wn as fused,Bn as gather,U_ as gatherND,rg as gather_util,$V as getBackend,Hh as getGradient,Mc as getKernel,_m as getKernelsForBackend,yW as grad,bW as grads,Qt as greater,to as greaterEqual,zi as ifft,Tu as imag,Rs as image,DU as inTopKAsync,dw as initializers,Qg as input,vr as io,Bu as irfft,__ as isFinite,w_ as isInf,k_ as isNaN,Dt as keep,Sr as kernel_impls,Yw as layers,Sa as leakyRelu,Eu as less,Mo as lessEqual,Z_ as linalg,v_ as linspace,uE as loadGraphModel,y1 as loadLayersModel,Gm as localResponseNormalization,ir as log,Au as log1p,C_ as logSigmoid,Du as logSoftmax,Um as logSumExp,fr as logicalAnd,Ta as logicalNot,$u as logicalOr,T_ as logicalXor,vj as losses,We as matMul,hN as math,ar as max,Ea as maxPool,jm as maxPool3d,E_ as maxPoolWithArgmax,qr as maximum,ht as mean,Hc as memory,ek as metrics,Li as min,As as minimum,Hm as mirrorPad,qm as mod,g1 as model,tk as models,Yc as moments,IU as movingAverage,O as mul,KW as multiRNNCell,A_ as multinomial,je as neg,uf as nextFrame,Wu as norm,Gn as notEqual,Is as oneHot,Cr as ones,er as onesLike,S as op,QW as outerProduct,$r as pad,r4 as pad1d,n4 as pad2d,i4 as pad3d,l4 as pad4d,D_ as pool,Rr as pow,Da as prelu,Kb as print,Ru as prod,EV as profile,x4 as rand,I4 as randomGamma,dg as randomNormal,Ds as randomUniform,Jc as range,DV as ready,al as real,Xm as reciprocal,bu as registerBackend,b1 as registerCallbackConstructor,Sb as registerGradient,Qa as registerKernel,R1 as registerOp,rk as regularizers,Ir as relu,Ou as relu6,RV as removeBackend,L as reshape,qt as reverse,F4 as reverse1d,P4 as reverse2d,L4 as reverse3d,B4 as reverse4d,Fa as rfft,Ym as round,Pu as rsqrt,le as scalar,W_ as scatterND,og as scatter_util,Mu as selu,Zm as separableConv2d,x1 as sequential,J as serialization,DN as setBackend,PV as setPlatform,Aee as setWasmPath,Dee as setWasmPaths,B_ as setdiff1dAsync,Ur as sigmoid,Jm as sign,kj as signal,Lu as sin,zu as sinh,Fe as slice,Qm as slice1d,hg as slice2d,ef as slice3d,Qc as slice4d,or as slice_util,$a as softmax,Es as softplus,Aa as spaceToBatchND,af as sparseToDense,wj as spectral,lr as split,xt as sqrt,Pe as square,Vu as squaredDifference,wo as squeeze,Bt as stack,$s as step,tf as stridedSlice,ue as sub,ye as sum,gu as sumOutType,rf as tan,Mi as tanh,Dr as tensor,Vt as tensor1d,Bi as tensor2d,Jb as tensor3d,mU as tensor4d,fU as tensor5d,dU as tensor6d,Ln as tensor_util,TN as test_util,B as tidy,Po as tile,AV as time,of as topk,ul as train,Ue as transpose,Gu as truncatedNormal,ep as unique,y3 as unregisterGradient,x3 as unregisterKernel,nf as unsortedSegmentSum,ur as unstack,mr as upcastType,y as util,_W as valueAndGrad,wW as valueAndGrads,V_ as variable,pg as variableGrads,_9 as version,_x as version_converter,n_ as version_core,hl as version_layers,$ee as version_wasm,$t as where,sf as whereAsync,gt as zeros,Ie as zerosLike}; /** * @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=tfjs.esm.js.map