/* Human homepage: author: ' */ var yV=Object.create;var ub=Object.defineProperty;var bV=Object.getOwnPropertyDescriptor;var CV=Object.getOwnPropertyNames;var SV=Object.getPrototypeOf,wV=Object.prototype.hasOwnProperty;var bm=(r=>typeof require!="undefined"?require:typeof Proxy!="undefined"?new Proxy(r,{get:(e,t)=>(typeof require!="undefined"?require:e)[t]}):r)(function(r){if(typeof require!="undefined")return require.apply(this,arguments);throw new Error('Dynamic require of "'+r+'" is not supported')});var qt=(r,e)=>()=>(e||r((e={exports:{}}).exports,e),e.exports),Ue=(r,e)=>{for(var t in e)ub(r,t,{get:e[t],enumerable:!0})},IV=(r,e,t,o)=>{if(e&&typeof e=="object"||typeof e=="function")for(let n of CV(e))!wV.call(r,n)&&n!==t&&ub(r,n,{get:()=>e[n],enumerable:!(o=bV(e,n))||o.enumerable});return r};var rp=(r,e,t)=>(t=r!=null?yV(SV(r)):{},IV(e||!r||!r.__esModule?ub(t,"default",{value:r,enumerable:!0}):t,r));var r0=qt((Pne,t0)=>{t0.exports=It;var xo=null;try{xo=new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([0,97,115,109,1,0,0,0,1,13,2,96,0,1,127,96,4,127,127,127,127,1,127,3,7,6,0,1,1,1,1,1,6,6,1,127,1,65,0,11,7,50,6,3,109,117,108,0,1,5,100,105,118,95,115,0,2,5,100,105,118,95,117,0,3,5,114,101,109,95,115,0,4,5,114,101,109,95,117,0,5,8,103,101,116,95,104,105,103,104,0,0,10,191,1,6,4,0,35,0,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,126,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,127,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,128,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,129,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,130,34,4,66,32,135,167,36,0,32,4,167,11])),{}).exports}catch(r){}function It(r,e,t){this.low=r|0,this.high=e|0,this.unsigned=!!t}It.prototype.__isLong__;Object.defineProperty(It.prototype,"__isLong__",{value:!0});function Lr(r){return(r&&r.__isLong__)===!0}It.isLong=Lr;var qI={},KI={};function eu(r,e){var t,o,n;return e?(r>>>=0,(n=0<=r&&r<256)&&(o=KI[r],o)?o:(t=vt(r,(r|0)<0?-1:0,!0),n&&(KI[r]=t),t)):(r|=0,(n=-128<=r&&r<128)&&(o=qI[r],o)?o:(t=vt(r,r<0?-1:0,!1),n&&(qI[r]=t),t))}It.fromInt=eu;function yo(r,e){if(isNaN(r))return e?Ji:bo;if(e){if(r<0)return Ji;if(r>=QI)return e0}else{if(r<=-XI)return Mr;if(r+1>=XI)return JI}return r<0?yo(-r,e).neg():vt(r%Ep|0,r/Ep|0,e)}It.fromNumber=yo;function vt(r,e,t){return new It(r,e,t)}It.fromBits=vt;var Rm=Math.pow;function Ib(r,e,t){if(r.length===0)throw Error("empty string");if(r==="NaN"||r==="Infinity"||r==="+Infinity"||r==="-Infinity")return bo;if(typeof e=="number"?(t=e,e=!1):e=!!e,t=t||10,t<2||360)throw Error("interior hyphen");if(o===0)return Ib(r.substring(1),e,t).neg();for(var n=yo(Rm(t,8)),s=bo,a=0;a>>0:this.low};me.toNumber=function(){return this.unsigned?(this.high>>>0)*Ep+(this.low>>>0):this.high*Ep+(this.low>>>0)};me.toString=function(e){if(e=e||10,e<2||36>>0,c=u.toString(e);if(a=p,a.isZero())return c+i;for(;c.length<6;)c="0"+c;i=""+c+i}};me.getHighBits=function(){return this.high};me.getHighBitsUnsigned=function(){return this.high>>>0};me.getLowBits=function(){return this.low};me.getLowBitsUnsigned=function(){return this.low>>>0};me.getNumBitsAbs=function(){if(this.isNegative())return this.eq(Mr)?64:this.neg().getNumBitsAbs();for(var e=this.high!=0?this.high:this.low,t=31;t>0&&(e&1<=0};me.isOdd=function(){return(this.low&1)===1};me.isEven=function(){return(this.low&1)===0};me.equals=function(e){return Lr(e)||(e=ts(e)),this.unsigned!==e.unsigned&&this.high>>>31===1&&e.high>>>31===1?!1:this.high===e.high&&this.low===e.low};me.eq=me.equals;me.notEquals=function(e){return!this.eq(e)};me.neq=me.notEquals;me.ne=me.notEquals;me.lessThan=function(e){return this.comp(e)<0};me.lt=me.lessThan;me.lessThanOrEqual=function(e){return this.comp(e)<=0};me.lte=me.lessThanOrEqual;me.le=me.lessThanOrEqual;me.greaterThan=function(e){return this.comp(e)>0};me.gt=me.greaterThan;me.greaterThanOrEqual=function(e){return this.comp(e)>=0};me.gte=me.greaterThanOrEqual;me.ge=me.greaterThanOrEqual;me.compare=function(e){if(Lr(e)||(e=ts(e)),this.eq(e))return 0;var t=this.isNegative(),o=e.isNegative();return t&&!o?-1:!t&&o?1:this.unsigned?e.high>>>0>this.high>>>0||e.high===this.high&&e.low>>>0>this.low>>>0?-1:1:this.sub(e).isNegative()?-1:1};me.comp=me.compare;me.negate=function(){return!this.unsigned&&this.eq(Mr)?Mr:this.not().add(_p)};me.neg=me.negate;me.add=function(e){Lr(e)||(e=ts(e));var t=this.high>>>16,o=this.high&65535,n=this.low>>>16,s=this.low&65535,a=e.high>>>16,i=e.high&65535,p=e.low>>>16,u=e.low&65535,c=0,l=0,m=0,d=0;return d+=s+u,m+=d>>>16,d&=65535,m+=n+p,l+=m>>>16,m&=65535,l+=o+i,c+=l>>>16,l&=65535,c+=t+a,c&=65535,vt(m<<16|d,c<<16|l,this.unsigned)};me.subtract=function(e){return Lr(e)||(e=ts(e)),this.add(e.neg())};me.sub=me.subtract;me.multiply=function(e){if(this.isZero())return bo;if(Lr(e)||(e=ts(e)),xo){var t=xo.mul(this.low,this.high,e.low,e.high);return vt(t,xo.get_high(),this.unsigned)}if(e.isZero())return bo;if(this.eq(Mr))return e.isOdd()?Mr:bo;if(e.eq(Mr))return this.isOdd()?Mr:bo;if(this.isNegative())return e.isNegative()?this.neg().mul(e.neg()):this.neg().mul(e).neg();if(e.isNegative())return this.mul(e.neg()).neg();if(this.lt(YI)&&e.lt(YI))return yo(this.toNumber()*e.toNumber(),this.unsigned);var o=this.high>>>16,n=this.high&65535,s=this.low>>>16,a=this.low&65535,i=e.high>>>16,p=e.high&65535,u=e.low>>>16,c=e.low&65535,l=0,m=0,d=0,f=0;return f+=a*c,d+=f>>>16,f&=65535,d+=s*c,m+=d>>>16,d&=65535,d+=a*u,m+=d>>>16,d&=65535,m+=n*c,l+=m>>>16,m&=65535,m+=s*u,l+=m>>>16,m&=65535,m+=a*p,l+=m>>>16,m&=65535,l+=o*c+n*u+s*p+a*i,l&=65535,vt(d<<16|f,l<<16|m,this.unsigned)};me.mul=me.multiply;me.divide=function(e){if(Lr(e)||(e=ts(e)),e.isZero())throw Error("division by zero");if(xo){if(!this.unsigned&&this.high===-2147483648&&e.low===-1&&e.high===-1)return this;var t=(this.unsigned?xo.div_u:xo.div_s)(this.low,this.high,e.low,e.high);return vt(t,xo.get_high(),this.unsigned)}if(this.isZero())return this.unsigned?Ji:bo;var o,n,s;if(this.unsigned){if(e.unsigned||(e=e.toUnsigned()),e.gt(this))return Ji;if(e.gt(this.shru(1)))return ZI;s=Ji}else{if(this.eq(Mr)){if(e.eq(_p)||e.eq(wb))return Mr;if(e.eq(Mr))return _p;var a=this.shr(1);return o=a.div(e).shl(1),o.eq(bo)?e.isNegative()?_p:wb:(n=this.sub(e.mul(o)),s=o.add(n.div(e)),s)}else if(e.eq(Mr))return this.unsigned?Ji:bo;if(this.isNegative())return e.isNegative()?this.neg().div(e.neg()):this.neg().div(e).neg();if(e.isNegative())return this.div(e.neg()).neg();s=bo}for(n=this;n.gte(e);){o=Math.max(1,Math.floor(n.toNumber()/e.toNumber()));for(var i=Math.ceil(Math.log(o)/Math.LN2),p=i<=48?1:Rm(2,i-48),u=yo(o),c=u.mul(e);c.isNegative()||c.gt(n);)o-=p,u=yo(o,this.unsigned),c=u.mul(e);u.isZero()&&(u=_p),s=s.add(u),n=n.sub(c)}return s};me.div=me.divide;me.modulo=function(e){if(Lr(e)||(e=ts(e)),xo){var t=(this.unsigned?xo.rem_u:xo.rem_s)(this.low,this.high,e.low,e.high);return vt(t,xo.get_high(),this.unsigned)}return this.sub(this.div(e).mul(e))};me.mod=me.modulo;me.rem=me.modulo;me.not=function(){return vt(~this.low,~this.high,this.unsigned)};me.and=function(e){return Lr(e)||(e=ts(e)),vt(this.low&e.low,this.high&e.high,this.unsigned)};me.or=function(e){return Lr(e)||(e=ts(e)),vt(this.low|e.low,this.high|e.high,this.unsigned)};me.xor=function(e){return Lr(e)||(e=ts(e)),vt(this.low^e.low,this.high^e.high,this.unsigned)};me.shiftLeft=function(e){return Lr(e)&&(e=e.toInt()),(e&=63)===0?this:e<32?vt(this.low<>>32-e,this.unsigned):vt(0,this.low<>>e|this.high<<32-e,this.high>>e,this.unsigned):vt(this.high>>e-32,this.high>=0?0:-1,this.unsigned)};me.shr=me.shiftRight;me.shiftRightUnsigned=function(e){if(Lr(e)&&(e=e.toInt()),e&=63,e===0)return this;var t=this.high;if(e<32){var o=this.low;return vt(o>>>e|t<<32-e,t>>>e,this.unsigned)}else return e===32?vt(t,0,this.unsigned):vt(t>>>e-32,0,this.unsigned)};me.shru=me.shiftRightUnsigned;me.shr_u=me.shiftRightUnsigned;me.toSigned=function(){return this.unsigned?vt(this.low,this.high,!1):this};me.toUnsigned=function(){return this.unsigned?this:vt(this.low,this.high,!0)};me.toBytes=function(e){return e?this.toBytesLE():this.toBytesBE()};me.toBytesLE=function(){var e=this.high,t=this.low;return[t&255,t>>>8&255,t>>>16&255,t>>>24,e&255,e>>>8&255,e>>>16&255,e>>>24]};me.toBytesBE=function(){var e=this.high,t=this.low;return[e>>>24,e>>>16&255,e>>>8&255,e&255,t>>>24,t>>>16&255,t>>>8&255,t&255]};It.fromBytes=function(e,t,o){return o?It.fromBytesLE(e,t):It.fromBytesBE(e,t)};It.fromBytesLE=function(e,t){return new It(e[0]|e[1]<<8|e[2]<<16|e[3]<<24,e[4]|e[5]<<8|e[6]<<16|e[7]<<24,t)};It.fromBytesBE=function(e,t){return new It(e[4]<<24|e[5]<<16|e[6]<<8|e[7],e[0]<<24|e[1]<<16|e[2]<<8|e[3],t)}});var M0=qt(()=>{});var L0=qt(()=>{});var Bk=qt((Lk,cC)=>{(function(r,e,t){function o(i){var p=this,u=a();p.next=function(){var c=2091639*p.s0+p.c*23283064365386963e-26;return p.s0=p.s1,p.s1=p.s2,p.s2=c-(p.c=c|0)},p.c=1,p.s0=u(" "),p.s1=u(" "),p.s2=u(" "),p.s0-=u(i),p.s0<0&&(p.s0+=1),p.s1-=u(i),p.s1<0&&(p.s1+=1),p.s2-=u(i),p.s2<0&&(p.s2+=1),u=null}function n(i,p){return p.c=i.c,p.s0=i.s0,p.s1=i.s1,p.s2=i.s2,p}function s(i,p){var u=new o(i),c=p&&p.state,l=u.next;return l.int32=function(){return u.next()*4294967296|0},l.double=function(){return l()+(l()*2097152|0)*11102230246251565e-32},l.quick=l,c&&(typeof c=="object"&&n(c,u),l.state=function(){return n(u,{})}),l}function a(){var i=4022871197,p=function(u){u=String(u);for(var c=0;c>>0,l-=i,l*=i,i=l>>>0,l-=i,i+=l*4294967296}return(i>>>0)*23283064365386963e-26};return p}e&&e.exports?e.exports=s:t&&t.amd?t(function(){return s}):this.alea=s})(Lk,typeof cC=="object"&&cC,typeof define=="function"&&define)});var zk=qt((Vk,lC)=>{(function(r,e,t){function o(a){var i=this,p="";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:p+=a;for(var u=0;u>>0)/4294967296};return c.double=function(){do var l=p.next()>>>11,m=(p.next()>>>0)/4294967296,d=(l+m)/(1<<21);while(d===0);return d},c.int32=p.next,c.quick=c,u&&(typeof u=="object"&&n(u,p),c.state=function(){return n(p,{})}),c}e&&e.exports?e.exports=s:t&&t.amd?t(function(){return s}):this.xor128=s})(Vk,typeof lC=="object"&&lC,typeof define=="function"&&define)});var Uk=qt((Wk,mC)=>{(function(r,e,t){function o(a){var i=this,p="";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:p+=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 p=new o(a),u=i&&i.state,c=function(){return(p.next()>>>0)/4294967296};return c.double=function(){do var l=p.next()>>>11,m=(p.next()>>>0)/4294967296,d=(l+m)/(1<<21);while(d===0);return d},c.int32=p.next,c.quick=c,u&&(typeof u=="object"&&n(u,p),c.state=function(){return n(p,{})}),c}e&&e.exports?e.exports=s:t&&t.amd?t(function(){return s}):this.xorwow=s})(Wk,typeof mC=="object"&&mC,typeof define=="function"&&define)});var Hk=qt((Gk,dC)=>{(function(r,e,t){function o(a){var i=this;i.next=function(){var u=i.x,c=i.i,l,m,d;return l=u[c],l^=l>>>7,m=l^l<<24,l=u[c+1&7],m^=l^l>>>10,l=u[c+3&7],m^=l^l>>>3,l=u[c+4&7],m^=l^l<<7,l=u[c+7&7],l=l^l<<13,m^=l^l<<9,u[c]=m,i.i=c+1&7,m};function p(u,c){var l,m,d=[];if(c===(c|0))m=d[0]=c;else for(c=""+c,l=0;l0;--l)u.next()}p(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 p=new o(a),u=i&&i.state,c=function(){return(p.next()>>>0)/4294967296};return c.double=function(){do var l=p.next()>>>11,m=(p.next()>>>0)/4294967296,d=(l+m)/(1<<21);while(d===0);return d},c.int32=p.next,c.quick=c,u&&(u.x&&n(u,p),c.state=function(){return n(p,{})}),c}e&&e.exports?e.exports=s:t&&t.amd?t(function(){return s}):this.xorshift7=s})(Gk,typeof dC=="object"&&dC,typeof define=="function"&&define)});var Kk=qt((qk,fC)=>{(function(r,e,t){function o(a){var i=this;i.next=function(){var u=i.w,c=i.X,l=i.i,m,d;return i.w=u=u+1640531527|0,d=c[l+34&127],m=c[l=l+1&127],d^=d<<13,m^=m<<17,d^=d>>>15,m^=m>>>12,d=c[l]=d^m,i.i=l,d+(u^u>>>16)|0};function p(u,c){var l,m,d,f,h,g=[],x=128;for(c===(c|0)?(m=c,c=null):(c=c+"\0",m=0,x=Math.max(x,c.length)),d=0,f=-32;f>>15,m^=m<<4,m^=m>>>13,f>=0&&(h=h+1640531527|0,l=g[f&127]^=m+h,d=l==0?d+1:0);for(d>=128&&(g[(c&&c.length||0)&127]=-1),d=127,f=4*128;f>0;--f)m=g[d+34&127],l=g[d=d+1&127],m^=m<<13,l^=l<<17,m^=m>>>15,l^=l>>>12,g[d]=m^l;u.w=h,u.X=g,u.i=d}p(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 p=new o(a),u=i&&i.state,c=function(){return(p.next()>>>0)/4294967296};return c.double=function(){do var l=p.next()>>>11,m=(p.next()>>>0)/4294967296,d=(l+m)/(1<<21);while(d===0);return d},c.int32=p.next,c.quick=c,u&&(u.X&&n(u,p),c.state=function(){return n(p,{})}),c}e&&e.exports?e.exports=s:t&&t.amd?t(function(){return s}):this.xor4096=s})(qk,typeof fC=="object"&&fC,typeof define=="function"&&define)});var Xk=qt((jk,hC)=>{(function(r,e,t){function o(a){var i=this,p="";i.next=function(){var c=i.b,l=i.c,m=i.d,d=i.a;return c=c<<25^c>>>7^l,l=l-m|0,m=m<<24^m>>>8^d,d=d-c|0,i.b=c=c<<20^c>>>12^l,i.c=l=l-m|0,i.d=m<<16^l>>>16^d,i.a=d-c|0},i.a=0,i.b=0,i.c=-1640531527,i.d=1367130551,a===Math.floor(a)?(i.a=a/4294967296|0,i.b=a|0):p+=a;for(var u=0;u>>0)/4294967296};return c.double=function(){do var l=p.next()>>>11,m=(p.next()>>>0)/4294967296,d=(l+m)/(1<<21);while(d===0);return d},c.int32=p.next,c.quick=c,u&&(typeof u=="object"&&n(u,p),c.state=function(){return n(p,{})}),c}e&&e.exports?e.exports=s:t&&t.amd?t(function(){return s}):this.tychei=s})(jk,typeof hC=="object"&&hC,typeof define=="function"&&define)});var Yk=qt(()=>{});var Zk=qt((Qk,kd)=>{(function(r,e,t){var o=256,n=6,s=52,a="random",i=t.pow(o,n),p=t.pow(2,s),u=p*2,c=o-1,l;function m(C,w,k){var _=[];w=w==!0?{entropy:!0}:w||{};var $=g(h(w.entropy?[C,b(e)]:C==null?x():C,3),_),A=new d(_),R=function(){for(var D=A.g(n),P=i,M=0;D=u;)D/=2,P/=2,M>>>=1;return(D+M)/P};return R.int32=function(){return A.g(4)|0},R.quick=function(){return A.g(4)/4294967296},R.double=R,g(b(A.S),e),(w.pass||k||function(D,P,M,L){return L&&(L.S&&f(L,A),D.state=function(){return f(A,{})}),M?(t[a]=D,P):D})(R,$,"global"in w?w.global:this==t,w.state)}function d(C){var w,k=C.length,_=this,$=0,A=_.i=_.j=0,R=_.S=[];for(k||(C=[k++]);${var eG=Bk(),tG=zk(),rG=Uk(),oG=Hk(),nG=Kk(),sG=Xk(),du=Zk();du.alea=eG;du.xor128=tG;du.xorwow=rG;du.xorshift7=oG;du.xor4096=nG;du.tychei=sG;Jk.exports=du});var Rl=qt(()=>{});var qw=qt(()=>{});var l3=qt(()=>{});var m3=qt(()=>{});var d3=qt(()=>{});var f3=qt((wg,jw)=>{var Kw=(()=>{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 J.buffer!=Oe&&Nt(J.buffer),mt}function o(){return J.buffer!=Oe&&Nt(J.buffer),at}function n(){return J.buffer!=Oe&&Nt(J.buffer),ft}function s(){return J.buffer!=Oe&&Nt(J.buffer),Fr}function a(){return J.buffer!=Oe&&Nt(J.buffer),Ot}function i(){return J.buffer!=Oe&&Nt(J.buffer),Kr}function p(){return J.buffer!=Oe&&Nt(J.buffer),er}var u=typeof e!="undefined"?e:{},c,l;u.ready=new Promise(function(F,B){c=F,l=B});var m;typeof process!="undefined"&&process.listeners&&(m={uncaughtException:process.listeners("uncaughtException"),unhandledRejection:process.listeners("unhandledRejection")});var d=Object.assign({},u),f=[],h="./this.program",g=(F,B)=>{throw B},x=typeof window=="object",b=typeof importScripts=="function",C=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",w=u.ENVIRONMENT_IS_PTHREAD||!1,k="";function _(F){return u.locateFile?u.locateFile(F,k):k+F}var $,A,R,D;function P(F){if(F instanceof Hi)return;q("exiting due to exception: "+F)}if(C){b?k=Rl().dirname(k)+"/":k=__dirname+"/";var M,L;typeof bm=="function"&&(M=qw(),L=Rl()),$=(B,ne)=>(B=L.normalize(B),M.readFileSync(B,ne?void 0:"utf8")),R=B=>{var ne=$(B,!0);return ne.buffer||(ne=new Uint8Array(ne)),ne},A=(B,ne,fe)=>{B=L.normalize(B),M.readFile(B,function(Te,Ze){Te?fe(Te):ne(Ze.buffer)})},process.argv.length>1&&(h=process.argv[1].replace(/\\/g,"/")),f=process.argv.slice(2),process.on("uncaughtException",function(B){if(!(B instanceof Hi))throw B}),process.on("unhandledRejection",function(B){throw B}),g=(B,ne)=>{if(Fo())throw process.exitCode=B,ne;P(ne),process.exit(B)},u.inspect=function(){return"[Emscripten Module object]"};let F;try{F=l3()}catch(B){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),B}global.Worker=F.Worker}else(x||b)&&(b?k=self.location.href:typeof document!="undefined"&&document.currentScript&&(k=document.currentScript.src),typeof r!="undefined"&&r&&(k=r),k.indexOf("blob:")!==0?k=k.substr(0,k.replace(/[?#].*/,"").lastIndexOf("/")+1):k="",C||($=F=>{var B=new XMLHttpRequest;return B.open("GET",F,!1),B.send(null),B.responseText},b&&(R=F=>{var B=new XMLHttpRequest;return B.open("GET",F,!1),B.responseType="arraybuffer",B.send(null),new Uint8Array(B.response)}),A=(F,B,ne)=>{var fe=new XMLHttpRequest;fe.open("GET",F,!0),fe.responseType="arraybuffer",fe.onload=()=>{if(fe.status==200||fe.status==0&&fe.response){B(fe.response);return}ne()},fe.onerror=ne,fe.send(null)}),D=F=>document.title=F);C&&typeof performance=="undefined"&&(global.performance=m3().performance);var W=console.log.bind(console),V=console.warn.bind(console);C&&(W=F=>M.writeSync(1,F+` `),V=F=>M.writeSync(2,F+` `));var U=u.print||W,q=u.printErr||V;Object.assign(u,d),d=null,u.arguments&&(f=u.arguments),u.thisProgram&&(h=u.thisProgram),u.quit&&(g=u.quit);var H=4,j=Atomics.load,X=Atomics.store,Z=Atomics.compareExchange,ee;u.wasmBinary&&(ee=u.wasmBinary);var Y=u.noExitRuntime||!0;typeof WebAssembly!="object"&&Xu("no native wasm support detected");var J,ie,pe=!1,he;function we(F,B){F||Xu(B)}var ve=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function $e(F,B,ne){for(var fe=B+ne,Te=B;F[Te]&&!(Te>=fe);)++Te;if(Te-B>16&&F.buffer&&ve)return ve.decode(F.buffer instanceof SharedArrayBuffer?F.slice(B,Te):F.subarray(B,Te));for(var Ze="";B>10,56320|Qr&1023)}}return Ze}function Le(F,B){return F?$e(o(),F,B):""}function nt(F,B,ne,fe){if(!(fe>0))return 0;for(var Te=ne,Ze=ne+fe-1,Ae=0;Ae=55296&&Pe<=57343){var zt=F.charCodeAt(++Ae);Pe=65536+((Pe&1023)<<10)|zt&1023}if(Pe<=127){if(ne>=Ze)break;B[ne++]=Pe}else if(Pe<=2047){if(ne+1>=Ze)break;B[ne++]=192|Pe>>6,B[ne++]=128|Pe&63}else if(Pe<=65535){if(ne+2>=Ze)break;B[ne++]=224|Pe>>12,B[ne++]=128|Pe>>6&63,B[ne++]=128|Pe&63}else{if(ne+3>=Ze)break;B[ne++]=240|Pe>>18,B[ne++]=128|Pe>>12&63,B[ne++]=128|Pe>>6&63,B[ne++]=128|Pe&63}}return B[ne]=0,ne-Te}function pt(F,B,ne){return nt(F,o(),B,ne)}var Oe,mt,at,ft,wt,Fr,Ot,Kr,er;w&&(Oe=u.buffer);function Nt(F){Oe=F,u.HEAP8=mt=new Int8Array(F),u.HEAP16=ft=new Int16Array(F),u.HEAP32=Fr=new Int32Array(F),u.HEAPU8=at=new Uint8Array(F),u.HEAPU16=wt=new Uint16Array(F),u.HEAPU32=Ot=new Uint32Array(F),u.HEAPF32=Kr=new Float32Array(F),u.HEAPF64=er=new Float64Array(F)}var tr=u.INITIAL_MEMORY||16777216;if(w)J=u.wasmMemory,Oe=u.buffer;else if(u.wasmMemory)J=u.wasmMemory;else if(J=new WebAssembly.Memory({initial:tr/65536,maximum:32768,shared:!0}),!(J.buffer instanceof SharedArrayBuffer))throw q("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"),C&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");J&&(Oe=J.buffer),tr=Oe.byteLength,Nt(Oe);var rr,jr=[],Xr=[],pr=[],Js=!1;function Fo(){return Y}function Ka(){if(u.preRun)for(typeof u.preRun=="function"&&(u.preRun=[u.preRun]);u.preRun.length;)Ac(u.preRun.shift());Pc(jr)}function Kt(){Js=!0,!w&&Pc(Xr)}function ea(){if(!w){if(u.postRun)for(typeof u.postRun=="function"&&(u.postRun=[u.postRun]);u.postRun.length;)SI(u.postRun.shift());Pc(pr)}}function Ac(F){jr.unshift(F)}function Rc(F){Xr.unshift(F)}function SI(F){pr.unshift(F)}var ja=0,ju=null,ta=null;function wI(F){ja++,u.monitorRunDependencies&&u.monitorRunDependencies(ja)}function II(F){if(ja--,u.monitorRunDependencies&&u.monitorRunDependencies(ja),ja==0&&(ju!==null&&(clearInterval(ju),ju=null),ta)){var B=ta;ta=null,B()}}function Xu(F){w?postMessage({cmd:"onAbort",arg:F}):u.onAbort&&u.onAbort(F),F="Aborted("+F+")",q(F),pe=!0,he=1,F+=". Build with -sASSERTIONS for more info.";var B=new WebAssembly.RuntimeError(F);throw l(B),B}var Ix="data:application/octet-stream;base64,";function jl(F){return F.startsWith(Ix)}function Fc(F){return F.startsWith("file://")}var fr;fr="tfjs-backend-wasm-threaded-simd.wasm",jl(fr)||(fr=_(fr));function Xl(F){try{if(F==fr&&ee)return new Uint8Array(ee);if(R)return R(F);throw"both async and sync fetching of the wasm failed"}catch(B){Xu(B)}}function vx(){if(!ee&&(x||b)){if(typeof fetch=="function"&&!Fc(fr))return fetch(fr,{credentials:"same-origin"}).then(function(F){if(!F.ok)throw"failed to load wasm binary file at '"+fr+"'";return F.arrayBuffer()}).catch(function(){return Xl(fr)});if(A)return new Promise(function(F,B){A(fr,function(ne){F(new Uint8Array(ne))},B)})}return Promise.resolve().then(function(){return Xl(fr)})}function kx(){var F={env:im,wasi_snapshot_preview1:im};function B(Ae,Pe){var zt=Ae.exports;if(u.asm=zt,Ox(u.asm._emscripten_tls_init),rr=u.asm.__indirect_function_table,Rc(u.asm.__wasm_call_ctors),ie=Pe,!w){var Qr=De.unusedWorkers.length;De.unusedWorkers.forEach(function(oa){De.loadWasmModuleToWorker(oa,function(){--Qr||II("wasm-instantiate")})})}}w||wI("wasm-instantiate");function ne(Ae){B(Ae.instance,Ae.module)}function fe(Ae){return vx().then(function(Pe){return WebAssembly.instantiate(Pe,F)}).then(function(Pe){return Pe}).then(Ae,function(Pe){q("failed to asynchronously prepare wasm: "+Pe),Xu(Pe)})}function Te(){return!ee&&typeof WebAssembly.instantiateStreaming=="function"&&!jl(fr)&&!Fc(fr)&&!C&&typeof fetch=="function"?fetch(fr,{credentials:"same-origin"}).then(function(Ae){var Pe=WebAssembly.instantiateStreaming(Ae,F);return Pe.then(ne,function(zt){return q("wasm streaming compile failed: "+zt),q("falling back to ArrayBuffer instantiation"),fe(ne)})}):fe(ne)}if(u.instantiateWasm)try{var Ze=u.instantiateWasm(F,B);return Ze}catch(Ae){q("Module.instantiateWasm callback failed with error: "+Ae),l(Ae)}return Te().catch(l),{}}var Nx,vI,Tx={};function Hi(F){this.name="ExitStatus",this.message="Program terminated with exit("+F+")",this.status=F}function _x(F){var B=De.pthreads[F];delete De.pthreads[F],B.terminate(),sb(F),De.runningWorkers.splice(De.runningWorkers.indexOf(B),1),B.pthread_ptr=0}function Ex(F){var B=De.pthreads[F];B.postMessage({cmd:"cancel"})}function Dc(F){var B=De.pthreads[F];we(B),De.returnWorkerToPool(B)}function Yl(F){var B=De.getNewWorker();if(!B)return 6;De.runningWorkers.push(B),De.pthreads[F.pthread_ptr]=B,B.pthread_ptr=F.pthread_ptr;var ne={cmd:"run",start_routine:F.startRoutine,arg:F.arg,pthread_ptr:F.pthread_ptr};return B.runPthread=()=>{ne.time=performance.now(),B.postMessage(ne,F.transferList)},B.loaded&&(B.runPthread(),delete B.runPthread),0}var Ql={varargs:void 0,get:function(){Ql.varargs+=4;var F=s()[Ql.varargs-4>>2];return F},getStr:function(F){var B=Le(F);return B}};function Oc(F){if(w)return Xa(1,1,F);he=F,Fo()||(De.terminateAllThreads(),u.onExit&&u.onExit(F),pe=!0),g(F,new Hi(F))}function kI(F,B){if(he=F,!B&&w)throw Jl(F),"unwind";Oc(F)}var Zl=kI;function $x(F){if(F instanceof Hi||F=="unwind")return he;g(1,F)}var De={unusedWorkers:[],runningWorkers:[],tlsInitFunctions:[],pthreads:{},init:function(){w?De.initWorker():De.initMainThread()},initMainThread:function(){for(var F=8;F--;)De.allocateUnusedWorker()},initWorker:function(){Y=!1},setExitStatus:function(F){he=F},terminateAllThreads:function(){for(var F of Object.values(De.pthreads))De.returnWorkerToPool(F);for(var F of De.unusedWorkers)F.terminate();De.unusedWorkers=[]},returnWorkerToPool:function(F){var B=F.pthread_ptr;delete De.pthreads[B],De.unusedWorkers.push(F),De.runningWorkers.splice(De.runningWorkers.indexOf(F),1),F.pthread_ptr=0,sb(B)},receiveObjectTransfer:function(F){},threadInitTLS:function(){De.tlsInitFunctions.forEach(F=>F())},loadWasmModuleToWorker:function(F,B){F.onmessage=ne=>{var fe=ne.data,Te=fe.cmd;if(F.pthread_ptr&&(De.currentProxiedOperationCallerThread=F.pthread_ptr),fe.targetThread&&fe.targetThread!=dm()){var Ze=De.pthreads[fe.targetThread];Ze?Ze.postMessage(fe,fe.transferList):q('Internal error! Worker sent a message "'+Te+'" to target pthread '+fe.targetThread+", but that thread no longer exists!"),De.currentProxiedOperationCallerThread=void 0;return}Te==="processProxyingQueue"?Mc(fe.queue):Te==="spawnThread"?Yl(fe):Te==="cleanupThread"?Dc(fe.thread):Te==="killThread"?_x(fe.thread):Te==="cancelThread"?Ex(fe.thread):Te==="loaded"?(F.loaded=!0,B&&B(F),F.runPthread&&(F.runPthread(),delete F.runPthread)):Te==="print"?U("Thread "+fe.threadId+": "+fe.text):Te==="printErr"?q("Thread "+fe.threadId+": "+fe.text):Te==="alert"?alert("Thread "+fe.threadId+": "+fe.text):fe.target==="setimmediate"?F.postMessage(fe):Te==="onAbort"?u.onAbort&&u.onAbort(fe.arg):Te&&q("worker sent an unknown command "+Te),De.currentProxiedOperationCallerThread=void 0},F.onerror=ne=>{var fe="worker sent an error!";throw q(fe+" "+ne.filename+":"+ne.lineno+": "+ne.message),ne},C&&(F.on("message",function(ne){F.onmessage({data:ne})}),F.on("error",function(ne){F.onerror(ne)}),F.on("detachedExit",function(){})),F.postMessage({cmd:"load",urlOrBlob:u.mainScriptUrlOrBlob||r,wasmMemory:J,wasmModule:ie})},allocateUnusedWorker:function(){var F=_("tfjs-backend-wasm-threaded-simd.worker.js");De.unusedWorkers.push(new Worker(F))},getNewWorker:function(){return De.unusedWorkers.length==0&&(De.allocateUnusedWorker(),De.loadWasmModuleToWorker(De.unusedWorkers[0])),De.unusedWorkers.pop()}};u.PThread=De;function Pc(F){for(;F.length>0;)F.shift()(u)}function Ax(F){var B=ab(),ne=F();return fm(B),ne}function NI(F){return F}function TI(F){var B=/\b_Z[\w\d_]+/g;return F.replace(B,function(ne){var fe=ne;return ne===fe?ne:fe+" ["+ne+"]"})}function Rx(){var F=dm(),B=s()[F+44>>2],ne=s()[F+48>>2],fe=B-ne;DI(B,fe),fm(B)}u.establishStackSpace=Rx;function Jl(F){if(w)return Xa(2,0,F);try{Zl(F)}catch(B){$x(B)}}var Yu=[];function Fx(F){var B=Yu[F];return B||(F>=Yu.length&&(Yu.length=F+1),Yu[F]=B=rr.get(F)),B}function Dx(F,B){var ne=Fx(F)(B);Fo()?De.setExitStatus(ne):FI(ne)}u.invokeEntryPoint=Dx;function _I(){var F=new Error;if(!F.stack){try{throw new Error}catch(B){F=B}if(!F.stack)return"(no stack trace available)"}return F.stack.toString()}function Ox(F){De.tlsInitFunctions.push(F)}function Px(F,B){t().set(F,B)}function Mx(F){$I(F,!b,1,!x),De.threadInitTLS()}function Lx(F){w?postMessage({cmd:"cleanupThread",thread:F}):Dc(F)}function em(F,B,ne,fe){return w?Xa(3,1,F,B,ne,fe):tm(F,B,ne,fe)}function tm(F,B,ne,fe){if(typeof SharedArrayBuffer=="undefined")return q("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;var Te=[],Ze=0;if(w&&(Te.length===0||Ze))return em(F,B,ne,fe);if(Ze)return Ze;var Ae={startRoutine:ne,pthread_ptr:F,arg:fe,transferList:Te};return w?(Ae.cmd="spawnThread",postMessage(Ae,Te),0):Yl(Ae)}function Bx(){return 2097152}var Vx=!0;function zx(){return Vx}function Mc(F){Atomics.store(s(),F>>2,1),dm()&&RI(F),Atomics.compareExchange(s(),F>>2,1,0)}u.executeNotifiedProxyingQueue=Mc;function Wx(F,B,ne,fe){if(F==B)setTimeout(()=>Mc(fe));else if(w)postMessage({targetThread:F,cmd:"processProxyingQueue",queue:fe});else{var Te=De.pthreads[F];if(!Te)return;Te.postMessage({cmd:"processProxyingQueue",queue:fe})}return 1}function Ux(F,B,ne){return-1}function Gx(){Xu("")}function qi(F){qi.shown||(qi.shown={}),qi.shown[F]||(qi.shown[F]=1,C&&(F="warning: "+F),q(F))}function Hx(){C||b||qi("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread")}function qx(){return Date.now()}function rm(){return 2147483648}function Kx(){return rm()}var Qu;C?Qu=()=>{var F=process.hrtime();return F[0]*1e3+F[1]/1e6}:w?Qu=()=>performance.now()-u.__performance_now_clock_drift:Qu=()=>performance.now();function jx(F,B,ne){o().copyWithin(F,B,B+ne)}function Xx(){return C?d3().cpus().length:navigator.hardwareConcurrency}function Xa(F,B){var ne=arguments.length-2,fe=arguments;return Ax(()=>{for(var Te=ne,Ze=hm(Te*8),Ae=Ze>>3,Pe=0;Pe>3,Te=0;Te>>16),Nt(J.buffer),1}catch(B){}}function Zx(F){var B=o().length;if(F=F>>>0,F<=B)return!1;var ne=rm();if(F>ne)return!1;let fe=(zt,Qr)=>zt+(Qr-zt%Qr)%Qr;for(var Te=1;Te<=4;Te*=2){var Ze=B*(1+.2/Te);Ze=Math.min(Ze,F+100663296);var Ae=Math.min(ne,fe(Math.max(F,Ze),65536)),Pe=Qx(Ae);if(Pe)return!0}return!1}function Jx(){throw"unwind"}function om(F){return w?Xa(4,1,F):52}function nm(F,B,ne,fe,Te){return w?Xa(5,1,F,B,ne,fe,Te):70}var ey=[null,[],[]];function ty(F,B){var ne=ey[F];B===0||B===10?((F===1?U:q)($e(ne,0)),ne.length=0):ne.push(B)}function sm(F,B,ne,fe){if(w)return Xa(6,1,F,B,ne,fe);for(var Te=0,Ze=0;Ze>2],Pe=a()[B+4>>2];B+=8;for(var zt=0;zt>2]=Te,0}function am(F){var B=u["_"+F];return B}function ry(F,B,ne,fe,Te){var Ze={string:Dr=>{var tp=0;if(Dr!=null&&Dr!==0){var MI=(Dr.length<<2)+1;tp=hm(MI),pt(Dr,tp,MI)}return tp},array:Dr=>{var tp=hm(Dr.length);return Px(Dr,tp),tp}};function Ae(Dr){return B==="string"?Le(Dr):B==="boolean"?Boolean(Dr):Dr}var Pe=am(F),zt=[],Qr=0;if(fe)for(var oa=0;oaAe==="number"||Ae==="boolean"),Ze=B!=="string";return Ze&&Te&&!fe?am(F):function(){return ry(F,B,ne,arguments,fe)}}De.init();var ny=[null,Oc,Jl,em,om,nm,sm],im={__emscripten_init_main_thread_js:Mx,__emscripten_thread_cleanup:Lx,__pthread_create_js:tm,_emscripten_default_pthread_stack_size:Bx,_emscripten_get_now_is_monotonic:zx,_emscripten_notify_task_queue:Wx,_emscripten_set_offscreencanvas_size:Ux,abort:Gx,emscripten_check_blocking_allowed:Hx,emscripten_date_now:qx,emscripten_get_heap_max:Kx,emscripten_get_now:Qu,emscripten_memcpy_big:jx,emscripten_num_logical_cores:Xx,emscripten_receive_on_main_thread_js:Yx,emscripten_resize_heap:Zx,emscripten_unwind_to_js_event_loop:Jx,exit:Zl,fd_close:om,fd_seek:nm,fd_write:sm,memory:J||u.wasmMemory},EI=kx(),sy=u.___wasm_call_ctors=function(){return(sy=u.___wasm_call_ctors=u.asm.__wasm_call_ctors).apply(null,arguments)},ay=u._init=function(){return(ay=u._init=u.asm.init).apply(null,arguments)},iy=u._init_with_threads_count=function(){return(iy=u._init_with_threads_count=u.asm.init_with_threads_count).apply(null,arguments)},uy=u._get_threads_count=function(){return(uy=u._get_threads_count=u.asm.get_threads_count).apply(null,arguments)},py=u._register_tensor=function(){return(py=u._register_tensor=u.asm.register_tensor).apply(null,arguments)},cy=u._dispose_data=function(){return(cy=u._dispose_data=u.asm.dispose_data).apply(null,arguments)},ly=u._dispose=function(){return(ly=u._dispose=u.asm.dispose).apply(null,arguments)},my=u._Abs=function(){return(my=u._Abs=u.asm.Abs).apply(null,arguments)},dy=u._Add=function(){return(dy=u._Add=u.asm.Add).apply(null,arguments)},fy=u._AddN=function(){return(fy=u._AddN=u.asm.AddN).apply(null,arguments)},hy=u._All=function(){return(hy=u._All=u.asm.All).apply(null,arguments)},gy=u._Any=function(){return(gy=u._Any=u.asm.Any).apply(null,arguments)},xy=u._ArgMax=function(){return(xy=u._ArgMax=u.asm.ArgMax).apply(null,arguments)},yy=u._AvgPool=function(){return(yy=u._AvgPool=u.asm.AvgPool).apply(null,arguments)},by=u._BatchMatMul=function(){return(by=u._BatchMatMul=u.asm.BatchMatMul).apply(null,arguments)},Cy=u._Ceil=function(){return(Cy=u._Ceil=u.asm.Ceil).apply(null,arguments)},Sy=u._ClipByValue=function(){return(Sy=u._ClipByValue=u.asm.ClipByValue).apply(null,arguments)},wy=u._Conv2D=function(){return(wy=u._Conv2D=u.asm.Conv2D).apply(null,arguments)},Iy=u._Conv2DBackpropInput=function(){return(Iy=u._Conv2DBackpropInput=u.asm.Conv2DBackpropInput).apply(null,arguments)},vy=u._Cos=function(){return(vy=u._Cos=u.asm.Cos).apply(null,arguments)},ky=u._Cosh=function(){return(ky=u._Cosh=u.asm.Cosh).apply(null,arguments)},Ny=u._CropAndResize=function(){return(Ny=u._CropAndResize=u.asm.CropAndResize).apply(null,arguments)},Ty=u._Cumprod=function(){return(Ty=u._Cumprod=u.asm.Cumprod).apply(null,arguments)},_y=u._Cumsum=function(){return(_y=u._Cumsum=u.asm.Cumsum).apply(null,arguments)},Ey=u._DepthToSpace=function(){return(Ey=u._DepthToSpace=u.asm.DepthToSpace).apply(null,arguments)},$y=u._DepthwiseConv2dNative=function(){return($y=u._DepthwiseConv2dNative=u.asm.DepthwiseConv2dNative).apply(null,arguments)},Ay=u._Elu=function(){return(Ay=u._Elu=u.asm.Elu).apply(null,arguments)},Ry=u._Equal=function(){return(Ry=u._Equal=u.asm.Equal).apply(null,arguments)},Fy=u._Exp=function(){return(Fy=u._Exp=u.asm.Exp).apply(null,arguments)},Dy=u._FlipLeftRight=function(){return(Dy=u._FlipLeftRight=u.asm.FlipLeftRight).apply(null,arguments)},Oy=u._Floor=function(){return(Oy=u._Floor=u.asm.Floor).apply(null,arguments)},Py=u._FloorDiv=function(){return(Py=u._FloorDiv=u.asm.FloorDiv).apply(null,arguments)},My=u._FusedBatchNorm=function(){return(My=u._FusedBatchNorm=u.asm.FusedBatchNorm).apply(null,arguments)},Ly=u._FusedConv2D=function(){return(Ly=u._FusedConv2D=u.asm.FusedConv2D).apply(null,arguments)},By=u._FusedDepthwiseConv2D=function(){return(By=u._FusedDepthwiseConv2D=u.asm.FusedDepthwiseConv2D).apply(null,arguments)},Vy=u._Gather=function(){return(Vy=u._Gather=u.asm.Gather).apply(null,arguments)},zy=u._GatherNd=function(){return(zy=u._GatherNd=u.asm.GatherNd).apply(null,arguments)},Wy=u._Greater=function(){return(Wy=u._Greater=u.asm.Greater).apply(null,arguments)},Uy=u._GreaterEqual=function(){return(Uy=u._GreaterEqual=u.asm.GreaterEqual).apply(null,arguments)},Gy=u._IsNan=function(){return(Gy=u._IsNan=u.asm.IsNan).apply(null,arguments)},Hy=u._LeakyRelu=function(){return(Hy=u._LeakyRelu=u.asm.LeakyRelu).apply(null,arguments)},qy=u._Less=function(){return(qy=u._Less=u.asm.Less).apply(null,arguments)},Ky=u._LessEqual=function(){return(Ky=u._LessEqual=u.asm.LessEqual).apply(null,arguments)},jy=u._Log=function(){return(jy=u._Log=u.asm.Log).apply(null,arguments)},Xy=u._LogicalAnd=function(){return(Xy=u._LogicalAnd=u.asm.LogicalAnd).apply(null,arguments)},Yy=u._LogicalNot=function(){return(Yy=u._LogicalNot=u.asm.LogicalNot).apply(null,arguments)},Qy=u._LogicalOr=function(){return(Qy=u._LogicalOr=u.asm.LogicalOr).apply(null,arguments)},Zy=u._LogicalXor=function(){return(Zy=u._LogicalXor=u.asm.LogicalXor).apply(null,arguments)},Jy=u._Max=function(){return(Jy=u._Max=u.asm.Max).apply(null,arguments)},eb=u._MaxPool=function(){return(eb=u._MaxPool=u.asm.MaxPool).apply(null,arguments)},um=u._Maximum=function(){return(um=u._Maximum=u.asm.Maximum).apply(null,arguments)},pm=u._Mean=function(){return(pm=u._Mean=u.asm.Mean).apply(null,arguments)},Bc=u._Min=function(){return(Bc=u._Min=u.asm.Min).apply(null,arguments)},tb=u._Minimum=function(){return(tb=u._Minimum=u.asm.Minimum).apply(null,arguments)},rb=u._MirrorPad=function(){return(rb=u._MirrorPad=u.asm.MirrorPad).apply(null,arguments)},Zu=u._Multiply=function(){return(Zu=u._Multiply=u.asm.Multiply).apply(null,arguments)},cm=u._Neg=function(){return(cm=u._Neg=u.asm.Neg).apply(null,arguments)},Ju=u._NonMaxSuppressionV3=function(){return(Ju=u._NonMaxSuppressionV3=u.asm.NonMaxSuppressionV3).apply(null,arguments)},ep=u._NonMaxSuppressionV4=function(){return(ep=u._NonMaxSuppressionV4=u.asm.NonMaxSuppressionV4).apply(null,arguments)},ob=u._NonMaxSuppressionV5=function(){return(ob=u._NonMaxSuppressionV5=u.asm.NonMaxSuppressionV5).apply(null,arguments)},G=u._NotEqual=function(){return(G=u._NotEqual=u.asm.NotEqual).apply(null,arguments)},oe=u._OneHot=function(){return(oe=u._OneHot=u.asm.OneHot).apply(null,arguments)},ke=u._PadV2=function(){return(ke=u._PadV2=u.asm.PadV2).apply(null,arguments)},je=u._Pow=function(){return(je=u._Pow=u.asm.Pow).apply(null,arguments)},Tt=u._Prelu=function(){return(Tt=u._Prelu=u.asm.Prelu).apply(null,arguments)},_t=u._Prod=function(){return(_t=u._Prod=u.asm.Prod).apply(null,arguments)},qe=u._RealDiv=function(){return(qe=u._RealDiv=u.asm.RealDiv).apply(null,arguments)},We=u._Reciprocal=function(){return(We=u._Reciprocal=u.asm.Reciprocal).apply(null,arguments)},Vt=u._Relu=function(){return(Vt=u._Relu=u.asm.Relu).apply(null,arguments)},Yr=u._Relu6=function(){return(Yr=u._Relu6=u.asm.Relu6).apply(null,arguments)},ra=u._ResizeBilinear=function(){return(ra=u._ResizeBilinear=u.asm.ResizeBilinear).apply(null,arguments)},lm=u._ResizeNearestNeighbor=function(){return(lm=u._ResizeNearestNeighbor=u.asm.ResizeNearestNeighbor).apply(null,arguments)},Vc=u._Reverse=function(){return(Vc=u._Reverse=u.asm.Reverse).apply(null,arguments)},nb=u._RotateWithOffset=function(){return(nb=u._RotateWithOffset=u.asm.RotateWithOffset).apply(null,arguments)},hr=u._Round=function(){return(hr=u._Round=u.asm.Round).apply(null,arguments)},Ya=u._Rsqrt=function(){return(Ya=u._Rsqrt=u.asm.Rsqrt).apply(null,arguments)},mm=u._ScatterNd=function(){return(mm=u._ScatterNd=u.asm.ScatterNd).apply(null,arguments)},BB=u._SelectV2=function(){return(BB=u._SelectV2=u.asm.SelectV2).apply(null,arguments)},VB=u._Sigmoid=function(){return(VB=u._Sigmoid=u.asm.Sigmoid).apply(null,arguments)},zB=u._Sin=function(){return(zB=u._Sin=u.asm.Sin).apply(null,arguments)},WB=u._Softmax=function(){return(WB=u._Softmax=u.asm.Softmax).apply(null,arguments)},UB=u._SparseFillEmptyRows=function(){return(UB=u._SparseFillEmptyRows=u.asm.SparseFillEmptyRows).apply(null,arguments)},GB=u._SparseReshape=function(){return(GB=u._SparseReshape=u.asm.SparseReshape).apply(null,arguments)},HB=u._SparseSegmentReduction=function(){return(HB=u._SparseSegmentReduction=u.asm.SparseSegmentReduction).apply(null,arguments)},qB=u._Sqrt=function(){return(qB=u._Sqrt=u.asm.Sqrt).apply(null,arguments)},KB=u._Square=function(){return(KB=u._Square=u.asm.Square).apply(null,arguments)},jB=u._SquaredDifference=function(){return(jB=u._SquaredDifference=u.asm.SquaredDifference).apply(null,arguments)},XB=u._Step=function(){return(XB=u._Step=u.asm.Step).apply(null,arguments)},YB=u._StridedSlice=function(){return(YB=u._StridedSlice=u.asm.StridedSlice).apply(null,arguments)},QB=u._Sub=function(){return(QB=u._Sub=u.asm.Sub).apply(null,arguments)},ZB=u._Sum=function(){return(ZB=u._Sum=u.asm.Sum).apply(null,arguments)},JB=u._Tan=function(){return(JB=u._Tan=u.asm.Tan).apply(null,arguments)},eV=u._Tanh=function(){return(eV=u._Tanh=u.asm.Tanh).apply(null,arguments)},tV=u._Tile=function(){return(tV=u._Tile=u.asm.Tile).apply(null,arguments)},rV=u._TopK=function(){return(rV=u._TopK=u.asm.TopK).apply(null,arguments)},oV=u._Transform=function(){return(oV=u._Transform=u.asm.Transform).apply(null,arguments)},nV=u._Transpose=function(){return(nV=u._Transpose=u.asm.Transpose).apply(null,arguments)},sV=u.__FusedMatMul=function(){return(sV=u.__FusedMatMul=u.asm._FusedMatMul).apply(null,arguments)},aV=u._malloc=function(){return(aV=u._malloc=u.asm.malloc).apply(null,arguments)},iV=u._free=function(){return(iV=u._free=u.asm.free).apply(null,arguments)},uV=u.__emscripten_tls_init=function(){return(uV=u.__emscripten_tls_init=u.asm._emscripten_tls_init).apply(null,arguments)},dm=u._pthread_self=function(){return(dm=u._pthread_self=u.asm.pthread_self).apply(null,arguments)},pV=u.___errno_location=function(){return(pV=u.___errno_location=u.asm.__errno_location).apply(null,arguments)},$I=u.__emscripten_thread_init=function(){return($I=u.__emscripten_thread_init=u.asm._emscripten_thread_init).apply(null,arguments)},cV=u.__emscripten_thread_crashed=function(){return(cV=u.__emscripten_thread_crashed=u.asm._emscripten_thread_crashed).apply(null,arguments)},lV=u._emscripten_main_thread_process_queued_calls=function(){return(lV=u._emscripten_main_thread_process_queued_calls=u.asm.emscripten_main_thread_process_queued_calls).apply(null,arguments)},mV=u._emscripten_main_browser_thread_id=function(){return(mV=u._emscripten_main_browser_thread_id=u.asm.emscripten_main_browser_thread_id).apply(null,arguments)},AI=u._emscripten_run_in_main_runtime_thread_js=function(){return(AI=u._emscripten_run_in_main_runtime_thread_js=u.asm.emscripten_run_in_main_runtime_thread_js).apply(null,arguments)},dV=u._emscripten_dispatch_to_thread_=function(){return(dV=u._emscripten_dispatch_to_thread_=u.asm.emscripten_dispatch_to_thread_).apply(null,arguments)},RI=u.__emscripten_proxy_execute_task_queue=function(){return(RI=u.__emscripten_proxy_execute_task_queue=u.asm._emscripten_proxy_execute_task_queue).apply(null,arguments)},sb=u.__emscripten_thread_free_data=function(){return(sb=u.__emscripten_thread_free_data=u.asm._emscripten_thread_free_data).apply(null,arguments)},FI=u.__emscripten_thread_exit=function(){return(FI=u.__emscripten_thread_exit=u.asm._emscripten_thread_exit).apply(null,arguments)},DI=u._emscripten_stack_set_limits=function(){return(DI=u._emscripten_stack_set_limits=u.asm.emscripten_stack_set_limits).apply(null,arguments)},ab=u.stackSave=function(){return(ab=u.stackSave=u.asm.stackSave).apply(null,arguments)},fm=u.stackRestore=function(){return(fm=u.stackRestore=u.asm.stackRestore).apply(null,arguments)},hm=u.stackAlloc=function(){return(hm=u.stackAlloc=u.asm.stackAlloc).apply(null,arguments)},fV=u.dynCall_iijjiiii=function(){return(fV=u.dynCall_iijjiiii=u.asm.dynCall_iijjiiii).apply(null,arguments)},hV=u.dynCall_jiji=function(){return(hV=u.dynCall_jiji=u.asm.dynCall_jiji).apply(null,arguments)};u.keepRuntimeAlive=Fo,u.wasmMemory=J,u.cwrap=oy,u.ExitStatus=Hi,u.PThread=De;var gm;ta=function F(){gm||OI(),gm||(ta=F)};function OI(F){if(F=F||f,ja>0)return;if(w){c(u),Kt(),postMessage({cmd:"loaded"});return}if(Ka(),ja>0)return;function B(){gm||(gm=!0,u.calledRun=!0,!pe&&(Kt(),c(u),u.onRuntimeInitialized&&u.onRuntimeInitialized(),ea()))}u.setStatus?(u.setStatus("Running..."),setTimeout(function(){setTimeout(function(){u.setStatus("")},1),B()},1)):B()}if(u.preInit)for(typeof u.preInit=="function"&&(u.preInit=[u.preInit]);u.preInit.length>0;)u.preInit.pop()();OI();var xm;m&&(xm={uncaughtException:process.listeners("uncaughtException").filter(function(F){return!m.uncaughtException.indexOf(F)>-1}),unhandledRejection:process.listeners("unhandledRejection").filter(function(F){return!m.unhandledRejection.indexOf(F)>-1})});var ym;if(typeof WasmBackendModule!="undefined")ym=WasmBackendModule;else if(typeof e!="undefined")ym=e;else throw new Error("Could not find wasm module in post.js");if(xm){var gV=ym._dispose;ym._dispose=function(){gV(),xm.uncaughtException.forEach(function(F){process.removeListener("uncaughtException",F)}),xm.unhandledRejection.forEach(function(F){process.removeListener("unhandledRejection",F)})}}return e.ready}})();typeof wg=="object"&&typeof jw=="object"?jw.exports=Kw:typeof define=="function"&&define.amd?define([],function(){return Kw}):typeof wg=="object"&&(wg.WasmBackendModuleThreadedSimd=Kw)});var g3=qt((kkt,h3)=>{h3.exports.wasmWorkerContents=`"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",data=>onmessage({data:data}));var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8"))},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}var initializedJS=false;var pendingNotifiedProxyingQueues=[];function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+" ");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=(info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports};self.onunhandledrejection=e=>{throw e.reason??e};self.onmessage=e=>{try{if(e.data.cmd==="load"){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)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance})}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.pthread_ptr,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInitTLS();if(!initializedJS){pendingNotifiedProxyingQueues.forEach(queue=>{Module["executeNotifiedProxyingQueue"](queue)});pendingNotifiedProxyingQueues=[];initializedJS=true}try{Module["invokeEntryPoint"](e.data.start_routine,e.data.arg)}catch(ex){if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["__emscripten_thread_exit"](ex.status)}}else{throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processProxyingQueue"){if(initializedJS){Module["executeNotifiedProxyingQueue"](e.data.queue)}else{pendingNotifiedProxyingQueues.push(e.data.queue)}}else if(e.data.cmd){err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}};`});var x3=qt((Ig,Yw)=>{var Xw=(()=>{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;t.ready=new Promise(function(G,oe){o=G,n=oe});var s;typeof process!="undefined"&&process.listeners&&(s={uncaughtException:process.listeners("uncaughtException"),unhandledRejection:process.listeners("unhandledRejection")});var a=Object.assign({},t),i=[],p="./this.program",u=(G,oe)=>{throw oe},c=typeof window=="object",l=typeof importScripts=="function",m=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",d="";function f(G){return t.locateFile?t.locateFile(G,d):d+G}var h,g,x,b;function C(G){if(G instanceof ju)return;$("exiting due to exception: "+G)}if(m){l?d=Rl().dirname(d)+"/":d=__dirname+"/";var w,k;typeof bm=="function"&&(w=qw(),k=Rl()),h=(G,oe)=>(G=k.normalize(G),w.readFileSync(G,oe?void 0:"utf8")),x=G=>{var oe=h(G,!0);return oe.buffer||(oe=new Uint8Array(oe)),oe},g=(G,oe,ke)=>{G=k.normalize(G),w.readFile(G,function(je,Tt){je?ke(je):oe(Tt.buffer)})},process.argv.length>1&&(p=process.argv[1].replace(/\\/g,"/")),i=process.argv.slice(2),process.on("uncaughtException",function(G){if(!(G instanceof ju))throw G}),process.on("unhandledRejection",function(G){throw G}),u=(G,oe)=>{if(at())throw process.exitCode=G,oe;C(oe),process.exit(G)},t.inspect=function(){return"[Emscripten Module object]"}}else(c||l)&&(l?d=self.location.href:typeof document!="undefined"&&document.currentScript&&(d=document.currentScript.src),r&&(d=r),d.indexOf("blob:")!==0?d=d.substr(0,d.replace(/[?#].*/,"").lastIndexOf("/")+1):d="",h=G=>{var oe=new XMLHttpRequest;return oe.open("GET",G,!1),oe.send(null),oe.responseText},l&&(x=G=>{var oe=new XMLHttpRequest;return oe.open("GET",G,!1),oe.responseType="arraybuffer",oe.send(null),new Uint8Array(oe.response)}),g=(G,oe,ke)=>{var je=new XMLHttpRequest;je.open("GET",G,!0),je.responseType="arraybuffer",je.onload=()=>{if(je.status==200||je.status==0&&je.response){oe(je.response);return}ke()},je.onerror=ke,je.send(null)},b=G=>document.title=G);var _=t.print||console.log.bind(console),$=t.printErr||console.warn.bind(console);Object.assign(t,a),a=null,t.arguments&&(i=t.arguments),t.thisProgram&&(p=t.thisProgram),t.quit&&(u=t.quit);var A=4,R;t.wasmBinary&&(R=t.wasmBinary);var D=t.noExitRuntime||!0;typeof WebAssembly!="object"&&pr("no native wasm support detected");var P,M=!1,L;function W(G,oe){G||pr(oe)}var V=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function U(G,oe,ke){for(var je=oe+ke,Tt=oe;G[Tt]&&!(Tt>=je);)++Tt;if(Tt-oe>16&&G.buffer&&V)return V.decode(G.subarray(oe,Tt));for(var _t="";oe>10,56320|Yr&1023)}}return _t}function q(G,oe){return G?U(ee,G,oe):""}function H(G,oe,ke,je){if(!(je>0))return 0;for(var Tt=ke,_t=ke+je-1,qe=0;qe=55296&&We<=57343){var Vt=G.charCodeAt(++qe);We=65536+((We&1023)<<10)|Vt&1023}if(We<=127){if(ke>=_t)break;oe[ke++]=We}else if(We<=2047){if(ke+1>=_t)break;oe[ke++]=192|We>>6,oe[ke++]=128|We&63}else if(We<=65535){if(ke+2>=_t)break;oe[ke++]=224|We>>12,oe[ke++]=128|We>>6&63,oe[ke++]=128|We&63}else{if(ke+3>=_t)break;oe[ke++]=240|We>>18,oe[ke++]=128|We>>12&63,oe[ke++]=128|We>>6&63,oe[ke++]=128|We&63}}return oe[ke]=0,ke-Tt}function j(G,oe,ke){return H(G,ee,oe,ke)}var X,Z,ee,Y,J,ie,pe,he,we;function ve(G){X=G,t.HEAP8=Z=new Int8Array(G),t.HEAP16=Y=new Int16Array(G),t.HEAP32=ie=new Int32Array(G),t.HEAPU8=ee=new Uint8Array(G),t.HEAPU16=J=new Uint16Array(G),t.HEAPU32=pe=new Uint32Array(G),t.HEAPF32=he=new Float32Array(G),t.HEAPF64=we=new Float64Array(G)}var $e=t.INITIAL_MEMORY||16777216,Le,nt=[],pt=[],Oe=[],mt=!1;function at(){return D}function ft(){if(t.preRun)for(typeof t.preRun=="function"&&(t.preRun=[t.preRun]);t.preRun.length;)Ot(t.preRun.shift());ta(nt)}function wt(){mt=!0,ta(pt)}function Fr(){if(t.postRun)for(typeof t.postRun=="function"&&(t.postRun=[t.postRun]);t.postRun.length;)er(t.postRun.shift());ta(Oe)}function Ot(G){nt.unshift(G)}function Kr(G){pt.unshift(G)}function er(G){Oe.unshift(G)}var Nt=0,tr=null,rr=null;function jr(G){Nt++,t.monitorRunDependencies&&t.monitorRunDependencies(Nt)}function Xr(G){if(Nt--,t.monitorRunDependencies&&t.monitorRunDependencies(Nt),Nt==0&&(tr!==null&&(clearInterval(tr),tr=null),rr)){var oe=rr;rr=null,oe()}}function pr(G){t.onAbort&&t.onAbort(G),G="Aborted("+G+")",$(G),M=!0,L=1,G+=". Build with -sASSERTIONS for more info.";var oe=new WebAssembly.RuntimeError(G);throw n(oe),oe}var Js="data:application/octet-stream;base64,";function Fo(G){return G.startsWith(Js)}function Ka(G){return G.startsWith("file://")}var Kt;Kt="tfjs-backend-wasm.wasm",Fo(Kt)||(Kt=f(Kt));function ea(G){try{if(G==Kt&&R)return new Uint8Array(R);if(x)return x(G);throw"both async and sync fetching of the wasm failed"}catch(oe){pr(oe)}}function Ac(){if(!R&&(c||l)){if(typeof fetch=="function"&&!Ka(Kt))return fetch(Kt,{credentials:"same-origin"}).then(function(G){if(!G.ok)throw"failed to load wasm binary file at '"+Kt+"'";return G.arrayBuffer()}).catch(function(){return ea(Kt)});if(g)return new Promise(function(G,oe){g(Kt,function(ke){G(new Uint8Array(ke))},oe)})}return Promise.resolve().then(function(){return ea(Kt)})}function Rc(){var G={env:Oc,wasi_snapshot_preview1:Oc};function oe(qe,We){var Vt=qe.exports;t.asm=Vt,P=t.asm.memory,ve(P.buffer),Le=t.asm.__indirect_function_table,Kr(t.asm.__wasm_call_ctors),Xr("wasm-instantiate")}jr("wasm-instantiate");function ke(qe){oe(qe.instance)}function je(qe){return Ac().then(function(We){return WebAssembly.instantiate(We,G)}).then(function(We){return We}).then(qe,function(We){$("failed to asynchronously prepare wasm: "+We),pr(We)})}function Tt(){return!R&&typeof WebAssembly.instantiateStreaming=="function"&&!Fo(Kt)&&!Ka(Kt)&&!m&&typeof fetch=="function"?fetch(Kt,{credentials:"same-origin"}).then(function(qe){var We=WebAssembly.instantiateStreaming(qe,G);return We.then(ke,function(Vt){return $("wasm streaming compile failed: "+Vt),$("falling back to ArrayBuffer instantiation"),je(ke)})}):je(ke)}if(t.instantiateWasm)try{var _t=t.instantiateWasm(G,oe);return _t}catch(qe){$("Module.instantiateWasm callback failed with error: "+qe),n(qe)}return Tt().catch(n),{}}var SI,ja;function ju(G){this.name="ExitStatus",this.message="Program terminated with exit("+G+")",this.status=G}function ta(G){for(;G.length>0;)G.shift()(t)}function wI(G){return G}function II(G){var oe=/\b_Z[\w\d_]+/g;return G.replace(oe,function(ke){var je=ke;return ke===je?ke:je+" ["+ke+"]"})}function Xu(){var G=new Error;if(!G.stack){try{throw new Error}catch(oe){G=oe}if(!G.stack)return"(no stack trace available)"}return G.stack.toString()}function Ix(G,oe){Z.set(G,oe)}function jl(){pr("")}function Fc(){return 2147483648}function fr(){return Fc()}function Xl(G,oe,ke){ee.copyWithin(G,oe,oe+ke)}function vx(G){try{return P.grow(G-X.byteLength+65535>>>16),ve(P.buffer),1}catch(oe){}}function kx(G){var oe=ee.length;G=G>>>0;var ke=Fc();if(G>ke)return!1;let je=(Vt,Yr)=>Vt+(Yr-Vt%Yr)%Yr;for(var Tt=1;Tt<=4;Tt*=2){var _t=oe*(1+.2/Tt);_t=Math.min(_t,G+100663296);var qe=Math.min(ke,je(Math.max(G,_t),65536)),We=vx(qe);if(We)return!0}return!1}var Nx={varargs:void 0,get:function(){Nx.varargs+=4;var G=ie[Nx.varargs-4>>2];return G},getStr:function(G){var oe=q(G);return oe}};function vI(G){return 52}function Tx(G,oe,ke,je,Tt){return 70}var Hi=[null,[],[]];function _x(G,oe){var ke=Hi[G];oe===0||oe===10?((G===1?_:$)(U(ke,0)),ke.length=0):ke.push(oe)}function Ex(G,oe,ke,je){for(var Tt=0,_t=0;_t>2],We=pe[oe+4>>2];oe+=8;for(var Vt=0;Vt>2]=Tt,0}function Dc(G){var oe=t["_"+G];return oe}function Yl(G,oe,ke,je,Tt){var _t={string:hr=>{var Ya=0;if(hr!=null&&hr!==0){var mm=(hr.length<<2)+1;Ya=Bc(mm),j(hr,Ya,mm)}return Ya},array:hr=>{var Ya=Bc(hr.length);return Ix(hr,Ya),Ya}};function qe(hr){return oe==="string"?q(hr):oe==="boolean"?Boolean(hr):hr}var We=Dc(G),Vt=[],Yr=0;if(je)for(var ra=0;raqe==="number"||qe==="boolean"),_t=oe!=="string";return _t&&Tt&&!je?Dc(G):function(){return Yl(G,oe,ke,arguments,je)}}var Oc={abort:jl,emscripten_get_heap_max:fr,emscripten_memcpy_big:Xl,emscripten_resize_heap:kx,fd_close:vI,fd_seek:Tx,fd_write:Ex},kI=Rc(),Zl=t.___wasm_call_ctors=function(){return(Zl=t.___wasm_call_ctors=t.asm.__wasm_call_ctors).apply(null,arguments)},$x=t._init=function(){return($x=t._init=t.asm.init).apply(null,arguments)},De=t._init_with_threads_count=function(){return(De=t._init_with_threads_count=t.asm.init_with_threads_count).apply(null,arguments)},Pc=t._get_threads_count=function(){return(Pc=t._get_threads_count=t.asm.get_threads_count).apply(null,arguments)},Ax=t._register_tensor=function(){return(Ax=t._register_tensor=t.asm.register_tensor).apply(null,arguments)},NI=t._dispose_data=function(){return(NI=t._dispose_data=t.asm.dispose_data).apply(null,arguments)},TI=t._dispose=function(){return(TI=t._dispose=t.asm.dispose).apply(null,arguments)},Rx=t._Abs=function(){return(Rx=t._Abs=t.asm.Abs).apply(null,arguments)},Jl=t._Add=function(){return(Jl=t._Add=t.asm.Add).apply(null,arguments)},Yu=t._AddN=function(){return(Yu=t._AddN=t.asm.AddN).apply(null,arguments)},Fx=t._All=function(){return(Fx=t._All=t.asm.All).apply(null,arguments)},Dx=t._Any=function(){return(Dx=t._Any=t.asm.Any).apply(null,arguments)},_I=t._ArgMax=function(){return(_I=t._ArgMax=t.asm.ArgMax).apply(null,arguments)},Ox=t._AvgPool=function(){return(Ox=t._AvgPool=t.asm.AvgPool).apply(null,arguments)},Px=t._BatchMatMul=function(){return(Px=t._BatchMatMul=t.asm.BatchMatMul).apply(null,arguments)},Mx=t._Ceil=function(){return(Mx=t._Ceil=t.asm.Ceil).apply(null,arguments)},Lx=t._ClipByValue=function(){return(Lx=t._ClipByValue=t.asm.ClipByValue).apply(null,arguments)},em=t._Conv2D=function(){return(em=t._Conv2D=t.asm.Conv2D).apply(null,arguments)},tm=t._Conv2DBackpropInput=function(){return(tm=t._Conv2DBackpropInput=t.asm.Conv2DBackpropInput).apply(null,arguments)},Bx=t._Cos=function(){return(Bx=t._Cos=t.asm.Cos).apply(null,arguments)},Vx=t._Cosh=function(){return(Vx=t._Cosh=t.asm.Cosh).apply(null,arguments)},zx=t._CropAndResize=function(){return(zx=t._CropAndResize=t.asm.CropAndResize).apply(null,arguments)},Mc=t._Cumprod=function(){return(Mc=t._Cumprod=t.asm.Cumprod).apply(null,arguments)},Wx=t._Cumsum=function(){return(Wx=t._Cumsum=t.asm.Cumsum).apply(null,arguments)},Ux=t._DepthToSpace=function(){return(Ux=t._DepthToSpace=t.asm.DepthToSpace).apply(null,arguments)},Gx=t._DepthwiseConv2dNative=function(){return(Gx=t._DepthwiseConv2dNative=t.asm.DepthwiseConv2dNative).apply(null,arguments)},qi=t._Elu=function(){return(qi=t._Elu=t.asm.Elu).apply(null,arguments)},Hx=t._Equal=function(){return(Hx=t._Equal=t.asm.Equal).apply(null,arguments)},qx=t._Exp=function(){return(qx=t._Exp=t.asm.Exp).apply(null,arguments)},rm=t._FlipLeftRight=function(){return(rm=t._FlipLeftRight=t.asm.FlipLeftRight).apply(null,arguments)},Kx=t._Floor=function(){return(Kx=t._Floor=t.asm.Floor).apply(null,arguments)},Qu=t._FloorDiv=function(){return(Qu=t._FloorDiv=t.asm.FloorDiv).apply(null,arguments)},jx=t._FusedBatchNorm=function(){return(jx=t._FusedBatchNorm=t.asm.FusedBatchNorm).apply(null,arguments)},Xx=t._FusedConv2D=function(){return(Xx=t._FusedConv2D=t.asm.FusedConv2D).apply(null,arguments)},Xa=t._FusedDepthwiseConv2D=function(){return(Xa=t._FusedDepthwiseConv2D=t.asm.FusedDepthwiseConv2D).apply(null,arguments)},Lc=t._Gather=function(){return(Lc=t._Gather=t.asm.Gather).apply(null,arguments)},Yx=t._GatherNd=function(){return(Yx=t._GatherNd=t.asm.GatherNd).apply(null,arguments)},Qx=t._Greater=function(){return(Qx=t._Greater=t.asm.Greater).apply(null,arguments)},Zx=t._GreaterEqual=function(){return(Zx=t._GreaterEqual=t.asm.GreaterEqual).apply(null,arguments)},Jx=t._IsNan=function(){return(Jx=t._IsNan=t.asm.IsNan).apply(null,arguments)},om=t._LeakyRelu=function(){return(om=t._LeakyRelu=t.asm.LeakyRelu).apply(null,arguments)},nm=t._Less=function(){return(nm=t._Less=t.asm.Less).apply(null,arguments)},ey=t._LessEqual=function(){return(ey=t._LessEqual=t.asm.LessEqual).apply(null,arguments)},ty=t._Log=function(){return(ty=t._Log=t.asm.Log).apply(null,arguments)},sm=t._LogicalAnd=function(){return(sm=t._LogicalAnd=t.asm.LogicalAnd).apply(null,arguments)},am=t._LogicalNot=function(){return(am=t._LogicalNot=t.asm.LogicalNot).apply(null,arguments)},ry=t._LogicalOr=function(){return(ry=t._LogicalOr=t.asm.LogicalOr).apply(null,arguments)},oy=t._LogicalXor=function(){return(oy=t._LogicalXor=t.asm.LogicalXor).apply(null,arguments)},ny=t._Max=function(){return(ny=t._Max=t.asm.Max).apply(null,arguments)},im=t._MaxPool=function(){return(im=t._MaxPool=t.asm.MaxPool).apply(null,arguments)},EI=t._Maximum=function(){return(EI=t._Maximum=t.asm.Maximum).apply(null,arguments)},sy=t._Mean=function(){return(sy=t._Mean=t.asm.Mean).apply(null,arguments)},ay=t._Min=function(){return(ay=t._Min=t.asm.Min).apply(null,arguments)},iy=t._Minimum=function(){return(iy=t._Minimum=t.asm.Minimum).apply(null,arguments)},uy=t._MirrorPad=function(){return(uy=t._MirrorPad=t.asm.MirrorPad).apply(null,arguments)},py=t._Multiply=function(){return(py=t._Multiply=t.asm.Multiply).apply(null,arguments)},cy=t._Neg=function(){return(cy=t._Neg=t.asm.Neg).apply(null,arguments)},ly=t._NonMaxSuppressionV3=function(){return(ly=t._NonMaxSuppressionV3=t.asm.NonMaxSuppressionV3).apply(null,arguments)},my=t._NonMaxSuppressionV4=function(){return(my=t._NonMaxSuppressionV4=t.asm.NonMaxSuppressionV4).apply(null,arguments)},dy=t._NonMaxSuppressionV5=function(){return(dy=t._NonMaxSuppressionV5=t.asm.NonMaxSuppressionV5).apply(null,arguments)},fy=t._NotEqual=function(){return(fy=t._NotEqual=t.asm.NotEqual).apply(null,arguments)},hy=t._OneHot=function(){return(hy=t._OneHot=t.asm.OneHot).apply(null,arguments)},gy=t._PadV2=function(){return(gy=t._PadV2=t.asm.PadV2).apply(null,arguments)},xy=t._Pow=function(){return(xy=t._Pow=t.asm.Pow).apply(null,arguments)},yy=t._Prelu=function(){return(yy=t._Prelu=t.asm.Prelu).apply(null,arguments)},by=t._Prod=function(){return(by=t._Prod=t.asm.Prod).apply(null,arguments)},Cy=t._RealDiv=function(){return(Cy=t._RealDiv=t.asm.RealDiv).apply(null,arguments)},Sy=t._Reciprocal=function(){return(Sy=t._Reciprocal=t.asm.Reciprocal).apply(null,arguments)},wy=t._Relu=function(){return(wy=t._Relu=t.asm.Relu).apply(null,arguments)},Iy=t._Relu6=function(){return(Iy=t._Relu6=t.asm.Relu6).apply(null,arguments)},vy=t._ResizeBilinear=function(){return(vy=t._ResizeBilinear=t.asm.ResizeBilinear).apply(null,arguments)},ky=t._ResizeNearestNeighbor=function(){return(ky=t._ResizeNearestNeighbor=t.asm.ResizeNearestNeighbor).apply(null,arguments)},Ny=t._Reverse=function(){return(Ny=t._Reverse=t.asm.Reverse).apply(null,arguments)},Ty=t._RotateWithOffset=function(){return(Ty=t._RotateWithOffset=t.asm.RotateWithOffset).apply(null,arguments)},_y=t._Round=function(){return(_y=t._Round=t.asm.Round).apply(null,arguments)},Ey=t._Rsqrt=function(){return(Ey=t._Rsqrt=t.asm.Rsqrt).apply(null,arguments)},$y=t._ScatterNd=function(){return($y=t._ScatterNd=t.asm.ScatterNd).apply(null,arguments)},Ay=t._SelectV2=function(){return(Ay=t._SelectV2=t.asm.SelectV2).apply(null,arguments)},Ry=t._Sigmoid=function(){return(Ry=t._Sigmoid=t.asm.Sigmoid).apply(null,arguments)},Fy=t._Sin=function(){return(Fy=t._Sin=t.asm.Sin).apply(null,arguments)},Dy=t._Softmax=function(){return(Dy=t._Softmax=t.asm.Softmax).apply(null,arguments)},Oy=t._SparseFillEmptyRows=function(){return(Oy=t._SparseFillEmptyRows=t.asm.SparseFillEmptyRows).apply(null,arguments)},Py=t._SparseReshape=function(){return(Py=t._SparseReshape=t.asm.SparseReshape).apply(null,arguments)},My=t._SparseSegmentReduction=function(){return(My=t._SparseSegmentReduction=t.asm.SparseSegmentReduction).apply(null,arguments)},Ly=t._Sqrt=function(){return(Ly=t._Sqrt=t.asm.Sqrt).apply(null,arguments)},By=t._Square=function(){return(By=t._Square=t.asm.Square).apply(null,arguments)},Vy=t._SquaredDifference=function(){return(Vy=t._SquaredDifference=t.asm.SquaredDifference).apply(null,arguments)},zy=t._Step=function(){return(zy=t._Step=t.asm.Step).apply(null,arguments)},Wy=t._StridedSlice=function(){return(Wy=t._StridedSlice=t.asm.StridedSlice).apply(null,arguments)},Uy=t._Sub=function(){return(Uy=t._Sub=t.asm.Sub).apply(null,arguments)},Gy=t._Sum=function(){return(Gy=t._Sum=t.asm.Sum).apply(null,arguments)},Hy=t._Tan=function(){return(Hy=t._Tan=t.asm.Tan).apply(null,arguments)},qy=t._Tanh=function(){return(qy=t._Tanh=t.asm.Tanh).apply(null,arguments)},Ky=t._Tile=function(){return(Ky=t._Tile=t.asm.Tile).apply(null,arguments)},jy=t._TopK=function(){return(jy=t._TopK=t.asm.TopK).apply(null,arguments)},Xy=t._Transform=function(){return(Xy=t._Transform=t.asm.Transform).apply(null,arguments)},Yy=t._Transpose=function(){return(Yy=t._Transpose=t.asm.Transpose).apply(null,arguments)},Qy=t.__FusedMatMul=function(){return(Qy=t.__FusedMatMul=t.asm._FusedMatMul).apply(null,arguments)},Zy=t._malloc=function(){return(Zy=t._malloc=t.asm.malloc).apply(null,arguments)},Jy=t._free=function(){return(Jy=t._free=t.asm.free).apply(null,arguments)},eb=t.___errno_location=function(){return(eb=t.___errno_location=t.asm.__errno_location).apply(null,arguments)},um=t.stackSave=function(){return(um=t.stackSave=t.asm.stackSave).apply(null,arguments)},pm=t.stackRestore=function(){return(pm=t.stackRestore=t.asm.stackRestore).apply(null,arguments)},Bc=t.stackAlloc=function(){return(Bc=t.stackAlloc=t.asm.stackAlloc).apply(null,arguments)},tb=t.dynCall_iijjiiii=function(){return(tb=t.dynCall_iijjiiii=t.asm.dynCall_iijjiiii).apply(null,arguments)},rb=t.dynCall_jiji=function(){return(rb=t.dynCall_jiji=t.asm.dynCall_jiji).apply(null,arguments)};t.cwrap=Ql;var Zu;rr=function G(){Zu||cm(),Zu||(rr=G)};function cm(G){if(G=G||i,Nt>0||(ft(),Nt>0))return;function oe(){Zu||(Zu=!0,t.calledRun=!0,!M&&(wt(),o(t),t.onRuntimeInitialized&&t.onRuntimeInitialized(),Fr()))}t.setStatus?(t.setStatus("Running..."),setTimeout(function(){setTimeout(function(){t.setStatus("")},1),oe()},1)):oe()}if(t.preInit)for(typeof t.preInit=="function"&&(t.preInit=[t.preInit]);t.preInit.length>0;)t.preInit.pop()();cm();var Ju;s&&(Ju={uncaughtException:process.listeners("uncaughtException").filter(function(G){return!s.uncaughtException.indexOf(G)>-1}),unhandledRejection:process.listeners("unhandledRejection").filter(function(G){return!s.unhandledRejection.indexOf(G)>-1})});var ep;if(typeof e!="undefined")ep=e;else if(typeof WasmBackendModuleThreadedSimd!="undefined")ep=WasmBackendModuleThreadedSimd;else throw new Error("Could not find wasm module in post.js");if(Ju){var ob=ep._dispose;ep._dispose=function(){ob(),Ju.uncaughtException.forEach(function(G){process.removeListener("uncaughtException",G)}),Ju.unhandledRejection.forEach(function(G){process.removeListener("unhandledRejection",G)})}}return e.ready}})();typeof Ig=="object"&&typeof Yw=="object"?Yw.exports=Xw:typeof define=="function"&&define.amd?define([],function(){return Xw}):typeof Ig=="object"&&(Ig.WasmBackendModule=Xw)});var Do=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}},Zr=class{refCount(e){return Or("refCount")}incRef(e){return Or("incRef")}timerAvailable(){return!0}time(e){return Or("time")}read(e){return Or("read")}readSync(e){return Or("readSync")}readToGPU(e,t){return Or("readToGPU")}numDataIds(){return Or("numDataIds")}disposeData(e,t){return Or("disposeData")}write(e,t,o){return Or("write")}move(e,t,o,n,s){return Or("move")}createTensorFromTexture(e,t,o){return Or("createTensorFromTexture")}memory(){return Or("memory")}floatPrecision(){return Or("floatPrecision")}epsilon(){return this.floatPrecision()===32?1e-7:1e-4}dispose(){return Or("dispose")}};function Or(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 LI(r){let e=r.length,t=0;for(;e>0;)t=Math.random()*e|0,e--,Cm(r,e,t)}function vV(r,e){if(r.length!==e.length)throw new 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=0;for(;t>0;)o=Math.random()*t|0,t--,Cm(r,t,o),Cm(e,t,o)}function op(r,e,t){return Math.max(r,Math.min(e,t))}function kV(r){return r%2===0?r:r+1}function Cm(r,e,t){let o=r[e];r[e]=r[t],r[t]=o}function NV(r){let e=0;for(let t=0;tt+` Shapes ${r} and ${e} must match`)}function Jr(r){E(r!=null,()=>"The input to the tensor constructor must be a non-null value.")}function Oo(r,e=[],t=!1){if(e==null&&(e=[]),Array.isArray(r)||Wt(r)&&!t)for(let o=0;o0,t,o){return new Promise((n,s)=>{let a=0,i=()=>{if(r()){n();return}a++;let p=e(a);if(t!=null&&a>=t){s();return}o!=null?o(i,p):setTimeout(i,p)};i()})}function DV(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} / ${t}`);let n=r.slice();return n[o]=e/t,n}function Qa(r,e){let t=e.length;return r=r==null?e.map((o,n)=>n):[].concat(r),E(r.every(o=>o>=-t&&o`All values in axis param must be in range [-${t}, ${t}) but got axis ${r}`),E(r.every(o=>na(o)),()=>`All values in axis param must be integers but got axis ${r}`),r.map(o=>o<0?t+o:o)}function pb(r,e){let t=[],o=[],n=e!=null&&Array.isArray(e)&&e.length===0,s=e==null||n?null:Qa(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 cb(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 lb(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 ${r}`);return t}function mb(r,e){for(let t=0;te+=t.length),e}function Po(r){return typeof r=="string"||r instanceof String}function BI(r){return typeof r=="boolean"}function VI(r){return typeof r=="number"}function np(r){return Array.isArray(r)?np(r[0]):r instanceof Float32Array?"float32":r instanceof Int32Array||r instanceof Uint8Array||r instanceof Uint8ClampedArray?"int32":VI(r)?"float32":Po(r)?"string":BI(r)?"bool":"float32"}function fs(r){return!!(r&&r.constructor&&r.call&&r.apply)}function sp(r,e){for(let t=e;t=0;--o)t[o]=t[o+1]*r[o+1];return t}function zI(r,e,t,o=!1){let n=new Array;if(e.length===1){let s=e[0]*(o?2:1);for(let a=0;ap*u)*(o?2:1);for(let p=0;pn*s)*(t?2:1);if(o===0)return[];if(o!==e.length)throw new Error(`[${r}] does not match the input size ${e.length}${t?" for a complex tensor":""}.`);return zI(0,r,e,t)}function zc(r,e){let t=ap(r,e);for(let o=0;oo*n,1);if(e==null||e==="float32")return Ki(r,new Float32Array(t));if(e==="int32")return Ki(r,new Int32Array(t));if(e==="bool")return Ki(r,new Uint8Array(t));throw new Error(`Unknown data type ${e}`)}function yt(r){r.forEach(e=>{E(Number.isInteger(e)&&e>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${r}].`)})}function MV(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]=WV(n,s)})}};function VV(r){let e={};return r.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(t,...o)=>(zV(e,o[0],o[1]),o.join("="))),e}function zV(r,e,t){r[decodeURIComponent(e)]=decodeURIComponent(t||"")}function WV(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 O(){return hb}var hb=null;function UI(r){hb=r}var gb;function xb(){if(gb==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");gb=r}return gb}function UV(){let r=xb();return r._tfGlobals==null&&(r._tfGlobals=new Map),r._tfGlobals}function Gc(r,e){let t=UV();if(t.has(r))return t.get(r);{let o=e();return t.set(r,o),t.get(r)}}var gs="Abs",sa="Acos",aa="Acosh",eo="Add",Mo="AddN",Lo="All",Bo="Any",Vo="ArgMax",Za="ArgMin",ia="Asin",ua="Asinh",pa="Atan",ca="Atanh",la="Atan2",zo="AvgPool",wm="AvgPoolGrad",ip="AvgPool3D",Im="AvgPool3DGrad",Wo="BatchMatMul",xs="BatchToSpaceND",Ja="Bincount",wne="BroadcastTo",up="BroadcastArgs",co="Cast",Uo="Ceil",lo="ClipByValue",ei="Complex",pp="ComplexAbs",ys="Concat",Go="Conv2D",cp="Conv2DBackpropFilter",Ho="Conv2DBackpropInput",lp="Conv3D",vm="Conv3DBackpropFilterV2",mp="Conv3DBackpropInputV2",qo="Cos",Ko="Cosh",jo="Cumprod",Xo="Cumsum",Yo="CropAndResize",ti="DenseBincount",Qo="DepthToSpace",Zo="DepthwiseConv2dNative",dp="DepthwiseConv2dNativeBackpropFilter",fp="DepthwiseConv2dNativeBackpropInput",hp="Diag",gp="Dilation2D",yb="Dilation2DBackpropInput",bb="Dilation2DBackpropFilter",Jo="RealDiv",ri="Einsum",en="Elu",km="EluGrad",ma="Erf",tn="Equal",rn="Exp",bs="ExpandDims",da="Expm1",oi="FFT",Cs="Fill",on="FlipLeftRight",nn="Floor",sn="FloorDiv",an="FusedBatchNorm",Ss="GatherV2",un="GatherNd",pn="Greater",cn="GreaterEqual",mo="Identity",ni="IFFT",si="Imag",fa="IsFinite",ha="IsInf",ln="IsNan",mn="LeakyRelu",dn="Less",fn="LessEqual",xp="LinSpace",hn="Log",ga="Log1p",gn="LogicalAnd",xn="LogicalNot",xa="LogicalOr",GI="LogicalXor",Ine="LogSoftmax",vne="LowerBound",yp="LRN",Nm="LRNGrad",yn="Max",bn="Maximum",Cn="MaxPool",Tm="MaxPoolGrad",bp="MaxPool3D",_m="MaxPool3DGrad",Cp="MaxPoolWithArgmax",Sn="Mean",wn="Min",In="Minimum",vn="MirrorPad",ya="Mod",Sp="Multinomial",kn="Multiply",ws="Neg",Nn="NotEqual",Tn="NonMaxSuppressionV3",ba="NonMaxSuppressionV4",_n="NonMaxSuppressionV5",Is="OnesLike",En="OneHot",vs="Pack",$n="PadV2",kne="Pool",An="Pow",Rn="Prelu",Fn="Prod",wp="RaggedGather",Ip="RaggedRange",vp="RaggedTensorToTensor",ks="Range",ai="Real",Dn="Reciprocal",On="Relu",Ns="Reshape",Pn="ResizeNearestNeighbor",Em="ResizeNearestNeighborGrad",Mn="ResizeBilinear",$m="ResizeBilinearGrad",Ln="Relu6",Bn="Reverse",Ca="Round",Vn="Rsqrt",zn="ScatterNd",ii="SearchSorted",Ts="Select",Xi="Selu",_s="Slice",Wn="Sin",Sa="Sinh",Yi="Sign",Un="Sigmoid",Qi="Softplus",Gn="Sqrt",Hn="Sum",Es="SpaceToBatchND",$s="SplitV",qn="Softmax",ui="SparseFillEmptyRows",wa="SparseReshape",pi="SparseSegmentMean",ci="SparseSegmentSum",li="SparseToDense",Kn="SquaredDifference",mi="Square",jn="StridedSlice",As="StringNGrams",di="StringSplit",fi="StringToHashBucketFast",Xn="Sub",Yn="Tan",Qn="Tanh",to="Tile",Zn="TopK",Jn="Transform",ro="Transpose",kp="Unique",Rs="Unpack",Np="UnsortedSegmentSum",Nne="UpperBound",Fs="ZerosLike",Ds="Step",Zi="FromPixels",es="RotateWithOffset",fo="_FusedMatMul",ho="FusedConv2D",go="FusedDepthwiseConv2D";function Os(...r){O().getBool("IS_TEST")||O().getBool("PROD")||console.warn(...r)}function GV(...r){O().getBool("IS_TEST")||O().getBool("PROD")||console.log(...r)}var Tp=Gc("kernelRegistry",()=>new Map),Hc=Gc("gradRegistry",()=>new Map);function qc(r,e){let t=Sb(r,e);return Tp.get(t)}function Cb(r){return Hc.get(r)}function Am(r){let e=Tp.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 Ia(r){let{kernelName:e,backendName:t}=r,o=Sb(e,t);Tp.has(o)&&Os(`The kernel '${e}' for backend '${t}' is already registered`),Tp.set(o,r)}function Ane(r){let{kernelName:e}=r;Hc.has(e)&&O().getBool("DEBUG")&&Os(`Overriding the gradient for '${e}'`),Hc.set(e,r)}function Rne(r,e){let t=Sb(r,e);if(!Tp.has(t))throw new Error(`The kernel '${r}' for backend '${e}' is not registered`);Tp.delete(t)}function Fne(r){if(!Hc.has(r))throw new Error(`The gradient '${r}' for backend is not registered`);Hc.delete(r)}function Dne(r,e){Am(r).forEach(o=>{let n=Object.assign({},o,{backendName:e});Ia(n)})}function Sb(r,e){return`${e}_${r}`}var y={};Ue(y,{arraysEqual:()=>Pr,assert:()=>E,assertNonNegativeIntegerDimensions:()=>yt,assertNonNull:()=>Jr,assertShapesMatch:()=>ht,bytesFromStringArray:()=>fb,bytesPerElement:()=>Sm,checkConversionForErrors:()=>mb,clamp:()=>op,computeStrides:()=>hs,createScalarValue:()=>QV,createShuffledIndices:()=>RV,decodeString:()=>Ap,distSquared:()=>_V,encodeString:()=>gi,fetch:()=>JV,fingerPrint64:()=>YV,flatten:()=>Oo,getArrayFromDType:()=>lb,getTypedArrayFromDType:()=>cb,hasEncodingLoss:()=>OV,hexToLong:()=>Kc,indexToLoc:()=>LV,inferDtype:()=>np,inferFromImplicitShape:()=>DV,isBoolean:()=>BI,isFunction:()=>fs,isInt:()=>na,isNumber:()=>VI,isPromise:()=>Wc,isScalarShape:()=>EV,isString:()=>Po,isTypedArray:()=>Wt,isValidDtype:()=>db,locToIndex:()=>MV,makeOnesTypedArray:()=>zc,makeZerosNestedTypedArray:()=>PV,makeZerosTypedArray:()=>ap,nearestDivisor:()=>sp,nearestLargerEven:()=>kV,now:()=>ou,parseAxisParam:()=>Qa,randUniform:()=>TV,repeatedTry:()=>FV,rightPad:()=>ji,shuffle:()=>LI,shuffleCombo:()=>vV,sizeFromShape:()=>ze,sizeToSquarishShape:()=>AV,squeezeShape:()=>pb,sum:()=>NV,swap:()=>Cm,tanh:()=>$V,toNestedArray:()=>Ki,toTypedArray:()=>$p});var kb=rp(r0());var ru=kb.default||kb;function Kc(r){return ru.fromString(r,!0,16)}var n0=Kc("c3a5c85c97cb3127"),tu=Kc("b492b66fbe98f273"),gr=Kc("9ae16a3b2f90404f");function vb(r){return r.xor(r.shru(47))}function s0(r,e,t){let o=r.slice(e,e+t);return ru.fromBytes(Array.from(o),!0,!0)}function bt(r,e){return s0(r,e,8)}function o0(r,e){return s0(r,e,4)}function jt(r,e){return e===0?r:r.shru(e).or(r.shl(64-e))}function hi(r,e,t=Kc("9ddfea08eb382d69")){let o=r.xor(e).mul(t);o=o.xor(o.shru(47));let n=e.xor(o).mul(t);return n=n.xor(n.shru(47)),n=n.mul(t),n}function qV(r,e,t,o,n,s){n=n.add(r),s=jt(s.add(n).add(o),21);let a=n;return n=n.add(e),n=n.add(t),s=s.add(jt(n,44)),[n.add(o),s.add(a)]}function Fm(r,e,t,o){return qV(bt(r,e),bt(r,e+8),bt(r,e+16),bt(r,e+24),t,o)}function KV(r,e=r.length){if(e>=8){let t=gr.add(e*2),o=bt(r,0).add(gr),n=bt(r,e-8),s=jt(n,37).mul(t).add(o),a=jt(o,25).add(n).mul(t);return hi(s,a,t)}if(e>=4){let t=gr.add(e*2),o=o0(r,0);return hi(o.shl(3).add(e),o0(r,e-4),t)}if(e>0){let t=r[0],o=r[e>>1],n=r[e-1],s=t+(o<<8),a=e+(n<<2);return vb(gr.mul(s).xor(n0.mul(a))).mul(gr)}return gr}function jV(r,e=r.length){let t=gr.add(e*2),o=bt(r,0).mul(tu),n=bt(r,8),s=bt(r,e-8).mul(t),a=bt(r,e-16).mul(gr);return hi(jt(o.add(n),43).add(jt(s,30)).add(a),o.add(jt(n.add(gr),18)).add(s),t)}function XV(r,e=r.length){let t=gr.add(e*2),o=bt(r,0).mul(gr),n=bt(r,8),s=bt(r,e-8).mul(t),a=bt(r,e-16).mul(gr),i=jt(o.add(n),43).add(jt(s,30)).add(a),p=hi(i,o.add(jt(n.add(gr),18)).add(s),t),u=bt(r,16).mul(t),c=bt(r,24),l=i.add(bt(r,e-32)).mul(t),m=p.add(bt(r,e-24)).mul(t);return hi(jt(u.add(c),43).add(jt(l,30)).add(m),u.add(jt(c.add(o),18)).add(l),t)}function YV(r,e=r.length){let t=ru.fromNumber(81,!0);if(e<=32)return e<=16?KV(r,e):jV(r,e);if(e<=64)return XV(r,e);let o=t,n=t.mul(tu).add(113),s=vb(n.mul(gr).add(113)).mul(gr),a=[ru.UZERO,ru.UZERO],i=[ru.UZERO,ru.UZERO];o=o.mul(gr).add(bt(r,0));let p=0,u=(e-1>>6)*64,c=u+(e-1&63)-63;do o=jt(o.add(n).add(a[0]).add(bt(r,p+8)),37).mul(tu),n=jt(n.add(a[1]).add(bt(r,p+48)),42).mul(tu),o=o.xor(i[1]),n=n.add(a[0]).add(bt(r,p+40)),s=jt(s.add(i[0]),33).mul(tu),a=Fm(r,p,a[1].mul(tu),o.add(i[0])),i=Fm(r,p+32,s.add(i[1]),n.add(bt(r,p+16))),[s,o]=[o,s],p+=64;while(p!==u);let l=tu.add(s.and(255).shl(1));return p=c,i[0]=i[0].add(e-1&63),a[0]=a[0].add(i[0]),i[0]=i[0].add(a[0]),o=jt(o.add(n).add(a[0]).add(bt(r,p+8)),37).mul(l),n=jt(n.add(a[1]).add(bt(r,p+48)),42).mul(l),o=o.xor(i[1].mul(9)),n=n.add(a[0].mul(9).add(bt(r,p+40))),s=jt(s.add(i[0]),33).mul(l),a=Fm(r,p,a[1].mul(l),o.add(i[0])),i=Fm(r,p+32,s.add(i[1]),n.add(bt(r,p+16))),[s,o]=[o,s],hi(hi(a[0],i[0],l).add(vb(n).mul(n0)).add(s),hi(a[1],i[1],l).add(o),l)}function QV(r,e){return e==="string"?gi(r):$p([r],e)}function ZV(r,e){return r instanceof Float32Array&&e==="float32"||r instanceof Int32Array&&e==="int32"||r instanceof Uint8Array&&e==="bool"}function $p(r,e){if(e==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(r)&&(r=Oo(r)),O().getBool("DEBUG")&&mb(r,e),ZV(r,e))return r;if(e==null||e==="float32"||e==="complex64")return new Float32Array(r);if(e==="int32")return new Int32Array(r);if(e==="bool"){let t=new Uint8Array(r.length);for(let o=0;o{n=o()},a,i=ou();if(this.backendTimer.timerAvailable())a=this.backendTimer.time(s);else{s();for(let u of n)u.dataSync();a=Promise.resolve({kernelMs:ou()-i})}if(O().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let u=0;u{ez(l,c.dtype,e)})}return{kernelName:e,outputs:n,inputs:t,timeMs:a.then(u=>u.kernelMs),extraInfo:a.then(u=>u.getExtraProfileInfo!=null?u.getExtraProfileInfo():"")}}logKernelProfile(e){let{kernelName:t,outputs:o,timeMs:n,inputs:s,extraInfo:a}=e;o.forEach(i=>{Promise.all([i.data(),n,a]).then(p=>{this.logger.logKernelProfile(t,i,p[0],p[1],s,p[2])})})}};function ez(r,e,t){if(e!=="float32")return!1;for(let o=0;o0?h:""} `}}console.log(`%c${p} %c${i} %c${u}D ${l} %c${c} %c${m} %c${a}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function a0(r,e,t){let o={},n={};for(let p=0;po[h.id]=!0),d=!0,n[u.id]=!0;break}if(d)break}}let s={};s[t.id]=!0;let a={};for(let p=r.length-1;p>=0;p--){let u=r[p],c=u.inputs;for(let l=0;l=0;n--){let s=e[n],a=[];if(s.outputs.forEach(p=>{let u=r[p.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 p in s.inputs){if(!(p in i))throw new Error(`Cannot backprop through input ${p}. Available gradients found: ${Object.keys(i)}.`);let u=t(()=>i[p]());if(u.dtype!=="float32")throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${p} must have 'float32' dtype, but has '${u.dtype}'`);let c=s.inputs[p];if(!Pr(u.shape,c.shape))throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${p}' 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 l=r[c.id];r[c.id]=o(l,u),l.dispose()}}}}var u0=20,jc=3,Tb=7;function p0(r,e,t,o){let n=hs(e),s=tz(r,e,t,n),a=e.length,i=Om(r,e,t,n,s),p=["Tensor"];return o&&(p.push(` dtype: ${t}`),p.push(` rank: ${a}`),p.push(` shape: [${e}]`),p.push(" values:")),p.push(i.map(u=>" "+u).join(` `)),p.join(` `)}function tz(r,e,t,o){let n=ze(e),s=o[o.length-1],a=new Array(s).fill(0),i=e.length,p=t==="complex64"?Yc(r):r;if(i>1)for(let u=0;uu0){let g=jc*a,x=Array.from(r.slice(0,g)),b=Array.from(r.slice((i-jc)*a,i*a));return t==="complex64"&&(x=Yc(x),b=Yc(b)),["["+x.map((C,w)=>Xc(C,n[w],t)).join(", ")+", ..., "+b.map((C,w)=>Xc(C,n[i-jc+w],t)).join(", ")+"]"]}return["["+(t==="complex64"?Yc(r):Array.from(r)).map((g,x)=>Xc(g,n[x],t)).join(", ")+"]"]}let u=e.slice(1),c=o.slice(1),l=o[0]*a,m=[];if(i>u0){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||lb(t,this.size),this.strides=hs(e)}set(e,...t){t.length===0&&(t=[0]),E(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;nAp(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}dataToGPU(e){return this.throwIfDisposed(),rs().readToGPU(this.dataId,e)}dataSync(){this.throwIfDisposed();let e=rs().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Ap(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 rs().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(rs().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Rp.print(this,e)}clone(){return this.throwIfDisposed(),Rp.clone(this)}toString(e=!1){let t=this.dataSync();return p0(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Rp.cast(this,e)}variable(e=!0,t,o){return this.throwIfDisposed(),rs().makeVariable(this,e,t,o)}};Object.defineProperty(it,Symbol.hasInstance,{value:r=>!!r&&r.data!=null&&r.dataSync!=null&&r.throwIfDisposed!=null});function oz(){return Gc("Tensor",()=>it)}oz();var va=class extends it{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(!Pr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);rs().disposeTensor(this),this.dataId=e.dataId,rs().incRef(this,null)}dispose(){rs().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(va,Symbol.hasInstance,{value:r=>r instanceof it&&r.assign!=null&&r.assign instanceof Function});var h0={};Ue(h0,{assertTypesMatch:()=>Fb,getTensorsInContainer:()=>Qc,isTensorInList:()=>sz,makeTypesMatch:()=>Re});var _b;(function(r){r.R0="R0",r.R1="R1",r.R2="R2",r.R3="R3",r.R4="R4",r.R5="R5",r.R6="R6"})(_b||(_b={}));var Eb;(function(r){r.float32="float32",r.int32="int32",r.bool="int32",r.complex64="complex64"})(Eb||(Eb={}));var $b;(function(r){r.float32="float32",r.int32="int32",r.bool="bool",r.complex64="complex64"})($b||($b={}));var Ab;(function(r){r.float32="float32",r.int32="float32",r.bool="float32",r.complex64="complex64"})(Ab||(Ab={}));var Rb;(function(r){r.float32="complex64",r.int32="complex64",r.bool="complex64",r.complex64="complex64"})(Rb||(Rb={}));var nz={float32:Ab,int32:Eb,bool:$b,complex64:Rb};function dt(r,e){if(r==="string"||e==="string"){if(r==="string"&&e==="string")return"string";throw new Error(`Can not upcast ${r} with ${e}`)}return nz[r][e]}function ka(r){return dt(r,"int32")}function Re(r,e){if(r.dtype===e.dtype)return[r,e];let t=dt(r.dtype,e.dtype);return[r.cast(t),e.cast(t)]}function Fb(r,e){E(r.dtype===e.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`)}function sz(r,e){return e.some(t=>t.id===r.id)}function Qc(r){let e=[];return f0(r,e,new Set),e}function f0(r,e,t){if(r==null)return;if(r instanceof it){e.push(r);return}if(!az(r))return;let o=r;for(let n in o){let s=o[n];t.has(s)||(t.add(s),f0(s,e,t))}}function az(r){return Array.isArray(r)||typeof r=="object"}function Db(r){return r.kernelName!=null}var Pm=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()}},xi=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Pm}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){Am(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 ${e}, no registration found.`);try{let o=t.factory();if(o&&!(o instanceof Zr)&&typeof o.then=="function"){let n=++this.pendingBackendInitId,s=o.then(a=>n(nthis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;tthis.startScope(o),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n))}scopedRun(e,t,o){e();try{let n=o();return t(),n}catch(n){throw t(),n}}nextTensorId(){return xi.nextTensorId++}nextVariableId(){return xi.nextVariableId++}clone(e){let t=T.runKernel(mo,{x:e}),o={x:e},n=a=>({x:()=>{let i="float32",p={x:a},u={dtype:i};return T.runKernel(co,p,u)}}),s=[];return this.addTapeNode(this.state.activeScope.name,o,[t],n,s,{}),t}runKernel(e,t,o){if(this.backendName==null&&this.backend,!(qc(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,o){let n=this.backend.numDataIds(),s=0;o.forEach(p=>{s+=p.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-s-a;if(i>0)throw new Error(`Backend '${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 p,u=Db(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Db(e)){let{kernelName:f,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=qc(f,this.backendName);E(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let w=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,w);o=this.saveTensorsForBackwardMode(k)}return w}}else{let{forwardFunc:f}=e,h=g=>{!n||(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=e,m=Db(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),n&&this.addTapeNode(u,c,t,m,o,l),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(f=>c[f]!=null?c[f].shape:null),outputShapes:t.map(f=>f.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(p)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=Cb(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?(E(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 p=o.filter((u,c)=>a[c]);return i.concat(p)}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"&&Po(e[0])&&(s=e.map(p=>gi(p)));let a=n.write(s,t,o),i=new it(t,o,a,this.nextTensorId());if(this.trackTensor(i,n),o==="string"){let p=this.state.tensorInfo.get(a),u=fb(s);this.state.numBytes+=u-p.bytes,p.bytes=u}return i}makeTensorFromDataId(e,t,o,n){o=o||"float32";let s={dataId:e,shape:t,dtype:o};return this.makeTensorFromTensorInfo(s,n)}makeTensorFromTensorInfo(e,t){let{dataId:o,shape:n,dtype:s}=e,a=new it(n,s,o,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,o,n){o=o||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let s=new va(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*Sm(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 va||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*Sm(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},p=Cb(e);p!=null&&(n=p.gradFunc),n!=null&&(i.gradient=u=>(u=u.map((c,l)=>{if(c==null){let m=o[l],d=ap(m.size,m.dtype);return this.makeTensor(d,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=Qc(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(E(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));E(s instanceof it,()=>"The result y returned by f() must be a tensor.");let a=a0(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?iz(s.shape):o,i0(i,a,u=>this.tidy(u),uz);let p=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:p}})}customGrad(e){return E(fs(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{E(t.every(i=>i instanceof it),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let o,n={};t.forEach((i,p)=>{n[p]=i});let s=(i,p)=>(o=e(...t,p),E(o.value instanceof it,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),E(fs(o.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),o.value),a=(i,p)=>{let u=o.gradFunc(i,p),c=Array.isArray(u)?u:[u];E(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(...)."),E(c.every(m=>m instanceof it),()=>"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 l={};return c.forEach((m,d)=>{l[d]=()=>m}),l};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)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){let t=ou(),o=await this.backend.time(e);return o.wallMs=ou()-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 Pm;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}};xi.nextTensorId=0;xi.nextVariableId=0;function iz(r){let e=zc(ze(r),"float32");return T.makeTensor(e,r,"float32")}function Ob(){let r=xb();if(r._tfengine==null){let e=new Uc(r);r._tfengine=new xi(e)}return UI(r._tfengine.ENV),l0(()=>r._tfengine),r._tfengine}var T=Ob();function uz(r,e){let t={a:r,b:e};return T.runKernel(eo,t)}var yi={};Ue(yi,{isBrowser:()=>Mb,isMobile:()=>lz,mockIsMobile:()=>cz});function pz(){return typeof navigator!="undefined"&&navigator!=null}var Pb;function cz(r){Pb=r}function lz(r){if(Pb!==void 0)return Pb;if(r||pz()){if(r||(r=navigator),r.product==="ReactNative")return!0;let e=r.userAgent||r.vendor||(typeof window!="undefined"?window.opera:"");if(!e){let t=r;return t.userAgentData&&t.userAgentData.mobile}return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(e)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(e.substr(0,4))}return!1}function Mb(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var oo=O();oo.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.")});oo.registerFlag("IS_BROWSER",()=>Mb());oo.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");oo.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));oo.registerFlag("PROD",()=>!1);oo.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>oo.getBool("DEBUG"));oo.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);oo.registerFlag("IS_TEST",()=>!1);oo.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);oo.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);oo.registerFlag("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU",()=>!1);oo.registerFlag("USE_SETTIMEOUTCUSTOM",()=>!1);function or(r,e){let t=r;if(Wt(r))return e==="string"?[]:[r.length];if(typeof r=="object"&&"texture"in r){let n=r.channels||"RGBA";return[r.height,r.width*n.length]}if(!Array.isArray(r))return[];let o=[];for(;Array.isArray(t)||Wt(t)&&e!=="string";)o.push(t.length),t=t[0];return Array.isArray(r)&&O().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&x0(r,o,[]),o}function x0(r,e,t){if(t=t||[],!Array.isArray(r)&&!Wt(r)){E(e.length===0,()=>`Element arr[${t.join("][")}] is a primitive, but should be an array/TypedArray of ${e[0]} elements`);return}E(e.length>0,()=>`Element arr[${t.join("][")}] should be a primitive, but is an array of ${r.length} elements`),E(r.length===e[0],()=>`Element arr[${t.join("][")}] should have ${e[0]} elements, but has ${r.length} elements`);let o=e.slice(1);for(let n=0;n=0&&(n=o),g0(o,n,e,t),r==null||!Wt(r)&&!Array.isArray(r)&&typeof r!="number"&&typeof r!="boolean"&&typeof r!="string"){let p=r==null?"null":r.constructor.name;throw new Error(`Argument '${e}' passed to '${t}' must be a Tensor or TensorLike, but got '${p}'`)}let s=or(r,n);!Wt(r)&&!Array.isArray(r)&&(r=[r]);let i=n!=="string"?$p(r,n):Oo(r,[],!0);return T.makeTensor(i,s,n)}function Na(r,e,t,o="numeric"){if(!Array.isArray(r))throw new Error(`Argument ${e} passed to ${t} must be a \`Tensor[]\` or \`TensorLike[]\``);return r.map((s,a)=>v(s,`${e}[${a}]`,t,o))}var Lb="__op";function N(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+Lb;let n=(...s)=>{T.startScope(t);try{let a=o(...s);return Wc(a)&&console.error("Cannot return a Promise inside of tidy."),T.endScope(a),a}catch(a){throw T.endScope(null),a}};return Object.defineProperty(n,"name",{value:t,configurable:!0}),n}function mz(r,e){let t=v(r,"real","complex"),o=v(e,"imag","complex");ht(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 T.runKernel(ei,n)}var Tr=N({complex_:mz});function xr(r,e,t,o){if(o==null&&(o=np(r)),o==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(typeof r=="object"&&"texture"in r){if(o!=="float32"&&o!=="int32")throw new Error(`Creating tensor from texture only supports 'float32'|'int32' dtype, while the dtype is ${o}.`);return r.channels=r.channels||"RGBA",T.backend.createTensorFromTexture(r,e||t,o)}if(!Wt(r)&&!Array.isArray(r)&&typeof r!="number"&&typeof r!="boolean"&&typeof r!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(e!=null){yt(e);let n=ze(e),s=ze(t);E(n===s,()=>`Based on the provided shape, [${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 (${e}). `)}}return!Wt(r)&&!Array.isArray(r)&&(r=[r]),e=e||t,r=o!=="string"?$p(r,o):Oo(r,[],!0),T.makeTensor(r,e,o)}function nr(r,e,t){let o=or(r,t);return xr(r,e,o,t)}var Zc={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8};var Mm=4;async function b0(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 p.bytes(),d=m.reduce((g,x)=>g+x.length,0)+Mm*m.length,f=new Uint8Array(d),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 y0(r){return Bb?Buffer.byteLength(r):new Blob([r]).size}function C0(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 Bm(r,e){let t={modelTopology:r.modelTopology,format:r.format,generatedBy:r.generatedBy,convertedBy:r.convertedBy,weightsManifest:e};return r.signature!=null&&(t.signature=r.signature),r.userDefinedMetadata!=null&&(t.userDefinedMetadata=r.userDefinedMetadata),r.modelInitializer!=null&&(t.modelInitializer=r.modelInitializer),r.initializerSignature!=null&&(t.initializerSignature=r.initializerSignature),r.trainingConfig!=null&&(t.trainingConfig=r.trainingConfig),t}function zb(r,e,t){let o={modelTopology:r.modelTopology,format:r.format,generatedBy:r.generatedBy,convertedBy:r.convertedBy};if(r.trainingConfig!=null&&(o.trainingConfig=r.trainingConfig),r.weightsManifest!=null){if(!e)throw new Error("modelJSON has weightsManifest but weightSpecs is null");if(!t)throw new Error("modelJSON has weightsManifest but weightData is null");o.weightSpecs=e,o.weightData=t}return r.signature!=null&&(o.signature=r.signature),r.userDefinedMetadata!=null&&(o.userDefinedMetadata=r.userDefinedMetadata),r.modelInitializer!=null&&(o.modelInitializer=r.modelInitializer),r.initializerSignature!=null&&(o.initializerSignature=r.initializerSignature),o}async function Dp(r,e){let t,o;return r.weightsManifest!=null&&([t,o]=await e(r.weightsManifest)),zb(r,t,o)}function Ps(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:y0(JSON.stringify(r.modelTopology)),weightSpecsBytes:r.weightSpecs==null?0:y0(JSON.stringify(r.weightSpecs)),weightDataBytes:r.weightData==null?0:r.weightData.byteLength}}function Vm(r){let e=[];for(let t of r)e.push(...t.weights);return e}function fz(){let r=t=>{let o=t<<13,n=0;for(;(o&8388608)===0;)n-=8388608,o<<=1;return o&=-8388609,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 hz(){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 gz(){let r=new Uint32Array(64);for(let e=0;e<64;e++)r[e]=1024;return r[0]=r[32]=0,r}function xz(){let r=fz(),e=hz(),t=gz();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]=p}return new Float32Array(n)}}var lt=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return lt.instance==null&&(lt.instance=new lt),lt.instance}static registerSaveRouter(e){lt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){lt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return lt.getHandlers(e,"save")}static getLoadHandlers(e,t){return lt.getHandlers(e,"load",t)}static getHandlers(e,t,o){let n=[];return(t==="load"?lt.getInstance().loadRouters:lt.getInstance().saveRouters).forEach(a=>{let i=a(e,o);i!==null&&n.push(i)}),n}},w0=r=>lt.registerSaveRouter(r),I0=r=>lt.registerLoadRouter(r),v0=r=>lt.getSaveHandlers(r),k0=(r,e)=>lt.getLoadHandlers(r,e);var Wb="tensorflowjs",Ub=1,nu="models_store",bi="model_info_store";function N0(){if(!O().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 Gb(r){let e=r.result;e.createObjectStore(nu,{keyPath:"modelPath"}),e.createObjectStore(bi,{keyPath:"modelPath"})}var Ms=class{constructor(e){if(this.indexedDB=N0(),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(Wb,Ub);s.onupgradeneeded=()=>Gb(s),s.onsuccess=()=>{let a=s.result;if(t==null){let i=a.transaction(nu,"readonly"),u=i.objectStore(nu).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=Ps(t),p=a.transaction(bi,"readwrite"),u=p.objectStore(bi),c=u.put({modelPath:this.modelPath,modelArtifactsInfo:i}),l;c.onsuccess=()=>{l=a.transaction(nu,"readwrite");let d=l.objectStore(nu).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:i});d.onsuccess=()=>o({modelArtifactsInfo:i}),d.onerror=f=>{u=p.objectStore(bi);let h=u.delete(this.modelPath);h.onsuccess=()=>(a.close(),n(d.error)),h.onerror=g=>(a.close(),n(d.error))}},c.onerror=m=>(a.close(),n(c.error)),p.oncomplete=()=>{l==null?a.close():l.oncomplete=()=>a.close()}}},s.onerror=a=>n(s.error)})}};Ms.URL_SCHEME="indexeddb://";var T0=r=>O().getBool("IS_BROWSER")&&!Array.isArray(r)&&r.startsWith(Ms.URL_SCHEME)?yz(r.slice(Ms.URL_SCHEME.length)):null;lt.registerSaveRouter(T0);lt.registerLoadRouter(T0);function yz(r){return new Ms(r)}function bz(r){return r.startsWith(Ms.URL_SCHEME)?r.slice(Ms.URL_SCHEME.length):r}var zm=class{constructor(){this.indexedDB=N0()}async listModels(){return new Promise((e,t)=>{let o=this.indexedDB.open(Wb,Ub);o.onupgradeneeded=()=>Gb(o),o.onsuccess=()=>{let n=o.result,s=n.transaction(bi,"readonly"),i=s.objectStore(bi).getAll();i.onsuccess=()=>{let p={};for(let u of i.result)p[u.modelPath]=u.modelArtifactsInfo;e(p)},i.onerror=p=>(n.close(),t(i.error)),s.oncomplete=()=>n.close()},o.onerror=n=>t(o.error)})}async removeModel(e){return e=bz(e),new Promise((t,o)=>{let n=this.indexedDB.open(Wb,Ub);n.onupgradeneeded=()=>Gb(n),n.onsuccess=()=>{let s=n.result,a=s.transaction(bi,"readwrite"),i=a.objectStore(bi),p=i.get(e),u;p.onsuccess=()=>{if(p.result==null)return s.close(),o(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{let c=i.delete(e),l=()=>{u=s.transaction(nu,"readwrite");let d=u.objectStore(nu).delete(e);d.onsuccess=()=>t(p.result.modelArtifactsInfo),d.onerror=f=>o(p.error)};c.onsuccess=l,c.onerror=m=>(l(),s.close(),o(p.error))}},p.onerror=c=>(s.close(),o(p.error)),a.oncomplete=()=>{u==null?s.close():u.oncomplete=()=>s.close()}},n.onerror=s=>o(n.error)})}};var Ta="/",Op="tensorflowjs_models",_0="info",Cz="model_topology",Sz="weight_specs",wz="weight_data",Iz="model_metadata";function E0(r){return{info:[Op,r,_0].join(Ta),topology:[Op,r,Cz].join(Ta),weightSpecs:[Op,r,Sz].join(Ta),weightData:[Op,r,wz].join(Ta),modelMetadata:[Op,r,Iz].join(Ta)}}function $0(r){for(let e of Object.values(r))window.localStorage.removeItem(e)}function vz(r){let e=r.split(Ta);if(e.length<3)throw new Error(`Invalid key format: ${r}`);return e.slice(1,e.length-1).join(Ta)}function kz(r){return r.startsWith(Ls.URL_SCHEME)?r.slice(Ls.URL_SCHEME.length):r}var Ls=class{constructor(e){if(!O().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=E0(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=Ps(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,C0(e.weightData));let s={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,signature:e.signature!=null?e.signature:void 0,userDefinedMetadata:e.userDefinedMetadata!=null?e.userDefinedMetadata:void 0,modelInitializer:e.modelInitializer!=null?e.modelInitializer:void 0,initializerSignature:e.initializerSignature!=null?e.initializerSignature:void 0,trainingConfig:e.trainingConfig!=null?e.trainingConfig:void 0};return this.LS.setItem(this.keys.modelMetadata,JSON.stringify(s)),{modelArtifactsInfo:n}}catch(s){throw $0(this.keys),new Error(`Failed to save model '${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),i.initializerSignature!=null&&(t.initializerSignature=i.initializerSignature),i.trainingConfig!=null&&(t.trainingConfig=i.trainingConfig)}let a=this.LS.getItem(this.keys.weightData);if(a==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=S0(a),t}};Ls.URL_SCHEME="localstorage://";var A0=r=>O().getBool("IS_BROWSER")&&!Array.isArray(r)&&r.startsWith(Ls.URL_SCHEME)?Nz(r.slice(Ls.URL_SCHEME.length)):null;lt.registerSaveRouter(A0);lt.registerLoadRouter(A0);function Nz(r){return new Ls(r)}var Wm=class{constructor(){E(O().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),E(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=Op+Ta,o=Ta+_0;for(let n=0;n"scheme must not be undefined or null."),e.endsWith(Pp)&&(e=e.slice(0,e.indexOf(Pp))),E(e.length>0,()=>"scheme must not be an empty string.");let o=Xt.getInstance();E(o.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),o.managers[e]=t}static getManager(e){let t=Xt.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(Xt.getInstance().managers)}};function Um(r){if(r.indexOf(Pp)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Xt.getSchemes().join(",")}`);return{scheme:r.split(Pp)[0],path:r.split(Pp)[1]}}async function R0(r,e,t=!1){E(r!==e,()=>`Old path and new path are the same: '${r}'`);let o=lt.getLoadHandlers(r);E(o.length>0,()=>`Copying failed because no load handler is found for source URL ${r}.`),E(o.length<2,()=>`Copying failed because more than one (${o.length}) load handlers for source URL ${r}.`);let n=o[0],s=lt.getSaveHandlers(e);E(s.length>0,()=>`Copying failed because no save handler is found for destination URL ${e}.`),E(s.length<2,()=>`Copying failed because more than one (${o.length}) save handlers for destination URL ${e}.`);let a=s[0],i=Um(r).scheme,p=Um(r).path,u=i===Um(r).scheme,c=await n.load();t&&u&&await Xt.getManager(i).removeModel(p);let l=await a.save(c);return t&&!u&&await Xt.getManager(i).removeModel(p),l.modelArtifactsInfo}async function F0(){let r=Xt.getSchemes(),e={};for(let t of r){let o=await Xt.getManager(t).listModels();for(let n in o){let s=t+Pp+n;e[s]=o[n]}}return e}async function D0(r){let e=Um(r);return Xt.getManager(e.scheme).removeModel(e.path)}async function O0(r,e){return R0(r,e,!1)}async function P0(r,e){return R0(r,e,!0)}var Hb=class{constructor(){this.messageName="setTimeoutCustom",this.functionRefs=[],this.handledMessageCount=0,this.hasEventListener=!1}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)}setTimeoutCustom(e,t){if(typeof window=="undefined"||!O().getBool("USE_SETTIMEOUTCUSTOM")){setTimeout(e,t);return}this.functionRefs.push(e),setTimeout(()=>{window.postMessage({name:this.messageName,index:this.functionRefs.length-1},"*")},t),this.hasEventListener||(this.hasEventListener=!0,window.addEventListener("message",o=>{if(o.source===window&&o.data.name===this.messageName){o.stopPropagation();let n=this.functionRefs[o.data.index];n(),this.handledMessageCount++,this.handledMessageCount===this.functionRefs.length&&(this.functionRefs=[],this.handledMessageCount=0)}},!0))}};if(O().get("IS_BROWSER")){O().setPlatform("browser",new Hb);try{Xt.registerManager(Ls.URL_SCHEME,new Wm)}catch(r){}try{Xt.registerManager(Ms.URL_SCHEME,new zm)}catch(r){}}var Tz={importFetch:()=>M0()},qb;var Kb=class{constructor(){this.util=L0(),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return O().global.fetch!=null?O().global.fetch(e,t):(qb==null&&(qb=Tz.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)}};O().get("IS_NODE")&&!O().get("IS_BROWSER")&&O().setPlatform("node",new Kb);function le(r,e="float32",t){return e=e||"float32",yt(r),new st(r,e,t)}function _z(r,e){let t=v(r,"x","cast");if(!db(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 T.runKernel(co,o,n)}var Ke=N({cast_:_z});function Ez(r){let t={x:v(r,"x","clone","string_or_numeric")};return T.runKernel(mo,t)}var Br=N({clone_:Ez});function Gm(r,e=!1){console.log(r.toString(e))}Ob();var $z={buffer:le,cast:Ke,clone:Br,print:Gm};m0($z);var Ea={};Ue(Ea,{browserFiles:()=>V0,browserHTTPRequest:()=>U0,concatenateArrayBuffers:()=>Fp,copyModel:()=>O0,decodeWeights:()=>Lm,encodeWeights:()=>b0,fromMemory:()=>G0,fromMemorySync:()=>Jb,getLoadHandlers:()=>k0,getModelArtifactsForJSON:()=>Dp,getModelArtifactsForJSONSync:()=>zb,getModelArtifactsInfoForJSON:()=>Ps,getSaveHandlers:()=>v0,getWeightSpecs:()=>Vm,http:()=>qm,isHTTPScheme:()=>Hm,listModels:()=>F0,loadWeights:()=>z0,moveModel:()=>P0,registerLoadRouter:()=>I0,registerSaveRouter:()=>w0,removeModel:()=>D0,weightsLoaderFactory:()=>Qb,withSaveHandler:()=>H0,withSaveHandlerSync:()=>q0});var Az="model",Rz=".json",Fz=".weights.bin";function B0(r){return new Promise(e=>setTimeout(e)).then(r)}var _a=class{constructor(e){if(!O().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(_a.URL_SCHEME)&&(e=e.slice(_a.URL_SCHEME.length)),(e==null||e.length===0)&&(e=Az),this.modelJsonFileName=e+Rz,this.weightDataFileName=e+Fz}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=Bm(e,o),s=window.URL.createObjectURL(new Blob([JSON.stringify(n)],{type:"application/json"})),a=this.modelJsonAnchor==null?document.createElement("a"):this.modelJsonAnchor;if(a.download=this.modelJsonFileName,a.href=s,await B0(()=>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 B0(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Ps(e)}}}};_a.URL_SCHEME="downloads://";var jb=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.jsonFile=e[0],this.weightsFiles=e.slice(1)}async load(){return new Promise((e,t)=>{let o=new FileReader;o.onload=n=>{let s=JSON.parse(n.target.result),a=s.modelTopology;if(a==null){t(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));return}if(s.weightsManifest==null){t(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));return}if(this.weightsFiles.length===0){e({modelTopology:a});return}let p=Dp(s,u=>this.loadWeights(u));e(p)},o.onerror=n=>t(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),o.readAsText(this.jsonFile)})}loadWeights(e){let t=[],o=[];for(let a of e)t.push(...a.weights),o.push(...a.paths);let n=this.checkManifestAndWeightFiles(e),s=o.map(a=>this.loadWeightsFile(a,n[a]));return Promise.all(s).then(a=>[t,Fp(a)])}loadWeightsFile(e,t){return new Promise((o,n)=>{let s=new FileReader;s.onload=a=>{let i=a.target.result;o(i)},s.onerror=a=>n(`Failed to weights data from file of path '${e}'.`),s.readAsArrayBuffer(t)})}checkManifestAndWeightFiles(e){let t=[],o=this.weightsFiles.map(s=>Vb(s.name)),n={};for(let s of e)s.paths.forEach(a=>{let i=Vb(a);if(t.indexOf(i)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${i}'`);if(t.push(i),o.indexOf(i)===-1)throw new Error(`Weight file with basename '${i}' is not provided.`);n[a]=this.weightsFiles[o.indexOf(i)]});if(t.length!==this.weightsFiles.length)throw new Error(`Mismatch in the number of files in weights manifest (${t.length}) and the number of weight files provided (${this.weightsFiles.length}).`);return n}},Dz=r=>O().getBool("IS_BROWSER")&&!Array.isArray(r)&&r.startsWith(_a.URL_SCHEME)?Oz(r.slice(_a.URL_SCHEME.length)):null;lt.registerSaveRouter(Dz);function Oz(r="model"){return new _a(r)}function V0(r){return new jb(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=p=>(p.then(u=>{let c=t+ ++n/r.length*(o-t);return e(c),u}),p);function a(p){E(p!=null&&Array.isArray(p)&&p.length>0,()=>"promises must be a none empty array")}function i(p,u){E(p>=0&&p<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${p}`),E(u>=0&&u<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${u}`),E(u>=p,()=>`startFraction must be no more than endFraction, but got startFraction ${p} and endFraction ${u}`)}return Promise.all(r.map(s))}async function Yb(r,e){e==null&&(e={});let t=e.fetchFunc==null?O().platform.fetch:e.fetchFunc,o=r.map(l=>t(l,e.requestInit,{isBinary:!0})),n=0,s=.5,i=(e.onProgress==null?await Promise.all(o):await Xb(o,e.onProgress,n,s)).map(l=>l.arrayBuffer()),p=.5,u=1;return e.onProgress==null?await Promise.all(i):await Xb(i,e.onProgress,p,u)}async function z0(r,e="",t,o){return Qb(a=>Yb(a,{requestInit:o}))(r,e,t)}function Qb(r){return async(e,t="",o)=>{let n=e.map(()=>!1),s={},a=o!=null?o.map(()=>!1):[],i=[];if(e.forEach((d,f)=>{let h=0;d.weights.forEach(g=>{let x="quantization"in g?g.quantization.dtype:g.dtype,b=Zc[x]*ze(g.shape),C=()=>{n[f]=!0,s[f]==null&&(s[f]=[]),s[f].push({manifestEntry:g,groupOffset:h,sizeBytes:b})};o!=null?o.forEach((w,k)=>{w===g.name&&(C(),a[k]=!0)}):C(),i.push(g.name),h+=b})}),!a.every(d=>d)){let d=o.filter((f,h)=>!a[h]);throw new Error(`Could not find weights in manifest with names: ${d.join(", ")}. Manifest JSON has weights with names: ${i.join(", ")}.`)}let p=n.reduce((d,f,h)=>(f&&d.push(h),d),[]),u=[];p.forEach(d=>{e[d].paths.forEach(f=>{let h=t+(t.endsWith("/")?"":"/")+f;u.push(h)})});let c=await r(u),l={},m=0;return p.forEach(d=>{let f=e[d].paths.length,h=0;for(let w=0;w{let k=g.slice(w.groupOffset,w.groupOffset+w.sizeBytes),_=Lm(k,[w.manifestEntry]);for(let $ in _)l[$]=_[$]}),m+=f}),l}}var Pz="application/octet-stream",Mz="application/json",Jc=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?(E(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=O().platform.fetch,E(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&E(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=Bm(e,o);t.body.append("model.json",new Blob([JSON.stringify(n)],{type:Mz}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:Pz}),"model.weights.bin");let s=await this.fetch(this.path,t);if(s.ok)return{modelArtifactsInfo:Ps(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(s){let a=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?a+=" 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.":a+=" Please make sure the server is serving valid JSON for this request.",new Error(a)}let o=t.modelTopology,n=t.weightsManifest;if(o==null&&n==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);return Dp(t,s=>this.loadWeights(s))}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[o,n]=Lz(t),s=this.weightPathPrefix||o,a=Vm(e),i=[],p=[];for(let c of e)for(let l of c.paths)this.weightUrlConverter!=null?p.push(this.weightUrlConverter(l)):i.push(s+l+n);this.weightUrlConverter&&i.push(...await Promise.all(p));let u=await Yb(i,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[a,Fp(u)]}};Jc.URL_SCHEME_REGEX=/^https?:\/\//;function Lz(r){let e=r.lastIndexOf("/"),t=r.lastIndexOf("?"),o=r.substring(0,e),n=t>e?r.substring(t):"";return[o+"/",n]}function Hm(r){return r.match(Jc.URL_SCHEME_REGEX)!=null}var W0=(r,e)=>{if(typeof fetch=="undefined"&&(e==null||e.fetchFunc==null))return null;{let t=!0;if(Array.isArray(r)?t=r.every(o=>Hm(o)):t=Hm(r),t)return qm(r,e)}return null};lt.registerSaveRouter(W0);lt.registerLoadRouter(W0);function qm(r,e){return new Jc(r,e)}function U0(r,e){return qm(r,e)}var el=class{constructor(e){this.modelArtifacts=e}load(){return this.modelArtifacts}},Km=class{constructor(e){this.saveHandler=e}save(e){return this.saveHandler(e)}},Zb=class{constructor(e){e.load&&(this.load=()=>Promise.resolve(e.load())),e.save&&(this.save=t=>Promise.resolve(e.save(t)))}};function G0(r,e,t,o){let n=arguments;return new Zb(Jb(...n))}function Jb(r,e,t,o){return arguments.length===1?r.modelTopology!=null||r.weightSpecs!=null?new el(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 el({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 el({modelTopology:r,weightSpecs:e,weightData:t,trainingConfig:o}))}function H0(r){return new Km(r)}function q0(r){return new Km(r)}var j0={};Ue(j0,{confusionMatrix:()=>K0});function Bz(r,e,t=!1,o=!1){let n=v(r,"a","matMul"),s=v(e,"b","matMul");[n,s]=Re(n,s);let a={a:n,b:s},i={transposeA:t,transposeB:o};return T.runKernel(Wo,a,i)}var Xe=N({matMul_:Bz});function Vz(r,e,t=1,o=0,n="int32"){if(e<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${e}`);let a={indices:v(r,"indices","oneHot","int32")},i={dtype:n,depth:e,onValue:t,offValue:o};return T.runKernel(En,a,i)}var tl=N({oneHot_:Vz});function wie(){O().set("PROD",!0)}function Iie(){O().set("DEBUG",!0)}function vie(){O().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function eC(r){O().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(r+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}d0(eC);function kie(){T.disposeVariables()}function cr(){return T}function Nie(){return T.memory()}function Tie(r){return T.profile(r)}function Ee(r,e){return T.tidy(r,e)}function Dt(r){Qc(r).forEach(t=>t.dispose())}function _r(r){return T.keep(r)}function _ie(r){return T.time(r)}function Eie(r){return T.setBackend(r)}function $ie(){return T.ready()}function Aie(){return T.backendName}function Rie(r){T.removeBackend(r)}function Fie(r){return T.findBackend(r)}function Die(r){return T.findBackendFactory(r)}function Ci(r,e,t=1){return T.registerBackend(r,e,t)}function Oie(){return T.backend}function Pie(r,e){O().setPlatform(r,e)}function zz(r){let t={input:v(r,"input","imag")};return T.runKernel(si,t)}var Si=N({imag_:zz});function Wz(r){let t={x:v(r,"x","neg")};return T.runKernel(ws,t)}var yr=N({neg_:Wz});function Uz(r){let t={input:v(r,"input","real")};return T.runKernel(ai,t)}var $a=N({real_:Uz});function Gz(r,e,t){let o=v(r,"x","transpose");if(e==null&&(e=o.shape.map((a,i)=>i).reverse()),E(o.rank===e.length,()=>`Error in transpose: rank of input ${o.rank} must match length of perm ${e}.`),e.forEach(a=>{E(a>=0&&a`All entries in 'perm' must be between 0 and ${o.rank-1} but got ${e}`)}),o.rank<=1)return o.clone();let n={x:o},s={perm:e};return o.dtype==="complex64"?Ee(()=>{let a=$a(o),i=Si(o);return a=T.runKernel(ro,{x:a},s),i=T.runKernel(ro,{x:i},s),t&&(i=yr(i)),Tr(a,i)}):T.runKernel(ro,n,s)}var Mp=N({transpose_:Gz});function Hz(r,e,t){let o=v(r,"labels","confusionMatrix"),n=v(e,"predictions","confusionMatrix");E(t==null||t>0&&Number.isInteger(t),()=>`If provided, numClasses must be a positive integer, but got ${t}`),E(o.rank===1,()=>`Expected the rank of labels to be 1, but got ${o.rank}`),E(n.rank===1,()=>`Expected the rank of predictions to be 1, but got ${n.rank}`),E(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.`),E(t>0&&Number.isInteger(t),()=>`numClasses is required to be a positive integer, but got ${t}`);let s=tl(Ke(o,"int32"),t),a=tl(Ke(n,"int32"),t),i=Mp(s),p=Xe(i,a);return Ke(p,"int32")}var K0=N({confusionMatrix_:Hz});var br={};Ue(br,{assertAndGetBroadcastShape:()=>Je,getBroadcastDims:()=>X0,getReductionAxes:()=>jm});function X0(r,e){let t=r.length,o=[];for(let n=0;n1&&a===1&&o.unshift(s)}return o}function jm(r,e){let t=[];for(let o=0;o1)&&t.unshift(s)}return t}function Je(r,e){let t=[],o=Math.max(r.length,e.length);for(let n=0;nZz,fromPixelsAsync:()=>Yz,toPixels:()=>Qz});function Xm(r,e,t){if(Jr(r),e!=null&&e.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let o=or(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 xr(r,e,o,t)}var su;function Y0(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(qc(Zi,T.backendName)!=null){let f={pixels:r},h={numChannels:e};return T.runKernel(Zi,f,h)}let[u,c]=n?[r.videoWidth,r.videoHeight]:[r.width,r.height],l;if(a)l=r.getContext("2d").getImageData(0,0,u,c).data;else if(o||t)l=r.data;else if(s||n||i){if(su==null)if(typeof document=="undefined")if(typeof OffscreenCanvas!="undefined"&&typeof OffscreenCanvasRenderingContext2D!="undefined")su=new OffscreenCanvas(1,1).getContext("2d");else throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");else su=document.createElement("canvas").getContext("2d",{willReadFrequently:!0});su.canvas.width=u,su.canvas.height=c,su.drawImage(r,0,0,u,c),l=su.getImageData(0,0,u,c).data}let m;if(e===4)m=new Int32Array(l);else{let f=u*c;m=new Int32Array(f*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,p=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 ${d}.`)}else if(t.dtype==="int32"&&(d<0||d>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${d}.`);s===1?(c[0]=d*i,c[1]=d*i,c[2]=d*i):c[m]=d*i}let l=u*4;p[l+0]=Math.round(c[0]),p[l+1]=Math.round(c[1]),p[l+2]=Math.round(c[2]),p[l+3]=Math.round(c[3])}if(e!=null){e.width=n,e.height=o;let u=e.getContext("2d"),c=new ImageData(p,n,o);u.putImageData(c,0,0)}return t!==r&&t.dispose(),p}var Zz=N({fromPixels_:Y0});var Ym={};Ue(Ym,{prepareAndValidate:()=>Z0});function Z0(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(ze(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 l=0;ll/u),1].slice(0,s);return[p,a,u,c]}var rl={};Ue(rl,{calculateShapes:()=>J0,validateInput:()=>Qm,validateUpdateShape:()=>tC});function tC(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: ${n}.`;if(t.rank1?e.shape[o-1]:1,s=t.length,a=1;for(let l=n;leW,computeFlatOffset:()=>sW,computeOutShape:()=>rW,getNormalizedAxes:()=>oW,isSliceContinous:()=>nW,maskToAxes:()=>tW,parseSliceParams:()=>aW,sliceInfo:()=>iW,startForAxis:()=>iv,startIndicesWithElidedDims:()=>nv,stopForAxis:()=>uv,stopIndicesWithElidedDims:()=>sv,stridesForAxis:()=>av,stridesWithElidedDims:()=>tv});var rC=-2,Jz=-1;function eW(r,e,t){let o=r.shape.length;E(o===e.length,()=>`Error in slice${o}D: Length of begin ${e} must match the rank of the array (${o}).`),E(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 tW(r){let e=[],t=0;for(;r>0;)r&1&&e.push(t),r/=2,t++;return e}function rW(r,e,t){let o=[];for(let n=0;n0){let d=e[0],f=t+1;c=nv(a,d,f,o,r),l=sv(i,d,f,n,r),m=tv(s,d,f,r)}else for(let d=0;d-1)s[i]=0;else{let p=rv(e,t,i),u=o[p];r&1<-1)s[i]=Number.MAX_SAFE_INTEGER;else{let p=rv(e,t,i),u=o[p];r&1<0?a=Number.MIN_SAFE_INTEGER:a=Number.MAX_SAFE_INTEGER);let p=o[n];return a<0&&(a+=p),a=op(0,a,p-1),a}function uv(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 p=o[n];return a<0&&(a+=p),i>0?a=op(0,a,p):a=op(-1,a,p-1),a}function nW(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 sW(r,e){let t=r.length>0?r[r.length-1]:1;for(let o=0;o{E(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:(E(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 iW(r,e,t,o,n,s,a,i,p){let u;if(o==null?(u=new Array(e.length),u.fill(1)):u=o,a!=null&&(a&a-1)!==0)throw new Error("Multiple ellipses in slice is not allowed.");let c=!1,l={dims:u.length,numAddAxisAfterEllipsis:0,begin:e.slice(),end:t.slice(),strides:u.slice(),beginMask:n,endMask:s,ellipsisMask:a,newAxisMask:i,shrinkAxisMask:p};for(let C=0;C0?0:-1,m.strides[C]>0?k:k-1];if(w&&m.strides[C]<=0)throw Error("only stride 1 allowed on non-range indexing.");h=h&&m.strides[C]===1;let A=!!(m.beginMask&1<=k)throw Error(`slice index ${m.begin[C]} of dimension ${C} out of bounds.`)}else m.begin[C]=ev(m.begin[C],0,m.strides[C],k,_,$),m.end[C]=ev(m.end[C],1,m.strides[C],k,_,$);let P=m.strides[C]===1&&m.begin[C]===0&&m.end[C]===k;d=d&&P,f=f&&(C===0&&m.strides[C]===1||P)}else d=d&&m.strides[C]===1&&A,f=f&&(C===0&&m.strides[C]===1||A);let R,D=!1;if(m.beginValid&&m.endValid?(R=m.end[C]-m.begin[C],D=!0):w?(R=1,D=!0):A&&k>=0&&(m.strides[C]<0?R=-k:R=k,D=!0),D){let P;R===0||R<0!=m.strides[C]<0?P=0:P=Math.trunc(R/m.strides[C])+(R%m.strides[C]!==0?1:0),g.push(P)}else g.push(-1)}for(let C=0;C=0?x.push(g[w]):w===rC&&x.push(1)}return{finalShapeSparse:x.filter((C,w)=>m.finalShapeGatherIndices[w]!==rC),finalShape:x,isIdentity:d,sliceDim0:f,isSimpleSlice:h,begin:m.begin,end:m.end,strides:m.strides}}function uW(r,e){e.beginMask=0,e.endMask=0,e.shrinkAxisMask=0;let t=0;e.beginValid=r.begin!=null,e.endValid=r.end!=null,e.begin=new Array(e.dims),e.end=new Array(e.dims),e.strides=new Array(e.dims),e.finalShapeGatherIndices=[],e.finalShapeGatherIndicesSparse=[],e.inputShapeGatherIndicesSparse=new Array(e.dims);for(let o=0;o0?s[e]:s[e+1&1];{let a=r<0?o+r:r;return as[1]?s[1]:a}}var pv={};Ue(pv,{Serializable:()=>ol,SerializationMap:()=>Bs,registerClass:()=>Er});var ol=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},Bs=class{constructor(){this.classNameMap={}}static getMap(){return Bs.instance==null&&(Bs.instance=new Bs),Bs.instance}static register(e){Bs.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function Er(r){E(r.className!=null,()=>"Class being registered does not have the static className property defined."),E(typeof r.className=="string",()=>"className is required to be a string, but got type "+typeof r.className),E(r.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),Bs.register(r)}var dv={};Ue(dv,{TEST_EPSILON_FLOAT16:()=>cv,createVideoElement:()=>hW,encodeStrings:()=>mv,expectArrayBuffersEqual:()=>fW,expectArraysClose:()=>cW,expectArraysEqual:()=>mW,expectNumbersClose:()=>lv,expectPromiseToFail:()=>lW,expectValuesInRange:()=>dW,play:()=>gW,testEpsilon:()=>Zm});var pW=.001,cv=.1;function cW(r,e,t){return t==null&&(t=Zm()),oC(r,e,(o,n)=>nC(o,n,t))}function Zm(){return T.backend.floatPrecision()===32?pW:cv}function oC(r,e,t){let o=!0;if((Wt(r)||Wt(e))&&(o=!1),Wt(r)&&Wt(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: ${i}`)}if(Array.isArray(r)&&Array.isArray(e)){let a=or(r),i=or(e);if(!Pr(a,i))throw new Error(`Arrays have different shapes. Actual: [${a}]. Expected: [${i}]`)}let n=Wt(r)?r:Oo(r),s=Wt(e)?e:Oo(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()),typeof expect!="undefined"&&expect().nothing()}function mW(r,e){let t=typeof e=="string"||typeof e=="number"||typeof e=="boolean"?[e]:e;return Po(r)||Po(r[0])||Po(e)||Po(e[0])?oC(r,t,(o,n)=>o==n):oC(r,e,(o,n)=>nC(o,n,0))}function lv(r,e,t){if(t==null&&(t=Zm()),!nC(r,e,t))throw new Error(`Numbers differ: actual === ${r}, expected === ${e}`);typeof expect!="undefined"&&expect().nothing()}function nC(r,e,t){return!isFinite(r)&&!isFinite(e)?!0:!(isNaN(r)||isNaN(e)||Math.abs(r-e)>t)}function dW(r,e,t){for(let o=0;ot)throw new Error(`Value out of range:${r[o]} low: ${e}, high: ${t}`)}function fW(r,e){let t=new Float32Array(r),o=new Float32Array(e);if(t.length!==o.length)throw new Error(`Expected ArrayBuffer to be of length ${o.length}, but it was ${t.length}`);for(let n=0;n{e.addEventListener("loadeddata",o=>t(e)),e.load()})}async function gW(r){await r.play(),"requestVideoFrameCallback"in r&&await new Promise(e=>{r.requestVideoFrameCallback(e)})}var xW="4.1.0";function yW(r,e){let t=v(r,"a","add"),o=v(e,"b","add");[t,o]=Re(t,o);let n={a:t,b:o};return T.runKernel(eo,n)}var xe=N({add_:yW});function bW(r,e){let t=v(r,"a","floorDiv"),o=v(e,"b","floorDiv");[t,o]=Re(t,o);let n={a:t,b:o};return T.runKernel(sn,n)}var Jm=N({floorDiv_:bW});function CW(r,e){let t=v(r,"a","div"),o=v(e,"b","div");if([t,o]=Re(t,o),t.dtype==="int32"&&o.dtype==="int32")return Jm(t,o);let n={a:t,b:o},s={};return T.runKernel(Jo,n,s)}var Ge=N({div_:CW});function SW(r,e){let t=v(r,"a","mul"),o=v(e,"b","mul");[t,o]=Re(t,o);let n={a:t,b:o};return T.runKernel(kn,n)}var ae=N({mul_:SW});function wW(r){let e=v(r,"x","abs");if(e.dtype==="complex64"){let t={x:e};return T.runKernel(pp,t)}else{let t={x:e};return T.runKernel(gs,t)}}var Yt=N({abs_:wW});function IW(r){let t={x:v(r,"x","acos")};return T.runKernel(sa,t)}var fv=N({acos_:IW});function vW(r){let t={x:v(r,"x","acosh")};return T.runKernel(aa,t)}var hv=N({acosh_:vW});function kW(r){E(Array.isArray(r),()=>"The argument passed to tf.addN() must be a list of tensors"),E(r.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${r.length}`);let e=r.map((n,s)=>v(n,`tensors${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(!Pr(n.shape,t.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let o=e;return T.runKernel(Mo,o)}var gv=N({addN_:kW});function NW(r,e=null,t=!1){let n={x:v(r,"x","all","bool")},s={axis:e,keepDims:t};return T.runKernel(Lo,n,s)}var xv=N({all_:NW});function TW(r,e=null,t=!1){let n={x:v(r,"x","any","bool")},s={axis:e,keepDims:t};return T.runKernel(Bo,n,s)}var yv=N({any_:TW});function _W(r,e=0){let o={x:v(r,"x","argMax")},n={axis:e};return T.runKernel(Vo,o,n)}var bv=N({argMax_:_W});function EW(r,e=0){let o={x:v(r,"x","argMin")},n={axis:e};return T.runKernel(Za,o,n)}var Cv=N({argMin_:EW});function $W(r){let t={x:v(r,"x","asin")};return T.runKernel(ia,t)}var Sv=N({asin_:$W});function AW(r){let t={x:v(r,"x","asinh")};return T.runKernel(ua,t)}var wv=N({asinh_:AW});function RW(r){let t={x:v(r,"x","atan")};return T.runKernel(pa,t)}var Iv=N({atan_:RW});function FW(r,e){let t=v(r,"a","atan2"),o=v(e,"b","atan2");[t,o]=Re(t,o);let n={a:t,b:o};return T.runKernel(la,n)}var vv=N({atan2_:FW});function DW(r){let t={x:v(r,"x","atanh")};return T.runKernel(ca,t)}var kv=N({atanh_:DW});function OW(r,e,t,o,n="NHWC",s){let a=r[3],i=[...e,a],p=Tv(n);return uu(r,i,t,s,o,null,null,p)}function aC(r,e,t,o,n,s,a="channelsLast"){let[i,p]=ed(e),u;if(a==="channelsLast")u=[i,p,r[3],r[3]];else if(a==="channelsFirst")u=[i,p,r[1],r[1]];else throw new Error(`Unknown dataFormat ${a}`);return uu(r,u,t,o,n,s,!1,a)}function PW(r,e,t,o,n,s,a="NDHWC"){let[i,p,u]=sC(e),c,l;if(a==="NDHWC")l="channelsLast",c=[i,p,u,r[4],r[4]];else if(a==="NCDHW")l="channelsFirst",c=[i,p,u,r[1],r[1]];else throw new Error(`Unknown dataFormat ${a}`);return Nv(r,c,t,o,n,!1,l,s)}function uu(r,e,t,o,n,s,a=!1,i="channelsLast"){let[p,u,c,l]=[-1,-1,-1,-1];if(i==="channelsLast")[p,u,c,l]=r;else if(i==="channelsFirst")[p,l,u,c]=r;else throw new Error(`Unknown dataFormat ${i}`);let[m,d,,f]=e,[h,g]=ed(t),[x,b]=ed(o),C=Lp(m,x),w=Lp(d,b),{padInfo:k,outHeight:_,outWidth:$}=BW(n,u,c,h,g,C,w,s,i),A=a?f*l:f,R;return i==="channelsFirst"?R=[p,A,_,$]:i==="channelsLast"&&(R=[p,_,$,A]),{batchSize:p,dataFormat:i,inHeight:u,inWidth:c,inChannels:l,outHeight:_,outWidth:$,outChannels:A,padInfo:k,strideHeight:h,strideWidth:g,filterHeight:m,filterWidth:d,effectiveFilterHeight:C,effectiveFilterWidth:w,dilationHeight:x,dilationWidth:b,inShape:r,outShape:R,filterShape:e}}function Nv(r,e,t,o,n,s=!1,a="channelsLast",i){let[p,u,c,l,m]=[-1,-1,-1,-1,-1];if(a==="channelsLast")[p,u,c,l,m]=r;else if(a==="channelsFirst")[p,m,u,c,l]=r;else throw new Error(`Unknown dataFormat ${a}`);let[d,f,h,,g]=e,[x,b,C]=sC(t),[w,k,_]=sC(o),$=Lp(d,w),A=Lp(f,k),R=Lp(h,_),{padInfo:D,outDepth:P,outHeight:M,outWidth:L}=VW(n,u,c,l,x,b,C,$,A,R,i),W=s?g*m:g,V;return a==="channelsFirst"?V=[p,W,P,M,L]:a==="channelsLast"&&(V=[p,P,M,L,W]),{batchSize:p,dataFormat:a,inDepth:u,inHeight:c,inWidth:l,inChannels:m,outDepth:P,outHeight:M,outWidth:L,outChannels:W,padInfo:D,strideDepth:x,strideHeight:b,strideWidth:C,filterDepth:d,filterHeight:f,filterWidth:h,effectiveFilterDepth:$,effectiveFilterHeight:A,effectiveFilterWidth:R,dilationDepth:w,dilationHeight:k,dilationWidth:_,inShape:r,outShape:V,filterShape:e}}function MW(r,e,t,o,n){o==null&&(o=iC(r,e,t));let s=r[0],a=r[1],i=au((s-e+2*o)/t+1,n),p=au((a-e+2*o)/t+1,n);return[i,p]}function LW(r,e,t,o,n,s){n==null&&(n=iC(r,e,o));let a=r[0],i=r[1],p=r[2],u=au((a-e+2*n)/o+1,s),c=au((i-e+2*n)/o+1,s),l=au((p-e+2*n)/o+1,s);return[u,c,l,t]}function iC(r,e,t,o=1){let n=Lp(e,o);return Math.floor((r[0]*(t-1)-t+n)/2)}function ed(r){return typeof r=="number"?[r,r,r]:r.length===2?[r[0],r[1],1]:r}function sC(r){return typeof r=="number"?[r,r,r]:r}function Lp(r,e){return e<=1?r:r+(r-1)*(e-1)}function BW(r,e,t,o,n,s,a,i,p){let u,c,l;if(typeof r=="number"){u={top:r,bottom:r,left:r,right:r,type:r===0?"VALID":"NUMBER"};let d=MW([e,t],s,o,r,i);c=d[0],l=d[1]}else if(r==="same"){c=Math.ceil(e/o),l=Math.ceil(t/n);let m=Math.max(0,(c-1)*o+s-e),d=Math.max(0,(l-1)*n+a-t),f=Math.floor(m/2),h=m-f,g=Math.floor(d/2),x=d-g;u={top:f,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),l=Math.ceil((t-a+1)/n);else if(typeof r=="object"){let m=p==="channelsLast"?r[1][0]:r[2][0],d=p==="channelsLast"?r[1][1]:r[2][1],f=p==="channelsLast"?r[2][0]:r[3][0],h=p==="channelsLast"?r[2][1]:r[3][1];u={top:m,bottom:d,left:f,right:h,type:m===0&&d===0&&f===0&&h===0?"VALID":"EXPLICIT"},c=au((e-s+m+d)/o+1,i),l=au((t-a+f+h)/n+1,i)}else throw Error(`Unknown padding parameter: ${r}`);return{padInfo:u,outHeight:c,outWidth:l}}function VW(r,e,t,o,n,s,a,i,p,u,c){let l,m,d,f;if(typeof r=="number"){l={top:r,bottom:r,left:r,right:r,front:r,back:r,type:r===0?"VALID":"NUMBER"};let g=LW([e,t,o,1],i,1,n,r,c);m=g[0],d=g[1],f=g[2]}else if(r==="same"){m=Math.ceil(e/n),d=Math.ceil(t/s),f=Math.ceil(o/a);let h=(m-1)*n+i-e,g=(d-1)*s+p-t,x=(f-1)*a+u-o,b=Math.floor(h/2),C=h-b,w=Math.floor(g/2),k=g-w,_=Math.floor(x/2),$=x-_;l={top:w,bottom:k,left:_,right:$,front:b,back:C,type:"SAME"}}else if(r==="valid")l={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},m=Math.ceil((e-i+1)/n),d=Math.ceil((t-p+1)/s),f=Math.ceil((o-u+1)/a);else throw Error(`Unknown padding parameter: ${r}`);return{padInfo:l,outDepth:m,outHeight:d,outWidth:f}}function au(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 iu(r){let[e,t,o]=ed(r);return e===1&&t===1&&o===1}function lr(r,e){return iu(r)||iu(e)}function Tv(r){if(r==="NHWC")return"channelsLast";if(r==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${r}`)}function Pt(r,e,t){if(t!=null){if(typeof e=="string")throw Error(`Error in ${r}: pad must be an integer when using dimRoundingMode ${t} but got pad ${e}.`);if(typeof e=="number")E(na(e),()=>`Error in ${r}: pad must be an integer when using dimRoundingMode ${t} but got pad ${e}.`);else if(typeof e=="object")e.forEach(o=>{o.forEach(n=>{E(na(n),()=>`Error in ${r}: pad must be an integer when using dimRoundingMode ${t} but got pad ${n}.`)})});else throw Error(`Error in ${r}: Unknown padding parameter: ${e}`)}}function zW(r,e){let o={x:v(r,"x","reshape","string_or_numeric")},n={shape:e};return T.runKernel(Ns,o,n)}var z=N({reshape_:zW});function WW(r,e,t,o,n){let s=v(r,"x","avgPool","float32"),a=1;E(lr(t,a),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${t} and dilations '${a}'`);let i=s,p=!1;s.rank===3&&(p=!0,i=z(s,[1,s.shape[0],s.shape[1],s.shape[2]])),E(i.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${i.rank}.`),Pt("avgPool",o,n);let u={x:i},c={filterSize:e,strides:t,pad:o,dimRoundingMode:n},l=T.runKernel(zo,u,c);return l=Ke(l,s.dtype),p?z(l,[l.shape[1],l.shape[2],l.shape[3]]):l}var td=N({avgPool_:WW});function UW(r,e,t,o,n,s="NDHWC"){let a=v(r,"x","avgPool3d","float32"),i=a,p=!1;a.rank===4&&(p=!0,i=z(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),E(i.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`),E(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),Pt("avgPool3d",o,n);let u={x:i},c={filterSize:e,strides:t,pad:o,dimRoundingMode:n,dataFormat:s},l=T.runKernel(ip,u,c);return l=Ke(l,i.dtype),p?z(l,[l.shape[1],l.shape[2],l.shape[3],l.shape[4]]):l}var _v=N({avgPool3d_:UW});function GW(r,e=0){E(r.length>=1,()=>"Pass at least one tensor to concat");let t=Na(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 ${s.dtype}. `)}),t.length===1)return Br(t[0]);let o=t,n={axis:e};return T.runKernel(ys,o,n)}var gt=N({concat_:GW});function HW(r){let t={x:v(r,"x","sigmoid","float32")};return T.runKernel(Un,t)}var zs=N({sigmoid_:HW});function qW(r,e,t){let o=v(r,"x","slice","string_or_numeric");if(o.rank===0)throw new Error("Slicing scalar is not possible");let n={x:o},s={begin:e,size:t};return T.runKernel(_s,n,s)}var He=N({slice_:qW});function KW(r){let t={x:v(r,"x","tanh","float32")};return T.runKernel(Qn,t)}var nl=N({tanh_:KW});function jW(r,e,t,o,n,s){let a=v(r,"forgetBias","basicLSTMCell"),i=v(e,"lstmKernel","basicLSTMCell"),p=v(t,"lstmBias","basicLSTMCell"),u=v(o,"data","basicLSTMCell"),c=v(n,"c","basicLSTMCell"),l=v(s,"h","basicLSTMCell"),m=gt([u,l],1),d=Xe(m,i),f=xe(d,p),h=f.shape[0],g=f.shape[1]/4,x=[h,g],b=He(f,[0,0],x),C=He(f,[0,g],x),w=He(f,[0,g*2],x),k=He(f,[0,g*3],x),_=xe(ae(zs(b),nl(C)),ae(c,zs(xe(a,w)))),$=ae(nl(_),zs(k));return[_,$]}var Ev=N({basicLSTMCell_:jW});function XW(r,e,t){let o=v(r,"x","batchToSpaceND"),n=e.reduce((i,p)=>i*p);E(o.rank>=1+e.length,()=>`input rank is ${o.rank} but should be > than blockShape.length ${e.length}`),E(t.length===e.length,()=>`crops.length is ${t.length} but should be equal to blockShape.length ${e.length}`),E(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 T.runKernel(xs,s,a)}var rd=N({batchToSpaceND_:XW});function $v(r){let e;return r.rank===0||r.rank===1?e=z(r,[1,1,1,r.size]):r.rank===2?e=z(r,[1,1,r.shape[0],r.shape[1]]):r.rank===3?e=z(r,[1,r.shape[0],r.shape[1],r.shape[2]]):e=r,e}function YW(r,e,t,o,n,s){s==null&&(s=.001);let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),p=v(t,"variance","batchNorm"),u;n!=null&&(u=v(n,"scale","batchNorm"));let c;o!=null&&(c=v(o,"offset","batchNorm")),E(i.rank===p.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),E(c==null||i.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),E(u==null||i.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let m={x:$v(a),scale:u,offset:c,mean:i,variance:p},d={varianceEpsilon:s},f=T.runKernel(an,m,d);return z(f,a.shape)}var wi=N({batchNorm_:YW});function QW(r,e,t,o,n,s){let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),p=v(t,"variance","batchNorm"),u;n!=null&&(u=v(n,"scale","batchNorm"));let c;return o!=null&&(c=v(o,"offset","batchNorm")),E(a.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`),E(i.rank===2||i.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`),E(p.rank===2||p.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),wi(a,i,p,c,u,s)}var Av=N({batchNorm2d_:QW});function ZW(r,e,t,o,n,s){let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),p=v(t,"variance","batchNorm"),u;n!=null&&(u=v(n,"scale","batchNorm"));let c;return o!=null&&(c=v(o,"offset","batchNorm")),E(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),E(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),E(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),wi(a,i,p,c,u,s)}var Rv=N({batchNorm3d_:ZW});function JW(r,e,t,o,n,s){let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),p=v(t,"variance","batchNorm"),u;n!=null&&(u=v(n,"scale","batchNorm"));let c;return o!=null&&(c=v(o,"offset","batchNorm")),E(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),E(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),E(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),wi(a,i,p,c,u,s)}var Fv=N({batchNorm4d_:JW});function eU(r,e,t){let o=v(r,"x","bincount"),n=v(e,"weights","bincount");E(o.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${o.dtype}`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(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 T.runKernel(Ja,s,a)}var od=N({bincount_:eU});function tU(r,e){let t=v(r,"s0","broadcastArgs","int32"),o=v(e,"s1","broadcastArgs","int32");if(t.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${t.rank}`);if(o.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${o.rank}`);let n={s0:t,s1:o};return T.runKernel(up,n)}var Dv=N({broadcastArgs_:tU});function rU(r,e){let t=v(r,"broadcastTo","x"),o=t.shape;if(yt(e),e.lengtht.rank){let u=t.shape.slice();for(;u.length=0;u--)if(n[u]===e[u])s[u]=1;else if(t.shape[u]!==1)throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${e}].`);if(s.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return Br(t);let i={x:t},p={reps:s};return T.runKernel(to,i,p)}var Ii=N({broadcastTo_:rU});function oU(r){let t={x:v(r,"x","ceil","float32")};return T.runKernel(Uo,t)}var Ov=N({ceil_:oU});function Ws(r,e,t){yt(r);let o={shape:r,value:e,dtype:t};return T.runKernel(Cs,{},o)}function nU(r,e,t){let o=v(r,"x","clipByValue");if(E(e<=t,()=>`Error in clip: min (${e}) must be less than or equal to max (${t}).`),e===t)return Ws(o.shape,e,o.dtype);let n={x:o},s={clipValueMin:e,clipValueMax:t};return T.runKernel(lo,n,s)}var Pv=N({clipByValue_:nU});function sU(r){return gt(r,0)}var Mv=N({concat1d_:sU});function aU(r,e){return gt(r,e)}var Lv=N({concat2d_:aU});function iU(r,e){return gt(r,e)}var Bv=N({concat3d_:iU});function uU(r,e){return gt(r,e)}var Vv=N({concat4d_:uU});function pU(r,e,t,o,n="NHWC",s=[1,1],a){let i=v(r,"x","conv2d","float32"),p=v(e,"filter","conv2d","float32"),u=i,c=!1;i.rank===3&&(c=!0,u=z(i,[1,i.shape[0],i.shape[1],i.shape[2]])),E(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),E(p.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${p.rank}.`),Pt("conv2d",o,a);let l=n==="NHWC"?u.shape[3]:u.shape[1];E(l===p.shape[2],()=>`Error in conv2d: depth of input (${l}) must match input depth for filter ${p.shape[2]}.`),E(lr(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`);let m={x:u,filter:p},d={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},f=T.runKernel(Go,m,d);return c?z(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var vi=N({conv2d_:pU});function cU(r,e,t,o,n="NWC",s=1,a){let i=v(r,"x","conv1d"),p=v(e,"filter","conv1d"),u=i,c=!1;i.rank===2&&(c=!0,u=z(i,[1,i.shape[0],i.shape[1]])),E(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),E(p.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${p.rank}.`),Pt("conv1d",o,a),E(u.shape[2]===p.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${p.shape[1]}.`),E(lr(t,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${t} and dilation '${s}'`),E(n==="NWC",()=>`Error in conv1d: got dataFormat of ${n} but only NWC is currently supported.`);let l=z(p,[1,p.shape[0],p.shape[1],p.shape[2]]),m=z(u,[u.shape[0],1,u.shape[1],u.shape[2]]),g=vi(m,l,[1,t],o,"NHWC",[1,s],a);return c?z(g,[g.shape[2],g.shape[3]]):z(g,[g.shape[0],g.shape[2],g.shape[3]])}var zv=N({conv1d_:cU});function lU(r,e,t,o,n,s="NHWC",a){E(r.length===e.rank,()=>`Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);let i=r,p=e,u=!1;e.rank===3&&(u=!0,p=z(e,[1,e.shape[0],e.shape[1],e.shape[2]]),i=[1,r[0],r[1],r[2]]),E(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),E(p.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${p.rank}`),E(t.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${t.rank}`);let c=s==="NHWC"?i[3]:i[1],l=s==="NHWC"?p.shape[3]:p.shape[1];E(c===t.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${t.shape[2]}.`),E(l===t.shape[3],()=>`Error in conv2dDerInput: depth of output (${l}) must match output depth for filter ${t.shape[3]}.`),Pt("conv2dDerInput",n,a);let m={dy:p,filter:t},d={strides:o,pad:n,dataFormat:s,dimRoundingMode:a,inputShape:i},f=T.runKernel(Ho,m,d);return u?z(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var nd=N({conv2DBackpropInput_:lU});function mU(r,e,t,o,n,s){let a=v(r,"x","conv2dTranspose"),i=v(e,"filter","conv2dTranspose");return nd(t,a,i,o,n,"NHWC",s)}var Wv=N({conv2dTranspose_:mU});function dU(r,e,t,o,n="NDHWC",s=[1,1,1]){let a=v(r,"x","conv3d"),i=v(e,"filter","conv3d"),p=a,u=!1;a.rank===4&&(u=!0,p=z(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),E(p.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${p.rank}.`),E(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),E(p.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${p.shape[4]}) must match input depth for filter ${i.shape[3]}.`),E(lr(t,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`),E(n==="NDHWC",()=>`Error in conv3d: got dataFormat of ${n} but only NDHWC is currently supported.`);let c={x:p,filter:i},l={strides:t,pad:o,dataFormat:n,dilations:s},m=T.runKernel(lp,c,l);return u?z(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var Uv=N({conv3d_:dU});function fU(r,e,t,o,n){E(r.length===e.rank,()=>`Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);let s=r,a=e,i=!1;e.rank===4&&(i=!0,a=z(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 p=s[4],u=a.shape[4];E(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),E(a.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${a.rank}`),E(t.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${t.rank}`),E(p===t.shape[3],()=>`Error in conv3dDerInput: depth of input (${p}) must match input depth for filter ${t.shape[3]}.`),E(u===t.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${t.shape[4]}.`);let c={dy:a,filter:t},l={pad:n,strides:o,inputShape:s},m=T.runKernel(mp,c,l);return i?z(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var Gv=N({conv3DBackpropInput_:fU});function hU(r,e,t,o,n){let s=v(r,"x","conv3dTranspose"),a=v(e,"filter","conv3dTranspose");return Gv(t,s,a,o,n)}var Hv=N({conv3dTranspose_:hU});function gU(r){let t={x:v(r,"x","cos","float32")};return T.runKernel(qo,t)}var qv=N({cos_:gU});function xU(r){let t={x:v(r,"x","cosh","float32")};return T.runKernel(Ko,t)}var Kv=N({cosh_:xU});function yU(r,e=0,t=!1,o=!1){let s={x:v(r,"x","cumprod")},a={axis:e,exclusive:t,reverse:o};return T.runKernel(jo,s,a)}var jv=N({cumprod_:yU});function bU(r,e=0,t=!1,o=!1){let s={x:v(r,"x","cumsum")},a={axis:e,exclusive:t,reverse:o};return T.runKernel(Xo,s,a)}var Xv=N({cumsum_:bU});function CU(r,e,t,o=!1){let n=v(r,"x","denseBincount"),s=v(e,"weights","denseBincount");E(n.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${n.dtype}`),E(n.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(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 T.runKernel(ti,a,i)}var Yv=N({denseBincount_:CU});function SU(r,e,t="NHWC"){let o=v(r,"x","depthToSpace","float32"),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];E(e>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${e}`),E(n*e>=0,()=>`Negative dimension size caused by overflow when multiplying ${n} and ${e} for depthToSpace with input shape ${o.shape}`),E(s*e>=0,()=>`Negative dimension size caused by overflow when multiplying ${s} and ${e} for depthToSpace with input shape ${o.shape}`),E(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},p={blockSize:e,dataFormat:t};return T.runKernel(Qo,i,p)}var Qv=N({depthToSpace_:SU});function wU(r,e,t,o,n="NHWC",s=[1,1],a){let i=v(r,"x","depthwiseConv2d","float32"),p=v(e,"filter","depthwiseConv2d","float32"),u=i,c=!1;i.rank===3&&(c=!0,u=z(i,[1,i.shape[0],i.shape[1],i.shape[2]])),E(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),E(p.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${p.rank}.`);let l=n==="NHWC"?u.shape[3]:u.shape[1];E(l===p.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${l}) must match the inChannels dimension in filter ${p.shape[2]}.`),Pt("depthwiseConv2d",o,a);let m={x:u,filter:p},d={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},f=T.runKernel(Zo,m,d);return c?z(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Bp=N({depthwiseConv2d_:wU});function IU(r){let t={x:v(r,"x","diag")};return T.runKernel(hp,t)}var Zv=N({diag_:IU});function vU(r,e,t,o,n=[1,1],s="NHWC"){let a=v(r,"x","dilation2d"),i=v(e,"filter","dilation2d");E(a.rank===3||a.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`),E(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),E(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let p=a,u=!1;a.rank===3&&(p=z(a,[1,a.shape[0],a.shape[1],a.shape[2]]),u=!0);let c={x:p,filter:i},l={strides:t,pad:o,dilations:n},m=T.runKernel(gp,c,l);return u?z(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Jv=N({dilation2d_:vU});function kU(r,e){let t=v(r,"a","equal","string_or_numeric"),o=v(e,"b","equal","string_or_numeric");[t,o]=Re(t,o),Je(t.shape,o.shape);let n={a:t,b:o};return T.runKernel(tn,n)}var sd=N({equal_:kU});function NU(r,e,t){let o=v(e,"a","where"),n=v(t,"b","where"),s=v(r,"condition","where","bool"),a=Je(Je(s.shape,o.shape),n.shape),i=Ii(s,a),p=Ii(o,a),u=Ii(n,a),c={condition:i,t:p,e:u};return T.runKernel(Ts,c)}var os=N({where_:NU});function TU(r){let t={x:v(r,"x","zerosLike")};return T.runKernel(Fs,t)}var Ut=N({zerosLike_:TU});function _U(r,e){let t=v(r,"a","div"),o=v(e,"b","div");[t,o]=Re(t,o);let n=Ge(t,o),s=Ut(n),a=sd(o,s);return os(a,s,n)}var ek=N({divNoNan_:_U});function EU(r,e){let t=v(r,"t1","dot"),o=v(e,"t2","dot");E((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(E(n===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${n} and ${s}.`),t.rank===1&&o.rank===1){let a=z(t,[1,-1]),i=z(o,[-1,1]),p=Xe(a,i);return z(p,[])}else if(t.rank===1&&o.rank===2){let a=z(t,[1,-1]),i=z(o,[o.shape[0],o.shape[1]]),p=Xe(a,i);return z(p,[p.size])}else if(t.rank===2&&o.rank===1){let a=z(o,[-1,1]),i=Xe(t,a);return z(i,[i.size])}else{let a=z(o,[o.shape[0],o.shape[1]]);return Xe(t,a)}}var tk=N({dot_:EU});function $U(r,...e){let t=e.map((n,s)=>v(n,`tensors${s}`,"einsum")),o={equation:r};return T.runKernel(ri,t,o)}var rk=N({einsum_:$U});function AU(r){let t={x:v(r,"x","elu","float32")};return T.runKernel(en,t)}var ad=N({elu_:AU});function RU(r){let e=v(r,"x","erf");E(e.dtype==="int32"||e.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),e.dtype==="int32"&&(e=Ke(e,"float32"));let t={x:e};return T.runKernel(ma,t)}var ok=N({erf_:RU});function uC(r,e){for(let t=0;tr[s]);return[t,n]}function Aa(r,e){let t=e.map(o=>1);return nk(r,t,e)}function DU(r,e,t){E(uC(e,t),()=>`${r} supports only inner-most axes for now. Got axes ${e} and rank-${t} input.`)}function OU(r,e){if(uC(r,e))return null;let t=[];for(let o=0;ot.push(o)),t}function PU(r){return r.map((e,t)=>[t,e]).sort((e,t)=>e[1]-t[1]).map(e=>e[0])}function MU(r,e){let t=[];for(let o=e-r;o"Axis must be <= rank of the tensor");let o={input:t},n={dim:e};return T.runKernel(bs,o,n)}var Fa=N({expandDims_:jU});function XU(r){let t={x:v(r,"x","expm1")};return T.runKernel(da,t)}var ik=N({expm1_:XU});function YU(r,e){let t=v(r,"x","tile","string_or_numeric");E(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 T.runKernel(to,o,n)}var ki=N({tile_:YU});function QU(r,e,t,o="float32"){e==null&&(e=r);let n=le([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}.`),E(na(e),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${e}.`);let a=s,i=!1;s.rank===3&&(i=!0,a=z(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let p={x:a},u={depthRadius:e,bias:t,alpha:o,beta:n},c=T.runKernel(yp,p,u);return i?z(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var dk=N({localResponseNormalization_:u4});function p4(r){let t={x:v(r,"x","log","float32")};return T.runKernel(hn,t)}var Da=N({log_:p4});function c4(r){let t={x:v(r,"x","log1p")};return T.runKernel(ga,t)}var md=N({log1p_:c4});function l4(r){return E(fs(r),()=>"The f passed in grad(f) must be a function"),(e,t)=>{let o=v(e,"x","tf.grad","string_or_numeric"),n=t!=null?v(t,"dy","tf.grad"):null;return T.tidy(()=>{let{value:s,grads:a}=T.gradients(()=>r(o),[o],n);return n!=null&&ht(s.shape,n.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),dd(a),a[0]})}}function m4(r){return E(fs(r),()=>"The f passed in grads(f) must be a function"),(e,t)=>{E(Array.isArray(e),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let o=Na(e,"args","tf.grads","string_or_numeric"),n=t!=null?v(t,"dy","tf.grads"):null;return T.tidy(()=>{let{value:s,grads:a}=T.gradients(()=>r(...o),o,n);return n!=null&&ht(s.shape,n.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),dd(a),a})}}function d4(r){return E(fs(r),()=>"The f passed in valueAndGrad(f) must be a function"),(e,t)=>{E(e instanceof it,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),E(t==null||t instanceof it,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:o,value:n}=T.gradients(()=>r(e),[e],t);return dd(o),{grad:o[0],value:n}}}function f4(r){return E(fs(r),()=>"The f passed in valueAndGrads(f) must be a function"),(e,t)=>{E(Array.isArray(e)&&e.every(n=>n instanceof it),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),E(t==null||t instanceof it,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let o=T.gradients(()=>r(...e),e,t);return t!=null&&ht(o.value.shape,t.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),dd(o.grads),o}}function pC(r,e){E(fs(r),()=>"The f passed in variableGrads(f) must be a function"),E(e==null||Array.isArray(e)&&e.every(u=>u instanceof va),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let t=e!=null;if(!t){e=[];for(let u in T.registeredVariables)e.push(T.registeredVariables[u])}let o=t?e.filter(u=>!u.trainable):null,n=e.length;e=e.filter(u=>u.trainable),E(e.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${n} variables is trainable.`);let s=!0,{value:a,grads:i}=T.gradients(r,e,null,s);E(i.some(u=>u!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),E(a.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${a.rank} tensor`);let p={};return e.forEach((u,c)=>{i[c]!=null&&(p[u.name]=i[c])}),o!=null&&o.forEach(u=>p[u.name]=null),{value:a,grads:p}}function Cr(r){return T.customGrad(r)}function dd(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 h4(r){let t={x:v(r,"x","softplus")};return T.runKernel(Qi,t)}var fd=N({softplus_:h4});function g4(r){let e=v(r,"x","logSigmoid");return Cr(o=>({value:yr(fd(yr(o))),gradFunc:a=>ae(a,zs(yr(o)))}))(e)}var fk=N({logSigmoid_:g4});function x4(r,e){let t=v(r,"a","sub"),o=v(e,"b","sub");[t,o]=Re(t,o);let n={a:t,b:o};return T.runKernel(Xn,n)}var Ne=N({sub_:x4});function y4(r,e=-1){let t=v(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 Cr((n,s)=>{let i=Us(n,e,!0),p=Ne(n,i),u=Ne(Ke(p,"float32"),Da(et(Co(p),e,!0)));return s([u]),{value:u,gradFunc:(l,m)=>{let[d]=m,f=!0,h=Co(d);return Ne(l,ae(et(l,e,f),h))}}})(t)}var hk=N({logSoftmax_:y4});function b4(r,e=null,t=!1){let o=v(r,"x","logSumExp"),n=Qa(e,o.shape),s=Us(o,n,!0),a=Ne(o,s),i=Co(a),p=et(i,n),u=Da(p),c=xe(z(s,u.shape),u);if(t){let l=Aa(c.shape,n);return z(c,l)}return c}var hd=N({logSumExp_:b4});function C4(r,e){let t=v(r,"a","logicalAnd","bool"),o=v(e,"b","logicalAnd","bool");Je(t.shape,o.shape);let n={a:t,b:o};return T.runKernel(gn,n)}var lu=N({logicalAnd_:C4});function S4(r){let t={x:v(r,"x","logicalNot","bool")};return T.runKernel(xn,t)}var gd=N({logicalNot_:S4});function w4(r,e){let t=v(r,"a","logicalOr","bool"),o=v(e,"b","logicalOr","bool");Je(t.shape,o.shape);let n={a:t,b:o};return T.runKernel(xa,n)}var xd=N({logicalOr_:w4});function I4(r,e){let t=v(r,"a","logicalXor","bool"),o=v(e,"b","logicalXor","bool");return Je(t.shape,o.shape),lu(xd(r,e),gd(lu(r,e)))}var gk=N({logicalXor_:I4});var yd=2147483648;function v4(r,e,t="left"){let o=v(r,"sortedSequence","searchSorted"),n=v(e,"values","searchSorted"),s=o.shape[o.shape.length-1],a=n.shape[n.shape.length-1],i=z(o,[-1,s]),p=z(n,[-1,a]);if(i.rank<2)throw new Error("Sorted input argument must be at least 2-dimensional");if(i.shape[0]!==p.shape[0])throw new Error("Leading dimension of 'sortedSequence' and 'values' must match.");if(ze(p.shape)>=yd)throw new Error(`values tensor size must less than ${yd}`);if(i.shape[1]>=yd)throw new Error(`trailing dim_size must less than ${yd} for int32 output type, was ${i.shape[1]}`);let u={sortedSequence:i,values:p},c={side:t};return T.runKernel(ii,u,c)}var al=N({searchSorted_:v4});function xk(r,e){return al(r,e,"left")}function k4(r,e,t,o,n){let s=v(r,"x","maxPool"),a=1,i=s,p=!1;s.rank===3&&(p=!0,i=z(s,[1,s.shape[0],s.shape[1],s.shape[2]])),E(i.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${i.rank}.`),E(lr(t,a),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${t} and dilations '${a}'`),Pt("maxPool",o,n);let u={x:i},c={filterSize:e,strides:t,pad:o,dimRoundingMode:n},l=T.runKernel(Cn,u,c);return p?z(l,[l.shape[1],l.shape[2],l.shape[3]]):l}var bd=N({maxPool_:k4});function N4(r,e=[1,1,1],t,o,n,s="NDHWC"){let a=v(r,"x","maxPool3d"),i=a,p=!1;a.rank===4&&(p=!0,i=z(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),E(i.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${i.rank}.`),E(s==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),Pt("maxPool3d",o,n);let u={x:i},c={filterSize:e,strides:t,pad:o,dimRoundingMode:n,dataFormat:s},l=T.runKernel(bp,u,c);return p?z(l,[l.shape[1],l.shape[2],l.shape[3],l.shape[4]]):l}var yk=N({maxPool3d_:N4});function T4(r,e,t,o,n=!1){let a={x:v(r,"x","maxPoolWithArgmax")},i={filterSize:e,strides:t,pad:o,includeBatchInIndex:n},p=T.runKernel(Cp,a,i);return{result:p[0],indexes:p[1]}}var bk=N({maxPoolWithArgmax_:T4});function _4(r,e){let t=v(r,"a","maximum"),o=v(e,"b","maximum");[t,o]=Re(t,o),t.dtype==="bool"&&(t=Ke(t,"int32"),o=Ke(o,"int32")),Je(t.shape,o.shape);let n={a:t,b:o};return T.runKernel(bn,n)}var Cd=N({maximum_:_4});function E4(r,e=null,t=!1){let n={x:v(r,"x","mean")},s={axis:e,keepDims:t};return T.runKernel(Sn,n,s)}var mu=N({mean_:E4});function Vr(r,e="float32"){if(yt(r),e==="complex64"){let o=Vr(r,"float32"),n=Vr(r,"float32");return Tr(o,n)}let t=ap(ze(r),e);return T.makeTensor(t,r,e)}function Gs(r,e="float32"){if(yt(r),e==="complex64"){let o=Gs(r,"float32"),n=Vr(r,"float32");return Tr(o,n)}let t=zc(ze(r),e);return T.makeTensor(t,r,e)}function Ck(r,e,{indexing:t="xy"}={}){if(t!=="xy"&&t!=="ij")throw new TypeError(`${t} is not a valid third argument to meshgrid`);if(r===void 0)return[];let o=v(r,"x","meshgrid",r instanceof it?r.dtype:"float32");if(e===void 0)return[o];let n=v(e,"y","meshgrid",e instanceof it?e.dtype:"float32"),s=ze(o.shape),a=ze(n.shape);return t==="xy"?(o=z(o,[1,-1]),n=z(n,[-1,1]),[Xe(Gs([a,1],o.dtype),o),Xe(n,Gs([1,s],n.dtype))]):(o=z(o,[-1,1]),n=z(n,[1,-1]),[Xe(o,Gs([1,a],o.dtype)),Xe(Gs([s,1],n.dtype),n)])}function $4(r,e){let t=v(r,"a","minimum"),o=v(e,"b","minimum");[t,o]=Re(t,o),t.dtype==="bool"&&(t=Ke(t,"int32"),o=Ke(o,"int32")),Je(t.shape,o.shape);let n={a:t,b:o};return T.runKernel(In,n)}var Sd=N({minimum_:$4});function A4(r,e,t){E(t==="reflect"||t==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${t}.`);let o=v(r,"x","mirrorPad");if(o.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");E(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."),E(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 T.runKernel(vn,a,s)}var Sk=N({mirrorPad_:A4});function R4(r,e){let t=v(r,"a","mod"),o=v(e,"b","mod");[t,o]=Re(t,o);let n={a:t,b:o};return T.runKernel(ya,n)}var wk=N({mod_:R4});function F4(r,e=null,t=!1){r=v(r,"x","moments");let o=Qa(e,r.shape),n=mu(r,o,t),s=n.shape;t||(s=Aa(n.shape,o));let a=Qt(Ne(Ke(r,"float32"),z(n,s))),i=mu(a,o,t);return{mean:n,variance:i}}var Ik=N({moments_:F4});function D4(r,e,t,o){let n=v(e,"data","multiRNNCell"),s=Na(t,"c","multiRNNCell"),a=Na(o,"h","multiRNNCell"),i=n,p=[];for(let l=0;l2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${a}`);t=t||Math.random();let p={logits:a===1?z(n,[1,-1]):n},u={numSamples:e,seed:t,normalized:o},c=T.runKernel(Sp,p,u);return a===1?z(c,[c.size]):c}var kk=N({multinomial_:O4});function P4(r,e){let t=v(r,"a","notEqual","string_or_numeric"),o=v(e,"b","notEqual","string_or_numeric");[t,o]=Re(t,o),Je(t.shape,o.shape);let n={a:t,b:o};return T.runKernel(Nn,n)}var wd=N({notEqual_:P4});function M4(r){let t={x:v(r,"x","onesLike")};return T.runKernel(Is,t)}var Nk=N({onesLike_:M4});function L4(r,e){let t=v(r,"v1","outerProduct"),o=v(e,"v2","outerProduct");E(t.rank===1&&o.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${t.rank} and ${o.rank}.`);let n=z(t,[-1,1]),s=z(o,[1,-1]);return Xe(n,s)}var Tk=N({outerProduct_:L4});function B4(r,e,t=0){let o=v(r,"x","pad");if(o.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let n={paddings:e,constantValue:t},s={x:o};return T.runKernel($n,s,n)}var Hs=N({pad_:B4});function V4(r,e,t=0){return E(e.length===2,()=>"Invalid number of paddings. Must be length of 2."),Hs(r,[e],t)}var _k=N({pad1d_:V4});function z4(r,e,t=0){return E(e.length===2&&e[0].length===2&&e[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),Hs(r,e,t)}var Ek=N({pad2d_:z4});function W4(r,e,t=0){return E(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."),Hs(r,e,t)}var $k=N({pad3d_:W4});function U4(r,e,t=0){return E(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."),Hs(r,e,t)}var Ak=N({pad4d_:U4});function G4(r,e,t){let o=v(r,"x","spaceToBatchND");E(o.rank>=1+e.length,()=>`input rank ${o.rank} should be > than [blockShape] ${e.length}`),E(t.length===e.length,()=>`paddings.shape[0] ${t.length} must be equal to [blockShape] ${e.length}`),E(o.shape.reduce((a,i,p)=>p>0&&p<=e.length?a&&(i+t[p-1][0]+t[p-1][1])%e[p-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 T.runKernel(Es,n,s)}var Id=N({spaceToBatchND_:G4});function H4(r,e,t,o,n,s,a){n==null&&(n=[1,1]),s==null&&(s=1),o===0&&(o="valid");let i=v(r,"x","maxPool"),p=i,u=!1;i.rank===3&&(u=!0,p=z(i,[1,i.shape[0],i.shape[1],i.shape[2]])),E(lr(s,n),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${n}'`);let c=aC(p.shape,e,s,n,o),l=[c.dilationHeight,c.dilationWidth],m;o==="same"?m=K4([c.filterHeight,c.filterWidth],l):m=[[0,0],[0,0]];let d=l[0]===1&&l[1]===1,[f,h]=q4([c.inHeight,c.inWidth],l,m),g=d?o:"valid",x=d?p:Id(p,l,f),C=(t==="avg"?()=>td(x,e,s,g,a):()=>bd(x,e,s,g,a))(),w=d?C:rd(C,l,h);return u?z(w,[w.shape[1],w.shape[2],w.shape[3]]):w}function q4(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,l)=>(c-s[l]%c)%c),i=n.map((c,l)=>c+a[l]),p=e.map((c,l)=>[o[l],i[l]]),u=e.map((c,l)=>[0,a[l]]);return[p,u]}function K4(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 Rk=N({pool_:H4});function j4(r,e){let t=v(r,"x","prelu"),o=v(e,"alpha","prelu"),n={x:t,alpha:o};return T.runKernel(Rn,n)}var vd=N({prelu_:j4});function X4(r,e=null,t=!1){let o=v(r,"x","prod");o.dtype==="bool"&&(o=Ke(o,"int32"));let n={x:o},s={axis:e,keepDims:t};return T.runKernel(Fn,n,s)}var Fk=N({prod_:X4});function Y4(r,e,t,o){let n=r.map((c,l)=>v(c,`tensors${l}`,"raggedGather","int32")),s=v(e,"paramsDenseValues","raggedGather"),a=v(t,"indices","raggedGather","int32"),i={paramsNestedSplits:n,paramsDenseValues:s,indices:a},p={outputRaggedRank:o},u=T.runKernel(wp,i,p);return{outputNestedSplits:u.slice(0,u.length-1),outputDenseValues:u[u.length-1]}}var Dk=N({raggedGather_:Y4});function Q4(r,e,t){let o=v(r,"starts","raggedRange"),n=v(e,"limits","raggedRange",o.dtype),s=v(t,"deltas","raggedRange",o.dtype),a={starts:o,limits:n,deltas:s},i=T.runKernel(Ip,a);return{rtNestedSplits:i[0],rtDenseValues:i[1]}}var Ok=N({raggedRange_:Q4});function Z4(r,e,t,o,n){let s=v(r,"shape","raggedTensorToTensor","int32"),a=v(e,"values","raggedTensorToTensor"),i=v(t,"defaultValue","raggedTensorToTensor",a.dtype),p=o.map((l,m)=>v(l,`tensors${m}`,"raggedTensorToTensor","int32")),u={shape:s,values:a,defaultValue:i,rowPartitionTensors:p},c={rowPartitionTypes:n};return T.runKernel(vp,u,c)}var Pk=N({raggedTensorToTensor_:Z4});function J4(r,e,t){yt(r);let o=ze(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}},Nd=class{constructor(e,t,o,n){this.alpha=e,this.beta=1/t,this.dtype=o;let s=n||Math.random();this.randu=_d.alea(s.toString()),this.randn=new fu(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=_d.alea(n)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function aG(r,e,t=1,o="float32",n){if(yt(r),t==null&&(t=1),o==null&&(o="float32"),o!=="float32"&&o!=="int32")throw new Error(`Unsupported data type ${o}`);let s=new Nd(e,t,o,n),a=le(r,o);for(let i=0;i`Error in reverse1D: x must be rank 1 but got rank ${e.rank}.`),no(e,0)}var o1=N({reverse1d_:fG});function hG(r,e){let t=v(r,"x","reverse");return E(t.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${t.rank}.`),no(t,e)}var n1=N({reverse2d_:hG});function gG(r,e){let t=v(r,"x","reverse");return E(t.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${t.rank}.`),no(t,e)}var s1=N({reverse3d_:gG});function xG(r,e){let t=v(r,"x","reverse");return E(t.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${t.rank}.`),no(t,e)}var a1=N({reverse4d_:xG});function yG(r){let t={x:v(r,"x","round")};return T.runKernel(Ca,t)}var Rd=N({round_:yG});function bG(r){let t={x:v(r,"x","rsqrt","float32")};return T.runKernel(Vn,t)}var i1=N({rsqrt_:bG});function CG(r){let t={x:v(r,"x","selu")};return T.runKernel(Xi,t)}var u1=N({selu_:CG});function SG(r,e,t,o,n,s=[1,1],a="NHWC"){let i=v(r,"x","separableConv2d"),p=v(e,"depthwiseFilter","separableConv2d"),u=v(t,"pointwiseFilter","separableConv2d"),c=i,l=!1;if(i.rank===3&&(l=!0,c=z(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");E(c.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`),E(p.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${p.rank}.`),E(u.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${p.rank}.`),E(u.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${u.shape[0]}.`),E(u.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${u.shape[1]}.`);let m=p.shape[2],d=p.shape[3];E(u.shape[2]===m*d,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${m*d}, but got ${u.shape[2]}.`);let f=Bp(c,p,o,n,a,s),g=vi(f,u,1,"valid",a);return l?z(g,[g.shape[1],g.shape[2],g.shape[3]]):g}var p1=N({separableConv2d_:SG});async function wG(r,e){let t=v(r,"x","setdiff1d"),o=v(e,"y","setdiff1d");E(t.dtype===o.dtype,()=>`x and y should have the same dtype, but got x (${t.dtype}) and y (${o.dtype}).`),E(t.rank===1,()=>`x should be 1D tensor, but got x (${t.shape}).`),E(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`),He(o,[e],[t])}var f1=N({slice1d_:NG});function TG(r,e,t){let o=v(r,"x","slice2d");return E(o.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${o.rank} tensor`),He(o,e,t)}var h1=N({slice2d_:TG});function _G(r,e,t){let o=v(r,"x","slice3d");return E(o.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${o.rank} tensor`),He(o,e,t)}var g1=N({slice3d_:_G});function EG(r,e,t){let o=v(r,"x","slice4d");return E(o.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${o.rank} tensor`),He(o,e,t)}var x1=N({slice4d_:EG});function $G(r,e=-1){let t=v(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 ${e}`);let o={logits:t},n={dim:e};return T.runKernel(qn,o,n)}var y1=N({softmax_:$G});function AG(r){E(r.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${r.dtype}.`);let e={input:r};return T.runKernel(oi,e)}var zp=N({fft_:AG});function RG(r){E(r.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${r.dtype}.`);let e={input:r};return T.runKernel(ni,e)}var hu=N({ifft_:RG});function FG(r){let e=r.shape[r.shape.length-1],t=r.size/e,o;if(e<=2){let n=z(r,[t,e]);o=hu(n)}else{let n=[t,2*(e-1)],s=z($a(r),[t,e]),a=z(Si(r),[t,e]),i=no(He(s,[0,1],[t,e-2]),1),p=ae(no(He(a,[0,1],[t,e-2]),1),be(-1)),u=gt([s,i],1),c=gt([a,p],1),l=z(Tr(u,c),[n[0],n[1]]);o=hu(l)}if(o=$a(o),r.rank===3&&r.shape[0]!==0){let n=o,s=r.shape[0];o=z(o,[s,o.shape[0]/s,o.shape[1]]),n.dispose()}return o}var Fd=N({irfft_:FG});function DG(r,e,t=0){let n={x:v(r,"x","split")},s={numOrSizeSplits:e,axis:t};return T.runKernel($s,n,s)}var Oa=N({split_:DG});function OG(r,e){E(r.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${r.dtype}`);let t=r.shape[r.shape.length-1],o=r.size/t,n;if(e!=null&&e0),h=r.shape.map(g=>g);h[r.shape.length-1]=e,n=He(r,f,h),t=e}else if(e!=null&&e>t){let f=r.shape.map(h=>h);f[r.shape.length-1]=e-t,n=gt([r,Vr(f)],r.shape.length-1),t=e}else n=r;let s=Ut(n),a=z(Tr(n,s),[o,t]),i=zp(a),p=Math.floor(t/2)+1,u=$a(i),c=Si(i),l=Oa(u,[p,t-p],u.shape.length-1),m=Oa(c,[p,t-p],c.shape.length-1),d=n.shape.slice();return d[n.shape.length-1]=p,z(Tr(l[0],m[0]),d)}var Wp=N({rfft_:OG});function PG(r,e){let t=v(r,"a","squaredDifference"),o=v(e,"b","squaredDifference");[t,o]=Re(t,o),Je(t.shape,o.shape);let n={a:t,b:o},s={};return T.runKernel(Kn,n,s)}var Dd=N({squaredDifference_:PG});function MG(r,e){let t=v(r,"x","squeeze","string_or_numeric");return z(t,pb(t.shape,e).newShape)}var Up=N({squeeze_:MG});function LG(r,e=0){let t=Na(r,"tensors","stack","string_or_numeric");E(t.length>=1,()=>"Pass at least one tensor to tf.stack"),t.length>0&&E(e<=t[0].rank,()=>"Axis must be <= rank of the tensor");let o=t,n={axis:e};return T.runKernel(vs,o,n)}var Sr=N({stack_:LG});function BG(r,e=0){let o={x:v(r,"x","step")},n={alpha:e};return T.runKernel(Ds,o,n)}var Od=N({step_:BG});function VG(r,e,t,o,n=0,s=0,a=0,i=0,p=0){let c={x:v(r,"x","stridedSlice","string_or_numeric")},l={begin:e,end:t,strides:o,beginMask:n,endMask:s,ellipsisMask:a,newAxisMask:i,shrinkAxisMask:p};return T.runKernel(jn,c,l)}var b1=N({stridedSlice_:VG});function zG(r){let t={x:v(r,"x","tan","float32")};return T.runKernel(Yn,t)}var C1=N({tan_:zG});function mr(r,e){Jr(r);let t=or(r,e);if(t.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return xr(r,null,t,e)}function _i(r,e,t){if(Jr(r),e!=null&&e.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let o=or(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 xr(r,e,o,t)}function S1(r,e,t){if(Jr(r),e!=null&&e.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let o=or(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 xr(r,e,o,t)}function w1(r,e,t){if(Jr(r),e!=null&&e.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let o=or(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 xr(r,e,o,t)}function I1(r,e,t){if(Jr(r),e!=null&&e.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let o=or(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,xr(r,e,o,t)}function WG(r,e=1,t=!0){let o=v(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<0)throw new Error(`'k' passed to topk() must be >= 0 but got ${e}`);if(e>n)throw new Error(`'k' passed to topk() must be <= the last dimension (${n}) but got ${e}`);let s={x:o},a={k:e,sorted:t},[i,p]=T.runKernel(Zn,s,a);return{values:i,indices:p}}var v1=N({topk_:WG});function UG(r,e=0,t=1,o,n){if(yt(r),o!=null&&o==="bool")throw new Error("Unsupported data type $ { dtype }");let s=new fu(e,t,o,!0,n),a=le(r,o);for(let i=0;i0,()=>"The input tensor must be at least 1D");let o={x:t},n={axis:e},[s,a]=T.runKernel(kp,o,n);return{values:s,indices:a}}var N1=N({unique_:GG});function HG(r,e,t){let o=v(r,"x","unsortedSegmentSum"),n=v(e,"segmentIds","unsortedSegmentSum","int32");E(na(t),()=>"numSegments must be of dtype int");let s={x:o,segmentIds:n},a={numSegments:t};return T.runKernel(Np,s,a)}var T1=N({unsortedSegmentSum_:HG});function qG(r,e=0){let t=v(r,"x","unstack","string_or_numeric");E(e>=-t.shape.length&&e`Axis = ${e} is not in [-${t.shape.length}, ${t.shape.length})`);let o={value:t},n={axis:e};return T.runKernel(Rs,o,n)}var so=N({unstack_:qG});function _1(r,e){return al(r,e,"right")}function E1(r,e=!0,t,o){return T.makeVariable(r,e,t,o)}function Pd(r,e){let t=[];for(let s=0;s0,()=>"mask cannot be scalar"),ht(i.slice(s,s+a),n.shape,"mask's shape must match the first K dimensions of tensor's shape,");let p=1;for(let h=s;h"Shape mismatch in v and x");let p=be(1),u=Ne(p,i),c=ae(Ne(a,s),u);if(n){E(o!=null,()=>"When using zeroDebias: true, step is required.");let l=v(o,"step","movingAverage");c=Ge(c,Ne(p,Ra(i,l)))}return xe(s,c)}var QG=N({movingAverage_:YG});function ZG(r,e,t){yt(t);let o=v(r,"indices","scatterND","int32"),n=v(e,"updates","scatterND");Qm(n,o,t);let s={indices:o,updates:n},a={shape:t};return T.runKernel(zn,s,a)}var JG=N({scatterND_:ZG});function $1(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 tH(r,e,t,o=0){yt(t);let n=v(r,"sparseIndices","sparseToDense","int32"),s=v(e,"sparseValues","sparseToDense","string_or_numeric"),a=v(o,"defaultValue","sparseToDense",s.dtype);$1(n,s,t,a);let i={sparseIndices:n,sparseValues:s,defaultValue:a},p={outputShape:t};return T.runKernel(li,i,p)}var rH=N({sparseToDense_:tH});function oH(r,e){let t=v(e,"indices","gatherND","int32"),n={params:v(r,"x","gatherND","string_or_numeric"),indices:t};return T.runKernel(un,n)}var nH=N({gatherND_:oH});function A1(r,e){if(e==null)return r.shape.slice();if(Pr(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.`),E(e>=0&&e<1,()=>`rate must be a float in the range [0, 1), but got ${e}.`),e===0)return r instanceof it?n.clone():n;let s=A1(n,t),a=1-e,i=Ge(ud(xe($d(s,0,1,"float32",o),a)),a);return ae(n,i)}var aH=N({dropout_:sH});function xC(r){return Math.floor(Math.pow(2,Math.ceil(Math.log(r)/Math.log(2))))}function il(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}`),E(o.rank-1===n.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${o.rank} and targets rank ${n.rank}`),ht(o.shape.slice(0,o.shape.length-1),n.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let s=o.shape[o.shape.length-1];E(t>0&&t<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${t}`);let a=await o.data(),i=await n.data(),[p,u]=[a.length/s,s],c=cb("bool",p);for(let l=0;lg.value-h.value),c[l]=0;for(let h=0;hF1,depthwiseConv2d:()=>P1,matMul:()=>M1});function pH(r,e,t,o,n,s="NHWC",a){let i=r;r.rank===3&&(i=z(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let p=e;p.rank===3&&(p=z(e,[1,e.shape[0],e.shape[1],e.shape[2]])),E(i.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`),E(p.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${p.shape}.`),E(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"?p.shape[3]:p.shape[1];E(u===t[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${t[2]}.`),E(c===t[3],()=>`Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${t[3]}).`),Pt("conv2dDerFilter",n,a);let l={x:i,dy:p},m={strides:o,pad:n,dataFormat:s,dimRoundingMode:a,filterShape:t};return T.runKernel(cp,l,m)}var R1=N({conv2DBackpropFilter_:pH});function gu(r,e,t){if(t==null||t==="linear")return r;if(t==="relu")return ae(r,Od(e));throw new Error(`Cannot compute gradient for fused activation ${t}.`)}function xu(r,e){let t=e,o=jm(r.shape,e.shape);return o.length>0&&(t=et(t,o)),z(t,r.shape)}function yu(r,e,t,o){if(e==="linear")return r;if(e==="relu")return Ti(r);if(e==="elu")return ad(r);if(e==="relu6")return Ad(r);if(e==="prelu")return vd(r,t);if(e==="leakyrelu")return ld(r,o);if(e==="sigmoid")return zs(r);throw new Error(`Unknown fused activation ${e}.`)}var bu=(r,e)=>!(r>0)||e==="linear";function cH({x:r,filter:e,strides:t,pad:o,dataFormat:n="NHWC",dilations:s=[1,1],dimRoundingMode:a,bias:i,activation:p="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(p=p||"linear",bu(T.state.gradientDepth,p)===!1){E(n==="NHWC",()=>`Error in fused conv2d: got dataFormat of ${n} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);let _=vi(r,e,t,o,n,s,a);return i!=null&&(_=xe(_,i)),yu(_,p,u,c)}let l=v(r,"x","conv2d","float32"),m=v(e,"filter","conv2d","float32"),d=l,f=!1;l.rank===3&&(f=!0,d=z(l,[1,l.shape[0],l.shape[1],l.shape[2]])),E(d.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${d.rank}.`),E(m.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`),Pt("fused conv2d",o,a);let h=n==="NHWC"?d.shape[3]:d.shape[1];E(m.shape[2]===h,()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${m.shape[2]}.`),E(lr(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`);let g=uu(d.shape,m.shape,t,s,o,a),x;i!=null&&(x=v(i,"bias","fused conv2d"),[x]=Re(x,l),n==="NHWC"?Je(g.outShape,x.shape):(E(x.shape.length<=1,()=>`Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${x.shape.length}.`),E(x.shape.length===0||x.shape[0]===g.outChannels||x.shape[0]===1,()=>`Error in fused conv2d: bias shape (${x.shape}) is not compatible with the number of output channels (${g.outChannels})`)));let b;if(u!=null){let _=u.shape;if(E(_.length<=1||_.length===3,()=>`Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${_.length}.`),_.length===1)E(_[0]===1||_[0]===g.outChannels,()=>`Error in fused conv2d: PReLU activation weights (${_}) is not compatible with the number of output channels (${g.outChannels}).`);else if(_.length===3)try{Je(_,g.outShape)}catch($){let A=`Error in fused conv2d: PReLU activation weights (${_}) is not compatible with the output shape of the conv2d (${g.outShape}).`;throw Error(A)}b=v(u,"prelu weights","fused conv2d")}let C=(_,$)=>{E(n==="NHWC",()=>`Error in gradient of fused conv2D: got dataFormat of ${n} but only NHWC is currently supported.`);let[A,R,D,P]=$,M=gu(_,D,p);E(iu(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let L=nd(R.shape,M,A,t,o),W=R1(R,M,A.shape,t,o),V=[L,W];if(P!=null){let U=xu(P,M);V.push(U)}return V},w={x:d,filter:m,bias:x,preluActivationWeights:b},k={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a,activation:p,leakyreluAlpha:c};return i==null?Cr(($,A,R)=>{let D=T.runKernel(ho,w,k);return R([A,$,D]),f&&(D=z(D,[D.shape[1],D.shape[2],D.shape[3]])),{value:D,gradFunc:C}})(d,m):Cr(($,A,R,D)=>{let P=T.runKernel(ho,w,k);return D([A,$,P,R]),f&&(P=z(P,[P.shape[1],P.shape[2],P.shape[3]])),{value:P,gradFunc:C}})(d,m,x)}var F1=N({fusedConv2d_:cH});function lH(r,e,t,o,n,s=[1,1],a){let i=r;r.rank===3&&(i=z(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let p=e;p.rank===3&&(p=z(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let u={x:i,dy:p},c={strides:o,pad:n,dimRoundingMode:a,dilations:s,filterShape:t};return T.runKernel(dp,u,c)}var D1=N({depthwiseConv2dNativeBackpropFilter_:lH});function mH(r,e,t,o,n,s=[1,1],a){let i=e,p=!1;e.rank===3&&(p=!0,i=z(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},l=T.runKernel(fp,u,c);return p?z(l,[l.shape[1],l.shape[2],l.shape[3]]):l}var O1=N({depthwiseConv2dNativeBackpropInput_:mH});function dH({x:r,filter:e,strides:t,pad:o,dataFormat:n="NHWC",dilations:s=[1,1],dimRoundingMode:a,bias:i,activation:p="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(bu(T.state.gradientDepth,p)===!1){let k=Bp(r,e,t,o,n,s,a);return i!=null&&(k=xe(k,i)),yu(k,p,u,c)}let l=v(r,"x","depthwiseConv2d","float32"),m=v(e,"filter","depthwiseConv2d","float32"),d=l,f=!1;l.rank===3&&(f=!0,d=z(l,[1,l.shape[0],l.shape[1],l.shape[2]])),E(d.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${d.rank}.`),E(m.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${m.rank}.`),E(d.shape[3]===m.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${d.shape[3]}) must match the inChannels dimension in filter ${m.shape[2]}.`),s==null&&(s=[1,1]),E(lr(t,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`),Pt("fused depthwiseConv2d",o,a);let h=uu(d.shape,m.shape,t,s,o,a,!0),g;i!=null&&(g=v(i,"bias","fused conv2d"),[g]=Re(g,l),Je(h.outShape,g.shape));let x;u!=null&&(x=v(u,"prelu weights","fused depthwiseConv2d"));let b=(k,_)=>{E(iu(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[$,A,R,D]=_,P=gu(k,R,p),M=O1(A.shape,P,$,t,o,s,a),L=D1(A,P,$.shape,t,o,s,a);if(D!=null){let W=xu(g,P);return[M,L,W]}return[M,L]},C={x:d,filter:m,bias:g,preluActivationWeights:x},w={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a,activation:p,leakyreluAlpha:c};return i==null?Cr((_,$,A)=>{let R=T.runKernel(go,C,w);return A([$,_,R]),f&&(R=z(R,[R.shape[1],R.shape[2],R.shape[3]])),{value:R,gradFunc:b}})(d,m):Cr((_,$,A,R)=>{let D=T.runKernel(go,C,w);return R([$,_,D,A]),f&&(D=z(D,[D.shape[1],D.shape[2],D.shape[3]])),{value:D,gradFunc:b}})(d,m,g)}var P1=N({fusedDepthwiseConv2d_:dH});function fH({a:r,b:e,transposeA:t=!1,transposeB:o=!1,bias:n,activation:s="linear",preluActivationWeights:a,leakyreluAlpha:i=.2}){if(bu(T.state.gradientDepth,s)===!1){let P=Xe(r,e,t,o);return n!=null&&(P=xe(P,n)),yu(P,s,a,i)}let p=v(r,"a","fused matMul"),u=v(e,"b","fused matMul");[p,u]=Re(p,u);let c=t?p.shape[p.rank-2]:p.shape[p.rank-1],l=o?u.shape[u.rank-1]:u.shape[u.rank-2],m=t?p.shape[p.rank-1]:p.shape[p.rank-2],d=o?u.shape[u.rank-2]:u.shape[u.rank-1],f=p.shape.slice(0,-2),h=u.shape.slice(0,-2),g=ze(f),x=ze(h);E(c===l,()=>`Error in fused matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${p.shape} and ${u.shape} and transposeA=${t} and transposeB=${o} must match.`);let C=Je(p.shape.slice(0,-2),u.shape.slice(0,-2)).concat([m,d]),w=t?z(p,[g,c,m]):z(p,[g,m,c]),k=o?z(u,[x,d,l]):z(u,[x,l,d]),_;n!=null&&(_=v(n,"bias","fused matMul"),[_]=Re(_,p),Je(C,_.shape));let $;a!=null&&($=v(a,"prelu weights","fused matMul"));let A=(P,M)=>{let[L,W,V,U]=M,q=gu(z(P,V.shape),V,s),H,j;if(!t&&!o?(H=Xe(q,W,!1,!0),j=Xe(L,q,!0,!1)):!t&&o?(H=Xe(q,W,!1,!1),j=Xe(q,L,!0,!1)):t&&!o?(H=Xe(W,q,!1,!0),j=Xe(L,q,!1,!1)):(H=Xe(W,q,!0,!0),j=Xe(q,L,!0,!0)),n!=null){let X=xu(U,q);return[H,j,X]}else return[H,j]},R={a:w,b:k,bias:_,preluActivationWeights:$},D={transposeA:t,transposeB:o,activation:s,leakyreluAlpha:i};return n==null?Cr((M,L,W)=>{let V=T.runKernel(fo,R,D);return W([M,L,V]),{value:z(V,C),gradFunc:A}})(w,k):Cr((M,L,W,V)=>{let U=T.runKernel(fo,R,D);return V([M,L,U,W]),{value:z(U,C),gradFunc:A}})(w,k,_)}var M1=N({fusedMatMul_:fH});function hH(r){return il(r,.54,.46)}var L1=N({hammingWindow_:hH});function gH(r){return il(r,.5,.5)}var Ld=N({hannWindow_:gH});function xH(r,e,t,o=!1,n=0){let s=0,a=[];for(;s+e<=r.size;)a.push(He(r,s,e)),s+=t;if(o)for(;s`Error in cropAndResize: image must be rank 4,but got rank ${a.rank}.`),E(i.rank===2&&i.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${u},4] but had shape ${i.shape}.`),E(p.rank===1&&p.shape[0]===u,()=>`Error in cropAndResize: boxInd must be have size [${u}] but had shape ${i.shape}.`),E(o.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${o.length}.`),E(o[0]>=1&&o[1]>=1,()=>`cropSize must be atleast [1,1], but was ${o}`),E(n==="bilinear"||n==="nearest",()=>`method must be bilinear or nearest, but was ${n}`);let c={image:a,boxes:i,boxInd:p},l={method:n,extrapolationValue:s,cropSize:o};return T.runKernel(Yo,c,l)}var V1=N({cropAndResize_:bH});function CH(r){let e=v(r,"image","flipLeftRight","float32");E(e.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${e.rank}.`);let t={image:e};return T.runKernel(on,t,{})}var z1=N({flipLeftRight_:CH});function SH(r){let e=v(r,"image","grayscaleToRGB"),t=e.rank-1,o=e.shape[t];E(e.rank>=2,()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${e.rank}.`),E(o===1,()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${o}.`);let n=new Array(e.rank);return n.fill(1,0,t),n[t]=3,ki(e,n)}var W1=N({grayscaleToRGB_:SH});function wH(r,e,t=0,o=.5){let n=v(r,"image","rotateWithOffset","float32");E(n.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${n.rank}.`);let s={image:n},a={radians:e,fillValue:t,center:o};return T.runKernel(es,s,a)}var U1=N({rotateWithOffset_:wH});function So(r,e,t,o,n,s){o==null&&(o=.5),n==null&&(n=Number.NEGATIVE_INFINITY),s==null&&(s=0);let a=r.shape[0];return t=Math.min(t,a),E(0<=o&&o<=1,()=>`iouThreshold must be in [0, 1], but was '${o}'`),E(r.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${r.rank}'`),E(r.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`),E(e.rank===1,()=>"scores must be a 1D tensor"),E(e.shape[0]===a,()=>`scores has incompatible shape with boxes. Expected ${a}, but was ${e.shape[0]}`),E(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:t,iouThreshold:o,scoreThreshold:n,softNmsSigma:s}}function IH(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY){let s=v(r,"boxes","nonMaxSuppression","float32"),a=v(e,"scores","nonMaxSuppression","float32"),i=So(s,a,t,o,n);t=i.maxOutputSize,o=i.iouThreshold,n=i.scoreThreshold;let p={maxOutputSize:t,iouThreshold:o,scoreThreshold:n};return T.runKernel(Tn,{boxes:s,scores:a},p)}var G1=N({nonMaxSuppression_:IH});function H1(r,e,t){let o=vH(r,e,t),n=o<0?-(o+1):o;r.splice(n,0,e)}function vH(r,e,t){return NH(r,e,t||kH)}function kH(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 Vd(r,e,t,o,n){return bC(r,e,t,o,n,0)}function zd(r,e,t,o,n,s){return bC(r,e,t,o,n,0,!1,s,!0)}function Wd(r,e,t,o,n,s){return bC(r,e,t,o,n,s,!0)}function bC(r,e,t,o,n,s,a=!1,i=!1,p=!1){let u=[];for(let g=0;gn&&u.push({score:e[g],boxIndex:g,suppressBeginIndex:0});u.sort(q1);let c=s>0?-.5/s:0,l=[],m=[];for(;l.length0;){let g=u.pop(),{score:x,boxIndex:b,suppressBeginIndex:C}=g;if(x=C;--k){let _=TH(r,b,l[k]);if(_>=o){w=!0;break}if(g.score=g.score*_H(o,c,_),g.score<=n)break}g.suppressBeginIndex=l.length,w||(g.score===x?(l.push(b),m.push(g.score)):g.score>n&&H1(u,g,q1))}let d=l.length,f=t-d;i&&f>0&&(l.push(...new Array(f).fill(0)),m.push(...new Array(f).fill(0)));let h={selectedIndices:l};return a&&(h.selectedScores=m),p&&(h.validOutputs=d),h}function TH(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]),p=Math.max(o[1],o[3]),u=Math.min(n[0],n[2]),c=Math.min(n[1],n[3]),l=Math.max(n[0],n[2]),m=Math.max(n[1],n[3]),d=(i-s)*(p-a),f=(l-u)*(m-c);if(d<=0||f<=0)return 0;let h=Math.max(s,u),g=Math.max(a,c),x=Math.min(i,l),b=Math.min(p,m),C=Math.max(x-h,0)*Math.max(b-g,0);return C/(d+f-C)}function _H(r,e,t){let o=Math.exp(e*t*t);return t<=r?o:0}function q1(r,e){return r.score-e.score||r.score===e.score&&e.boxIndex-r.boxIndex}async function EH(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY){let s=v(r,"boxes","nonMaxSuppressionAsync"),a=v(e,"scores","nonMaxSuppressionAsync"),i=So(s,a,t,o,n);t=i.maxOutputSize,o=i.iouThreshold,n=i.scoreThreshold;let p=await Promise.all([s.data(),a.data()]),u=p[0],c=p[1],{selectedIndices:l}=Vd(u,c,t,o,n);return s!==r&&s.dispose(),a!==e&&a.dispose(),mr(l,"int32")}var K1=EH;function $H(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=0){let a=v(r,"boxes","nonMaxSuppression"),i=v(e,"scores","nonMaxSuppression"),p=So(a,i,t,o,n,s);t=p.maxOutputSize,o=p.iouThreshold,n=p.scoreThreshold,s=p.softNmsSigma;let u={boxes:a,scores:i},c={maxOutputSize:t,iouThreshold:o,scoreThreshold:n,softNmsSigma:s},l=T.runKernel(_n,u,c);return{selectedIndices:l[0],selectedScores:l[1]}}var j1=N({nonMaxSuppressionWithScore_:$H});async function AH(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=0){let a=v(r,"boxes","nonMaxSuppressionAsync"),i=v(e,"scores","nonMaxSuppressionAsync"),p=So(a,i,t,o,n,s);t=p.maxOutputSize,o=p.iouThreshold,n=p.scoreThreshold,s=p.softNmsSigma;let u=await Promise.all([a.data(),i.data()]),c=u[0],l=u[1],{selectedIndices:m,selectedScores:d}=Wd(c,l,t,o,n,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:mr(m,"int32"),selectedScores:mr(d)}}var X1=AH;function RH(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=!1){let a=v(r,"boxes","nonMaxSuppression"),i=v(e,"scores","nonMaxSuppression"),p=So(a,i,t,o,n,null),u=p.maxOutputSize,c=p.iouThreshold,l=p.scoreThreshold,m={boxes:a,scores:i},d={maxOutputSize:u,iouThreshold:c,scoreThreshold:l,padToMaxOutputSize:s},f=T.runKernel(ba,m,d);return{selectedIndices:f[0],validOutputs:f[1]}}var Y1=N({nonMaxSuppressionPadded_:RH});async function FH(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=!1){let a=v(r,"boxes","nonMaxSuppressionAsync"),i=v(e,"scores","nonMaxSuppressionAsync"),p=So(a,i,t,o,n,null),u=p.maxOutputSize,c=p.iouThreshold,l=p.scoreThreshold,[m,d]=await Promise.all([a.data(),i.data()]),{selectedIndices:f,validOutputs:h}=zd(m,d,u,c,l,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:mr(f,"int32"),validOutputs:be(h,"int32")}}var Q1=FH;function DH(r,e,t=!1,o=!1){let n=v(r,"images","resizeBilinear");E(n.rank===3||n.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${n.rank}.`),E(e.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${e}.`),E(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=z(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let[]=e,i={images:s},p={alignCorners:t,halfPixelCenters:o,size:e},u=T.runKernel(Mn,i,p);return a?z(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var Z1=N({resizeBilinear_:DH});function OH(r,e,t=!1,o=!1){let n=v(r,"images","resizeNearestNeighbor");E(n.rank===3||n.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${n.rank}.`),E(e.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${e}.`),E(n.dtype==="float32"||n.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),E(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=z(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let[]=e,i={images:s},p={alignCorners:t,halfPixelCenters:o,size:e},u=T.runKernel(Pn,i,p);return a?z(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var J1=N({resizeNearestNeighbor_:OH});function PH(r,e="binary",t=!1,o=.5){let n=v(r,"image","threshold"),s=.2989,a=.587,i=.114,p=n.shape[0]*n.shape[1],u=ae(mr([o]),255),c,l,m,d;if(E(n.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${n.rank}.`),E(n.shape[2]===3||n.shape[2]===1,()=>`Error in threshold: image color channel must be equal to 3 or 1but got ${n.shape[2]}.`),E(n.dtype==="int32"||n.dtype==="float32",()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${n.dtype}.`),E(e==="otsu"||e==="binary",()=>`Method must be binary or otsu, but was ${e}`),n.shape[2]===3){[c,l,m]=Oa(n,[1,1,1],-1);let g=ae(c,s),x=ae(l,a),b=ae(m,i);d=xe(xe(g,x),b)}else d=r;if(e==="otsu"){let g=od(Ke(Rd(d),"int32"),nr([]),256);u=MH(g,p)}let f=t?Vp(d,u):cu(d,u);return Ke(ae(f,255),"int32")}function MH(r,e){let t=mr([-1]),o=mr([0]),n=mr([0]),s,a,i,p,u,c;for(let l=0;l`Error in transform: image must be rank 4,but got rank ${a.rank}.`),E(i.rank===2&&(i.shape[0]===a.shape[0]||i.shape[0]===1)&&i.shape[1]===8,()=>"Error in transform: Input transform should be batch x 8 or 1 x 8"),E(s==null||s.length===2,()=>`Error in transform: outputShape must be [height, width] or null, but got ${s}.`);let p={image:a,transforms:i},u={interpolation:t,fillMode:o,fillValue:n,outputShape:s};return T.runKernel(Jn,p,u)}var tN=N({transform_:LH});function BH(r,e,t){E(e%1===0,()=>`bandPart(): numLower must be an integer, got ${e}.`),E(t%1===0,()=>`bandPart(): numUpper must be an integer, got ${t}.`);let o=v(r,"a","bandPart");E(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 (${a}).`);e<0&&(e=s),t<0&&(t=a);let i=z(Ni(0,s,1,"int32"),[-1,1]),p=Ni(0,a,1,"int32"),u=Ne(i,p),c=lu(Vp(u,be(+e,"int32")),cd(u,be(-t,"int32"))),l=Vr([s,a],o.dtype);return z(Sr(so(z(o,[-1,s,a])).map(m=>os(c,m,l))),n)}var rN=N({bandPart_:BH});function VH(r){let e;if(Array.isArray(r)){e=!1,E(r!=null&&r.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let n=r[0].shape[0];for(let s=1;s`Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${n})`)}else e=!0,r=Oa(r,r.shape[0],0).map(n=>Up(n,[0]));E(r.length<=r[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${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 nN(r,e);{let t=r.shape.slice(0,r.shape.length-2).reduce((p,u)=>p*u),o=so(z(r,[t,r.shape[r.shape.length-2],r.shape[r.shape.length-1]]),0),n=[],s=[];o.forEach(p=>{let[u,c]=nN(p,e);n.push(u),s.push(c)});let a=z(Sr(n,0),r.shape),i=z(Sr(s,0),r.shape);return[a,i]}}function nN(r,e=!1){return T.tidy(()=>{E(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=id(t),s=Br(r),a=_i([[1]],[1,1]),i=Br(a),p=t>=o?o:t;for(let u=0;u{let d=He(s,[u,u],[t-u,1]),f=pu(d),h=He(s,[u,u],[1,1]),g=os(cu(h,0),_i([[-1]]),_i([[1]])),x=Ne(h,ae(g,f)),b=Ge(d,x);b.shape[0]===1?i=Br(a):i=gt([a,He(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let C=yr(Ge(Xe(g,x),f)),w=He(s,[u,0],[t-u,o]),k=ae(C,i),_=Mp(i);if(u===0)s=Ne(w,Xe(k,Xe(_,w)));else{let R=Ne(w,Xe(k,Xe(_,w)));s=gt([He(s,[0,0],[u,o]),R],0)}let $=Mp(k),A=He(n,[0,u],[t,n.shape[1]-u]);if(u===0)n=Ne(A,Xe(Xe(A,i),$));else{let R=Ne(A,Xe(Xe(A,i),$));n=gt([He(n,[0,0],[t,u]),R],1)}return[i,s,n]}),Dt([c,l,m])}return!e&&t>o&&(n=He(n,[0,0],[t,o]),s=He(s,[0,0],[o,o])),[n,s]})}var sN=N({qr_:zH});var Et;(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"})(Et||(Et={}));function WH(r,e,t=Et.SUM_BY_NONZERO_WEIGHTS){let o=v(r,"losses","computeWeightedLoss"),n=null;e!=null&&(n=v(e,"weights","computeWeightedLoss"));let s=n==null?o:ae(o,n);if(t===Et.NONE)return s;if(t===Et.SUM)return et(s);if(t===Et.MEAN){if(n==null)return mu(s);{let a=o.size/n.size,i=Ge(et(s),et(n));return a>1?Ge(i,be(a)):i}}if(t===Et.SUM_BY_NONZERO_WEIGHTS){if(n==null)return Ge(et(s),be(o.size));{let a=ae(n,Gs(o.shape)),i=Ke(et(wd(a,be(0))),"float32");return Ge(et(s),i)}}throw Error(`Unknown reduction: ${t}`)}var sr=N({computeWeightedLoss_:WH});function UH(r,e,t,o=Et.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","absoluteDifference"),s=v(e,"predictions","absoluteDifference"),a=null;t!=null&&(a=v(t,"weights","absoluteDifference")),ht(n.shape,s.shape,"Error in absoluteDifference: ");let i=Yt(Ne(n,s));return sr(i,a,o)}var aN=N({absoluteDifference_:UH});function GH(r,e,t,o,n=Et.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","cosineDistance"),a=v(e,"predictions","cosineDistance"),i=null;o!=null&&(i=v(o,"weights","cosineDistance")),ht(s.shape,a.shape,"Error in cosineDistance: ");let p=be(1),u=Ne(p,et(ae(s,a),t,!0));return sr(u,i,n)}var iN=N({cosineDistance_:GH});function HH(r,e,t,o=Et.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","hingeLoss"),s=v(e,"predictions","hingeLoss"),a=null;t!=null&&(a=v(t,"weights","hingeLoss")),ht(n.shape,s.shape,"Error in hingeLoss: ");let i=be(1);n=Ne(ae(be(2),n),i);let p=Ti(Ne(i,ae(n,s)));return sr(p,a,o)}var uN=N({hingeLoss_:HH});function qH(r,e,t,o=1,n=Et.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","huberLoss"),a=v(e,"predictions","huberLoss"),i=null;t!=null&&(i=v(t,"weights","huberLoss")),ht(s.shape,a.shape,"Error in huberLoss: ");let p=be(o),u=Yt(Ne(a,s)),c=Sd(u,p),l=Ne(u,c),m=xe(ae(be(.5),Qt(c)),ae(p,l));return sr(m,i,n)}var pN=N({huberLoss_:qH});function KH(r,e,t,o=1e-7,n=Et.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","logLoss"),a=v(e,"predictions","logLoss"),i=null;t!=null&&(i=v(t,"weights","logLoss")),ht(s.shape,a.shape,"Error in logLoss: ");let p=be(1),u=be(o),c=yr(ae(s,Da(xe(a,u)))),l=ae(Ne(p,s),Da(xe(Ne(p,a),u))),m=Ne(c,l);return sr(m,i,n)}var cN=N({logLoss_:KH});function jH(r,e,t,o=Et.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","meanSquaredError"),s=v(e,"predictions","meanSquaredError"),a=null;t!=null&&(a=v(t,"weights","meanSquaredError")),ht(n.shape,s.shape,"Error in meanSquaredError: ");let i=Dd(n,s);return sr(i,a,o)}var lN=N({meanSquaredError_:jH});function XH(r,e){let t=v(r,"labels","sigmoidCrossEntropyWithLogits"),o=v(e,"logits","sigmoidCrossEntropyWithLogits");ht(t.shape,o.shape,"Error in sigmoidCrossEntropyWithLogits: ");let n=Ti(o),s=ae(o,t),a=md(Co(yr(Yt(o))));return xe(Ne(n,s),a)}function YH(r,e,t,o=0,n=Et.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"multiClassLabels","sigmoidCrossEntropy"),a=v(e,"logits","sigmoidCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","sigmoidCrossEntropy")),ht(s.shape,a.shape,"Error in sigmoidCrossEntropy: "),o>0){let u=be(o),c=be(1),l=be(.5);s=xe(ae(s,Ne(c,u)),ae(l,u))}let p=XH(s,a);return sr(p,i,n)}var mN=N({sigmoidCrossEntropy_:YH});function QH(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 Cr((n,s,a)=>{let p=hd(s,[t],!0),u=Ne(Ke(s,"float32"),p);a([n,u]);let c=yr(ae(u,n));return{value:et(c,[t]),gradFunc:(d,f)=>{let[h,g]=f,x=Aa(d.shape,[t]);return[ae(z(d,x),Ne(Ke(h,"float32"),Co(g))),ae(z(d,x),Ne(Co(g),Ke(h,"float32")))]}}})(r,e)}function ZH(r,e,t,o=0,n=Et.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"onehotLabels","softmaxCrossEntropy"),a=v(e,"logits","softmaxCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","softmaxCrossEntropy")),ht(s.shape,a.shape,"Error in softmaxCrossEntropy: "),o>0){let u=be(o),c=be(1),l=be(s.shape[1]);s=xe(ae(s,Ne(c,u)),Ge(u,l))}let p=QH(s,a);return sr(p,i,n)}var dN=N({softmaxCrossEntropy_:ZH});function JH(r,e,t,o){let n=v(r,"indices","sparseFillEmptyRows","int32"),s=v(e,"values","sparseFillEmptyRows"),a=v(t,"denseShape","sparseFillEmptyRows","int32"),i=v(o,"defaultValue","sparseFillEmptyRows",s.dtype);if(n.rank!==2)throw new Error(`Indices should be Tensor2D but received shape ${n.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(a.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${a.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let p={indices:n,values:s,denseShape:a,defaultValue:i},u=T.runKernel(ui,p);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var fN=N({sparseFillEmptyRows_:JH});function eq(r,e,t){let o=v(r,"inputIndices","sparseReshape","int32"),n=v(e,"inputShape","sparseReshape","int32"),s=v(t,"newShape","sparseReshape","int32");if(o.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape ${o.shape}`);if(n.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${n.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let a={inputIndices:o,inputShape:n,newShape:s},i=T.runKernel(wa,a);return{outputIndices:i[0],outputShape:i[1]}}var hN=N({sparseReshape_:eq});function tq(r,e,t){let o=v(r,"data","sparseSegmentMean"),n=v(e,"indices","sparseSegmentMean","int32"),s=v(t,"segmentIds","sparseSegmentMean","int32");if(o.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(pi,a)}var gN=N({sparseSegmentMean_:tq});function rq(r,e,t){let o=v(r,"data","sparseSegmentSum"),n=v(e,"indices","sparseSegmentSum","int32"),s=v(t,"segmentIds","sparseSegmentSum","int32");if(o.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(ci,a)}var xN=N({sparseSegmentSum_:rq});function oq(r,e,t,o,n,s,a,i){let p=v(r,"data","stringNGrams","string");if(p.dtype!=="string")throw new Error("Data must be of datatype string");if(p.shape.length!==1)throw new Error(`Data must be a vector, saw: ${p.shape}`);let u=v(e,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let c={separator:t,nGramWidths:o,leftPad:n,rightPad:s,padWidth:a,preserveShortSequences:i},l={data:p,dataSplits:u},m=T.runKernel(As,l,c);return{nGrams:m[0],nGramsSplits:m[1]}}var yN=N({stringNGrams_:oq});function nq(r,e,t=!0){let o=v(r,"input","stringSplit","string"),n=v(e,"delimiter","stringSplit","string");if(o.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${o.shape}`);if(n.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`);let s={skipEmpty:t},a={input:o,delimiter:n},i=T.runKernel(di,a,s);return{indices:i[0],values:i[1],shape:i[2]}}var bN=N({stringSplit_:nq});function sq(r,e){let t=v(r,"input","stringToHashBucketFast","string"),o={numBuckets:e};if(e<=0)throw new Error("Number of buckets must be at least 1");let n={input:t};return T.runKernel(fi,n,o)}var CN=N({stringToHashBucketFast_:sq});var aq={fft:zp,ifft:hu,rfft:Wp,irfft:Fd},iq={hammingWindow:L1,hannWindow:Ld,frame:Bd,stft:B1},uq={flipLeftRight:z1,grayscaleToRGB:W1,resizeNearestNeighbor:J1,resizeBilinear:Z1,rotateWithOffset:U1,cropAndResize:V1,nonMaxSuppression:G1,nonMaxSuppressionAsync:K1,nonMaxSuppressionWithScore:j1,nonMaxSuppressionWithScoreAsync:X1,nonMaxSuppressionPadded:Y1,nonMaxSuppressionPaddedAsync:Q1,threshold:eN,transform:tN},pq={bandPart:rN,gramSchmidt:oN,qr:sN},cq={absoluteDifference:aN,computeWeightedLoss:sr,cosineDistance:iN,hingeLoss:uN,huberLoss:pN,logLoss:cN,meanSquaredError:lN,sigmoidCrossEntropy:mN,softmaxCrossEntropy:dN},lq={sparseFillEmptyRows:fN,sparseReshape:hN,sparseSegmentMean:gN,sparseSegmentSum:xN},mq={stringNGrams:yN,stringSplit:bN,stringToHashBucketFast:CN};var wr=class extends ol{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 Dt(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 pC(e,t)}dispose(){this.iterations_!=null&&Dt(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:be(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(wr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var Ei=class extends wr{constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;Ee(()=>{let c=xe(ae(p,this.rho),ae(Qt(i),1-this.rho)),l=ae(Ge($r(xe(u,this.epsilon)),$r(xe(p,this.epsilon))),i),m=xe(ae(u,this.rho),ae(Qt(l),1-this.rho));p.assign(c),u.assign(m);let d=xe(ae(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Dt(this.accumulatedGrads.map(e=>e.variable)),Dt(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)}};Ei.className="Adadelta";Er(Ei);var $i=class extends wr{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:Ee(()=>Ws(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;Ee(()=>{let p=xe(i,Qt(a));i.assign(p);let u=xe(ae(Ge(a,$r(xe(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Dt(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};$i.className="Adagrad";Er($i);var Ai=class extends wr{constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Ee(()=>{this.accBeta1=be(t).variable(),this.accBeta2=be(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=Ne(1,this.accBeta1),n=Ne(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ee(()=>Ut(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:Ee(()=>Ut(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=xe(ae(c,this.beta1),ae(u,1-this.beta1)),d=xe(ae(l,this.beta2),ae(Qt(u),1-this.beta2)),f=Ge(m,o),h=Ge(d,n);c.assign(m),l.assign(d);let g=xe(ae(Ge(f,xe($r(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(ae(this.accBeta1,this.beta1)),this.accBeta2.assign(ae(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Dt(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Dt(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),Ee(()=>{this.accBeta1.assign(Ra(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ra(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)}};Ai.className="Adam";Er(Ai);var Ri=class extends wr{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=[],Ee(()=>{this.iteration=be(0).variable(),this.accBeta1=be(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);Ee(()=>{let o=Ne(1,this.accBeta1),n=Ge(-this.learningRate,xe(ae(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ut(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ut(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=xe(ae(c,this.beta1),ae(u,1-this.beta1)),d=ae(l,this.beta2),f=Yt(u),h=Cd(d,f);c.assign(m),l.assign(h);let g=xe(ae(Ge(n,o),Ge(m,xe(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(xe(this.iteration,1)),this.accBeta1.assign(ae(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Dt(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Dt(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)}};Ri.className="Adamax";Er(Ri);var qs=class extends wr{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=T.registeredVariables[o];Ee(()=>{let i=xe(ae(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=_r(be(-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)}};qs.className="SGD";Er(qs);var Fi=class extends qs{constructor(e,t,o=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=be(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:Ee(()=>Ut(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&Ee(()=>{let p,u=xe(ae(this.m,a),i);this.useNesterov?p=xe(ae(this.c,xe(i,ae(u,this.m))),s):p=xe(ae(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Dt(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Fi.className="Momentum";Er(Fi);var Di=class extends wr{constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:Ee(()=>Ut(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:Ee(()=>Ut(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;Ee(()=>{let c=xe(ae(p,this.decay),ae(Qt(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=xe(ae(l,this.decay),ae(i,1-this.decay)),d=Ge(ae(i,this.learningRate),$r(Ne(c,xe(Qt(m),this.epsilon)))),f=xe(ae(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Ne(s,f);s.assign(h)}else{let l=xe(ae(p,this.decay),ae(Qt(i),1-this.decay)),m=xe(ae(u,this.momentum),Ge(ae(i,this.learningRate),$r(xe(l,this.epsilon))));p.assign(l),u.assign(m);let d=Ne(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Dt(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Dt(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Dt(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)}};Di.className="RMSProp";Er(Di);var ns=class{static sgd(e){return new qs(e)}static momentum(e,t,o=!1){return new Fi(e,t,o)}static rmsprop(e,t=.9,o=0,n=null,s=!1){return new Di(e,t,o,n,s)}static adam(e=.001,t=.9,o=.999,n=null){return new Ai(e,t,o,n)}static adadelta(e=.001,t=.95,o=null){return new Ei(e,t,o)}static adamax(e=.002,t=.9,o=.999,n=null,s=0){return new Ri(e,t,o,n,s)}static adagrad(e,t=.1){return new $i(e,t)}};var hMe={sgd:ns.sgd,momentum:ns.momentum,adadelta:ns.adadelta,adagrad:ns.adagrad,rmsprop:ns.rmsprop,adamax:ns.adamax,adam:ns.adam};var dq=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:r=>r())();function CC(){return new Promise(r=>dq(()=>r()))}var S={};Ue(S,{ERF_A1:()=>$q,ERF_A2:()=>Aq,ERF_A3:()=>Rq,ERF_A4:()=>Fq,ERF_A5:()=>Dq,ERF_P:()=>Eq,PARALLELIZE_THRESHOLD:()=>Ud,RowPartitionType:()=>Ks,SELU_SCALE:()=>_q,SELU_SCALEALPHA:()=>Tq,applyActivation:()=>yu,assertAndGetBroadcastShape:()=>Je,assertAxesAreInnerMostDims:()=>DU,assertParamsConsistent:()=>fq,assignToTypedArray:()=>Vq,axesAreInnerMostDims:()=>uC,calculateShapes:()=>J0,checkEinsumDimSizes:()=>qq,checkPadOnDimRoundingMode:()=>Pt,combineLocations:()=>nk,combineRaggedTensorToTensorShapes:()=>gq,complexWithEvenIndex:()=>Mq,complexWithOddIndex:()=>Lq,computeConv2DInfo:()=>uu,computeConv3DInfo:()=>Nv,computeDefaultPad:()=>iC,computeDilation2DInfo:()=>OW,computeOptimalWindowSize:()=>Cq,computeOutAndReduceShapes:()=>FU,computeOutShape:()=>hq,computePool2DInfo:()=>aC,computePool3DInfo:()=>PW,convertConv2DDataFormat:()=>Tv,decodeEinsumEquation:()=>Gq,eitherStridesOrDilationsAreOne:()=>lr,expandShapeToKeepDim:()=>Aa,exponent:()=>Wq,exponents:()=>zq,fromStringArrayToUint8:()=>dK,fromUint8ToStringArray:()=>mK,getAxesPermutation:()=>OU,getBroadcastDims:()=>X0,getComplexWithIndex:()=>Bq,getEinsumComputePath:()=>Kq,getEinsumPermutation:()=>Hq,getFusedBiasGradient:()=>xu,getFusedDyActivation:()=>gu,getImageCenter:()=>Sq,getInnerMostAxes:()=>MU,getPermuted:()=>Iq,getRaggedRank:()=>yq,getReductionAxes:()=>jm,getReshaped:()=>wq,getReshapedPermuted:()=>vq,getRowPartitionTypesHelper:()=>xq,getSliceBeginCoords:()=>kq,getSliceSize:()=>Nq,getSparseFillEmptyRowsIndicesDenseShapeMismatch:()=>Qq,getSparseFillEmptyRowsNegativeIndexErrorMessage:()=>Zq,getSparseFillEmptyRowsOutOfRangeIndexErrorMessage:()=>Jq,getSparseReshapeEmptyTensorZeroOutputDimErrorMessage:()=>rK,getSparseReshapeInputOutputMismatchErrorMessage:()=>nK,getSparseReshapeInputOutputMultipleErrorMessage:()=>oK,getSparseReshapeMultipleNegativeOneOutputDimErrorMessage:()=>eK,getSparseReshapeNegativeOutputDimErrorMessage:()=>tK,getSparseSegmentReductionIndicesOutOfRangeErrorMessage:()=>uK,getSparseSegmentReductionNegativeSegmentIdsErrorMessage:()=>sK,getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage:()=>aK,getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage:()=>iK,getUndoAxesPermutation:()=>PU,isIdentityPermutation:()=>jq,log:()=>GV,mergeRealAndImagArrays:()=>Oq,prepareAndValidate:()=>Z0,prepareSplitSize:()=>Yq,segment_util:()=>wC,shouldFuse:()=>bu,slice_util:()=>ut,splitRealAndImagArrays:()=>Pq,tupleValuesAreOne:()=>iu,upcastType:()=>dt,validateDefaultValueShape:()=>bq,validateInput:()=>Qm,validateUpdateShape:()=>tC,warn:()=>Os});function fq(r,e){let t=r[0].length;r.forEach((n,s)=>{E(n.length===t,()=>`Error in concat${t}D: rank of tensors[${s}] must be the same as the rank of the rest (${t})`)}),E(e>=0&&e`Error in concat${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 ${s}.`)})}function hq(r,e){let t=r[0].slice();for(let o=1;o=0)if(i>=0){if(i!==s)throw new Error(`rt input.shape and shape=${e} are incompatible: rt input.shape[${n+r}] = ${s} but shape[${n+r}] = ${i}`)}else o[a]=s}return o}function xq(r){let e={FIRST_DIM_SIZE:Ks.FIRST_DIM_SIZE,VALUE_ROWIDS:Ks.VALUE_ROWIDS,ROW_LENGTHS:Ks.ROW_LENGTHS,ROW_SPLITS:Ks.ROW_SPLITS,ROW_LIMITS:Ks.ROW_LIMITS,ROW_STARTS:Ks.ROW_STARTS},t=[];for(let o of r)if(o in e)t.push(e[o]);else break;return t}function yq(r){return r.length===0?0:r[0]===Ks.FIRST_DIM_SIZE?r.length-1:r.length}function bq(r,e){if(r==null||e==null)return;let t=r.length,o=e.length;if(t>=o)throw new Error(`defaultValue.shape=${r} and ragged tensor flatValues.shape=${e}, are incompatible: defaultValue.rank = ${t} must be less than ragged tensor input flatValues.rank = ${o})`);for(let n=0;n=0&&a>=0&&s!==1&&s!==a)throw new Error(`defaultValue.shape=${r}, and ragged tensor input flatValues.shape=${e} are incompatible: defaultValue.shape[${n-r.length}] = ${s} but ragged tensor input.flatValues.shape[${n-r.length}] = ${a}`)}}var Ud=30;function Cq(r){return r<=Ud?r:sp(r,Math.floor(Math.sqrt(r)))}function Sq(r,e,t){let o=t*(typeof r=="number"?r:r[0]),n=e*(typeof r=="number"?r:r[1]);return[o,n]}function wq(r,e,t,o=!0){let n=[];if(o)n=n.concat(e.slice(0)),n.push(r[0]/t),n=n.concat(r.slice(1));else{n=n.concat(r[0]);let s=e.length;for(let a=0;a=e*2+1||a%2===1?s.push(a):n.push(a);o.push(...n),o.push(0),o.push(...s)}return o}function vq(r,e,t,o=!0){let n=[];o?n.push(r[0]/t):n.push(r[0]*t);for(let s=1;s/g,SN=",",wN="...";function Gq(r,e){r=r.replace(/\s/g,"");let t=(r.length-r.replace(Uq,"").length)/SC.length;if(t<1)throw new Error("Equations without an arrow are not supported.");if(t>1)throw new Error(`Equation must contain exactly one arrow ("${SC}").`);let[o,n]=r.split(SC);E(o.indexOf(wN)===-1,()=>`The ellipsis notation ("${wN}") is not supported yet.`);let s=o.split(SN),a=s.length;if(e!==a)throw new Error(`Expected ${a} input tensors, received ${e}`);if(a>2)throw new Error("Support for more than 2 input tensors is not implemented yet.");let i=[];for(let m=0;mf.indexOf(d)!==-1))throw new Error(`Output subscripts contain the label ${d} not present in the input subscripts.`);i.indexOf(d)===-1&&i.push(d)}for(let m=0;mn!==-1),{permutationIndices:t,expandDims:o}}function qq(r,e,t){let o=new Array(r);for(let n=0;n`Expected dimension ${o[e[n][a]]} at axis ${a} of input shaped ${JSON.stringify(s)}, but got dimension ${s[a]}`)}}function Kq(r,e){let t=r,o=[],n=0;r.length===0&&t.push(-1),n=r.length+1;for(let a=0;ae===t)}function Xq(r,e){let t=[];for(let o=0;o"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);E(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,p)=>p>0?i+p:i);e[s]=r.shape[t]-a}E(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}function Qq(r){return`Received SparseTensor with denseShape[0] = 0 but indices.shape[0] = ${r}`}function Zq(r,e){return`indices(${r}, 0) is invalid: ${e} < 0`}function Jq(r,e,t){return`indices(${r}, 0) is invalid: ${e} >= ${t}`}function eK(r,e){return`only one output dimension may be -1, not both ${r} and ${e}`}function tK(r,e){return`size ${r} must be non-negative, not ${e}`}function rK(){return"reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"}function oK(r,e){let t=ze(r),o=ze(e);return`Input to reshape is a SparseTensor with ${t} dense values, but the requested shape requires a multiple of ${o}. inputShape=${r} outputShape= ${e}`}function nK(r,e){let t=ze(r),o=ze(e);return`Input to reshape is a tensor with ${t} dense values, but the requested shape has ${o}. inputShape=${r} outputShape=${e}`}function sK(){return"segment ids must be >= 0"}function aK(){return"segment ids are not increasing"}function iK(r,e){return`Segment id ${r} out of range [0, ${e}), possibly because segmentIds input is not sorted.`}function uK(r,e,t){return`Bad: indices[${r}] == ${e} out of range [0, ${t})`}var wC={};Ue(wC,{collectGatherOpShapeInfo:()=>lK,computeOutShape:()=>cK,segOpComputeOptimalWindowSize:()=>pK});function pK(r,e){let t=!1,o;for(r<=Ud?(o=r,t=!0):o=sp(r,Math.floor(Math.sqrt(r)));!t;)o>e||o===r?t=!0:o=sp(r,o+1);return o}function cK(r,e,t){let o=[],n=r.length;for(let s=0;sn))throw new Error(`Expect batchDims in the range of [-${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(tAp(e))}catch(e){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${e}`)}}function dK(r){return r.map(e=>gi(e))}var Lt={};Ue(Lt,{nonMaxSuppressionV3Impl:()=>Vd,nonMaxSuppressionV4Impl:()=>zd,nonMaxSuppressionV5Impl:()=>Wd,whereImpl:()=>Pd});var fK=O();fK.registerFlag("KEEP_INTERMEDIATE_TENSORS",()=>!1,r=>{r&&console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance.")});var ao;(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_QINT16=15]="DT_QINT16",r[r.DT_QUINT16=16]="DT_QUINT16",r[r.DT_UINT16=17]="DT_UINT16",r[r.DT_COMPLEX128=18]="DT_COMPLEX128",r[r.DT_HALF=19]="DT_HALF",r[r.DT_RESOURCE=20]="DT_RESOURCE",r[r.DT_VARIANT=21]="DT_VARIANT",r[r.DT_UINT32=22]="DT_UINT32",r[r.DT_UINT64=23]="DT_UINT64",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",r[r.DT_QINT16_REF=115]="DT_QINT16_REF",r[r.DT_QUINT16_REF=116]="DT_QUINT16_REF",r[r.DT_UINT16_REF=117]="DT_UINT16_REF",r[r.DT_COMPLEX128_REF=118]="DT_COMPLEX128_REF",r[r.DT_HALF_REF=119]="DT_HALF_REF",r[r.DT_RESOURCE_REF=120]="DT_RESOURCE_REF",r[r.DT_VARIANT_REF=121]="DT_VARIANT_REF",r[r.DT_UINT32_REF=122]="DT_UINT32_REF",r[r.DT_UINT64_REF=123]="DT_UINT64_REF"})(ao||(ao={}));var IN;(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={}))})(IN||(IN={}));var vC={};function gK(r,e){let t={tfOpName:r,category:"custom",inputs:[],attrs:[],customExecutor:e};vC[r]=t}function Gd(r){return vC[r]}function xK(r){delete vC[r]}function I(r,e,t,o,n){let s=e.inputParams[r];if(s&&s.inputIndexStart!==void 0){let i=s.inputIndexStart,p=s.inputIndexEnd===0?void 0:s.inputIndexEnd===void 0?i+1:s.inputIndexEnd;if(s.type==="tensor")return Gt(e.inputNames[s.inputIndexStart],t,o,n);if(s.type==="tensors")return e.inputNames.slice(i,p).map(m=>Gt(m,t,o,n));let u=Gt(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 Gt(r,e,t,o){let[n,s]=Ir(r);if(o!=null){let i=o.getHashTableHandleByName(n);if(i!=null)return i}let a=t.currentContextIds.find(i=>!!e[Hd(n,i)]);return a!==void 0?e[Hd(n,a)][s]:void 0}function vN(r,e,t){return e[Hd(r,t.currentContextId)]}function ss(r,e){let[t,o,n]=Ir(r);return[Hd(t,e&&e.currentContextId),o,n]}function Hd(r,e){return e?`${r}-${e}`:r}function Ir(r){let e=r.split(":");if(e.length===1)return[r,0,void 0];let t=e[0],o=e.length===3?e[1]:void 0,n=Number(e[e.length-1]);return[t,n,o]}function ul(r,e,t){let o=I("pad",r,e,t);if(o==="explicit"){o=I("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 as(r){return r.kept?r:Br(r)}var kC={};Ue(kC,{json:()=>yK});var yK=[{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 NC={};Ue(NC,{json:()=>bK});var bK=[{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}]},{tfOpName:"IsNan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];var TC={};Ue(TC,{json:()=>CK});var CK=[{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:"TensorListConcatV2",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"}]},{tfOpName:"TensorListLength",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"}]},{tfOpName:"TensorListResize",category:"control",inputs:[{start:0,name:"tensorListId",type:"tensor"},{start:1,name:"size",type:"number"}]}];var _C={};Ue(_C,{json:()=>SK});var SK=[{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",defaultValue:.2}]},{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 EC={};Ue(EC,{json:()=>wK});var wK=[{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"}]},{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:"RandomStandardNormal",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"seed",name:"seed",type:"number",defaultValue:0},{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:"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 $C={};Ue($C,{json:()=>IK});var IK=[{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 AC={};Ue(AC,{json:()=>vK});var vK=[{tfOpName:"LowerBound",category:"evaluation",inputs:[{start:0,name:"sortedSequence",type:"tensor"},{start:1,name:"values",type:"tensor"}]},{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:"UpperBound",category:"evaluation",inputs:[{start:0,name:"sortedSequence",type:"tensor"},{start:1,name:"values",type:"tensor"}]},{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 RC={};Ue(RC,{json:()=>kK});var kK=[{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 FC={};Ue(FC,{json:()=>NK});var NK=[{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}]},{tfOpName:"LookupTableSize",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"}]},{tfOpName:"LookupTableSizeV2",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"}]},{tfOpName:"InitializeTable",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"values",type:"tensor"}]},{tfOpName:"InitializeTableV2",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"values",type:"tensor"}]}];var DC={};Ue(DC,{json:()=>TK});var TK=[{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"}]},{tfOpName:"ImageProjectiveTransformV3",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"transforms",type:"tensor"},{start:2,name:"outputShape",type:"number[]"},{start:3,name:"fillValue",type:"number"}],attrs:[{tfName:"interpolation",name:"interpolation",type:"string"},{tfName:"fill_mode",name:"fillMode",type:"string"}]}];var OC={};Ue(OC,{json:()=>_K});var _K=[{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 PC={};Ue(PC,{json:()=>EK});var EK=[{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:"leakyrelu_alpha",name:"leakyreluAlpha",type:"number",defaultValue:.2},{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}]},{tfOpName:"Einsum",category:"matrices",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"equation",name:"equation",type:"string"},{tfName:"N",name:"n",type:"number",defaultValue:2},{tfName:"T",name:"dtype",type:"dtype"}]}];var MC={};Ue(MC,{json:()=>$K});var $K=[{tfOpName:"EuclideanNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool",defaultValue:!1}]},{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 LC={};Ue(LC,{json:()=>AK});var AK=[{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:"Cumprod",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"}]},{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 BC={};Ue(BC,{json:()=>RK});var RK=[{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 VC={};Ue(VC,{json:()=>FK});var FK=[{tfOpName:"SparseFillEmptyRows",category:"sparse",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"denseShape",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}]},{tfOpName:"SparseReshape",category:"sparse",inputs:[{start:0,name:"inputIndices",type:"tensor"},{start:1,name:"inputShape",type:"tensor"},{start:2,name:"newShape",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SparseSegmentMean",category:"sparse",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"segmentIds",type:"tensor"}]},{tfOpName:"SparseSegmentSum",category:"sparse",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"segmentIds",type:"tensor"}]}];var zC={};Ue(zC,{json:()=>DK});var DK=[{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 WC={};Ue(WC,{json:()=>OK});var OK=[{tfOpName:"StringNGrams",category:"string",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"dataSplits",type:"tensor"}],attrs:[{tfName:"separator",name:"separator",type:"string"},{tfName:"ngram_widths",name:"nGramWidths",type:"number[]"},{tfName:"left_pad",name:"leftPad",type:"string"},{tfName:"right_pad",name:"rightPad",type:"string"},{tfName:"pad_width",name:"padWidth",type:"number"},{tfName:"preserve_short_sequences",name:"preserveShortSequences",type:"bool"}],outputs:["ngrams","ngrams_splits"]},{tfOpName:"StringSplit",category:"string",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"delimiter",type:"tensor"}],attrs:[{tfName:"skip_empty",name:"skipEmpty",type:"bool"}],outputs:["indices","values","shape"]},{tfOpName:"StringToHashBucketFast",category:"string",inputs:[{start:0,name:"input",type:"tensor"}],attrs:[{tfName:"num_buckets",name:"numBuckets",type:"number"}]}];var UC={};Ue(UC,{json:()=>PK});var PK=[{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:[]},{tfOpName:"BroadcastArgs",category:"transformation",inputs:[{start:0,name:"s0",type:"tensor"},{start:1,name:"s1",type:"tensor"}],attrs:[]}];var pl=class{constructor(){let e=[kC,NC,TC,_C,EC,$C,AC,RC,FC,DC,OC,PC,MC,LC,BC,VC,zC,WC,UC],t=[].concat(...e.map(o=>o.json));this.opMappers=t.reduce((o,n)=>(o[n.tfOpName]=n,o),{})}static get Instance(){return this._instance||(this._instance=new this)}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),{}),p=[],u=[],c={},l={};t!=null&&(c=this.mapSignatureEntries(t.inputs),l=this.mapSignatureEntries(t.outputs));let m=Object.keys(i);m.forEach(h=>{let g=i[h];g.inputNames.forEach((x,b)=>{let[C,,w]=ss(x),k=i[C];if(k.outputs!=null){let _=k.outputs.indexOf(w);if(_!==-1){let $=`${C}:${_}`;g.inputNames[b]=$}}g.inputs.push(k),k.children.push(g)})}),Object.keys(l).length===0?m.forEach(h=>{let g=i[h];g.children.length===0&&u.push(g)}):Object.keys(l).forEach(h=>{let[g]=ss(h),x=i[g];x!=null&&(x.signatureKey=l[h],u.push(x))}),Object.keys(c).length>0?Object.keys(c).forEach(h=>{let[g]=ss(h),x=i[g];x&&(x.signatureKey=c[h],p.push(x))}):p=n;let d={};e.library!=null&&e.library.function!=null&&(d=e.library.function.reduce((h,g)=>(h[g.signature.name]=this.mapFunction(g),h),{}));let f={nodes:i,inputs:p,outputs:u,weights:s,placeholders:n,signature:t,functions:d};return a.length>0&&(f.initNodes=a),f}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,o)=>(t[e[o].name]=o,t),{})}mapNode(e){let t=Gd(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.slice(1):n),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr,outputs:t.outputs};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=qd(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=qd(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"string[]":i=Jd(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=Jd(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"number":i=jd(e.attr,s.tfName,s.defaultValue||0),i===void 0&&!!s.tfDeprecatedName&&(i=jd(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"number[]":i=Zd(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=Zd(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"bool":i=Kd(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=Kd(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"bool[]":i=tf(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=tf(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"shape":i=Qd(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=Qd(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"shape[]":i=ef(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=ef(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"dtype":i=Xd(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=Xd(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"dtype[]":i=Yd(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=Yd(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"func":i=kN(e.attr,s.tfName,s.defaultValue),i===void 0&&!!s.tfDeprecatedName&&(i=kN(e.attr,s.tfDeprecatedName,s.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${s.type} for op: ${e.op}`)}return n[s.name]={value:i,type:a},n},{})),o}mapFunction(e){let t=e.nodeDef,o=[],n=[],s={};t!=null&&(s=t.reduce((l,m)=>(l[m.name]=this.mapNode(m),m.op==="Const"&&n.push(l[m.name]),l),{}));let a=[],i=[];e.signature.inputArg.forEach(l=>{let[m]=ss(l.name),d={name:m,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:GC(l.type),type:"dtype"}},children:[]};d.signatureKey=l.name,a.push(d),s[m]=d}),Object.keys(s).forEach(l=>{let m=s[l];m.inputNames.forEach((d,f)=>{let[h,,g]=ss(d),x=s[h];if(x.outputs!=null){let b=x.outputs.indexOf(g);if(b!==-1){let C=`${h}:${b}`;m.inputNames[f]=C}}m.inputs.push(x),x.children.push(m)})});let u=e.ret;e.signature.outputArg.forEach(l=>{let[m,d]=ss(u[l.name]),f=s[m];f!=null&&(f.defaultOutput=d,i.push(f))});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 MK(r){let e=O().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 NN(r,e){let t=Array.isArray(r)?String.fromCharCode.apply(null,r):MK(r);return e?t:t.toLowerCase()}function qd(r,e,t,o=!1){let n=r[e];return n!=null?NN(n.s,o):t}function Kd(r,e,t){let o=r[e];return o?o.b:t}function jd(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 GC(r){switch(typeof r=="string"&&(r=ao[r]),r){case ao.DT_FLOAT:case ao.DT_HALF:return"float32";case ao.DT_INT32:case ao.DT_INT64:case ao.DT_INT8:case ao.DT_UINT8:return"int32";case ao.DT_BOOL:return"bool";case ao.DT_DOUBLE:return"float32";case ao.DT_STRING:return"string";default:return null}}function kN(r,e,t){let o=r[e];return o&&o.func?o.func.name:t}function Xd(r,e,t){let o=r[e];return o&&o.type?GC(o.type):t}function Yd(r,e,t){let o=r[e];return o&&o.list&&o.list.type?o.list.type.map(n=>GC(n)):t}function TN(r){if(!r.unknownRank)return r.dim!=null?r.dim.map(e=>typeof e.size=="number"?e.size:parseInt(e.size,10)):[]}function Qd(r,e,t){let o=r[e];return o&&o.shape?TN(o.shape):t}function Zd(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 Jd(r,e,t,o=!1){let n=r[e];return n&&n.list&&n.list.s?n.list.s.map(s=>NN(s,o)):t}function ef(r,e,t){let o=r[e];return o&&o.list&&o.list.shape?o.list.shape.map(n=>TN(n)):t}function tf(r,e,t){let o=r[e];return o&&o.list&&o.list.b?o.list.b:t}var rf=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 Gt(e,this.tensorMap,this.context)}getAttr(e,t){let o=this.node.rawAttrs[e];if(o.tensor!=null)return Gt(e,this.tensorMap,this.context);if(o.i!=null||o.f!=null)return jd(this.node.rawAttrs,e,t);if(o.s!=null)return qd(this.node.rawAttrs,e,t);if(o.b!=null)return Kd(this.node.rawAttrs,e,t);if(o.shape!=null)return Qd(this.node.rawAttrs,e,t);if(o.type!=null)return Xd(this.node.rawAttrs,e,t);if(o.list!=null){if(o.list.i!=null||o.list.f!=null)return Zd(this.node.rawAttrs,e,t);if(o.list.s!=null)return Jd(this.node.rawAttrs,e,t);if(o.list.shape!=null)return ef(this.node.rawAttrs,e,t);if(o.list.b!=null)return tf(this.node.rawAttrs,e,t);if(o.list.type!=null)return Yd(this.node.rawAttrs,e,t)}return t}};var Ye={};Ue(Ye,{OP_SCOPE_SUFFIX:()=>Lb,abs:()=>Yt,acos:()=>fv,acosh:()=>hv,add:()=>xe,addN:()=>gv,all:()=>xv,any:()=>yv,argMax:()=>bv,argMin:()=>Cv,asin:()=>Sv,asinh:()=>wv,atan:()=>Iv,atan2:()=>vv,atanh:()=>kv,avgPool:()=>td,avgPool3d:()=>_v,basicLSTMCell:()=>Ev,batchNorm:()=>wi,batchNorm2d:()=>Av,batchNorm3d:()=>Rv,batchNorm4d:()=>Fv,batchToSpaceND:()=>rd,bincount:()=>od,booleanMaskAsync:()=>XG,broadcastArgs:()=>Dv,broadcastTo:()=>Ii,buffer:()=>le,cast:()=>Ke,ceil:()=>Ov,clipByValue:()=>Pv,clone:()=>Br,complex:()=>Tr,concat:()=>gt,concat1d:()=>Mv,concat2d:()=>Lv,concat3d:()=>Bv,concat4d:()=>Vv,conv1d:()=>zv,conv2d:()=>vi,conv2dTranspose:()=>Wv,conv3d:()=>Uv,conv3dTranspose:()=>Hv,cos:()=>qv,cosh:()=>Kv,cosineWindow:()=>il,cumprod:()=>jv,cumsum:()=>Xv,denseBincount:()=>Yv,depthToSpace:()=>Qv,depthwiseConv2d:()=>Bp,diag:()=>Zv,dilation2d:()=>Jv,div:()=>Ge,divNoNan:()=>ek,dot:()=>tk,dropout:()=>aH,einsum:()=>rk,elu:()=>ad,enclosingPowerOfTwo:()=>xC,equal:()=>sd,erf:()=>ok,euclideanNorm:()=>ak,exp:()=>Co,expandDims:()=>Fa,expm1:()=>ik,eye:()=>id,fft:()=>zp,fill:()=>Ws,floor:()=>ud,floorDiv:()=>Jm,fused:()=>yC,gather:()=>pd,gatherND:()=>nH,greater:()=>cu,greaterEqual:()=>cd,ifft:()=>hu,imag:()=>Si,image:()=>uq,inTopKAsync:()=>uH,irfft:()=>Fd,isFinite:()=>uk,isInf:()=>pk,isNaN:()=>ck,leakyRelu:()=>ld,less:()=>lk,lessEqual:()=>Vp,linalg:()=>pq,linspace:()=>mk,localResponseNormalization:()=>dk,log:()=>Da,log1p:()=>md,logSigmoid:()=>fk,logSoftmax:()=>hk,logSumExp:()=>hd,logicalAnd:()=>lu,logicalNot:()=>gd,logicalOr:()=>xd,logicalXor:()=>gk,losses:()=>cq,lowerBound:()=>xk,matMul:()=>Xe,max:()=>Us,maxPool:()=>bd,maxPool3d:()=>yk,maxPoolWithArgmax:()=>bk,maximum:()=>Cd,mean:()=>mu,meshgrid:()=>Ck,min:()=>sl,minimum:()=>Sd,mirrorPad:()=>Sk,mod:()=>wk,moments:()=>Ik,movingAverage:()=>QG,mul:()=>ae,multiRNNCell:()=>vk,multinomial:()=>kk,neg:()=>yr,norm:()=>pu,notEqual:()=>wd,oneHot:()=>tl,ones:()=>Gs,onesLike:()=>Nk,op:()=>N,outerProduct:()=>Tk,pad:()=>Hs,pad1d:()=>_k,pad2d:()=>Ek,pad3d:()=>$k,pad4d:()=>Ak,pool:()=>Rk,pow:()=>Ra,prelu:()=>vd,print:()=>Gm,prod:()=>Fk,raggedGather:()=>Dk,raggedRange:()=>Ok,raggedTensorToTensor:()=>Pk,rand:()=>Mk,randomGamma:()=>e1,randomNormal:()=>Ed,randomStandardNormal:()=>t1,randomUniform:()=>$d,range:()=>Ni,real:()=>$a,reciprocal:()=>r1,relu:()=>Ti,relu6:()=>Ad,reshape:()=>z,reverse:()=>no,reverse1d:()=>o1,reverse2d:()=>n1,reverse3d:()=>s1,reverse4d:()=>a1,rfft:()=>Wp,round:()=>Rd,rsqrt:()=>i1,scalar:()=>be,scatterND:()=>JG,searchSorted:()=>al,selu:()=>u1,separableConv2d:()=>p1,setdiff1dAsync:()=>c1,sigmoid:()=>zs,sign:()=>l1,signal:()=>iq,sin:()=>m1,sinh:()=>d1,slice:()=>He,slice1d:()=>f1,slice2d:()=>h1,slice3d:()=>g1,slice4d:()=>x1,softmax:()=>y1,softplus:()=>fd,spaceToBatchND:()=>Id,sparse:()=>lq,sparseToDense:()=>rH,spectral:()=>aq,split:()=>Oa,sqrt:()=>$r,square:()=>Qt,squaredDifference:()=>Dd,squeeze:()=>Up,stack:()=>Sr,step:()=>Od,stridedSlice:()=>b1,string:()=>mq,sub:()=>Ne,sum:()=>et,tan:()=>C1,tanh:()=>nl,tensor:()=>nr,tensor1d:()=>mr,tensor2d:()=>_i,tensor3d:()=>Xm,tensor4d:()=>S1,tensor5d:()=>w1,tensor6d:()=>I1,tile:()=>ki,topk:()=>v1,transpose:()=>Mp,truncatedNormal:()=>k1,unique:()=>N1,unsortedSegmentSum:()=>T1,unstack:()=>so,upperBound:()=>_1,variable:()=>E1,where:()=>os,whereAsync:()=>Md,zeros:()=>Vr,zerosLike:()=>Ut});var _N=(r,e,t,o=Ye)=>{switch(r.op){case"BiasAdd":case"AddV2":case"Add":return[o.add(I("a",r,e,t),I("b",r,e,t))];case"AddN":return[o.addN(I("tensors",r,e,t))];case"FloorMod":case"Mod":return[o.mod(I("a",r,e,t),I("b",r,e,t))];case"Mul":return[o.mul(I("a",r,e,t),I("b",r,e,t))];case"RealDiv":case"Div":return[o.div(I("a",r,e,t),I("b",r,e,t))];case"DivNoNan":return[o.divNoNan(I("a",r,e,t),I("b",r,e,t))];case"FloorDiv":return[o.floorDiv(I("a",r,e,t),I("b",r,e,t))];case"Sub":return[o.sub(I("a",r,e,t),I("b",r,e,t))];case"Minimum":return[o.minimum(I("a",r,e,t),I("b",r,e,t))];case"Maximum":return[o.maximum(I("a",r,e,t),I("b",r,e,t))];case"Pow":return[o.pow(I("a",r,e,t),I("b",r,e,t))];case"SquaredDifference":return[o.squaredDifference(I("a",r,e,t),I("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var EN=(r,e,t,o=Ye)=>{switch(r.op){case"Abs":case"ComplexAbs":return[o.abs(I("x",r,e,t))];case"Acos":return[o.acos(I("x",r,e,t))];case"Acosh":return[o.acosh(I("x",r,e,t))];case"Asin":return[o.asin(I("x",r,e,t))];case"Asinh":return[o.asinh(I("x",r,e,t))];case"Atan":return[o.atan(I("x",r,e,t))];case"Atan2":return[o.atan2(I("x",r,e,t),I("y",r,e,t))];case"Atanh":return[o.atanh(I("x",r,e,t))];case"Ceil":return[o.ceil(I("x",r,e,t))];case"Complex":return[o.complex(I("real",r,e,t),I("imag",r,e,t))];case"Cos":return[o.cos(I("x",r,e,t))];case"Cosh":return[o.cosh(I("x",r,e,t))];case"Elu":return[o.elu(I("x",r,e,t))];case"Erf":return[o.erf(I("x",r,e,t))];case"Exp":return[o.exp(I("x",r,e,t))];case"Expm1":return[o.expm1(I("x",r,e,t))];case"Floor":return[o.floor(I("x",r,e,t))];case"Log":return[o.log(I("x",r,e,t))];case"Log1p":return[o.log1p(I("x",r,e,t))];case"Imag":return[o.imag(I("x",r,e,t))];case"Neg":return[o.neg(I("x",r,e,t))];case"Reciprocal":return[o.reciprocal(I("x",r,e,t))];case"Real":return[o.real(I("x",r,e,t))];case"Relu":return[o.relu(I("x",r,e,t))];case"Round":return[o.round(I("x",r,e,t))];case"Selu":return[o.selu(I("x",r,e,t))];case"Sigmoid":return[o.sigmoid(I("x",r,e,t))];case"Sin":return[o.sin(I("x",r,e,t))];case"Sign":return[o.sign(I("x",r,e,t))];case"Sinh":return[o.sinh(I("x",r,e,t))];case"Softplus":return[o.softplus(I("x",r,e,t))];case"Sqrt":return[o.sqrt(I("x",r,e,t))];case"Square":return[o.square(I("x",r,e,t))];case"Tanh":return[o.tanh(I("x",r,e,t))];case"Tan":return[o.tan(I("x",r,e,t))];case"ClipByValue":return[o.clipByValue(I("x",r,e,t),I("clipValueMin",r,e,t),I("clipValueMax",r,e,t))];case"Relu6":return[o.relu6(I("x",r,e,t))];case"Rsqrt":return[o.rsqrt(Gt(r.inputNames[0],e,t))];case"Prod":return[o.prod(I("x",r,e,t),I("axes",r,e,t))];case"LeakyRelu":return[o.leakyRelu(I("x",r,e,t),I("alpha",r,e,t))];case"Prelu":return[o.prelu(I("x",r,e,t),I("alpha",r,e,t))];case"IsNan":return[o.isNaN(Gt(r.inputNames[0],e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function zr(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 $N(r){return!(typeof r=="number"||r.some(e=>e<0))}function Gp(r,e,t){let o=of(r,t),n=!$N(o);if(n&&e.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${o}`);if(n&&e.forEach(s=>{o=of(s.shape,o)}),!$N(o))throw new Error(`Non-fully-defined elementShape: ${o}`);return o}function of(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 nf=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=be(0),_r(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),zr(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,_r(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,so(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(p=>(o+=p,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=[];Ee(()=>{t=z(t,[1,o,s]);for(let p=0;p{if(o!==s.dtype)throw new Error(`Invalid data types; op elements ${o}, but list elements ${s.dtype}`);zr(t,s.shape,"TensorList shape mismatch: "),_r(s)}),this.idTensor=be(0),this.maxNumElements=n,_r(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Pa([...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.`);zr(e,this.elementShape,"TensorList shape mismatch: ");let n=Gp(this.elementShape,this.tensors,e);return Ee(()=>{let s=this.tensors.map(a=>z(a,n));return Sr(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=Gp(this.elementShape,this.tensors,e),n=this.tensors.pop();return n.kept=!1,zr(n.shape,e,"TensorList shape mismatch: "),z(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(zr(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");_r(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}.`);let t=new Pa([],this.elementShape,this.elementDtype,this.maxNumElements);t.tensors.length=e;for(let o=0;othis.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.`);zr(this.tensors[e].shape,t,"TensorList shape mismatch: ");let n=Gp(this.elementShape,this.tensors,t);return z(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.`);zr(this.elementShape,t.shape,"TensorList shape mismatch: "),_r(t),this.tensors[e]!=null&&(this.tensors[e].kept=!1),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}`);zr(this.elementShape,o,"TensorList shape mismatch: "),e=e.slice(0,this.size());let n=Gp(this.elementShape,this.tensors,o);return e.length===0?nr([],[0].concat(n)):Ee(()=>{let s=e.map(a=>z(this.tensors[a],n));return Sr(s,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);zr(this.elementShape,t,"TensorList shape mismatch: ");let o=Gp(this.elementShape,this.tensors,t);return this.size()===0?nr([],[0].concat(o)):Ee(()=>{let n=this.tensors.map(s=>z(s,o));return gt(n,0)})}};function AN(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);zr(n,e,"TensorList shape mismatch: ");let s=so(r);return new Pa(s,e,o)}function RN(r,e,t,o){return new Pa([],r,e,o)}function FN(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 Pa([],t,r.dtype,o),a=so(r,0);return e.forEach((i,p)=>{s.setItem(i,a[p])}),s}function DN(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=of(s,t),i=o===0?0:r.size/o,p=Ee(()=>{let c=[];r=z(r,[1,o,i]);for(let l=0;l{switch(r.op){case"If":case"StatelessIf":{let o=I("thenBranch",r,e,t),n=I("elseBranch",r,e,t),s=I("cond",r,e,t),a=I("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=I("body",r,e,t),n=I("cond",r,e,t),s=I("args",r,e,t),a=await t.functionMap[n].executeFunctionAsync(s,t.tensorArrayMap,t.tensorListMap),i=s.map(c=>c.id),p=await a[0].data();a.forEach(c=>{!c.kept&&i.indexOf(c.id)===-1&&c.dispose()});let u=s;for(;p[0];){let c=u;u=await t.functionMap[o].executeFunctionAsync(u,t.tensorArrayMap,t.tensorListMap);let l=u.map(d=>d.id);c.forEach(d=>{!d.kept&&i.indexOf(d.id)===-1&&l.indexOf(d.id)===-1&&d.dispose()});let m=await t.functionMap[n].executeFunctionAsync(u,t.tensorArrayMap,t.tensorListMap);p=await m[0].data(),m.forEach(d=>{!d.kept&&i.indexOf(d.id)===-1&&l.indexOf(d.id)===-1&&d.dispose()})}return u}case"LoopCond":{let o=I("pred",r,e,t);return[as(o)]}case"Switch":{let o=I("pred",r,e,t),n=I("data",r,e,t);return n.kept||(n=as(n)),(await o.data())[0]?[void 0,n]:[n,void 0]}case"Merge":{let o=r.inputNames.find(n=>Gt(n,e,t)!==void 0);if(o){let n=Gt(o,e,t);return[as(n)]}return}case"Enter":{let o=I("frameName",r,e,t),n=I("tensor",r,e,t);return t.enterFrame(o),[as(n)]}case"Exit":{let o=I("tensor",r,e,t);return t.exitFrame(),[as(o)]}case"NextIteration":{let o=I("tensor",r,e,t);return t.nextIteration(),[as(o)]}case"TensorArrayV3":{let o=I("size",r,e,t),n=I("dtype",r,e,t),s=I("elementShape",r,e,t),a=I("dynamicSize",r,e,t),i=I("clearAfterRead",r,e,t),p=I("identicalElementShapes",r,e,t),u=I("name",r,e,t),c=new nf(u,n,o,s,p,a,i);return t.addTensorArray(c),[c.idTensor,be(1)]}case"TensorArrayWriteV3":{let o=I("tensorArrayId",r,e,t),n=I("index",r,e,t),s=I("tensor",r,e,t),a=t.getTensorArray(o.id);return a.write(n,s),[a.idTensor]}case"TensorArrayReadV3":{let o=I("tensorArrayId",r,e,t),n=I("index",r,e,t);return[t.getTensorArray(o.id).read(n)]}case"TensorArrayGatherV3":{let o=I("tensorArrayId",r,e,t),n=I("indices",r,e,t),s=I("dtype",r,e,t);return[t.getTensorArray(o.id).gather(n,s)]}case"TensorArrayScatterV3":{let o=I("tensorArrayId",r,e,t),n=I("indices",r,e,t),s=I("tensor",r,e,t),a=t.getTensorArray(o.id);return a.scatter(n,s),[a.idTensor]}case"TensorArrayConcatV3":{let o=I("tensorArrayId",r,e,t),n=t.getTensorArray(o.id),s=I("dtype",r,e,t);return[n.concat(s)]}case"TensorArraySplitV3":{let o=I("tensorArrayId",r,e,t),n=I("tensor",r,e,t),s=I("lengths",r,e,t),a=t.getTensorArray(o.id);return a.split(s,n),[a.idTensor]}case"TensorArraySizeV3":{let o=I("tensorArrayId",r,e,t),n=t.getTensorArray(o.id);return[be(n.size(),"int32")]}case"TensorArrayCloseV3":{let o=I("tensorArrayId",r,e,t),n=t.getTensorArray(o.id);return n.clearAndClose(),[n.idTensor]}case"TensorListSetItem":{let o=I("tensorListId",r,e,t),n=I("index",r,e,t),s=I("tensor",r,e,t),a=t.getTensorList(o.id);return a.setItem(n,s),[a.idTensor]}case"TensorListGetItem":{let o=I("tensorListId",r,e,t),n=I("index",r,e,t),s=I("elementShape",r,e,t),a=I("elementDType",r,e,t);return[t.getTensorList(o.id).getItem(n,s,a)]}case"TensorListScatterV2":case"TensorListScatter":{let o=I("indices",r,e,t),n=I("tensor",r,e,t),s=I("elementShape",r,e,t),a=I("numElements",r,e,t),i=FN(n,o,s,a);return t.addTensorList(i),[i.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let o=I("elementShape",r,e,t),n=I("elementDType",r,e,t),s;r.op==="TensorListReserve"?s="numElements":s="maxNumElements";let a=I(s,r,e,t),i=r.op==="TensorListReserve"?-1:a,p=RN(o,n,a,i);return t.addTensorList(p),[p.idTensor]}case"TensorListGather":{let o=I("tensorListId",r,e,t),n=I("indices",r,e,t),s=I("elementShape",r,e,t),a=I("elementDType",r,e,t);return[t.getTensorList(o.id).gather(n,a,s)]}case"TensorListStack":{let o=I("tensorListId",r,e,t),n=I("elementShape",r,e,t),s=I("elementDType",r,e,t),a=I("numElements",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case"TensorListFromTensor":{let o=I("tensor",r,e,t),n=I("elementShape",r,e,t),s=I("elementDType",r,e,t),a=AN(o,n,s);return t.addTensorList(a),[a.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let o=I("tensorListId",r,e,t),n=t.getTensorList(o.id),s=I("dtype",r,e,t),a=I("elementShape",r,e,t);return[n.concat(s,a)]}case"TensorListPushBack":{let o=I("tensorListId",r,e,t),n=I("tensor",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case"TensorListPopBack":{let o=I("tensorListId",r,e,t),n=I("elementShape",r,e,t),s=I("elementDType",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case"TensorListSplit":{let o=I("tensor",r,e,t),n=I("elementShape",r,e,t),s=I("lengths",r,e,t),a=DN(o,s,n);return t.addTensorList(a),[a.idTensor]}case"TensorListLength":{let o=I("tensorListId",r,e,t),n=t.getTensorList(o.id);return[be(n.size(),"int32")]}case"TensorListResize":{let o=I("tensorListId",r,e,t),n=I("size",r,e,t),a=t.getTensorList(o.id).resize(n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function PN(r,e,t){let[o,n]=I("fusedOps",r,e,t),s=o==="biasadd",a=!s,i=n==="prelu",p=o==="fusedbatchnorm",u=I("numArgs",r,e,t);if(s){if(i&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(p)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let c=I("strides",r,e,t),l=ul(r,e,t),m=I("dataFormat",r,e,t).toUpperCase(),d=I("dilations",r,e,t),[f,h]=I("args",r,e,t);a&&(h=f,f=void 0);let g=I("leakyreluAlpha",r,e,t);return{stride:c,pad:l,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var MN=(r,e,t,o=Ye)=>{switch(r.op){case"Conv1D":{let n=I("stride",r,e,t),s=I("pad",r,e,t),a=I("dataFormat",r,e,t).toUpperCase(),i=I("dilation",r,e,t);return[o.conv1d(I("x",r,e,t),I("filter",r,e,t),n,s,a,i)]}case"Conv2D":{let n=I("strides",r,e,t),s=ul(r,e,t),a=I("dataFormat",r,e,t).toUpperCase(),i=I("dilations",r,e,t);return[o.conv2d(I("x",r,e,t),I("filter",r,e,t),[n[1],n[2]],s,a,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=PN(r,e,t);return[o.fused.conv2d({x:I("x",r,e,t),filter:I("filter",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case"FusedDepthwiseConv2dNative":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=PN(r,e,t);return[o.fused.depthwiseConv2d({x:I("x",r,e,t),filter:I("filter",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let n=I("outputShape",r,e,t),s=I("strides",r,e,t),a=ul(r,e,t);return[o.conv2dTranspose(I("x",r,e,t),I("filter",r,e,t),n,[s[1],s[2]],a)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let n=I("strides",r,e,t),s=ul(r,e,t),a=I("dilations",r,e,t),i=I("dataFormat",r,e,t).toUpperCase();return[o.depthwiseConv2d(I("input",r,e,t),I("filter",r,e,t),[n[1],n[2]],s,i,[a[1],a[2]])]}case"Conv3D":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("dataFormat",r,e,t).toUpperCase(),i=I("dilations",r,e,t);return[o.conv3d(I("x",r,e,t),I("filter",r,e,t),[n[1],n[2],n[3]],s,a,[i[1],i[2],i[3]])]}case"AvgPool":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t);return[o.avgPool(I("x",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case"MaxPool":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t);return[o.maxPool(I("x",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case"MaxPoolWithArgmax":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t),i=I("includeBatchInIndex",r,e,t),{result:p,indexes:u}=o.maxPoolWithArgmax(I("x",r,e,t),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case"AvgPool3D":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t);return[o.avgPool3d(I("x",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case"MaxPool3D":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("kernelSize",r,e,t);return[o.maxPool3d(I("x",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case"Dilation2D":{let n=I("strides",r,e,t),s=I("pad",r,e,t),a=I("dilations",r,e,t),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I("x",r,e,t),I("filter",r,e,t),[i,p],s,[u,c],"NHWC")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var LN=(r,e,t,o=Ye)=>{switch(r.op){case"Fill":{let n=I("shape",r,e,t),s=I("dtype",r,e,t),a=I("value",r,e,t);return[o.fill(n,a,s)]}case"LinSpace":{let n=I("start",r,e,t),s=I("stop",r,e,t),a=I("num",r,e,t);return[o.linspace(n,s,a)]}case"Multinomial":{let n=I("logits",r,e,t),s=I("numSamples",r,e,t),a=I("seed",r,e,t);return[o.multinomial(n,s,a)]}case"OneHot":{let n=I("indices",r,e,t),s=I("depth",r,e,t),a=I("onValue",r,e,t),i=I("offValue",r,e,t),p=I("dtype",r,e,t);return[o.oneHot(n,s,a,i,p)]}case"Ones":return[o.ones(I("shape",r,e,t),I("dtype",r,e,t))];case"OnesLike":return[o.onesLike(I("x",r,e,t))];case"RandomStandardNormal":return[o.randomStandardNormal(I("shape",r,e,t),I("dtype",r,e,t),I("seed",r,e,t))];case"RandomUniform":return[o.randomUniform(I("shape",r,e,t),I("minval",r,e,t),I("maxval",r,e,t),I("dtype",r,e,t))];case"Range":{let n=I("start",r,e,t),s=I("stop",r,e,t),a=I("step",r,e,t);return[o.range(n,s,a,I("dtype",r,e,t))]}case"TruncatedNormal":{let n=I("shape",r,e,t),s=I("mean",r,e,t),a=I("stdDev",r,e,t),i=I("seed",r,e,t);return[o.truncatedNormal(n,s,a,I("dtype",r,e,t),i)]}case"Zeros":return[o.zeros(I("shape",r,e,t),I("dtype",r,e,t))];case"ZerosLike":return[o.zerosLike(I("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function HC(r,e,t){let o=I("boxes",r,e,t),n=I("scores",r,e,t),s=I("maxOutputSize",r,e,t),a=I("iouThreshold",r,e,t),i=I("scoreThreshold",r,e,t),p=I("softNmsSigma",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:p}}var BN=async(r,e,t,o,n=Ye)=>{switch(r.op){case"NonMaxSuppressionV5":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u,softNmsSigma:c}=HC(r,e,t),l=await n.image.nonMaxSuppressionWithScoreAsync(s,a,i,p,u,c);return[l.selectedIndices,l.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=HC(r,e,t),c=I("padToMaxOutputSize",r,e,t),l=await n.image.nonMaxSuppressionPaddedAsync(s,a,i,p,u,c);return[l.selectedIndices,l.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u}=HC(r,e,t);return[await n.image.nonMaxSuppressionAsync(s,a,i,p,u)]}case"Where":{let s=n.cast(I("condition",r,e,t),"bool"),a=[await n.whereAsync(s)];return s.dispose(),a}case"ListDiff":return n.setdiff1dAsync(I("x",r,e,t),I("y",r,e,t));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var VN=(r,e,t,o=Ye)=>{switch(r.op){case"LowerBound":{let n=I("sortedSequence",r,e,t),s=I("values",r,e,t);return[o.lowerBound(n,s)]}case"TopKV2":{let n=I("x",r,e,t),s=I("k",r,e,t),a=I("sorted",r,e,t),i=o.topk(n,s,a);return[i.values,i.indices]}case"UpperBound":{let n=I("sortedSequence",r,e,t),s=I("values",r,e,t);return[o.upperBound(n,s)]}case"Unique":{let n=I("x",r,e,t),s=o.unique(n);return[s.values,s.indices]}case"UniqueV2":{let n=I("x",r,e,t),s=I("axis",r,e,t),a=o.unique(n,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var zN=(r,e,t,o=Ye)=>{switch(r.op){case"Const":return e[r.name];case"PlaceholderWithDefault":let n=I("default",r,e,t);return[Gt(r.name,e,t)||n];case"Placeholder":return[Gt(r.name,e,t)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=I("x",r,e,t);return[as(c)]}case"IdentityN":return I("x",r,e,t).map(c=>as(c));case"Snapshot":let s=I("x",r,e,t);return[as(s)];case"Shape":return[o.tensor1d(I("x",r,e,t).shape,"int32")];case"ShapeN":return I("x",r,e,t).map(c=>o.tensor1d(c.shape));case"Size":return[o.scalar(I("x",r,e,t).size,"int32")];case"Rank":return[o.scalar(I("x",r,e,t).rank,"int32")];case"NoOp":return[o.scalar(1)];case"Print":let a=I("x",r,e,t),i=I("data",r,e,t),p=I("message",r,e,t),u=I("summarize",r,e,t);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(p);for(let c=0;ce.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return be(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),Ee(()=>{let n=so(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=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I("keyDType",r,e,t),a=I("valueDType",r,e,t),i=new sf(s,a);return o.addHashTable(r.name,i),[i.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let n=I("tableHandle",r,e,t,o),s=I("keys",r,e,t),a=I("values",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case"LookupTableFind":case"LookupTableFindV2":{let n=I("tableHandle",r,e,t,o),s=I("keys",r,e,t),a=I("defaultValue",r,e,t);return[await o.getHashTableById(n.id).find(s,a)]}case"LookupTableSize":case"LookupTableSizeV2":{let n=I("tableHandle",r,e,t,o);return[o.getHashTableById(n.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var UN=(r,e,t,o=Ye)=>{switch(r.op){case"ResizeBilinear":{let n=I("images",r,e,t),s=I("size",r,e,t),a=I("alignCorners",r,e,t),i=I("halfPixelCenters",r,e,t);return[o.image.resizeBilinear(n,[s[0],s[1]],a,i)]}case"ResizeNearestNeighbor":{let n=I("images",r,e,t),s=I("size",r,e,t),a=I("alignCorners",r,e,t),i=I("halfPixelCenters",r,e,t);return[o.image.resizeNearestNeighbor(n,[s[0],s[1]],a,i)]}case"CropAndResize":{let n=I("image",r,e,t),s=I("boxes",r,e,t),a=I("boxInd",r,e,t),i=I("cropSize",r,e,t),p=I("method",r,e,t),u=I("extrapolationValue",r,e,t);return[o.image.cropAndResize(n,s,a,i,p,u)]}case"ImageProjectiveTransformV3":{let n=I("images",r,e,t),s=I("transforms",r,e,t),a=I("outputShape",r,e,t),i=I("fillValue",r,e,t),p=I("interpolation",r,e,t),u=I("fillMode",r,e,t);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GN=(r,e,t,o=Ye)=>{switch(r.op){case"Equal":return[o.equal(I("a",r,e,t),I("b",r,e,t))];case"NotEqual":return[o.notEqual(I("a",r,e,t),I("b",r,e,t))];case"Greater":return[o.greater(I("a",r,e,t),I("b",r,e,t))];case"GreaterEqual":return[o.greaterEqual(I("a",r,e,t),I("b",r,e,t))];case"Less":return[o.less(I("a",r,e,t),I("b",r,e,t))];case"LessEqual":return[o.lessEqual(I("a",r,e,t),I("b",r,e,t))];case"LogicalAnd":return[o.logicalAnd(I("a",r,e,t),I("b",r,e,t))];case"LogicalNot":return[o.logicalNot(I("a",r,e,t))];case"LogicalOr":return[o.logicalOr(I("a",r,e,t),I("b",r,e,t))];case"Select":case"SelectV2":return[o.where(I("condition",r,e,t),I("a",r,e,t),I("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var HN=(r,e,t,o=Ye)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[o.matMul(I("a",r,e,t),I("b",r,e,t),I("transposeA",r,e,t),I("transposeB",r,e,t))];case"Einsum":return[o.einsum(I("equation",r,e,t),...I("tensors",r,e,t))];case"Transpose":return[o.transpose(I("x",r,e,t),I("perm",r,e,t))];case"_FusedMatMul":let[n,s]=I("fusedOps",r,e,t),a=n==="biasadd",i=s==="prelu",p=I("numArgs",r,e,t),u=I("leakyreluAlpha",r,e,t);if(a){if(i&&p!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&p!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,l]=I("args",r,e,t);return[o.fused.matMul({a:I("a",r,e,t),b:I("b",r,e,t),transposeA:I("transposeA",r,e,t),transposeB:I("transposeB",r,e,t),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var qN=(r,e,t,o=Ye)=>{switch(r.op){case"EuclideanNorm":return[o.euclideanNorm(I("x",r,e,t),I("axis",r,e,t),I("keepDims",r,e,t))];case"FusedBatchNorm":case"FusedBatchNormV2":return[o.batchNorm(I("x",r,e,t),I("mean",r,e,t),I("variance",r,e,t),I("offset",r,e,t),I("scale",r,e,t),I("epsilon",r,e,t))];case"FusedBatchNormV3":return[o.batchNorm(I("x",r,e,t),I("mean",r,e,t),I("variance",r,e,t),I("offset",r,e,t),I("scale",r,e,t),I("epsilon",r,e,t))];case"LRN":return[o.localResponseNormalization(I("x",r,e,t),I("radius",r,e,t),I("bias",r,e,t),I("alpha",r,e,t),I("beta",r,e,t))];case"Softmax":return[o.softmax(I("x",r,e,t))];case"LogSoftmax":return[o.logSoftmax(I("x",r,e,t))];case"SparseToDense":return[o.sparseToDense(I("sparseIndices",r,e,t),I("outputShape",r,e,t),I("sparseValues",r,e,t),I("defaultValue",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var KN=(r,e,t,o=Ye)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I("paramsNestedSplits",r,e,t),I("paramsDenseValues",r,e,t),I("indices",r,e,t),I("outputRaggedRank",r,e,t));return n.concat(s)}case"RaggedRange":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I("starts",r,e,t),I("limits",r,e,t),I("splits",r,e,t));return[n,s]}case"RaggedTensorToTensor":return[o.raggedTensorToTensor(I("shape",r,e,t),I("values",r,e,t),I("defaultValue",r,e,t),I("rowPartitionTensors",r,e,t),I("rowPartitionTypes",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var jN=(r,e,t,o=Ye)=>{switch(r.op){case"Max":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.max(I("x",r,e,t),i,p)]}case"Mean":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.mean(I("x",r,e,t),i,p)]}case"Min":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.min(I("x",r,e,t),i,p)]}case"Sum":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.sum(I("x",r,e,t),i,p)]}case"All":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.all(I("x",r,e,t),i,p)]}case"Any":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.any(I("x",r,e,t),i,p)]}case"ArgMax":{let i=I("axis",r,e,t);return[o.argMax(I("x",r,e,t),i)]}case"ArgMin":{let i=I("axis",r,e,t);return[o.argMin(I("x",r,e,t),i)]}case"Prod":{let i=I("axis",r,e,t),p=I("keepDims",r,e,t);return[o.prod(I("x",r,e,t),i,p)]}case"Cumprod":{let i=I("axis",r,e,t),p=I("exclusive",r,e,t),u=I("reverse",r,e,t);return[o.cumprod(I("x",r,e,t),i,p,u)]}case"Cumsum":{let i=I("axis",r,e,t),p=I("exclusive",r,e,t),u=I("reverse",r,e,t);return[o.cumsum(I("x",r,e,t),i,p,u)]}case"Bincount":let n=I("x",r,e,t),s=I("weights",r,e,t),a=I("size",r,e,t);return[o.bincount(n,s,a)];case"DenseBincount":{let i=I("x",r,e,t),p=I("weights",r,e,t),u=I("size",r,e,t),c=I("binaryOutput",r,e,t);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var XN=(r,e,t,o=Ye)=>{switch(r.op){case"ConcatV2":case"Concat":{let n=I("n",r,e,t),s=I("axis",r,e,t),a=I("tensors",r,e,t);return a=a.slice(0,n),[o.concat(a,s)]}case"Gather":{let n=I("x",r,e,t),s=I("indices",r,e,t);return[o.gather(n,o.cast(s,"int32"),0)]}case"GatherV2":{let n=I("axis",r,e,t),s=I("batchDims",r,e,t),a=I("x",r,e,t),i=I("indices",r,e,t);return[o.gather(a,o.cast(i,"int32"),n,s)]}case"Reverse":{let n=I("dims",r,e,t),s=[];for(let i=0;i{let n=I("axis",r,e,t),s=I("tensors",r,e,t),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error("the input tensors shape does not match");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case"Unpack":{let n=I("axis",r,e,t),s=I("tensor",r,e,t);return o.unstack(s,n)}case"Tile":{let n=I("reps",r,e,t);return[o.tile(I("x",r,e,t),n)]}case"Split":case"SplitV":{let n=I("axis",r,e,t),s=I("numOrSizeSplits",r,e,t),a=I("x",r,e,t);return o.split(a,s,n)}case"ScatterNd":{let n=I("indices",r,e,t),s=I("values",r,e,t),a=I("shape",r,e,t);return[o.scatterND(n,s,a)]}case"GatherNd":{let n=I("x",r,e,t),s=I("indices",r,e,t);return[o.gatherND(n,s)]}case"SparseToDense":{let n=I("sparseIndices",r,e,t),s=I("outputShape",r,e,t),a=I("sparseValues",r,e,t),i=I("defaultValue",r,e,t);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var YN=(r,e,t,o=Ye)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I("indices",r,e,t),I("values",r,e,t),I("denseShape",r,e,t),I("defaultValue",r,e,t));return[n,s,a,i]}case"SparseReshape":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I("inputIndices",r,e,t),I("inputShape",r,e,t),I("newShape",r,e,t));return[n,s]}case"SparseSegmentMean":return[o.sparse.sparseSegmentMean(I("data",r,e,t),I("indices",r,e,t),I("segmentIds",r,e,t))];case"SparseSegmentSum":return[o.sparse.sparseSegmentSum(I("data",r,e,t),I("indices",r,e,t),I("segmentIds",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var QN=(r,e,t,o=Ye)=>{switch(r.op){case"FFT":return[o.fft(I("x",r,e,t))];case"IFFT":return[o.ifft(I("x",r,e,t))];case"RFFT":return[o.rfft(I("x",r,e,t))];case"IRFFT":return[o.irfft(I("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ZN=(r,e,t,o=Ye)=>{switch(r.op){case"StringNGrams":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I("data",r,e,t),I("dataSplits",r,e,t),I("separator",r,e,t),I("nGramWidths",r,e,t),I("leftPad",r,e,t),I("rightPad",r,e,t),I("padWidth",r,e,t),I("preserveShortSequences",r,e,t));return[n,s]}case"StringSplit":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I("input",r,e,t),I("delimiter",r,e,t),I("skipEmpty",r,e,t));return[n,s,a]}case"StringToHashBucketFast":return[o.string.stringToHashBucketFast(I("input",r,e,t),I("numBuckets",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var JN=(r,e,t,o=Ye)=>{switch(r.op){case"Cast":return[o.cast(I("x",r,e,t),I("dtype",r,e,t))];case"ExpandDims":{let n=I("axis",r,e,t);return[o.expandDims(I("x",r,e,t),n)]}case"Squeeze":{let n=I("axis",r,e,t);return[o.squeeze(I("x",r,e,t),n)]}case"Reshape":return[o.reshape(I("x",r,e,t),I("shape",r,e,t))];case"MirrorPad":return[o.mirrorPad(I("x",r,e,t),I("padding",r,e,t),I("mode",r,e,t))];case"PadV2":case"Pad":return[o.pad(I("x",r,e,t),I("padding",r,e,t),I("constantValue",r,e,t))];case"SpaceToBatchND":{let n=I("blockShape",r,e,t),s=I("paddings",r,e,t);return[o.spaceToBatchND(I("x",r,e,t),n,s)]}case"BatchToSpaceND":{let n=I("blockShape",r,e,t),s=I("crops",r,e,t);return[o.batchToSpaceND(I("x",r,e,t),n,s)]}case"DepthToSpace":{let n=I("blockSize",r,e,t),s=I("dataFormat",r,e,t).toUpperCase();return[o.depthToSpace(I("x",r,e,t),n,s)]}case"BroadcastTo":return[o.broadcastTo(I("x",r,e,t),I("shape",r,e,t))];case"BroadcastArgs":return[o.broadcastArgs(I("s0",r,e,t),I("s1",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function qC(r,e,t,o,n=Ee){let s=((a,i,p)=>{switch(a.category){case"arithmetic":return n(()=>_N(a,i,p));case"basic_math":return n(()=>EN(a,i,p));case"control":return ON(a,i,p);case"convolution":return n(()=>MN(a,i,p));case"creation":return n(()=>LN(a,i,p));case"dynamic":return BN(a,i,p);case"evaluation":return n(()=>VN(a,i,p));case"image":return n(()=>UN(a,i,p));case"graph":return n(()=>zN(a,i,p));case"logical":return n(()=>GN(a,i,p));case"matrices":return n(()=>HN(a,i,p));case"normalization":return n(()=>qN(a,i,p));case"ragged":return n(()=>KN(a,i,p));case"reduction":return n(()=>jN(a,i,p));case"slice_join":return n(()=>XN(a,i,p));case"sparse":return n(()=>YN(a,i,p));case"spectral":return n(()=>QN(a,i,p));case"string":return n(()=>ZN(a,i,p));case"transformation":return n(()=>JN(a,i,p));case"hash_table":return WN(a,i,p,o);case"custom":let u=Gd(a.op);if(u&&u.customExecutor)return u.customExecutor(new rf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.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(s)?s.then(a=>[].concat(a)):[].concat(s)}var cl=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 KC(r,e,t,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=Object.keys(r).map(m=>Ir(m)[0]),c=[];o!=null&&(c=o.map(m=>Ir(m.name)[0]));let l=[...e];for(;l.length>0;){let m=l.pop();if((jC(m)||i6(m)||u6(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),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(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function eT(r,e,t){let{usedNodes:o,inputs:n}=t,s=[],a=Object.keys(n).map(c=>Ir(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 p=new Set,u=[];for(;s.length>0;){let c=s.pop();p.add(c.name),e[c.name]||u.push(c),c.children.forEach(l=>{!p.has(l.name)&&o.has(l.name)&&l.inputs.every(m=>p.has(m.name))&&s.push(l)})}return u}var n6=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],s6=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],a6=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function jC(r){return n6.indexOf(r.op)>=0}function i6(r){return s6.indexOf(r.op)>=0}function u6(r){return a6.indexOf(r.op)>=0}var Cu=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,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 Cu(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=KC(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 [${a}]`);if(n.length>0){let i=t.map(u=>u.name),p=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${p}]. Missing the following inputs: [${n}]`)}return eT(this.graph,this.weightMap,o)}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return _r(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),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(l=>this.graph.nodes[Ir(l)[0]]),s=t.map(l=>Ir(l)[0]),a=s.map(l=>this.graph.nodes[l]);a.length===0&&(a=this._outputs);let i=this.getCompilationKey(n,a),p=this.compiledMap.get(i);p==null&&(p=this.compile(e,a),this.compiledMap.set(i,p));try{this.keepIntermediateTensors=O().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(l){this.keepIntermediateTensors=!1,console.warn(l.message)}let u={},c={};return Ee(()=>{let l=new cl(this.weightMap,u,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(h=>{let[g,x]=Ir(h),b=[];b[x]=e[h],m[g]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[g]=this.cloneTensorList(b))});let d=this.getFrozenTensorIds(m),f={};for(let h=0;hGt(h,m,l))})}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(p=>{p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length)}),t.inputs.forEach(p=>{if(p.category!=="control"){let u=vN(p.name,o,n);u!=null&&u.forEach(c=>{if(c&&!c.kept&&!s.has(c.id)){let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.clonedTensorsMap||(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=O().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new cl(this.weightMap,n,s,this.functionExecutorMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Gt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}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(C=>this.graph.nodes[Ir(C)[0]]),i=o.map(C=>Ir(C)[0]),p=i.map(C=>this.graph.nodes[C]);p.length===0&&(p=this._outputs);let{usedNodes:u,missingInputs:c,dynamicNode:l,syncInputs:m}=KC(e,p,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(C=>({node:C,contexts:t.currentContext})),f=Object.assign({},this.weightMap);Object.keys(e).forEach(C=>{let[w,k]=Ir(C),_=[];_[k]=e[C],f[w]=_});let h={},g=this.getFrozenTensorIds(f),x={};for(;d.length>0;){let C=this.processStack(a,d,t,f,x,g,i,h,u);await Promise.all(C)}l==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=p.filter(C=>!jC(C)&&!Gt(C.name,f,t)).map(C=>C.name);if(b.length>0){let C="";throw l!=null&&(C=`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}]. ${C}`)}return f}processStack(e,t,o,n,s,a,i,p,u){let c=[];for(;t.length>0;){let l=t.pop();o.currentContext=l.contexts;let m="";if(l.node.op==="Enter"&&I("isConstant",l.node,n,o)&&([m]=ss(l.node.name,o)),n[l.node.name]==null){let d=qC(l.node,n,o,this._resourceManager);m||([m]=ss(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u))}else this.processChildNodes(l.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=ss(i.name,o);s[p]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!Gt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Gt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Ir(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${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 ${o.dtype}`)})}mapInputs(e){var t,o;let n={};for(let s in e){let a=(o=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=e[s]:n[s]=e[s]}return n}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Ir(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[t];return s!=null?s.name:t},{})}checkOutputs(e){e.forEach(t=>{let[o]=Ir(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var af=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}};var p6="?tfjs-format=file",c6="model.json",ll=class{constructor(e,t={},o=Ea){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=o,t==null&&(this.loadOptions={}),this.resourceManager=new af}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return y.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=o,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Cu(pl.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=pl.Instance.transformGraph(e.modelInitializer);this.initializer=new Cu(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let o=this.io.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${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)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof it?[e]:e,o={};return t.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return e}predict(e,t){let o=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(e,t){let o=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(e){var t;if(!(e instanceof it)&&!Array.isArray(e)){let s=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(e[a]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let o=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+o!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-o} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((s,a)=>{var i,p,u;let c=(u=(p=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||p===void 0?void 0:p[a])===null||u===void 0?void 0:u.resourceId;return c!=null?s[a]=this.resourceIdToCapturedInput[c]:s[a]=e[n++],s},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,o=Object.keys(t);for(let n=0;n1?o:o[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=await this.executor.executeAsync(e,t);return o.length>1?o:o[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,o)=>(t[o]=[e[o]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Dt(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function l6(r,e={},t=Ea){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&&typeof r=="string"&&(r=d6(r));let o=new ll(r,e,t);return await o.load(),o}function m6(r){if(r==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let e;if(r instanceof Array){let[o,n]=r;if(!o)throw new Error("modelJSON must be the first element of the array");if(!n||!(n instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in o))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in o))throw new Error("Model JSON is missing 'weightsManifest'");let s=Ea.getWeightSpecs(o.weightsManifest),a=Ea.getModelArtifactsForJSONSync(o,s,n);e=Ea.fromMemorySync(a)}else if("load"in r)e=r;else if("modelTopology"in r&&"weightSpecs"in r&&"weightData"in r)e=Ea.fromMemorySync(r);else throw new Error("Unknown model format");let t=new ll(e);return t.load(),t}function d6(r){return r.endsWith("/")||(r=r+"/"),`${r}${c6}${p6}`}var f6="4.1.0";function K(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 h6=Lt.whereImpl,Oi=class extends Zr{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Do(this,cr())}nextDataId(){return Oi.nextDataId++}write(e,t,o){this.firstUse&&(this.firstUse=!1,O().get("IS_NODE")&&S.warn(` ============================ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, 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 S.mergeRealAndImagArrays(n,s)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return le(e.shape,e.dtype,t)}makeOutput(e,t,o){return cr().makeTensorFromTensorInfo(this.makeTensorInfo(t,o,e),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){K([e],"where");let t=this.readSync(e.dataId);return h6(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};Oi.nextDataId=0;var Qp={};Ue(Qp,{addImpl:()=>QC,bincountImpl:()=>Kp,bincountReduceImpl:()=>uf,castImpl:()=>YC,ceilImpl:()=>ZC,concatImpl:()=>Su,equalImpl:()=>JC,expImpl:()=>tS,expm1Impl:()=>oS,floorImpl:()=>nS,gatherNdImpl:()=>pf,gatherV2Impl:()=>cf,greaterEqualImpl:()=>aS,greaterImpl:()=>sS,lessEqualImpl:()=>uS,lessImpl:()=>iS,linSpaceImpl:()=>lf,logImpl:()=>pS,maxImpl:()=>mf,maximumImpl:()=>cS,minimumImpl:()=>lS,multiplyImpl:()=>ml,negImpl:()=>mS,notEqualImpl:()=>dS,prodImpl:()=>fS,raggedGatherImpl:()=>df,raggedRangeImpl:()=>ff,raggedTensorToTensorImpl:()=>hf,rangeImpl:()=>Iu,rsqrtImpl:()=>hS,scatterImpl:()=>Ma,sigmoidImpl:()=>ET,simpleAbsImpl:()=>XC,sliceImpl:()=>vu,sparseFillEmptyRowsImpl:()=>gf,sparseReshapeImpl:()=>xf,sparseSegmentReductionImpl:()=>Yp,sqrtImpl:()=>RT,squaredDifferenceImpl:()=>xS,stridedSliceImpl:()=>yf,stringNGramsImpl:()=>ku,stringSplitImpl:()=>Nu,stringToHashBucketFastImpl:()=>Tu,subImpl:()=>bS,tileImpl:()=>bf,topKImpl:()=>Cf,transposeImpl:()=>jp,uniqueImpl:()=>Sf});function XC(r){let e=new Float32Array(r.length);for(let t=0;t{let{x:e}=r.inputs,t=r.backend;K(e,"abs");let o=new Float32Array(y.sizeFromShape(e.shape)),n=t.data.get(e.dataId).values;return o=XC(n),t.makeOutput(o,e.shape,e.dtype)},tT={kernelName:gs,backendName:"cpu",kernelFunc:g6};function Be(r){return(e,t,o,n,s)=>{let a=S.assertAndGetBroadcastShape(e,t),i=a.length,p=y.computeStrides(a),u=y.sizeFromShape(a),c=y.getTypedArrayFromDType(s,u),l=e.length,m=t.length,d=y.computeStrides(e),f=y.computeStrides(t),h=S.getBroadcastDims(e,a),g=S.getBroadcastDims(t,a);if(h.length+g.length===0)for(let x=0;xC[$]=0);let w=y.locToIndex(C,l,d),k=b.slice(-m);g.forEach($=>k[$]=0);let _=y.locToIndex(k,m,f);c[x]=r(o[w],n[_])}return[c,a]}}function Ht(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"),p=t.data.get(i.dataId);return p.complexTensorInfos={real:t.makeTensorInfo(o.shape,"float32",s),imag:t.makeTensorInfo(n.shape,"float32",a)},i}var rT={kernelName:ei,backendName:"cpu",kernelFunc:Ht};function Hp(r,e,t="float32"){if(t==="complex64"){let n=Hp(r,e,"float32"),s=Hp(r,e,"float32");return Ht({inputs:{real:n,imag:s},backend:r})}let o=y.makeZerosTypedArray(y.sizeFromShape(e),t);return r.makeTensorInfo(e,t,o)}function ar(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 oT={kernelName:mo,backendName:"cpu",kernelFunc:ar};function wo(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 nT={kernelName:ai,backendName:"cpu",kernelFunc:wo};function YC(r,e,t,o){if(o==="int32"){let n=Int32Array.from(r);return[e,"int32",n]}if(o==="bool"){let n=y.toTypedArray([0],t),[s,a]=Be((i,p)=>i!==p?1:0)(e,[],r,n,"bool");return[a,"bool",s]}throw new Error(`Error in Cast: failed to cast ${t} to ${o}`)}function Io(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return ar({inputs:{x:n},backend:t});let c=Hp(t,n.shape,n.dtype),l=Io({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),m=Ht({inputs:{real:l,imag:c},backend:t});return t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(l),m}if(n.dtype==="complex64"){let c=wo({inputs:{input:n},backend:t}),l=Io({inputs:{x:c},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(c),l}if(!y.hasEncodingLoss(n.dtype,s)){let c=ar({inputs:{x:n},backend:t});return{dataId:c.dataId,shape:c.shape,dtype:s}}let a=t.data.get(n.dataId).values,[i,p,u]=YC(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}var sT={kernelName:co,backendName:"cpu",kernelFunc:Io};function Qe(r,e,t,o){return t==null?({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;K([a,i],r);let u=p.data.get(a.dataId).values,c=p.data.get(i.dataId).values,l=a.dtype==="string"?S.fromUint8ToStringArray(u):u,m=a.dtype==="string"?S.fromUint8ToStringArray(c):c,d=o||a.dtype,[f,h]=e(a.shape,i.shape,l,m,d);return p.makeTensorInfo(h,d,f)}:({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(a.dtype==="complex64"||i.dtype==="complex64"){let u=Io({inputs:{x:a},backend:p,attrs:{dtype:"complex64"}}),c=p.data.get(u.dataId),l=c.complexTensorInfos.real,m=c.complexTensorInfos.imag,d=p.data.get(l.dataId).values,f=p.data.get(m.dataId).values,h=Io({inputs:{x:i},backend:p,attrs:{dtype:"complex64"}}),g=p.data.get(h.dataId),x=g.complexTensorInfos.real,b=g.complexTensorInfos.imag,C=p.data.get(x.dataId).values,w=p.data.get(b.dataId).values,[k,_,$]=t(a.shape,i.shape,d,f,C,w),A=p.makeTensorInfo($,"float32",k),R=p.makeTensorInfo($,"float32",_),D=Ht({inputs:{real:A,imag:R},backend:p});return p.disposeIntermediateTensorInfo(u),p.disposeIntermediateTensorInfo(h),p.disposeIntermediateTensorInfo(A),p.disposeIntermediateTensorInfo(R),D}else{let u=p.data.get(a.dataId).values,c=p.data.get(i.dataId).values,l=o||a.dtype,[m,d]=e(a.shape,i.shape,u,c,l);return p.makeTensorInfo(d,l,m)}}}function qp(r){return(e,t,o,n,s,a)=>{let i=S.assertAndGetBroadcastShape(e,t),p=y.sizeFromShape(i),u=i.length,c=y.computeStrides(i),l=y.getTypedArrayFromDType("float32",p),m=y.getTypedArrayFromDType("float32",p),d=S.getBroadcastDims(e,i),f=S.getBroadcastDims(t,i),h=S.mergeRealAndImagArrays(o,n),g=S.mergeRealAndImagArrays(s,a),x=e.length,b=y.computeStrides(e),C=t.length,w=y.computeStrides(t);if(d.length+f.length===0)for(let k=0;k$[M]=0);let A=y.locToIndex($,x,b),R=_.slice(-C);f.forEach(M=>R[M]=0);let D=y.locToIndex(R,C,w),P=r(h[A*2],h[A*2+1],g[D*2],g[D*2+1]);l[k]=P.real,m[k]=P.imag}return[l,m,i]}}var QC=Be((r,e)=>r+e),x6=qp((r,e,t,o)=>({real:r+t,imag:e+o})),js=Qe(eo,QC,x6),aT={kernelName:eo,backendName:"cpu",kernelFunc:js};function Kp(r,e,t,o,n){let s=y.sizeFromShape(o),a=y.makeZerosTypedArray(n,t);for(let i=0;i=n||(s>0?a[p]+=e[i]:a[p]+=1)}return a}function uf(r,e,t,o=!1){let n=r.shape[0],s=r.shape[1],a=le([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,p),i,u):a.set(a.get(i,u)+1,i,u))}return a}function vr(r){return(e,t,o)=>{let n=y.getTypedArrayFromDType(t,e.length);for(let s=0;s{let{x:a}=o;if(K(a,r),a.dtype==="string"||t==="string")throw new Error("unaryKernelFunc does not support string input/output");let i=s,p=i.data.get(a.dataId).values,u=y.sizeFromShape(a.shape),c=t||a.dtype,l=y.getArrayFromDType(c,u);for(let m=0;m{let{x:a}=o;if(K(a,r),a.dtype==="string"||t==="string")throw new Error("unaryKernelFunc does not support string input/output");let i=s,p=i.data.get(a.dataId).values,u=t||a.dtype,c=e(p,u,n);return i.makeTensorInfo(a.shape,u,c)}}var ZC=vr(r=>Math.ceil(r)),y6=vo(Uo,ZC),iT={kernelName:Uo,backendName:"cpu",kernelFunc:y6};function Su(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"?S.fromUint8ToStringArray(a.vals):a.vals,p=0;for(let u=0;ur===e?1:0),eS=Qe(tn,JC,null,"bool"),uT={kernelName:tn,backendName:"cpu",kernelFunc:eS};var tS=vr(r=>Math.exp(r)),rS=vo(rn,tS,"float32"),pT={kernelName:rn,backendName:"cpu",kernelFunc:rS};var oS=vr(r=>Math.expm1(r)),b6=vo(da,oS),cT={kernelName:da,backendName:"cpu",kernelFunc:b6};var nS=vr(r=>Math.floor(r)),C6=vo(nn,nS),lT={kernelName:nn,backendName:"cpu",kernelFunc:C6};function pf(r,e,t,o,n,s,a,i,p){let u=le([o,s],t);for(let c=0;c=p/s)throw new Error(`Invalid indices: ${l} does not index into ${i}`);for(let d=0;dr>e?1:0),S6=Qe(pn,sS,null,"bool"),mT={kernelName:pn,backendName:"cpu",kernelFunc:S6};var aS=Be((r,e)=>r>=e?1:0),w6=Qe(cn,aS,null,"bool"),dT={kernelName:cn,backendName:"cpu",kernelFunc:w6};var iS=Be((r,e)=>rr<=e?1:0),v6=Qe(fn,uS,null,"bool"),hT={kernelName:fn,backendName:"cpu",kernelFunc:v6};function lf(r,e,t){let o=(e-r)/(t-1),n=y.makeZerosTypedArray(t,"float32");n[0]=r;for(let s=1;sMath.log(r)),k6=vo(hn,pS),gT={kernelName:hn,backendName:"cpu",kernelFunc:k6};function mf(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 cS=Be((r,e)=>Math.max(r,e)),N6=Qe(bn,cS),xT={kernelName:bn,backendName:"cpu",kernelFunc:N6};var lS=Be((r,e)=>Math.min(r,e)),T6=Qe(In,lS),yT={kernelName:In,backendName:"cpu",kernelFunc:T6};var ml=Be((r,e)=>r*e),_6=qp((r,e,t,o)=>({real:r*t-e*o,imag:r*o+e*t})),wu=Qe(kn,ml,_6),bT={kernelName:kn,backendName:"cpu",kernelFunc:wu};function mS(r,e,t){let o=y.createScalarValue(-1,t);return ml([],e,o,r,t)}function E6(r){let{inputs:e,backend:t}=r,{x:o}=e;K(o,"neg");let n=t.data.get(o.dataId).values,[s,a]=mS(n,o.shape,o.dtype);return t.makeTensorInfo(a,o.dtype,s)}var CT={kernelName:ws,backendName:"cpu",kernelFunc:E6};var dS=Be((r,e)=>r!==e?1:0),$6=Qe(Nn,dS,null,"bool"),ST={kernelName:Nn,backendName:"cpu",kernelFunc:$6};function jp(r,e,t,o,n){let s=e.length,a=y.sizeFromShape(e),i=y.computeStrides(e),p=y.computeStrides(n),u=y.getTypedArrayFromDType(t,y.sizeFromShape(n));for(let c=0;ct.disposeIntermediateTensorInfo(b)),t.makeTensorInfo(x,g,f)}var IT={kernelName:Fn,backendName:"cpu",kernelFunc:A6};function R6(r,e,t){r.forEach((o,n)=>{if(o<0||o>=t){let s=y.indexToLoc(n,e.length,y.computeStrides(e)).join(",");throw new Error(`indices[${s}] = ${o} is not in [0, ${t})`)}})}function F6(r,e){for(let t=0;tn)throw new Error("Ragged splits must not point past values");for(let s=1;so[s])throw new Error("Ragged splits must be sorted in ascending order")}}function D6(r,e,t,o){let n=[],s=0,a=e.length-1+t.length,i=new Array(a).fill(null).map(()=>[0]);F6(t,o);let p=1;for(let u=0;u=0){let h=i[f],g=h[h.length-1]-d[c];for(let x=c;xn[a]=s)}return e}function vT(r,e){let t=r.slice(0,e);for(;t.length1)throw new Error("starts must be a scalar or vector");if(n.length>1)throw new Error("limits must be a scalar or vector");if(a.length>1)throw new Error("deltas must be a scalar or vector");let i=e.length===0,p=n.length===0,u=a.length===0,c=[];i||c.push(e[0]),p||c.push(n[0]),u||c.push(a[0]);for(let g=1;g0&&bx)w=0;else if(w=Math.ceil(Math.abs((b-x)/C)),w>kT)throw new Error(`Requires ((limit - start) / delta) <= ${kT}`);m[g+1]=m[g]+w}let d=m[l],f=y.getArrayFromDType(t,d),h=0;for(let g=0;go&&(o=s)}return o}static getMaxWidthValueRowID(e){let t=e.length;if(t===0)return 0;let o=0,n=e[0],s=0;for(let a=1;a"Final length of result must be equal to firstDimension."),s}calculateOutputIndexRowSplit(e,t,o,n){let s=e.length,a=[];for(let i=0;i0&&a.length!==e[s-1])throw new Error("Invalid row split size.");return a}calculateOutputIndexValueRowID(e,t,o,n){let s=e.length,a=[];if(s===0)return[];let i=0,p=e[0];if(p>=t.length)throw new Error(`Got currentValueRowId=${p}, which is not less than ${t.length}`);let u=t[p];a.push(u);for(let c=1;c=0&&(++i,i=t.length)throw new Error(`Got nextValueRowId=${l} which is not less than ${t.length}`);u=t[l]}a.push(u)}if(a.length!==e.length)throw new Error("Invalid row ids.");return a}calculateOutputIndex(e,t,o,n){let s=this.getRowPartitionTensor(e),a=this.getRowPartitionTypeByDimension(e);switch(a){case ko.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(s,t,o,n);case ko.ROW_SPLITS:if(s.length-1>t.length)throw new Error(`Row partition size is greater than output size: ${s.length-1} > ${t.length}`);return this.calculateOutputIndexRowSplit(s,t,o,n);default:throw new Error(`Unsupported partition type: ${ko[a]}`)}}getFirstDimensionSize(){let e=this.rowPartitionValues[0];if(this.rowPartitionTypes.length===0)throw new Error("No row_partition_types given.");let t=this.rowPartitionTypes[0];switch(t){case ko.FIRST_DIM_SIZE:return e[0];case ko.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case ko.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${ko[t]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. Tensor requires at least one element.");let t=this.getFirstDimensionSize(),o=this.calculateOutputSize(t),n=new Array(this.raggedRank+1);n[n.length-1]=1;for(let p=n.length-2;p>=0;--p)n[p]=n[p+1]*o[p+1];let s=TT(o,!1),a=y.getArrayFromDType(this.valuesDType,y.sizeFromShape(s));if(n[0]*o[0]>0){let p=this.calculateFirstParentOutputIndex(t,n[0],o[0]);for(let u=1;u<=this.raggedRank;++u)p=this.calculateOutputIndex(u-1,p,n[u],o[u]);this.setOutput(this.raggedRank,p,a,s)}return[s,a]}setOutput(e,t,o,n){if(o.length===0)return;let s=this.values,a=o,i=n.slice();i=i.slice(e+1);let p=y.sizeFromShape(i),u=t.length,c=this.defaultValue;if(c.length!==p&&c.length!==1){let f=this.defaultValueShape;Ee(()=>{let h=z(c,f);c=Ii(h,i).dataSync()})}let l=0,m=0,d=0;for(let f=0;f<=u;++f){let h=f=u){let g=o.length;h=Math.floor(g/p)}if(h>d)if(this.defaultValue.length===1)a.subarray(d*p,h*p).fill(this.defaultValue[0]),d=h;else for(;h>d;){let g=a.slice(d*p);NT(g,c,p),++d}h<0?(l=f+1,m=d):(l=f,m=d,d=m+1)}}};function NT(r,e,t){for(let o=0;o= 0`);if(o<-1)throw new Error(`Dimension ${o} must be >= -1`);o=-1}t.push(o)}return t}function hf(r,e,t,o,n,s,a,i,p,u){return new Xp(r,e,t,o,n,s,a,i,p,u).compute()}function Iu(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)),p=y.makeZerosTypedArray(i,o);e1/Math.sqrt(r)),L6=vo(Vn,hS),_T={kernelName:Vn,backendName:"cpu",kernelFunc:L6};function Ma(r,e,t,o,n,s,a,i,p,u){let c=[o/n,n],l=r.values,m=e.values;if(o===0)return le(t,e.dtype);let d=le(c,e.dtype);typeof p=="string"||typeof p=="number"?d.values.fill(p):typeof p=="boolean"&&d.values.fill(+p);for(let f=0;f=o/n)throw new Error(`Invalid indices: ${h} does not index into ${t}`);for(let x=0;x1/(1+Math.exp(-r))),gS=Ie(Un,r=>1/(1+Math.exp(-r))),$T={kernelName:Un,backendName:"cpu",kernelFunc:gS};function vu(r,e,t,o,n){let s=ut.isSliceContinous(o,e,t),a=y.sizeFromShape(t),i=y.computeStrides(o);if(s){let l=ut.computeFlatOffset(e,i);return n==="string"?r.slice(l,l+a):r.subarray(l,l+a)}let p=n==="string"?S.fromUint8ToStringArray(r):r,u=le(o,n,p),c=le(t,n);for(let l=0;lf+e[h]);c.set(u.get(...d),...m)}return n==="string"?S.fromStringArrayToUint8(c.values):c.values}function No(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o;K(n,"slice");let[i,p]=ut.parseSliceParams(n,s,a);ut.assertParamsValid(n,i,p);let u=t.data.get(n.dataId).values,c=vu(u,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,c)}var AT={kernelName:_s,backendName:"cpu",kernelFunc:No};function gf(r,e,t,o,n,s,a){let i=e[0],p=s[0],u=new Array(p),c=new Array(i),l=e[1];if(p===0){if(i!==0)throw new Error(S.getSparseFillEmptyRowsIndicesDenseShapeMismatch(i));let g=y.getArrayFromDType(t,0),x=y.getArrayFromDType(n,0);return[g,[0,l],x,u,c]}let m=!0,d=0,f=new Array(p).fill(0);for(let g=0;g=p)throw new Error(S.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g,x,p));++f[x],m=m&&x>=d,d=x}let h=!0;for(let g=0;g0&&(f[g]+=f[g-1])}if(h&&m){let g=r,x=o;for(let b=0;b0){d[m-1]=1;for(let g=m-2;g>=0;--g)d[g]=d[g+1]*o[g+1]}let f=[];if(i>0){f[i-1]=1;for(let g=i-2;g>=0;--g)f[g]=f[g+1]*p[g+1]}let h=y.getArrayFromDType(t,a*i);for(let g=0;g0?n[i-1]+1:0;if(l<0)throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let m=e.slice();m[0]=l;let d=m.reduce((C,w)=>C*w,1),f=y.getArrayFromDType(t,d);if(i===0)return l>0&&f.fill(a),[f,m];if(l<=0)throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let h=0,g=1,x=0,b=n[h];for(;;){let C=0;if(g=C)throw new Error(S.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage())}if(b<0||b>=l)throw new Error(S.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(b,l));b>x&&f.fill(a,x*u,b*u);for(let w=h;w=p[0])throw new Error(S.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(w,o[w],p[0]));for(let _=0;_i)break}return xMath.sqrt(r)),B6=Ie(Gn,r=>Math.sqrt(r)),FT={kernelName:Gn,backendName:"cpu",kernelFunc:B6};var xS=Be((r,e)=>{let t=r-e;return t*t}),V6=Qe(Kn,xS),DT={kernelName:Kn,backendName:"cpu",kernelFunc:V6};function yf(r,e,t,o){let n=le(r,e.dtype);for(let s=0;s0?0:i-p),d=0;d+=u*this.leftPad.length;for(let b=0;bb.forEach(C=>h[g++]=C);for(let b=0;b0){x(e[m+l-1]);for(let b=0;b0){let p=t[0];if(p!==0)throw new Error(`First split value must be 0, got ${p}`);for(let u=1;u=p;if(c=c&&t[u]<=o,!c)throw new Error(`Invalid split value ${t[u]}, must be in [${p}, ${o}]`);p=t[u]}if(p!==o)throw new Error(`Last split value must be data size. Expected ${o}, got ${p}`)}let s=n-1,a=y.getArrayFromDType("int32",n);if(o===0||n===0){let p=new Array(o);for(let u=0;u<=s;++u)a[u]=0;return[p,a]}a[0]=0;for(let p=1;p<=s;++p){let u=t[p]-t[p-1],c=0;this.nGramWidths.forEach(l=>{c+=this.getNumNGrams(u,l)}),this.preserveShort&&u>0&&c===0&&(c=1),a[p]=a[p-1]+c}let i=new Array(a[s]);for(let p=0;p{let m=t[p+1]-t[p],d=this.getNumNGrams(m,l);this.createNGrams(e,u,i,c,d,l),c+=d}),this.preserveShort&&c===a[p]){let l=t[p+1]-t[p];if(l===0)continue;let m=l+2*this.padWidth,d=1;this.createNGrams(e,u,i,c,d,m)}}return[i,a]}};function ku(r,e,t,o,n,s,a,i){return new yS(t,o,n,s,a,i).compute(r,e)}function z6(r,e,t,o){if(!r.length)return;if(e.length===0){for(let s=0;sr-e),W6=qp((r,e,t,o)=>({real:r-t,imag:e-o})),dl=Qe(Xn,bS,W6),OT={kernelName:Xn,backendName:"cpu",kernelFunc:dl};function bf(r,e){let t=new Array(r.rank);for(let n=0;n{let t=e.value-r.value;return t===0?r.index-e.index:t};function PT(r,e,t=0,o=r.length-1){for(;o>t;){if(o-t>600){let i=o-t+1,p=e-t+1,u=Math.log(i),c=.5*Math.exp(2*u/3),l=.5*Math.sqrt(u*c*(i-c)/i)*Math.sign(p-i/2),m=Math.max(t,Math.floor(e-p*c/i+l)),d=Math.min(o,Math.floor(e+(i-p)*c/i+l));PT(r,e,m,d)}let n=r[e],s=t,a=o;for(y.swap(r,t,e),fl(r[o],n)>0&&y.swap(r,t,o);s0;)a=a-1}fl(r[t],n)===0?y.swap(r,t,a):(a=a+1,y.swap(r,a,o)),a<=e&&(t=a+1),e<=a&&(o=a-1)}}function Cf(r,e,t,o,n){let s=e[e.length-1],[a,i]=[r.length/s,s],p=y.getTypedArrayFromDType(t,a*o),u=y.getTypedArrayFromDType("int32",a*o);for(let l=0;lf[C]={value:b,index:C}),o{for(let g=0;gnew Oi,1);var CS=Ie(en,r=>r>=0?r:Math.exp(r)-1),MT={kernelName:en,backendName:"cpu",kernelFunc:CS};function SS(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o;K([n],"leakyRelu");let a=y.sizeFromShape(n.shape),i=t.data.get(n.dataId).values,p=y.getTypedArrayFromDType("float32",a);for(let u=0;ur<0?e*r:r);function wS(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e;K([o,n],"prelu");let s=t.data.get(o.dataId).values,a=t.data.get(n.dataId).values,[i,p]=G6(o.shape,n.shape,s,a,"float32");return t.makeTensorInfo(p,"float32",i)}var BT={kernelName:Rn,backendName:"cpu",kernelFunc:wS};var IS=Ie(On,r=>Math.max(0,r)),VT={kernelName:On,backendName:"cpu",kernelFunc:IS};var vS=Ie(Ln,r=>Math.min(Math.max(0,r),6)),zT={kernelName:Ln,backendName:"cpu",kernelFunc:vS};function _u(r,e,t,o,n){if(t==="linear")return ar({inputs:{x:e},backend:r});if(t==="relu")return IS({inputs:{x:e},backend:r});if(t==="elu")return CS({inputs:{x:e},backend:r});if(t==="relu6")return vS({inputs:{x:e},backend:r});if(t==="prelu")return wS({inputs:{x:e,alpha:o},backend:r});if(t==="leakyrelu")return SS({inputs:{x:e},backend:r,attrs:{alpha:n}});if(t==="sigmoid")return gS({inputs:{x:e},backend:r});throw new Error(`Activation ${t} has not been implemented for the CPU backend.`)}function Me(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{shape:s}=o,a=y.sizeFromShape(n.shape),i=y.inferFromImplicitShape(s,a),p=y.sizeFromShape(i);y.assert(a===p,()=>`The new shape (${i}) has ${p} elements and the old shape (${n.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),t.incRef(n.dataId);let u=t.data.get(n.dataId);if(u.complexTensorInfos!=null){let c=u.complexTensorInfos.real,l=u.complexTensorInfos.imag;c.shape=i,l.shape=i}return{dataId:n.dataId,shape:i,dtype:n.dtype}}var WT={kernelName:Ns,backendName:"cpu",kernelFunc:Me};function kS(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;K([n,s],"matMul");let p=n.shape.length,u=s.shape.length,c=a?n.shape[p-2]:n.shape[p-1],l=i?s.shape[u-1]:s.shape[u-2],m=a?n.shape[p-1]:n.shape[p-2],d=i?s.shape[u-2]:s.shape[u-1],f=n.shape.slice(0,-2),h=s.shape.slice(0,-2),g=y.sizeFromShape(f),x=y.sizeFromShape(h),C=br.assertAndGetBroadcastShape(n.shape.slice(0,-2),s.shape.slice(0,-2)).concat([m,d]);y.assert(c===l,()=>`Error in matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${n.shape} and ${s.shape} and transposeA=${a} and transposeB=${i} must match.`);let w=a?[g,c,m]:[g,m,c],k=i?[x,d,l]:[x,l,d],_=Me({inputs:{x:n},backend:t,attrs:{shape:w}}),$=Me({inputs:{x:s},backend:t,attrs:{shape:k}}),A=a?_.shape[1]:_.shape[2],R=a?_.shape[2]:_.shape[1],D=i?$.shape[1]:$.shape[2],P=Math.max(g,x),M=t.data.get(_.dataId).values,L=t.data.get($.dataId).values,W=y.computeStrides(_.shape),V=y.computeStrides($.shape),[U,q,H]=a?[W[0],1,W[1]]:[W[0],W[1],1],[j,X,Z]=i?[1,V[1],V[0]]:[V[1],1,V[0]],ee=R*D,Y=le([P,R,D],_.dtype),J=Y.values,ie=t.blockSize;for(let pe=0;peMath.acos(r)),HT={kernelName:sa,backendName:"cpu",kernelFunc:q6};var K6=Ie(aa,r=>Math.acosh(r)),qT={kernelName:aa,backendName:"cpu",kernelFunc:K6};function j6(r){let{inputs:e,backend:t}=r,o=e;K(e,"addN");let n=o.map(i=>t.data.get(i.dataId).values),s=le(o[0].shape,o[0].dtype),a=s.values;for(let i=0;ib&&(b=k,C=w)}d[g]=C}return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),t.makeTensorInfo(c,"int32",d)}var YT={kernelName:Vo,backendName:"cpu",kernelFunc:Q6};function Z6(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o;K(n,"argMin");let a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Ct({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),a=[a[0]],S.assertAxesAreInnerMostDims("argMin",a,p.shape.length);let[c,l]=S.computeOutAndReduceShapes(p.shape,a),m=y.sizeFromShape(c),d=y.makeZerosTypedArray(m,"int32"),f=y.sizeFromShape(l),h=t.data.get(p.dataId).values;for(let g=0;gt.disposeIntermediateTensorInfo(g)),t.makeTensorInfo(c,"int32",d)}var QT={kernelName:Za,backendName:"cpu",kernelFunc:Z6};var J6=Ie(ia,r=>Math.asin(r)),ZT={kernelName:ia,backendName:"cpu",kernelFunc:J6};var ej=Ie(ua,r=>Math.asinh(r)),JT={kernelName:ua,backendName:"cpu",kernelFunc:ej};var tj=Ie(pa,r=>Math.atan(r)),e2={kernelName:pa,backendName:"cpu",kernelFunc:tj};var rj=Be((r,e)=>Math.atan2(r,e)),oj=Qe(la,rj),t2={kernelName:la,backendName:"cpu",kernelFunc:oj};var nj=Ie(ca,r=>Math.atanh(r)),r2={kernelName:ca,backendName:"cpu",kernelFunc:nj};function Zp(r,e,t,o,n,s){let a=n.strideHeight,i=n.strideWidth,p=n.dilationHeight,u=n.dilationWidth,c=n.effectiveFilterHeight,l=n.effectiveFilterWidth,m=n.padInfo.top,d=n.padInfo.left,f=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,h=le(n.outShape,t),g=h.values,x=n.outShape[1]*n.outShape[2]*n.outShape[3],b=n.outShape[2]*n.outShape[3],C=n.outShape[3];for(let w=0;wq?q=ie:s==="avg"&&(H+=ie,j++)}if(isNaN(q))break}let X=M+L*C+$;g[X]=s==="avg"?H/j:q}}}return h}function wf(r,e,t,o,n=!1,s=!1){let a=le(o.outShape,"int32"),i=o.strideHeight,p=o.strideWidth,u=o.dilationHeight,c=o.dilationWidth,l=o.effectiveFilterHeight,m=o.effectiveFilterWidth,d=o.padInfo.top,f=o.padInfo.left,h=le(e,t,r);for(let g=0;gD&&(D=U,n?P=s?((g*o.inHeight+M)*o.inWidth+W)*o.inChannels+x:(M*o.inWidth+W)*o.inChannels+x:P=L*m+V)}}a.set(P,g,b,_,x)}}return a}function If(r,e,t,o,n,s){let a=n.strideDepth,i=n.strideHeight,p=n.strideWidth,u=n.dilationDepth,c=n.dilationHeight,l=n.dilationWidth,m=n.effectiveFilterDepth,d=n.effectiveFilterHeight,f=n.effectiveFilterWidth,h=n.padInfo.front,g=n.padInfo.top,x=n.padInfo.left,b=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,C=le(n.outShape,t),w=C.values,k=n.outShape[1]*n.outShape[2]*n.outShape[3]*n.outShape[4],_=n.outShape[2]*n.outShape[3]*n.outShape[4],$=n.outShape[3]*n.outShape[4],A=n.outShape[4];for(let R=0;Rwe?we=wt:s==="avg"&&(ve+=wt,$e++),isNaN(we))break}if(isNaN(we))break}if(isNaN(we))break}let Le=he+M;w[Le]=s==="avg"?ve/$e:we}}}}return C}function o2(r,e){let t=le(e.outShape,"int32"),o=e.strideDepth,n=e.strideHeight,s=e.strideWidth,a=e.dilationDepth,i=e.dilationHeight,p=e.dilationWidth,u=e.effectiveFilterDepth,c=e.effectiveFilterHeight,l=e.effectiveFilterWidth,m=e.padInfo.front,d=e.padInfo.top,f=e.padInfo.left;for(let h=0;h=L&&(L=Z,W=U*c*l+H*c+X)}}}t.set(W,h,x,k,R,g)}}}return t}function sj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;K(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,p),l;if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))l=ar({inputs:{x:n},backend:t});else{let m=t.data.get(n.dataId).values,d=y.computeStrides(n.shape),f=Zp(m,n.shape,n.dtype,d,c,"avg");l=t.makeTensorInfo(c.outShape,n.dtype,f.values)}return l}var n2={kernelName:zo,backendName:"cpu",kernelFunc:sj};function aj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o;K(n,"avgPool3d");let c=S.computePool3DInfo(n.shape,s,a,1,i,p,u),l=t.data.get(n.dataId).values,m=If(l,n.shape,n.dtype,y.computeStrides(n.shape),c,"avg");return t.makeTensorInfo(m.shape,"float32",m.values)}var s2={kernelName:ip,backendName:"cpu",kernelFunc:aj};function ij(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o;K([n,s],"avgPool3DGrad");let c=S.computePool3DInfo(s.shape,a,i,1,p,u),l=c.strideDepth,m=c.strideHeight,d=c.strideWidth,f=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,C=c.dilationWidth,w=c.effectiveFilterDepth,k=c.effectiveFilterHeight,_=c.effectiveFilterWidth,$=w-1-c.padInfo.front,A=_-1-c.padInfo.left,R=k-1-c.padInfo.top,D=le(s.shape,"float32"),P=1/(f*h*g),M=t.bufferSync(n);for(let L=0;L=c.outDepth||Math.floor(Y)!==Y))for(let J=0;J=c.outHeight||Math.floor(ie)!==ie))for(let pe=0;pe<_;pe+=C){let he=(X+pe)/d;if(he<0||he>=c.outWidth||Math.floor(he)!==he)continue;let we=M.get(L,Y,ie,he,W);Z+=we}}}D.set(Z*P,L,V,U,q,W)}return t.makeTensorInfo(D.shape,D.dtype,D.values)}var a2={kernelName:Im,backendName:"cpu",kernelFunc:ij};function uj(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;K([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=S.computePool2DInfo(a.shape,i,p,1,u),l=c.strideHeight,m=c.strideWidth,d=c.filterHeight,f=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,C=b-1-c.padInfo.left,w=x-1-c.padInfo.top,k=le(a.shape,"float32"),_=1/(d*f),$=t.data.get(n.dataId).values,A=le(n.shape,"float32",$);for(let R=0;R=c.outHeight||Math.floor(q)!==q))for(let H=0;H=c.outWidth||Math.floor(j)!==j)continue;let X=A.get(R,q,j,D);V+=X}}k.set(V*_,R,P,M,D)}return t.makeTensorInfo(k.shape,k.dtype,k.values)}var i2={kernelName:wm,backendName:"cpu",kernelFunc:uj};function pj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,scale:s,offset:a,mean:i,variance:p}=e;y.assert(i.shape.length===p.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(s==null||i.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),K([n,i,p,s,a],"batchNorm");let{varianceEpsilon:u}=o;u==null&&(u=.001);let c=t.data.get(n.dataId).values,l=t.data.get(i.dataId).values,m=t.data.get(p.dataId).values,d=s?t.data.get(s.dataId).values:new Float32Array([1]),f=a?t.data.get(a.dataId).values:new Float32Array([0]),h=new Float32Array(c.length),g=f.length,x=d.length,b=m.length,C=l.length,w=0,k=0,_=0,$=0;for(let A=0;A=g&&(w=0),k>=C&&(k=0),_>=x&&(_=0),$>=b&&($=0);return t.makeTensorInfo(n.shape,n.dtype,h)}var u2={kernelName:an,backendName:"cpu",kernelFunc:pj};function cj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;K([n],"batchToSpaceND");let i=s.reduce((x,b)=>x*b),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=Me({inputs:{x:n},backend:t,attrs:{shape:p}}),f=Ct({inputs:{x:d},backend:t,attrs:{perm:u}}),h=Me({inputs:{x:f},backend:t,attrs:{shape:c}}),g=No({inputs:{x:h},backend:t,attrs:{begin:l,size:m}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),g}var p2={kernelName:xs,backendName:"cpu",kernelFunc:cj};function lj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.data.get(n.dataId).values,p=t.data.get(s.dataId).values,u=Kp(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var c2={kernelName:Ja,backendName:"cpu",kernelFunc:lj};function mj(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.data.get(o.dataId).values,a=t.data.get(n.dataId).values,i=S.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var l2={kernelName:up,backendName:"cpu",kernelFunc:mj};var dj=Ie(lo,(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,p=t.data.get(a.dataId).values;for(let u=0;uh.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(h=>h.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(h=>y.sizeFromShape(h.shape)>0);if(p.length===1)return ar({inputs:{x:p[0]},backend:t});if(p[0].dtype==="complex64"){let h=p.map(w=>wo({inputs:{input:w},backend:t})),g=p.map(w=>Xs({inputs:{input:w},backend:t})),x=Pi({inputs:h,backend:t,attrs:{axis:s}}),b=Pi({inputs:g,backend:t,attrs:{axis:s}}),C=Ht({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),C}let u=p.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return Me({inputs:{x:h},backend:t,attrs:{shape:x}})}),c=u.map(h=>({vals:t.data.get(h.dataId).values,shape:h.shape}));i=S.computeOutShape(u.map(h=>h.shape),1);let l=u[0].shape[0]===1,m=Su(c,i,e[0].dtype,l),d=S.computeOutShape(p.map(h=>h.shape),s),f=t.makeTensorInfo(d,e[0].dtype,m);return u.forEach(h=>t.disposeIntermediateTensorInfo(h)),f}var h2={kernelName:ys,backendName:"cpu",kernelFunc:Pi};function NS(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o;K([n,s],"conv2d");let l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,C=m.dataFormat==="channelsLast",w=new st(m.outShape,n.dtype),k=y.computeStrides(n.shape),_=y.computeStrides(s.shape),$=k[0],A=C?k[1]:k[2],R=C?k[2]:1,D=C?1:k[1],P=w.strides[0],M=C?w.strides[1]:w.strides[2],L=C?w.strides[2]:1,W=C?1:w.strides[1],V=t.data.get(n.dataId).values,U=t.data.get(s.dataId).values,q=w.values;for(let H=0;H=m.inHeight)continue;let pe=J*_[0],he=j+ie*A;for(let we=0;we=m.inWidth)continue;let pt=pe+Le*_[1],Oe=he+nt*R,mt=pt;for(let at=0;at=u.inDepth)continue;let H=U*R[0],j=P+q*A[1];for(let X=0;X=u.inHeight)continue;let ie=H+Y*R[1],pe=j+J*A[2];for(let he=0;he=u.inWidth)continue;let nt=ie+$e*R[2],pt=pe+Le*u.inChannels,Oe=nt;for(let mt=0;mtMath.cos(r)),w2={kernelName:qo,backendName:"cpu",kernelFunc:Cj};var Sj=Ie(Ko,r=>Math.cosh(r)),I2={kernelName:Ko,backendName:"cpu",kernelFunc:Sj};function wj(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=le([f,h,g,d],"float32"),b=t.data.get(s.dataId).values,C=t.data.get(a.dataId).values,w=t.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let $=0;$=c)continue;let W=h>1?(P-R)*(l-1)/(h-1):0,V=g>1?(M-D)*(m-1)/(g-1):0;for(let U=0;U1?R*(l-1)+U*W:.5*(R+P)*(l-1);if(q<0||q>l-1){for(let H=0;H1?D*(m-1)+Z*V:.5*(D+M)*(m-1);if(ee<0||ee>m-1){for(let pe=0;pe1?D*(m-1)+H*V:.5*(D+M)*(m-1);if(j<0||j>m-1){for(let ee=0;eex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);let i=n.shape[0],p=n.shape[1],u=n.shape[2],c=n.shape[3],l=p*s,m=u*s,d=c/(s*s),f=t.data.get(n.dataId).values,h=new Float32Array(i*l*m*d),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`);let d=S.computeConv2DInfo(n.shape,s.shape,a,m,i,u,!0),{filterHeight:f,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=d,C=b.left,w=b.top,k=d.outChannels/d.inChannels,_=new st(d.outShape,n.dtype),$=t.data.get(n.dataId).values,A=t.data.get(s.dataId).values,R=_.values;for(let D=0;D=d.inHeight)continue;let H=U*l[0],j=P+q*c[1];for(let X=0;X=d.inWidth)continue;let ie=H+Y*l[1],pe=j+J*d.inChannels,he=Z,we=ie;for(let ve=0;ve{let{x:o,filter:n}=r,{strides:s,pad:a,dilations:i}=t,p=e,u=p.data.get(o.dataId).values,c=o.shape.length,l=p.data.get(n.dataId).values,m=n.shape.length,{batchSize:d,inHeight:f,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:C,strideHeight:w,strideWidth:k,filterHeight:_,filterWidth:$,dilationHeight:A,dilationWidth:R,outShape:D}=S.computeDilation2DInfo(o.shape,n.shape,s,a,"NHWC",i),P=y.sizeFromShape(D),M=D.length,L=y.getArrayFromDType(o.dtype,P);for(let V=0;V=0&&J=0&&peZ&&(Z=ve)}}}let ee=y.locToIndex([V,U,H,X],M,y.computeStrides(D));L[ee]=Z}}}return{dataId:p.write(y.toTypedArray(L,o.dtype),D,o.dtype),shape:D,dtype:o.dtype}}};var D2={kernelName:bb,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:w,filterHeight:k,filterWidth:_,dilationHeight:$,dilationWidth:A,outShape:R}=S.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",p);y.assert(s.rank===R.length,()=>`Error in ${bb}, dy must have the same rank as output ${R.length}, but got ${s.rank}`);let D=y.toNestedArray(R,u.data.get(s.dataId).values),P=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let L=0;L=0&&Y=0&&iej&&(j=pe,X=ee,Z=J)}}}P[X][Z][H]+=D[L][W][U][H]}}}return{dataId:u.write(y.toTypedArray(P,o.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var O2={kernelName:yb,backendName:"cpu",kernelFunc:({inputs:r,backend:e,attrs:t})=>{let{x:o,filter:n,dy:s}=r,{strides:a,pad:i,dilations:p}=t,u=e,c=y.toNestedArray(o.shape,u.data.get(o.dataId).values),l=y.toNestedArray(n.shape,u.data.get(n.dataId).values),{batchSize:m,inHeight:d,inWidth:f,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:C,strideWidth:w,filterHeight:k,filterWidth:_,dilationHeight:$,dilationWidth:A,outShape:R}=S.computeDilation2DInfo(o.shape,n.shape,a,i,"NHWC",p);y.assert(s.rank===R.length,()=>`Error in ${yb}, dy must have the same rank as output ${R.length}, but got ${s.rank}`);let D=y.toNestedArray(R,u.data.get(s.dataId).values),P=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let L=0;L=0&&Y=0&&iej&&(j=pe,X=Y,Z=ie)}}}P[L][X][Z][H]+=D[L][W][U][H]}}}return{dataId:u.write(y.toTypedArray(P,o.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};function La(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;K(n,"sum");let i;n.dtype==="bool"?i=Io({inputs:{x:n},backend:t,attrs:{dtype:"int32"}}):i=ar({inputs:{x:n},backend:t});let p=i.shape.length,u=y.parseAxisParam(s,i.shape),c=S.getAxesPermutation(u,p),l=u,m=i;c!=null&&(m=Ct({inputs:{x:i},backend:t,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,p)),S.assertAxesAreInnerMostDims("sum",l,m.shape.length);let[d,f]=S.computeOutAndReduceShapes(m.shape,l),h=S.upcastType(m.dtype,"int32"),g=Hp(t,d,h),x=y.sizeFromShape(f),b=t.data.get(g.dataId).values,C=t.data.get(m.dataId).values;for(let w=0;w=0&&(m=La({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var M2={kernelName:ri,backendName:"cpu",kernelFunc:$j};function Aj(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e;K([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 p=0;p=1?s[p]=i[p]:s[p]=i[p]*(u+1)}return t.makeTensorInfo(n.shape,"float32",s)}var L2={kernelName:km,backendName:"cpu",kernelFunc:Aj};var Rj=S.ERF_P,Fj=S.ERF_A1,Dj=S.ERF_A2,Oj=S.ERF_A3,Pj=S.ERF_A4,Mj=S.ERF_A5,Lj=Ie(ma,r=>{let e=Math.sign(r),t=Math.abs(r),o=1/(1+Rj*t);return e*(1-((((Mj*o+Pj)*o+Oj)*o+Dj)*o+Fj)*o*Math.exp(-t*t))}),B2={kernelName:ma,backendName:"cpu",kernelFunc:Lj};function Jp(r){let{inputs:e,backend:t,attrs:o}=r,{input:n}=e,{dim:s}=o,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),Me({inputs:{x:n},backend:t,attrs:{shape:i}})}var V2={kernelName:bs,backendName:"cpu",kernelFunc:Jp};var Bj=Be((r,e)=>r/e),hl=Qe(Jo,Bj),gl={kernelName:Jo,backendName:"cpu",kernelFunc:hl};function vf(r,e,t){let o=r.shape,n=o[0],s=o[1],a=t.data.get(r.dataId),i=a.complexTensorInfos.real,p=a.complexTensorInfos.imag,u=[n,s],c=y.sizeFromShape(u),l=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,p,u]=o.shape,c=n.data.get(o.dataId).values;for(let m=0;m=0&&CMath.floor(r/e)),qj=Qe(sn,Hj,null,"int32"),G2={kernelName:sn,backendName:"cpu",kernelFunc:qj};function Kj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=NS({inputs:{x:n,filter:s},backend:t,attrs:{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m}});if(a){let g=h;if(c==="NCHW"&&a.shape.length===1&&a.shape[0]!==1){let x=Me({inputs:{x:a},backend:t,attrs:{shape:[a.shape[0],1,1]}});h=js({inputs:{a:h,b:x},backend:t}),t.disposeIntermediateTensorInfo(x)}else h=js({inputs:{a:h,b:a},backend:t});t.disposeIntermediateTensorInfo(g)}if(d){let g=h;if(c==="NCHW"&&d==="prelu"&&i.shape.length===1&&i.shape[0]!==1){let x=Me({inputs:{x:i},backend:t,attrs:{shape:[i.shape[0],1,1]}});h=_u(t,h,d,x,f),t.disposeIntermediateTensorInfo(x)}else h=_u(t,h,d,i,f);t.disposeIntermediateTensorInfo(g)}return h}var H2={kernelName:ho,backendName:"cpu",kernelFunc:Kj};function jj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=TS({inputs:{x:n,filter:s},backend:t,attrs:{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m}});if(a){let g=h;h=js({inputs:{a:h,b:a},backend:t}),t.disposeIntermediateTensorInfo(g)}if(d){let g=h;h=_u(t,h,d,i,f),t.disposeIntermediateTensorInfo(g)}return h}var q2={kernelName:go,backendName:"cpu",kernelFunc:jj};function Xj(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],[p,u,c,l]=S.prepareAndValidate(o,n);if(u===0)return t.makeTensorInfo(p,o.dtype,[]);let m=t.data.get(n.dataId).values,d=t.bufferSync(o),f=pf(m,d,o.dtype,u,i,c,l,o.shape,s);return t.makeTensorInfo(p,o.dtype,f.values)}var K2={kernelName:un,backendName:"cpu",kernelFunc:Xj};function Yj(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o;K([n,s],"gatherV2");let p=y.parseAxisParam(a,n.shape)[0],u=t.data.get(s.dataId).values,c=n.shape[p];for(let w=0;w=0,()=>`GatherV2: the index value ${k} is not in [0, ${c-1}]`)}let l=i;i==null&&(l=0);let m=y.sizeFromShape(s.shape),d=S.segment_util.collectGatherOpShapeInfo(n,s,p,l),f=Me({inputs:{x:n},backend:t,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),h=Me({inputs:{x:s},backend:t,attrs:{shape:[d.batchSize,m/d.batchSize]}}),g=[d.batchSize,d.outerSize,m/d.batchSize,d.sliceSize],x=t.bufferSync(h),b=t.bufferSync(f),C=cf(b,x,g);return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),t.makeTensorInfo(d.outputShape,C.dtype,C.values)}var j2={kernelName:Ss,backendName:"cpu",kernelFunc:Yj};function Qj(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=Me({inputs:{x:o},backend:t,attrs:{shape:[a,s]}}),p=vf(i,!0,t),u=Me({inputs:{x:p},backend:t,attrs:{shape:o.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(p),u}var X2={kernelName:ni,backendName:"cpu",kernelFunc:Qj};var Zj=Ie(fa,r=>Number.isFinite(r)?1:0,"bool"),Y2={kernelName:fa,backendName:"cpu",kernelFunc:Zj};var Jj=Ie(ha,r=>Math.abs(r)===1/0?1:0,"bool"),Q2={kernelName:ha,backendName:"cpu",kernelFunc:Jj};var eX=Ie(ln,r=>Number.isNaN(r)?1:0,"bool"),Z2={kernelName:ln,backendName:"cpu",kernelFunc:eX};function tX(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=lf(o,n,s);return e.makeTensorInfo([a.length],"float32",a)}var J2={kernelName:xp,backendName:"cpu",kernelFunc:tX};var rX=Ie(ga,r=>Math.log1p(r)),e_={kernelName:ga,backendName:"cpu",kernelFunc:rX};var oX=Be((r,e)=>r&&e),nX=Qe(gn,oX,null,"bool"),t_={kernelName:gn,backendName:"cpu",kernelFunc:nX};var sX=Ie(xn,r=>r?0:1,"bool"),r_={kernelName:xn,backendName:"cpu",kernelFunc:sX};var aX=Be((r,e)=>r||e),iX=Qe(xa,aX,null,"bool"),o_={kernelName:xa,backendName:"cpu",kernelFunc:iX};function uX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o;K(n,"LRN");let u=n.shape[3],c=u-1,l=t.data.get(n.dataId).values,m=y.sizeFromShape(n.shape),d=new Float32Array(m);function f(h){let g=h%u,x=h-g+Math.max(0,g-s),b=h-g+Math.min(g+s,c),C=0;for(;x<=b;x++){let w=l[x];C+=w*w}return C}for(let h=0;h`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,p),l;if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))l=ar({inputs:{x:n},backend:t});else{let m=t.data.get(n.dataId).values,d=y.computeStrides(n.shape),f=Zp(m,n.shape,n.dtype,d,c,"max");l=t.makeTensorInfo(c.outShape,n.dtype,f.values)}return l}var i_={kernelName:Cn,backendName:"cpu",kernelFunc:cX};function lX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o;K(n,"maxPool3d");let c=S.computePool3DInfo(n.shape,s,a,1,i,p,u),l=t.data.get(n.dataId).values,m=If(l,n.shape,n.dtype,y.computeStrides(n.shape),c,"max");return t.makeTensorInfo(m.shape,"float32",m.values)}var u_={kernelName:bp,backendName:"cpu",kernelFunc:lX};function mX(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o;K([n,s],"maxPool3DGrad");let c=S.computePool3DInfo(s.shape,a,i,1,p,u),l=t.bufferSync(s),m=o2(l,c),d=c.strideDepth,f=c.strideHeight,h=c.strideWidth,g=c.dilationDepth,x=c.dilationHeight,b=c.dilationWidth,C=c.effectiveFilterDepth,w=c.effectiveFilterHeight,k=c.effectiveFilterWidth,_=C-1-c.padInfo.front,$=k-1-c.padInfo.left,A=w-1-c.padInfo.top,R=le(s.shape,"float32"),D=t.bufferSync(n);for(let P=0;P=c.outDepth||Math.floor(Z)!==Z))for(let ee=0;ee=c.outHeight||Math.floor(Y)!==Y))for(let J=0;J=c.outWidth||Math.floor(ie)!==ie)continue;let pe=C*w*k-1-m.get(P,Z,Y,ie,M),he=X*w*k+ee*k+J,we=pe===he?1:0;if(we===0)continue;let ve=D.get(P,Z,Y,ie,M);j+=ve*we}}}R.set(j,P,L,W,V,M)}return t.makeTensorInfo(R.shape,R.dtype,R.values)}var p_={kernelName:_m,backendName:"cpu",kernelFunc:mX};function dX(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;K([s,a],"maxPoolGrad");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=S.computePool2DInfo(i.shape,p,u,1,c,l),d=t.data.get(i.dataId).values,f=le(m.outShape,i.dtype,wf(d,i.shape,i.dtype,m).values),h=m.strideHeight,g=m.strideWidth,x=m.dilationHeight,b=m.dilationWidth,C=m.effectiveFilterHeight,w=m.effectiveFilterWidth,k=w-1-m.padInfo.left,_=C-1-m.padInfo.top,$=le(i.shape,"float32"),A=t.data.get(n.dataId).values,R=le(n.shape,"float32",A);for(let D=0;D=m.outHeight||Math.floor(H)!==H))for(let j=0;j=m.outWidth||Math.floor(X)!==X)continue;let Z=C*w-1-f.get(D,H,X,P),ee=q*w+j,Y=Z===ee?1:0;if(Y===0)continue;let J=R.get(D,H,X,P);U+=J*Y}}$.set(U,D,M,L,P)}return t.makeTensorInfo($.shape,$.dtype,$.values)}var c_={kernelName:Tm,backendName:"cpu",kernelFunc:dX};function l_(r,e,t,o,n){let s=y.computeStrides(e),a=Zp(r,e,t,s,n,"max"),i=wf(r,e,t,n,!0,o);return[a.values,i.values]}var m_={kernelName:Cp,backendName:"cpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;K(o,"MaxPoolWithArgmax");let u=p.data.get(o.dataId).values,c=S.computePool2DInfo(o.shape,n,s,[1,1],a),[l,m]=l_(u,o.shape,o.dtype,i,c),d=p.write(l,c.outShape,o.dtype),f=p.write(m,c.outShape,o.dtype);return[{dataId:d,shape:c.outShape,dtype:o.dtype},{dataId:f,shape:c.outShape,dtype:"int32"}]}};function fX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=y.parseAxisParam(s,n.shape),u=S.computeOutAndReduceShapes(n.shape,i)[1],c=y.sizeFromShape(u),l=[],m=t.makeTensorInfo([],"float32",new Float32Array([c]));l.push(m);let d=Io({inputs:{x:n},backend:t,attrs:{dtype:"float32"}});l.push(d);let f=hl({inputs:{a:d,b:m},backend:t});l.push(f);let h=La({inputs:{x:f},backend:t,attrs:{axis:s,keepDims:a}});return l.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}var d_={kernelName:Sn,backendName:"cpu",kernelFunc:fX};function hX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;K(n,"min");let i=y.parseAxisParam(s,n.shape),p=i,u=S.getAxesPermutation(p,n.shape.length),c=n;u!=null&&(c=Ct({inputs:{x:n},backend:t,attrs:{perm:u}}),p=S.getInnerMostAxes(p.length,n.shape.length)),S.assertAxesAreInnerMostDims("min",p,c.shape.length);let[l,m]=S.computeOutAndReduceShapes(c.shape,p),d=y.sizeFromShape(m),f=y.makeZerosTypedArray(y.sizeFromShape(l),c.dtype),h=t.data.get(c.dataId).values;for(let x=0;xC[0]+n.shape[w]+C[1]),p=s.map(C=>C[0]),u=s.map((C,w)=>C[0]+n.shape[w]),c=a==="reflect"?0:1,l=t.data.get(n.dataId).values,m=n.shape.length,d=y.computeStrides(n.shape),f=y.sizeFromShape(i),h=i.length,g=y.computeStrides(i),x=y.getTypedArrayFromDType(n.dtype,f);for(let C=0;C=u[_]&&(w[_]=(u[_]-1)*2-w[_]+c);w=w.map((_,$)=>_-p[$]);let k=y.locToIndex(w,m,d);x[C]=l[k]}return{dataId:t.write(x,i,n.dtype),shape:i,dtype:n.dtype}}var h_={kernelName:vn,backendName:"cpu",kernelFunc:gX};var xX=Be((r,e)=>{let t=r%e;return r<0&&e<0||r>=0&&e>=0?t:(t+e)%e}),yX=Qe(ya,xX),g_={kernelName:ya,backendName:"cpu",kernelFunc:yX};var y_=rp(gC());function $S(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 ${i}`);let p=y.parseAxisParam([i],n.shape),u=ES({inputs:{x:n},backend:t,attrs:{reductionIndices:p,keepDims:!1}}),c=S.expandShapeToKeepDim(u.shape,p),l=Me({inputs:{x:u},backend:t,attrs:{shape:c}}),m=dl({inputs:{a:n,b:l},backend:t}),d=rS({inputs:{x:m},backend:t}),f=La({inputs:{x:d},backend:t,attrs:{axis:p,keepDims:!1}}),h=Me({inputs:{x:f},backend:t,attrs:{shape:c}}),g=hl({inputs:{a:d,b:h},backend:t});return t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),g}var x_={kernelName:qn,backendName:"cpu",kernelFunc:$S};function bX(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o;K(n,"multinomial");let p=i?n:$S({inputs:{logits:n},backend:t,attrs:{dim:-1}}),u=p.shape[0],c=p.shape[1],l=t.data.get(p.dataId).values,m=[u,s],d=y.makeZerosTypedArray(y.sizeFromShape(m),"int32");for(let f=0;f=0&&l[m]{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],p=e.map(c=>{let l=Jp({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=Pi({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var T_={kernelName:vs,backendName:"cpu",kernelFunc:AS};function TX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;K(n,"pad");let i=s.map((b,C)=>b[0]+n.shape[C]+b[1]),p=s.map(b=>b[0]),u=t.data.get(n.dataId).values,c=y.sizeFromShape(n.shape),l=n.shape.length,m=y.computeStrides(n.shape),d=y.sizeFromShape(i),f=i.length,h=y.computeStrides(i),g=y.getTypedArrayFromDType(n.dtype,d);a!==0&&g.fill(a);for(let b=0;b_+p[$]),k=y.locToIndex(w,f,h);g[k]=u[b]}return{dataId:t.write(g,i,n.dtype),shape:i,dtype:n.dtype}}var kf={kernelName:$n,backendName:"cpu",kernelFunc:TX};var _X=Be((r,e)=>Math.pow(r,e)),EX=Qe(An,_X),__={kernelName:An,backendName:"cpu",kernelFunc:EX};function $X(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.data.get(x.dataId).values),u=n.map(x=>x.shape),c=t.data.get(s.dataId).values,l=t.data.get(a.dataId).values,[m,d,f]=df(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],"int32",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var E_={kernelName:wp,backendName:"cpu",kernelFunc:$X};function AX(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.data.get(o.dataId).values,i=t.data.get(n.dataId).values,p=t.data.get(s.dataId).values,[u,c]=ff(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],"int32",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var $_={kernelName:Ip,backendName:"cpu",kernelFunc:AX};function RX(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.data.get(n.dataId).values,c=t.data.get(s.dataId).values,l=t.data.get(a.dataId).values,m=i.map(g=>t.data.get(g.dataId).values),d=i.map(g=>g.shape),[f,h]=hf(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var A_={kernelName:vp,backendName:"cpu",kernelFunc:RX};function FX(r){let{backend:e,attrs:t}=r,{start:o,stop:n,dtype:s,step:a}=t,i=Iu(o,n,a,s);return e.makeTensorInfo([i.length],s,i)}var R_={kernelName:ks,backendName:"cpu",kernelFunc:FX};var DX=Ie(Dn,r=>1/r),F_={kernelName:Dn,backendName:"cpu",kernelFunc:DX};function OX(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o;K(n,"resizeBilinear");let p=y.computeStrides(n.shape),[u,c]=i,[l,m,d,f]=n.shape,h=t.data.get(n.dataId).values,g=new Float32Array(y.sizeFromShape([l,u,c,f])),x=[s&&u>1?m-1:m,s&&c>1?d-1:d],b=[s&&u>1?u-1:u,s&&c>1?c-1:c],C=0,w=x[0]/b[0],k=x[1]/b[1];for(let _=0;_1?u-1:u,a&&d>1?c-1:c],g=[a&&m>1?m-1:m,a&&d>1?d-1:d],x=h[0]/g[0],b=h[1]/g[1],C=t.data.get(s.dataId).values,w=0;for(let k=0;k1?m-1:m,s&&c>1?d-1:d],b=[s&&u>1?u-1:u,s&&c>1?c-1:c],C=x[0]/b[0],w=x[1]/b[1],k=0;for(let _=0;_1?c-1:c,a&&f>1?l-1:l],b=[a&&d>1?d-1:d,a&&f>1?f-1:f],C=x[0]/b[0],w=x[1]/b[1],k=1/C,_=1/w,$=Math.ceil(k)*2+2,A=Math.ceil(_)*2+2;for(let R=0;R=d)continue;let Y=D+ee*p[1],J=ee*C,ie=Math.min(c-1,a?Math.round(J):Math.floor(J));if(P===ie)for(let pe=0;pe=f)continue;let we=Y+he*p[2],ve=he*w,$e=Math.min(l-1,a?Math.round(ve):Math.floor(ve));V===$e&&(X+=g[we+j])}}h[U+j]=X}}}}return t.makeTensorInfo(n.shape,n.dtype,h)}var M_={kernelName:Em,backendName:"cpu",kernelFunc:LX};function BX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o;K(n,"reverse");let a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return ar({inputs:{x:n},backend:t});let p=new st(n.shape,n.dtype),u=t.bufferSync(n);for(let c=0;cm[d]=n.shape[d]-1-m[d]),p.set(u.get(...m),...l)}return t.makeTensorInfo(p.shape,p.dtype,p.values)}var L_={kernelName:Bn,backendName:"cpu",kernelFunc:BX};var B_={kernelName:es,backendName:"cpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=y.getTypedArrayFromDType(o.dtype,y.sizeFromShape(o.shape)),[u,c,l,m]=o.shape,[d,f]=S.getImageCenter(a,c,l),h=255,g=Math.sin(n),x=Math.cos(n),b=i.data.get(o.dataId).values;for(let w=0;w=0&&W=0&&V{let e=Math.floor(r);return r-e<.5?Math.floor(r):r-e>.5?Math.ceil(r):e%2===0?e:e+1}),V_={kernelName:Ca,backendName:"cpu",kernelFunc:VX};function zX(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=S.calculateShapes(s,n,a),m=!0,d=t.bufferSync(n),f=t.bufferSync(s),h=Ma(d,f,a,l,u,p,i,c,0,m);return t.makeTensorInfo(a,h.dtype,h.values)}var z_={kernelName:zn,backendName:"cpu",kernelFunc:zX};function WX(r,e){let t=0,o=r.length,n=0;for(;t1||n.shape.length===1?1:y.sizeFromShape(n.shape.slice(1));for(let f=0;fr>=0?KX*r:qX*(Math.exp(r)-1)),H_={kernelName:Xi,backendName:"cpu",kernelFunc:jX};var XX=Ie(Yi,r=>r<0?-1:r>0?1:0),q_={kernelName:Yi,backendName:"cpu",kernelFunc:XX};var YX=Ie(Wn,r=>Math.sin(r)),K_={kernelName:Wn,backendName:"cpu",kernelFunc:YX};var QX=Ie(Sa,r=>Math.sinh(r)),j_={kernelName:Sa,backendName:"cpu",kernelFunc:QX};var ZX=11920928955078125e-23,X_=Math.log(ZX)+2,JX=Ie(Qi,r=>{let e=r>-X_,t=rNumber(g)))),t.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var Z_={kernelName:ui,backendName:"cpu",kernelFunc:t5};function r5(r){let{inputs:e,backend:t}=r,{inputIndices:o,inputShape:n,newShape:s}=e;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(t.data.get(n.dataId).values),i=t.data.get(o.dataId).values,p=Array.from(t.data.get(s.dataId).values),[u,c,l]=xf(i,o.shape,o.dtype,a,p);return[t.makeTensorInfo(c,o.dtype,u),t.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var J_={kernelName:wa,backendName:"cpu",kernelFunc:r5};function o5(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape ${s.shape}`);if(n.shape[0]!==s.shape[0])throw new Error("segmentIds and indices should have same size.");let a=t.data.get(o.dataId).values,i=t.data.get(n.dataId).values,p=t.data.get(s.dataId).values,[u,c]=Yp(a,o.shape,o.dtype,i,p,!0);return t.makeTensorInfo(c,o.dtype,u)}var eE={kernelName:pi,backendName:"cpu",kernelFunc:o5};function n5(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape ${s.shape}`);if(n.shape[0]!==s.shape[0])throw new Error("segmentIds and indices should have same size.");let a=t.data.get(o.dataId).values,i=t.data.get(n.dataId).values,p=t.data.get(s.dataId).values,[u,c]=Yp(a,o.shape,o.dtype,i,p);return t.makeTensorInfo(c,o.dtype,u)}var tE={kernelName:ci,backendName:"cpu",kernelFunc:n5};function s5(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=S.calculateShapes(s,n,i),d=!1,f=t.bufferSync(n),h;switch(s.dtype){case"bool":{let g=t.bufferSync(s),x=Boolean(t.data.get(a.dataId).values[0]);h=Ma(f,g,i,m,c,u,p,l,x,d);break}case"float32":{let g=t.bufferSync(s),x=t.data.get(a.dataId).values[0];h=Ma(f,g,i,m,c,u,p,l,x,d);break}case"int32":{let g=t.bufferSync(s),x=t.data.get(a.dataId).values[0];h=Ma(f,g,i,m,c,u,p,l,x,d);break}case"string":{let g=t.bufferSync(s),x=y.decodeString(t.data.get(a.dataId).values[0]);h=Ma(f,g,i,m,c,u,p,l,x,d);break}default:throw new Error(`Unsupported type ${s.dtype}`)}return t.makeTensorInfo(i,h.dtype,h.values)}var rE={kernelName:li,backendName:"cpu",kernelFunc:s5};function a5(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=S.prepareSplitSize(n,s,i),u=new Array(n.shape.length).fill(0),c=n.shape.slice();return p.map(l=>{let m=[...c];m[i]=l;let d=No({inputs:{x:n},backend:t,attrs:{begin:u,size:m}});return u[i]+=l,d})}var oE={kernelName:$s,backendName:"cpu",kernelFunc:a5};var nE={kernelName:mi,backendName:"cpu",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e;K(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}),sE={kernelName:Ds,backendName:"cpu",kernelFunc:i5};function u5(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o;K(n,"stridedSlice");let{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:w}=ut.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=Me({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=ut.computeOutShape(b,C,w),$=No({inputs:{x:n},backend:t,attrs:{begin:b,size:_}});k=Me({inputs:{x:$},backend:t,attrs:{shape:f}}),t.disposeIntermediateTensorInfo($)}else{let _=t.bufferSync(n),$=yf(d,_,w,b);k=t.makeTensorInfo(f,$.dtype,$.values)}return k}var aE={kernelName:jn,backendName:"cpu",kernelFunc:u5};function p5(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.data.get(c.dataId).values,d=t.data.get(l.dataId).values,[f,h]=ku(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],"string",f),t.makeTensorInfo(l.shape,"int32",h)]}var iE={kernelName:As,backendName:"cpu",kernelFunc:p5};function c5(r){let{inputs:e,backend:t,attrs:o}=r,{skipEmpty:n}=o,{input:s,delimiter:a}=e;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(a.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`);let i=t.data.get(s.dataId).values,p=t.data.get(a.dataId).values[0],[u,c,l]=Nu(i,p,n),m=c.length;return[t.makeTensorInfo([m,2],"int32",u),t.makeTensorInfo([m],"string",c),t.makeTensorInfo([2],"int32",new Int32Array(l))]}var uE={kernelName:di,backendName:"cpu",kernelFunc:c5};function l5(r){let{inputs:e,backend:t,attrs:o}=r,{numBuckets:n}=o,{input:s}=e;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(n<=0)throw new Error("Number of buckets must be at least 1");let a=t.data.get(s.dataId).values,i=Tu(a,n);return t.makeTensorInfo(s.shape,"int32",i)}var pE={kernelName:fi,backendName:"cpu",kernelFunc:l5};var m5=Ie(Yn,r=>Math.tan(r)),cE={kernelName:Yn,backendName:"cpu",kernelFunc:m5};var d5=Ie(Qn,r=>Math.tanh(r)),lE={kernelName:Qn,backendName:"cpu",kernelFunc:d5};function f5(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reps:s}=o;K(n,"tile");let a=bf(t.bufferSync(n),s);return t.makeTensorInfo(a.shape,a.dtype,a.values)}var mE={kernelName:to,backendName:"cpu",kernelFunc:f5};function h5(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{k:s,sorted:a}=o;K(n,"topk");let i=t.data.get(n.dataId).values,[p,u]=Cf(i,n.shape,n.dtype,s,a);return[t.makeTensorInfo(p.shape,p.dtype,p.values),t.makeTensorInfo(u.shape,u.dtype,u.values)]}var dE={kernelName:Zn,backendName:"cpu",kernelFunc:h5};function g5(r){let{inputs:e,attrs:t,backend:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=t,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=y.computeStrides(n.shape),b=x[0],C=x[1],w=x[2],k=y.computeStrides(g),_=k[0],$=k[1],A=k[2],R=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(g));R.fill(p);let D=o.data.get(n.dataId).values,P=o.data.get(s.dataId).values;for(let L=0;Le-1)if(e<=1)t=0;else{let o=2*e;t-=o*Math.trunc(t/o),t>=e&&(t=o-t-1)}return y.clamp(0,t,e-1)}function y5(r,e){let t=r;if(t<0)if(e<=1)t=0;else{let o=e-1;t+=e*(Math.trunc(-t/o)+1)}else if(t>e-1)if(e<=1)t=0;else{let o=e-1;t-=e*Math.trunc(t/o)}return y.clamp(0,t,e-1)}function b5(r,e){return r}function C5(r,e){return y.clamp(0,r,e-1)}function bl(r,e,t,o,n,s,a,i,p,u,c){let l=a*o+i*n+p*s+u;return 0<=i&&it.disposeIntermediateTensorInfo(f)),d}var yE={kernelName:Np,backendName:"cpu",kernelFunc:k5};var N5=[GT,tT,HT,qT,aT,KT,jT,XT,YT,QT,ZT,JT,e2,t2,r2,n2,s2,a2,i2,UT,u2,p2,c2,l2,sT,iT,m2,rT,d2,h2,g2,x2,y2,b2,C2,S2,w2,I2,v2,k2,N2,T2,_2,E2,$2,A2,R2,F2,D2,O2,M2,MT,L2,uT,B2,pT,V2,cT,z2,W2,U2,lT,G2,H2,q2,K2,j2,mT,dT,oT,X2,f2,Y2,Q2,Z2,LT,fT,hT,J2,gT,e_,t_,r_,o_,n_,s_,a_,xT,i_,u_,p_,c_,m_,d_,f_,yT,h_,g_,b_,bT,CT,C_,S_,w_,ST,I_,N_,T_,kf,__,BT,IT,E_,$_,A_,R_,nT,gl,F_,VT,zT,WT,D_,O_,P_,M_,L_,B_,V_,_T,z_,U_,G_,H_,$T,q_,K_,j_,AT,x_,Y_,Q_,Z_,J_,eE,tE,rE,oE,FT,nE,DT,sE,aE,iE,uE,pE,OT,P2,cE,lE,mE,dE,hE,wT,gE,xE,yE,v_];for(let r of N5)Ia(r);var oc={};Ue(oc,{assertNotComplex:()=>is,bindCanvasToFramebuffer:()=>O5,bindColorTextureToFramebuffer:()=>Il,bindTextureToProgramUniformSampler:()=>KS,bindTextureUnit:()=>wE,bindVertexBufferToProgramAttribute:()=>Af,callAndCheck:()=>ce,canBeRepresented:()=>OS,createFragmentShader:()=>MS,createFramebuffer:()=>GS,createProgram:()=>LS,createStaticIndexBuffer:()=>zS,createStaticVertexBuffer:()=>VS,createTexture:()=>WS,createVertexShader:()=>PS,getBatchDim:()=>Va,getExtensionOrThrow:()=>ec,getFramebufferErrorMessage:()=>IE,getMaxTexturesInShader:()=>YS,getNumChannels:()=>F5,getProgramUniformLocation:()=>qS,getProgramUniformLocationOrThrow:()=>HS,getRowsCols:()=>za,getShapeAs3D:()=>rc,getTextureShapeFromLogicalShape:()=>jS,getWebGLDisjointQueryTimerVersion:()=>QS,getWebGLErrorMessage:()=>SE,getWebGLMaxTextureSize:()=>XS,hasExtension:()=>Ur,isCapableOfRenderingToFloatTexture:()=>ZS,isDownloadFloatTextureEnabled:()=>JS,isReshapeFree:()=>Li,isWebGLFenceEnabled:()=>ew,isWebGLVersionEnabled:()=>Ff,linkProgram:()=>BS,logShaderSourceAndInfoLog:()=>$f,resetMaxTextureSize:()=>P5,resetMaxTexturesInShader:()=>M5,unbindColorTextureFromFramebuffer:()=>Rf,unbindTextureUnit:()=>D5,validateFramebuffer:()=>tc,validateProgram:()=>wl,validateTextureSize:()=>US});var Eu={},Nf={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function RS(r,e){Eu[r]=e}function Wr(r,e){if(!(r in Eu)||e!=null){let o=_5(r,e);if(o!==null)Eu[r]=o;else return console.log("Could not get context for WebGL version",r),null}let t=Eu[r];return t==null||t.isContextLost()?(delete Eu[r],Wr(r)):(t.disable(t.DEPTH_TEST),t.disable(t.STENCIL_TEST),t.disable(t.BLEND),t.disable(t.DITHER),t.disable(t.POLYGON_OFFSET_FILL),t.disable(t.SAMPLE_COVERAGE),t.enable(t.SCISSOR_TEST),t.enable(t.CULL_FACE),t.cullFace(t.BACK),Eu[r])}function T5(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 _5(r,e){if(r!==1&&r!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=e==null?T5(r):e;return t.addEventListener("webglcontextlost",o=>{o.preventDefault(),delete Eu[r]},!1),O().getBool("SOFTWARE_WEBGL_ENABLED")&&(Nf.failIfMajorPerformanceCaveat=!1),r===1?t.getContext("webgl",Nf)||t.getContext("experimental-webgl",Nf):t.getContext("webgl2",Nf)}var Mi;(function(r){r[r.DENSE=0]="DENSE",r[r.SHARED_BATCH=1]="SHARED_BATCH"})(Mi||(Mi={}));var ir;(function(r){r[r.RENDER=0]="RENDER",r[r.UPLOAD=1]="UPLOAD",r[r.PIXELS=2]="PIXELS",r[r.DOWNLOAD=3]="DOWNLOAD"})(ir||(ir={}));var Zt;(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"})(Zt||(Zt={}));function $u(r,e){return[e,r]}function bE(r,e){return r*e}function Cl(r){let e=y.sizeFromShape(r),t=Math.ceil(e/4);return y.sizeToSquarishShape(t)}function Ys(r,e){return[Math.max(1,Math.ceil(e/2)),Math.max(1,Math.ceil(r/2))]}function CE(r,e){let[t,o]=Ys(r,e);return t*o*4}function Sl(r,e){let t=r,o,n,s,a,i,p,u,c,l,m;return O().getNumber("WEBGL_VERSION")===2?(o=t.R32F,n=t.R16F,s=t.RGBA16F,a=t.RGBA32F,i=t.RED,u=4,c=1,l=t.HALF_FLOAT,m=t.FLOAT,p=t.RGBA8):(o=r.RGBA,n=r.RGBA,s=r.RGBA,a=t.RGBA,i=r.RGBA,u=4,c=4,l=e!=null?e.HALF_FLOAT_OES:null,m=r.FLOAT,p=r.RGBA),{internalFormatFloat:o,internalFormatHalfFloat:n,internalFormatPackedHalfFloat:s,internalFormatPackedFloat:a,textureFormatFloat:i,downloadTextureFormat:p,downloadUnpackNumChannels:u,defaultNumChannels:c,textureTypeHalfFloat:l,textureTypeFloat:m}}function ce(r,e){let t=e();return O().getBool("DEBUG")&&E5(r),t}function E5(r){let e=r.getError();if(e!==r.NO_ERROR)throw new Error("WebGL Error: "+SE(r,e))}var $5=596e-10,A5=65504;function OS(r){return!!(O().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||r===0||$5r.getExtension(e),'Extension "'+e+'" not supported on this browser.')}function PS(r,e){let t=Ba(r,()=>r.createShader(r.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(ce(r,()=>r.shaderSource(t,e)),ce(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 MS(r,e){let t=Ba(r,()=>r.createShader(r.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(ce(r,()=>r.shaderSource(t,e)),ce(r,()=>r.compileShader(t)),O().get("ENGINE_COMPILE_ONLY"))return t;if(r.getShaderParameter(t,r.COMPILE_STATUS)===!1)throw $f(e,r.getShaderInfoLog(t)),new Error("Failed to compile fragment shader.");return t}var R5=/ERROR: [0-9]+:([0-9]+):/g;function $f(r,e){let t=R5.exec(e);if(t==null){console.log(`Couldn't parse line number in error: ${e}`),console.log(r);return}let o=+t[1],n=r.split(` `),s=n.length.toString().length+2,a=n.map((l,m)=>y.rightPad((m+1).toString(),s)+l),i=0;for(let l=0;lr.createProgram(),"Unable to create WebGLProgram.")}function BS(r,e){if(ce(r,()=>r.linkProgram(e)),!O().get("ENGINE_COMPILE_ONLY")&&r.getProgramParameter(e,r.LINK_STATUS)===!1)throw console.log(r.getProgramInfoLog(e)),new Error("Failed to link vertex and fragment shaders.")}function wl(r,e){if(ce(r,()=>r.validateProgram(e)),r.getProgramParameter(e,r.VALIDATE_STATUS)===!1)throw console.log(r.getProgramInfoLog(e)),new Error("Shader program validation failed.")}function VS(r,e){let t=Ba(r,()=>r.createBuffer(),"Unable to create WebGLBuffer");return ce(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),ce(r,()=>r.bufferData(r.ARRAY_BUFFER,e,r.STATIC_DRAW)),t}function zS(r,e){let t=Ba(r,()=>r.createBuffer(),"Unable to create WebGLBuffer");return ce(r,()=>r.bindBuffer(r.ELEMENT_ARRAY_BUFFER,t)),ce(r,()=>r.bufferData(r.ELEMENT_ARRAY_BUFFER,e,r.STATIC_DRAW)),t}function F5(){return O().getNumber("WEBGL_VERSION")===2?1:4}function WS(r){return Ba(r,()=>r.createTexture(),"Unable to create WebGLTexture.")}function US(r,e){let t=O().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(r<=0||e<=0){let o=`[${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 GS(r){return Ba(r,()=>r.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function Af(r,e,t,o,n,s,a){let i=r.getAttribLocation(e,t);return i===-1?!1:(ce(r,()=>r.bindBuffer(r.ARRAY_BUFFER,o)),ce(r,()=>r.vertexAttribPointer(i,n,r.FLOAT,!1,s,a)),ce(r,()=>r.enableVertexAttribArray(i)),!0)}function wE(r,e,t){vE(r,t),ce(r,()=>r.activeTexture(r.TEXTURE0+t)),ce(r,()=>r.bindTexture(r.TEXTURE_2D,e))}function D5(r,e){vE(r,e),ce(r,()=>r.activeTexture(r.TEXTURE0+e)),ce(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function HS(r,e,t){return Ba(r,()=>r.getUniformLocation(e,t),'uniform "'+t+'" not present in program.')}function qS(r,e,t){return r.getUniformLocation(e,t)}function KS(r,e,t,o){ce(r,()=>wE(r,e,o)),ce(r,()=>r.uniform1i(t,o))}function O5(r){ce(r,()=>r.bindFramebuffer(r.FRAMEBUFFER,null)),ce(r,()=>r.viewport(0,0,r.canvas.width,r.canvas.height)),ce(r,()=>r.scissor(0,0,r.canvas.width,r.canvas.height))}function Il(r,e,t){ce(r,()=>r.bindFramebuffer(r.FRAMEBUFFER,t)),ce(r,()=>r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,e,0))}function Rf(r,e){ce(r,()=>r.bindFramebuffer(r.FRAMEBUFFER,e)),ce(r,()=>r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,null,0))}function tc(r){let e=r.checkFramebufferStatus(r.FRAMEBUFFER);if(e!==r.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+IE(r,e))}function IE(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 ${e}`}}function Ba(r,e,t){let o=ce(r,()=>e());if(o==null)throw new Error(t);return o}function vE(r,e){let t=r.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,o=e+r.TEXTURE0;if(ot){let n=`[gl.TEXTURE0, gl.TEXTURE${t}]`;throw new Error(`textureUnit must be in ${n}.`)}}function Va(r,e=2){return y.sizeFromShape(r.slice(0,r.length-e))}function za(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 rc(r){let e=[1,1,1];return r.length===0||r.length===1&&r[0]===1||(e=[Va(r),...za(r)]),e}function jS(r,e=!1){let t=O().getNumber("WEBGL_MAX_TEXTURE_SIZE"),o=O().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE");o===1/0&&O().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE")&&(o=t/2),e&&(t=t*2,o=o*2,r=r.map((i,p)=>p>=r.length-2?y.nearestLargerEven(r[p]):r[p]),r.length===1&&(r=[2,r[0]])),r.length!==2&&(r=y.squeezeShape(r).newShape);let n=y.sizeFromShape(r),s=null;r.length<=1&&n<=t?s=[1,n]:r.length===2&&r[0]<=t&&r[1]<=t?s=r:r.length===3&&r[0]*r[1]<=t&&r[2]<=t?s=[r[0]*r[1],r[2]]:r.length===3&&r[0]<=t&&r[1]*r[2]<=t?s=[r[0],r[1]*r[2]]:r.length===4&&r[0]*r[1]*r[2]<=t&&r[3]<=t?s=[r[0]*r[1]*r[2],r[3]]:r.length===4&&r[0]<=t&&r[1]*r[2]*r[3]<=t&&(s=[r[0],r[1]*r[2]*r[3]]);let a=s!=null&&Math.max(...s)>o&&Math.min(...s)<=(e?2:1)&&Math.min(...s)>0;if(s==null||a)if(e){let i=Va(r),p=2,u=2;r.length&&([p,u]=za(r)),n=i*(p/2)*(u/2),s=y.sizeToSquarishShape(n).map(c=>c*2)}else s=y.sizeToSquarishShape(n);return s}function Tf(r){return r%2===0}function Li(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||Tf(t)&&Tf(o)&&(r[0]===1||e[0]===1))return!0}return r[1]===e[1]&&Tf(r[0])&&Tf(e[0])}var _f,Ef;function XS(r){if(_f==null){let e=Wr(r);_f=e.getParameter(e.MAX_TEXTURE_SIZE)}return _f}function P5(){_f=null}function M5(){Ef=null}function YS(r){if(Ef==null){let e=Wr(r);Ef=e.getParameter(e.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Ef)}function QS(r){if(r===0)return 0;let e,t=Wr(r);return Ur(t,"EXT_disjoint_timer_query_webgl2")&&r===2?e=2:Ur(t,"EXT_disjoint_timer_query")?e=1:e=0,e}function Ur(r,e){return r.getExtension(e)!=null}function Ff(r){try{if(Wr(r)!=null)return!0}catch(e){return console.log("Error when getting WebGL context: ",e),!1}return!1}function ZS(r){if(r===0)return!1;let e=Wr(r);if(r===1){if(!Ur(e,"OES_texture_float"))return!1}else if(!Ur(e,"EXT_color_buffer_float"))return!1;return DS(e)}function JS(r){if(r===0)return!1;let e=Wr(r);if(r===1){if(!Ur(e,"OES_texture_float")||!Ur(e,"WEBGL_color_buffer_float"))return!1}else{if(Ur(e,"EXT_color_buffer_float"))return DS(e);let o="EXT_color_buffer_half_float";if(Ur(e,o)){let n=e.getExtension(o);return L5(e,n)}return!1}return DS(e)}function DS(r){let e=Sl(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 L5(r,e){let t=Sl(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 ew(r){return r!==2?!1:Wr(r).fenceSync!=null}function is(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 Ce=O();Ce.registerFlag("HAS_WEBGL",()=>Ce.getNumber("WEBGL_VERSION")>0);Ce.registerFlag("WEBGL_VERSION",()=>Ff(2)?2:Ff(1)?1:0);Ce.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ce.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ce.get("WEBGL_VERSION")===2);Ce.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ce.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ce.registerFlag("WEBGL_PACK",()=>Ce.getBool("HAS_WEBGL"));Ce.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_CLIP",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_REDUCE",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_CONV_IM2COL",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>XS(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>YS(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let r=Ce.getNumber("WEBGL_VERSION");return r===0?0:QS(r)});Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ce.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!yi.isMobile());Ce.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>ZS(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ce.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ce.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ce.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>JS(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_FENCE_API_ENABLED",()=>ew(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ce.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ce.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}.`)});Ce.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>yi.isMobile()?1:-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});Ce.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Ce.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Ce.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Ce.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);Ce.registerFlag("WEBGL_EXP_CONV",()=>!1);Ce.registerFlag("SOFTWARE_WEBGL_ENABLED",()=>Ce.getBool("IS_TEST"));Ce.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE",()=>1/0);Ce.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE",()=>!1);Ce.registerFlag("WEBGL2_ISNAN_CUSTOM",()=>!1);Ce.registerFlag("ENGINE_COMPILE_ONLY",()=>!1);function St(){let r,e,t,o,n,s,a,i,p,u;return O().getNumber("WEBGL_VERSION")===2?(r="#version 300 es",e="in",t="out",o="in",n="texture",s="outputColor",a="out vec4 outputColor;",i=O().getBool("WEBGL2_ISNAN_CUSTOM")?` bool isnan_custom(float val) { uint floatToUint = floatBitsToUint(val); return (floatToUint & 0x7fffffffu) > 0x7f800000u; } 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) `:"",p="",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)); } `,p=` 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:p,defineRound:u}}function us(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 Au(r,e,t="index"){let o=y.computeStrides(e);return o.map((n,s)=>{let a=`int ${r[s]} = ${t} / outShapeStrides[${s}]`,i=s===o.length-1?`int ${r[s+1]} = ${t} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${a}; ${i};`}).join("")}function B5(r,e){let t=r.length,o=r.map(s=>`${e}[${s}]`),n=new Array(t-1);n[t-2]=o[t-1];for(let s=t-3;s>=0;--s)n[s]=`(${n[s+1]} * ${o[s+1]})`;return n}function kE(r,e,t="index"){let o=r.map((s,a)=>a),n=B5(o,e);return n.map((s,a)=>{let i=`int ${r[a]} = ${t} / ${n[a]}`,p=a===n.length-1?`int ${r[a+1]} = ${t} - ${r[a]} * ${n[a]}`:`index -= ${r[a]} * ${n[a]}`;return`${i}; ${p};`}).join("")}function nc(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; } `}function sc(){return` int getFlatIndex(ivec3 coords) { return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z; } `}var Df=` 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{getBroadcastDims:NE}=S;function TE(r,e,t){let o=[];if(r.forEach(d=>{let f=y.sizeFromShape(d.shapeInfo.logicalShape);if(d.shapeInfo.isUniform?o.push(`uniform float ${d.name}${f>1?`[${f}]`:""};`):(o.push(`uniform sampler2D ${d.name};`),o.push(`uniform int offset${d.name};`)),t.enableShapeUniforms){let{uniformShape:h}=Of(t.packedInputs,d.shapeInfo.logicalShape,d.shapeInfo.texShape);switch(h.length){case 1:o.push(`uniform int ${d.name}Shape;`);break;case 2:o.push(`uniform ivec2 ${d.name}Shape;`);break;case 3:o.push(`uniform ivec3 ${d.name}Shape;`);break;case 4:o.push(`uniform ivec4 ${d.name}Shape;`);break;default:break}o.push(`uniform ivec2 ${d.name}TexShape;`)}}),t.enableShapeUniforms){switch(e.logicalShape.length){case 1:o.push("uniform int outShape;");break;case 2:o.push("uniform ivec2 outShape;"),o.push("uniform int outShapeStrides;");break;case 3:o.push("uniform ivec3 outShape;"),o.push("uniform ivec2 outShapeStrides;");break;case 4:o.push("uniform ivec4 outShape;"),o.push("uniform ivec3 outShapeStrides;");break;default:break}o.push("uniform ivec2 outTexShape;")}t.customUniforms&&t.customUniforms.forEach(d=>{o.push(`uniform ${d.type} ${d.name}${d.arrayIndex?`[${d.arrayIndex}]`:""};`)});let n=o.join(` `),s=r.map(d=>V5(d,e,t.packedInputs,t.enableShapeUniforms)).join(` `),a=e.texShape,i=St(),p=U5(i),u,c,l=q5(i);return e.isPacked?(u=z5(e.logicalShape,a,t.enableShapeUniforms),c=H5(i)):(u=W5(e.logicalShape,a,t.enableShapeUniforms),c=G5(i)),t.packedInputs&&(l+=Y5),[l,p,c,n,u,s,t.userCode].join(` `)}function ic(r,e=!1){let t=r.shapeInfo.logicalShape;switch(t.length){case 0:return u8(r,e);case 1:return c8(r,e);case 2:return m8(r,e);case 3:return f8(r,e);case 4:return g8(r,e);case 5:return x8(r);case 6:return y8(r);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function _E(r,e){switch(r.shapeInfo.logicalShape.length){case 0:return i8(r);case 1:return p8(r,e);case 2:return l8(r,e);case 3:return d8(r,e);default:return h8(r,e)}}function V5(r,e,t=!1,o){let n="";t?n+=_E(r,o):n+=ic(r,o);let s=r.shapeInfo.logicalShape,a=e.logicalShape;return s.length<=a.length&&(t?n+=b8(r,e):n+=C8(r,e)),n}function z5(r,e,t){switch(r.length){case 0:return EE();case 1:return Q5(r,e,t);case 2:return s8(r,e,t);case 3:return J5(r,e,t);default:return t8(r,e,t)}}function W5(r,e,t){switch(r.length){case 0:return EE();case 1:return Z5(r,e,t);case 2:return a8(r,e,t);case 3:return e8(r,e,t);case 4:return r8(r,e,t);case 5:return o8(r,e);case 6:return n8(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function U5(r){return` float sampleTexture(sampler2D textureSampler, vec2 uv) { return ${r.texture2D}(textureSampler, uv).r; } `}function G5(r){return` void setOutput(float val) { ${r.output} = vec4(val, 0, 0, 0); } `}function H5(r){return` void setOutput(vec4 val) { ${r.output} = val; } `}function q5(r){return`${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); } ${K5} ${j5} ${X5} `}var K5=` 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); } `,j5=` 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); } `,X5=` 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); } `,Y5=` 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 EE(){return` int getOutputCoords() { return 0; } `}function Q5(r,e,t){let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return o[0]===1?t?` int getOutputCoords() { return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0)); } `:` int getOutputCoords() { return 2 * int(resultUV.x * ${o[1]}.0); } `:o[1]===1?t?` int getOutputCoords() { return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0)); } `:` int getOutputCoords() { return 2 * int(resultUV.y * ${o[0]}.0); } `:t?` int getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y); } `:` int getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${o[0]}, ${o[1]})); return 2 * (resTexRC.x * ${o[1]} + resTexRC.y); } `}function Z5(r,e,t){return e[0]===1?t?` int getOutputCoords() { return int(resultUV.x * float(outTexShape[1])); } `:` int getOutputCoords() { return int(resultUV.x * ${e[1]}.0); } `:e[1]===1?t?` int getOutputCoords() { return int(resultUV.y * float(outTexShape[0])); } `:` int getOutputCoords() { return int(resultUV.y * ${e[0]}.0); } `:t?` int getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); return resTexRC.x * outTexShape[1] + resTexRC.y; } `:` int getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]})); return resTexRC.x * ${e[1]} + resTexRC.y; } `}function J5(r,e,t){if(t)return` ivec3 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0)); int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); int index = resTexRC.x * packedTexShape[1] + resTexRC.y; int b = index / texelsInBatch; index -= b * texelsInBatch; int r = 2 * (index / texelsInLogicalRow); int c = imod(index, texelsInLogicalRow) * 2; return ivec3(b, r, c); } `;let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],n=Math.ceil(r[2]/2),s=n*Math.ceil(r[1]/2);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${o[0]}, ${o[1]})); int index = resTexRC.x * ${o[1]} + resTexRC.y; int b = index / ${s}; index -= b * ${s}; int r = 2 * (index / ${n}); int c = imod(index, ${n}) * 2; return ivec3(b, r, c); } `}function e8(r,e,t){if(t)return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1])); int index = resTexRC.x * outTexShape[1] + resTexRC.y; ${Au(["r","c","d"],r)} return ivec3(r, c, d); } `;let o=us(["r","c","d"],r);return` ivec3 getOutputCoords() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]})); int index = resTexRC.x * ${e[1]} + resTexRC.y; ${o} return ivec3(r, c, d); } `}function t8(r,e,t){if(t)return` ivec4 getOutputCoords() { ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0)); ivec2 resTexRC = ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1])); int index = resTexRC.x * packedTexShape[1] + resTexRC.y; int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0)); int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0)); int texelsInBatchN = texelsInBatch * outShape[1]; int b2 = index / texelsInBatchN; index -= b2 * texelsInBatchN; int b = index / texelsInBatch; index -= b * texelsInBatch; int r = 2 * (index / texelsInLogicalRow); int c = imod(index, texelsInLogicalRow) * 2; return ivec4(b2, b, r, c); } `;let o=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],n=Math.ceil(r[r.length-1]/2),s=n*Math.ceil(r[r.length-2]/2),a=s,i="",p="b, r, c";for(let u=2;u=1?c="coords = 0;":c=i.map(b=>`coords.${l[b+u]} = 0;`).join(` `);let m="";a<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,C)=>`coords.${l[C+u]}`).join(", ");let d="return outputValue;",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(e.logicalShape)===1;if(s===1&&!h&&!x)d=` return vec4(outputValue.xy, outputValue.xy); `;else if(h&&!x)a===1?d=` return vec4(outputValue.x, outputValue.x, 0., 0.); `:d=` return vec4(outputValue.x); `;else if(i.length){let b=s-2,C=s-1;i.indexOf(b)>-1&&i.indexOf(C)>-1?d="return vec4(outputValue.x);":i.indexOf(b)>-1?d="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(C)>-1&&(d="return vec4(outputValue.xx, outputValue.zz);")}return` vec4 ${n}() { ${p} coords = getOutputCoords(); ${c} vec4 outputValue = get${o}(${m}); ${d} } `}function C8(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,p=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===p&&r.shapeInfo.flatOffset==null&&y.arraysEqual(a,s))return` float ${n}() { return sampleTexture(${t}, resultUV); } `;let u=_e(p),c=NE(r.shapeInfo.logicalShape,e.logicalShape),l=p-i,m,d=["x","y","z","w","u","v"];i===0?m="":p<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${d[h+l]} = 0;`).join(` `);let f="";return p<2&&i>0?f="coords":f=r.shapeInfo.logicalShape.map((h,g)=>`coords.${d[g+l]}`).join(", "),` float ${n}() { ${u} coords = getOutputCoords(); ${m} return get${o}(${f}); } `}function _e(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 Of(r,e,t){let{newShape:o,keptDims:n}=y.squeezeShape(e),s=e.length,a=r&&s===3&&e[0]===1,i=a?e.slice(1):o,p=!r&&s>1&&!y.arraysEqual(e,t)&&o.lengthr[t]).join(", ")}function AE(r,e,t,o){let n=t.map((c,l)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:e.variableNames[l],shapeInfo:m}}),s=n.map(c=>c.shapeInfo),a={logicalShape:o.shape,texShape:o.texData.texShape,isUniform:!1,isPacked:o.texData.isPacked,flatOffset:null},i=TE(n,a,e),p=MS(r.gl,i),u=r.createProgram(p);return O().get("ENGINE_COMPILE_ONLY")?{program:e,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:e,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a},tw(r,e,u))}function tw(r,e,t){let o={},n={},s={},a=[],i,p,u,c=null,l=null;l=r.getUniformLocation(t,"NAN",!1),O().getNumber("WEBGL_VERSION")===1&&(c=r.getUniformLocation(t,"INFINITY",!1));let m=!1;for(let d=0;d{a[f]=r.getUniformLocation(t,d.name,m)}),{uniformLocations:o,customUniformLocations:a,infLoc:c,nanLoc:l,inShapesLocations:n,inTexShapesLocations:s,outShapeLocation:i,outShapeStridesLocation:u,outTexShapeLocation:p}}function $E(r,e){if(r.length!==e.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${e.length} inputs`);r.forEach((t,o)=>{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,p=s.isUniform?null:s.texData.texShape;if(!y.arraysEqual(i,p))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${p} must match`)})}function RE(r,e,t,o,n){e.program.enableShapeUniforms||($E(e.inShapeInfos,t),$E([e.outShapeInfo],[o]));let s=o.texData.texture,a=o.texData.texShape;o.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,a[0],a[1]):r.setOutputMatrixTexture(s.texture,a[0],a[1]),r.setProgram(e.webGLProgram),O().getNumber("WEBGL_VERSION")===1&&e.infLoc!==null&&r.gl.uniform1f(e.infLoc,1/0),e.nanLoc!==null&&r.gl.uniform1f(e.nanLoc,NaN),t.forEach((p,u)=>{let c=e.program.variableNames[u],l=e.uniformLocations[c],m=e.uniformLocations[`offset${c}`],d=e.inShapesLocations[`${c}Shape`],f=e.inTexShapesLocations[`${c}TexShape`];if(d){let{uniformShape:h}=Of(e.program.packedInputs,p.shape,p.texData.texShape);switch(h.length){case 1:r.gl.uniform1iv(d,new Int32Array(h));break;case 2:r.gl.uniform2iv(d,new Int32Array(h));break;case 3:r.gl.uniform3iv(d,new Int32Array(h));break;case 4:r.gl.uniform4iv(d,new Int32Array(h));break;default:break}}if(f&&r.gl.uniform2i(f,p.texData.texShape[0],p.texData.texShape[1]),l!=null){if(p.isUniform){if(y.sizeFromShape(p.shape)<2)r.gl.uniform1f(l,p.uniformValues[0]);else{let h=p.uniformValues;h instanceof Float32Array||(h=new Float32Array(h)),r.gl.uniform1fv(l,h)}return}p.texData.slice!=null&&m!=null&&r.gl.uniform1i(m,p.texData.slice.flatOffset),r.setInputMatrixTexture(p.texData.texture.texture,l,u)}});let i=e.outShapeLocation;if(i)switch(o.shape.length){case 1:r.gl.uniform1iv(i,new Int32Array(o.shape));break;case 2:r.gl.uniform2iv(i,new Int32Array(o.shape));break;case 3:r.gl.uniform3iv(i,new Int32Array(o.shape));break;case 4:r.gl.uniform4iv(i,new Int32Array(o.shape));break;default:break}if(e.outShapeStridesLocation){let p=y.computeStrides(o.shape);switch(o.shape.length){case 2:r.gl.uniform1iv(e.outShapeStridesLocation,new Int32Array(p));break;case 3:r.gl.uniform2iv(e.outShapeStridesLocation,new Int32Array(p));break;case 4:r.gl.uniform3iv(e.outShapeStridesLocation,new Int32Array(p));break;default:break}}e.outTexShapeLocation&&r.gl.uniform2i(e.outTexShapeLocation,o.texData.texShape[0],o.texData.texShape[1]),e.program.customUniforms&&n&&e.program.customUniforms.forEach((p,u)=>{let c=e.customUniformLocations[u],l=n[u];if(p.type==="float")r.gl.uniform1fv(c,l);else if(p.type==="vec2")r.gl.uniform2fv(c,l);else if(p.type==="vec3")r.gl.uniform3fv(c,l);else if(p.type==="vec4")r.gl.uniform4fv(c,l);else if(p.type==="int")r.gl.uniform1iv(c,l);else if(p.type==="ivec2")r.gl.uniform2iv(c,l);else if(p.type==="ivec3")r.gl.uniform3iv(c,l);else if(p.type==="ivec4")r.gl.uniform4iv(c,l);else throw Error(`uniform type ${p.type} is not supported yet.`)}),r.executeProgram()}function FE(r,e,t){let o="";e.concat(t).forEach(a=>{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!a.isUniform){let p=a.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:l}=Of(r.packedInputs,a.shape,p),m="",d="",f="";if(c.length===1&&r.packedInputs){let k=[Math.ceil(p[0]/2),Math.ceil(p[1]/2)];m=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!r.packedInputs)d=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let k=y.computeStrides(c);f=`${k[0]===p[1]}_${k[k.length-1]===p[1]}`}let h=a.shape.length,g=c.length===2&&y.arraysEqual(a.shape,p),x=y.sizeFromShape(a.shape)===1,b=S.getBroadcastDims(a.shape,t.shape),C=!r.packedInputs&&h===t.shape.length&&y.arraysEqual(p,t.texData.texShape),w=r.packedInputs||c.length>2?"":`${p[0]>1}_${p[1]>1}`;o+=`${h}_${C}_${u?l:""}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${w}_${i}`}else{let p=a.isUniform?"uniform":a.texData.texShape;o+=`${a.shape}_${p}_${i}`}});let n=r.userCode,s=r.constructor.name;return s+="_"+o+"_"+n+`${O().getNumber("WEBGL_VERSION")}`,s}function ct(r){return O().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&r<=4}var Pf=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Mi.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=St();this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { ${this.enableShapeUniforms?Au(["r","c","d"],e):us(["r","c","d"],e)} return ivec3(r, c, d); } void main() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1])); int index = 4 * (resTexRC.x * texShape[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); } ${t.output} = result; } `}};var Mf=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Mi.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=St();this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=` ivec3 outCoordsFromFlatIndex(int index) { ${this.enableShapeUniforms?Au(["r","c","d"],e):us(["r","c","d"],e)} return ivec3(r, c, d); } void main() { ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1])); int index = 4 * (resTexRC.x * texShape[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)); } ${t.output} = result; } `}};var Lf=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ir.DOWNLOAD;let t=St();this.outputShape=e,this.userCode=` ${Df} void main() { float x = getAAtOutCoords(); ${t.output} = encode_float(x); } `}};var Bf=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ir.DOWNLOAD;let t=St();this.outputShape=e,this.userCode=` ${Df} void main() { ivec3 coords = getOutputCoords(); float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z)); ${t.output} = encode_float(x); } `}};var I8={R:0,G:1,B:2,A:3},vl=class{constructor(e,t=!1,o="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=St();this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)");let a="";for(let i=0;icw,createBufferFromOutputTexture:()=>dw,createFloat16MatrixTexture:()=>aw,createFloat16PackedMatrixTexture:()=>pw,createFloat32MatrixTexture:()=>sw,createIndexBuffer:()=>nw,createPackedMatrixTexture:()=>uw,createUnsignedBytesMatrixTexture:()=>iw,createVertexBuffer:()=>ow,createVertexShader:()=>rw,downloadByteEncodedFloatMatrixFromOutputTexture:()=>hw,downloadFloat32MatrixFromBuffer:()=>fw,downloadMatrixFromPackedOutputTexture:()=>xw,downloadPackedMatrixFromBuffer:()=>gw,getInternalFormatForFloat16MatrixTexture:()=>Wf,getInternalFormatForFloat16PackedMatrixTexture:()=>Hf,getInternalFormatForFloat32MatrixTexture:()=>zf,getInternalFormatForPackedMatrixTexture:()=>Gf,getInternalFormatForUnsignedBytesMatrixTexture:()=>Uf,uploadDenseMatrixToTexture:()=>lw,uploadPixelDataToTexture:()=>mw});function rw(r){let e=St(),t=`${e.version} precision highp float; ${e.attribute} vec3 clipSpacePos; ${e.attribute} vec2 uv; ${e.varyingVs} vec2 resultUV; void main() { gl_Position = vec4(clipSpacePos, 1); resultUV = uv; }`;return PS(r,t)}function ow(r){let e=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return VS(r,e)}function nw(r){let e=new Uint16Array([0,1,2,2,1,3]);return zS(r,e)}function kl(r,e,t,o,n,s){US(e,t);let a=WS(r),i=r.TEXTURE_2D;return ce(r,()=>r.bindTexture(i,a)),ce(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),ce(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),ce(r,()=>r.texParameteri(i,r.TEXTURE_MIN_FILTER,r.NEAREST)),ce(r,()=>r.texParameteri(i,r.TEXTURE_MAG_FILTER,r.NEAREST)),O().getNumber("WEBGL_VERSION")===1?ce(r,()=>r.texImage2D(i,0,o,e,t,0,n,s,null)):ce(r,()=>r.texStorage2D(i,1,o,e,t)),ce(r,()=>r.bindTexture(r.TEXTURE_2D,null)),{texture:a,texShape:[t,e]}}function zf(r){return r.internalFormatFloat}function sw(r,e,t,o){let[n,s]=$u(e,t);return kl(r,n,s,zf(o),o.textureFormatFloat,r.FLOAT)}function Wf(r){return r.internalFormatHalfFloat}function aw(r,e,t,o){let[n,s]=$u(e,t);return kl(r,n,s,Wf(o),o.textureFormatFloat,o.textureTypeHalfFloat)}function Uf(r){return r.downloadTextureFormat}function iw(r,e,t,o){let[n,s]=$u(e,t);return kl(r,n,s,Uf(o),r.RGBA,r.UNSIGNED_BYTE)}function Gf(r){return r.internalFormatPackedFloat}function uw(r,e,t,o){let[n,s]=Ys(e,t);return kl(r,n,s,Gf(o),r.RGBA,r.FLOAT)}function Hf(r){return r.internalFormatPackedHalfFloat}function pw(r,e,t,o){let[n,s]=Ys(e,t);return kl(r,n,s,Hf(o),r.RGBA,o.textureTypeHalfFloat)}function cw(r,e,t){return ce(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),Af(r,e,"clipSpacePos",t,3,20,0)&&Af(r,e,"uv",t,2,20,12)}function lw(r,e,t,o,n,s){ce(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,p;n instanceof Uint8Array?(a=new Uint8Array(t*o*4),i=r.UNSIGNED_BYTE,p=r.RGBA):(a=new Float32Array(t*o*4),i=r.FLOAT,p=s.internalFormatPackedFloat),a.set(n),O().getNumber("WEBGL_VERSION")===2?ce(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,t,o,r.RGBA,i,a)):ce(r,()=>r.texImage2D(r.TEXTURE_2D,0,p,t,o,0,r.RGBA,i,a)),ce(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function mw(r,e,t){ce(r,()=>r.bindTexture(r.TEXTURE_2D,e)),t.data instanceof Uint8Array?O().getNumber("WEBGL_VERSION")===2?ce(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,t.width,t.height,r.RGBA,r.UNSIGNED_BYTE,t.data)):ce(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,t.width,t.height,0,r.RGBA,r.UNSIGNED_BYTE,t.data)):O().getNumber("WEBGL_VERSION")===2?ce(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,t)):ce(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,t)),ce(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function dw(r,e,t,o){let n=r.createBuffer();ce(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,n));let i=4*4*e*t;return ce(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),ce(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),ce(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),n}function fw(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 hw(r,e,t,o){let[n,s]=$u(e,t),a=4,i=new Uint8Array(bE(e*t,a));return ce(r,()=>r.readPixels(0,0,n,s,o.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function gw(r,e,t,o,n,s,a,i){let p=r,u=new Float32Array(CE(s,a));return p.bindBuffer(p.PIXEL_PACK_BUFFER,e),p.getBufferSubData(p.PIXEL_PACK_BUFFER,0,u),p.bindBuffer(p.PIXEL_PACK_BUFFER,null),u}function xw(r,e,t){let o=new Float32Array(e*t*4);return ce(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,o)),o}var Fu=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=O().getNumber("WEBGL_VERSION");if(e!=null?(this.gl=e,RS(t,e)):this.gl=Wr(t),e=this.gl,O().getNumber("WEBGL_VERSION")===2){let s=e;this.createVertexArray=()=>ce(s,()=>s.createVertexArray()),this.bindVertexArray=a=>ce(s,()=>s.bindVertexArray(a)),this.deleteVertexArray=a=>ce(s,()=>s.deleteVertexArray(a)),this.getVertexArray=()=>ce(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(e!=null){let s=e.getExtension("OES_vertex_array_object");if(s==null)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>ce(e,()=>s.createVertexArrayOES()),this.bindVertexArray=a=>ce(e,()=>s.bindVertexArrayOES(a)),this.deleteVertexArray=a=>ce(e,()=>s.deleteVertexArrayOES(a)),this.getVertexArray=()=>ce(e,()=>e.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let o="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),O().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=ec(this.gl,s),Ur(this.gl,a))this.textureHalfFloatExtension=ec(this.gl,a);else if(O().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),Ur(this.gl,n))this.colorBufferHalfFloatExtension=ec(this.gl,n);else if(O().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",Ur(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(Ur(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=ow(this.gl),this.indexBuffer=nw(this.gl),this.framebuffer=GS(this.gl),this.textureConfig=Sl(this.gl,this.textureHalfFloatExtension)}get debug(){return O().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;ce(e,()=>e.finish()),ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ce(e,()=>e.deleteFramebuffer(this.framebuffer)),ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ce(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),sw(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),aw(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),iw(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),mw(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,o,n){this.throwIfDisposed(),lw(this.gl,e,t,o,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),pw(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),uw(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Rf(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,o){return this.downloadMatrixDriver(e,()=>hw(this.gl,t,o,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,o,n,s,a){return gw(this.gl,e,t,o,n,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return fw(this.gl,e,t)}createBufferFromTexture(e,t,o){this.bindTextureToFrameBuffer(e);let n=dw(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(O().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 O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),o=()=>this.isQueryAvailable(t,O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):o=()=>!0;return{query:t,isFencePassed:o}}downloadMatrixFromPackedTexture(e,t,o){return this.downloadMatrixDriver(e,()=>xw(this.gl,t,o))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=rw(t));let o=LS(t);ce(t,()=>t.attachShader(o,this.vertexShader)),ce(t,()=>t.attachShader(o,e)),BS(t,o);let n;return n=Object.assign(o,{vao:this.createVertexArray()}),this.bindVertexArray(n.vao),ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),console.assert(cw(t,n,this.vertexBuffer),"gpgpu_util.bindVertexProgramAttributeStreams not fully successful."),this.debug&&wl(t,n),this.setProgram(n),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&(this.bindVertexArray(this.program.vao),this.debug&&wl(this.gl,this.program)),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?HS(this.gl,e,t):qS(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(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(),KS(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Ys(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&&wl(this.gl,this.program),tc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}ce(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ce(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=ec(this.gl,O().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(O().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(O().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,O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,O().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=v8(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){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let o;"setTimeoutCustom"in O().platform&&(o=O().platform.setTimeoutCustom.bind(O().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,o)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Il(this.gl,e,this.framebuffer),this.debug&&tc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Il(this.gl,this.outputTexture,this.framebuffer),this.debug&&tc(this.gl)):Rf(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;Il(n,e,this.framebuffer),this.debug&&tc(n),this.outputTexture=e,ce(n,()=>n.viewport(0,0,t,o)),ce(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),ce(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 v8(r){let e=0;for(;e`${r}.${t}`)}function $t(r,e){return e===1?[r]:bw(r,e)}function I$(r,e){if(r===1)return"rc";let t="";for(let o=0;o ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let o=this.rank-2;o= ${this.enableShapeUniforms?`outShape[${o}]`:this.outputShape[o]}`,o= ${o}; bool rEdge = rp1 >= ${n}; `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}), cEdge ? 0. : getA(${t[1]}), rEdge ? 0. : getA(${t[2]}), rEdge || cEdge ? 0. : getA(${t[3]})`}};var lc=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length);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=` ${k8(t,this.enableShapeUniforms)} ${this.enableShapeUniforms?sc():nc(e)} void main() { ivec3 rc = getOutputCoords(); vec4 result = vec4(0.); ivec3 thisRC; int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]}; int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]}; ${o} setOutput(result); } `}};function k8(r,e){return` ivec3 inputCoordsFromReshapedOutCoords(int index) { ${e?kE(["r","c","d"],"inputShape"):us(["r","c","d"],r)} return ivec3(r, c, d); } `}var Yf=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,o){let n=k$(t,o),s=N$(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=v$(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 p=this.freeTextures[s].shift();return this.usedTextures[s].push(p),p}let i;return n===Zt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===Zt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===Zt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===Zt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===Zt.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=k$(o,n),a=N$(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=v$(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=O().get("WEBGL_DELETE_TEXTURE_THRESHOLD");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function N8(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;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function v$(r,e,t,o,n){let s=T8(e,o),a;if(n){let[p,u]=Ys(r[0],r[1]);a=p*u}else{let[p,u]=$u(r[0],r[1]);a=p*u}let i=N8(t,s);return a*i}function T8(r,e){switch(r){case Zt.PACKED_2X2_FLOAT32:return Gf(e);case Zt.PACKED_2X2_FLOAT16:return Hf(e);case Zt.UNPACKED_FLOAT32:return zf(e);case Zt.UNPACKED_FLOAT16:return Wf(e);case Zt.PACKED_4X1_UNSIGNED_BYTE:return Uf(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function _8(r){return O().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?Zt.PACKED_2X2_FLOAT32:Zt.UNPACKED_FLOAT32:r?Zt.PACKED_2X2_FLOAT16:Zt.UNPACKED_FLOAT16}function k$(r,e){if(r===ir.UPLOAD)return Zt.PACKED_2X2_FLOAT32;if(r===ir.RENDER||r==null)return _8(e);if(r===ir.DOWNLOAD||r===ir.PIXELS)return Zt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function N$(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var Jt=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } void main() { float x = getAAtOutCoords(); float y = unaryOperation(x); setOutput(y); } `}},Bt="if (isnan(x)) return x;",T$="return x;",Cw="return abs(x);";var _$="return (x >= 0.0) ? x : (exp(x) - 1.0);",E$=Bt+` return (x < 0.0) ? 0.0 : x; `,$$=Bt+` return (x < 0.0) ? 0.0 : min(6.0, x); `,Qs="return x;",A$="return 1.0 / (1.0 + exp(-1.0 * x));";var F$="return x;",D$=` 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; `,O$=` 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; `,P$=` 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; `,M$="return 1.0 / (1.0 + exp(-1.0 * x));",Ar=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } void main() { vec4 x = getAAtOutCoords(); vec4 y = unaryOperation(x); setOutput(y); } `}};var Qf=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ct(this.outputShape.length);let t=e.length,o=$t("rc",t),n=_e(t),s=I$(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 $8=Lt.whereImpl,A8=1e-7,R8=1e-4,Zf={};function F8(r){return r in Zf||(Zf[r]={}),Zf[r]}var D8=O().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),O8=600;function P8(){return O().global.screen==null?1024:O().global.screen.height*O().global.screen.width*window.devicePixelRatio*O8/1024/1024}var Bi=class extends Zr{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!O().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Fu)t=e;else{let o=Wr(O().getNumber("WEBGL_VERSION"),e);t=new Fu(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Wr(O().getNumber("WEBGL_VERSION"));t=new Fu(o),this.binaryCache=F8(O().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Yf(this.gpgpu),this.numMBBeforeWarning=P8(),this.texData=new Do(this,cr())}nextDataId(){return Bi.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,o,n,s,a){let i=this.makeTensorInfo(t,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:e,texShape:[n,s]},p.texShape=[n,s];let u=rc(t),c=new vl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=t,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(e,t,o){if((O().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||O().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:ir.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(O().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:ir.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:p}=t;if(a!=null){let m;p?m=new Ar(i,Qs):m=new Jt(i,Qs);let d=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n==="complex64"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=S.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let f=this.pendingRead.get(e);return new Promise(h=>f.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=t;if(s!=null){let f;p?f=new Ar(n,Qs):f=new Jt(n,Qs);let h=this.runWebGLProgram(f,[{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(O().getBool("DEBUG")&&!O().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&O().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"&&O().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...Cl(n))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let l;if(a==="complex64"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=S.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(e);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(f=>f(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&cr().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){let o=this.texData.get(e),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;p?d=new Ar(s,Qs):d=new Jt(s,Qs);let f=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:i}],i),h=this.readToGPU(f,t);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(e,t.customTexShape),l=cr().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return le(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}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(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.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 O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(O().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),p=i&&i.origDataId||e,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),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.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=D8){return O().getBool("WEBGL_CPU_FORWARD")&&e.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.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){return cr().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,o),this)}unpackTensor(e){let t=new Qf(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Xf(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[Va(e.shape),...za(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[Va(t),...za(t)],a=new lc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],e.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let o=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=o;if(t!=null){let m=y.sizeFromShape(s),d=t[0]*t[1]*4;y.assert(m<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=rc(s),p;n?p=new Mf(i):p=new Pf(i);let u=!0,c=[t!=null?t:Cl(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:e}],a,c,u,t);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,o,n,s=!1,a){let i=this.makeTensorInfo(e.outputShape,o),p=this.texData.get(i.dataId);if(e.packedOutput&&(p.isPacked=!0),e.outPackingScheme===Mi.DENSE){let x=a!=null?a:Cl(e.outputShape);p.texShape=x.map(b=>b*2)}if(e.outTexUsage!=null&&(p.usage=e.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=t.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!e.packedInputs&&y.sizeFromShape(x.shape)<=O().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!e.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Li(b.shape,x.shape)){let C=x,w=x.shape;x.shape=b.shape,x=this.packedReshape(x,w),u.push(x),b=this.texData.get(x.dataId),C.shape=w}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=FE(e,c,l),d=this.getAndSaveBinary(m,()=>AE(this.gpgpu,e,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),O().get("ENGINE_COMPILE_ONLY")||RE(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let g=O().get("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!O().getBool("WEBGL_LAZILY_UNPACK")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}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||(O().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=Ee(()=>{if(!O().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=O().getBool("DEBUG");O().set("DEBUG",!1);let t=this.abs(be(1e-8)).dataSync()[0];if(O().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?A8:R8}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=t.texShape;if(l==null&&(l=jS(o,p),t.texShape=l),s!=null){let m=rc(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Ys(l[0],l[1])),p?d=new Vf(m,g):d=new vl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=ir.PIXELS:C.usage=ir.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let w=[[h,f]],k=!0,_=this.runWebGLProgram(d,[b],n,w,k),$=this.texData.get(_.dataId);t.texShape=$.texShape,t.isPacked=$.isPacked,t.usage=$.usage,O().get("ENGINE_COMPILE_ONLY")?this.disposeData(_.dataId):(t.texture=$.texture,t.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return t!=null&&(o.values=M8(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)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(s){throw s}});e.push(o)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await CC(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?($f(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:o,infLoc:n,nanLoc:s,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:p,outShapeStridesLocation:u,outTexShapeLocation:c}=tw(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=o,e.infLoc=n,e.nanLoc=s,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=p,e.outShapeStridesLocation=u,e.outTexShapeLocation=c}}createTensorFromTexture(e,t,o){let{texture:n,height:s,width:a,channels:i}=e,p=cr().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=p.writeTexture(n,t,o,s,a,i);return cr().makeTensorFromDataId(u,t,o,p)}};Bi.nextDataId=0;function M8(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 Bi,2);var L9e={forceHalfFloat:L$};var mc=` if (isnan(a)) return a; if (isnan(b)) return b; `;var io=class{constructor(e,t,o){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.enableShapeUniforms=ct(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${e} } void main() { float a = getAAtOutCoords(); float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } `}};var Zs=` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; `;var To=class{constructor(e,t,o,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length;this.enableShapeUniforms=ct(s);let a="";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=` result.y = 0.; result.z = 0.; result.w = 0.; `;else if(a=` ${_e(s)} coords = getOutputCoords(); `,s===1)this.enableShapeUniforms?a+=` result.y = (coords + 1) >= outShape ? 0. : result.y; result.z = 0.; result.w = 0.; `:a+=` result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; `;else{let p=$t("coords",s);this.enableShapeUniforms?a+=` bool nextRowOutOfBounds = (${p[s-2]} + 1) >= outShape[${s} - 2]; bool nextColOutOfBounds = (${p[s-1]} + 1) >= outShape[${s} - 1]; result.y = nextColOutOfBounds ? 0. : result.y; result.z = nextRowOutOfBounds ? 0. : result.z; result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; `:a+=` bool nextRowOutOfBounds = (${p[s-2]} + 1) >= ${this.outputShape[s-2]}; bool nextColOutOfBounds = (${p[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 At(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 B$={kernelName:mo,backendName:"webgl",kernelFunc:At};function Rr(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=At({inputs:{x:o},backend:t}),p=At({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var V$={kernelName:ei,backendName:"webgl",kernelFunc:Rr};var Sw="return (a < 0.) ? b * a : a;",ww=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function B8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),i=O().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new To(ww,n.shape,a.shape):new io(Sw,n.shape,a.shape),p=t.runWebGLProgram(i,[n,a],"float32");return t.disposeIntermediateTensorInfo(a),p}var z$={kernelName:mn,backendName:"webgl",kernelFunc:B8};var Iw="return (a < 0.) ? b * a : a;",vw=` vec4 aLessThanZero = vec4(lessThan(a, vec4(0.))); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function V8(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=O().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new To(vw,o.shape,n.shape):new io(Iw,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],"float32")}var W$={kernelName:Rn,backendName:"webgl",kernelFunc:V8};var _o="if (isnan(x)) return x;";function ge({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.texData.get(a.dataId),m=t(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=O().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new Ar(a.shape,e):c=new Jt(a.shape,r),i.runWebGLProgram(c,[a],p)}}function tt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype==="complex64"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[w,k]=C,_={dataId:w.dataId,dtype:w.dtype,shape:p.shape},$={dataId:k.dataId,dtype:k.dtype,shape:u.shape},A=new io(r,p.shape,u.shape);return c.runWebGLProgram(A,[_,$],dt(w.dtype,k.dtype))}),b=Rr({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype==="string"?S.fromUint8ToStringArray(f):f,x=p.dtype==="string"?S.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),w=c.makeTensorInfo(C,l),k=c.texData.get(w.dataId);return k.values=b,w}let m=O().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,d;return m?d=new To(e,p.shape,u.shape,t):d=new io(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function Wa(r,e=!1){if(r==="linear")return e?F$:T$;if(r==="relu")return e?O$:E$;if(r==="elu")return e?D$:_$;if(r==="relu6")return e?P$:$$;if(r==="prelu")return e?vw:Iw;if(r==="leakyrelu")return e?ww:Sw;if(r==="sigmoid")return e?M$:A$;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var dc=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=ct(this.outputShape.length);let c=n?e[1]:e[2],l=Math.ceil(c/2),m=n?"i * 2, rc.y":"rc.y, i * 2",d=s?"rc.z, i * 2":"i * 2, rc.z",f=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";i&&(p?g=`vec4 activation(vec4 a) { vec4 b = getPreluActivationWeightsAtOutCoords(); ${i} }`:u?g=`vec4 activation(vec4 a) { vec4 b = getLeakyreluAlphaAtOutCoords(); ${i} }`:g=`vec4 activation(vec4 x) { ${i} }`,x="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let C="rc.x",w="rc.x";e[0]`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!Li(n.shape,p)&&!(c.texture!==null&&Li(c.shape,p))?H$(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var q$={kernelName:Ns,backendName:"webgl",kernelFunc:te};var _l=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,p=o%4,u="sumValue += dot(values, ones);";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, 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 + ${i}; if (${p===1}) { vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0); ${u} } else if (${p===2}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), 0.0, 0.0); ${u} } else if (${p===3}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), 0.0); ${u} } setOutput(sumValue); } `}};var Jf=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i="0.0",p="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",p="min"):t==="max"&&(i="-1.0 / 1e-20",p="max");let u=`${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,l=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 = ${p}(values, minMaxValue); if (${t==="min"} || ${t==="max"}) { minMaxValue = ${p}(values, minMaxValue); bvec4 isNaN = isnan(values); if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) { minMaxValue = vec4(NAN); } } } `,d="vec4";t==="all"?(i="1.0",m=` bool reducedAllValue = all(values); float floatedReducedAllValue = float(reducedAllValue); allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0); `,d="bvec4"):t==="any"&&(i="0.0",m=` bool reducedAnyValue = any(values); float floatedReducedAnyValue = float(reducedAnyValue); anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0); `,d="bvec4");let f="";s%o>0&&(f=` 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) { ${f} 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; ${d} values = ${d}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); ${m} } int inIdx = inOffset + ${c}; if (${l===1}) { ${d} values = ${d}( getValue(batch, inIdx), initializationValue, initializationValue, initializationValue ); ${m} } else if (${l===2}) { ${d} values = ${d}( getValue(batch, inIdx), getValue(batch, inIdx + 1), initializationValue, initializationValue ); ${m} } else if (${l===3}) { ${d} values = ${d}( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), initializationValue ); ${m} } setOutput(${u}); } `}};function W8(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=S.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function Gr(r,e,t,o){let n=W8(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 ${this.rank} is not yet supported.`);let n=_e(this.rank),s=bw("rc",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],$=te({inputs:{x:r},backend:n,attrs:{shape:k}}),A=te({inputs:{x:e},backend:n,attrs:{shape:_}}),R=[$,A],D=Math.max(x,b),P=t?$.shape[1]:$.shape[2],M=s!=null,L=a!=null,W=p==="leakyrelu",V=p!=null?Wa(p,!0):null,U=M||L||W||V!=null,q;if((d===1||f===1)&&P>Nw&&U===!1){let j=$,X=A;t&&(j=xt({inputs:{x:$},backend:n,attrs:{perm:[0,2,1]}}),R.push(j)),o&&(X=xt({inputs:{x:A},backend:n,attrs:{perm:[0,2,1]}}),R.push(X));let Z=f!==1,ee=f===1,Y=j;Z&&(Y=te({inputs:{x:j},backend:n,attrs:{shape:[D,P,1]}}),R.push(Y));let J=f===1?2:1,ie=X;ee&&(ie=te({inputs:{x:X},backend:n,attrs:{shape:[D,1,P]}}),R.push(ie));let pe=Tl({inputs:{a:Y,b:ie},backend:n});q=Ou({inputs:{x:pe},backend:n,attrs:{axis:J,keepDims:!0}}),R.push(pe)}else{let j=dt(r.dtype,e.dtype),X=new dc(k,_,[D,d,f],t,o,M,V,L,W),Z=[$,A];if(s!=null&&Z.push(s),L&&Z.push(a),W){let ee=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));Z.push(ee),R.push(ee)}q=n.runWebGLProgram(X,Z,j)}let H=te({inputs:{x:q},backend:n,attrs:{shape:w}});R.push(q);for(let j of R)n.disposeIntermediateTensorInfo(j);return H}function G8(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Pu({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var Y$={kernelName:fo,backendName:"webgl",kernelFunc:G8};var Q$="return abs(x);";function H8(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=Kf(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return O().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Ar(o.shape,Q$):n=new Jt(o.shape,Q$),t.runWebGLProgram(n,[o],o.dtype)}var Z$={kernelName:gs,backendName:"webgl",kernelFunc:H8};var q8=Bt+` if (abs(x) > 1.) { return NAN; } return acos(x); `,K8=ge({opSnippet:q8}),J$={kernelName:sa,backendName:"webgl",kernelFunc:K8};var j8=Bt+` if (x < 1.0) return NAN; return log(x + sqrt(x * x - 1.0));`,X8=ge({opSnippet:j8}),eA={kernelName:aa,backendName:"webgl",kernelFunc:X8};var tA="return a + b;",Y8=tt({opSnippet:tA,packedOpSnippet:tA,supportsComplex:!0,cpuKernelImpl:DE}),rA={kernelName:eo,backendName:"webgl",kernelFunc:Y8};var rh=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 = ${n}; setOutput(result); } `}};var oh=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${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 nh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return At({inputs:{x:o[0]},backend:t});if(o.length>O().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let p=Math.floor(o.length/2),u=nh({inputs:o.slice(0,p),backend:t}),c=nh({inputs:o.slice(p),backend:t});return nh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=O().getBool("WEBGL_PACK")?new oh(o[0].shape,s):new rh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var oA={kernelName:Mo,backendName:"webgl",kernelFunc:nh};function Q8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=n;c!=null&&(l=xt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims("all",u,i);let[m,d]=S.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Gr(h,h.dtype,"all",t),x;if(a){let b=S.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var nA={kernelName:Lo,backendName:"webgl",kernelFunc:Q8};function Z8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=n;c!=null&&(l=xt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims("any",u,i);let[m,d]=S.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Gr(h,h.dtype,"any",t),x;if(a){let b=S.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var sA={kernelName:Bo,backendName:"webgl",kernelFunc:Z8};var sh=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"?">":"<",p=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 = ${p}; float candidate = getA(batch, inIdx); if (candidate ${i} bestValue) { bestValue = candidate; bestIndex = inIdx; } } setOutput(float(bestIndex)); } `}};var ah=class{constructor(e,t,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${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,p=i.length,u=_e(p),c=$t("coords",p),l,m;if(a===1){m=p+1;let A=_e(m);l=` ${A} sourceLocR = ${A}(${c.join()}, 0); ++${c[p-1]}; ${A} sourceLocG = ${A}(${c.join()}, 0); ++${c[p-2]}; ${A} sourceLocA = ${A}(${c.join()}, 0); --${c[p-1]}; ${A} sourceLocB = ${A}(${c.join()}, 0); --${c[p-2]};`}else m=p,l=` ${u} sourceLocR = coords; ++${c[p-1]}; ${u} sourceLocG = coords; ++${c[p-2]}; ${u} sourceLocA = coords; --${c[p-1]}; ${u} sourceLocB = coords; --${c[p-2]};`;let d=["x","y","z","w","u","v"].slice(0,m),f="."+d[m-1],h=d.map(A=>"int "+A),g=$t("sourceLocR",m-1).concat("inIdx.r"),x=$t("sourceLocG",m-1).concat("inIdx.g"),b=$t("sourceLocB",m-1).concat("inIdx.b"),C=$t("sourceLocA",m-1).concat("inIdx.a"),w=o==="max"?"greaterThan":"lessThan",k=n?"":` inIdx = round(vec4(getBestIndicesAChannel(${g.join()}), getBestIndicesAChannel(${x.join()}), getBestIndicesAChannel(${b.join()}), getBestIndicesAChannel(${C.join()})));`,_=`vec4( getAChannel(${g.join()}), hasNextCol ? getAChannel(${x.join()}) : 0., hasNextRow ? getAChannel(${b.join()}) : 0., hasNextRow && hasNextCol ? getAChannel(${C.join()}) : 0.)`,$=n?"":` float getBestIndicesAChannel(${h.join()}) { return getChannel(getBestIndicesA(${d.join()}), vec2(${d.slice(-2).join()})); }`;this.userCode=` float getAChannel(${h.join()}) { return getChannel(getA(${d.join()}), vec2(${d.slice(-2).join()})); } ${$} void main() { ${u} coords = getOutputCoords(); bool hasNextCol = ${c[p-1]} < ${i[p-1]-1}; bool hasNextRow = ${c[p-2]} < ${i[p-2]-1}; ${l} ivec4 srcIdx = ivec4(sourceLocR${f}, sourceLocG${f}, sourceLocB${f}, sourceLocA${f}) * ${t}; ivec4 inIdx = srcIdx; vec4 bestIndex = vec4(inIdx); vec4 bestValue = ${_}; for (int i = 0; i < ${t}; i++) { inIdx = srcIdx; ${k} 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 aA(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=S.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},p=new sh(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(p,u,"int32");if(c.shape[1]===1)return c;let l=aA(r,e,t,c);return r.disposeIntermediateTensorInfo(c),l}function iA(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=S.computeOptimalWindowSize(s),i=new ah(n,a,t,o==null),p=o==null?[e]:[e,o],u=r.runWebGLProgram(i,p,"int32");if(u.shape.length===e.shape.length){let c=iA(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function ih(r,e,t,o){let n=[t];if(S.assertAxesAreInnerMostDims("arg"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!O().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],a=r.texData.get(e.dataId),i=a!==null&&a.isPacked,p=e;i&&(p=r.unpackTensor(e),s.push(p));let[u,c]=S.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=te({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=aA(r,m,o);s.push(d);let f=te({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return iA(r,e,o)}function J8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims("argMax",[a[0]],p.shape.length);let c=ih(t,p,a[0],"max");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var uA={kernelName:Vo,backendName:"webgl",kernelFunc:J8};function eY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims("argMin",[a[0]],p.shape.length);let c=ih(t,p,a[0],"min");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var pA={kernelName:Za,backendName:"webgl",kernelFunc:eY};var tY=Bt+` if (abs(x) > 1.) { return NAN; } return asin(x); `,rY=ge({opSnippet:tY}),cA={kernelName:ia,backendName:"webgl",kernelFunc:rY};var oY=Bt+"return log(x + sqrt(x * x + 1.0));",nY=ge({opSnippet:oY}),lA={kernelName:ua,backendName:"webgl",kernelFunc:nY};var sY=Bt+` return atan(x); `,aY=ge({opSnippet:sY}),mA={kernelName:pa,backendName:"webgl",kernelFunc:aY};var iY=mc+` return atan(a, b); `,uY=` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); `+Zs+` return result; `,pY=tt({opSnippet:iY,packedOpSnippet:uY}),dA={kernelName:la,backendName:"webgl",kernelFunc:pY};var cY=Bt+` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,lY=ge({opSnippet:cY}),fA={kernelName:ca,backendName:"webgl",kernelFunc:lY};var ps=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,p=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=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) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),o){let A=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${p}); const ivec2 pads = ivec2(${d}, ${f}); 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 < ${l}; 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 ${A} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`}; } } } setOutput(float(minMaxPosition)); } `;return}let C="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let k=Math.floor(a/4)*4,_=a%4,$=` if (${h}) { avgValue += dot(values, ones); } else { minMaxValue = ${C}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${i}, ${p}); const ivec2 pads = ivec2(${d}, ${f}); 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 < ${l}; wR += ${u}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${k}; 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) ); ${$} } int xC = xCCorner + ${k}; if (${_===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, initializationValue, initializationValue ); ${$} } else if (${_===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${c}, d), initializationValue, initializationValue ); ${$} } 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 ); ${$} } } setOutput(${w}); } `}},zi=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let C=t==="avg",w="0.0";if(C||(w="-1.0 / 1e-20"),o){let D=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${p}, ${u}); const ivec3 pads = ivec3(${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 < ${d}; wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${f}; wR += ${l}) { 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 ${D} 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 * ${f} * ${h} + wR * ${h} + wC`}; } } } } setOutput(float(minMaxPosition)); } `;return}let k="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let $=Math.floor(a/4)*4,A=a%4,R=` if (${C}) { avgValue += dot(values, ones); } else { minMaxValue = ${k}(values, minMaxValue); } `;this.userCode=` const ivec3 strides = ivec3(${i}, ${p}, ${u}); const ivec3 pads = ivec3(${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 < ${d}; wD += ${c}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${f}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${$}; 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) ); ${R} } int xC = xCCorner + ${$}; if (${A===1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), initializationValue, initializationValue, initializationValue ); ${R} } else if (${A===2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${m}, ch), initializationValue, initializationValue ); ${R} } else if (${A===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 ); ${R} } } setOutput(${_}); } } `}};function mY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;is(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:t});let l=new ps(c,"avg",!1);return t.runWebGLProgram(l,[n],"float32")}var hA={kernelName:zo,backendName:"webgl",kernelFunc:mY};function dY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=S.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new zi(l,"avg",!1);return t.runWebGLProgram(m,[n],"float32")}var gA={kernelName:ip,backendName:"webgl",kernelFunc:dY};var uh=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,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=p-1-e.padInfo.top,l=u-1-e.padInfo.left,m=1/(t*o);this.userCode=` const ivec2 pads = ivec2(${c}, ${l}); const float avgMultiplier = float(${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 < ${p}; 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 >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); dotProd += dyValue * avgMultiplier; } } setOutput(dotProd); } `}},ph=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterDepth,m=e.effectiveFilterHeight,d=e.effectiveFilterWidth,f=l-1-e.padInfo.front,h=m-1-e.padInfo.top,g=d-1-e.padInfo.left,x=1/(t*o*n);this.userCode=` const ivec3 pads = ivec3(${f}, ${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 < ${l}; wD += ${p}) { 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 < ${d}; 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 fY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=S.computePool3DInfo(a.shape,i,p,l,u,c),d=new ph(m);return t.runWebGLProgram(d,[n],a.dtype)}var xA={kernelName:Im,backendName:"webgl",kernelFunc:fY};function hY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;is([n,s],"avgPoolGrad");let{filterSize:i,strides:p,pad:u}=o,c=S.computePool2DInfo(a.shape,i,p,1,u),l=new uh(c);return t.runWebGLProgram(l,[n],a.dtype)}var yA={kernelName:wm,backendName:"webgl",kernelFunc:hY};function gY(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Pu({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var bA={kernelName:Wo,backendName:"webgl",kernelFunc:gY};var ch=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,o);let i="0.0";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let p="1.0";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); float variance = getVarianceAtOutCoords(); float offset = ${i}; float scale = ${p}; float inv = scale * inversesqrt(variance + float(${a})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } `}};var lh=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,o);let i="vec4(0.0)";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let p="vec4(1.0)";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${p}; vec4 x = getXAtOutCoords(); vec4 mean = getMeanAtOutCoords(); vec4 variance = getVarianceAtOutCoords(); vec4 inv = scale * inversesqrt(variance + vec4(${a})); setOutput((x - mean) * inv + offset); } `}};var xY=({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:p}=t;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=O().getBool("WEBGL_PACK_NORMALIZATION")?new lh(o.shape,n.shape,s.shape,c,l,p):new ch(o.shape,n.shape,s.shape,c,l,p);return e.runWebGLProgram(m,u,u[0].dtype)},CA={kernelName:an,backendName:"webgl",kernelFunc:xY};var mh=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=_e(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let o=yY(this.rank),n,s=e.map((a,i)=>`sourceLoc.${Tw[i]} = start[${i}] + coords.${Tw[i]};`);n=` ${t} sourceLoc; ${t} coords = getOutputCoords(); ${s.join(` `)} `,this.userCode=` void main() { ${n} setOutput(getSource(${o})); } `}},Tw=["x","y","z","w","u","v"];function yY(r){if(r===1)return"sourceLoc";if(r<=6)return Tw.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var dh=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=_e(this.rank),o=$t("coords",this.rank),n=$t("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${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]}; } `,p=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,l)=>`start[${l}]`).join()});`:e.map((c,l)=>`${n[l]} = ${o[l]} + start[${l}];`).join(` `);this.userCode=` void main() { ${t} coords = getOutputCoords(); ${t} sourceLoc; ${u} vec4 result = vec4(0.); ${i} ${p} setOutput(result); } `}};function bY(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=ut.computeFlatOffset(e,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let p=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,p+1),s}function cs(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ut.parseSliceParams(n,s,a);if(ut.assertParamsValid(n,i,p),y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype==="string"){let l=t.texData.get(n.dataId),m=l$(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=ut.isSliceContinous(n.shape,i,p);if(u||!c){let l=O().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new dh(p):new mh(p),m=[i];return t.runWebGLProgram(l,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),bY(n,i,p,t)}var SA={kernelName:_s,backendName:"webgl",kernelFunc:cs};var CY=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,C)=>b*C),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=[],f=te({inputs:{x:n},backend:t,attrs:{shape:p}}),h=xt({inputs:{x:f},backend:t,attrs:{perm:u}}),g=te({inputs:{x:h},backend:t,attrs:{shape:c}}),x=cs({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},wA={kernelName:xs,backendName:"webgl",kernelFunc:CY};function SY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),p=t.readSync(s.dataId),u=qf(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var IA={kernelName:Ja,backendName:"webgl",kernelFunc:SY};function wY(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.readSync(o.dataId),a=t.readSync(n.dataId),i=S.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var vA={kernelName:up,backendName:"webgl",kernelFunc:wY};var IY="return float(a != b);",_w=tt({opSnippet:IY,cpuKernelImpl:r$,dtype:"bool"}),kA={kernelName:Nn,backendName:"webgl",kernelFunc:_w};function Ua(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.real},backend:t})}var NA={kernelName:ai,backendName:"webgl",kernelFunc:Ua};var vY="return float(int(x));";function TA(r,e){let t=new Jt(r.shape,vY),o=e.runWebGLProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function Ew(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return At({inputs:{x:n},backend:t});let a=Vr(n.shape),i=Ew({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),p=Rr({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),p}if(n.dtype==="complex64"){let a=Ua({inputs:{input:n},backend:t}),i=Ew({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=At({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.texData.get(n.dataId).values,[i,p,u]=PE(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s==="int32")return TA(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),p=_w({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var _A={kernelName:co,backendName:"webgl",kernelFunc:Ew};var EA="return ceil(x);",kY=ge({opSnippet:EA,packedOpSnippet:EA,cpuKernelImpl:ME}),$A={kernelName:Uo,backendName:"webgl",kernelFunc:kY};var fh=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { float value = getAAtOutCoords(); if (isnan(value)) { setOutput(value); return; } setOutput(clamp(value, minVal, maxVal)); } `}};var hh=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=` void main() { vec4 value = getAAtOutCoords(); if (any(isnan(value))) { setOutput(value); return; } setOutput(clamp(value, vec4(minVal), vec4(maxVal))); } `}};function NY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;O().getBool("WEBGL_PACK_CLIP")?i=new hh(n.shape):i=new fh(n.shape);let p=[[s],[a]];return t.runWebGLProgram(i,[n],n.dtype,p)}var AA={kernelName:lo,backendName:"webgl",kernelFunc:NY};var gh=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 RA(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function TY(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new gh(o.shape),a=[RA(o,n.complexTensorInfos.real),RA(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var FA={kernelName:pp,backendName:"webgl",kernelFunc:TY};var xh=class{constructor(e){this.outputShape=[],this.outputShape=S.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 p=new Array(e.length-1);p[0]=e[0][t];for(let h=1;h= ${p[h-1]}) { return getChannel( getT${h}(${yh(i,u,g)}), vec2(${yh(c,u,g)})); }`}let d=p.length,f=p[p.length-1];m+=` return getChannel( getT${d}(${yh(i,u,f)}), vec2(${yh(c,u,f)}));`,this.userCode=` float getValue(${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 yh(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function Mu(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.imag},backend:t})}var DA={kernelName:si,backendName:"webgl",kernelFunc:Mu};function fc(r,e,t){let o=r[0].dtype;if(o==="complex64"){let d=r.map(b=>Ua({inputs:{input:b},backend:t})),f=r.map(b=>Mu({inputs:{input:b},backend:t})),h=fc(d,e,t),g=fc(f,e,t),x=Rr({inputs:{real:h,imag:g},backend:t});return d.forEach(b=>t.disposeIntermediateTensorInfo(b)),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),x}let n=t.shouldExecuteOnCPU(r);if(o==="string"&&(n=!0),n){let d=r.map(w=>{let _=[-1,y.sizeFromShape(w.shape.slice(e))];return te({inputs:{x:w},backend:t,attrs:{shape:_}})}),f=d.map(w=>({vals:t.readSync(w.dataId),shape:w.shape})),h=S.computeOutShape(d.map(w=>w.shape),1),g=d[0].shape[0]===1,x=LE(f,h,o,g),b=S.computeOutShape(r.map(w=>w.shape),e),C=t.makeTensorInfo(b,o,x);return d.forEach(w=>t.disposeIntermediateTensorInfo(w)),C}let s=r.filter(d=>y.sizeFromShape(d.shape)>0),a=O().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let d=a?new Jt(r[0].shape,Qs):new Ar(r[0].shape,Qs);return t.runWebGLProgram(d,r,o)}let i=O().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>i){let d=[];for(let h=0;hf.shape),e);return t.runWebGLProgram(d,s,o)}let{tensors2D:p,outShape:u}=_Y(s,e,t),c=new xh(p.map(d=>d.shape)),l=t.runWebGLProgram(c,p,o);p.forEach(d=>t.disposeIntermediateTensorInfo(d));let m=te({inputs:{x:l},attrs:{shape:u},backend:t});return t.disposeIntermediateTensorInfo(l),m}function _Y(r,e,t){let o=S.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>te({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function $w(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?At({inputs:{x:p[0]},backend:t}):fc(p,s,t)}var OA={kernelName:ys,backendName:"webgl",kernelFunc:$w};var hc=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,p=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,C=g?3:1,w="",k="";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} } `,k="result = activation(result);");let _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${w} const ivec2 strides = ivec2(${p}, ${u}); const ivec2 pads = ivec2(${a}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d2 = coords[${C}]; 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 < ${d}; wC++) { int xC = xCCorner + wC * ${l}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${f}; d1 += 4) { vec4 wValues = vec4( getW(wR, wC, d1, d2), getW(wR, wC, d1 + 1, d2), getW(wR, wC, d1 + 2, d2), getW(wR, wC, d1 + 3, d2) ); if (${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, ${f}) * getW(wR, wC, ${f}, d2); } else { dotProd += getX(batch, ${f}, xR, xC) * getW(wR, wC, ${f}, d2); } } else if (${h===2}) { vec2 wValues = vec2( getW(wR, wC, ${f}, d2), getW(wR, wC, ${f} + 1, d2) ); if (${g}) { vec2 xValues = vec2( getX(batch, xR, xC, ${f}), getX(batch, xR, xC, ${f} + 1) ); dotProd += dot(xValues, wValues); } else { vec2 xValues = vec2( getX(batch, ${f}, xR, xC), getX(batch, ${f} + 1, xR, xC) ); dotProd += dot(xValues, wValues); } } else if (${h===3}) { vec3 wValues = vec3( getW(wR, wC, ${f}, d2), getW(wR, wC, ${f} + 1, d2), getW(wR, wC, ${f} + 2, d2) ); if (${g}) { vec3 xValues = vec3( getX(batch, xR, xC, ${f}), getX(batch, xR, xC, ${f} + 1), getX(batch, xR, xC, ${f} + 2) ); dotProd += dot(xValues, wValues); } else { vec3 xValues = vec3( getX(batch, ${f}, xR, xC), getX(batch, ${f} + 1, xR, xC), getX(batch, ${f} + 2, xR, xC) ); dotProd += dot(xValues, wValues); } } } } float result = dotProd; ${_} ${k} setOutput(result); } `}},Ch=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.filterDepth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${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 < ${l}; wF++) { int xF = xFCorner + wF * ${p}; 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 < ${d}; wC++) { int xC = xCCorner + wC * ${c}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${f}; d1 += 4) { vec4 xValues = vec4( getX(batch, xF, xR, xC, d1), getX(batch, xF, xR, xC, d1 + 1), getX(batch, xF, xR, xC, d1 + 2), getX(batch, xF, xR, xC, d1 + 3) ); vec4 wValues = vec4( getW(wF, wR, wC, d1, d2), getW(wF, wR, wC, d1 + 1, d2), getW(wF, wR, wC, d1 + 2, d2), getW(wF, wR, wC, d1 + 3, d2) ); dotProd += dot(xValues, wValues); } if (${h===1}) { dotProd += getX(batch, xF, xR, xC, ${f}) * getW(wF, wR, wC, ${f}, d2); } else if (${h===2}) { vec2 xValues = vec2( getX(batch, xF, xR, xC, ${f}), getX(batch, xF, xR, xC, ${f} + 1) ); vec2 wValues = vec2( getW(wF, wR, wC, ${f}, d2), getW(wF, wR, wC, ${f} + 1, d2) ); dotProd += dot(xValues, wValues); } else if (${h===3}) { vec3 xValues = vec3( getX(batch, xF, xR, xC, ${f}), getX(batch, xF, xR, xC, ${f} + 1), getX(batch, xF, xR, xC, ${f} + 2) ); vec3 wValues = vec3( getW(wF, wR, wC, ${f}, d2), getW(wF, wR, wC, ${f} + 1, d2), getW(wF, wR, wC, ${f} + 2, d2) ); dotProd += dot(xValues, wValues); } } } } setOutput(dotProd); } `}};var gc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ct(this.outputShape.length);let a=e.padInfo.left,i=e.strideWidth,p=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,l=c,m=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) { `;for(let g=0;g<(l+1)/2;g++){let x=g*2;if(m+=` xC = xCCorner + ${x*p}; `,i===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) { xTexelC${x} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${x}.zw = vec2(0.0); } xTexelC${x}Ready = 1; } `,p===1&&x>0?m+=` xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy); `:m+=` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { previous.zw = vec2(0.0); } xC${x} = vec4(previous.zw, xTexelC${x}.xy); } else { xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy); } `):m+=` if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) { xTexelC${x} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${x}.zw = vec2(0.0); } xTexelC${x}Ready = 1; } xC${x} = xTexelC${x}; `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) { xTexelC${x+1} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${x+1}.zw = vec2(0.0); } xTexelC${x+1}Ready = 1; } `,p>1?m+=` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy); } else { xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy); } `:m+=` xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy); `):b===1?m+=` xC${x+1} = xTexelC${x}; `:m+=` xCOffset = xC + ${b}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) { xTexelC${x+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${x+1}.zw = vec2(0.0); } xTexelC${x+1}Ready = 1; } xC${x+1} = xTexelC${x+1}; `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) { xTexelC${x} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${x}.zw = vec2(0.0); } xTexelC${x}Ready = 1; } if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) { xTexelC${x+1} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= inDims[1]) { xTexelC${x+1}.zw = vec2(0.0); } xTexelC${x+1}Ready = 1; } xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw); `,x+1= 0 && xCOffset < inDims[1]) { final = getX(batch, xR, xCOffset, d1); } xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy); `)):(m+=` if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) { xTexelC${x} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${x}.zw = vec2(0.0); } xTexelC${x}Ready = 1; } xCOffset = xC + strides[1]; if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) { xTexelC${x+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${x+1}.zw = vec2(0.); } xTexelC${x+1}Ready = 1; } xC${x} = vec4( xTexelC${x}.xy, xTexelC${x+1}.xy); `,x+1= 0) { // Use custom imod instead mod. On Intel GPU, mod may generate // unexpected value. // https://github.com/tensorflow/tfjs/issues/5447 offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1]; d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) / inChannels); if(d1 < inputShape[${i}] && d1 >= 0) { ch = imod(pos, inChannels); if (${s}) { innerDims = vec2(d1, ch); result[${c*2+l}] = getChannel( getA(rc.x, d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); result[${c*2+l}] = getChannel( getA(rc.x, ch, int(innerDims.x), int(innerDims.y)), innerDims); } } } } `;this.userCode=` void main() { ivec3 rc = getOutputCoords(); vec4 result = vec4(0); int blockIndex, pos, offsetY, d0, offsetX, d1, ch; vec2 innerDims; ${u} ${n.output} = result; } `}};function wh(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Ih({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,l=p[0]*p[1]*p[2],m=t.outChannels,d=t.dataFormat==="channelsLast",f=!1,h=!1,g,x=[];if(s!=null){let w=wh(s.shape,d);w!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:w}}),x.push(s))}if(n!=null){let w=wh(n.shape,d);w!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:w}}),x.push(n))}if(!((l===1||m===1)&&c>Nw)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let w=p[0]*p[1]*(p[2]+1),k={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(Li(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let $=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push($);let A=Pu({a:k,b:$,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),R=o.texData.get(A.dataId);y.assert(R.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=_,R.shape=t.outShape,g=At({inputs:{x:A},backend:o}),g.shape=t.outShape,x.push(A)}else{let w=t.outHeight*t.outWidth,k=te({inputs:{x:r},backend:o,attrs:{shape:d?[t.batchSize,w,t.inChannels]:[t.batchSize,t.inChannels,w]}}),_=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),$=Pu({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=te({inputs:{x:$},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(_),x.push($)}for(let w of x)o.disposeIntermediateTensorInfo(w);return g}function vh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=t,f=d==="channelsLast",h=p*u*c,g=m*l,x=[t.batchSize,h,g],b=!0,C=!1,w=[];if(s!=null){let H=wh(s.shape,f);H!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:H}}),w.push(s))}if(n!=null){let H=wh(n.shape,f);H!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:H}}),w.push(n))}let k=te({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});w.push(k);let _=new Sh(x,t),$=[r.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],A=o.runWebGLProgram(_,[r],"float32",$),R=te({inputs:{x:A},backend:o,attrs:{shape:x}});w.push(A),w.push(R);let D=n!=null,P=s!=null,M=i==="leakyrelu",L=i?Wa(i,!0):null,W=new dc(f?R.shape:k.shape,f?k.shape:R.shape,f?[t.batchSize,g,t.outChannels]:[t.batchSize,t.outChannels,g],b,C,D,L,P,M),V=f?[R,k]:[k,R];if(n&&V.push(n),P&&V.push(s),M){let H=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));V.push(H),w.push(H)}let U=o.runWebGLProgram(W,V,"float32"),q=te({inputs:{x:U},backend:o,attrs:{shape:t.outShape}});w.push(U);for(let H of w)o.disposeIntermediateTensorInfo(H);return q}function EY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;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"))d=Ih({x:n,filter:s,convInfo:m,backend:t});else if(m.strideWidth<=2&&l==="channelsLast"&&O().getBool("WEBGL_EXP_CONV")){let h=new gc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=t.runWebGLProgram(h,[n,s],"float32",g)}else if(O().getBool("WEBGL_CONV_IM2COL"))d=vh({x:n,filter:s,convInfo:m,backend:t});else{let h=new hc(m);d=t.runWebGLProgram(h,[n,s],"float32")}let f=te({inputs:{x:d},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(d),f}var PA={kernelName:Go,backendName:"webgl",kernelFunc:EY};var kh=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); } `}},Nh=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,p=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${p}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[${l}]; 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); } `}},Th=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 >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yF, yR, yC, d2); float xValue = getX(b, xF, xR, xC, d1); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},_h=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${p}, ${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 $Y(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new kh(m);return t.runWebGLProgram(d,[n,s],"float32")}var MA={kernelName:cp,backendName:"webgl",kernelFunc:$Y};function AY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=new Nh(m);return t.runWebGLProgram(d,[n,s],"float32")}var LA={kernelName:Ho,backendName:"webgl",kernelFunc:AY};function RY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Ch(u);return t.runWebGLProgram(c,[n,s],"float32")}var BA={kernelName:lp,backendName:"webgl",kernelFunc:RY};function FY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=S.computeConv3DInfo(n.shape,p,a,1,i),c=new Th(u);return t.runWebGLProgram(c,[n,s],"float32")}var VA={kernelName:vm,backendName:"webgl",kernelFunc:FY};function DY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:p}=o,u=S.computeConv3DInfo(p,s.shape,i,1,a),c=new _h(u);return t.runWebGLProgram(c,[n,s],"float32")}var zA={kernelName:mp,backendName:"webgl",kernelFunc:DY};var OY=_o+` return cos(x); `,PY=ge({opSnippet:OY}),WA={kernelName:qo,backendName:"webgl",kernelFunc:PY};var MY=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,LY=ge({opSnippet:MY}),UA={kernelName:Ko,backendName:"webgl",kernelFunc:LY};var Eh=class{constructor(e,t,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,p,u]=e,[c]=t,[l,m]=o;this.outputShape=[c,l,m,u];let d=n==="bilinear"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,"(y2-y1) * height_ratio",`y1*${f} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${f}`],[C,w,k]=m>1?[`${(p-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(${C}); 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 > ${f} ) { setOutput(float(${s})); return; } float in_x = ${k}; if( in_x < 0.0 || in_x > ${h} ) { setOutput(float(${s})); return; } vec2 sourceFracIndexCR = vec2(in_x,in_y); if(${d} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d); float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d); float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d); float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d); vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR); float top = topLeft + (topRight - topLeft) * fracCR.x; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; float newValue = top + (bottom - top) * fracCR.y; setOutput(newValue); } else { // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestCR = ivec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d); setOutput(newValue); } } `}};var BY=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Eh(n.shape,s.shape,i,p,u);return t.runWebGLProgram(c,[n,s,a],"float32")},GA={kernelName:Yo,backendName:"webgl",kernelFunc:BY};var Lu;(function(r){r.Prod="*",r.Sum="+"})(Lu||(Lu={}));var El=class{constructor(e,t,o,n){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,a=this.op===Lu.Prod?"1.0":"0.0",i=o?a:`getX(${HA(s,"coords",this.op)})`,p=this.outputShape[this.outputShape.length-1],u="",c="";o?(u=n?`end != ${p-1}`:"end != 0",c=n?"end + 1":"end - 1"):(u=n?`end + pow2 < ${p}`:"end >= pow2",c=n?"end + pow2":"end - pow2"),this.userCode=` void main() { ${_e(s)} coords = getOutputCoords(); int end = ${qA(s,"coords",this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${u}) { int idx = ${c}; ${qA(s,"coords",this.op)} = idx; val ${this.op}= getX(${HA(s,"coords",this.op)}); } setOutput(val); } `}};function HA(r,e,t){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 new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function qA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function $h(r,e,t,o,n,s){let a=e.shape.length,i=S.getAxesPermutation([o],a),p=e;i!=null&&(p=xt({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=S.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new El(r,p.shape,!1,s),f=[[m]],h=l;l=t.runWebGLProgram(d,[l],l.dtype,f),t.disposeIntermediateTensorInfo(h)}if(n){let m=new El(r,p.shape,n,s),d=l;l=t.runWebGLProgram(m,[l],l.dtype),t.disposeIntermediateTensorInfo(d)}if(i!=null){let m=S.getUndoAxesPermutation(i),d=xt({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(p),d}return l}function VY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return $h(Lu.Prod,n,t,s,a,i)}var KA={kernelName:jo,backendName:"webgl",kernelFunc:VY};function zY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return $h(Lu.Sum,n,t,s,a,i)}var jA={kernelName:Xo,backendName:"webgl",kernelFunc:zY};function WY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=qf(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),c=OE(p,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var XA={kernelName:ti,backendName:"webgl",kernelFunc:WY};var Ah=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 UY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=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],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=new Ah(f,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var YA={kernelName:Qo,backendName:"webgl",kernelFunc:UY};var xc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ct(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,p=e.outChannels/e.inChannels,u="",c="";o&&(n?u=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${o} }`:s?u=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${o} }`:u=` float activation(float x) { ${o} } `,c="result = activation(result);");let l=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${u} void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; ivec2 xRCCorner = coords.yz * strides - pads; int d2 = coords.w; int d1 = d2 / ${p}; int q = d2 - d1 * ${p}; 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 < ${a}; wR++) { int xR = xRCorner + wR * dilations[0]; if (xR < 0 || xR >= inDims[0]) { continue; } for (int wC = 0; wC < ${i}; wC++) { int xC = xCCorner + wC * dilations[1]; if (xC < 0 || xC >= inDims[1]) { continue; } float xVal = getX(batch, xR, xC, d1); float wVal = getW(wR, wC, d1, q); dotProd += xVal * wVal; } } float result = dotProd; ${l} ${c} setOutput(result); } `}};var yc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ct(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,p=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,m=l,d=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) { `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(d+=` xC = xCCorner + ${b*u}; `,p===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${b}.zw = vec2(0.0); } xTexelC${b}Ready = 1; } `,u===1&&b>0?d+=` xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy); `:d+=` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { previous.zw = vec2(0.0); } xC${b} = vec4(previous.zw, xTexelC${b}.xy); } else { xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy); } `):d+=` if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${b}.zw = vec2(0.0); } xTexelC${b}Ready = 1; } xC${b} = xTexelC${b}; `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { xTexelC${b+1} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${b+1}.zw = vec2(0.0); } xTexelC${b+1}Ready = 1; } `,u>1?d+=` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy); } else { xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy); } `:d+=` xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy); `):C===1?d+=` xC${b+1} = xTexelC${b}; `:d+=` xCOffset = xC + ${C}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { xTexelC${b+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${b+1}.zw = vec2(0.0); } xTexelC${b+1}Ready = 1; } xC${b+1} = xTexelC${b+1}; `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${b}.zw = vec2(0.0); } xTexelC${b}Ready = 1; } if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) { xTexelC${b+1} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= inDims[1]) { xTexelC${b+1}.zw = vec2(0.0); } xTexelC${b+1}Ready = 1; } xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw); `,b+1= 0 && xCOffset < inDims[1]) { final = getX(batch, xR, xCOffset, d1); } xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy); `)):(d+=` if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) { xTexelC${b} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${b}.zw = vec2(0.0); } xTexelC${b}Ready = 1; } xCOffset = xC + strides[1]; if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) { xTexelC${b+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${b+1}.zw = vec2(0.); } xTexelC${b+1}Ready = 1; } xC${b} = vec4( xTexelC${b}.xy, xTexelC${b+1}.xy); `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=S.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;O().getBool("WEBGL_PACK_DEPTHWISECONV")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new yc(l):m=new xc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return t.runWebGLProgram(m,[n,s],"float32",d)}var QA={kernelName:Zo,backendName:"webgl",kernelFunc:GY};var Rh=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 >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } } } setOutput(dotProd); } `}},Fh=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,p=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${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 < ${p}; dm++) { int d2 = d1 * ${p} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; } } } setOutput(dotProd); } `}};function HY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=S.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Rh(l);return t.runWebGLProgram(m,[n,s],"float32")}var ZA={kernelName:dp,backendName:"webgl",kernelFunc:HY};function qY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=S.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Fh(l);return t.runWebGLProgram(m,[n,s],"float32")}var JA={kernelName:fp,backendName:"webgl",kernelFunc:qY};var Dh=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } `}};function KY(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Dh(s),p=t.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(p),u}var eR={kernelName:hp,backendName:"webgl",kernelFunc:KY};var Oh=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=e,{top:l,left:m}=n;this.userCode=` const ivec2 strides = ivec2(${s}, ${a}); const ivec2 pads = ivec2(${l}, ${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 < ${p}; 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 jY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=S.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",p),c,l=new Oh(u);c=t.runWebGLProgram(l,[n,s],"float32");let m=te({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var tR={kernelName:gp,backendName:"webgl",kernelFunc:jY};function XY(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=S.decodeEinsumEquation(n,s.length);S.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=S.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Ou({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var rR={kernelName:ri,backendName:"webgl",kernelFunc:XY};var YY="return (x >= 0.0) ? x : (exp(x) - 1.0);",QY=` 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; `,ZY=ge({opSnippet:YY,packedOpSnippet:QY}),oR={kernelName:en,backendName:"webgl",kernelFunc:ZY};var JY="return (b >= 1.0) ? a : a * (b + 1.0);",eQ=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `,tQ=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=O().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new To(eQ,o.shape,n.shape):new io(JY,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},nR={kernelName:km,backendName:"webgl",kernelFunc:tQ};var rQ=` return vec4(equal(a, b)); `,oQ="return float(a == b);",nQ=tt({opSnippet:oQ,packedOpSnippet:rQ,dtype:"bool",cpuKernelImpl:BE}),sR={kernelName:tn,backendName:"webgl",kernelFunc:nQ};var sQ=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. float p = ${S.ERF_P}; float a1 = ${S.ERF_A1}; float a2 = ${S.ERF_A2}; float a3 = ${S.ERF_A3}; float a4 = ${S.ERF_A4}; float a5 = ${S.ERF_A5}; float sign = sign(x); x = abs(x); float t = 1.0 / (1.0 + p * x); return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x)); `,aQ=ge({opSnippet:sQ}),aR={kernelName:ma,backendName:"webgl",kernelFunc:aQ};var iQ=_o+` return exp(x); `,uQ=` vec4 result = exp(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,Aw=ge({opSnippet:iQ,packedOpSnippet:uQ,cpuKernelImpl:VE,dtype:"float32"}),iR={kernelName:rn,backendName:"webgl",kernelFunc:Aw};function Ph(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var uR={kernelName:bs,backendName:"webgl",kernelFunc:Ph};var pR="return exp(x) - 1.0;",pQ=ge({opSnippet:pR,packedOpSnippet:pR,cpuKernelImpl:zE}),cR={kernelName:da,backendName:"webgl",kernelFunc:pQ};var $l=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 Mh(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=te({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),p=i.shape,u=new $l("real",p,e),c=new $l("imag",p,e),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=t.runWebGLProgram(u,l,"float32"),d=t.runWebGLProgram(c,l,"float32"),f=Rr({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function cQ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Mh(o,!1,t)}var lR={kernelName:oi,backendName:"webgl",kernelFunc:cQ};var Lh=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); } `}};function Ga(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 Lh(o,n),i=[[n]];return e.runWebGLProgram(a,[],s,i)}}var mR={kernelName:Cs,backendName:"webgl",kernelFunc:Ga};var Bh=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 - 1; float outputValue; if(coordX >= 0 && coordX < ${t}) { outputValue = getImage(coords[0], coords[1], coordX, coords[3]); } else { outputValue = getImage(coords[0], coords[1], coords[2], coords[3]); } setOutput(outputValue); } `}};var dR={kernelName:on,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Bh(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var fR="return floor(x);",lQ=ge({opSnippet:fR,packedOpSnippet:fR,cpuKernelImpl:WE}),hR={kernelName:nn,backendName:"webgl",kernelFunc:lQ};var mQ=` float s = sign(a) * sign(b); int ia = round(a); int ib = round(b); if (ib != 0) { // Windows (D3D) wants guaranteed non-zero int division at compile-time. return float(idiv(ia, ib, s)); } else { return NAN; } `,dQ=` ivec4 ia = round(a); ivec4 ib = round(b); bvec4 cond = notEqual(ib, ivec4(0)); ivec4 result = ivec4(0); vec4 s = sign(a) * sign(b); // Windows (D3D) wants guaranteed non-zero int division at compile-time. if (cond[0]) { result[0] = idiv(ia[0], ib[0], s[0]); } if (cond[1]) { result[1] = idiv(ia[1], ib[1], s[1]); } if (cond[2]) { result[2] = idiv(ia[2], ib[2], s[2]); } if (cond[3]) { result[3] = idiv(ia[3], ib[3], s[3]); } return vec4(result); `,fQ=tt({opSnippet:mQ,packedOpSnippet:dQ,dtype:"int32"}),gR={kernelName:sn,backendName:"webgl",kernelFunc:fQ};var Vh=class{constructor(e){this.variableNames=["A"];let t=St(),[o,n]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0); vec4 values = ${t.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } setOutput(floor(value * 255.0 + 0.5)); } `}};var zh=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=St(),[o,n]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec4 result = vec4(0.); for(int row=0; row<=1; row++) { for(int col=0; col<=1; col++) { texC = coords[1] + row; depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${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 xR={kernelName:Zi,backendName:"webgl",kernelFunc:hQ},bc,Rw=O().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function hQ(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,[p,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,p],l=[u,p,s];if(i||a){let h=O().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(bc==null||h!==Rw)&&(Rw=h,bc=document.createElement("canvas").getContext("2d",{willReadFrequently:Rw})),bc.canvas.width=p,bc.canvas.height=u,bc.drawImage(n,0,0,p,u),n=bc.canvas}let m=t.makeTensorInfo(c,"int32");t.texData.get(m.dataId).usage=ir.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),n);let d=O().getBool("WEBGL_PACK")?new zh(l):new Vh(l),f=t.runWebGLProgram(d,[m],"int32");return t.disposeData(m.dataId),f}function gQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h),x,b=[],C=a!=null,w=i!=null,k=d==="leakyrelu",_=()=>{let A=[n,s],R=(D,P)=>{if(P==="NCHW"&&D.shape.length===1&&D.shape[0]!==1){let M=te({inputs:{x:D},backend:t,attrs:{shape:[D.shape[0],1,1]}});return b.push(M),M}return D};if(C&&A.push(R(a,c)),w&&A.push(R(i,c)),k){let D=t.makeTensorInfo([],"float32",y.createScalarValue(f,"float32"));A.push(D),b.push(D)}return A};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=Ih({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&h==="channelsLast"&&O().getBool("WEBGL_EXP_CONV")){let A=d?Wa(d,!0):null,R=new gc(g,C,A,w,k),D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],P=_();x=t.runWebGLProgram(R,P,"float32",D)}else if(O().getBool("WEBGL_CONV_IM2COL"))x=vh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else{let A=d?Wa(d,!1):null,R=new hc(g,C,A,w,k),D=_();x=t.runWebGLProgram(R,D,"float32")}let $=te({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(A=>t.disposeIntermediateTensorInfo(A)),$}var yR={kernelName:ho,backendName:"webgl",kernelFunc:gQ};function xQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(p,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${h}'`);let g=S.computeConv2DInfo(n.shape,s.shape,p,h,u,l,!0),x=O().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?Wa(m,x):null,C=[n,s],w=a!=null,k=i!=null,_=m==="leakyrelu";if(w&&C.push(a),k&&C.push(i),_){let D=t.makeTensorInfo([],"float32",y.createScalarValue(d,"float32"));C.push(D),f.push(D)}let $;x?$=new yc(g,w,b,k,_):$=new xc(g,w,b,k,_);let A=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=t.runWebGLProgram($,C,"float32",A);return f.forEach(D=>t.disposeIntermediateTensorInfo(D)),R}var bR={kernelName:go,backendName:"webgl",kernelFunc:xQ};var Wh=class{constructor(e,t,o,n){this.sliceDim=e,this.strides=t,this.paramsShape=n,this.variableNames=["x","indices"],this.outputShape=o;let s=_e(o.length),a=` int index;`;for(let i=0;i= ${this.paramsShape[i]}; flattenIndex += index * ${this.strides[i]};`;this.userCode=` void main() { ${s} coords = getOutputCoords(); int flattenIndex = 0; bool out_of_bounds = false; ${a} setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1])); } `}};function yQ(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=S.prepareAndValidate(o,n),m=te({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=te({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype==="string"){let x=t.readSync(n.dataId),b=t.bufferSync(o),C=UE(x,b,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,C.values)}let f=new Wh(a,l,[u,c],o.shape),h=t.runWebGLProgram(f,[d,m],d.dtype),g=te({inputs:{x:h},backend:t,attrs:{shape:p}});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var CR={kernelName:un,backendName:"webgl",kernelFunc:yQ};var Uh=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let o=_e(this.rank),n=bQ(e,2);this.userCode=` void main() { ${o} resRC = getOutputCoords(); int index = int(getIndices(resRC.x, resRC.z)); float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0; setOutput(inBounds * getA(${n})); } `}};function bQ(r,e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let n=0;n=0,()=>`GatherV2: the index value ${k} is not in [0, ${C-1}]`)}}let u=S.segment_util.collectGatherOpShapeInfo(n,s,p,i),c=y.sizeFromShape(s.shape),l=[],m=te({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),d=te({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});l.push(m),l.push(d);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype==="string"){let b=t.bufferSync(d),C=t.bufferSync(m),w=GE(C,b,f);return l.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,w.dtype,w.values)}let h=new Uh(m.shape,f),g=t.runWebGLProgram(h,[m,d],m.dtype);l.push(g);let x=te({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var SR={kernelName:Ss,backendName:"webgl",kernelFunc:Fw};var CQ="return float(a > b);",SQ=` return vec4(greaterThan(a, b)); `,wQ=tt({opSnippet:CQ,packedOpSnippet:SQ,cpuKernelImpl:HE,dtype:"bool"}),wR={kernelName:pn,backendName:"webgl",kernelFunc:wQ};var IQ="return float(a >= b);",vQ=` return vec4(greaterThanEqual(a, b)); `,kQ=tt({opSnippet:IQ,packedOpSnippet:vQ,dtype:"bool",cpuKernelImpl:qE}),IR={kernelName:cn,backendName:"webgl",kernelFunc:kQ};function NQ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Mh(o,!0,t)}var vR={kernelName:ni,backendName:"webgl",kernelFunc:NQ};var TQ="return float(!isnan(x) && !isinf(x));",_Q=ge({opSnippet:TQ,dtype:"bool"}),kR={kernelName:fa,backendName:"webgl",kernelFunc:_Q};var EQ="return float(isinf(x));",$Q=ge({opSnippet:EQ,dtype:"bool"}),NR={kernelName:ha,backendName:"webgl",kernelFunc:$Q};var AQ="return float(isnan(x));",RQ=ge({opSnippet:AQ,dtype:"bool"}),TR={kernelName:ln,backendName:"webgl",kernelFunc:RQ};var FQ="return float(a < b);",DQ=` return vec4(lessThan(a, b)); `,OQ=tt({opSnippet:FQ,packedOpSnippet:DQ,cpuKernelImpl:KE,dtype:"bool"}),_R={kernelName:dn,backendName:"webgl",kernelFunc:OQ};var PQ="return float(a <= b);",MQ=` return vec4(lessThanEqual(a, b)); `,LQ=tt({opSnippet:PQ,packedOpSnippet:MQ,cpuKernelImpl:jE,dtype:"bool"}),ER={kernelName:fn,backendName:"webgl",kernelFunc:LQ};function BQ(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=XE(o,n,s);return e.makeTensorInfo([a.length],"float32",a)}var $R={kernelName:xp,backendName:"webgl",kernelFunc:BQ};var VQ=_o+` return x < 0.0 ? 0./0. : log(x); `,zQ=` vec4 result = log(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r); result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g); result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b); result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; `,WQ=ge({opSnippet:VQ,packedOpSnippet:zQ,cpuKernelImpl:YE}),AR={kernelName:hn,backendName:"webgl",kernelFunc:WQ};var UQ=_o+` return log(1.0 + x); `,GQ=ge({opSnippet:UQ}),RR={kernelName:ga,backendName:"webgl",kernelFunc:GQ};var HQ="return float(a >= 1.0 && b >= 1.0);",qQ=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); `,KQ=tt({opSnippet:HQ,packedOpSnippet:qQ,dtype:"bool"}),FR={kernelName:gn,backendName:"webgl",kernelFunc:KQ};var jQ="return float(!(x >= 1.0));",XQ=ge({opSnippet:jQ}),DR={kernelName:xn,backendName:"webgl",kernelFunc:XQ};var YQ="return float(a >= 1.0 || b >= 1.0);",QQ=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); `,ZQ=tt({opSnippet:YQ,packedOpSnippet:QQ,dtype:"bool"}),OR={kernelName:xa,backendName:"webgl",kernelFunc:ZQ};var Gh=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${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 * ${p}; setOutput(val); } `}};var Hh=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${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 * ${p}; setOutput(result); } `}};var JQ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=O().getBool("WEBGL_PACK_NORMALIZATION")?new Hh(n.shape,s,a,i,p):new Gh(n.shape,s,a,i,p);return t.runWebGLProgram(u,[n],n.dtype)},PR={kernelName:yp,backendName:"webgl",kernelFunc:JQ};var qh=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 e7=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new qh(n.shape,i,p,u,c);return t.runWebGLProgram(l,[n,s,a],n.dtype)},MR={kernelName:Nm,backendName:"webgl",kernelFunc:e7};function LR(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Gr(i,r.dtype,"max",o),u=te({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function Dw(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=S.getAxesPermutation(u,i),l=c!=null,m=t.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let C=t.texData.get(d.dataId).values,w=new Array(i);for(let $=0;$`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return At({inputs:{x:n},backend:t});let l=new ps(c,"max",!1);return t.runWebGLProgram(l,[n],n.dtype)}var zR={kernelName:Cn,backendName:"webgl",kernelFunc:n7};function s7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=S.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new zi(l,"max",!1);return t.runWebGLProgram(m,[n],n.dtype)}var WR={kernelName:bp,backendName:"webgl",kernelFunc:s7};var Kh=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,p=a-1-e.padInfo.left,u=s*a-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${p}); 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 * ${a} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } setOutput(dotProd); } `}},jh=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=p-1-e.padInfo.front,m=u-1-e.padInfo.top,d=c-1-e.padInfo.left,f=p*u*c-1;this.userCode=` const ivec3 pads = ivec3(${l}, ${m}, ${d}); 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 < ${p}; 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 = ${f} - 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 a7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=S.computePool3DInfo(a.shape,i,p,l,u,c),d=new zi(m,"max",!0),f=t.runWebGLProgram(d,[a],a.dtype),h=new jh(m),g=t.runWebGLProgram(h,[n,f],a.dtype);return t.disposeIntermediateTensorInfo(f),g}var UR={kernelName:_m,backendName:"webgl",kernelFunc:a7};function i7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;is([s,a],"maxPoolGrad");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=S.computePool2DInfo(i.shape,p,u,1,c,l),d=!0,f=new ps(m,"max",d),h=t.runWebGLProgram(f,[i],i.dtype),g=new Kh(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var GR={kernelName:Tm,backendName:"webgl",kernelFunc:i7};function HR(r,e,t,o){let n=new ps(t,"max",!1),s=o.runWebGLProgram(n,[r],"float32");n=new ps(t,"max",!0,!0,e);let a=o.runWebGLProgram(n,[r],"float32");return[s,a]}var qR={kernelName:Cp,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=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(S.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=S.computePool2DInfo(o.shape,n,s,u,a),[l,m]=HR(o,i,c,p);return[l,m]}};function KR(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Gr(i,"float32","mean",o),u=te({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}var jR={kernelName:Sn,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{keepDims:n,axis:s}=e,a=t,i=o.shape.length,p=y.parseAxisParam(s,o.shape),u=p,c=S.getAxesPermutation(u,i),l=c!=null,m=a.shouldExecuteOnCPU([o]),d=[],f=o;if(l){if(m){let w=a.texData.get(f.dataId).values,k=new Array(i);for(let A=0;Ac[0]+e[l]+c[1]);let n=e.length,s=_e(n),a=t.map(c=>c[0]).join(","),i=t.map((c,l)=>c[0]+e[l]).join(","),p=["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}; } } ${s} coords = outC - start; setOutput(getX(${p})); } `}};var Yh=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,h)=>f[0]+e[h]+f[1]);let n=e.length,s=_e(n),a=t.map(f=>f[0]).join(","),i=t.map((f,h)=>f[0]+e[h]).join(","),p=$t("rc",n),u=$t("source",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?"source":`vec2(${u.slice(-2).join()})`,m=o==="reflect"?0:1,d="";if(n===1){let f=` ${s} source = rc; if (source < start) { source = start * 2 - source - ${m}; } else if (source >= end) { source = (end - 1) * 2 - source + ${m}; } source -= start; `;d=` ${s} rc = outputLoc; ${f} result[0] = getChannel(getX(${u.join()}), ${l}); ${p[n-1]} += 1; if(${c}) { ${f} result[1] = getChannel(getX(${u.join()}), ${l}); } `}else{let f=` ${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; `;d=` ${s} rc = outputLoc; ${f} result[0] = getChannel(getX(${u.join()}), ${l}); ${p[n-1]} += 1; if(${c}) { ${f} result[1] = getChannel(getX(${u.join()}), ${l}); } rc = outputLoc; ${p[n-2]} += 1; if(${p[n-2]} < ${this.outputShape[n-2]}) { ${f} result[2] = getChannel(getX(${u.join()}), ${l}); ${p[n-1]} += 1; if(${c}) { ${f} result[3] = getChannel(getX(${u.join()}), ${l}); } } `}this.userCode=` const ${s} start = ${s}(${a}); const ${s} end = ${s}(${i}); void main() { ${s} outputLoc = getOutputCoords(); vec4 result = vec4(0.); ${d} setOutput(result); } `}};var m7=({inputs:r,backend:e,attrs:t})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=O().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Yh(o.shape,n,s):new Xh(o.shape,n,s);return e.runWebGLProgram(a,[o],o.dtype)},QR={kernelName:vn,backendName:"webgl",kernelFunc:m7};var d7=`if (b == 0.0) return NAN; return mod(a, b);`,f7=` vec4 result = mod(a, b); bvec4 isNaN = equal(b, vec4(0.0)); `+Zs+` return result; `,h7=tt({opSnippet:d7,packedOpSnippet:f7}),ZR={kernelName:ya,backendName:"webgl",kernelFunc:h7};var Qh=class{constructor(e,t,o){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,o],this.userCode=` 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})); } `}};var g7=` if (a == b) { return 1.0; }; return a / b;`,x7=` // 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; `,Ow=tt({opSnippet:g7,packedOpSnippet:x7,checkOutOfBounds:!0}),JR={kernelName:Jo,backendName:"webgl",kernelFunc:Ow};var eF="return a - b;",Pw=tt({opSnippet:eF,packedOpSnippet:eF,supportsComplex:!0,cpuKernelImpl:b$}),tF={kernelName:Xn,backendName:"webgl",kernelFunc:Pw};function Mw(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=Dw({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),p=S.expandShapeToKeepDim(i.shape,a),u=te({inputs:{x:i},backend:t,attrs:{shape:p}}),c=Pw({inputs:{a:n,b:u},backend:t}),l=Aw({inputs:{x:c},backend:t}),m=Ou({inputs:{x:l},backend:t,attrs:{axis:a,keepDims:!1}}),d=te({inputs:{x:m},backend:t,attrs:{shape:p}}),f=Ow({inputs:{a:l,b:d},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),f}var rF={kernelName:qn,backendName:"webgl",kernelFunc:Mw};function y7(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,p=i?n:Mw({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new Qh(u,c,s),m=[[a]],d=t.runWebGLProgram(l,[p],"int32",m);return i||t.disposeIntermediateTensorInfo(p),d}var oF={kernelName:Sp,backendName:"webgl",kernelFunc:y7};var b7=Bt+` return -x; `,C7=` vec4 result = -x; bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `;function S7(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.texData.get(o.dataId),[a,i]=t$(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n;return O().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Ar(o.shape,C7):n=new Jt(o.shape,b7),t.runWebGLProgram(n,[o],o.dtype)}var nF={kernelName:ws,backendName:"webgl",kernelFunc:S7};var w7=Lt.nonMaxSuppressionV3Impl;function I7(r){S.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:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=w7(u,c,a,i,p);return t.makeTensorInfo([l.length],"int32",new Int32Array(l))}var sF={kernelName:Tn,backendName:"webgl",kernelFunc:I7};var v7=Lt.nonMaxSuppressionV4Impl;function k7(r){S.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:p,padToMaxOutputSize:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),{selectedIndices:m,validOutputs:d}=v7(c,l,a,i,p,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([d]))]}var aF={kernelName:ba,backendName:"webgl",kernelFunc:k7};var N7=Lt.nonMaxSuppressionV5Impl;function T7(r){S.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:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=N7(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var iF={kernelName:_n,backendName:"webgl",kernelFunc:T7};var Zh=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 _7=r=>{let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new Zh(u,a,i,p),l=te({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=t.runWebGLProgram(c,[l],s);t.disposeIntermediateTensorInfo(l);let d=[...n.shape,a],f=te({inputs:{x:m},backend:t,attrs:{shape:d}});return t.disposeIntermediateTensorInfo(m),f},uF={kernelName:En,backendName:"webgl",kernelFunc:_7};function Al(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=Ua({inputs:{input:o},backend:t}),s=Al({inputs:{x:n},backend:t}),a=Mu({inputs:{input:o},backend:t}),i=Al({inputs:{x:a},backend:t}),p=Rr({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),p}else return Ga({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var pF={kernelName:Fs,backendName:"webgl",kernelFunc:Al};function cF(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=Ua({inputs:{input:o},backend:t}),s=cF({inputs:{x:n},backend:t}),a=Mu({inputs:{input:o},backend:t}),i=Al({inputs:{x:a},backend:t}),p=Rr({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),p}else return Ga({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var lF={kernelName:Is,backendName:"webgl",kernelFunc:cF};function E7(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Ph({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=[],p=e.map(c=>{let l=Ph({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=$w({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var mF={kernelName:vs,backendName:"webgl",kernelFunc:E7};var Jh=class{constructor(e,t,o){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=_e(n),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),p=["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(value); } 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(value); } else { ${s} coords = outC - start; setOutput(getX(${p})); } } `}};var eg=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=_e(n),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),p=$t("rc",n),u=$t("source",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1; if(${c}) { `,n===1?"":`} rc = outputLoc; ${p[n-2]} += 1; if(${p[n-2]} < ${this.outputShape[n-2]}) {`,n===1?"":` ${p[n-1]} += 1; if(${c}) {`],d=n===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",f="";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;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return Ga({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=O().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new eg(n.shape,s,a):new Jh(n.shape,s,a),p=[[a]];return t.runWebGLProgram(i,[n],n.dtype,p)},dF={kernelName:$n,backendName:"webgl",kernelFunc:Lw};var $7=` 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); `,A7=` // 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; bvec4 isNaN1 = lessThan(a, vec4(0.0)); bvec4 isNaN2 = lessThan(floor(b), b); bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w); `+Zs+` return result; `,R7=tt({opSnippet:$7,packedOpSnippet:A7}),fF={kernelName:An,backendName:"webgl",kernelFunc:R7};function F7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=[],u=y.parseAxisParam(s,n.shape),c=u,l=S.getAxesPermutation(c,i),m=n;l!=null&&(m=xt({inputs:{x:n},backend:t,attrs:{perm:l}}),c=S.getInnerMostAxes(c.length,i),p.push(m)),S.assertAxesAreInnerMostDims("prod",c,i);let d;if(t.shouldExecuteOnCPU([m])){let f=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=o$(m.shape,m.dtype,f,c);d=t.makeTensorInfo(g,x,h)}else{let[f,h]=S.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=te({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=ka(n.dtype),C=Gr(x,b,"prod",t);d=te({inputs:{x:C},backend:t,attrs:{shape:f}}),p.push(x),p.push(C)}if(a){p.push(d);let f=S.expandShapeToKeepDim(d.shape,u);d=te({inputs:{x:d},backend:t,attrs:{shape:f}})}return p.forEach(f=>t.disposeIntermediateTensorInfo(f)),d}var hF={kernelName:Fn,backendName:"webgl",kernelFunc:F7};function D7(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),c=t.readSync(s.dataId),l=t.readSync(a.dataId),[m,d,f]=n$(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],"int32",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var gF={kernelName:wp,backendName:"webgl",kernelFunc:D7};function O7(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=s$(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],"int32",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var xF={kernelName:Ip,backendName:"webgl",kernelFunc:O7};function P7(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),l=t.readSync(a.dataId),m=i.map(g=>t.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=a$(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var yF={kernelName:vp,backendName:"webgl",kernelFunc:P7};var Bw=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=i$(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},bF={kernelName:ks,backendName:"webgl",kernelFunc:Bw};var M7="return 1.0 / x;",L7=ge({opSnippet:M7}),CF={kernelName:Dn,backendName:"webgl",kernelFunc:L7};var B7=Bt+` return (x < 0.0) ? 0.0 : x; `,V7=` 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; `,z7=ge({opSnippet:B7,packedOpSnippet:V7}),SF={kernelName:On,backendName:"webgl",kernelFunc:z7};var W7=Bt+` return (x < 0.0) ? 0.0 : min(6.0, x); `,U7=` 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; `,G7=ge({opSnippet:W7,packedOpSnippet:U7}),wF={kernelName:Ln,backendName:"webgl",kernelFunc:G7};var tg=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[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]/l[0]}, ${c[1]/l[1]}); const vec2 inputShapeRC = vec2(${i}.0, ${p}.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 rg=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[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]/l[0]}, ${c[1]/l[1]}, ${c[1]/l[1]}); const vec3 inputShapeRC = vec3(${i}.0, ${p}.0, ${p}.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 H7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=O().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new rg(n.shape,p,u,s,a):new tg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],"float32")}var IF={kernelName:Mn,backendName:"webgl",kernelFunc:H7};var og=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[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=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*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(${l}); const float invHeightScale = float(${m}); const float invWidthScale = float(${d}); const int winHeight = int(${f}); 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), ${s-1}.0)); float dxCLerp = dxC - float(leftDxCIndex); float inverseDxCLerp = 1.0 - dxCLerp; if (r == topDxRIndex && c == leftDxCIndex) { // topLeft accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp; } if (r == topDxRIndex && c == rightDxCIndex) { // topRight accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp; } if (r == bottomDxRIndex && c == leftDxCIndex) { // bottomLeft accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp; } if (r == bottomDxRIndex && c == rightDxCIndex) { // bottomRight accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp; } } } // End loop over dy setOutput(accumulator); } `}};function q7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new og(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var vF={kernelName:$m,backendName:"webgl",kernelFunc:q7};var ng=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?"0.5":"0.0",d;s?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${c[0]/l[0]}, ${c[1]/l[1]}); const vec2 inputShapeRC = vec2(${i}.0, ${p}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${d}; // 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); } `}};var sg=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?"0.5":"0.0",d;s?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${c[0]/l[0]}, ${c[1]/l[1]}, ${c[1]/l[1]}); const vec3 inputShapeRC = vec3(${i}.0, ${p}.0, ${p}.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 = ${d}; // Compute the coordinators of nearest neighbor point. ivec3 sourceNearestRC = ivec3( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m}))); // Should we calculate next column and row elements in 2x2 packed cell. bool hasNextCol = d < ${u-1}; bool hasNextRow = coords.z < ${o-1}; vec4 newValue = vec4( getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d), hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0); setOutput(newValue); } `}};function K7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=O().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new sg(n.shape,p,u,s,a):new ng(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var kF={kernelName:Pn,backendName:"webgl",kernelFunc:K7};var ag=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${c}); const float widthScale = float(${l}); const float invHeightScale = float(${m}); const float invWidthScale = float(${d}); const int winHeight = int(${f}); 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 >= ${i}) { continue; } float sourceFracRow = float(${p[0]}) * (float(dyR) / float(${u[0]})); float sourceFracCol = float(${p[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), ${o} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { accumulator += getDy(b, dyR, dyC, d); } } } // End loop over dy setOutput(accumulator); } `}};function j7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new ag(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var NF={kernelName:Em,backendName:"webgl",kernelFunc:j7};var ig=class{constructor(e,t){this.variableNames=["x"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${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,p)=>n(p)).join(","),a=_e(o);this.userCode=` void main() { ${a} coords = getOutputCoords(); setOutput(getX(${s})); } `}};var ug=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-${o} tensor is not yet supported`);this.outputShape=e;let n=$t("rc",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=_e(o);o===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); result.r = getChannel(getX(${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 = ${p(n.slice())}; if(${s}){ result.g = ${u(n.slice())}; } if(${a}) { result.b = ${c(n.slice())}; if(${s}) { result.a = ${l(n.slice())}; } } setOutput(result); } `;function p(f){return m(f)}function u(f){return f[o-1]="("+f[o-1]+" + 1)",m(f)}function c(f){return f[o-2]="("+f[o-2]+" + 1)",m(f)}function l(f){return f[o-1]="("+f[o-1]+" + 1)",f[o-2]="("+f[o-2]+" + 1)",m(f)}function m(f){let h=e.map((b,C)=>d(C,f)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return t.indexOf(f)!==-1&&e[f]!==1?`${e[f]} - ${h[f]} - 1`:`${h[f]}`}}};function X7(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 At({inputs:{x:n},backend:t});let p=O().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ug(n.shape,i):new ig(n.shape,i);return t.runWebGLProgram(p,[n],n.dtype)}var TF={kernelName:Bn,backendName:"webgl",kernelFunc:X7};var pg=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let o=e[1],n=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=` vec3 fill = vec3(${t.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) - params[0]) * params[3] - (float(y) - params[1]) * params[2]; float coordYFloat = (float(x) - params[0]) * params[2] + (float(y) - params[1]) * params[3]; int coordX = int(round(coordXFloat + params[0])); int coordY = int(round(coordYFloat + params[1])); ${s} if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) { outputValue = getImage(coords[0], coordY, coordX, coords[3]); } setOutput(outputValue); } `}};var _F={kernelName:es,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new pg(o.shape,s),[u,c]=S.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var Y7=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); if ((x - base) < 0.5) { return floor(x); } else if ((x - base) > 0.5) { return ceil(x); } else { if (mod(base, 2.0) == 0.0) { return base; } else { return base + 1.0; } } `,Q7=ge({opSnippet:Y7}),EF={kernelName:Ca,backendName:"webgl",kernelFunc:Q7};var Z7="return inversesqrt(x);",J7=ge({opSnippet:Z7,cpuKernelImpl:u$}),$F={kernelName:Vn,backendName:"webgl",kernelFunc:J7};var Cc=class{constructor(e,t,o,n,s,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let p=_e(s.length),u=_e(a.length),c="";o===1?c="i":o===2&&(c="i, j");let l=`getIndices(${c})`,m="";n===1?m="i":n===2&&(m="i, coords[1]");let d=`getUpdates(${m})`,f=t>1?"strides[j]":"strides";this.userCode=` ${p} strides = ${p}(${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 < ${t}; j++) { int index = round(${l}); flattenedIndex += index * ${f}; } if (flattenedIndex == coords[0]) { sum += ${d}; found = true; } } setOutput(mix(getDefaultValue(), sum, float(found))); } `}};function eZ(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=S.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=t.makeTensorInfo([],"float32",new Float32Array([0])),g=new Cc(p,i,d.shape.length,f.shape.length,c,m),x=t.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var AF={kernelName:zn,backendName:"webgl",kernelFunc:eZ};var cg=class{constructor(e,t,o,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,o];let s="while (left < right) {",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=O().getNumber("WEBGL_VERSION")===2?s:a,p=n==="left"?"<":"<=";this.userCode=` int findBound(int batch, float value) { int left = 0; int right = numInputs; int mid; ${i} mid = (left + right) / 2; if (getSortedSequence(batch, mid) ${p} value) { left = mid + 1; } else { right = mid; } } return right; } void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int valueIndex = coords[1]; float value = getValues(batch, valueIndex); setOutput(float(findBound(batch, value))); } `}};function tZ(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new cg(n.shape[0],n.shape[1],s.shape[1],a),p=[[n.shape[1]]];return t.runWebGLProgram(i,[n,s],"int32",p)}var RF={kernelName:ii,backendName:"webgl",kernelFunc:tZ};var lg=class{constructor(e,t,o){this.variableNames=["c","a","b"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],p=[],u=[];for(let c=0;c= 1.0) { setOutput(getA(${s})); } else { setOutput(getB(${s})); } } `}};function rZ(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new lg(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var FF={kernelName:Ts,backendName:"webgl",kernelFunc:rZ};var oZ=` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 float scaleAlpha = ${S.SELU_SCALEALPHA}; float scale = ${S.SELU_SCALE}; return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); `,nZ=ge({opSnippet:oZ}),DF={kernelName:Xi,backendName:"webgl",kernelFunc:nZ};var sZ=_o+` return 1.0 / (1.0 + exp(-1.0 * x)); `,aZ=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,iZ=ge({opSnippet:sZ,packedOpSnippet:aZ,cpuKernelImpl:c$}),OF={kernelName:Un,backendName:"webgl",kernelFunc:iZ};var uZ=` if (isnan(x)) { return 0.0; } return sign(x); `,pZ=ge({opSnippet:uZ}),PF={kernelName:Yi,backendName:"webgl",kernelFunc:pZ};var cZ=_o+` return sin(x); `,lZ=ge({opSnippet:cZ}),MF={kernelName:Wn,backendName:"webgl",kernelFunc:lZ};var mZ=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; `,dZ=ge({opSnippet:mZ}),LF={kernelName:Sa,backendName:"webgl",kernelFunc:dZ};var fZ=` 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; `,hZ=ge({opSnippet:fZ}),BF={kernelName:Qi,backendName:"webgl",kernelFunc:hZ};var gZ=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),p=[[0,0]];p.push(...a);for(let x=1+s.length;xt.disposeIntermediateTensorInfo(x)),g},VF={kernelName:Es,backendName:"webgl",kernelFunc:gZ};function xZ(r){let{inputs:e,backend:t}=r,{indices:o,values:n,denseShape:s,defaultValue:a}=e;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: ${s.shape}`);if(o.shape.length!==2)throw new Error(`Indices must be a matrix, saw: ${o.shape}`);if(n.shape.length!==1)throw new Error(`Values must be a vector, saw: ${n.shape}`);if(a.shape.length!==0)throw new Error(`Default value must be a scalar, saw: ${a.shape}`);let i=t.readSync(o.dataId),p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=t.readSync(a.dataId)[0],[l,m,d,f,h]=m$(i,o.shape,o.dtype,p,n.dtype,u,c);return[t.makeTensorInfo(m,o.dtype,l),t.makeTensorInfo([m[0]],n.dtype,d),t.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),t.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var zF={kernelName:ui,backendName:"webgl",kernelFunc:xZ};function yZ(r){let{inputs:e,backend:t}=r,{inputIndices:o,inputShape:n,newShape:s}=e;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(t.readSync(n.dataId)),i=t.readSync(o.dataId),p=Array.from(t.readSync(s.dataId)),[u,c,l]=d$(i,o.shape,o.dtype,a,p);return[t.makeTensorInfo(c,o.dtype,u),t.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var WF={kernelName:wa,backendName:"webgl",kernelFunc:yZ};function bZ(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=jf(a,o.shape,o.dtype,i,p,!0);return t.makeTensorInfo(c,o.dtype,u)}var UF={kernelName:pi,backendName:"webgl",kernelFunc:bZ};function CZ(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=jf(a,o.shape,o.dtype,i,p);return t.makeTensorInfo(c,o.dtype,u)}var GF={kernelName:ci,backendName:"webgl",kernelFunc:CZ};function SZ(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=S.calculateShapes(s,n,i),d=!1;if(s.dtype==="string"){let x=t.bufferSync(n),b=t.bufferSync(s),C=y.decodeString(t.readSync(a.dataId)[0]),w=p$(x,b,i,m,c,u,p,l,C,d);return t.makeTensorInfo(i,w.dtype,w.values)}let f=new Cc(u,p,n.shape.length,s.shape.length,l,[m,1],d),h=t.runWebGLProgram(f,[s,n,a],s.dtype),g=te({inputs:{x:h},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(h),g}var HF={kernelName:li,backendName:"webgl",kernelFunc:SZ};function wZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=S.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=cs({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var qF={kernelName:$s,backendName:"webgl",kernelFunc:wZ};var KF="return sqrt(x);",IZ=ge({opSnippet:KF,packedOpSnippet:KF,cpuKernelImpl:f$}),jF={kernelName:Gn,backendName:"webgl",kernelFunc:IZ};var vZ="return x * x;",kZ=ge({opSnippet:vZ}),XF={kernelName:mi,backendName:"webgl",kernelFunc:kZ};var YF="return (a - b) * (a - b);",NZ=tt({opSnippet:YF,packedOpSnippet:YF}),QF={kernelName:Kn,backendName:"webgl",kernelFunc:NZ};function TZ({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=Bt+` return x > 0.0 ? 1.0 : float(${e.alpha}); `,s=new Jt(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var ZF={kernelName:Ds,backendName:"webgl",kernelFunc:TZ};var mg=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=o;let n=o.length,s=_e(o.length),a=_e(o.length),i="";if(n===1)i="coords * strides + begin";else{let p=0;i=o.map((u,c)=>(p++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${p-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 _Z(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:w}=ut.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=te({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let $=ut.computeOutShape(b,C,w),A=cs({inputs:{x:n},backend:t,attrs:{begin:b,size:$}});k=te({inputs:{x:A},backend:t,attrs:{shape:f}}),t.disposeIntermediateTensorInfo(A)}else if(t.shouldExecuteOnCPU([n])){let A=t.readSync(n.dataId),R=le(n.shape,n.dtype,A),D=h$(d,R,w,b);k=t.makeTensorInfo(f,n.dtype,D.values)}else{let A=new mg(b,w,d);k=t.runWebGLProgram(A,[n],n.dtype)}let _=te({inputs:{x:k},backend:t,attrs:{shape:f}});return t.disposeIntermediateTensorInfo(k),_}var JF={kernelName:jn,backendName:"webgl",kernelFunc:_Z};function EZ(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=g$(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],"string",f),t.makeTensorInfo(l.shape,"int32",h)]}var eD={kernelName:As,backendName:"webgl",kernelFunc:EZ};function $Z(r){let{inputs:e,backend:t,attrs:o}=r,{skipEmpty:n}=o,{input:s,delimiter:a}=e;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(a.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`);let i=t.readSync(s.dataId),p=t.readSync(a.dataId)[0],[u,c,l]=x$(i,p,n),m=c.length;return[t.makeTensorInfo([m,2],"int32",u),t.makeTensorInfo([m],"string",c),t.makeTensorInfo([2],"int32",new Int32Array(l))]}var tD={kernelName:di,backendName:"webgl",kernelFunc:$Z};function AZ(r){let{inputs:e,backend:t,attrs:o}=r,{numBuckets:n}=o,{input:s}=e;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(n<=0)throw new Error("Number of buckets must be at least 1");let a=t.readSync(s.dataId),i=y$(a,n);return t.makeTensorInfo(s.shape,"int32",i)}var rD={kernelName:fi,backendName:"webgl",kernelFunc:AZ};var RZ="return tan(x);",FZ=ge({opSnippet:RZ}),oD={kernelName:Yn,backendName:"webgl",kernelFunc:FZ};var DZ=` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); `,OZ=ge({opSnippet:DZ}),nD={kernelName:Qn,backendName:"webgl",kernelFunc:OZ};var dg=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, ${r[0]})`;let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],o=[];for(let n=0;n5){let p=t.readSync(n.dataId),u=n.dtype==="string"?p.map(m=>y.decodeString(m)):p,c=le(n.shape,n.dtype,u),l=C$(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new dg(n.shape,s);return t.runWebGLProgram(a,[n],n.dtype)}var sD={kernelName:to,backendName:"webgl",kernelFunc:Vw};var fg=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int elemIdx = coords[1]; // We compare elements pair-wise within a group of size 2 * inc. // The comparing rule for each group alternates between ascending // and descending. Within each group, we compare each pair at // positions i and i+inc. To decide whether an element at position i // is x0 or x1, we mod it by 2 * inc, if the result is smaller than // inc, it is in the first half of the group, we denote it as x0, // otherwise we denote it as x1. // For example, as shown in the Bitonic top K paper referenced above, // Figure5(a) shows that element[1] is in the // second half of the group when group size is 2, but it is in the // first half of the group when group size is 4. bool isFirstInPair = imod(elemIdx, 2 * inc) < inc; int i = isFirstInPair ? elemIdx : elemIdx - inc; int i0 = firstPass == 1 ? i : int(getIndices(batch, i)); int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc)); float x0 = i0 < n ? getX(batch, i0) : negativeInf; float x1 = i1 < n ? getX(batch, i1) : negativeInf; // Denotes which direction indices are in (ascending or descending). bool reverse = imod(elemIdx, 2 * dir) >= dir; bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0); if (reverse == isGreater) { // Elements in opposite order of direction int iTemp = i0; i0 = i1; i1 = iTemp; } if (isFirstInPair) { setOutput(float(i0)); } else { setOutput(float(i1)); } } `}},hg=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=` void main() { // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ... ivec2 coords = getOutputCoords(); int batch = coords[0]; int elemIdx = coords[1]; // The output size is half of the previous size. // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4), // we only need to output the indices at positions |, the indices at // positions _ can be thrown away, see Figure5(b) After Phase 2 // (Merge phase) in the Bitonic Top K paper referenced above. // For example, the paper shows we only need to output the orange bars. // The output sequence should look like this | | | | | | | |. // Because the sequence is halved, to map the output index back // to the previous sequence to find the corresponding value, // we need to double the index. When we double the index, // we basically interpolate a position, so 2i looks like // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position // of each 2k positions by - elemIdx % k. E.g. for output at // index 4,5,6,7, we want to get the corresponding element at // original index 8,9,10,11, for output at index 8,9,10,11, // we want to get the corresponding element at original index // 16,17,18,19, so on and so forth. int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k)); int i0 = firstPass == 1 ? i : int(getIndices(batch, i)); int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k)); float x0 = getX(batch, i0); float x1 = i1 < n ? getX(batch, i1) : x0; setOutput(x0 >= x1 ? float(i0) : float(i1)); } `}};function Bu(r,e){e!==null&&r.disposeIntermediateTensorInfo(e)}function aD(r){let e=1;for(;ep){let D=t.readSync(n.dataId),[P,M]=S$(D,u,n.dtype,s,a);return[t.makeTensorInfo(P.shape,P.dtype,P.values),t.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[t.makeTensorInfo(u,n.dtype,[]),t.makeTensorInfo(u,"int32",[])];if(c===1)return[n,Ga({attrs:{shape:u,dtype:"int32",value:0},backend:t})];let l=t.texData.get(n.dataId),m=l!==null&&l.isPacked,d=m?t.unpackTensor(n):n,h=y.sizeFromShape(u)/c,g=te({inputs:{x:d},attrs:{shape:[h,c]},backend:t});m&&Bu(t,d);let x=aD(s),b=aD(c),C=null,w=()=>C===null?[g,g]:[g,C],k=(D,P,M)=>{let L=w(),W=new fg(M),U=[[c],[C===null?1:0],[Number.NEGATIVE_INFINITY],[D],[P]],q=C;C=t.runWebGLProgram(W,L,"int32",U),Bu(t,q)};for(let D=1;D=1;M/=2)k(P,M,[h,b])}for(let D=b;D>x;D/=2){let P=w(),M=new hg([h,D/2]),W=[[c],[C===null?1:0],[x]],V=C;C=t.runWebGLProgram(M,P,"int32",W),Bu(t,V);let U=x/2,q=U*2;for(let H=U;H>=1;H/=2)k(q,H,C.shape)}let _=C;C=cs({inputs:{x:C},backend:t,attrs:{begin:0,size:[h,s]}}),Bu(t,_);let $=Fw({inputs:{x:g,indices:C},backend:t,attrs:{axis:1,batchDims:1}});Bu(t,g);let A=u.slice(0,-1);A.push(s),_=C,C=te({inputs:{x:C},attrs:{shape:A},backend:t}),Bu(t,_);let R=$;return $=te({inputs:{x:$},attrs:{shape:A},backend:t}),Bu(t,R),[$,C]}var iD={kernelName:Zn,backendName:"webgl",kernelFunc:MZ};var gg=class{constructor(e,t,o,n,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=o==="nearest"?1:2,p;switch(n){case"constant":p=1;break;case"reflect":p=2;break;case"wrap":p=3;break;case"nearest":p=4;break;default:p=1;break}this.userCode=` float mapCoord(float outCoord, float len) { float inCoord = outCoord; if(${p} == 2) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; if (inCoord < sz2) { inCoord = sz2 * float(int(float(-inCoord / sz2))) + inCoord; } inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0; } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; inCoord -= sz2 * float(int(float(inCoord / sz2))); if (inCoord >= len) { inCoord = sz2 - inCoord - 1.0; } } } return clamp(inCoord, 0.0, len - 1.0); } else if (${p} == 3) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz = len - 1.0; inCoord += len * (float(int(float(-inCoord / sz))) + 1.0); } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz = len - 1.0; inCoord -= len * float(int(float(inCoord / sz))); } } return clamp(inCoord, 0.0, len - 1.0); } else if (${p} == 4) { return clamp(outCoord, 0.0, len - 1.0); } else { return outCoord; } } float readWithFillValue(int batch, int coordY, int coordX, int channel) { float outputValue; if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) { outputValue = getImage(batch, coordY, coordX, channel); } else { outputValue = float(${s}); } return outputValue; } void main() { ivec4 coords = getOutputCoords(); float outputValue; int batch = coords[0]; int x = coords[2]; int y = coords[1]; int channel = coords[3]; float xf = float(x); float yf = float(y); float a1 = getTransforms(batch, 0); float a2 = getTransforms(batch, 1); float a3 = getTransforms(batch, 2); float b1 = getTransforms(batch, 3); float b2 = getTransforms(batch, 4); float b3 = getTransforms(batch, 5); float c1 = getTransforms(batch, 6); float c2 = getTransforms(batch, 7); float projection = c1 * xf + c2 * yf + 1.0; if (projection == 0.0) { outputValue = float(${s}); } else { float inX = (a1 * xf + a2 * yf + a3) / projection; float inY = (b1 * xf + b2 * yf + b3) / projection; float mapX = mapCoord(inX, float(${t})); float mapY = mapCoord(inY, float(${e})); if (${i} == 1) { int coordY = int(round(mapY)); int coordX = int(round(mapX)); outputValue = readWithFillValue(batch, coordY, coordX, channel); } else { float yFloor = floor(mapY); float xFloor = floor(mapX); float yCeil = yFloor + 1.0; float xCeil = xFloor + 1.0; float valueYFloor = (xCeil - mapX) * readWithFillValue(batch, int(yFloor), int(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, int(yFloor), int(xCeil), channel); float valueYCeil = (xCeil - mapX) * readWithFillValue(batch, int(yCeil), int(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, int(yCeil), int(xCeil), channel); outputValue = (yCeil - mapY) * valueYFloor + (mapY - yFloor) * valueYCeil; } } setOutput(outputValue); } `}};function LZ(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new gg(l,m,a,i,p,g);return t.runWebGLProgram(x,[n,s],"float32")}var uD={kernelName:Jn,backendName:"webgl",kernelFunc:LZ};function BZ(r){let{inputs:e,attrs:t,backend:o}=r,{axis:n}=t,{x:s}=e;is(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let a=o.readSync(s.dataId),{outputValues:i,outputShape:p,indices:u}=w$(a,n,s.shape,s.dtype);return[o.makeTensorInfo(p,s.dtype,i),o.makeTensorInfo([u.length],"int32",u)]}var pD={kernelName:kp,backendName:"webgl",kernelFunc:BZ};function VZ(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,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeIntermediateTensorInfo(h)),f}var cD={kernelName:Rs,backendName:"webgl",kernelFunc:VZ};var xg=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 p="0.0",u="sumValue",c=Math.floor(o/4)*4,l=o%4,m=` sumValue += dot(values, segFilter); `,d="";s%o>0&&(d=` if (inIdx < 0 || inIdx >= ${s}) { return initializationValue; } `);let f="";s%o>0&&(f=` if (inIdx < 0 || inIdx >= ${s}) { return -1.0; } `),this.userCode=` const float initializationValue = ${p}; float getValue(int batch, int inIdx) { ${d} return getX(batch, inIdx); } float getSegmentIdAtIndex(int inIdx) { ${f} 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 + ${c}; if (${l===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 ); ${m} } else if (${l===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 ); ${m} } else if (${l===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 zZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=S.getAxesPermutation([u],i),l=n;c!=null&&(l=xt({inputs:{x:n},backend:t,attrs:{perm:c}}),p.push(l),u=S.getInnerMostAxes(1,i)[0]);let m=S.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=te({inputs:{x:l},backend:t,attrs:{shape:[-1,d]}});p.push(f);let h=ka(n.dtype),g=(w,k,_,$,A)=>{let R=w.shape[0],D=w.shape[1],P=S.segment_util.segOpComputeOptimalWindowSize(D,A),M={windowSize:P,inSize:D,batchSize:R,numSegments:A},L=new xg(M,k),W=t.compileAndRun(L,[w,_],$);if(p.push(W),W.shape[1]===A)return W;let V=Bw({backend:t,attrs:{start:0,stop:A,step:1,dtype:"float32"}}),U=Vw({inputs:{x:V},backend:t,attrs:{reps:[D/P]}});return p.push(V),p.push(U),g(W,k,U,$,A)},x=g(f,"unsortedSegmentSum",s,h,a),b=te({inputs:{x},backend:t,attrs:{shape:m}}),C=b;if(c!=null){p.push(b);let w=S.getUndoAxesPermutation(c);C=xt({inputs:{x:C},backend:t,attrs:{perm:w}})}return p.forEach(w=>t.disposeIntermediateTensorInfo(w)),C}var lD={kernelName:Np,backendName:"webgl",kernelFunc:zZ};var WZ=[Y$,Z$,J$,eA,rA,oA,nA,sA,uA,pA,cA,lA,mA,dA,fA,hA,gA,xA,yA,bA,CA,wA,IA,vA,_A,$A,AA,V$,FA,OA,PA,MA,LA,BA,VA,zA,WA,UA,GA,KA,jA,XA,YA,QA,ZA,JA,eR,tR,rR,oR,nR,sR,aR,iR,uR,cR,lR,mR,dR,hR,gR,xR,yR,bR,CR,SR,wR,IR,B$,vR,DA,kR,NR,TR,z$,_R,ER,$R,AR,RR,FR,DR,OR,PR,MR,BR,VR,zR,WR,UR,GR,qR,jR,XR,YR,QR,ZR,oF,G$,nF,sF,aF,iF,kA,uF,lF,mF,dF,fF,W$,hF,gF,xF,yF,bF,NA,JR,CF,SF,wF,q$,IF,vF,kF,NF,TF,_F,EF,$F,AF,RF,FF,DF,OF,PF,MF,LF,SA,rF,BF,VF,zF,WF,UF,GF,HF,qF,jF,XF,QF,ZF,JF,eD,tD,rD,tF,j$,oD,nD,sD,iD,uD,X$,pD,cD,lD,pF];for(let r of WZ)Ia(r);var Fe;(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"})(Fe||(Fe={}));var Wi;(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",r[r.sigmoid=5]="sigmoid",r[r.elu=6]="elu"})(Wi||(Wi={}));var mD;function UZ(r){mD=r.wasm.cwrap(fo,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function GZ(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:p,transposeB:u,activation:c,leakyreluAlpha:l}=o,m=t.dataIdMap.get(n.dataId).id,d=t.dataIdMap.get(s.dataId).id,f=0;if(a!=null){let A=t.dataIdMap.get(a.dataId);if(A.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${A.shape.length}.`);f=A.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=Wi[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=p?n.shape[2]:n.shape[1],b=u?s.shape[1]:s.shape[2],C=br.assertAndGetBroadcastShape(n.shape.slice(0,-2),s.shape.slice(0,-2)),w=t.makeOutput([...C,x,b],n.dtype),k=t.dataIdMap.get(w.dataId).id,_=new Uint8Array(new Int32Array(n.shape).buffer),$=new Uint8Array(new Int32Array(s.shape).buffer);return mD(m,_,n.shape.length,d,$,s.shape.length,p,u,g,f,h,l||0,k),w}var dD={kernelName:fo,backendName:"wasm",setupFunc:UZ,kernelFunc:GZ};function Ve(r,e){let t;function o(s){t=s.wasm.cwrap(r,null,["number","number","number"])}function n(s){let{backend:a,inputs:{x:i}}=s,p=a.dataIdMap.get(i.dataId).id,u=a.makeOutput(i.shape,e||i.dtype),c=a.dataIdMap.get(u.dataId).id;return y.sizeFromShape(u.shape)===0||t(p,Fe[i.dtype],c),u}return{kernelName:r,backendName:"wasm",setupFunc:o,kernelFunc:n}}var fD=Ve(gs);function rt(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:p}=a,{a:u,b:c}=p,l=i.dataIdMap.get(u.dataId).id,m=i.dataIdMap.get(c.dataId).id,d=t!=null?t:u.dtype,f=S.assertAndGetBroadcastShape(u.shape,c.shape),h=i.makeOutput(f,d);if(y.sizeFromShape(f)===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;return(()=>o(l,g,u.shape.length,m,x,c.shape.length,Fe[u.dtype],b))(),h}return{kernelName:r,backendName:"wasm",setupFunc:n,kernelFunc:s}}var HZ=!0,hD=rt(eo,HZ);var gD;function qZ(r){gD=r.wasm.cwrap(Mo,null,["array","number","number","number"])}function KZ(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 gD(s,n.length,Fe[o.dtype],a),o}var xD={kernelName:Mo,backendName:"wasm",setupFunc:qZ,kernelFunc:KZ};function Vu(r){let{inputs:{x:e},backend:t}=r;if(e.dtype==="string")return nr(t.readSync(e.dataId),e.shape,e.dtype);let o=t.makeOutput(e.shape,e.dtype),n=t.typedArrayFromHeap(e);return t.typedArrayFromHeap(o).set(n),o}var yD={kernelName:mo,backendName:"wasm",kernelFunc:Vu};var bD;function jZ(r){bD=r.wasm.cwrap(ro,null,["number","array","number","number","number","array","number"])}function uo(r){let{inputs:e,backend:t,attrs:o}=r,[n,s]=YZ(e.x.shape,o.perm),a=!0;for(let f=0;f=n&&(s===-1||o[s]>o[a])&&(s=a);o[s]=n}return[t,o]}var CD={kernelName:ro,backendName:"wasm",kernelFunc:uo,setupFunc:jZ};function kr(r,e,t){let o=r.shape,n=r.shape.length,s=y.parseAxisParam(e,o),a=s,i=S.getAxesPermutation(a,n),p=null,u=!1;if(i!=null){let c=new Array(n);for(let d=0;d`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 ED={kernelName:Ns,backendName:"wasm",kernelFunc:Mt};var $D;function s9(r){$D=r.wasm.cwrap(Wo,null,["number","array","number","number","array","number","number","number","number"])}function a9(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 p=n.shape.length,u=s.shape.length,c=a?n.shape[p-2]:n.shape[p-1],l=i?s.shape[u-1]:s.shape[u-2],m=a?n.shape[p-1]:n.shape[p-2],d=i?s.shape[u-2]:s.shape[u-1],f=n.shape.slice(0,-2),h=s.shape.slice(0,-2),g=y.sizeFromShape(f),x=y.sizeFromShape(h),C=br.assertAndGetBroadcastShape(n.shape.slice(0,-2),s.shape.slice(0,-2)).concat([m,d]);y.assert(c===l,()=>`Error in matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${n.shape} and ${s.shape} and transposeA=${a} and transposeB=${i} must match.`);let w=a?[g,c,m]:[g,m,c],k=i?[x,d,l]:[x,l,d],_=Mt({inputs:{x:n},backend:t,attrs:{shape:w}}),$=Mt({inputs:{x:s},backend:t,attrs:{shape:k}}),A=t.dataIdMap.get(_.dataId).id,R=t.dataIdMap.get($.dataId).id,D=a?_.shape[2]:_.shape[1],P=i?$.shape[1]:$.shape[2],M=Math.max(g,x),L=t.makeOutput([M,D,P],_.dtype),W=t.dataIdMap.get(L.dataId).id,V=new Uint8Array(new Int32Array(_.shape).buffer),U=new Uint8Array(new Int32Array($.shape).buffer);return $D(A,V,_.shape.length,R,U,$.shape.length,a,i,W),t.disposeData(_.dataId),t.disposeData($.dataId),L.shape=C,L}var AD={kernelName:Wo,backendName:"wasm",setupFunc:s9,kernelFunc:a9};function Eo(r){let{inputs:{x:e},attrs:{begin:t,size:o},backend:n}=r,[s,a]=ut.parseSliceParams(e,t,o),i=ut.isSliceContinous(e.shape,s,a),p=n.readSync(e.dataId),u=n.makeOutput(a,e.dtype),c=y.computeStrides(e.shape),l=n.dataIdMap.get(u.dataId);if(i){let f=ut.computeFlatOffset(s,c);return e.dtype==="string"?l.stringBytes=p.slice(f,f+y.sizeFromShape(a)):n.typedArrayFromHeap(u).set(p.subarray(f,f+y.sizeFromShape(a))),u}if(e.dtype==="string"){let f=vu(p,s,a,e.shape,e.dtype);return l.stringBytes=f,u}let m=n.typedArrayFromHeap(u),d=e.shape.length;if(d===2)i9(p,c[0],m,s,a);else if(d===3)u9(p,c[0],c[1],m,s,a);else if(d===4)p9(p,c[0],c[1],c[2],m,s,a);else{let f=vu(p,s,a,e.shape,e.dtype);m.set(f)}return u}function i9(r,e,t,o,n){let s=0,a=o[0],i=o[1],p=a+n[0];for(let u=a;ux*b),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=Mt({inputs:{x:n},backend:t,attrs:{shape:p}}),f=uo({inputs:{x:d},backend:t,attrs:{perm:u}}),h=Mt({inputs:{x:f},backend:t,attrs:{shape:c}}),g=Eo({inputs:{x:h},backend:t,attrs:{begin:l,size:m}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(d.dataId),g}var FD={kernelName:xs,backendName:"wasm",kernelFunc:c9};function ls(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 DD={kernelName:co,backendName:"wasm",kernelFunc:ls};var OD=Ve(Uo);var PD;function l9(r){PD=r.wasm.cwrap(lo,null,["number","number","number","number"])}function m9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i=t.dataIdMap.get(n.dataId).id,p=t.makeOutput(n.shape,n.dtype),u=t.dataIdMap.get(p.dataId).id;return PD(i,s,a,u),p}var MD={kernelName:lo,backendName:"wasm",setupFunc:l9,kernelFunc:m9};function zw(r){let{inputs:e,backend:t}=r,o=y.parseAxisParam(r.attrs.axis,e[0].shape)[0],n=e.map(d=>d.shape);S.assertParamsConsistent(n,o);let s=S.computeOutShape(e.map(d=>d.shape),o),a=e.filter(d=>y.sizeFromShape(d.shape)>0);if(a.length===1)return Vu({inputs:{x:a[0]},backend:t});let i=t.makeOutput(s,e[0].dtype);if(y.sizeFromShape(s)===0)return i;if(a[0].dtype==="string"){let d=a.map(C=>{let k=[-1,y.sizeFromShape(C.shape.slice(o))];return Mt({inputs:{x:C},backend:t,attrs:{shape:k}})}),f=d.map(C=>({vals:t.readSync(C.dataId),shape:C.shape}));s=S.computeOutShape(d.map(C=>C.shape),1);let h=d[0].shape[0]===1,g=Su(f,s,e[0].dtype,h),x=S.computeOutShape(a.map(C=>C.shape),o);i.shape=x;let b=t.dataIdMap.get(i.dataId);return b.stringBytes=S.fromStringArrayToUint8(g),d.forEach(C=>t.disposeData(C.dataId)),i}let p=y.sizeFromShape(a[0].shape.slice(0,o)),u=0,c=a.map(d=>{let f=y.sizeFromShape(d.shape.slice(o));return u+=f,f}),l=a.map(d=>t.typedArrayFromHeap(d)),m=t.typedArrayFromHeap(i);for(let d=0;d`cumprod does not support ${n.dtype} tensors in the WASM backend`);let u=S.getAxesPermutation([s],p),c=n;u!==null&&(c=uo({inputs:{x:n},attrs:{perm:u},backend:t}));let l=S.getInnerMostAxes(1,p)[0];S.assertAxesAreInnerMostDims("cumprod",[l],p);let m=t.makeOutput(c.shape,c.dtype),d=c.shape[l],f=t.dataIdMap.get(c.dataId).id,h=t.dataIdMap.get(m.dataId).id;KD(f,a?1:0,i?1:0,d,h,Fe[n.dtype]);let g=m;if(u!==null){let x=S.getUndoAxesPermutation(u);g=uo({inputs:{x:m},attrs:{perm:x},backend:t}),t.disposeData(c.dataId),t.disposeData(m.dataId)}return g}var jD={kernelName:jo,backendName:"wasm",setupFunc:b9,kernelFunc:C9};var XD;function S9(r){XD=r.wasm.cwrap(Xo,null,["number","number","number","number","number","number"])}function w9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o,p=n.shape.length;y.assert(n.dtype==="float32"||n.dtype==="int32",()=>`cumsum does not support ${n.dtype} tensors in the WASM backend`);let u=S.getAxesPermutation([s],p),c=n;u!==null&&(c=uo({inputs:{x:n},attrs:{perm:u},backend:t}));let l=S.getInnerMostAxes(1,p)[0];S.assertAxesAreInnerMostDims("cumsum",[l],p);let m=t.makeOutput(c.shape,c.dtype),d=c.shape[l],f=t.dataIdMap.get(c.dataId).id,h=t.dataIdMap.get(m.dataId).id;XD(f,a?1:0,i?1:0,d,h,Fe[n.dtype]);let g=m;if(u!==null){let x=S.getUndoAxesPermutation(u);g=uo({inputs:{x:m},attrs:{perm:x},backend:t}),t.disposeData(c.dataId),t.disposeData(m.dataId)}return g}var YD={kernelName:Xo,backendName:"wasm",setupFunc:S9,kernelFunc:w9};var QD;function I9(r){QD=r.wasm.cwrap(Qo,null,["number","number","number","array","number","array","array","number","number"])}function v9(r){let{backend:e,inputs:t,attrs:o}=r,{x:n}=t,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=e.makeOutput(f,"float32"),x=e.dataIdMap.get(n.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer),C=new Uint8Array(new Int32Array(f).buffer),w=new Uint8Array(new Int32Array(y.computeStrides(f)).buffer),k=e.dataIdMap.get(h.dataId).id;return QD(x,s,a==="NHWC"?1:0,b,n.shape.length-1,C,w,f.length,k),h}var ZD={kernelName:Qo,backendName:"wasm",setupFunc:I9,kernelFunc:v9};var JD;function k9(r){JD=r.wasm.cwrap(Zo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function N9(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,a=o.dataIdMap.get(n.dataId).id,i=o.dataIdMap.get(s.dataId).id,{strides:p,dilations:u,pad:c,dimRoundingMode:l}=t,m=u==null?[1,1]:u,d=S.computeConv2DInfo(n.shape,s.shape,p,m,c,l,!0),f=d.filterHeight,h=d.filterWidth,g=d.padInfo.top,x=d.padInfo.right,b=d.padInfo.bottom,C=d.padInfo.left,w=d.dilationHeight,k=d.dilationWidth,_=d.strideHeight,$=d.strideWidth,A=d.inChannels,R=d.outChannels,D=d.padInfo.type==="SAME"?1:0;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let P=o.makeOutput(d.outShape,"float32"),M=o.dataIdMap.get(P.dataId).id;return JD(a,n.shape[0],n.shape[1],n.shape[2],i,f,h,g,x,b,C,D,w,k,_,$,A,R,M),P}var eO={kernelName:Zo,backendName:"wasm",setupFunc:k9,kernelFunc:N9};var tO=Ve(en);var T9=!1,rO=rt(tn,T9,"bool");var oO=Ve(rn,"float32");function yg(r){let{inputs:e,attrs:t,backend:o}=r,{input:n}=e,{dim:s}=t,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),Mt({inputs:{x:n},backend:o,attrs:{shape:i}})}var nO={kernelName:bs,backendName:"wasm",kernelFunc:yg};function Uw(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 sO={kernelName:Cs,backendName:"wasm",kernelFunc:Uw};var aO;function _9(r){aO=r.wasm.cwrap(on,null,["number","number","number","number","number","number"])}function E9(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,p,u,c]=o.shape;return aO(s,i,p,u,c,a),n}var iO={kernelName:on,backendName:"wasm",kernelFunc:E9,setupFunc:_9};var uO=Ve(nn);var $9=!1,pO=rt(sn,$9);var cO;function A9(r){cO=r.wasm.cwrap(an,null,["number","number","number","number","number","number","number"])}function R9(r){let{backend:e,inputs:t,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:p,scale:u}=t,c=e.dataIdMap.get(s.dataId).id,l=e.dataIdMap.get(a.dataId).id,m=e.dataIdMap.get(i.dataId).id,d=p!=null?e.dataIdMap.get(p.dataId).id:0,f=u!=null?e.dataIdMap.get(u.dataId).id:0,h=e.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=e.dataIdMap.get(h.dataId).id;return cO(c,l,m,d,f,n,g),h}var lO={kernelName:an,backendName:"wasm",setupFunc:A9,kernelFunc:R9};var mO;function F9(r){mO=r.wasm.cwrap(ho,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function D9(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=S.computeConv2DInfo(n.shape,s.shape,p,c,u,m),g=Wi[d];if(g==null)throw new Error(`${d} 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,C=h.outChannels,w=0;if(a!=null){let Y=o.dataIdMap.get(a.dataId);if(Y.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Y.shape.length}.`);if(Y.shape[0]!==C)throw new Error(`FusedConv2D bias shape (${Y.shape}) does not match the number of output channels (${C})`);w=Y.id}let k=h.filterHeight,_=h.filterWidth,$=h.padInfo.top,A=h.padInfo.right,R=h.padInfo.bottom,D=h.padInfo.left,P=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,W=h.strideWidth,V=h.inChannels,U=h.padInfo.type==="SAME"?1:0,q=h.batchSize,H=h.inHeight,j=h.inWidth;if(l!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let X=o.makeOutput(h.outShape,"float32"),Z=o.dataIdMap.get(X.dataId).id,ee=i==null?0:o.dataIdMap.get(i.dataId).id;return mO(x,q,H,j,b,k,_,w,$,A,R,D,U,P,M,L,W,V,C,g,ee,f||0,Z),X}var dO={kernelName:ho,backendName:"wasm",setupFunc:F9,kernelFunc:D9};var fO;function O9(r){fO=r.wasm.cwrap(go,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 P9(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=S.computeConv2DInfo(n.shape,s.shape,p,c,u,m,!0),g=Wi[d];if(g==null)throw new Error(`${d} 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,C=h.outChannels,w=0;if(a!=null){let Y=o.dataIdMap.get(a.dataId);if(Y.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Y.shape.length}.`);if(Y.shape[0]!==C)throw new Error(`FusedDepthwiseConv2D bias shape (${Y.shape}) does not match the number of output channels (${C})`);w=Y.id}let k=h.filterHeight,_=h.filterWidth,$=h.padInfo.top,A=h.padInfo.right,R=h.padInfo.bottom,D=h.padInfo.left,P=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,W=h.strideWidth,V=h.inChannels,U=h.padInfo.type==="SAME"?1:0,q=h.batchSize,H=h.inHeight,j=h.inWidth;if(l!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let X=o.makeOutput(h.outShape,"float32"),Z=o.dataIdMap.get(X.dataId).id,ee=i==null?0:o.dataIdMap.get(i.dataId).id;return fO(x,q,H,j,b,k,_,w,$,A,R,D,U,P,M,L,W,V,C,g,ee,f||0,Z),X}var hO={kernelName:go,backendName:"wasm",setupFunc:O9,kernelFunc:P9};var gO;function M9(r){gO=r.wasm.cwrap(un,null,["number","number","number","number","number","number","array","number"])}function L9(r){let{backend:e,inputs:t}=r,{params:o,indices:n}=t,[s,a,i,p]=Ym.prepareAndValidate(o,n),u=e.makeOutput(s,o.dtype);if(a===0)return u;let c=n.shape,l=c[c.length-1],d=e.dataIdMap.get(o.dataId).id,h=e.dataIdMap.get(n.dataId).id,g=new Uint8Array(new Int32Array(p).buffer),x=e.dataIdMap.get(u.dataId).id;return gO(d,Fe[o.dtype],h,a,l,i,g,x),u}var xO={kernelName:un,backendName:"wasm",setupFunc:M9,kernelFunc:L9};var yO;function B9(r){yO=r.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function V9(r){let{backend:e,inputs:t,attrs:o}=r,{x:n,indices:s}=t,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0],u=e.readSync(s.dataId),c=n.shape[p];for(let R=0;R=0,()=>`GatherV2: the index value ${D} is not in [0, ${c-1}]`)}let l=S.segment_util.collectGatherOpShapeInfo(n,s,p,i),m=Mt({inputs:{x:n},attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]},backend:e}),d=y.sizeFromShape(s.shape),f=Mt({inputs:{x:s},attrs:{shape:[l.batchSize,d/l.batchSize]},backend:e}),h=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize],g=e.makeOutput(h,n.dtype);if(y.sizeFromShape(n.shape)===0)return g;let x=m.shape.length-1,C=e.dataIdMap.get(m.dataId).id,k=e.dataIdMap.get(f.dataId).id,_=e.dataIdMap.get(g.dataId).id,$=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),A=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return yO(C,Fe[n.dtype],$,x,k,l.batchSize,A,_),e.disposeData(m.dataId),e.disposeData(f.dataId),g.shape=l.outputShape,g}var bO={kernelName:Ss,backendName:"wasm",setupFunc:B9,kernelFunc:V9};var z9=!1,CO=rt(pn,z9,"bool");var W9=!1,SO=rt(cn,W9,"bool");var wO=Ve(ln,"bool");var IO;function U9(r){IO=r.wasm.cwrap(mn,null,["number","number","number","number"])}function G9(r){let{inputs:{x:e},attrs:{alpha:t},backend:o}=r,n=o.dataIdMap.get(e.dataId).id,s=o.makeOutput(e.shape,"float32");if(y.sizeFromShape(e.shape)!==0){let a=o.dataIdMap.get(s.dataId).id;IO(n,Fe[e.dtype],t,a)}return s}var vO={kernelName:mn,backendName:"wasm",setupFunc:U9,kernelFunc:G9};var H9=!1,kO=rt(dn,H9,"bool");var q9=!1,NO=rt(fn,q9,"bool");var TO=Ve(hn);var K9=!1,_O=rt(gn,K9,"bool");var EO=Ve(xn);var j9=!1,$O=rt(xa,j9,"bool");var X9=!1,AO=rt(GI,X9,"bool");var RO;function Y9(r){RO=r.wasm.cwrap(yn,null,["number","number","number","number"])}function Q9(r){let{backend:e,inputs:t,attrs:o}=r,{reductionIndices:n,keepDims:s}=o,{x:a}=t,p=e.dataIdMap.get(a.dataId).id,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=kr(a,n,e);if(d){let C=e.dataIdMap.get(c.dataId).id;u=c,p=C}let f=u.shape.length;S.assertAxesAreInnerMostDims("max",l,f);let[h,g]=S.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=e.makeOutput(h,a.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;RO(p,Fe[a.dtype],x,C)}if(d&&e.disposeData(c.dataId),s){let C=S.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var FO={kernelName:yn,backendName:"wasm",setupFunc:Y9,kernelFunc:Q9};var Z9=!1,DO=rt(bn,Z9);var OO;function J9(r){OO=r.wasm.cwrap(Cn,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function eJ(r){let{inputs:e,attrs:t,backend:o}=r,n=e.x,s=o.dataIdMap.get(n.dataId).id;y.assert(n.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);let{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=t,c=S.computePool2DInfo(n.shape,a,i,1,p,u),l=c.filterHeight,m=c.filterWidth,d=c.padInfo.top,f=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,C=c.strideHeight,w=c.strideWidth,k=c.inChannels,_=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let $=o.makeOutput(c.outShape,"float32"),A=o.dataIdMap.get($.dataId).id;return OO(s,n.shape[0],n.shape[1],n.shape[2],l,m,d,f,h,g,x,b,C,w,k,_,A),$}var PO={kernelName:Cn,backendName:"wasm",setupFunc:J9,kernelFunc:eJ};var MO;function tJ(r){MO=r.wasm.cwrap(Sn,null,["number, number, number"])}function rJ(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=kr(a,n,e),f=l;if(d){let w=e.dataIdMap.get(c.dataId).id;w!==i&&(u=c,p=w,f=S.getInnerMostAxes(f.length,u.shape.length))}S.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[h,g]=S.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=u;u.dtype!=="float32"&&(b=ls({backend:e,inputs:{x:u},attrs:{dtype:"float32"}}),p=e.dataIdMap.get(b.dataId).id);let C=e.makeOutput(h,"float32");if(y.sizeFromShape(u.shape)!==0){let w=e.dataIdMap.get(C.dataId).id;MO(p,x,w)}if(d&&e.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(C.shape,m);C.shape=w}return u.dtype!=="float32"&&e.disposeData(b.dataId),C}var LO={kernelName:Sn,backendName:"wasm",setupFunc:tJ,kernelFunc:rJ};var BO;function oJ(r){BO=r.wasm.cwrap(wn,null,["number","number","number","number"])}function nJ(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=kr(a,n,e);if(d){let C=e.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C)}let f=u.shape.length;S.assertAxesAreInnerMostDims("min",l,f);let[h,g]=S.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;BO(p,Fe[a.dtype],x,C)}if(d&&e.disposeData(c.dataId),s){let C=S.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var VO={kernelName:wn,backendName:"wasm",setupFunc:oJ,kernelFunc:nJ};var sJ=!1,zO=rt(In,sJ);var Gw;(function(r){r[r.reflect=0]="reflect",r[r.symmetric=1]="symmetric"})(Gw||(Gw={}));var WO;function aJ(r){WO=r.wasm.cwrap(vn,null,["number","array","number","number","array","array","number","number"])}function iJ(r){let{inputs:{x:e},backend:t,attrs:{paddings:o,mode:n}}=r,s=o.map((f,h)=>f[0]+e.shape[h]+f[1]),a=t.dataIdMap.get(e.dataId).id,i=t.makeOutput(s,e.dtype),p=t.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(e.shape).buffer),c=o.map(f=>f[0]),l=o.map(f=>f[1]),m=new Uint8Array(new Int32Array(c).buffer),d=new Uint8Array(new Int32Array(l).buffer);return WO(a,u,e.shape.length,Fe[e.dtype],m,d,Gw[n],p),i}var UO={kernelName:vn,backendName:"wasm",kernelFunc:iJ,setupFunc:aJ};var uJ=!0,GO=rt(kn,uJ);var HO=Ve(ws);function Sc(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 qO;function pJ(r){qO=r.wasm.cwrap(Tn,"number",["number","number","number","number","number"])}function cJ(r){let{backend:e,inputs:t,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a}=o,{boxes:i,scores:p}=t,u=e.dataIdMap.get(i.dataId).id,c=e.dataIdMap.get(p.dataId).id,l=qO(u,c,s,n,a),{pSelectedIndices:m,selectedSize:d,pSelectedScores:f,pValidOutputs:h}=Sc(e,l);return e.wasm._free(f),e.wasm._free(h),e.makeOutput([d],"int32",m)}var KO={kernelName:Tn,backendName:"wasm",setupFunc:pJ,kernelFunc:cJ};var jO;function lJ(r){jO=r.wasm.cwrap(ba,"number",["number","number","number","number","number","bool"])}function mJ(r){let{backend:e,inputs:t,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a,padToMaxOutputSize:i}=o,{boxes:p,scores:u}=t,c=e.dataIdMap.get(p.dataId).id,l=e.dataIdMap.get(u.dataId).id,m=jO(c,l,s,n,a,i),{pSelectedIndices:d,selectedSize:f,pSelectedScores:h,pValidOutputs:g}=Sc(e,m);e.wasm._free(h);let x=e.makeOutput([f],"int32",d),b=e.makeOutput([],"int32",g);return[x,b]}var XO={kernelName:ba,backendName:"wasm",setupFunc:lJ,kernelFunc:mJ};var YO;function dJ(r){YO=r.wasm.cwrap(_n,"number",["number","number","number","number","number","number"])}function fJ(r){let{backend:e,inputs:t,attrs:o}=r,{iouThreshold:n,maxOutputSize:s,scoreThreshold:a,softNmsSigma:i}=o,{boxes:p,scores:u}=t,c=e.dataIdMap.get(p.dataId).id,l=e.dataIdMap.get(u.dataId).id,m=YO(c,l,s,n,a,i),{pSelectedIndices:d,selectedSize:f,pSelectedScores:h,pValidOutputs:g}=Sc(e,m);e.wasm._free(g);let x=e.makeOutput([f],"int32",d),b=e.makeOutput([f],"float32",h);return[x,b]}var QO={kernelName:_n,backendName:"wasm",setupFunc:dJ,kernelFunc:fJ};var hJ=!1,ZO=rt(Nn,hJ,"bool");var JO;function gJ(r){JO=r.wasm.cwrap(En,null,["number","number","number","number","number"])}function xJ(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=t.makeOutput([...n.shape,a],s),c=t.dataIdMap.get(u.dataId).id,m=t.dataIdMap.get(n.dataId).id;return JO(m,a,i,p,c),u}var eP={kernelName:En,backendName:"wasm",setupFunc:gJ,kernelFunc:xJ};function yJ(r){let{inputs:{x:e},backend:t}=r,o=t.makeOutput(e.shape,e.dtype);return t.typedArrayFromHeap(o).fill(1),o}var tP={kernelName:Is,backendName:"wasm",kernelFunc:yJ};function bJ(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return yg({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=[],p=e.map(c=>{let l=yg({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=zw({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var rP={kernelName:vs,backendName:"wasm",kernelFunc:bJ};var oP;function CJ(r){oP=r.wasm.cwrap($n,null,["number","array","number","number","array","array","number","number"])}function SJ(r){let{inputs:{x:e},backend:t,attrs:{paddings:o,constantValue:n}}=r,s=o.map((h,g)=>h[0]+e.shape[g]+h[1]);if(y.sizeFromShape(e.shape)===0)return Uw({backend:t,attrs:{shape:s,value:n,dtype:e.dtype}});let a=t.dataIdMap.get(e.dataId).id,i=t.makeOutput(s,e.dtype),u=t.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(e.shape).buffer),l=o.map(h=>h[0]),m=o.map(h=>h[1]),d=new Uint8Array(new Int32Array(l).buffer),f=new Uint8Array(new Int32Array(m).buffer);return oP(a,c,e.shape.length,Fe[e.dtype],d,f,n,u),i}var bg={kernelName:$n,backendName:"wasm",kernelFunc:SJ,setupFunc:CJ};var wJ=!1,nP=rt(An,wJ);var sP;function IJ(r){sP=r.wasm.cwrap(Rn,null,["number","number","number"])}function vJ(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=s,p=o,u=p;p.dtype!=="float32"&&(u=ls({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),i=t.dataIdMap.get(u.dataId).id);let c=t.makeOutput(o.shape,"float32"),l=t.dataIdMap.get(c.dataId).id;return sP(i,a,l),p.dtype!=="float32"&&t.disposeData(u.dataId),c}var aP={kernelName:Rn,backendName:"wasm",setupFunc:IJ,kernelFunc:vJ};var iP;function kJ(r){iP=r.wasm.cwrap(Fn,null,["number","number","number","number"])}function NJ(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=kr(a,n,e),f=l;if(d){let C=e.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C,f=S.getInnerMostAxes(f.length,u.shape.length))}S.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[h,g]=S.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;iP(p,x,Fe[b.dtype],C)}if(d&&e.disposeData(c.dataId),s){let C=S.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var uP={kernelName:Fn,backendName:"wasm",setupFunc:kJ,kernelFunc:NJ};var TJ=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=Iu(o,n,s,a),p=e.makeOutput([i.length],a);return e.typedArrayFromHeap(p).set(i),p},pP={kernelName:ks,backendName:"wasm",kernelFunc:TJ};var _J=!0,cP=rt(Jo,_J);var lP=Ve(Dn);var mP=Ve(On);var dP=Ve(Ln);var fP;function EJ(r){fP=r.wasm.cwrap(Mn,null,["number","number","number","number","number","number","number","number","number","number"])}function $J(r){let{backend:e,inputs:t,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,[c,l,m,d]=n.shape,f=[c,p,u,d],h=e.dataIdMap.get(n.dataId),g;h.dtype!=="float32"&&(g=ls({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),h=e.dataIdMap.get(g.dataId));let x=h.id,b=e.makeOutput(f,"float32");if(y.sizeFromShape(n.shape)===0)return b;let C=e.dataIdMap.get(b.dataId).id;return fP(x,c,l,m,d,p,u,s?1:0,a?1:0,C),g!=null&&e.disposeData(g.dataId),b}var hP={kernelName:Mn,backendName:"wasm",setupFunc:EJ,kernelFunc:$J};var gP;function AJ(r){gP=r.wasm.cwrap(Pn,null,["number","number","number","number","number","number","number","number","number","number"])}function RJ(r){let{backend:e,inputs:t,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,[c,l,m,d]=n.shape,f=[c,p,u,d],h=e.makeOutput(f,"float32");if(y.sizeFromShape(n.shape)===0)return h;let g=e.dataIdMap.get(n.dataId),x;g.dtype!=="float32"&&(x=ls({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),g=e.dataIdMap.get(x.dataId));let b=g.id,C=e.dataIdMap.get(h.dataId).id;return gP(b,c,l,m,d,p,u,s?1:0,a?1:0,C),x!=null&&e.disposeData(x.dataId),h}var xP={kernelName:Pn,backendName:"wasm",setupFunc:AJ,kernelFunc:RJ};var yP;function FJ(r){yP=r.wasm.cwrap(Bn,null,["number","array","number","array","number","number"])}function DJ(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 Vu({inputs:{x:n},backend:t});let i=t.makeOutput(n.shape,n.dtype),p=t.dataIdMap.get(n.dataId).id,u=t.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),l=new Uint8Array(new Int32Array(n.shape).buffer);yP(p,c,a.length,l,n.shape.length,u);let m=Mt({inputs:{x:i},attrs:{shape:n.shape},backend:t});return t.disposeData(i.dataId),m}var bP={kernelName:Bn,backendName:"wasm",kernelFunc:DJ,setupFunc:FJ};var CP;function OJ(r){CP=r.wasm.cwrap(es,null,["number","number","number","number","number","number","number","number","array","number","number"])}function PJ(r){let{inputs:e,backend:t,attrs:o}=r,{image:n}=e,{radians:s,fillValue:a,center:i}=o,p=t.makeOutput(n.shape,n.dtype),u=t.dataIdMap.get(n.dataId).id,c=t.dataIdMap.get(p.dataId).id,[l,m,d,f]=n.shape,[h,g]=S.getImageCenter(i,m,d),x=a===0,b=255,C=typeof a=="number"?[a,a,a,x?0:b]:[...a,b],w=new Uint8Array(new Int32Array(C).buffer);return CP(u,l,m,d,f,s,h,g,w,C.length,c),p}var SP={kernelName:es,backendName:"wasm",kernelFunc:PJ,setupFunc:OJ};var wP=Ve(Ca);var IP=Ve(Vn);var vP;function MJ(r){vP=r.wasm.cwrap(zn,null,["number","number","number","number","number","number","array","number","number"])}function LJ(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:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=rl.calculateShapes(s,n,a),f=e.dataIdMap.get(n.dataId).id,g=e.dataIdMap.get(s.dataId).id,x=new Uint8Array(new Int32Array(l).buffer),b=e.dataIdMap.get(i.dataId).id;return vP(f,g,Fe[s.dtype],p,u,c,x,m,b),i}var kP={kernelName:zn,backendName:"wasm",setupFunc:MJ,kernelFunc:LJ};var NP;function BJ(r){NP=r.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function VJ(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,p=t.dataIdMap.get(s.dataId).id,u=t.makeOutput(n.shape,n.dtype),c=t.dataIdMap.get(u.dataId).id,l=o.shape.length,m=n.shape.length,d=l===0||l>1||m===1?1:y.sizeFromShape(n.shape.slice(1));return NP(a,i,p,d,c),u}var TP={kernelName:Ts,backendName:"wasm",kernelFunc:VJ,setupFunc:BJ};var _P;function zJ(r){_P=r.wasm.cwrap(Un,null,["number","number"])}function WJ(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||_P(o,s),n}var EP={kernelName:"Sigmoid",backendName:"wasm",setupFunc:zJ,kernelFunc:WJ};var $P=Ve(Wn);var AP;function UJ(r){AP=r.wasm.cwrap(qn,null,["number","number","number","number"])}function GJ(r){let{backend:e,inputs:{logits:t},attrs:{dim:o}}=r,n=e.dataIdMap.get(t.dataId).id,s=e.makeOutput(t.shape,t.dtype),a=e.dataIdMap.get(s.dataId).id,i=t.shape[o],p=y.sizeFromShape(t.shape)/i;return y.sizeFromShape(s.shape)===0||AP(n,a,i,p),s}var RP={kernelName:qn,backendName:"wasm",setupFunc:UJ,kernelFunc:GJ};function HJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o,i=y.sizeFromShape(s),p=[[0,0]];p.push(...a);for(let _=1+s.length;_0?p+1:0;if(c<0)throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let l=n.shape.slice();l[0]=c;let m=t.dataIdMap.get(n.dataId).id,d=t.dataIdMap.get(s.dataId).id,f=t.dataIdMap.get(a.dataId).id,h=t.makeOutput(l,n.dtype),g=t.dataIdMap.get(h.dataId).id,x=t.makeOutput([4],"int32"),b=t.dataIdMap.get(x.dataId).id;LP(m,Fe[n.dtype],n.shape[0],d,f,g,b,e,0);let C=t.readSync(x.dataId),w;switch(C[0]){case 0:{w=S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{w=S.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:w=S.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(C[1],C[2]);break;case 3:w=S.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(C[1],C[2],C[3]);break;default:w=""}if(t.disposeData(x.dataId),w)throw t.disposeData(h.dataId),new Error(w);return h}function YJ(r){return Sg(r,!0)}var BP={kernelName:pi,backendName:"wasm",setupFunc:Cg,kernelFunc:YJ};function QJ(r){return Sg(r,!1)}var VP={kernelName:ci,backendName:"wasm",setupFunc:Cg,kernelFunc:QJ};function ZJ(r){let{inputs:e,attrs:t,backend:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=t,i=y.parseAxisParam(a,n.shape)[0],p=S.prepareSplitSize(n,s,i),u=new Array(n.shape.length).fill(0),c=n.shape.slice();return p.map(l=>{let m=[...c];m[i]=l;let d=Eo({inputs:{x:n},attrs:{begin:u,size:m},backend:o});return u[i]+=l,d})}var zP={kernelName:$s,backendName:"wasm",kernelFunc:ZJ};var WP=Ve(Gn);var UP=Ve(mi);var JJ=!0,GP=rt(Kn,JJ);var HP;function eee(r){HP=r.wasm.cwrap(Ds,null,["number","number","number","number"])}function tee(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),p=e.dataIdMap.get(i.dataId).id;return HP(a,n,Fe[s.dtype],p),i}var qP={kernelName:Ds,backendName:"wasm",setupFunc:eee,kernelFunc:tee};var KP;function ree(r){KP=r.wasm.cwrap(jn,null,["number","array","number","array","array","array","array","array","number","number"])}function oee(r){let{backend:e,inputs:t,attrs:o}=r,{x:n}=t,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:w}=ut.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=Mt({inputs:{x:n},backend:e,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=ut.computeOutShape(b,C,w),$=Eo({inputs:{x:n},backend:e,attrs:{begin:b,size:_}});k=Mt({inputs:{x:$},backend:e,attrs:{shape:f}}),e.disposeData($.dataId)}else{let _=e.makeOutput(d,"float32"),$=e.dataIdMap.get(n.dataId).id,A=new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer),R=new Uint8Array(new Int32Array(b).buffer),D=new Uint8Array(new Int32Array(C).buffer),P=new Uint8Array(new Int32Array(w).buffer),M=new Uint8Array(new Int32Array(d).buffer),L=new Uint8Array(new Int32Array(y.computeStrides(d)).buffer),W=e.dataIdMap.get(_.dataId).id;KP($,A,n.shape.length,R,D,P,M,L,d.length,W),k=Mt({inputs:{x:_},backend:e,attrs:{shape:f}}),e.disposeData(_.dataId)}return k}var jP={kernelName:jn,backendName:"wasm",setupFunc:ree,kernelFunc:oee};function nee(r){let{backend:e,inputs:t,attrs:o}=r,{data:n,dataSplits:s}=t,{separator:a,nGramWidths:i,leftPad:p,rightPad:u,padWidth:c,preserveShortSequences:l}=o,m=e.readSync(n.dataId),d=e.readSync(s.dataId),[f,h]=ku(m,d,a,i,p,u,c,l),g=e.makeOutput([f.length],"string"),x=e.dataIdMap.get(g.dataId);x.stringBytes=f;let b=e.makeOutput(s.shape,"int32");return e.typedArrayFromHeap(b).set(h),[g,b]}var XP={kernelName:As,backendName:"wasm",kernelFunc:nee};function see(r){let{backend:e,inputs:t,attrs:o}=r,{input:n,delimiter:s}=t,{skipEmpty:a}=o,i=e.readSync(n.dataId),p=e.readSync(s.dataId),[u,c,l]=Nu(i,p[0],a),m=c.length,d=e.makeOutput([m,2],"int32");e.typedArrayFromHeap(d).set(u);let h=e.makeOutput([m],"string"),g=e.dataIdMap.get(h.dataId);g.stringBytes=c;let x=e.makeOutput([2],"int32");return e.typedArrayFromHeap(x).set(l),[d,h,x]}var YP={kernelName:di,backendName:"wasm",kernelFunc:see};function aee(r){let{backend:e,inputs:t,attrs:o}=r,{input:n}=t,{numBuckets:s}=o,a=e.readSync(n.dataId),i=Tu(a,s),p=e.makeOutput(n.shape,"int32");return e.typedArrayFromHeap(p).set(i),p}var QP={kernelName:fi,backendName:"wasm",kernelFunc:aee};var iee=!0,ZP=rt(Xn,iee);var JP;function uee(r){JP=r.wasm.cwrap(Hn,null,["number","number","number","number"])}function pee(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=kr(a,n,e),f=l;if(d){let C=e.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C,f=S.getInnerMostAxes(f.length,u.shape.length))}S.assertAxesAreInnerMostDims("sum",f,u.shape.length);let[h,g]=S.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;JP(p,x,Fe[b.dtype],C)}if(d&&e.disposeData(c.dataId),s){let C=S.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var e3={kernelName:Hn,backendName:"wasm",setupFunc:uee,kernelFunc:pee};var t3=Ve(Yn);var r3=Ve(Qn);var o3;function cee(r){o3=r.wasm.cwrap(to,null,["number","array","number","array","number","number"])}function lee(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),p=o.shape.slice();p[p.length-1]=n;let u=e.makeOutput(p,o.dtype),c=e.dataIdMap.get(u.dataId).id,l=e.makeOutput(p,"int32"),m=e.dataIdMap.get(l.dataId).id;return s3(a,i,o.shape.length,Fe[o.dtype],n,s,c,m),[u,l]},a3={kernelName:Zn,backendName:"wasm",setupFunc:mee,kernelFunc:dee};var i3;function fee(r){i3=r.wasm.cwrap(Jn,null,["number","number","bool","number","number","number","number","number","number","array","number","array","number","number","number","number","number"])}function hee(r){let{backend:e,inputs:t,attrs:o}=r,{image:n,transforms:s}=t,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer),b=new Uint8Array(new Int32Array(y.computeStrides(g)).buffer),C=e.makeOutput(g,n.dtype),w=e.dataIdMap.get(C.dataId).id,_=e.dataIdMap.get(n.dataId).id,A=e.dataIdMap.get(s.dataId).id,R=a==="nearest"?1:2,D;switch(i){case"constant":D=1;break;case"reflect":D=2;break;case"wrap":D=3;break;case"nearest":D=4;break;default:D=1;break}return i3(_,A,s.shape[0]>1,c,f,h,d,m,l,x,n.shape.length-1,b,g.length-1,R,D,p,w),C}var u3={kernelName:Jn,backendName:"wasm",setupFunc:fee,kernelFunc:hee};function gee(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,p=new Array(i-1),u=0;for(let d=0;d({dataId:d,dtype:f,shape:p}))}var p3={kernelName:Rs,backendName:"wasm",kernelFunc:gee};function xee(r){let{inputs:{x:e},backend:t}=r,o=t.makeOutput(e.shape,e.dtype);return t.typedArrayFromHeap(o).fill(0),o}var c3={kernelName:Fs,backendName:"wasm",kernelFunc:xee};var yee=[dD,fD,hD,xD,wD,vD,ND,_D,AD,FD,DD,OD,MD,LD,VD,WD,UD,GD,qD,jD,YD,ZD,eO,tO,rO,oO,nO,sO,iO,uO,pO,lO,dO,hO,xO,bO,CO,SO,yD,wO,vO,kO,NO,TO,_O,EO,$O,AO,FO,DO,PO,LO,VO,zO,UO,GO,HO,KO,XO,QO,ZO,eP,tP,rP,bg,nP,aP,uP,pP,cP,lP,mP,dP,ED,hP,xP,bP,SP,wP,IP,kP,TP,EP,$P,RD,RP,FP,OP,MP,BP,VP,zP,WP,UP,GP,qP,jP,XP,YP,QP,ZP,e3,t3,r3,n3,a3,u3,CD,p3,c3];for(let r of yee)Ia(r);var Hw=O();Hw.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>{try{return 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]))}catch(r){return!1}});Hw.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(Hw.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 Zw=rp(f3()),C3=rp(g3()),Jw=rp(x3());var y3=Zw.default||Zw,bee=Jw.default||Jw,Pl=class extends Zr{constructor(e){super(),this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(w3),Qw=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new Do(this,cr())}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),p=i*y.bytesPerElement(n),u=this.wasm._malloc(p);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,p),u)}async read(e){return this.readSync(e)}readSync(e,t,o){let{memoryOffset:n,dtype:s,shape:a,stringBytes:i}=this.dataIdMap.get(e);if(s==="string")return(t==null||t===0)&&(o==null||o>=i.length)?i:i.slice(t,o);t=t||0,o=o||y.sizeFromShape(a);let p=y.bytesPerElement(s),u=this.wasm.HEAPU8.slice(n+t*p,n+o*p);return See(u.buffer,s)}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(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),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 ${t}`)}}};function Cee(r){return(e,t)=>(y.fetch(r,{credentials:"same-origin"}).then(o=>{o.ok||e.env.a(`failed to load wasm binary file at '${r}'`),o.arrayBuffer().then(n=>{WebAssembly.instantiate(n,e).then(s=>{t(s.instance,s.module)})})}),{})}function b3(r,e,t){if(vg!=null)return vg;let o="tfjs-backend-wasm.wasm";return r&&e?o="tfjs-backend-wasm-threaded-simd.wasm":r&&(o="tfjs-backend-wasm-simd.wasm"),Dl!=null&&Dl[o]!=null?Dl[o]:t+o}async function S3(){let[r,e]=await Promise.all([O().getAsync("WASM_HAS_SIMD_SUPPORT"),O().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((t,o)=>{let n={};n.locateFile=(i,p)=>{if(i.endsWith(".worker.js")){let u=C3.wasmWorkerContents.replace(/\n/g,"\\n"),c=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(c)}return i.endsWith(".wasm")?b3(r,e,Fl!=null?Fl:p):p+i},eI&&(n.instantiateWasm=Cee(b3(r,e,Fl!=null?Fl:"")));let s=!1;n.onAbort=()=>{if(s||Ol)return;Ol=!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"})};let a;e&&r&&vg==null?(n.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+y3.toString()],{type:"text/javascript"}),a=y3(n)):a=bee(n),a.then(i=>{s=!0,Ol=!1;let p=null;i.tfjs={init:i.cwrap("init",null,[]),initWithThreadsCount:i.cwrap("init_with_threads_count",null,["number"]),getThreadsCount:i.cwrap("get_threads_count","number",[]),registerTensor:i.cwrap("register_tensor",null,["number","number","number"]),disposeData:i.cwrap("dispose_data",p,["number"]),dispose:i.cwrap("dispose",p,[])},t({wasm:i})}).catch(o)})}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 wee=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],vg=null,Fl=null,Dl={},Ol=!1,eI=!1;function Iee(r,e=!1){if(eC("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),Ol)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");vg=r,eI=e}function vee(r,e=!1){if(Ol)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")Fl=r;else{Dl=r;let t=wee.filter(o=>Dl[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.`)}eI=e}var w3=-1,Qw=-1;function kee(r){w3=r}function Nee(){if(Qw===-1)throw new Error("WASM backend not initialized.");return Qw}var Tee="4.1.0";var _ee=2;Ci("wasm",async()=>{let{wasm:r}=await S3();return new Pl(r)},_ee);var ms=O();ms.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);ms.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);ms.registerFlag("WEBGPU_MATMUL_PROGRAM_TYPE",()=>-1);ms.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);ms.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);ms.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);ms.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);ms.registerFlag("WEBGPU_IMPORT_EXTERNAL_TEXTURE",()=>!0);ms.registerFlag("WEBGPU_USE_NAIVE_CONV2D_DEBUG",()=>!1);ms.registerFlag("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL",()=>0);ms.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);var kg=class{constructor(e){e&&(this.vendor=e.vendor,this.architecture=e.architecture,this.intelGPUGeneration=this.getIntelGPUGeneration())}getIntelGPUGeneration(){if(this.isIntel()){if(this.architecture.startsWith("gen"))return Number(this.architecture.match(/\d+/));if(this.architecture.startsWith("xe"))return 12}return 0}isIntel(){return this.vendor==="intel"}};var Ng=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,o=!1){let n=I3(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(a),a}this.numBytesAllocated+=e;let s=this.device.createBuffer({size:e,usage:t,mappedAtCreation:o});return this.usedBuffers.get(n).push(s),s}releaseBuffer(e,t,o){if(this.freeBuffers.size===0)return;let n=I3(t,o);this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.freeBuffers.get(n).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let s=this.usedBuffers.get(n),a=s.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");s.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,o){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,o)},n=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function I3(r,e){return`${r}_${e}`}var Tg=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,o,n){let s=k3(o),a=e*t*s,i=v3(e,t,o,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let u=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(u),u}this.numBytesAllocated+=a;let p=this.device.createTexture({size:[e,t],format:o,usage:n});return this.usedTextures.get(i).push(p),p}releaseTexture(e,t,o,n,s){if(this.freeTextures.size===0)return;let a=v3(t,o,n,s);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(a),p=i.indexOf(e);if(p<0)throw new Error("Cannot release a texture that was never provided by this texture manager");i.splice(p,1);let u=k3(n),c=t*o*u;this.numBytesUsed-=c}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function v3(r,e,t,o){return`${r}_${e}_${t}_${o}`}function k3(r){if(r==="rgba8unorm")return 16;throw new Error(`${r} is not supported!`)}function N3(r,e){if(Math.max(...r)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let t=r.length,o=r.map(s=>`${e}[${s}]`),n=new Array(t-1);n[t-2]=o[t-1];for(let s=t-3;s>=0;--s)n[s]=`(${n[s+1]} * ${o[s+1]})`;return n}var A3=(r,e,t,o)=>{let n={dtype:o.dtype,shape:o.shape},s=$ee(t,n,e),a=r.createShaderModule({code:s,label:e.constructor.name});return r.createComputePipeline({compute:{module:a,entryPoint:"_start"},label:e.constructor.name,layout:"auto"})};function Rt(r){if(r<=1)return"i32";if(r===2)return"vec2";if(r===3)return"vec3";if(r===4)return"vec4";if(r===5)return"vec5";if(r===6)return"vec6";throw Error(`GPU for rank ${r} is not yet supported`)}function $o(r){if(r===0)return"x";if(r===1)return"y";if(r===2)return"z";if(r===3)return"w";if(r===4)return"u";if(r===5)return"v";throw Error(`Index ${r} is not yet supported`)}function se(...r){let e;switch(r.length){case 0:e=` fn main() `;break;case 1:e=` fn main(${r[0]} : i32) `;break;default:throw Error("Unreachable")}return e}function T3(r){let e;return e=` ${Eee()} fn _start(@builtin(local_invocation_id) LocalId : vec3, @builtin(global_invocation_id) GlobalId : vec3, @builtin(local_invocation_index) LocalIndex: u32, @builtin(workgroup_id) WorkgroupId : vec3, @builtin(num_workgroups) NumWorkgroups : vec3) { localId = LocalId; localIndex = LocalIndex; globalId = GlobalId; numWorkgroups = NumWorkgroups; workgroupId = WorkgroupId; ${r?"main(getGlobalIndex());":"main();"}; } `,e}function Eee(){return` @compute @workgroup_size(workgroupSizeX, workgroupSizeY, workgroupSizeZ) `}function $ee(r,e,t){let o=[],n=t.workgroupSize[0]*t.workgroupSize[1]*t.workgroupSize[2];if(o.push(` const workgroupSizeX = ${t.workgroupSize[0]}u; const workgroupSizeY = ${t.workgroupSize[1]}u; const workgroupSizeZ = ${t.workgroupSize[2]}u; var localId: vec3; var localIndex: u32; var globalId: vec3; var numWorkgroups: vec3; var workgroupId: vec3; // Only used when the y/z dimension of workgroup size is 1. fn getGlobalIndex() -> i32 { ${F3(t)?" return i32(globalId.x);":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y + workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n} + localIndex); `} } `),t.isFromPixels){o.push(` struct Uniform { size : i32, numChannels : i32, outShapeStrides : vec2, }; @group(0) @binding(0) var result: array<${wc(e.dtype,t.isVec4)}>; @group(0) @binding(2) var uniforms: Uniform; `);let f=$3(t);return[_3,o.join(` `),E3(e.shape),t.getUserCode(),T3(f)].join(` `)}let s="struct Uniforms { NAN : f32, INFINITY : f32, ";t.variableNames.forEach((f,h)=>{let g=Rt(r[h].shape.length);s+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `});let a=Rt(e.shape.length);s+=`outShape : ${a}, `;let i=e.shape.length-1,p=Rt(i);s+=` outShapeStrides: ${p}, `,t.size&&(s+="size : i32, "),t.uniforms&&(s+=t.uniforms),s+="};",s=Lee(s),o.push(s),t.atomic?o.push(` @group(0) @binding(0) var result: array>; `):o.push(` @group(0) @binding(0) var result: array<${wc(e.dtype,t.isVec4)}>; `),t.variableNames.forEach((f,h)=>{o.push(` @group(0) @binding(${1+h}) var ${f}: array<${t.variableTypes?t.variableTypes[h]:wc(r[h].dtype,t.isVec4)}>; `)}),s!==""&&o.push(` @group(0) @binding(${1+t.variableNames.length}) var uniforms: Uniforms; `);let u=Oee(e.shape,t.dispatchLayout),c=[_3+Aee,o.join(` `),E3(e.shape),u,Pee(e.shape.length)];t.atomic||c.push(Mee(e.shape,e.dtype,t.isVec4));let l=r.map((f,h)=>Dee(f,e.shape,t.variableTypes?t.variableTypes[h]==="vec4":t.isVec4,t.dispatchLayout.x.length===e.shape.length)).join(` `);c.push(l),c.push(t.getUserCode());let m=$3(t);return c.push(T3(m)),c.join(` `)}function R3(r,e,t,o){let n=r.shaderKey;if(r.isFromPixels)return n;let s=t.map(c=>c.dtype).concat(o.dtype),a=t.map(c=>S.getBroadcastDims(c.shape,o.shape)),i=t.map(c=>y.arraysEqual(c.shape,o.shape)).join("_"),p=a.map(c=>c.join("_")).join(";"),u=F3(r)?"flatDispatch":"";return n+="_"+(r.workgroupSize?r.workgroupSize.join(","):"")+e.map(c=>c.length).join(",")+s.join(",")+r.variableNames.join(",")+p+i+u,n}var _3=` struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32}; struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32}; // Checks whether coordinates lie within the bounds of the shape. fn coordsInBounds2D(coord : vec2, shape : vec2) -> bool { return all(coord >= vec2(0)) && all(coord < shape); } fn coordsInBounds3D(coord : vec3, shape : vec3) -> bool { return all(coord >= vec3(0)) && all(coord < shape); } fn coordsInBounds4D(coord : vec4, shape : vec4) -> bool { return all(coord >= vec4(0)) && all(coord < shape); } fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 { return coord; } fn getIndexFromCoords2D(coords : vec2, shape : vec2) -> i32 { return dot(coords, vec2(shape.y, 1)); } fn getIndexFromCoords3D(coords : vec3, shape : vec3) -> i32 { return dot(coords, vec3(shape.y * shape.z, shape.z, 1)); } fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { return dot(coords, vec4( shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); } fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 { let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1); return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u; } fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 { let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1); return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v; } fn idiv(a: i32, b: i32, sign: f32) -> i32 { var res: i32 = a / b; let modulo: i32 = a % b; if (sign < 0. && modulo != 0) { res = res - 1; } return res; } // NaN defination in IEEE 754-1985 is : // - sign = either 0 or 1. // - biased exponent = all 1 bits. // - fraction = anything except all 0 bits (since all 0 bits represents infinity). // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers fn isnan(val: f32) -> bool { let floatToUint: u32 = bitcast(val); return (floatToUint & 0x7fffffffu) > 0x7f800000u; } fn isnanVec4(val : vec4) -> vec4 { return vec4(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3])); } `,Aee=` fn isinf(val: f32) -> bool { return abs(val) == uniforms.INFINITY; } `;function E3(r){let e=r.length;if(e<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let t=y.computeStrides(r),o=Rt(e),n=[];for(let a=0;a vec2 { let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides; return vec2(d0, d1); }`;let s;return s="var index2 = index;"+t.map((a,i)=>{let p=`let ${n[i]} = index2 / uniforms.outShapeStrides.${$o(i)}`,u=i===t.length-1?`let ${n[i+1]} = index2 - ${n[i]} * uniforms.outShapeStrides.${$o(i)}`:`index2 = index2 - ${n[i]} * uniforms.outShapeStrides.${$o(i)}`;return`${p}; ${u};`}).join(""),` fn getCoordsFromIndex(index : i32) -> ${o} { ${s} return ${o}(${n.join(",")}); } `}function Ree(r,e){let t=r.name,o=r.shape.length,n=Rt(o),s="get"+t.charAt(0).toUpperCase()+t.slice(1),a=["d0","d1","d2","d3","d4","d5"].slice(0,o),i=a.map(c=>`${c} : i32`).join(", ");if(o<1)return e?` fn ${s}() -> vec4 { return vec4(${t}[0]); } `:` fn ${s}() ->f32 { return f32(${t}[0]); } `;let p=`uniforms.${t.charAt(0).toLowerCase()+t.slice(1)}Shape`,u=`${o}D`;return o===0&&(u="1D"),e?` fn ${s}(${i}) -> vec4 { return vec4(${t}[getIndexFromCoords${u}(${n}(${a.join(",")}), ${p}) / 4]); } `:` fn ${s}(${i}) -> f32 { return f32(${t}[getIndexFromCoords${u}(${n}(${a.join(",")}), ${p})]); } `}function Fee(r,e,t,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a="get"+s+"ByOutput",i=r.shape.length,p=e.length,u=Rt(p);if(y.arraysEqual(r.shape,e)&&o)return t?` fn ${a}Index(globalIndex : i32) -> vec4 { return vec4(${n}[globalIndex]); } fn ${a}Coords(coords : ${u}) -> vec4 { return vec4(${n}[${p>1?"getOutputIndexFromCoords(coords)":"coords"} / 4]); } `:` fn ${a}Index(globalIndex : i32) -> f32 { return f32(${n}[globalIndex]); } fn ${a}Coords(coords : ${u}) -> f32 { return f32(${n}[${p>1?"getOutputIndexFromCoords(coords)":"coords"}]); } `;let c=S.getBroadcastDims(r.shape,e),l=p-i,m="";if(i===0)return t?` fn ${a}Index(globalIndex : i32) -> vec4 { return get${s}(); } fn ${a}Coords(coords : ${u}) -> vec4 { return get${s}(); } `:` fn ${a}Index(globalIndex : i32) -> f32{ return get${s}(); } fn ${a}Coords(coords : ${u}) -> f32{ return get${s}(); } `;p<2&&c.length>=1?m="coords = 0;":m=c.map(g=>`coords.${$o(g+l)} = 0;`).join(` `);let d="";if(p<2&&i>0)d="coords";else if(p>1){let g=Rt(i),x=r.shape.map((b,C)=>`coords.${$o(C+l)}`).join(", ");d=`${g}(${x})`}else d="coords";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return t?` fn ${a}Index(globalIndex : i32) -> vec4 { var coords = getCoordsFromIndex(globalIndex); ${m} return ${n}[getIndexFromCoords${h}(${d}, ${f}) / 4]; } fn ${a}Coords(coordsIn : ${u}) -> vec4 { var coords = coordsIn; ${m} return ${n}[getIndexFromCoords${h}(${d}, ${f}) / 4]; } `:` fn ${a}Index(globalIndex : i32) -> f32 { var coords = getCoordsFromIndex(globalIndex); ${m} return f32(${n}[getIndexFromCoords${h}(${d}, ${f})]); } fn ${a}Coords(coordsIn : ${u}) -> f32 { var coords = coordsIn; ${m} return f32(${n}[getIndexFromCoords${h}(${d}, ${f})]); } `}function Dee(r,e,t,o){let n=Ree(r,t);return r.shape.length<=e.length&&(n+=Fee(r,e,t,o)),n}function Oee(r,e){let{x:t,y:o=[],z:n=[]}=e,s=r.length,a=t.length+o.length+n.length;if(a!==s)return"";if(t.length===s)return`fn getOutputCoords() -> ${Rt(s)}{ let globalIndex = getGlobalIndex(); return getCoordsFromIndex(globalIndex); } `;let i="",p=[t,o,n];for(let m=0;m ${c} { ${i} `;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(",")}); }`,l}function Pee(r){let e="";switch(r){case 0:case 1:e+=` fn getOutputIndexFromCoords(coords : i32) -> i32 { return coords; } `;break;case 2:e+=` fn getOutputIndexFromCoords(coords : vec2) -> i32 { return dot(coords, vec2(uniforms.outShapeStrides, 1)); } `;break;case 3:e+=` fn getOutputIndexFromCoords(coords : vec3) -> i32 { return dot(coords, vec3(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1)); } `;break;case 4:e+=` fn getOutputIndexFromCoords(coords : vec4) -> i32 { return dot(coords, vec4( uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1)); } `;break;case 5:e+=` fn getOutputIndexFromCoords(coords : vec5) -> i32 { return coords.x * uniforms.outShapeStrides.x + coords.y * uniforms.outShapeStrides.y + coords.z * uniforms.outShapeStrides.z + coords.w * uniforms.outShapeStrides.w + coords.u; } `;break;case 6:e+=` fn getOutputIndexFromCoords(coords : vec6) -> i32 { return coords.x * uniforms.outShapeStrides.x + coords.y * uniforms.outShapeStrides.y + coords.z * uniforms.outShapeStrides.z + coords.w * uniforms.outShapeStrides.w + coords.u * uniforms.outShapeStrides.u + coords.v; } `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return e}function F3(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function wc(r,e){return r==="float32"?e?"vec4":"f32":r==="int32"||r==="bool"?e?"vec4":"i32":r}function Mee(r,e,t){let o=r.length,n=wc(e,t),s;if(t?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4) { result[flatIndex] = ${n}(value); } fn setOutputAtIndexI32(flatIndex : i32, value : vec4) { result[flatIndex] = ${n}(value); }`:s=`fn setOutputAtIndex(flatIndex : i32, value : f32) { result[flatIndex] = ${n}(value); } fn setOutputAtIndexI32(flatIndex : i32, value : i32) { result[flatIndex] = ${n}(value); }`,o>=2){let a=["d0","d1","d2","d3","d4","d5"].slice(0,o),i=Rt(o);t?s+=` fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(", ")}, value : vec4) { let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")})); setOutputAtIndex(flatIndex / 4, value); } fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(", ")}, value : vec4) { let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")})); setOutputAtIndexI32(flatIndex / 4, value); } `:s+=` fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(", ")}, value : f32) { let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")})); setOutputAtIndex(flatIndex, value); } fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(", ")}, value : i32) { let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")})); setOutputAtIndexI32(flatIndex, value); } `}return s}function Lee(r){let e=/(\w+)\s*:\s*vec(5|6)/g;r=r.replace(e,o=>"@align(16) "+o);let t=/vec(5|6)\s*,\s*(\w+)/g;return r=r.replace(t,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function $3(r){return!(r.dispatchLayout.hasOwnProperty("y")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty("z")&&r.dispatchLayout.z.length!==0)}var nI={};Ue(nI,{ArrayBufferToTypedArray:()=>oI,GPUBytesPerElement:()=>rI,MatMulProgramType:()=>Ao,computeDispatch:()=>re,computeWorkPerThreadForConv2d:()=>Ll,computeWorkgroupInfoForMatMul:()=>tI,computeWorkgroupSizeForConv2d:()=>Ml,flatDispatchLayout:()=>ue,isWebGPUSupported:()=>Bl,tilesFitEvenlyIntoShape:()=>Vee});var zu=r=>{let e=1;for(let t=0;tt%r[o]===0)}function re(r,e,t=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(zu(r.x.map(i=>e[i]))/(t[0]*o[0])),r.y?Math.ceil(zu(r.y.map(i=>e[i]))/(t[1]*o[1])):1,r.z?Math.ceil(zu(r.z.map(i=>e[i]))/(t[2]*o[2])):1];return[n,s,a]}function tI(r,e,t,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),e<=16&&t<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function Ml(r,e,t=!1){if(t)return[8,8,1];let o=zu(r.x.map(s=>e[s])),n=zu(r.y.map(s=>e[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function Ll(r,e,t=!1){if(t)return[4,4,1];let o=zu(r.x.map(s=>e[s])),n=zu(r.y.map(s=>e[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function ue(r){return{x:r.map((e,t)=>t)}}function rI(r){if(r==="float32"||r==="int32"||r==="bool"||r==="string")return 4;if(r==="complex64")return 8;throw new Error(`Unknown dtype ${r}`)}function oI(r,e){if(e==="float32")return new Float32Array(r);if(e==="int32")return new Int32Array(r);if(e==="bool"||e==="string")return Uint8Array.from(new Int32Array(r));throw new Error(`Unknown dtype ${e}`)}function Bl(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}var Ao;(function(r){r[r.MatMulReduceProgram=0]="MatMulReduceProgram",r[r.MatMulSplitKProgram=1]="MatMulSplitKProgram",r[r.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",r[r.MatMulPackedProgram=3]="MatMulPackedProgram",r[r.MatMulMax=4]="MatMulMax"})(Ao||(Ao={}));var zee=O().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Wee=(r,e)=>{let t=r.limits.maxComputeWorkgroupsPerDimension,o=e.dispatchLayout,n=e.dispatch;if(n.every(a=>a<=t))return n;y.assert(n[0]>t&&o.y===void 0&&o.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(n[0]));return s>t?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=t,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},Ui=class extends Zr{constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!Bl())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has("timestamp-query-inside-passes"),this.adapterInfo=new kg(t),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Ng(this.device),this.textureManager=new Tg(this.device),this.tensorMap=new Do(this,cr()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),O().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return Ui.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let o=this.tensorMap.get(e);if(this.decRef(e),!t&&o.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:n}=this.tensorMap.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if("texture"in t.resourceInfo){let o=t.resourceInfo;o.texture instanceof GPUTexture&&this.textureManager.releaseTexture(o.texture,o.width,o.height,o.format,o.usage),o.texture=null}else{let o=t.resourceInfo;this.bufferManager.releaseBuffer(o.buffer,o.size,o.usage),o.buffer=null}t.resourceInfo=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,o){if(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.tensorMap.set(n,{dtype:o,shape:t,values:e,refCount:1}),n}move(e,t,o,n,s){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(e,{dtype:n,shape:o,values:t,refCount:s})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.size,e.usage)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let o=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,o,0,t),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),O().getBool("WEBGPU_USE_PROFILE_TOOL")&&(y.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let o=this.tensorMap.get(e);return this.releaseResource(e),o.values=t,o.values}readSync(e){let t=this.tensorMap.get(e),{values:o}=t;if(o==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return o}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:o}=t;if(o!=null)return this.convertAndCacheOnCPU(e,o);let n;if(t.dtype==="complex64"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=S.mergeRealAndImagArrays(a,i)}else{let s=t.resourceInfo,a=await this.getBufferData(s.buffer,s.size);n=oI(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}readToGPU(e){let t=this.tensorMap.get(e),{values:o,dtype:n,shape:s,resourceInfo:a}=t;if(n==="complex64")throw new Error("Does not support reading buffer for complex64 dtype.");if(a==null)throw o!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let i=a.size,p=this.bufferManager.acquireBuffer(i,a.usage);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(a.buffer,0,p,0,i),this.submitQueue();let u=this.makeTensorInfo(s,n),c=cr().makeTensorFromTensorInfo(u),l=this.tensorMap.get(u.dataId);return l.resourceInfo={size:i,usage:this.defaultGpuBufferUsage(),buffer:p},{tensorRef:c,buffer:p,bufSize:i}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let o=t.map(n=>y.decodeString(n));return le(e.shape,e.dtype,o)}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return le(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn("This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.");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(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,o){return t==="string"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if("texture"in t.resourceInfo){let n=t.resourceInfo;return n.texture instanceof GPUExternalTexture?n.texture:n.texture.createView()}let o=t.resourceInfo;return{offset:0,size:o.size,buffer:o.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let o=rI(t.dtype)*y.sizeFromShape(t.shape),n=this.bufferManager.acquireBuffer(o,this.defaultGpuBufferUsage());if(t.resourceInfo={size:o,usage:this.defaultGpuBufferUsage(),buffer:n},t.values){let s=this.bufferManager.acquireUploadBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),a=s.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),s.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s,0,n,0,o);let i={size:o,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:s};this.stagingPendingDisposal.push(i)}}makeUniforms(e){let t=0,o=0,n=[];e.forEach(p=>{p.data.length===0&&(p.data=[1]);let u;switch(p.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:y.assert(!1,()=>`Unsupported ${p.data.length}D shape`)}(o===5||o===6)&&(u=16),t=Math.ceil(t/u)*u,o=p.data.length,n.push(t),t+=p.data.length*4});let s=new ArrayBuffer(t);e.forEach((p,u)=>{let c=n[u];p.type==="int32"?new Int32Array(s,c,p.data.length).set(p.data):p.type==="uint32"?new Uint32Array(s,c,p.data.length).set(p.data):new Float32Array(s,c,p.data.length).set(p.data)});let a=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(a,0,s,0,t);let i={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:a};return this.uniformPendingDisposal.push(i),{offset:0,size:t,buffer:a}}runWebGPUProgram(e,t,o,n,s){if(s||(s=this.makeTensorInfo(e.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),e.dispatch=Wee(this.device,e);let a=[],i=[];if(!e.isFromPixels){a.push({type:"float32",data:[NaN]},{type:"float32",data:[1/0]}),i=t.concat(s).map(x=>x.shape);let h="int32";i.map(x=>{a.push({type:h,data:x})});let g=y.computeStrides(s.shape);if(a.push({type:h,data:g}),e.size){let x=y.sizeFromShape(e.outputShape);a.push({type:h,data:[e.isVec4?x/4:x]})}}let p=t.map((h,g)=>{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.");return this.uploadToGPU(h.dataId),{dtype:this.tensorMap.get(h.dataId).dtype,shape:h.shape,name:e.variableNames[g]}}),u=R3(e,i,p,s),c;u in this.pipelineCache?c=this.pipelineCache[u]:(c=A3(this.device,e,p,s),this.pipelineCache[u]=c),n&&(a=[...a,...n]);let l=[this.tensorToBinding(s),...t.map(h=>this.tensorToBinding(h)),this.makeUniforms(a)],m=this.device.createBindGroup({layout:c.getBindGroupLayout(0),entries:l.map((h,g)=>({binding:g,resource:h}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),f=this.activeTimers!=null;return f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(c),d.setBindGroup(0,m),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),f&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(h=>{this.commandQueueOwnedIds.add(h.dataId)}),this.commandQueueOwnedIds.add(s.dataId),O().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),f&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),s}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),o=this.bufferManager.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,o,0,16),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(o.getMappedRange()),s=Number(n[1]-n[0]);return o.unmap(),this.bufferManager.releaseBuffer(o,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),s/1e6}shouldExecuteOnCPU(e,t=zee){return O().getBool("WEBGPU_CPU_FORWARD")&&e.every(o=>this.tensorMap.get(o.dataId).resourceInfo==null&&y.sizeFromShape(o.shape){O().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let r={powerPreference:O().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},e=await navigator.gpu.requestAdapter(r),t={};e.features.has("timestamp-query-inside-passes")&&(t.requiredFeatures=["timestamp-query-inside-passes"]);let o=e.limits;t.requiredLimits={maxComputeWorkgroupStorageSize:o.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:o.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:o.maxStorageBufferBindingSize};let n=await e.requestDevice(t),s=await e.requestAdapterInfo();return new Ui(n,s)},3);var ye;(function(r){r[r.ADD=0]="ADD",r[r.ATAN2=1]="ATAN2",r[r.COMPLEX_MULTIPLY_IMAG=2]="COMPLEX_MULTIPLY_IMAG",r[r.COMPLEX_MULTIPLY_REAL=3]="COMPLEX_MULTIPLY_REAL",r[r.DIV=4]="DIV",r[r.EQUAL=5]="EQUAL",r[r.GREATER=6]="GREATER",r[r.GREATER_EQUAL=7]="GREATER_EQUAL",r[r.INT_DIV=8]="INT_DIV",r[r.LESS=9]="LESS",r[r.LESS_EQUAL=10]="LESS_EQUAL",r[r.LOGICAL_AND=11]="LOGICAL_AND",r[r.MAX=12]="MAX",r[r.MIN=13]="MIN",r[r.MOD=14]="MOD",r[r.MUL=15]="MUL",r[r.NOT_EQUAL=16]="NOT_EQUAL",r[r.POW=17]="POW",r[r.PRELU=18]="PRELU",r[r.SQUARED_DIFFERENCE=19]="SQUARED_DIFFERENCE",r[r.SUB=20]="SUB"})(ye||(ye={}));var D3=` if (isnan(a)) { return a; } if (isnan(b)) { return b; } `,O3=` if (isNaN.r) { resultTemp.r = valueForNaN; } if (isNaN.g) { resultTemp.g = valueForNaN; } if (isNaN.b) { resultTemp.b = valueForNaN; } if (isNaN.a) { resultTemp.a = valueForNaN; } `,aI=` let isNaN = isnanVec4(a) | isnanVec4(b); ${O3} `,Uee="return a + b;",Gee="return areal * breal - aimag * bimag;",Hee="return areal * bimag + aimag * breal;",qee="return a / b;",Kee="return f32(a == b);",jee="return vec4(a == b);",Xee="return f32(a > b);",Yee="return vec4(a > b);",Qee="return f32(a >= b);",Zee="return vec4(a >= b);",Jee=` let s = sign(a) * sign(b); let ia = i32(round(a)); let ib = i32(round(b)); return f32(idiv(ia, ib, s)); `,ete=` let ia = vec4(round(a)); let ib = vec4(round(b)); let cond = ib != vec4(0); var resultTemp = vec4(0); let s = sign(a) * sign(b); // Windows (D3D) wants guaranteed non-zero int division at compile-time. if (cond[0]) { resultTemp[0] = idiv(ia[0], ib[0], s[0]); } if (cond[1]) { resultTemp[1] = idiv(ia[1], ib[1], s[1]); } if (cond[2]) { resultTemp[2] = idiv(ia[2], ib[2], s[2]); } if (cond[3]) { resultTemp[3] = idiv(ia[3], ib[3], s[3]); } return vec4(resultTemp); `,tte="return f32(a < b);",rte="return vec4(a < b);",ote="return f32(a <= b);",nte="return vec4(a <= b);",ste="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",ate=`return (vec4(a >= vec4(1.0)) * vec4(b >= vec4(1.0)));`,ite=` ${D3} if (b == 0.) { return uniforms.NAN; } var resultTemp = a % b; if ((a < 0. && b < 0.) || (a >= 0. && b > 0.)) { return resultTemp; } else { return (resultTemp + b) % b; } `,ute=` let valueForNaN = uniforms.NAN; var resultTemp = vec4(a % b); ${aI} if (b[0] == 0.) { resultTemp[0] = uniforms.NAN; } if (b[1] == 0.) { resultTemp[1] = uniforms.NAN; } if (b[2] == 0.) { resultTemp[2] = uniforms.NAN; } if (b[3] == 0.) { resultTemp[3] = uniforms.NAN; } if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) { resultTemp[0] = (resultTemp[0] + b[0]) % b[0]; } if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) { resultTemp[1] = (resultTemp[1] + b[1]) % b[1]; } if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) { resultTemp[2] = (resultTemp[2] + b[2]) % b[2]; } if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) { resultTemp[3] = (resultTemp[3] + b[3]) % b[3]; } return resultTemp; `,pte="return a * b;",cte=` if (isnan(a) || isnan(b)) { return 1.0; } return f32(a != b); `,lte=` var resultTemp = vec4(a != b); let valueForNaN = 1.0; ${aI} return resultTemp; `,mte=` if(a < 0.0 && floor(b) < b) { return uniforms.NAN; } if (b == 0.0) { return 1.0; } if (round(abs(b) % 2.0) != 1.0) { return pow(abs(a), b); } return sign(a) * pow(abs(a), b); `,dte=` let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1); let isModRound1 = vec4(isModRound1Bool); let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); var resultTemp = multiplier * pow(abs(a), b); // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS let isExpZero = b == vec4(0.0); if (isExpZero.r) { resultTemp.r = 1.0; } if (isExpZero.g) { resultTemp.g = 1.0; } if (isExpZero.b) { resultTemp.b = 1.0; } if (isExpZero.a) { resultTemp.a = 1.0; } let isNaN = (a < vec4(0.0)) & (floor(b) < b); let valueForNaN = uniforms.NAN; ${O3} return resultTemp; `,fte="if (a < 0.0) { return b * a; } return a;",hte=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `,gte="return (a - b) * (a - b);",xte="return a - b;";function sI(r,e,t="uniforms.NAN"){let o=e?aI:D3;return e?` let valueForNaN = ${t}; var resultTemp = vec4(${r}(a, b)); `+o+` return resultTemp; `:o+` return ${r}(a, b); `}function Ic(r,e){switch(r){case ye.ADD:return Uee;case ye.ATAN2:return sI("atan2",e);case ye.COMPLEX_MULTIPLY_IMAG:return Hee;case ye.COMPLEX_MULTIPLY_REAL:return Gee;case ye.DIV:return qee;case ye.EQUAL:return e?jee:Kee;case ye.GREATER:return e?Yee:Xee;case ye.GREATER_EQUAL:return e?Zee:Qee;case ye.INT_DIV:return e?ete:Jee;case ye.LESS:return e?rte:tte;case ye.LESS_EQUAL:return e?nte:ote;case ye.LOGICAL_AND:return e?ate:ste;case ye.MAX:return sI("max",e);case ye.MIN:return sI("min",e);case ye.MOD:return e?ute:ite;case ye.MUL:return pte;case ye.NOT_EQUAL:return e?lte:cte;case ye.POW:return e?dte:mte;case ye.PRELU:return e?hte:fte;case ye.SQUARED_DIFFERENCE:return gte;case ye.SUB:return xte;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var Q;(function(r){r[r.ABS=0]="ABS",r[r.ACOS=1]="ACOS",r[r.ACOSH=2]="ACOSH",r[r.ASIN=3]="ASIN",r[r.ASINH=4]="ASINH",r[r.ATAN=5]="ATAN",r[r.ATANH=6]="ATANH",r[r.CEIL=7]="CEIL",r[r.COS=8]="COS",r[r.COSH=9]="COSH",r[r.ELU=10]="ELU",r[r.ERF=11]="ERF",r[r.EXP=12]="EXP",r[r.EXPM1=13]="EXPM1",r[r.FLOOR=14]="FLOOR",r[r.IS_FINITE=15]="IS_FINITE",r[r.IS_INF=16]="IS_INF",r[r.IS_NAN=17]="IS_NAN",r[r.LINEAR=18]="LINEAR",r[r.LOG=19]="LOG",r[r.LOG1P=20]="LOG1P",r[r.LOGICAL_NOT=21]="LOGICAL_NOT",r[r.NEG=22]="NEG",r[r.RELU=23]="RELU",r[r.RELU6=24]="RELU6",r[r.LEAKYRELU=25]="LEAKYRELU",r[r.RECIPROCAL=26]="RECIPROCAL",r[r.RSQRT=27]="RSQRT",r[r.SIN=28]="SIN",r[r.SINH=29]="SINH",r[r.SIGMOID=30]="SIGMOID",r[r.SQRT=31]="SQRT",r[r.SQUARE=32]="SQUARE",r[r.TAN=33]="TAN",r[r.TANH=34]="TANH",r[r.TO_INT=35]="TO_INT"})(Q||(Q={}));var yte="return abs(a);",bte=` if (abs(a) > 1.) { return uniforms.NAN; } return acos(a); `,Cte=` if (a < 1.) { return uniforms.NAN; } return acosh(a); `,Ste=` if (abs(a) > 1.) { return uniforms.NAN; } return asin(a); `,wte="return asinh(a);",Ite=` if (isnan(a)) { return uniforms.NAN; } return atan(a); `,vte=` if (abs(a) > 1.) { return uniforms.NAN; } if (a == 1.) { return uniforms.INFINITY; } if (a == -1.) { return -uniforms.INFINITY; } return atanh(a); `,kte="return ceil(a);",Nte="return cos(a);",Tte=` let e2x = exp(-a); return (e2x + 1.0 / e2x) / 2.0; `,_te="return exp(a) - 1.0;",Ete="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",$te=` var resFloat = exp(a) - vec4(1.0); if (a.r >= 0.0) { resFloat.r = a.r; } if (a.g >= 0.0) { resFloat.g = a.g; } if (a.b >= 0.0) { resFloat.b = a.b; } if (a.a >= 0.0) { resFloat.a = a.a; } return resFloat; `,Ate=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. let p = ${S.ERF_P}; let a1 = ${S.ERF_A1}; let a2 = ${S.ERF_A2}; let a3 = ${S.ERF_A3}; let a4 = ${S.ERF_A4}; let a5 = ${S.ERF_A5}; let sign = sign(a); let absA = abs(a); let t = 1.0 / (1.0 + p * absA); return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA)); `,Rte="return exp(a);",Fte="return floor(a);",Dte="return f32(!isnan(a) && !isinf(a));",Ote="return f32(isinf(a));",Pte="return f32(isnan(a));",Mte="return a;",Lte=`if (a < 0.0) { return uniforms.NAN; } return log(a);`,Bte=` if (isnan(a)) { return a; } return log(1.0 + a); `,Vte="return f32(!(a >= 1.0));",zte="return -a;",Wte="if (a < 0.0) { return uniforms.alpha * a; } return a;",Ute=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a); `,Gte="return 1.0 / a;",Hte="return select(a, 0.0, a < 0.0);",qte="return clamp(a, 0.0, 6.0);",Kte="return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));",jte=` return select(a, vec4(0.0), a < vec4(0.0)); `,Xte="return inverseSqrt(a);",Yte="return 1.0 / (1.0 + exp(-1.0 * a));",Qte="return sin(a);",Zte=` let e2x = exp(a); return (e2x - 1.0 / e2x) / 2.0; `,Jte="return sqrt(a);",ere="return a * a;",tre="return tan(a);",rre=` let e2x = exp(-2.0 * abs(a)); return sign(a) * (1.0 - e2x) / (1.0 + e2x); `,ore="return f32(i32((a)));";function Ha(r,e){switch(r){case Q.ABS:return yte;case Q.ACOS:return bte;case Q.ACOSH:return Cte;case Q.ASIN:return Ste;case Q.ASINH:return wte;case Q.ATAN:return Ite;case Q.ATANH:return vte;case Q.COS:return Nte;case Q.COSH:return Tte;case Q.CEIL:return kte;case Q.ELU:return e?$te:Ete;case Q.ERF:return Ate;case Q.EXP:return Rte;case Q.EXPM1:return _te;case Q.FLOOR:return Fte;case Q.IS_FINITE:return Dte;case Q.IS_INF:return Ote;case Q.IS_NAN:return Pte;case Q.LINEAR:return Mte;case Q.LOG:return Lte;case Q.LOG1P:return Bte;case Q.LOGICAL_NOT:return Vte;case Q.NEG:return zte;case Q.LEAKYRELU:return e?Ute:Wte;case Q.RECIPROCAL:return Gte;case Q.RELU:return e?jte:Hte;case Q.RELU6:return e?Kte:qte;case Q.RSQRT:return Xte;case Q.SIGMOID:return Yte;case Q.SIN:return Qte;case Q.SINH:return Zte;case Q.SQRT:return Jte;case Q.SQUARE:return ere;case Q.TAN:return tre;case Q.TANH:return rre;case Q.TO_INT:return ore;default:throw new Error(`BinaryType ${r} is not implemented!`)}}var kt=r=>{switch(r){case 1:return"f32";case 2:return"vec2";case 3:return"vec3";case 4:return"vec4";default:throw new Error(`${r}-component is not supported.`)}};function ur(r,e=!1,t=!1,o=3){if(r===null)return"";let n="";if(r==="linear")n=Ha(Q.LINEAR);else if(r==="relu")n=Ha(Q.RELU,t);else if(r==="elu")n=Ha(Q.ELU,t);else if(r==="relu6")n=Ha(Q.RELU6,t);else if(r==="prelu")n=Ic(ye.PRELU,t);else if(r==="sigmoid")n=Ha(Q.SIGMOID,t);else if(r==="leakyrelu")n=Ha(Q.LEAKYRELU,t);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=kt(t?4:1),i="";return e?i=` fn activation(a : ${a}, coords : vec${o}) -> ${a} { let b = getPreluActivationWeightsByOutputCoords(coords); ${n} }`:i=` fn activation(a : ${a}, coords : vec${o}) -> ${a} { ${n} }`,i}function Hr(r,e){return` ${r?"value = value + getBiasByOutputCoords(coords);":""} ${e?"value = activation(value, coords);":""} `}function iI(r,e,t,o,n=!1,s=!1,a=!1,i=1){y.assert(t&&i===1||!t,()=>`transposeA ${t} is not compatible with component size ${i}`);let p=` let batch = ${r?"0":"batchIn"}; ${t?"value = getA(batch, col, row);":"value = getA(batch, row, col);"} `,u=o?"value = getB(batch, col, row);":"value = getB(batch, row, col);";return` fn mm_readA(batchIn: i32, row: i32, colIn: i32) -> ${kt(i)} { var value = ${kt(i)}(0.0); let col = colIn * ${i}; ${n&&a?p:` ${t?"if(row < uniforms.dimAOuter && col < uniforms.dimInner)":"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])"} { ${p} } `} return value; } fn mm_readB(batchIn: i32, row: i32, colIn: i32) -> ${kt(i)} { let col = colIn * ${i}; let batch = ${e?"0":"batchIn"}; var value = ${kt(i)}(0.0); ${u} return value; } `}function Vl(r,e,t,o,n,s,a=!1,i=!1,p=!1,u=1){return` ${iI(t,o,n,s,a,i,p,u)} fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${kt(u)}) { let col = colIn * ${u}; ${a&&i?"":"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"} { var value = valueIn; let coords = vec3(batch, row, col); ${Hr(r,e)} setOutputAtCoords(coords[0], coords[1], coords[2], value); } } `}var nre=r=>r?` mm_Asub[inputRow][inputCol] = mm_readA(batch, kStart + inputRow, globalRowStart / innerElementSize + inputCol); `:` mm_Asub[inputRow][inputCol] = mm_readA(batch, globalRow + innerRow, kStart / innerElementSize + inputCol); `,sre=(r,e)=>r?` let ACached0 = mm_Asub[k * innerElementSize][localRow]; let ACached1 = mm_Asub[k * innerElementSize + 1][localRow]; let ACached2 = mm_Asub[k * innerElementSize + 2][localRow]; ${e===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"} for (var i = 0; i < rowPerThread; i = i + 1) { acc[i] = BCached0 * ACached0[i] + acc[i]; acc[i] = BCached1 * ACached1[i] + acc[i]; acc[i] = BCached2 * ACached2[i] + acc[i]; ${e===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"} }`:` for (var i = 0; i < rowPerThread; i = i + 1) { let ACached = mm_Asub[tileRow + i][k]; acc[i] = BCached0 * ACached.x + acc[i]; acc[i] = BCached1 * ACached.y + acc[i]; acc[i] = BCached2 * ACached.z + acc[i]; ${e===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"} }`;function Wu(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=e[1]*r[1],p=e[0]*r[0],u=t?i:o,c=t?o:i,l=u/e[0],m=o/e[1];return y.assert((t&&l===4&&r[1]===4||!t&&(l===3||l===4))&&u%e[0]===0&&o%e[1]===0&&r[0]===4,()=>`If transposeA ${t} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4. Otherwise, innerElementSize ${l} must be 3 or 4. tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`),` var mm_Asub : array, ${u/l}>, ${c}>; var mm_Bsub : array, ${p/r[0]}>, ${o}>; const rowPerThread = ${r[1]}; const colPerThread = ${r[0]}; const innerElementSize = ${l}; const tileInner = ${o}; ${se()} { let localRow = i32(localId.y); let tileRow = ${a?"0":"localRow * rowPerThread"}; let tileCol = i32(localId.x); let globalRow = ${a?"0":"i32(globalId.y) * rowPerThread"}; let globalCol = i32(globalId.x); let batch = ${n?"0":"i32(globalId.z)"}; let globalRowStart = i32(workgroupId.y) * ${i}; let numTiles = ${n?`${Math.ceil(s/o)}`:"(uniforms.dimInner - 1) / tileInner + 1"}; var kStart = ${n?`i32(globalId.z) * ${s}`:"0"}; var acc: array, rowPerThread>; // Loop over shared dimension. let tileRowB = localRow * ${m}; for (var t = 0; t < numTiles; t = t + 1) { // Load one tile of A into local memory. for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { let inputRow = tileRow + innerRow; let inputCol = tileCol; ${nre(t)} } // Load one tile of B into local memory. for (var innerRow = 0; innerRow < ${m}; innerRow = innerRow + 1) { let inputRow = tileRowB + innerRow; let inputCol = tileCol; mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol); } kStart = kStart + tileInner; workgroupBarrier(); // Compute acc values for a single thread. for (var k = 0; k < tileInner / innerElementSize; k = k + 1) { let BCached0 = mm_Bsub[k * innerElementSize][tileCol]; let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol]; let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol]; ${l===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} ${sre(t,l)} } workgroupBarrier(); } for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); } }`}var P3=r=>r?` mm_Asub[inputRow][inputCol] = mm_readA(batch, kStart + inputRow, globalRowStart + inputCol); `:` mm_Asub[inputRow][inputCol] = mm_readA(batch, globalRowStart + inputRow, kStart + inputCol); `,are=r=>r?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function Uu(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=r[1]*e[1],p=r[0]*e[0],u=t?i:o,c=t?o:i;y.assert(c%e[1]===0&&u%e[0]===0&&o%e[1]===0,()=>`tileAHight ${c} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let l=c/e[1],m=u/e[0],d=o/e[1],f=a?` let localRow = i32(localId.y); let localCol = i32(localId.x); let globalRowStart = i32(workgroupId.y) * ${i}; let globalColStart = i32(workgroupId.x) * ${p}; // Loop over shared dimension. for (var t = 0; t < numTiles; t = t + 1) { // Load one tile of A into local memory. for (var inputRow = localRow; inputRow < ${c}; inputRow = inputRow + ${e[1]}) { for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) { ${P3(t)} } } // Load one tile of B into local memory. for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) { for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${e[0]}) { mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalColStart + inputCol); } } kStart = kStart + tileInner; workgroupBarrier(); // Compute acc values for a single thread. var BCached : array; for (var k = 0; k < tileInner; k = k + 1) { for (var inner = 0; inner < colPerThread; inner = inner + 1) { BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}]; } for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { let ACached = ${t?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[1]}][k];`} for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; } } } workgroupBarrier(); } for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { let gRow = globalRowStart + localRow + innerRow * ${e[1]}; for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { let gCol = globalColStart + localCol + innerCol * ${e[0]}; mm_write(batch, gRow, gCol, acc[innerRow][innerCol]); } } `:` let tileRow = i32(localId.y) * rowPerThread; let tileCol = i32(localId.x) * colPerThread; let globalRow = i32(globalId.y) * rowPerThread; let globalCol = i32(globalId.x) * colPerThread; let globalRowStart = i32(workgroupId.y) * ${i}; let tileRowA = i32(localId.y) * ${l}; let tileColA = i32(localId.x) * ${m}; let tileRowB = i32(localId.y) * ${d}; // Loop over shared dimension. for (var t = 0; t < numTiles; t = t + 1) { // Load one tile of A into local memory. for (var innerRow = 0; innerRow < ${l}; innerRow = innerRow + 1) { for (var innerCol = 0; innerCol < ${m}; innerCol = innerCol + 1) { let inputRow = tileRowA + innerRow; let inputCol = tileColA + innerCol; ${P3(t)} } } // Load one tile of B into local memory. for (var innerRow = 0; innerRow < ${d}; innerRow = innerRow + 1) { for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { let inputRow = tileRowB + innerRow; let inputCol = tileCol + innerCol; mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol + innerCol); } } kStart = kStart + tileInner; workgroupBarrier(); // Compute acc values for a single thread. var BCached : array; for (var k = 0; k < tileInner; k = k + 1) { for (var inner = 0; inner < colPerThread; inner = inner + 1) { BCached[inner] = mm_Bsub[k][tileCol + inner]; } for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { ${are(t)} for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; } } } workgroupBarrier(); } for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { mm_write(batch, globalRow + innerRow, globalCol + innerCol, acc[innerRow][innerCol]); } } `;return` var mm_Asub : array, ${c}>; var mm_Bsub : array, ${o}>; const rowPerThread = ${r[1]}; const colPerThread = ${r[0]}; const tileInner = ${o}; ${se()} { let batch = ${n?"0":"i32(globalId.z)"}; let numTiles = ${n?`${Math.ceil(s/o)}`:"(uniforms.dimInner - 1) / tileInner + 1"}; var kStart = ${n?`i32(globalId.z) * ${s}`:"0"}; var acc : array, rowPerThread>; // Without this initialization strange values show up in acc. for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { acc[innerRow][innerCol] = 0.0; } } ${f} } `}var ire=r=>r?` mm_readA(batch, colA, globalRow), mm_readA(batch, colA + 1, globalRow), mm_readA(batch, colA + 2, globalRow), mm_readA(batch, colA + 3, globalRow) `:` mm_readA(batch, globalRow, colA), mm_readA(batch, globalRow, colA + 1), mm_readA(batch, globalRow, colA + 2), mm_readA(batch, globalRow, colA + 3) `;function ure(r,e=!1){return y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`),` const tileSize = ${r[0]*4}; var mm_Asub : array, ${r[0]}>; ${se()} { let tileCol = i32(localId.x); let globalCol = i32(globalId.x); let globalRow = i32(globalId.y); let numTiles = (uniforms.dimInner - 1) / tileSize + 1; let batch = i32(globalId.z); // Without this initialization strange values show up in acc. var acc = 0.0; // Loop over shared dimension. for (var t = 0; t < numTiles; t = t + 1) { // Load one tile of A into local memory. let colA = t * tileSize + tileCol * 4; mm_Asub[tileCol] = vec4(${ire(e)}); workgroupBarrier(); // Compute acc values for a single thread. for (var k = 0; k < tileSize / 4; k = k + 1) { let rowB = t * tileSize + k * 4; let BCached = vec4(mm_readB(batch, rowB, globalCol), mm_readB(batch, rowB + 1, globalCol), mm_readB(batch, rowB + 2, globalCol), mm_readB(batch, rowB + 3, globalCol)); let ACached = mm_Asub[k]; acc = acc + dot(ACached, BCached); } workgroupBarrier(); } mm_write(batch, globalRow, globalCol, acc); } `}var _g=class{constructor(e,t,o,n,s=!1,a=!1,i=null,p=null,u=null,c=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=s?e[1]:e[2];if(this.isVec4=(l%4===0&&!s||t[1]%4===0&&s)&&t[2]%4===0&&!a,this.isVectorA=t[1]===1&&!s,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let f=tI(t[1],l,t[2],s);this.workgroupSize=f.workgroupSize,this.elementsPerThread=f.elementsPerThread}this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let m=i!=null,d=u!=null;m&&this.variableNames.push("bias"),d&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=c,this.transposeA=s,this.transposeB=a,this.addBias=m,this.activation=p,this.hasPreluActivationWeights=d,this.batchAEqualOne=o,this.batchBEqualOne=n,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],l),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${s}_${a}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.batchAEqualOne}_${this.batchBEqualOne}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=e%n===0,i=t%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return` ${ur(this.activation,this.hasPreluActivationWeights,this.isVec4)} ${Vl(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} ${this.isVec4?Wu(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.isVectorA):this.isVectorA?ure(this.workgroupSize,this.transposeA):Uu(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads)} `}};function pre(){return` var sumValues : array; ${se()} { let coords = getOutputCoords(); let batch = coords[0]; let row = coords[1]; let col = coords[2]; var sum = 0.0; let Length = uniforms.dimInner; for (var k = i32(localId.x); k < Length; k = k + i32(workgroupSizeX)) { let dataA = mm_readA(batch, row, k); let dataB = mm_readB(batch, k, col); sum = sum + dataA * dataB; } sumValues[localId.x] = sum; workgroupBarrier(); for(var currentSize = workgroupSizeX / 2u; currentSize > 1u; currentSize = currentSize / 2u) { if (localId.x < currentSize) { sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize]; } workgroupBarrier(); } if (localId.x == 0u) { sum = sumValues[0] + sumValues[1]; mm_write(batch, row, col, sum); } } `}var Eg=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize);let u=a!=null,c=p!=null;u&&this.variableNames.push("bias"),c&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.batchAEqualOne=t,this.batchBEqualOne=o,this.shaderKey=`matMulReduce_${this.activation}_${n}_${s}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){return` ${ur(this.activation,this.hasPreluActivationWeights)} ${Vl(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,this.transposeA,this.transposeB)} ${pre()} `}};function cre(r){let e=r[1],t=r[0],o=e>t?e:t;return` var mm_Asub : array, ${e}>; var mm_Bsub : array, ${o}>; // If the output size is small for matrix multiplication, avoid to use vec4 // and handle some elements per thread to optimally utilize the ALU. // Read data from global memory to registers firstly, then store them into // shared memory, so it is instruction-Level parallelism for arithmetic // operations and others handle IO operations between barrier api, makes ALU // and load/store units work simultaneously, could improves the performance. ${se()} { let tileRow = i32(localId.y); let tileCol = i32(localId.x); let globalRow = i32(globalId.y); let globalCol = i32(globalId.x); let batch = i32(globalId.z); // uniforms.dimInner should be greater than 0. let numTiles = (uniforms.dimInner - 1) / ${o} + 1; var acc = 0.0; var globalColA = tileCol; var globalRowB = 0; var regA = mm_readA(batch, globalRow, globalColA); var regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol); var regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol); globalColA = globalColA + ${o}; globalRowB = globalRowB + ${o}; for (var t = 0; t < numTiles; t = t + 1) { mm_Asub[tileRow][tileCol] = regA; mm_Bsub[2 * tileRow][tileCol] = regB0; mm_Bsub[2 * tileRow + 1][tileCol] = regB1; workgroupBarrier(); regA = mm_readA(batch, globalRow, globalColA); regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol); regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol); globalColA = globalColA + ${o}; globalRowB = globalRowB + ${o}; for (var k = 0; k < ${o}; k = k + 1) { acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol]; } workgroupBarrier(); } mm_write(batch, globalRow, globalCol, acc); } `}var $g=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push("bias");let c=p!=null;c&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.batchAEqualOne=e[0]===1,this.batchBEqualOne=t[0]===1,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){return` ${ur(this.activation,this.hasPreluActivationWeights)} ${Vl(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,this.transposeA,this.transposeB)} ${cre(this.workgroupSize)} `}};var Ag=class{constructor(e,t,o,n,s=!1,a=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workgroupSize=[8,8,1],this.atomic=!0,this.isVec4=!1,this.splitedDimInner=128,y.assert(e[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]},this.isVec4=(s&&this.outputShape[1]%4===0||!s&&t%4===0)&&this.outputShape[2]%4===0,this.elementsPerThread=[4,4,this.splitedDimInner],this.isVec4||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=re(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=s,this.transposeB=a,this.batchAEqualOne=o,this.batchBEqualOne=n,this.shaderKey=`matMulSplitK_${s}_${a}_${o}_${n}_${this.elementsPerThread}_${this.isVec4}`}getUserCode(){let e=n=>` for (var i = 0; i < ${n}; i = i + 1) { var oldValue = atomicLoad(&(result[flatIndex + i])); var exchanged = false; for (; !exchanged;) { let newValueF32 = bitcast(oldValue) + ${n>1?"value[i]":"value"}; let newValue = bitcast(newValueF32); let res = atomicCompareExchangeWeak(&(result[flatIndex + i]), oldValue, newValue); oldValue = res.old_value; exchanged = res.exchanged; } } `,t=this.isVec4?4:1;return` ${iI(this.batchAEqualOne,this.batchBEqualOne,!1,this.transposeB,!1,!1,!1,t)} fn mm_write(batch: i32, row : i32, colIn : i32, value : ${kt(t)}) { let col = colIn * ${t}; if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { let coords = vec3(batch, row, col); let flatIndex = getOutputIndexFromCoords(coords); // The problem is that we should initialize output to zero before using. // Otherwise, the original value will be added to the result. ${e(t)} } } ${this.isVec4?Wu(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Uu(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)} `}},Rg=class{constructor(e,t=null,o=null,n=null){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return` ${ur(this.activation,this.hasPreluActivationWeights)} ${se("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var value = getXByOutputIndex(index); ${Hr(this.addBias,this.activation)} setOutputAtIndex(index, value); } } `}};var Fg=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="fill"}getUserCode(){return` ${se("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.value); } } `}};function dr(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Fg(o),i=[{type:"float32",data:[n]}];return e.runWebGPUProgram(a,[],s,i)}}var M3={kernelName:Cs,backendName:"webgpu",kernelFunc:dr};function de(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var L3={kernelName:Ns,backendName:"webgpu",kernelFunc:de};function Gu({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),w=br.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],$=de({inputs:{x:r},backend:n,attrs:{shape:k}}),A=de({inputs:{x:e},backend:n,attrs:{shape:_}}),R=[$,A],D=Math.max(x,b),P=x===1,M=b===1,L=[$,A],W=[{type:"int32",data:[d]},{type:"int32",data:[f]},{type:"int32",data:[l]}],V,U,q=[D,d,f],H=O().get("WEBGPU_MATMUL_PROGRAM_TYPE");if(H<0){let X=O().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),Z=X>0?X:n.thresholdToIncreaseWorkgroups,ee=D*Math.ceil(d/32)*Math.ceil(f/32);ee<=Z||d<=8&&ee<=Z*2?D*d*f<=128?H=Ao.MatMulReduceProgram:D===1&&m>=2e3?H=Ao.MatMulSplitKProgram:H=Ao.MatMulSmallOutputSizeProgram:H=Ao.MatMulPackedProgram}switch(H){case Ao.MatMulReduceProgram:V=new Eg(q,P,M,t,o,s,p,a);break;case Ao.MatMulSplitKProgram:{if(U=dr({backend:n,attrs:{shape:q,value:0,dtype:r.dtype}}),V=new Ag(q,m,P,M,t,o),s||p){U=n.runWebGPUProgram(V,L,r.dtype,W,U);let Z=new Rg(U.shape,s,p,a),ee=null,Y=[U];s&&Y.push(s),a&&Y.push(a),p==="leakyrelu"&&(ee=[{type:"float32",data:[i]}],Z.uniforms+=" alpha : f32,");let J=n.runWebGPUProgram(Z,Y,U.dtype,ee);R.push(U);let ie=de({inputs:{x:J},backend:n,attrs:{shape:w}});R.push(J);for(let pe of R)n.disposeData(pe.dataId);return ie}break}case Ao.MatMulSmallOutputSizeProgram:V=new $g(k,_,q,t,o,s,p,a);break;case Ao.MatMulPackedProgram:let X=n.adapterInfo.isIntel();V=new _g(k,q,P,M,t,o,s,p,a,X);break;default:throw new Error(`Unsupported MatMulProgramType ${H}.`)}s&&L.push(s),a&&L.push(a),p==="leakyrelu"&&(W.push({type:"float32",data:[i]}),V.uniforms+=" alpha : f32,"),U=n.runWebGPUProgram(V,L,r.dtype,W,U);let j=de({inputs:{x:U},backend:n,attrs:{shape:w}});R.push(U);for(let X of R)n.disposeData(X.dataId);return j}function lre(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Gu({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var B3={kernelName:fo,backendName:"webgpu",kernelFunc:lre};var zl=class{constructor(e,t,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return` fn binaryOpComplex( areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 { ${Ic(this.op,!1)} } ${se("index")} { if(index < uniforms.size) { let areal = getARealByOutputIndex(index); let aimag = getAImagByOutputIndex(index); let breal = getBRealByOutputIndex(index); let bimag = getBImagByOutputIndex(index); setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag)); } } `}};var Hu=class{constructor(e,t,o){this.size=!0,this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,o),this.dispatchLayout=ue(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&o.length>1&&t[0]<128,this.useSharedMemoryWithB=o.length<=1&&t.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB?(this.isVec4=!1,this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:t[0],this.shaderKey=`binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`,this.type="shared",this.workgroupSize=[256,1,1],this.workPerThread=1):(y.arraysEqual(t,o)&&y.sizeFromShape(t)%4===0?(this.isVec4=!0,this.type="vec4",this.workPerThread=4):(this.isVec4=!1,this.type="plain",this.workPerThread=1),this.shaderKey=`binary_${this.type}_${e}`,this.workgroupSize=[128,1,1]),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1])}getUserCode(){let e,t=this.isVec4?"vec4":"f32",o=` fn binaryOperation(a : ${t}, b : ${t}) -> ${t} { ${Ic(this.op,this.isVec4)} }; `;if(this.type==="shared"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index); let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}]; let b = getBByOutputIndex(index);`;e=` ${o} var sharedBuf : array; ${se("index")} { // Fill in the shared memory buffer. let localIndex = i32(localId.x); if(localIndex < ${this.lastDimensionSize}) { sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?"B":"A"}[localIndex]); } workgroupBarrier(); if(index < uniforms.size) { let coords = getCoordsFromIndex(index); ${s} setOutputAtIndex(index, binaryOperation(a, b)); } } `}else e=` ${o} ${se("index")} { if (index < uniforms.size) { let a = getAByOutputIndex(index); let b = getBByOutputIndex(index); setOutputAtIndex(index, binaryOperation(a, b)); } } `;return e}};function Ft(r){let{inputs:e}=r,{x:t}=e;return r.backend.incRef(t.dataId),{dataId:t.dataId,shape:t.shape,dtype:t.dtype}}var V3={kernelName:mo,backendName:"webgpu",kernelFunc:Ft};function po(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.tensorMap.get(s.dataId),i=Ft({inputs:{x:o},backend:t}),p=Ft({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var z3={kernelName:ei,backendName:"webgpu",kernelFunc:po};var Ro=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let o=128;this.workgroupSize=[o,1,1],this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return` fn unaryOperation(a : f32) -> f32 { ${Ha(this.op,!1)} } ${se("index")} { if (index < uniforms.size) { let a = getAByOutputIndex(index); setOutputAtIndex(index, unaryOperation(a)); } } `}};function Se({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),c=e(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Ro(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function ot({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype==="complex64"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==ye.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},w={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Hu(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,w],dt(x.dtype,b.dtype))});else{let g=new zl(ye.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new zl(ye.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,"float32"),f=p.runWebGPUProgram(x,b,"float32")}let h=po({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype==="string"||i.dtype==="string"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype==="string"?S.fromUint8ToStringArray(l):l,f=a.dtype==="string"?S.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new Hu(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:W3,castImpl:U3,ceilImpl:G3,concatImpl:H3,equalImpl:q3,expImpl:K3,expm1Impl:j3,floorImpl:X3,gatherNdImpl:Y3,gatherV2Impl:Q3,greaterEqualImpl:Z3,greaterImpl:J3,lessEqualImpl:eM,lessImpl:tM,logImpl:rM,maxImpl:oM,maximumImpl:nM,minimumImpl:sM,multiplyImpl:aM,negImpl:iM,notEqualImpl:uM,prodImpl:pM,rangeImpl:cM,rsqrtImpl:lM,scatterImpl:mM,simpleAbsImpl:dM,sliceImpl:fM,stridedSliceImpl:hM,stringNGramsImpl:gM,subImpl:xM,tileImpl:yM,topKImpl:bM,transposeImpl:CM,uniqueImpl:kNt}=Qp;var mre=Se({opType:Q.ABS,cpuKernelImpl:dM}),SM={kernelName:gs,backendName:"webgpu",kernelFunc:mre};var dre=Se({opType:Q.ACOS}),wM={kernelName:sa,backendName:"webgpu",kernelFunc:dre};var fre=Se({opType:Q.ACOSH}),IM={kernelName:aa,backendName:"webgpu",kernelFunc:fre};var hre=ot({opType:ye.ADD,cpuKernelImpl:W3,supportsComplex:!0}),vM={kernelName:eo,backendName:"webgpu",kernelFunc:hre};var Dg=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=this.variableNames.map(n=>`v${n}`).join(" + ");return` ${se("index")} { for (var i = 0; i < ${this.workPerThread}; i = i + 1) { let flatIndex = index * ${this.workPerThread} + i; if (flatIndex < uniforms.size) { let coords = getCoordsFromIndex(flatIndex); ${e.join(` `)} setOutputAtIndex(flatIndex, ${t}); } } } `}};function gre(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Ft({inputs:{x:o[0]},backend:t});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new Dg(s);return t.runWebGPUProgram(a,o,n)}var kM={kernelName:Mo,backendName:"webgpu",kernelFunc:gre};var Og=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[16,16,1];let o=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`),` const tileSize = ${this.workgroupSize[0]}; var tile : array, ${this.workgroupSize[0]}>; ${se()} { var x = i32(workgroupId.x) * tileSize + i32(localId.x); var y = i32(workgroupId.y) * tileSize + i32(localId.y); let width = uniforms.outShape[0]; let height = uniforms.outShape[1]; if (x < width && y < height) { tile[localId.y][localId.x] = f32(A[y * width + x]); } workgroupBarrier(); x = i32(workgroupId.y) * tileSize + i32(localId.x); y = i32(workgroupId.x) * tileSize + i32(localId.y); if (x < height && y < width) { setOutputAtIndex((y * height + x), tile[localId.x] [localId.y]); } } `}};var Pg=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=new Array(e);for(let o=0;o"} bestValue) { bestValue = candidate; }`,t="f32(x[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"?(e=" bestValue = bestValue * candidate; ",t="1.0"):this.reduceType==="all"?(e=" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); ",t="1.0"):this.reduceType==="any"&&(e=" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); ",t="0.0");let o=this.reduceType==="mean"?"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputAtIndex(outputIndex, bestValue);";return` fn DIV_CEIL(a : u32, b : u32) -> u32 { return ((a - 1u) / b + 1u); } ${` var xBestValues : array; `} fn getOffset(outputIndex : i32) -> i32 { let outputCoords = getCoordsFromIndex(outputIndex); let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize; return offset; } ${se("index")} { let outputIndex = index / i32(workgroupSizeX); let offset = getOffset(outputIndex); var bestValue = ${t}; let Length = uniforms.reduceSize; let WorkPerThread = DIV_CEIL(u32(Length), workgroupSizeX); for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size; k = k + i32(workgroupSizeX)) { let candidate = f32(x[offset + k]); ${e} } xBestValues[localId.x] = bestValue; workgroupBarrier(); var reduceSize = min(u32(Length), workgroupSizeX); for (var currentSize = reduceSize / 2u; reduceSize > 1u; currentSize = reduceSize / 2u) { let interval = DIV_CEIL(reduceSize, 2u); if (localId.x < currentSize) { let candidate = xBestValues[localId.x + interval]; ${e} xBestValues[localId.x] = bestValue; } reduceSize = interval; workgroupBarrier(); } if (localId.x == 0u && outputIndex < uniforms.size) { ${o} } } `}};function qr(r,e,t,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(e,r.shape),p=i,u=S.getAxesPermutation(p,s),c=r;u!=null&&(c=Nr({inputs:{x:r},attrs:{perm:u},backend:n}),p=S.getInnerMostAxes(p.length,s),a.push(c)),S.assertAxesAreInnerMostDims(o,p,s);let[l,m]=S.computeOutAndReduceShapes(c.shape,p),d=l;t&&(d=S.expandShapeToKeepDim(l,i));let f;if((o==="max"||o==="prod")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case"max":let g=oM(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case"prod":let{outVals:x,outShape:b,outDtype:C}=pM(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},C=o==="mean"?"float32":ka(r.dtype),w=[{type:"int32",data:[h]}],k=new Mg(b,o),_=n.runWebGPUProgram(k,[c],C,w);a.push(_),f=de({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function yre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return qr(n,a,s,"all",t)}var TM={kernelName:Lo,backendName:"webgpu",kernelFunc:yre};function bre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return qr(n,a,s,"any",t)}var _M={kernelName:Bo,backendName:"webgpu",kernelFunc:bre};var vc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let n=[t];this.op=o==="min"?"<":">";let[s,a]=S.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=ue(this.outputShape),y.sizeFromShape(a)<32||y.sizeFromShape(s)>1e3?(this.type="plain",this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type="shared",this.dispatch=re(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${$o(this.inputShape.length-1)}`,t=()=>{let o="";if(this.outputShape.length===1)this.inputShape.length!==1&&(o+="outputCoords,");else for(let n=0;n u32 { return ((a - 1u) / b + 1u); } ${` var xBestIndices : array; var xBestValues : array; `} ${se("index")} { let outputIndex = index / i32(workgroupSizeX); let reduceLength = ${e()}; var bestIndex = i32(localId.x); var bestValue = uniforms.infinityValue; let outputCoords = getCoordsFromIndex(outputIndex); for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size; k = k + i32(workgroupSizeX)) { let candidate = getX(${t()} k); if (!isnan(candidate) && candidate ${this.op} bestValue) { bestValue = candidate; bestIndex = k; } } xBestValues[localId.x] = bestValue; xBestIndices[localId.x] = bestIndex; workgroupBarrier(); var reduceSize = min(u32(reduceLength), workgroupSizeX); for (var currentSize = reduceSize / 2u; reduceSize > 1u; currentSize = reduceSize / 2u) { let interval = DIV_CEIL(reduceSize, 2u); if (localId.x < currentSize) { let candidate = xBestValues[localId.x + interval]; if (candidate ${this.op} bestValue) { bestValue = candidate; xBestValues[localId.x] = bestValue; xBestIndices[localId.x] = xBestIndices[localId.x + interval]; } } reduceSize = interval; workgroupBarrier(); } if (localId.x == 0u && outputIndex < uniforms.size) { setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]); } } `:` ${se("index")} { if (index < uniforms.size) { let outputCoords = getCoordsFromIndex(index); var bestIndex = 0; var bestValue = getX(${t()} 0); let reduceLength = ${e()}; for (var i = 1; i < reduceLength; i++) { let candidate = getX(${t()} i); if (candidate ${this.op} bestValue) { bestValue = candidate; bestIndex = i; } } setOutputAtIndexI32(index, bestIndex); } } `}};function Cre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Nr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims("argMax",[a[0]],p.shape.length);let c=new vc(p.shape,a[0],"max"),l=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],"int32",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var EM={kernelName:Vo,backendName:"webgpu",kernelFunc:Cre};function Sre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=Nr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=S.getInnerMostAxes(a.length,p.shape.length)),S.assertAxesAreInnerMostDims("argMin",[a[0]],p.shape.length);let c=new vc(p.shape,a[0],"min"),l=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],"int32",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var $M={kernelName:Za,backendName:"webgpu",kernelFunc:Sre};var wre=Se({opType:Q.ASIN}),AM={kernelName:ia,backendName:"webgpu",kernelFunc:wre};var Ire=Se({opType:Q.ASINH}),RM={kernelName:ua,backendName:"webgpu",kernelFunc:Ire};var vre=Se({opType:Q.ATAN}),FM={kernelName:pa,backendName:"webgpu",kernelFunc:vre};var kre=ot({opType:ye.ATAN2}),DM={kernelName:la,backendName:"webgpu",kernelFunc:kre};var Nre=Se({opType:Q.ATANH}),OM={kernelName:ca,backendName:"webgpu",kernelFunc:Nre};var Wl=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2, pad : vec2, dilation : vec2, convDims : vec2, filterDims : vec2,",this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),` ${se("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; let xRCCorner = vec2(coords.yz) * uniforms.stride - uniforms.pad; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; var resultValue = ${this.poolType==="avg"?"0.0":"-1.0 / pow(10.0, -20.0)"}; var count = 0.0; for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) { let xR = xRCorner + wR; if (xR < 0 || xR >= uniforms.convDims.x) { continue; } for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) { let xC = xCCorner + wC; if (xC < 0 || xC >= uniforms.convDims.y) { continue; } let value = getX(batch, xR, xC, coords[3]); ${e} } } setOutputAtIndex(index, ${t}); } } `}};var Lg=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2,",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return` ${se("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; let d = coords[3]; let xRCCorner = coords.yz * uniforms.stride; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; let value = getX(batch, xRCorner, xCCorner, d); setOutputAtIndex(index, value); } } `}};function Ul(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return qr(n,s,a,"max",t)}var PM={kernelName:yn,backendName:"webgpu",kernelFunc:Ul};function uI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return qr(n,a,s,"mean",t)}var MM={kernelName:Sn,backendName:"webgpu",kernelFunc:uI};function Bg(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return Ft({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type==="VALID"){let a=r.shape.length,i=de({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t==="avg"?p=uI({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t==="max",()=>`Invalid pool type ${t}`),p=Ul({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=de({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:"int32",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new Lg(e):(t==="avg"?n=new Wl(e,"avg"):(y.assert(t==="max",()=>`Invalid pool type ${t}`),n=new Wl(e,"max")),s.push({type:"int32",data:[e.padInfo.top,e.padInfo.left]},{type:"int32",data:[e.dilationHeight,e.dilationWidth]},{type:"int32",data:[e.inHeight,e.inWidth]},{type:"int32",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function Tre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=S.computePool2DInfo(n.shape,s,a,u,i,p);return Bg(n,c,"avg",t)}var LM={kernelName:zo,backendName:"webgpu",kernelFunc:Tre};function _re(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Gu({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var BM={kernelName:Wo,backendName:"webgpu",kernelFunc:_re};var Vg=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Rt(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Rt(this.rank),t=Ere(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>"sourceLoc = uniforms.start + coords;"):o=this.outputShape.map((s,a)=>`sourceLoc.${pI[a]} = uniforms.start.${$o(a)} + coords.${pI[a]};`),` ${se("index")} { if (index < uniforms.size) { var sourceLoc : ${e}; let coords = getCoordsFromIndex(index); ${o.join(` `)} setOutputAtIndex(index, getSource(${t})); } } `}},pI=["x","y","z","w","u","v"];function Ere(r){if(r===1)return"sourceLoc";if(r<=6)return pI.slice(0,r).map(e=>`sourceLoc.${e}`).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}function ds(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=ut.parseSliceParams(n,s,a);if(ut.assertParamsValid(n,i,p),t.shouldExecuteOnCPU([n])||n.dtype==="string"){let l=t.tensorMap.get(n.dataId),m=fM(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);let u=new Vg(i,p),c=[{type:"int32",data:i}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var VM={kernelName:_s,backendName:"webgpu",kernelFunc:ds};var $re=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=s.reduce((b,C)=>b*C),p=S.getReshaped(n.shape,s,i),u=S.getPermuted(p.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),l=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),d=[],f=de({inputs:{x:n},backend:t,attrs:{shape:p}}),h=Nr({inputs:{x:f},backend:t,attrs:{perm:u}}),g=de({inputs:{x:h},backend:t,attrs:{shape:c}}),x=ds({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeData(b.dataId)),x},zM={kernelName:xs,backendName:"webgpu",kernelFunc:$re};var Are=` fn bincount_write(index: i32, value: f32) { var oldValue = atomicLoad(& (result[index])); var exchanged = false; for (; !exchanged;) { let newValueF32 = bitcast(oldValue) + value; let newValue = bitcast(newValueF32); let res = atomicCompareExchangeWeak( &(result[index]), oldValue, newValue); oldValue = res.old_value; exchanged = res.exchanged; } } `,Rre=` fn bincount_write(index: i32, value: f32) { result[index] = value; } `,kc=class{constructor(e,t,o=!1){this.outputShape=[],this.variableNames=["x"],this.uniforms="binCountSize : i32,",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push("w"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return` ${this.binaryOutput?Rre:Are} ${se("index")} { ${this.rank===1?`if (index < uniforms.xShape) { let indexVal = i32(getX(index)); if (indexVal < uniforms.binCountSize) { let value = ${this.binaryOutput?1:this.hasWeights?"f32(getW(index))":"1."}; bincount_write(indexVal, value); } }`:`let coord = getCoordsFromIndex(index); if (coordsInBounds2D(coord, uniforms.xShape)) { let indexVal = i32(getX(coord[0], coord[1])); if (indexVal < uniforms.binCountSize) { let value = ${this.binaryOutput?1:this.hasWeights?"f32(getW(coord[0], coord[1]))":"1."}; bincount_write(coord.x * uniforms.binCountSize + indexVal, value); } }`} } `}};function Fre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=dr({backend:t,attrs:{shape:c,value:0,dtype:l}}),d=new kc([i],u),f=[{type:"int32",data:[a]}],h=u?[n,s]:[n];return t.runWebGPUProgram(d,h,l,f,m)}var WM={kernelName:Ja,backendName:"webgpu",kernelFunc:Fre};var cI=ot({opType:ye.NOT_EQUAL,dtype:"bool",cpuKernelImpl:uM}),UM={kernelName:Nn,backendName:"webgpu",kernelFunc:cI};function qa(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return Ft({inputs:{x:n.complexTensorInfos.real},backend:t})}var GM={kernelName:ai,backendName:"webgpu",kernelFunc:qa};function HM(r,e){let t=new Ro(r.shape,Q.TO_INT),o=e.runWebGPUProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function lI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return Ft({inputs:{x:n},backend:t});let a=Vr(n.shape),i=lI({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),p=po({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeData(i.dataId),p}if(n.dtype==="complex64"){let a=qa({inputs:{input:n},backend:t}),i=lI({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Ft({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.tensorMap.get(n.dataId).values,[i,p,u]=U3(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s==="int32")return HM(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),p=cI({inputs:{a:n,b:a},backend:t});return t.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var qM={kernelName:co,backendName:"webgpu",kernelFunc:lI};var Dre=Se({opType:Q.CEIL,cpuKernelImpl:G3}),KM={kernelName:Uo,backendName:"webgpu",kernelFunc:Dre};var zg=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workgroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return` ${se("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); var clampedValue : vec4; for (var i = 0; i < 4; i = i + 1) { if (isnan(value[i])) { clampedValue[i] = value[i]; } else { clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal); } } setOutputAtIndex(index, clampedValue); } } `}};var Wg=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="clip"}getUserCode(){return` ${se("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); if (isnan(value)) { setOutputAtIndex(index, value); return; } setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal)); } } `}};function Ore(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:"float32",data:[s]},{type:"float32",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new zg(n.shape):i=new Wg(n.shape),t.runWebGPUProgram(i,[n],n.dtype,p)}var jM={kernelName:lo,backendName:"webgpu",kernelFunc:Ore};var Ug=class{constructor(e){this.uniforms="",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let s=1;sqa({inputs:{input:C},backend:t})),h=r.map(C=>qu({inputs:{input:C},backend:t})),g=Nc(f,e,t),x=Nc(h,e,t),b=po({inputs:{real:g,imag:x},backend:t});return f.forEach(C=>t.disposeData(C.dataId)),h.forEach(C=>t.disposeData(C.dataId)),t.disposeData(g.dataId),t.disposeData(x.dataId),b}let n=t.shouldExecuteOnCPU(r);if(o==="string"&&(n=!0),n){let f=r.map(k=>{let $=[-1,y.sizeFromShape(k.shape.slice(e))];return de({inputs:{x:k},backend:t,attrs:{shape:$}})}),h=f.map(k=>({vals:t.readSync(k.dataId),shape:k.shape})),g=S.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=H3(h,g,o,x),C=S.computeOutShape(r.map(k=>k.shape),e),w=t.makeTensorInfo(C,o,b);return f.forEach(k=>t.disposeData(k.dataId)),w}let s=t.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new Ug(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:"int32",data:[l[0]]});for(let f=1;ft.disposeData(f.dataId));let d=de({inputs:{x:m},backend:t,attrs:{shape:i}});return t.disposeData(m.dataId),d}function Pre(r,e,t){let o=S.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>de({inputs:{x:s},backend:t,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,e)),y.sizeFromShape(s.shape.slice(e))]}})),outShape:o}}function mI(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);S.assertParamsConsistent(a,s);let i=S.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Ft({inputs:{x:p[0]},backend:t}):Nc(p,s,t)}var YM={kernelName:ys,backendName:"webgpu",kernelFunc:mI};function Mre(r,e,t,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=R=>{switch(R){case 1:return"resData = x[xIndex];";case 3:return"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${R} is not supported.`)}},l=R=>{switch(R){case 1:return"return W[row * uniforms.wShape[3] + colIn];";case 4:return"return W[row * uniforms.wShape[3] / 4 + colIn];";default:throw new Error(`innerElementSize ${R} is not supported.`)}},m=r?` let coord = vec4(batch, xRow, xCol, xCh); `:` let coord = vec4(batch, xCh, xRow, xCol); `,d=r?` let coords = vec4( batch, row / outWidth, row % outWidth, col); `:` let coords = vec4( batch, row, col / outWidth, col % outWidth); `,f=r?"uniforms.xShape[1]":"uniforms.xShape[2]",h=r?"uniforms.xShape[2]":"uniforms.xShape[3]",g=r?"row":"col",x=r?"col":"row",b=` let inChannels = uniforms.wShape[2]; let outWidth = ${r?"uniforms.outShape[2]":"uniforms.outShape[3]"}; let outRow = ${g} / outWidth; let outCol = ${g} % outWidth; let WRow = ${x} / (uniforms.filterDims[1] * inChannels); let WCol = ${x} / inChannels % uniforms.filterDims[1]; let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0]; let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1]; let xCh = ${x} % inChannels; var resData = ${kt(i)}(0.0); // The bounds checking is always needed since we use it to pad zero for // the 'same' padding type. if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) { ${m} let xIndex = getIndexFromCoords4D(coord, uniforms.xShape); ${c(i)} } return resData;`,C=r?e&&o?` let col = colIn * ${i}; ${b}`:` let col = colIn * ${i}; if (row < uniforms.dimAOuter && col < uniforms.dimInner) { ${b} } return ${kt(i)}(0.0);`:o&&t?` let col = colIn * ${i}; ${b}`:` let col = colIn * ${i}; if (row < uniforms.dimInner && col < uniforms.dimBOuter) { ${b} } return ${kt(i)}(0.0);`,w=`${l(p)}`,k=kt(u),_=r?kt(i):kt(p),$=r?kt(p):kt(i);return` ${ur(s,a,u===4,4)} fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${_} { ${r?C:w} } fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${$} { ${r?w:C} } fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${k}) { let col = colIn * ${u}; if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { var value = valueIn; let outWidth = ${r?"uniforms.outShape[2]":"uniforms.outShape[3]"}; ${d} ${Hr(n,s)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } }`}var Gg=class{constructor(e,t,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pad : vec2, stride : vec2, dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=Ml(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Ll(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableTypes=["f32","vec4"]):(this.innerElementSize=4,this.variableTypes=["vec4","vec4"]),s&&(this.variableNames.push("bias"),this.variableTypes.push("vec4")),i&&(this.variableNames.push("preluActivationWeights"),this.variableTypes.push("vec4"))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?Wu(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Uu(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return` ${Mre(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])} ${e} `}};var Hg=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2, pad: vec2, stride: vec2, dilation: vec2,",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=o,this.hasPreluActivationWeights=n,t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return` ${ur(this.activation,this.hasPreluActivationWeights,!1,4)} fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{ let coords = vec4(batch, row, col, chan); if (coordsInBounds4D(coords, uniforms.xShape)) { return getX(batch, row, col, chan); } else { return 0.0; } } fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{ let coords = vec4(row, col, xChannel, outChannel); if(coordsInBounds4D(coords, uniforms.wShape)) { return getW(row, col, xChannel, outChannel); } else { return 0.0; } } fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) { let coords = ${this.isChannelsLast?"vec4(batch, row, col, chan);":"vec4(batch, chan, row, col);"} if (coordsInBounds4D(coords, uniforms.outShape)) { var value = valueIn; ${Hr(this.addBias,this.activation)} setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value); } } ${se("index")} { let coords = getOutputCoords(); let batch = coords[0]; let outChannel = ${this.isChannelsLast?"coords[3];":"coords[1];"} let outRow = ${this.isChannelsLast?"coords[1];":"coords[2];"} let outCol = ${this.isChannelsLast?"coords[2];":"coords[3];"} var acc : f32 = 0.0; for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) { for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) { let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0]; let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1]; for (var xChannel = 0; xChannel < ${this.isChannelsLast?"uniforms.xShape[3];":"uniforms.xShape[1];"} xChannel = xChannel + 1) { ${this.isChannelsLast?"let v = readInp(batch, xRow, xCol, xChannel);":"let v = readInp(batch, xChannel, xRow, xCol);"} let f = readFilt(row, col, xChannel, outChannel); acc = acc + v * f; } } } writeResult(batch, outRow, outCol, outChannel, acc); } `}};var qg=class{constructor(e,t){this.variableNames=["x"],this.uniforms=`pad : vec2, stride : vec2, dilation : vec2, outWidth : i32, itemsPerBlockRow : i32, inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?"coords[1]":"coords[2]",n=this.isChannelsLast?"coords[2]":"coords[1]",s=this.isChannelsLast?"getX(batch, xRow, xCol, ch)":"getX(batch, ch, xRow, xCol)";return` ${se("index")} { let coords = getCoordsFromIndex(index); if(index < uniforms.size) { let batch = coords[0]; let row = ${o}; let col = ${n}; let offsetY = (row / uniforms.outWidth) * uniforms.stride[0] - uniforms.pad[0]; let xRow = offsetY + uniforms.dilation[0] * (col / uniforms.itemsPerBlockRow); var value = 0.0; if(xRow < uniforms.xShape[${e}] && xRow >= 0) { let offsetX = (row % uniforms.outWidth) * uniforms.stride[1] - uniforms.pad[1]; let xCol = offsetX + uniforms.dilation[1] * ((col % uniforms.itemsPerBlockRow) / uniforms.inChannels); let ch = col % uniforms.inChannels; if(xCol < uniforms.xShape[${t}] && xCol >= 0) { value = ${s}; } } setOutputAtIndex(index, value); } } `}};function Kg(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function Lre({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=t.dataFormat==="channelsLast",u=!p,c=!1,l=p&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type==="VALID",m=[],d,f;if(l){let x=t.inHeight*t.inWidth*t.inChannels;d=de({inputs:{x:r},backend:o,attrs:{shape:[1,t.batchSize,x]}}),f=de({inputs:{x:e},backend:o,attrs:{shape:[1,x,t.outChannels]}})}else d=de({inputs:{x:r},backend:o,attrs:{shape:p?[t.batchSize,t.inHeight*t.inWidth,t.inChannels]:[t.batchSize,t.inChannels,t.inHeight*t.inWidth]}}),f=de({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=Kg(s.shape,p);x!=null&&(s=de({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=Kg(n.shape,p);x!=null&&(n=de({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=Gu({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=de({inputs:{x:h},backend:o,attrs:{shape:t.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function Bre({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=t,C=b==="channelsLast",w=p*u*c,k=h*f,_=C?[t.batchSize,k,w]:[t.batchSize,w,k],$=new qg(_,C),A=[{type:"int32",data:[d.top,d.left]},{type:"int32",data:[m,l]},{type:"int32",data:[x,g]},{type:"int32",data:[f]},{type:"int32",data:[c*p]},{type:"int32",data:[c]}],R=o.runWebGPUProgram($,[r],r.dtype,A),D=[];D.push(R);let P=de({inputs:{x:e},backend:o,attrs:{shape:[1,w,-1]}});if(D.push(P),s!=null){let U=Kg(s.shape,C);U!=null&&(s=de({inputs:{x:s},backend:o,attrs:{shape:U}}),D.push(s))}if(n!=null){let U=Kg(n.shape,C);U!=null&&(n=de({inputs:{x:n},backend:o,attrs:{shape:U}}),D.push(n))}let W=Gu({a:C?R:P,b:C?P:R,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),V=de({inputs:{x:W},backend:o,attrs:{shape:t.outShape}});D.push(W);for(let U of D)o.disposeData(U.dataId);return V}function jg({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=t.dataFormat==="channelsLast",l=c&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type==="VALID",m=O().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!m&&(l||t.filterHeight===1&&t.filterWidth===1&&t.dilationHeight===1&&t.dilationWidth===1&&t.strideHeight===1&&t.strideWidth===1&&(t.padInfo.type==="SAME"||t.padInfo.type==="VALID")))return Lre({x:r,filter:e,convInfo:t,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=O().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"),f=d>0?d:o.thresholdToIncreaseWorkgroups,h=t.batchSize*Math.ceil(t.outHeight*t.outWidth/32)*Math.ceil(t.outChannels/32);if(O().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")||h<=f)return Bre({x:r,filter:e,convInfo:t,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[t.padInfo.top,t.padInfo.left],b=[{type:"int32",data:[t.filterHeight,t.filterWidth]},{type:"int32",data:[...x]},{type:"int32",data:[t.strideHeight,t.strideWidth]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]}];if(m)g=new Hg(t,p,i,u);else{let _=c?t.outHeight*t.outWidth:t.outChannels,$=c?t.outChannels:t.outHeight*t.outWidth,A=t.filterHeight*t.filterWidth*t.inChannels;b.push({type:"int32",data:[_]},{type:"int32",data:[$]},{type:"int32",data:[A]});let R=o.adapterInfo.isIntel();g=new Gg(t,_,$,A,p,i,u,R)}let C=[],w=[r,e];p&&(!c&&n.shape.length===1&&(n=de({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),w.push(n)),u&&(!c&&s.shape.length===1&&(s=de({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),w.push(s)),i==="leakyrelu"&&(b.push({type:"float32",data:[a]}),g.uniforms+=" alpha : f32,");let k=o.runWebGPUProgram(g,w,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function Vre(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=t,l=S.convertConv2DDataFormat(p),m=S.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return jg({x:n,filter:s,convInfo:m,backend:o})}var QM={kernelName:Go,backendName:"webgpu",kernelFunc:Vre};function zre(r=4){let e=s=>{switch(s){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return` let coord1 = vec4(coordX, coordY, col + 1, rowInner); let coord2 = vec4(coordX, coordY, col + 2, rowInner); let coord3 = vec4(coordX, coordY, col + 3, rowInner); let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)]; let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)]; let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)]; let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)]; return vec4(v0, v1, v2, v3); `;default:throw new Error(`innerElementSize ${s} is not supported.`)}},o=`if (row < uniforms.dimAOuter && col < uniforms.dimInner) { ${` let outRow = row / uniforms.outShape[2]; let outCol = row % uniforms.outShape[2]; let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]); let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1]; let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]); let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]); if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) { return ${kt(r)}(0.0); } if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) { return ${kt(r)}(0.0); } let coord = vec4( batch, i32(xR), i32(xC), col % uniforms.outBackprop[3]); return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`} } return ${kt(r)}(0.0);`;return` fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${kt(r)} { let col = colIn * ${r}; ${o} } fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${kt(r)} { let col = colIn * ${r}; let coordX = uniforms.filterDims.x - 1 - row / (uniforms.filterDims[1] * uniforms.outBackprop[3]); let coordY = uniforms.filterDims.y - 1 - (row / uniforms.outBackprop[3]) % uniforms.filterDims[1]; if (row < uniforms.dimInner && col < uniforms.dimBOuter && coordX >= 0 && coordY >= 0) { let rowInner = row % uniforms.outBackprop[3]; let coord = vec4(coordX, coordY, col, rowInner); ${e(r)} } return ${kt(r)}(0.0); } fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${kt(r)}) { let col = colIn * ${r}; if (row < uniforms.dimAOuter && (col + ${r-1}) < uniforms.dimBOuter) { var value = valueInput; let outCoord = vec4( batch, row / uniforms.outShape[2], row % uniforms.outShape[2], col); result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value; } }`}var Xg=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,y.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=Ml(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Ll(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.variableTypes=["vec4","f32"]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?Wu(this.elementsPerThread,this.workgroupSize):Uu(this.elementsPerThread,this.workgroupSize);return` ${zre(this.isVec4?4:1)} ${e} `}};var Yg=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1;return` ${se("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; let d1 = coords[${o}]; let dyCorner = vec2(coords[${e}], coords[${t}]) - uniforms.pads; let dyRCorner = dyCorner.x; let 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. var dotProd = 0.0; for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) { let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x); let wRPerm = uniforms.filterDims.x - 1 - wR; if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 || wRPerm < 0) { continue; } let idyR = i32(dyR); for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) { let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y); let wCPerm = uniforms.filterDims.y - 1 - wC; if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) || fract(dyC) > 0.0 || wCPerm < 0) { continue; } let idyC = i32(dyC); for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) { if (${this.isChannelsLast}) { let xValue = getDy(batch, idyR, idyC, d2); let wValue = getW(wRPerm, wCPerm, d1, d2); dotProd = dotProd + xValue * wValue; } else { let xValue = getDy(batch, d2, idyR, idyC); let wValue = getW(wRPerm, wCPerm, d1, d2); dotProd = dotProd + xValue * wValue; } } } } setOutputAtIndex(index, dotProd); } } `}};function Wre(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:"int32",data:[m.filterHeight,m.filterWidth]},{type:"int32",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(O().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||m.filterHeight<=2&&m.filterWidth<=2&&m.outChannels<=16&&m.inChannels===1)f=new Yg(m);else{f=new Xg(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:"uint32",data:[h]},{type:"uint32",data:[g]},{type:"uint32",data:[x]})}return t.runWebGPUProgram(f,[n,s],"float32",d)}var ZM={kernelName:Ho,backendName:"webgpu",kernelFunc:Wre};var Ure=Se({opType:Q.COS}),JM={kernelName:qo,backendName:"webgpu",kernelFunc:Ure};var Gre=Se({opType:Q.COSH}),eL={kernelName:Ko,backendName:"webgpu",kernelFunc:Gre};var Qg=class{constructor(e,t,o,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[o,n,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return` ${se("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let height_ratio = f32(${o}); let width_ratio = f32(${a}); let b = coords[0]; let y = coords[1]; let x = coords[2]; let d = coords[3]; // get box vals let y1 = getBoxes(b, 0); let x1 = getBoxes(b, 1); let y2 = getBoxes(b, 2); let x2 = getBoxes(b, 3); // get image in batch index let bInd = i32(round(getBoxInd(b))); if(bInd < 0 || bInd >= uniforms.outShape[0]) { return; } let height_scale = ${n}; let width_scale = ${i}; let in_y = ${s}; if( in_y < 0.0 || in_y > ${e} ) { setOutputAtIndex(index, uniforms.extrapolationValue); return; } let in_x = ${p}; if( in_x < 0.0 || in_x > ${t} ) { setOutputAtIndex(index, uniforms.extrapolationValue); return; } let sourceFracIndexCR = vec2(in_x,in_y); if(${this.methodId} == 1) { // Compute the four integer indices. let sourceFloorCR = vec2(sourceFracIndexCR); let sourceCeilCR = vec2(ceil(sourceFracIndexCR)); let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d); let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d); let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d); let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d); let fracCR = sourceFracIndexCR - vec2(sourceFloorCR); let top = topLeft + (topRight - topLeft) * fracCR.x; let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; let newValue = top + (bottom - top) * fracCR.y; setOutputAtIndex(index, newValue); } else { // Compute the coordinators of nearest neighbor point. let sourceNearestCR = vec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); let newValue = getImage( bInd, sourceNearestCR.y, sourceNearestCR.x, d); setOutputAtIndex(index, newValue); } } } `}};var Hre=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Qg(n.shape[3],s.shape,i,p),l=[{type:"float32",data:[u]}];return t.runWebGPUProgram(c,[n,s,a],"float32",l)},tL={kernelName:Yo,backendName:"webgpu",kernelFunc:Hre};var Ku;(function(r){r.Prod="*",r.Sum="+"})(Ku||(Ku={}));var Gl=class{constructor(e,t,o,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Ku.Prod?"1.0":"0.0",o=this.exclusive?t:`getX(${rL(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],s="",a="";return this.exclusive?(s=this.reverse?`end != ${n-1}`:"end != 0",a=this.reverse?"end + 1":"end - 1"):(s=this.reverse?`end + pow2 < ${n}`:"end >= pow2",a=this.reverse?"end + pow2":"end - pow2"),` ${se("index")} { if (index < uniforms.size) { var coords = getCoordsFromIndex(index); let end = ${oL(e,"coords",this.op)}; var val = ${o}; let pow2 = i32(pow(2.0, uniforms.index)); if (${s}) { let idx = ${a}; ${oL(e,"coords",this.op)} = idx; val ${this.op}= getX(${rL(e,"coords",this.op)}); } setOutputAtIndex(index, val); } } `}};function rL(r,e,t){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 ${t} for rank ${r} is not yet supported`)}function oL(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Zg(r,e,t,o,n,s){let a=e.shape.length,i=S.getAxesPermutation([o],a),p=e;i!=null&&(p=Nr({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=S.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Ft({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new Gl(r,p.shape,!1,s),f=l,h=[{type:"float32",data:[m]}];l=t.runWebGPUProgram(d,[l],l.dtype,h),t.disposeData(f.dataId)}if(n){let m=new Gl(r,p.shape,n,s),d=l,f=[{type:"float32",data:[0]}];l=t.runWebGPUProgram(m,[l],l.dtype,f),t.disposeData(d.dataId)}if(i!=null){let m=S.getUndoAxesPermutation(i),d=Nr({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeData(l.dataId),t.disposeData(p.dataId),d}return l}function qre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Zg(Ku.Prod,n,t,s,a,i)}var nL={kernelName:jo,backendName:"webgpu",kernelFunc:qre};function Kre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Zg(Ku.Sum,n,t,s,a,i)}var sL={kernelName:Xo,backendName:"webgpu",kernelFunc:Kre};function jre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=dr({backend:t,attrs:{shape:d,value:0,dtype:l}}),h=new kc(m,c,i),g=[{type:"int32",data:[a]}],x=c?[n,s]:[n];return t.runWebGPUProgram(h,x,l,g,f)}var aL={kernelName:ti,backendName:"webgpu",kernelFunc:jre};var Jg=class{constructor(e,t){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return` ${se("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; let h = ${this.getHeightCoordString()}; let w = ${this.getWidthCoordString()}; let d = ${this.getDepthCoordString()}; let in_h = h / uniforms.blockSize; let offset_h = h % uniforms.blockSize; let in_w = w / uniforms.blockSize; let offset_w = w % uniforms.blockSize; let offset_d = (offset_h * uniforms.blockSize + offset_w) * ${this.getOutputDepthSize()}; let in_d = d + offset_d; let rlt = ${this.getInputSamplingString()}; setOutputAtIndex(index, rlt); } }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Xre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a==="NHWC"?[i,l,m,d]:[i,d,l,m],h=[{type:"int32",data:[s]}],g=new Jg(f,a);return t.runWebGPUProgram(g,[n],n.dtype,h)}var iL={kernelName:Qo,backendName:"webgpu",kernelFunc:Xre};var ex=class{constructor(e,t,o,n=!1,s=null,a=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2, inDims : vec2,",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return` ${ur(this.activation,this.hasPreluActivation,!1,4)} var mm_Asub : array, ${o}>; var mm_Bsub : array, ${this.filterHeight}>; fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 { var value = 0.0; if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1]) { value = getX(batch, channel, row, col); } return value; } ${se()} { let coords = getOutputCoords(); let batch = coords[0]; let xRCCorner = vec2(coords.zw) - uniforms.pad; let channelMul = uniforms.wShape[3]; let d1 = coords[1] / channelMul; let q = coords[1] % channelMul; let inputRowStart = xRCCorner.x; let inputColStart = xRCCorner.y; let localRow = i32(localId.y); let localCol = i32(localId.x); // Load one tile of X into local memory. for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${this.workgroupSize[1]}) { for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workgroupSize[0]}) { let rowOffset = inputRow - localRow; let colOffset = inputCol - localCol; mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset); } } // Load one tile of W into local memory. var wIndex = i32(localIndex); ${e, inDims : vec2,",this.workgroupSize=[4,4,4],this.workPerThread=4,this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1]),y.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth;return` ${ur(this.activation,this.hasPreluActivation,!0,4)} fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 { var value = vec4(0.0); if (col >=0 && col < uniforms.inDims[1]) { value = getX(batch, row, col, channel); } return value; } const strideHeight = ${this.convInfo.strideHeight}; const strideWidth = ${this.convInfo.strideWidth}; ${se()} { let batch = i32(globalId.z) / uniforms.outShape[1]; let r = i32(globalId.z) % uniforms.outShape[1]; let c = i32(globalId.y) * ${this.workPerThread}; let d1 = i32(globalId.x) * 4; let xRCCorner = vec2(r, c) * vec2(strideHeight, strideWidth) - uniforms.pad; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; var xVals : array, ${e}>; var dotProd : array, ${this.workPerThread}>; for (var i = 0; i < ${this.workPerThread}; i++) { dotProd[i] = vec4(0.0); } // Use constant instead of uniform can give better performance. for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) { let xR = xRCorner + wR; if (xR >=0 && xR < uniforms.inDims[0]) { for (var i = 0; i < ${e}; i++) { xVals[i] = readX(batch, xR, xCCorner + i, d1); } for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) { let wValue = getW(wR, wC, d1, 0); for (var i = 0; i < ${this.workPerThread}; i++) { dotProd[i] = fma(xVals[i * strideWidth + wC], wValue, dotProd[i]); } } } } for (var i = 0; i < ${this.workPerThread}; i = i + 1) { let coords = vec4(batch, r, c + i, d1); if (coordsInBounds4D(coords, uniforms.outShape)) { var value = dotProd[i]; ${Hr(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } } `}};var _c=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2, inDims : vec2, filterHeight : i32, filterWidth : i32, stride : vec2, dilation : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return` ${ur(this.activation,this.hasPreluActivation,!1,4)} ${se("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); let batch = coords[0]; let xRCCorner = vec2(coords.${this.isChannelsLast?"yz":"zw"}) * uniforms.stride - uniforms.pad; let d2 = coords[${this.isChannelsLast?3:1}]; let channelMul = uniforms.wShape[3]; let d1 = d2 / channelMul; let q = d2 % channelMul; let inputRowStart = xRCCorner.x; let inputColStart = xRCCorner.y; let inputRowEnd = inputRowStart + uniforms.filterHeight * uniforms.dilation[0]; let inputColEnd = inputColStart + uniforms.filterWidth * uniforms.dilation[1]; // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC. // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW. var value = 0.0; // Extract if checking out of for loop for performance. if (inputRowStart >= 0 && inputColStart >= 0 && inputRowEnd < uniforms.inDims[0] && inputColEnd < uniforms.inDims[1]) { for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) { let xR = inputRowStart + wR * uniforms.dilation[0]; for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) { let xC = inputColStart + wC * uniforms.dilation[1]; let xVal = ${e}; let wVal = getW(wR, wC, d1, q); value = value + xVal * wVal; } } } else { for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) { let xR = inputRowStart + wR * uniforms.dilation[0]; if (xR < 0 || xR >= uniforms.inDims[0]) { continue; } for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) { let xC = inputColStart + wC * uniforms.dilation[1]; if (xC < 0 || xC >= uniforms.inDims[1]) { continue; } let xVal = ${e}; let wVal = getW(wR, wC, d1, q); value = value + xVal * wVal; } } } ${Hr(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } `}};function Yre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=S.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=S.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.inHeight,d.inWidth]}],h=d.dataFormat==="channelsLast",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new ex(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?g=new Tc(d):(g=new _c(d),f.push({type:"int32",data:[d.filterHeight]},{type:"int32",data:[d.filterWidth]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]})),t.runWebGPUProgram(g,[n,s],n.dtype,f)}var uL={kernelName:Zo,backendName:"webgpu",kernelFunc:Yre};var dI=ot({opType:ye.MUL,cpuKernelImpl:aM,supportsComplex:!0}),pL={kernelName:kn,backendName:"webgpu",kernelFunc:dI};function Hl(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return qr(n,s,a,"sum",t)}var cL={kernelName:Hn,backendName:"webgpu",kernelFunc:Hl};function Qre(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=S.decodeEinsumEquation(n,s.length);S.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=S.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=Hl({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var lL={kernelName:ri,backendName:"webgpu",kernelFunc:Qre};var Zre=Se({opType:Q.ELU}),mL={kernelName:en,backendName:"webgpu",kernelFunc:Zre};var Jre=ot({opType:ye.EQUAL,dtype:"bool",cpuKernelImpl:q3}),dL={kernelName:tn,backendName:"webgpu",kernelFunc:Jre};var eoe=Se({opType:Q.ERF}),fL={kernelName:ma,backendName:"webgpu",kernelFunc:eoe};var fI=Se({opType:Q.EXP,cpuKernelImpl:K3,dtype:"float32"}),hL={kernelName:rn,backendName:"webgpu",kernelFunc:fI};function tx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),de({inputs:{x:s},backend:o,attrs:{shape:i}})}var gL={kernelName:bs,backendName:"webgpu",kernelFunc:tx};var toe=Se({opType:Q.EXPM1,cpuKernelImpl:j3}),xL={kernelName:da,backendName:"webgpu",kernelFunc:toe};var ql=class{constructor(e,t){this.variableNames=["real","imag"],this.outputShape=[],this.uniforms="exponentMultiplier : f32, denominator: f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return` fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 { ${this.component==="real"?"return real * expR - imag * expI;":"return real * expI + imag * expR;"} } fn mulMatDFT(batch: i32, index: i32) -> f32 { let indexRatio = f32(index) / f32(uniforms.realShape[1]); let exponentMultiplierTimesIndexRatio = uniforms.exponentMultiplier * indexRatio; var result = 0.0; for (var i = 0; i < uniforms.realShape[1]; i = i + 1) { // x = (-2|2 * PI / N) * index * i; let x = exponentMultiplierTimesIndexRatio * f32(i); let expR = cos(x); let expI = sin(x); let real = getReal(batch, i); let imag = getImag(batch, i); result = result + unaryOpComplex(real, expR, imag, expI) / uniforms.denominator; } return result; } ${se("index")} { if (index < uniforms.size) { let coords = getOutputCoords(); setOutputAtIndex(index, mulMatDFT(coords[0], coords[1])); } } `}};function rx(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=de({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new ql("real",u),l=new ql("imag",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:"float32",data:[d]},{type:"float32",data:[f]}],g=t.runWebGPUProgram(c,m,"float32",h);i.push(g);let x=t.runWebGPUProgram(l,m,"float32",h);i.push(x);let b=po({inputs:{real:g,imag:x},backend:t});i.push(b);let C=de({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(w=>t.disposeData(w.dataId)),C}function roe(r){let{inputs:e,backend:t}=r,{input:o}=e;return rx(o,!1,t)}var yL={kernelName:oi,backendName:"webgpu",kernelFunc:roe};var ox=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` ${se("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordX = uniforms.xShape[2] - coords[2] - 1; let outputValue = getX(coords[0], coords[1], coordX, coords[3]); setOutputAtIndex(index, outputValue); } } `}};var bL={kernelName:on,backendName:"webgpu",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new ox(t.shape);return o.runWebGPUProgram(n,[t],t.dtype)}};var ooe=Se({opType:Q.FLOOR,cpuKernelImpl:X3}),CL={kernelName:nn,backendName:"webgpu",kernelFunc:ooe};var noe=ot({opType:ye.INT_DIV,dtype:"int32"}),SL={kernelName:sn,backendName:"webgpu",kernelFunc:noe};var nx=class{constructor(e,t,o=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` @binding(1) @group(0) var src: ${this.importVideo?"texture_external":"texture_2d"}; ${se("index")} { let flatIndex = index * uniforms.numChannels; if (flatIndex < uniforms.size) { let coords = getCoordsFromIndex(flatIndex); let values = ${e}; for (var i = 0; i < uniforms.numChannels; i = i + 1) { result[flatIndex + i] = i32(floor(255.0 * values[i])); } } } `}};var wL={kernelName:Zi,backendName:"webgpu",kernelFunc:soe},Ec,hI=O().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"),sx=new Map;function soe(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o;if(n==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let a=typeof HTMLVideoElement!="undefined"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&n instanceof HTMLImageElement,p=typeof HTMLCanvasElement!="undefined"&&n instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&n instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&n instanceof ImageBitmap,[c,l]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],m=[l,c,s],d=!1,f=a||i;if(u||p||f){let b;if(d){let D=n;if(!sx.has(D)||sx.get(D).expired){let P={source:D};sx.set(D,t.device.importExternalTexture(P))}b={width:c,height:l,format:null,usage:null,texture:sx.get(D)}}else{if(f){let L=O().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Ec==null||L!==hI)&&(hI=L,Ec=document.createElement("canvas").getContext("2d",{willReadFrequently:hI})),Ec.canvas.width=c,Ec.canvas.height=l,Ec.drawImage(n,0,0,c,l),n=Ec.canvas}let D=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,P="rgba8unorm",M=t.textureManager.acquireTexture(m[1],m[0],P,D);t.queue.copyExternalImageToTexture({source:n},{texture:M},[m[1],m[0]]),b={width:c,height:l,format:P,usage:D,texture:M}}let C=y.sizeFromShape(m),w=y.computeStrides(m),k=new nx(m,s,d),_=[{type:"uint32",data:[C]},{type:"uint32",data:[s]},{type:"uint32",data:[...w]}],$=t.makeTensorInfo([l,c],"int32"),A=t.tensorMap.get($.dataId);A.resourceInfo=b;let R=t.runWebGPUProgram(k,[$],"int32",_);return t.disposeData($.dataId),R}let h=n.data,g=h;if(s!=null&&s!==4){g=new Uint8Array(n.width*n.height*s);let b=h.length,C=0;for(let w=0;w(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0))); } } `}};var IL={kernelName:an,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=e,u=t,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new ax(o.shape,a.shape,i.shape,l,m),f=[{type:"float32",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function aoe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return jg({x:n,filter:s,convInfo:g,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var vL={kernelName:ho,backendName:"webgpu",kernelFunc:aoe};function ioe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=S.computeConv2DInfo(n.shape,s.shape,p,f,u,l,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let C=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],w;return h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?w=new Tc(h,x,m,b):(w=new _c(h,x,m,b),C.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),m==="leakyrelu"&&(C.push({type:"float32",data:[d]}),w.uniforms+=" alpha : f32,"),t.runWebGPUProgram(w,g,"float32",C)}var kL={kernelName:go,backendName:"webgpu",kernelFunc:ioe};var ix=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Rt(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",` ${se("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var flattenIndex = 0; for (var j = 0; j < uniforms.sliceDim; j = j + 1) { let indexTemp = i32(round(getIndices(coords[0], j))); let strideNum = ${e}; flattenIndex = flattenIndex + indexTemp * strideNum; } setOutputAtIndex(index, getA(flattenIndex, coords[1])); } } `}};function uoe(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=S.prepareAndValidate(o,n),m=de({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=de({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype==="string"){let b=t.readSync(n.dataId),C=t.bufferSync(o),w=Y3(b,C,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,w.values)}let f=new ix(a,[u,c]),h=[{type:"int32",data:[a]},{type:"int32",data:l}],g=t.runWebGPUProgram(f,[d,m],d.dtype,h),x=de({inputs:{x:g},backend:t,attrs:{shape:p}});return t.disposeData(m.dataId),t.disposeData(d.dataId),t.disposeData(g.dataId),x}var NL={kernelName:un,backendName:"webgpu",kernelFunc:uoe};var ux=class{constructor(e,t){this.variableNames=["A","indices"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="gather"}getUserCode(){let e=poe(this.aShape);return` ${se("index")} { if (index < uniforms.size) { let resRC = getCoordsFromIndex(index); let indexZ = i32(getIndices(resRC.x, resRC.z)); let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]); setOutputAtIndex(index, inBounds * getA(${e})); } } `}};function poe(r){let e=["resRC.x","resRC.y","resRC.z","resRC.w"],t=[];for(let o=0;ot.disposeData(R.dataId)),t.makeTensorInfo(u.outputShape,A.dtype,A.values)}let h=new ux(m.shape,f),g=t.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=de({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeData(b.dataId)),x}var TL={kernelName:Ss,backendName:"webgpu",kernelFunc:gI};var coe=ot({opType:ye.GREATER,cpuKernelImpl:J3,dtype:"bool"}),_L={kernelName:pn,backendName:"webgpu",kernelFunc:coe};var loe=ot({opType:ye.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:Z3}),EL={kernelName:cn,backendName:"webgpu",kernelFunc:loe};function moe(r){let{inputs:e,backend:t}=r,{input:o}=e;return rx(o,!0,t)}var $L={kernelName:ni,backendName:"webgpu",kernelFunc:moe};var doe=Se({opType:Q.IS_FINITE,dtype:"bool"}),AL={kernelName:fa,backendName:"webgpu",kernelFunc:doe};var foe=Se({opType:Q.IS_INF,dtype:"bool"}),RL={kernelName:ha,backendName:"webgpu",kernelFunc:foe};var hoe=Se({opType:Q.IS_NAN,dtype:"bool"}),FL={kernelName:ln,backendName:"webgpu",kernelFunc:hoe};function goe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=[{type:"float32",data:[s]}],i=new Ro(n.shape,Q.LEAKYRELU);return i.uniforms="alpha : f32,",t.runWebGPUProgram(i,[n],"float32",a)}var DL={kernelName:mn,backendName:"webgpu",kernelFunc:goe};var xoe=ot({opType:ye.LESS,dtype:"bool",cpuKernelImpl:tM}),OL={kernelName:dn,backendName:"webgpu",kernelFunc:xoe};var yoe=ot({opType:ye.LESS_EQUAL,dtype:"bool",cpuKernelImpl:eM}),PL={kernelName:fn,backendName:"webgpu",kernelFunc:yoe};var boe=Se({opType:Q.LOG,cpuKernelImpl:rM}),ML={kernelName:hn,backendName:"webgpu",kernelFunc:boe};var Coe=Se({opType:Q.LOG1P}),LL={kernelName:ga,backendName:"webgpu",kernelFunc:Coe};var Soe=ot({opType:ye.LOGICAL_AND,dtype:"bool"}),BL={kernelName:gn,backendName:"webgpu",kernelFunc:Soe};var woe=Se({opType:Q.LOGICAL_NOT}),VL={kernelName:xn,backendName:"webgpu",kernelFunc:woe};var Ioe=ot({opType:ye.MAX,cpuKernelImpl:nM}),zL={kernelName:bn,backendName:"webgpu",kernelFunc:Ioe};function voe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1,c=S.computePool2DInfo(n.shape,s,a,u,i,p);return Bg(n,c,"max",t)}var WL={kernelName:Cn,backendName:"webgpu",kernelFunc:voe};function koe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return qr(n,s,a,"min",t)}var UL={kernelName:wn,backendName:"webgpu",kernelFunc:koe};var Noe=ot({opType:ye.MIN,cpuKernelImpl:sM}),GL={kernelName:In,backendName:"webgpu",kernelFunc:Noe};var px=class{constructor(e,t,o){this.uniforms="",this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o==="reflect"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(","),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:""}`).join(","),n=e===1?"start":"start[i]",s=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",i=Rt(e),p=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` ${se("index")} { if (index < uniforms.size) { let start = ${i}(${t}); let end = ${i}(${o}); var outC = getCoordsFromIndex(index); for (var i = 0; i < ${e}; i = i + 1) { if (${a} < ${n}) { ${a} = ${n} * 2 - ${a} - ${this.offset}; } else if(${a} >= ${s}) { ${a} = (${s} - 1) * 2 - ${a} + ${this.offset}; } } let coords = outC - start; setOutputAtIndex(index, getX(${p})); } } `}};var HL={kernelName:vn,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=t,i=n.map(c=>({type:"int32",data:[c[0],c[1]]})),p=new px(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var Toe=ot({opType:ye.MOD}),qL={kernelName:ya,backendName:"webgpu",kernelFunc:Toe};function _oe(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.tensorMap.get(o.dataId),[a,i]=iM(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n=new Ro(o.shape,Q.NEG);return t.runWebGPUProgram(n,[o],o.dtype)}var KL={kernelName:ws,backendName:"webgpu",kernelFunc:_oe};function Eoe(r){console.warn("tf.nonMaxSuppression() in webgpu 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:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=Lt.nonMaxSuppressionV3Impl(u,c,a,i,p);return t.makeTensorInfo([l.length],"int32",new Int32Array(l))}var jL={kernelName:Tn,backendName:"webgpu",kernelFunc:Eoe};function $oe(r){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Lt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var XL={kernelName:_n,backendName:"webgpu",kernelFunc:$oe};var cx=class{constructor(e,t){this.variableNames=["x"],this.uniforms="onValue : f32, offValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="onehot"}getUserCode(){return` ${se("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue, f32(i32(round(getX(coords.x))) == coords.y))); } } `}};function Aoe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new cx(u,a),l=de({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=[{type:"float32",data:[i]},{type:"float32",data:[p]}],d=t.runWebGPUProgram(c,[l],s,m);t.disposeData(l.dataId);let f=[...n.shape,a],h=de({inputs:{x:d},backend:t,attrs:{shape:f}});return t.disposeData(d.dataId),h}var YL={kernelName:En,backendName:"webgpu",kernelFunc:Aoe};function Kl(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=qa({inputs:{input:o},backend:t}),s=Kl({inputs:{x:n},backend:t}),a=qu({inputs:{input:o},backend:t}),i=Kl({inputs:{x:a},backend:t}),p=po({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return dr({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var QL={kernelName:Fs,backendName:"webgpu",kernelFunc:Kl};function ZL(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=qa({inputs:{input:o},backend:t}),s=ZL({inputs:{x:n},backend:t}),a=qu({inputs:{input:o},backend:t}),i=Kl({inputs:{x:a},backend:t}),p=po({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return dr({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var JL={kernelName:Is,backendName:"webgpu",kernelFunc:ZL};function Roe(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return tx({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=[],p=e.map(c=>{let l=tx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=mI({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var eB={kernelName:vs,backendName:"webgpu",kernelFunc:Roe};var lx=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((o,n)=>o[0]+e[n]+o[1]),this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Rt(e),o=this.xShape.map((l,m)=>`uniforms.pad${m}[0]`).join(","),n=this.xShape.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${e>1?`[${m}]`:""}`).join(","),s=e>1?`${t}(${o})`:`${o}`,a=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",p=e>1?"any(outC >= end)":"outC >= end",u=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` ${se("index")} { if (index < uniforms.size) { let start = ${s}; let end = ${a}; let outC = getCoordsFromIndex(index); if (${i} || ${p}) { setOutputAtIndex(index, uniforms.constantValue); } else { let coords = outC - start; setOutputAtIndex(index, getX(${u})); } } } `}};var xI=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return Ft({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return dr({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:"float32",data:[a]}];s.map(u=>i.push({type:"int32",data:[u[0],u[1]]}));let p=new lx(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},tB={kernelName:$n,backendName:"webgpu",kernelFunc:xI};var Foe=ot({opType:ye.POW}),rB={kernelName:An,backendName:"webgpu",kernelFunc:Foe};function Doe(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new Hu(ye.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],"float32")}var oB={kernelName:Rn,backendName:"webgpu",kernelFunc:Doe};function Ooe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return qr(n,s,a,"prod",t)}var nB={kernelName:Fn,backendName:"webgpu",kernelFunc:Ooe};var Poe=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=cM(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},sB={kernelName:ks,backendName:"webgpu",kernelFunc:Poe};var yI=ot({opType:ye.DIV}),aB={kernelName:Jo,backendName:"webgpu",kernelFunc:yI};var Moe=Se({opType:Q.RECIPROCAL}),iB={kernelName:Dn,backendName:"webgpu",kernelFunc:Moe};var Loe=Se({opType:Q.RELU}),uB={kernelName:On,backendName:"webgpu",kernelFunc:Loe};var Boe=Se({opType:Q.RELU6}),pB={kernelName:Ln,backendName:"webgpu",kernelFunc:Boe};var mx=class{constructor(e,t,o){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` ${se("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; let d = coords[3]; let rc = coords.yz; let effectiveInSize = vec2( f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0], f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]); let effectiveOutSize = vec2( f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0], f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]); let effectiveInputOverOutputRatioRC = effectiveInSize / effectiveOutSize; // Fractional source index let sourceFracIndexRC = (vec2(rc) + vec2(uniforms.halfPixelCenters)) * effectiveInputOverOutputRatioRC - vec2(uniforms.halfPixelCenters); // Compute the four integer indices. let sourceFloorRC = vec2(sourceFracIndexRC); let sourceCeilRC = vec2( min(vec2(uniforms.xShape.yz) - vec2(1.0), ceil(sourceFracIndexRC))); let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d); let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d); let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d); let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d); let fracRC = sourceFracIndexRC - vec2(sourceFloorRC); let top = topLeft + (topRight - topLeft) * fracRC.y; let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y; let newValue = top + (bottom - top) * fracRC.x; setOutputAtIndex(index, newValue); } } `}};function Voe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:"float32",data:[c,l]},{type:"float32",data:[i?.5:0]}],f=new mx(n.shape,p,u);return t.runWebGPUProgram(f,[n],"float32",d)}var cB={kernelName:Mn,backendName:"webgpu",kernelFunc:Voe};var dx=class{constructor(e,t,o,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, roundBase : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":e="vec2(rc) * effectiveInputOverOutputRatioRC",` ${se("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; let d = coords[3]; let rc = coords.yz; let effectiveInSize = vec2( f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0], f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]); let effectiveOutSize = vec2( f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0], f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]); let effectiveInputOverOutputRatioRC = effectiveInSize / effectiveOutSize; // Fractional source index let sourceFracIndexRC = ${e}; // Compute the coordinators of nearest neighbor point. let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z)); let sourceNearestRC = vec2( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase))); let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutputAtIndex(index, newValue); } } `}};function zoe(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:"float32",data:[c,l]},{type:"float32",data:[s?.5:0]}],f=new dx(n.shape,p,u,a);return t.runWebGPUProgram(f,[n],n.dtype,d)}var lB={kernelName:Pn,backendName:"webgpu",kernelFunc:zoe};var fx=class{constructor(e){this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=" axis : vec4,",this.shaderKey="reverse"}getUserCode(){return` // Using uniform variables as judging conditions, so the function has // coherent execution within all threads. fn getReverseCoords(coords : vec4) -> vec4 { var reverseCoords = coords; if (uniforms.axis[0] == 1) { reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1; } if (uniforms.axis[1] == 1) { reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1; } if (uniforms.axis[2] == 1) { reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1; } if (uniforms.axis[3] == 1) { reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1; } return reverseCoords; } ${se("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let reverseCoords = getReverseCoords(coords); setOutputAtIndex(index, getX(reverseCoords[0], reverseCoords[1], reverseCoords[2], reverseCoords[3])); } } `}};function Woe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length;if(a===0)return Ft({inputs:{x:n},backend:t});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:"int32",data:c}],m=de({inputs:{x:n},backend:t,attrs:{shape:p}}),d=new fx(p),f=t.runWebGPUProgram(d,[m],m.dtype,l);t.disposeData(m.dataId);let h=de({inputs:{x:f},backend:t,attrs:{shape:i}});return t.disposeData(f.dataId),h}var mB={kernelName:Bn,backendName:"webgpu",kernelFunc:Woe};var hx=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32, cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return` ${se("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordXFloat = (f32(coords[2]) - uniforms.centerX) * uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) * uniforms.sinRadians; let coordYFloat = (f32(coords[2]) - uniforms.centerX) * uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) * uniforms.cosRadians; let coordX = i32(round(coordXFloat + uniforms.centerX)); let coordY = i32(round(coordYFloat + uniforms.centerY)); ${this.fillSnippet} if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 && coordY < uniforms.xShape[1]) { outputValue = getX(coords[0], coordY, coordX, coords[3]); } setOutputAtIndex(index, outputValue); } } `}};var dB={kernelName:es,backendName:"webgpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new hx(o.shape,s),[u,c]=S.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:"float32",data:[u]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(n)]},{type:"float32",data:[Math.cos(n)]}];return typeof s=="number"?l.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:"float32",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var Uoe=Se({opType:Q.RSQRT,cpuKernelImpl:lM}),fB={kernelName:Vn,backendName:"webgpu",kernelFunc:Uoe};var Gi=class{constructor(e,t,o,n,s,a,i,p=!0){this.variableNames=["updates","indices"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=ue(e),this.dispatch=re(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;let u=Rt(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,o=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",s="";this.dispatchLayout.x.length===1?(n="flattenedIndex",s=` fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 { return index; } `):this.dispatchLayout.x.length===2&&(n="vec2(flattenedIndex, coords[1])",s=` fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2 { // N.B. |updates| could be a scalar tensor, conceptually representing a // 2D tensor with all values equal to that. By design, its size must be // the same as |outShape[1]| in one dimension, and |indicesShape[0]| // gives the other. let sliceSize = uniforms.outShape[1]; let d0 = index / sliceSize; let d1 = index - d0 * sliceSize; return vec2(d0, d1); } `);let i=`getUpdates(${Array.from({length:this.updatesRank},(c,l)=>`coords[${l}]`).join(", ")})`,p=(c,l)=>{let m=`atomicAdd(${c}, bitcast(${l}))`;this.type==="float32"&&(m=` { var oldBits = 0; var newBits = bitcast(${l}); loop { let info = atomicCompareExchangeWeak(${c}, oldBits, newBits); if (info.exchanged) { break; } oldBits = info.old_value; let oldValue = bitcast(oldBits); let newValue = oldValue + (${l}); newBits = bitcast(newValue); } } `);let d=`atomicStore(${c}, bitcast(${l}));`;return this.sumDupeIndices?m:d};return` ${s} ${se("index")} { if (index < uniforms.updatesSize) { let coords = getUpdatesCoordsFromFlatIndex(index); var flattenedIndex = 0; for (var j = 0; j < uniforms.sliceDim; j = j + 1) { let indexInside = i32(round(${t})); flattenedIndex = flattenedIndex + indexInside * ${o}; } let updateValue = ${wc(this.type,!1)}(${i}); let flatIndex = getOutputIndexFromCoords(${n}); ${p("&result[flatIndex]","updateValue")}; } }`}};function Goe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=S.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=de({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=de({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=f.dtype,g=dr({backend:t,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[x]}],C=new Gi(f.shape,i,d.shape.length,f.shape.length,c,m,h),w=t.runWebGPUProgram(C,[f,d],h,b,g),k=de({inputs:{x:w},backend:t,attrs:{shape:a}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(w.dataId),k}var hB={kernelName:zn,backendName:"webgpu",kernelFunc:Goe};var gx=class{constructor(e,t){this.outputShape=[],this.variableNames=["sortedSequence","values"],this.uniforms="numInputs : i32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return` fn findBound(batch: i32, value: f32) -> i32 { var left = i32(0); var right = uniforms.numInputs; while (left < right) { var mid = (left + right) / 2; if (getSortedSequence(batch, mid) ${this.side==="left"?"<":"<="} value) { left = mid + 1; } else { right = mid; } } return right; } ${se("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let value = getValuesByOutputIndex(index); setOutputAtIndexI32(index, findBound(coords[0], value)); } } `}};function Hoe(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new gx([s.shape[0],s.shape[1]],a),p=[{type:"int32",data:[n.shape[1]]}];return t.runWebGPUProgram(i,[n,s],"int32",p)}var gB={kernelName:ii,backendName:"webgpu",kernelFunc:Hoe};var xx=class{constructor(e,t,o){this.variableNames=["c","a","b"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=o,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[],a=[];for(let i=0;i= 1.0) { setOutputAtIndex(index, getA(${t})); } else { setOutputAtIndex(index, getB(${t})); } } } `}};function qoe(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new xx(o.shape.length,n.shape,n.shape.length);return t.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var xB={kernelName:Ts,backendName:"webgpu",kernelFunc:qoe};var Koe=Se({opType:Q.SIGMOID}),yB={kernelName:Un,backendName:"webgpu",kernelFunc:Koe};var joe=Se({opType:Q.SIN}),bB={kernelName:Wn,backendName:"webgpu",kernelFunc:joe};var Xoe=Se({opType:Q.SINH}),CB={kernelName:Sa,backendName:"webgpu",kernelFunc:Xoe};var bI=ot({opType:ye.SUB,cpuKernelImpl:xM,supportsComplex:!0}),SB={kernelName:Xn,backendName:"webgpu",kernelFunc:bI};function Yoe(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=Ul({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),p=S.expandShapeToKeepDim(i.shape,a),u=de({inputs:{x:i},backend:t,attrs:{shape:p}}),c=bI({inputs:{a:n,b:u},backend:t}),l=fI({inputs:{x:c},backend:t}),m=Hl({inputs:{x:l},backend:t,attrs:{axis:a,keepDims:!1}}),d=de({inputs:{x:m},backend:t,attrs:{shape:p}}),f=yI({inputs:{a:l,b:d},backend:t});return t.disposeData(i.dataId),t.disposeData(u.dataId),t.disposeData(c.dataId),t.disposeData(l.dataId),t.disposeData(m.dataId),t.disposeData(d.dataId),f}var wB={kernelName:qn,backendName:"webgpu",kernelFunc:Yoe};var Qoe=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 WebGPU backend not implemented yet");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xt.disposeData(x.dataId)),g},IB={kernelName:Es,backendName:"webgpu",kernelFunc:Qoe};var yx=class{constructor(e,t){this.variableNames=["A"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${e}aShape)`;let t=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let n=0;n=5){let p=t.readSync(n.dataId),u=n.dtype==="string"?p.map(m=>y.decodeString(m)):p,c=le(n.shape,n.dtype,u),l=yM(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new yx(n.shape,s);return t.runWebGPUProgram(a,[n],n.dtype)}var vB={kernelName:to,backendName:"webgpu",kernelFunc:CI};function Joe(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=S.calculateShapes(s,n,i),d=!1;if(s.dtype==="string"){let A=t.bufferSync(n),R=t.bufferSync(s),D=y.decodeString(t.readSync(a.dataId)[0]),P=mM(A,R,i,m,c,u,p,l,D,d);return t.makeTensorInfo(i,P.dtype,P.values)}let f=[m/c,c],h=de({inputs:{x:n},backend:t,attrs:{shape:[u,p]}}),g=s.shape.length?de({inputs:{x:s},backend:t,attrs:{shape:[u,c]}}):Ft({inputs:{x:s},backend:t}),x=g.dtype,b=t.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=de({inputs:{x:a},backend:t,attrs:{shape:Array(f.length).fill(1)}}),w=CI({inputs:{x:C},backend:t,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:"int32",data:[p]},{type:"int32",data:l},{type:"int32",data:[k]}];switch(u){case 0:break;case 1:{let A=new Gi([u,c],p,h.shape.length,g.shape.length,l,f,x,d);t.runWebGPUProgram(A,[g,h],x,_,w)}break;default:{let A=new Gi([u,c],p,h.shape.length,b.shape.length,l,f,x,d);t.runWebGPUProgram(A,[b,h],x,_,w)}{let A=new Gi([u,c],p,h.shape.length,g.shape.length,l,f,x);t.runWebGPUProgram(A,[g,h],x,_,w)}}let $=de({inputs:{x:w},backend:t,attrs:{shape:i}});return t.disposeData(h.dataId),t.disposeData(g.dataId),t.disposeData(C.dataId),t.disposeData(b.dataId),t.disposeData(w.dataId),$}var kB={kernelName:li,backendName:"webgpu",kernelFunc:Joe};function ene(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=S.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=ds({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var NB={kernelName:$s,backendName:"webgpu",kernelFunc:ene};var tne=Se({opType:Q.SQRT}),TB={kernelName:Gn,backendName:"webgpu",kernelFunc:tne};var _B={kernelName:mi,backendName:"webgpu",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e,n=new Ro(t.shape,Q.SQUARE);return o.runWebGPUProgram(n,[t],t.dtype)}};var rne=ot({opType:ye.SQUARED_DIFFERENCE}),EB={kernelName:Kn,backendName:"webgpu",kernelFunc:rne};var bx=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=Rt(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let n=0;t=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return` ${se("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, getX(${t})); } } `}};function one(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:w}=ut.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=de({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=ut.computeOutShape(b,C,w),$=ds({inputs:{x:n},backend:t,attrs:{begin:b,size:_}});k=de({inputs:{x:$},backend:t,attrs:{shape:f}}),t.disposeData($.dataId)}else if(t.shouldExecuteOnCPU([n])){let $=t.readSync(n.dataId),A=le(n.shape,n.dtype,$),R=hM(d,A,w,b);k=t.makeTensorInfo(f,n.dtype,R.values)}else{let $=new bx(d),A=[{type:"int32",data:b},{type:"int32",data:w}],R=t.runWebGPUProgram($,[n],n.dtype,A);k=de({inputs:{x:R},backend:t,attrs:{shape:f}}),t.disposeData(R.dataId)}return k}var $B={kernelName:jn,backendName:"webgpu",kernelFunc:one};function nne(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=gM(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],"string",f),t.makeTensorInfo(l.shape,"int32",h)]}var AB={kernelName:As,backendName:"webgpu",kernelFunc:nne};var sne=Se({opType:Q.TAN}),RB={kernelName:Yn,backendName:"webgpu",kernelFunc:sne};var ane=Se({opType:Q.TANH}),FB={kernelName:Qn,backendName:"webgpu",kernelFunc:ane};var Cx=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32, dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return` ${se("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); let batch = outC[0]; let elemIdx = outC[1]; // We compare elements pair-wise within a group of size 2 * inc. // The comparing rule for each group alternates between ascending // and descending. Within each group, we compare each pair at // positions i and i+inc. To decide whether an element at position i // is x0 or x1, we mod it by 2 * inc, if the result is smaller than // inc, it is in the first half of the group, we denote it as x0, // otherwise we denote it as x1. // For example, as shown in the Bitonic top K paper referenced // above, Figure5(a) shows that element[1] is in the second half of // the group when group size is 2, but it is in the first half of // the group when group size is 4. let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc; var i = 0; if (isFirstInPair) { i = elemIdx; } else { i = elemIdx - uniforms.inc; } var i0 = 0; if (uniforms.firstPass == 1) { i0 = i; } else { i0 = i32(getIndices(batch, i)); } var i1 = 0; if (uniforms.firstPass == 1) { i1 = i + uniforms.inc; } else { i1 = i32(getIndices(batch, i + uniforms.inc)); } var x0 = f32(0.0); var x1 = f32(0.0); if (i0 < uniforms.inputSize) { x0 = getX(batch, i0); } else { x0 = uniforms.negativeInf; } if (i1 < uniforms.inputSize) { x1 = getX(batch, i1); } else { x1 = uniforms.negativeInf; } let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir; let isGreater = x0 > x1 || (x0 == x1 && i1 > i0); if (reverse == isGreater) { // Elements in opposite order of direction let iTemp = i0; i0 = i1; i1 = iTemp; } if (isFirstInPair) { setOutputAtIndex(index, f32(i0)); } else { setOutputAtIndex(index, f32(i1)); } } } `}},Sx=class{constructor(e){this.variableNames=["x","indices"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return` ${se("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); let batch = outC[0]; let elemIdx = outC[1]; // The output size is half of the previous size. // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ // (k=4), we only need to output the indices at positions |, the // indices at positions _ can be thrown away, see Figure5(b) After // Phase 2 (Merge phase) in the Bitonic Top K paper referenced // above. // For example, the paper shows we only need to output the orange // bars. The output sequence should look like this | | | | | | | |. // Because the sequence is halved, to map the output index back to // the previous sequence to find the corresponding value, we need // to double the index. When we double the index, we basically // interpolate a position, so 2i looks like // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k // position of each 2k positions by - elemIdx % k. E.g. for output // at index 4,5,6,7, we want to get the corresponding element at // original index 8,9,10,11, for output at index 8,9,10,11, // we want to get the corresponding element at original index // 16,17,18,19, so on and so forth. var i = 0; if (elemIdx < uniforms.k) { i = elemIdx; } else { i = elemIdx * 2 - elemIdx % uniforms.k; } var i0 = 0; if (uniforms.firstPass == 1) { i0 = i; } else { i0 = i32(getIndices(batch, i)); } var i1 = 0; if (uniforms.firstPass == 1) { i1 = i + uniforms.k; } else { i1 = i32(getIndices(batch, i + uniforms.k)); } let x0 = getX(batch, i0); var x1 = f32(0.0); if (i1 < uniforms.inputSize) { x1 = getX(batch, i1); } else { x1 = x0; } if (x0 >= x1) { setOutputAtIndex(index, f32(i0)); } else { setOutputAtIndex(index, f32(i1)); } } } `}};function $c(r,e){e!==null&&r.disposeData(e.dataId)}function DB(r){let e=1;for(;ef===null?[l,l]:[l,f],g=(k,_,$)=>{let A=h(),R=new Cx($),P=[{type:"int32",data:[p]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[_]}],M=f;f=t.runWebGPUProgram(R,A,"int32",P),$c(t,M)};for(let k=1;k=1;$/=2)g(_,$,[c,d])}for(let k=d;k>m;k/=2){let _=h(),$=new Sx([c,k/2]),R=[{type:"int32",data:[p]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[m]}],D=f;f=t.runWebGPUProgram($,_,"int32",R),$c(t,D);let P=m/2,M=P*2;for(let L=P;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=ds({inputs:{x:f},backend:t,attrs:{begin:0,size:[c,s]}}),$c(t,x);let b=gI({inputs:{x:l,indices:f},backend:t,attrs:{axis:1,batchDims:1}});$c(t,l);let C=i.slice(0,-1);C.push(s),x=f,f=de({inputs:{x:f},attrs:{shape:C},backend:t}),$c(t,x);let w=b;return b=de({inputs:{x:b},attrs:{shape:C},backend:t}),$c(t,w),[b,f]}var OB={kernelName:Zn,backendName:"webgpu",kernelFunc:ine};var wx=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=ue(this.outputShape),this.dispatch=re(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey="transform"}getUserCode(){return` fn mapCoord(outCoord : f32, len : f32) -> f32{ var inCoord = outCoord; if(uniforms.fillModeId == 2) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { let sz2 = 2.0 * len; if (inCoord < sz2) { inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) + inCoord; } if (inCoord < -len) { inCoord = inCoord + sz2; } else { inCoord = -inCoord - 1.0; } } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { let sz2 = 2.0 * len; inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2))); if (inCoord >= len) { inCoord = sz2 - inCoord - 1.0; } } } return clamp(inCoord, 0.0, len - 1.0); } else if (uniforms.fillModeId == 3) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { let sz = len - 1.0; inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0); } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { let sz = len - 1.0; inCoord = inCoord - len * f32(i32(f32(inCoord / sz))); } } return clamp(inCoord, 0.0, len - 1.0); } else if (uniforms.fillModeId == 4) { return clamp(outCoord, 0.0, len - 1.0); } return outCoord; } fn readWithFillValue(batch : i32, coordY : i32, coordX : i32, channel : i32) -> f32 { var outputValue : f32; if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) { outputValue = getImage(batch, coordY, coordX, channel); } else { outputValue = uniforms.fillValue; } return outputValue; } ${se("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var outputValue : f32; let batch = coords[0]; let x = coords[2]; let y = coords[1]; let channel = coords[3]; let xf = f32(x); let yf = f32(y); let a1 = getTransforms(batch, 0); let a2 = getTransforms(batch, 1); let a3 = getTransforms(batch, 2); let b1 = getTransforms(batch, 3); let b2 = getTransforms(batch, 4); let b3 = getTransforms(batch, 5); let c1 = getTransforms(batch, 6); let c2 = getTransforms(batch, 7); let projection = c1 * xf + c2 * yf + 1.0; if (projection == 0.0) { outputValue = uniforms.fillValue; } else { let inX = (a1 * xf + a2 * yf + a3) / projection; let inY = (b1 * xf + b2 * yf + b3) / projection; let mapX = mapCoord(inX, f32(uniforms.imageShape[2])); let mapY = mapCoord(inY, f32(uniforms.imageShape[1])); if (uniforms.interpolationModeId == 1) { let coordY = i32(round(mapY)); let coordX = i32(round(mapX)); outputValue = readWithFillValue(batch, coordY, coordX, channel); } else { let yFloor = floor(mapY); let xFloor = floor(mapX); let yCeil = yFloor + 1.0; let xCeil = xFloor + 1.0; let valueYFloor = (xCeil - mapX) * readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, i32(yFloor), i32(xCeil), channel); let valueYCeil = (xCeil - mapX) * readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, i32(yCeil), i32(xCeil), channel); outputValue = (yCeil - mapY) * valueYFloor + (mapY - yFloor) * valueYCeil; } } setOutputAtIndex(index, outputValue); } } `}};function une(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new wx(g),b=a==="nearest"?1:2,C;switch(i){case"constant":C=1;break;case"reflect":C=2;break;case"wrap":C=3;break;case"nearest":C=4;break;default:C=1;break}let w=[{type:"int32",data:[b]},{type:"int32",data:[C]},{type:"float32",data:[p]}];return t.runWebGPUProgram(x,[n,s],"float32",w)}var PB={kernelName:Jn,backendName:"webgpu",kernelFunc:une};function pne(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,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeData(h.dataId)),f}var MB={kernelName:Rs,backendName:"webgpu",kernelFunc:pne};var cne=[B3,SM,wM,IM,vM,kM,TM,_M,EM,$M,AM,RM,FM,DM,OM,LM,BM,zM,WM,qM,KM,jM,z3,YM,QM,ZM,JM,eL,tL,nL,sL,aL,iL,uL,lL,mL,dL,fL,hL,gL,xL,yL,M3,bL,wL,CL,SL,IL,vL,kL,NL,TL,_L,EL,V3,$L,XM,AL,RL,FL,DL,OL,PL,LL,ML,BL,VL,PM,zL,WL,MM,UL,GL,HL,qL,pL,KL,jL,XL,UM,YL,JL,eB,tB,rB,oB,nB,sB,GM,aB,iB,uB,pB,L3,cB,lB,mB,dB,fB,hB,gB,xB,yB,bB,CB,VM,$B,AB,wB,IB,kB,NB,TB,_B,EB,SB,cL,RB,FB,vB,OB,PB,NM,MB,QL];for(let r of cne)Ia(r);var LB="4.1.0",lne="4.1.0",mne="4.1.0",dne="4.1.0",fne="4.1.0",hne="0.0.1-alpha.16",gne={tfjs:LB,"tfjs-core":LB,"tfjs-converter":lne,"tfjs-backend-cpu":mne,"tfjs-backend-webgl":dne,"tfjs-backend-wasm":fne,"tfjs-backend-webgpu":hne};export{gs as Abs,sa as Acos,aa as Acosh,Ei as AdadeltaOptimizer,$i as AdagradOptimizer,Ai as AdamOptimizer,Ri as AdamaxOptimizer,eo as Add,Mo as AddN,Lo as All,Bo as Any,Vo as ArgMax,Za as ArgMin,ia as Asin,ua as Asinh,pa as Atan,la as Atan2,ca as Atanh,zo as AvgPool,ip as AvgPool3D,Im as AvgPool3DGrad,wm as AvgPoolGrad,Pl as BackendWasm,Wo as BatchMatMul,xs as BatchToSpaceND,Ja as Bincount,up as BroadcastArgs,wne as BroadcastTo,co as Cast,Uo as Ceil,lo as ClipByValue,ei as Complex,pp as ComplexAbs,ys as Concat,Go as Conv2D,cp as Conv2DBackpropFilter,Ho as Conv2DBackpropInput,lp as Conv3D,vm as Conv3DBackpropFilterV2,mp as Conv3DBackpropInputV2,qo as Cos,Ko as Cosh,Yo as CropAndResize,jo as Cumprod,Xo as Cumsum,Do as DataStorage,ti as DenseBincount,Qo as DepthToSpace,Zo as DepthwiseConv2dNative,dp as DepthwiseConv2dNativeBackpropFilter,fp as DepthwiseConv2dNativeBackpropInput,hp as Diag,gp as Dilation2D,bb as Dilation2DBackpropFilter,yb as Dilation2DBackpropInput,hb as ENV,ri as Einsum,en as Elu,km as EluGrad,Uc as Environment,tn as Equal,ma as Erf,rn as Exp,bs as ExpandDims,da as Expm1,oi as FFT,Cs as Fill,on as FlipLeftRight,nn as Floor,sn as FloorDiv,Zi as FromPixels,an as FusedBatchNorm,ho as FusedConv2D,go as FusedDepthwiseConv2D,Fu as GPGPUContext,un as GatherNd,Ss as GatherV2,ll as GraphModel,pn as Greater,cn as GreaterEqual,ni as IFFT,mo as Identity,si as Imag,fa as IsFinite,ha as IsInf,ln as IsNan,Zr as KernelBackend,yp as LRN,Nm as LRNGrad,mn as LeakyRelu,dn as Less,fn as LessEqual,xp as LinSpace,hn as Log,ga as Log1p,Ine as LogSoftmax,gn as LogicalAnd,xn as LogicalNot,xa as LogicalOr,GI as LogicalXor,vne as LowerBound,Oi as MathBackendCPU,Bi as MathBackendWebGL,yn as Max,Cn as MaxPool,bp as MaxPool3D,_m as MaxPool3DGrad,Tm as MaxPoolGrad,Cp as MaxPoolWithArgmax,bn as Maximum,Sn as Mean,wn as Min,In as Minimum,vn as MirrorPad,ya as Mod,Fi as MomentumOptimizer,Sp as Multinomial,kn as Multiply,ws as Neg,Tn as NonMaxSuppressionV3,ba as NonMaxSuppressionV4,_n as NonMaxSuppressionV5,Nn as NotEqual,Lb as OP_SCOPE_SUFFIX,En as OneHot,Is as OnesLike,wr as Optimizer,ns as OptimizerConstructors,vs as Pack,$n as PadV2,kne as Pool,An as Pow,Rn as Prelu,Fn as Prod,Di as RMSPropOptimizer,wp as RaggedGather,Ip as RaggedRange,vp as RaggedTensorToTensor,ks as Range,_b as Rank,ai as Real,Jo as RealDiv,Dn as Reciprocal,Et as Reduction,On as Relu,Ln as Relu6,Ns as Reshape,Mn as ResizeBilinear,$m as ResizeBilinearGrad,Pn as ResizeNearestNeighbor,Em as ResizeNearestNeighborGrad,Bn as Reverse,es as RotateWithOffset,Ca as Round,Vn as Rsqrt,qs as SGDOptimizer,zn as ScatterNd,ii as SearchSorted,Ts as Select,Xi as Selu,Un as Sigmoid,Yi as Sign,Wn as Sin,Sa as Sinh,_s as Slice,qn as Softmax,Qi as Softplus,Es as SpaceToBatchND,ui as SparseFillEmptyRows,wa as SparseReshape,pi as SparseSegmentMean,ci as SparseSegmentSum,li as SparseToDense,$s as SplitV,Gn as Sqrt,mi as Square,Kn as SquaredDifference,Ds as Step,jn as StridedSlice,As as StringNGrams,di as StringSplit,fi as StringToHashBucketFast,Xn as Sub,Hn as Sum,Yn as Tan,Qn as Tanh,it as Tensor,st as TensorBuffer,to as Tile,Zn as TopK,Jn as Transform,ro as Transpose,kp as Unique,Rs as Unpack,Np as UnsortedSegmentSum,Nne as UpperBound,va as Variable,Ui as WebGPUBackend,Fs as ZerosLike,fo as _FusedMatMul,Yt as abs,fv as acos,hv as acosh,xe as add,gv as addN,xv as all,yv as any,bv as argMax,Cv as argMin,Sv as asin,wv as asinh,Iv as atan,vv as atan2,kv as atanh,td as avgPool,_v as avgPool3d,Oie as backend,S as backend_util,Ev as basicLSTMCell,wi as batchNorm,Av as batchNorm2d,Rv as batchNorm3d,Fv as batchNorm4d,rd as batchToSpaceND,od as bincount,XG as booleanMaskAsync,Dv as broadcastArgs,Ii as broadcastTo,br as broadcast_util,Q0 as browser,le as buffer,Ke as cast,Ov as ceil,Pv as clipByValue,Br as clone,Tr as complex,gt as concat,Mv as concat1d,Lv as concat2d,Bv as concat3d,Vv as concat4d,zv as conv1d,vi as conv2d,Wv as conv2dTranspose,Uv as conv3d,Hv as conv3dTranspose,Dne as copyRegisteredKernels,qv as cos,Kv as cosh,il as cosineWindow,jv as cumprod,Xv as cumsum,Cr as customGrad,Yv as denseBincount,eC as deprecationWarn,Qv as depthToSpace,Bp as depthwiseConv2d,xK as deregisterOp,yi as device_util,Zv as diag,Jv as dilation2d,vie as disableDeprecationWarnings,Dt as dispose,kie as disposeVariables,Ge as div,ek as divNoNan,tk as dot,aH as dropout,rk as einsum,ad as elu,Iie as enableDebugMode,wie as enableProdMode,xC as enclosingPowerOfTwo,cr as engine,O as env,sd as equal,ok as erf,ak as euclideanNorm,Co as exp,Fa as expandDims,ik as expm1,id as eye,zp as fft,Ws as fill,Fie as findBackend,Die as findBackendFactory,ud as floor,Jm as floorDiv,L$ as forceHalfFloat,yC as fused,pd as gather,nH as gatherND,Ym as gather_util,Aie as getBackend,Cb as getGradient,qc as getKernel,Am as getKernelsForBackend,Nee as getThreadsCount,yw as gpgpu_util,l4 as grad,m4 as grads,cu as greater,cd as greaterEqual,hu as ifft,Si as imag,uq as image,uH as inTopKAsync,Ea as io,Fd as irfft,uk as isFinite,pk as isInf,ck as isNaN,_r as keep,Lt as kernel_impls,ld as leakyRelu,lk as less,Vp as lessEqual,pq as linalg,mk as linspace,l6 as loadGraphModel,m6 as loadGraphModelSync,dk as localResponseNormalization,Da as log,md as log1p,fk as logSigmoid,hk as logSoftmax,hd as logSumExp,lu as logicalAnd,gd as logicalNot,xd as logicalOr,gk as logicalXor,cq as losses,xk as lowerBound,Xe as matMul,j0 as math,Us as max,bd as maxPool,yk as maxPool3d,bk as maxPoolWithArgmax,Cd as maximum,mu as mean,Nie as memory,Ck as meshgrid,sl as min,Sd as minimum,Sk as mirrorPad,wk as mod,Ik as moments,QG as movingAverage,ae as mul,vk as multiRNNCell,kk as multinomial,yr as neg,CC as nextFrame,pu as norm,wd as notEqual,tl as oneHot,Gs as ones,Nk as onesLike,N as op,Tk as outerProduct,Hs as pad,_k as pad1d,Ek as pad2d,$k as pad3d,Ak as pad4d,Rk as pool,Ra as pow,vd as prelu,Gm as print,Fk as prod,Tie as profile,Dk as raggedGather,Ok as raggedRange,Pk as raggedTensorToTensor,Mk as rand,e1 as randomGamma,Ed as randomNormal,t1 as randomStandardNormal,$d as randomUniform,Ni as range,$ie as ready,$a as real,r1 as reciprocal,Ci as registerBackend,Ane as registerGradient,Ia as registerKernel,gK as registerOp,Ti as relu,Ad as relu6,Rie as removeBackend,z as reshape,no as reverse,o1 as reverse1d,n1 as reverse2d,s1 as reverse3d,a1 as reverse4d,Wp as rfft,Rd as round,i1 as rsqrt,be as scalar,JG as scatterND,rl as scatter_util,al as searchSorted,u1 as selu,p1 as separableConv2d,pv as serialization,Eie as setBackend,Pie as setPlatform,kee as setThreadsCount,Iee as setWasmPath,vee as setWasmPaths,RS as setWebGLContext,c1 as setdiff1dAsync,Qp as shared,zs as sigmoid,l1 as sign,iq as signal,m1 as sin,d1 as sinh,He as slice,f1 as slice1d,h1 as slice2d,g1 as slice3d,x1 as slice4d,ut as slice_util,y1 as softmax,fd as softplus,Id as spaceToBatchND,lq as sparse,rH as sparseToDense,aq as spectral,Oa as split,$r as sqrt,Qt as square,Dd as squaredDifference,Up as squeeze,Sr as stack,Od as step,b1 as stridedSlice,mq as string,Ne as sub,et as sum,ka as sumOutType,C1 as tan,nl as tanh,nr as tensor,mr as tensor1d,_i as tensor2d,Xm as tensor3d,S1 as tensor4d,w1 as tensor5d,I1 as tensor6d,h0 as tensor_util,dv as test_util,Ee as tidy,ki as tile,_ie as time,v1 as topk,hMe as train,Mp as transpose,k1 as truncatedNormal,N1 as unique,Fne as unregisterGradient,Rne as unregisterKernel,T1 as unsortedSegmentSum,so as unstack,dt as upcastType,_1 as upperBound,y as util,d4 as valueAndGrad,f4 as valueAndGrads,E1 as variable,pC as variableGrads,gne as version,f6 as version_converter,xW as version_core,U6 as version_cpu,Tee as version_wasm,L8 as version_webgl,L9e as webgl,oc as webgl_util,nI as webgpu_util,os as where,Md as whereAsync,Vr as zeros,Ut as zerosLike};